Machine Translated by Google Standard Methods for Estimation Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) MINISTRY OF ENVIRONMENT AND FORESTRY RESEARCH, DEVELOPMENT AND INNOVATION AGENCY © 2015 Machine Translated by Google Standard Methods for Estimation Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Indonesia's National Carbon Accounting System (INCAS) MINISTRY OF ENVIRONMENT AND FORESTRY RESEARCH, DEVELOPMENT AND INNOVATION AGENCY © 2015 Machine Translated by Google INDONESIAN NATIONAL CARBON CALCULATION SYSTEM (INCAS) Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Writer: Haruni Krisnawati, Rinaldi Imanuddin, Wahyu Catur Adinugroho, Silver Hutabarat National Reviewer: Rizaldi Boer, Ruandha Agung Sugardiman, Teddy Rusolono, Chairil Anwar Siregar, Maswar Bahri International Reviewers: Michael Parsons, Robert Waterworth, Thomas Harvey, Geoff Roberts, Nikki Fitzgerald Contributors: National Institute of Aeronautics and Space, Directorate General of Forestry Planning and Planning Environment Ministry of Environment and Forestry, Research and Development Agency Ministry of Agriculture © 2015 Ministry of Environment and Forestry Research, Development and Innovation Agency ISBN: 978-979-8452-70-3 Contents can be quoted by citing the source: Krisnawati, H., Imanuddin, R., Adinugroho, WC and Hutabarat, S. 2015. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia, Version 2. Research Development and Innovation Agency, Ministry of Environment and Forestry . Bogor, Indonesia. Published by: Research Development and Innovation Agency, Ministry of Environment and Forestry Research, Development and Innovation Agency Campus Jl. Rock Mountain No. 5, Bogor 16610, Indonesia Tel : +62-251 7520068 Email : datinfo@forda-mof.org | incas@forda-mof.org Website : http://www.forda-mof.org This publication is published with support from the Australian Government through the Center for International Forestry Research (CIFOR). Previous support was provided through the Indonesia–Australia Forest Carbon Partnership (IAFCP). Machine Translated by Google INTRODUCTION The Ministry of Environment and Forestry developed the Indonesian National Carbon Accounting System (INCAS) to meet the requirements for calculating greenhouse gases (GHG) for the land-based sector in Indonesia. This system uses a systematic and nationally consistent approach in measuring GHG emissions and removals in the land sector. I am proud to present this important publication, the second version of the INCAS Standard Method. This document clearly describes the approach used in estimating GHG emissions and removals under the INCAS framework. This method was developed from the first version of the INCAS Standard Method applied in the REDD+ Pilot Province of Central Kalimantan and launched in March 2015. This second version has been updated and is applied nationally for estimating net GHG emissions from forests and peatlands throughout Indonesia. I hope that the continued development and operation of INCAS will continue to improve GHG data and reporting capabilities. This will not only help us meet international requirements, including a measurement, reporting and verification (MRV) system for REDD+ activities, but will also enable us to effectively design, implement and monitor activities for reducing net GHG emissions from land use. I congratulate the INCAS team, the Research, Development and Innovation Agency, and the Directorate General of Forestry and Environmental Planning for developing INCAS. I would also like to express my appreciation for the invaluable contribution of the National Institute of Aeronautics and Space (LAPAN). I also thank the Australian Government and the Center for International Forestry Research (CIFOR), and in advance through the Indonesia Australia Forest Carbon Partnership (IAFCP) for targeted and effective assistance. I look forward to seeing the further development and expansion of INCAS for the Agriculture, Forestry and Other Land Use (AFOLU) sector, as well as the operationalization of INCAS as a functional system in the Ministry of Environment and Forestry. Jakarta, November 2015 Minister of Environment and Forestry Dr. Ir. Siti Nurbaya, M.Sc Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | iii Machine Translated by Google TABLE OF CONTENTS INTRODUCTION ................................................................. ................................................................. ................... iii LIST OF TABLES ................................................ ................................................................. ...............vii LIST OF FIGURES ................................................ ................................................................. ....... viii 1. INTRODUCTION ............................................... ................................................................. ......... 1 2. STANDARD METHOD – INITIAL CONDITIONS .......................................... ...................... 3 2.1 Objectives ................................................................ ................................................................. ................................. 3 2.2 Data collection................................................ ................................................................. ......................... 4 2.3 Analysis ................................................................ ................................................................. ............... 6 2.3.1 Estimation of aboveground biomass (BAPT).......................... 7 2.3.2 Estimation of belowground biomass (roots) ............................... 8 2.3.3 Estimation of litter.......... ................................................................. ................................ 9 2.3.4 Estimation of dead wood ............... ................................................................. ................... 9 2.4 Quality control and quality assurance ............................................................... .............9 2.5 Results and uncertainty analysis.......................................................... ................................ 10 2.6 Limitations ................................................................ ................................................................. ......... 14 2.7 Improvement plan ............................................................... ................................................. 14 3. STANDARD METHODS – FOREST GROWTH AND TRANSITION ......... 15 3.1 Objectives ................................................................ ................................................................. ............... 15 3.2 Data collection.......................... ................................................................. ................... 16 3.3 Analysis ................................................................ ................................................................. .................. 17 3.4 Quality control and quality assurance ............................................................... ........... 19 3.5 Results and uncertainty analysis.............................................................. ................................ 19 3.6 Limitations ............................................................... ................................................................. ......... 21 3.7 Improvement plan ............................................................... ................................................ 21 iv | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google 4. STANDARD METHODS – FOREST MANAGEMENT EVENTS AND REGAMES 22 4.1 Objectives ................................................................. ................................................................. .................. 22 4.2 Data collection............................................................... ................................................ 23 4.3 Analysis ................................................................ ................................................................. .................. 24 4.4 Quality control and quality assurance ............................................................... ........... 27 4.5 Results ................................................................ ................................................................. ...................... 27 4.6 Limitations ................................................................. ................................................................. ......... 29 4.7 Improvement plan ............................................................... ................................................ 30 5. STANDARD METHOD – FOREST COVER CHANGES............................... 31 5.1 Objectives ................................................................ ................................................................. .................. 31 5.2 Data collection ............................................................... ................................................ 31 5.3 Analysis ................................................................ ................................................................. .................. 32 5.4 Quality control and quality assurance ............................................................... ........... 34 5.5 Results and uncertainty analysis.............................................................. ................................ 34 5.6 Limitations ................................................................. ................................................................. ......... 36 5.7 Improvement plan ............................................................... ................................................ 36 6. STANDARD METHODS – SPATIAL ALLOCATION OF THE REGIM ............................................... .. 37 6.1 Objectives ................................................................ ................................................................. .................. 37 6.2 Data collection ............................................................... ................................................ 37 6.3 Analysis ................................................................ ................................................................. .................. 39 6.4 Quality control and quality assurance ............................................................... ...........41 6.5 Results ................................................................. ................................................................. .................42 6.6 Limitations ................................................................. ................................................................. ......... 42 6.7 Improvement plan ............................................................... ................................................ 42 7. STANDARD METHOD – PEATLAND GHG EMISSIONS ............................... 44 7.1 Objectives ................................................................ ................................................................. .................. 44 7.2 Data collection ............................................................... ................................................ 44 7.3 Analysis ................................................................. ................................................................. .................. 48 7.4 Quality control and quality assurance ............................................................... ........... 49 7.5 Results and uncertainty analysis.............................................................. ................................ 49 7.6 Limitations ............................................................... ................................................................. ......... 50 7.7 Improvement plan ............................................................... ................................................ 51 Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | v Machine Translated by Google 8. STANDARD METHODS – DATA INTEGRATION AND REPORTING............................. 52 8.1 Objectives ................................................................ ................................................................. .................. 52 8.2 Data collection............................................................... ................................................ 53 8.3 Analysis ................................................................ ................................................................. .................. 54 8.3.1 Forest land................................................................................. ................................................................. .. 56 8.3.2 Plantations and other agricultural land................................................................. ............ 63 8.3.3 Carbon emissions from mineral soils.......................................................... .................. 64 8.3.4 Emissions of N2O from mineral soils................................................................ .......................... 64 8.3.5 Non-CO2 emissions from surface fires................................................................ .... 65 8.4 Quality control and quality assurance ............................................................... ...........65 8.5 Results ................................................................ ................................................................. ......................... 66 8.5.1 Reporting year.......................................................... ............................................ 66 8.5.2 Land use transition matrix.......................................................... ............... 66 8.5.3 Units of reporting.......................................................... ................................................. 67 8.5.4 Reporting categories.......................................................... ........................................ 68 8.6 Uncertainty analysis ............................................................... ............................................... 71 8.6.1 Method................................................................. ................................................................. ......... 72 8.6.2 Uncertainty analysis results – plot level uncertainty............... 73 8.6.3 Results of uncertainty analysis – uncertainty at the national level ... 76 8.6.4 Discussion of uncertainty analysis and improvement plans... 76 8.7 Limitations ................................................................ ................................................................. ......... 77 8.8 Improvement plan ............................................................... ................................................ 78 REFERENCE................................................. ................................................................. ......................... 79 APPENDIX................................................. ................................................ ....................... 86 vi | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google LIST OF TABLES Table 2-1. Potential data sources used to determine conditions early................................................... ................................................ .................... 5 Table 2-2. The initial aboveground biomass (DBH 5 cm) for each forest type and area of analysis in Indonesia................................................................ .... 11 Table 2-3. Estimated unmeasured carbon source biomass based on proportion relative to aboveground biomass. .................................. 12 Table 4-1. Sources of data used to determine events and regimes forest management. ................................................................. ............................................... 24 Table 4-2. The possible conditions of each category used in determination of the management regime or suite. ................................................................. ... 25 Table 4-3. Summary of regime description............................................................... ................................. 28 Table 4-4. Summary of incident description............................................................... ................................ 29 Table 6-1. Spatial data sources. ................................................................. ............................................... 38 Table 7-1. Spatial data sources used. ................................................................. ......... 45 Table 7-2. Source of data for modeling input. ................................................................. ......... 45 Table 7-3. Emission factors for peat biological oxidation in Indonesia................................................. 46 Table 7-4. Input parameters and CO2 -C, CO and CH4 emissions per ha for fires in organic soils. ................................................................. ......................... 47 Table 7-5. Standard nitrogen oxide emission factor from organic soil........................... 48 Table 7-6. Modeling output and reporting units. ................................................................. 50 Table 8-1. Source of data for modeling input. ................................................................. ......... 54 Table 8-2. Summary of methodology and emission factors: land use sector, land use change and forestry. ................................................. 55 Table 8-3. Land use transition matrix................................................................................. .................. 67 Table 8-4. Output models and reporting units. ................................................................. .......... 68 Table 8-5. Comparison between UNFCCC reporting categories and activities REDD+ included in the national GHG inventory............... 69 Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | viii Machine Translated by Google LIST OF FIGURES Figure 2-1. General approach used to calculate biomass forest on each carbon source................................................................................. .................. 6 Figure 3-1. Stages of growth speed. ................................................................. ......... 18 Figure 3-2. Example of a growth curve of volume growth................................. 18 Figure 3-3. An example of the results of a secondary swamp forest growth analysis after fire................................................. ................................................................. ... 20 Figure 5-1. Flowchart of steps in the INCAS-LCCA processing sequence (EIGHT, 2014). ................................................ .......................................... 32 Figure 5-2. Examples of forest area products (2009) at national, regional and local scales. Local scale includes comparison with Landsat and high-resolution imagery (LAPAN, 2014). ................................................. 35 Figure 7-1. INCAS peat GHG emission estimation approach. ................................... 49 Figure 8-1. FullCAM components and carbon flow for carbon sources dead trees and organic matter. ................................................................. .............. 57 Figure 8ÿ2. Example of output of change in carbon mass by source carbon from deforestation. ................................................................. ............................ 60 Figure 8ÿ3. Example of output of change in carbon mass by source carbon from forest degradation. ................................................................. .................. 60 Figure 8ÿ4. Example of output of change in carbon mass by source carbon from sustainable forest management. ............................................... 61 Figure 8ÿ5. Example of output of change in carbon mass by source carbon from increasing forest carbon stocks................................................. 61 Figure 8ÿ6. Example of output of change in carbon mass by source carbon from forest conversion to plantations. ................................. 62 Figure 8ÿ7. Comparison of annual emissions from deforestation events, as estimated by FullCAM and CAMFor, shows very little variation between the two modeling tools. ......................... 73 Figure 8ÿ8. Mass distribution of net carbon emitted in swamp forest secondary to deforestation in the first year of simulation................... 74 viii | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google Figure 8ÿ9. Regression sensitivity of net carbon mass emitted in secondary swamp forest due to deforestation in the first year of simulation................................................ ................................................................. ........ 74 Figure 8ÿ10. Mass distribution of net carbon emitted in swamp forest secondary 10 years after deforestation.......................................................... ......... 75 Figure 8ÿ11. Regression sensitivity for mass net carbon emitted in secondary swamp forest 10 years after deforestation................................. 75 Figure 8ÿ12. National level uncertainty results for deforestation events caused by land clearing and forest fires. ........ 76 Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | ix Machine Translated by Google Machine Translated by Google PRELIMINARY This document (Annex) describes in detail the standard methods developed by the Indonesian National Carbon Accounting System (INCAS) in calculating net greenhouse gas (GHG) emissions in the Indonesian forestry sector in a transparent, accurate, complete, consistent and reliable manner. compared (TACCC). The first version of this standard method, described in Krisnawati et al. (2015a) was initially tested and refined to estimate GHG emissions and removals from forests and peatlands in the REDD+ pilot province of Central Kalimantan. The results are reported in the Estimation of Annual Greenhouse Gas Emissions from Forests and Peatlands in Central Kalimantan (Krisnawati et al., 2015b). The method was refined in line with the expansion of INCAS coverage for all provinces in Indonesia. Improvements were made due to access to new data sources and increased technical knowledge. This standard method describes the approaches and methods used in data collection, data analysis, quality control, quality assurance, modeling and reporting of GHG emissions and removals. The use of this standard method ensures the consistency of the method applied to GHG inventories across all forest land sectors, regardless of geographic scope or time. These standard methods include: 1. Standard method – initial conditions: describes the process of determining initial conditions used as inputs for modeling GHG emissions and removals. These initial conditions include aboveground biomass, belowground biomass, litter and dead wood for each biomass class (see Chapter 2 of this Annex). 2. Standard method – forest growth and turnover: describes the process of determining the growth rate, aboveground biomass transfer and belowground biomass and dead wood decay rate, for each component of the biomass class, which is used as input for modeling GHG emissions and removals ( see Chapter 3 of this Annex). 3. Standard method – forest management events and regimes: describes the process of determining forest management events and regimes and their impact on carbon stocks as inputs for modeling GHG emissions and removals (see Chapter 4 of this Annex). Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 1 Machine Translated by Google 4. Standard method – forest cover change: the standard method used to monitor forest cover change in Indonesia, is described in Indonesia's National Carbon Accounting System Remote Sensing Monitoring Program: Methodology and Results, Version 1 (LAPAN, 2014) (see Chapter 5 of this Annex ). 5. Standard method – regime spatial allocation: describes how existing spatial data are used consistently to allocate management regimes to the analyzed area and to derive annual area statistics used in INCAS (see Chapter 6 of this Annex). 6. Standard method – peatland GHG emissions: describes the process of calculating GHG emissions from biological oxidation of drained peatlands, direct emissions from draining organic soils and emissions from peat fires (see Chapter 7 of this Annex). 7. Standard method – data integration and reporting: describes the process used in integrating the data generated from the standard method INCAS no. 1–6 and estimate GHG emissions and removals from activities taking place on forest land including deforestation, forest degradation, sustainable forest management and enhancement of forest carbon stocks in Indonesia (see Chapter 8 of this Annex). The second version of this standard method describes the methods, assumptions and data inputs used to estimate GHG emissions and removals across all provinces in Indonesia as part of a national GHG inventory using INCAS for the first time. This standard method needs to be continuously updated as new data and technology become available, to ensure continuous improvement of INCAS continuously. 2 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google STANDARD METHOD – INITIAL CONDITION 2.1 PURPOSE This standard method describes the process used by INCAS in determining the initial conditions that will be used as input for modeling GHG emissions and removals from activities on forest land including deforestation, forest degradation, sustainable forest management and enhancement of forest carbon stocks in Indonesia. This process includes data collection, data analysis, quality control and quality assurance. In modeling GHG emissions and removals, initial conditions must be set for each biomass class. Biomass class denotes forests with similar initial carbon stocks that respond in the same way to forest management events. There are several factors that can affect the carbon stock stored in biomass, such as forest type, soil type, climate and land use history. For the purpose of estimating carbon stocks, each biomass class must be grouped into classes that are able to explain the diversity of carbon stocks well. This diversity needs to be identified so that a more detailed analysis of GHG emissions and removals can be carried out. Stratification of forests into biomass classes will reduce the variability and uncertainty of the predicted carbon stock results. Classification of biomass based on forest type and forest conditions in which management activities take place is considered sufficient to reduce variability and uncertainty in the estimation. The potential for biomass class is determined based on the type and condition of the forest, including natural forest (primary dry land forest, secondary dry land forest, primary swamp forest, secondary swamp forest, primary mangrove forest, and secondary mangrove forest) and plantation forest. The forest category is in accordance with the forest land classification contained in the land cover map of the Ministry of Environment and Forestry (KLHK). Biomass refers to all living organic matter that is above the soil surface and below the forest soil surface. Aboveground biomass includes trees (covering all diameter classes) and aboveground understorey vegetation. This includes stems, branches, bark and leaves. Subsoil biomass includes coarse and fine roots. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 3 Machine Translated by Google Litter and dead wood are included in the group of dead organic matter, however, both are included in the biomass estimation because they contribute to the value of the biomass class. For each biomass class, stock values reflecting each carbon source (aboveground biomass, belowground biomass, litter and dead wood) are estimated based on available data (such as forest inventory plots, research plots and information from publications). which is available. Soil organic carbon is not included in this chapter, but it is very important to consider in the calculations, especially in peat swamp forests because peat soils can continue to emit carbon after disturbance. The approach to estimating changes in soil organic carbon in peatlands is described in the Standard method – peatland GHG emissions (Chapter 7 of this Annex). The biomass estimates of each carbon source component (above ground biomass, below ground biomass and dead organic matter) for each biomass class were used as initial values at the start of the GHG emission and removal simulation. 2.2 DATA COLLECTION The data used to determine the initial conditions in the national GHG inventory were collected from various sources, mainly from forest inventory plots. Forest inventory data from temporary and permanent sample plots were used as the basis for estimating biomass in each biomass class. Research data, from studies related to biomass and carbon assessments, are used to fill important information gaps not covered by forest inventories. For aboveground biomass in primary dryland forest, secondary dryland forest, primary swamp forest and secondary swamp forest, the data used to determine initial conditions in the national GHG inventory are derived from the National Forest Inventory (NFI) plot, as presented in the publication. Directorate General of Forestry Planning (2014). NFI is a national program initiated by the previous Ministry of Forestry in 1989 and at that time supported by the United Nations Food and Agriculture Organization (FAO) and the World Bank through the NFI Project. To date, more than 3,900 sample plot clusters have been built and spread throughout Indonesia. These plots are spread systematically throughout the territory of Indonesia at every 20 km x 20 km. Each cluster contains nine plots consisting of a permanent sample plot (PSP) measuring 1 hectare (ha) and surrounded by eight temporary sample plots (TSP). The majority of plots are built in areas below 1000 meters above sea level. All trees with a minimum diameter of 5 cm were measured to obtain DBH and some trees were measured for their total height. Each tree in the plots was also classified according to local species name, canopy characteristics, damage and disease. Each plot is classified according to various types or conditions including land system, altitude class per 100 meters, land use, forest type, stand condition, plant status, topography, slope and aspect. Detailed protocols used in field sampling and system design for data processing of NFI sample plots in Indonesia are described in Revilla (1992). 4 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google A total of 4,450 PSP measurement data from NFIs throughout Indonesia are available for processing and analysis. Each tree in the plot is checked, and the information on each plot is checked to ensure the correctness of the information, as described in the quality control and quality assurance process (Section 2.4). Each individual tree in the plot is added with wood density information1 . Of the 4,450 measurement data available from the NFI PSP, 80% are located on forested land while the other data are located in bush or other land. Of the PSPs located on forest land, the data validation process reduced the number of measurement data that could be used for further analysis to 2,622 (74.1%). The data is grouped into seven (regions) main islands in Indonesia to take into account regional differences according to site conditions, namely Sumatra, Kalimantan, Sulawesi, Papua, Java, Bali and Nusa Tenggara, and Maluku. Values for each region are then applied to each province within that region. Given the unavailability of PSP measurement data from NFI plots for mangrove forest ecosystem types in Indonesia, data from research on carbon assessment of Indonesian mangrove forest ecosystems is used (eg Murdiyarso et al., 2009; 2015; Donato et al., 2011; Krisnawati et al., 2012 reported in Krisnawati et al., 2014). Forest inventory data is used as a basis for estimating aboveground tree biomass. Carbon sources not measured in the forest inventory (e.g. other components of aboveground biomass, roots or belowground biomass, litter and dead wood) are estimated using the relationship based on their proportion to aboveground tree biomass as described in section the following. Table 2-1. Potential data sources used to determine initial conditions. Data Description Source Inventory Tile National Forest Above ground biomass Ministry of Environment and Forestry (DBH ÿ 5cm) (KLHK) (NFI) Vegetation Above ground biomass (all growth Related projects under KLHK monitoring plot phases) Diverse (includes some or all Research plots components of aboveground tree Research activities below on forest carbon biomass, understorey, belowground assessment KLHK and other research institutions biomass (roots), dead wood, litter) Information available from Diverse (used to fill information Journal/Research Report gaps) various publications 1 Compilations of wood densities for Indonesian wood species can be found in the INCAS Timber Carapace Database compiled from various sources (eg Oey, 1964; Abdurrochim et al, 2004; Martawidjaya et al, 2005) Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 5 Machine Translated by Google 2.3 ANALYSIS The analytical approach described in this standard method follows the procedure for estimating forest biomass in calculating CO2 emissions , as described in Krisnawati et al. (2014). This procedure includes methods for estimating: • aboveground biomass (AGA): BAPT for trees (DBH 5cm) AGR for trees (DBH < 5cm; height > 1.5 m) BAPT for understorey vegetation (height < 1.5m); • below ground biomass (BBPT) or roots; • litter; • dead wood. The general approach used to calculate forest biomass for each carbon source is depicted in Figure 2-1. Condition Forest type and Biomass (Carbon Source) Stock early condition Carbon • Part of the Tree above ground level DBH 10 cm Model alometrik DBH < 10 cm, • Forest land Height >1.5 m primary dry • Secondary Proportion to the dryland forest • Undergrowth biomass of the Natural Forest (Height < 1.5m) aboveground part of • Primary the tree carbon 44/12 xstock O2 e= C swamp forest xbiomass Carbon stock 0.5 = • Secondary Root-shoot ratio swamp forest • The part of the plant (proportion to condition biomass forest each Total type and in below the soil surface aboveground biomass (roots) of plant parts) • Primary mangrove forest • Secondary mangrove Proportion to the forest • Litter biomass of the aboveground part of the tree Proportion to the biomass of the • Woody debris aboveground part of the tree Figure 2-1. The general approach used to calculate forest biomass for each carbon source. 6 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google Details of the methodology applied in calculating forest biomass for each carbon source are described below. 2.3.1 Estimation of aboveground biomass (BAPT) BAPT includes all trees of all diameter classes and understorey vegetation. Data for each individual tree in the inventory plot was used to estimate AGR for trees with a diameter at breast height (DBH) of 5 cm or more. Estimates are made as follows: BAPT pohon (DBH ÿ 5 cm) AGR of each tree (DBH 5 cm) in a plot was estimated using an allometric model developed for pantropical forests (Chave et al., 2005), which used DBH and wood density (WD) per species as the main parameters. Several other allometric models were also tested, including the local allometric models that have been reviewed and compiled in Krisnawati et al. (2012). However, local allometric models specific to all six forest types are not available for all seven main islands in Indonesia, so the general allometric model from Chave et al. (2005) was used. This model has been tested and performs as well as local models in Indonesia's tropical forests (Rutishauser et al., 2013; Manuri et al., 2014). The models are: * BAPTT = ÿ exp (-1.499 + (2.148*lnDBH)+(0.207*lnDBH)2 – (0.0281*lnDBH3 )) where BAPTT = BAPT tree (kg), = wood density2 , DBH = diameter at chest height (cm). The AGA produced is the total AGR for trees (including stems, branches, twigs, leaves and fruit/ flowers, if any) in dry weight (expressed in kilograms [kg]). Total BAPT for each plot (per hectare) is calculated by adding up the estimated values AGR of all trees in the plot (expressed in megagrams (Mg) or tons (t)): BAPT BAPT where BAPTP = BAPT plots (Mg ha-1), BAPTT = AGR trees (kg), AP = plot area (ha), n = number of trees per plot. The average BAPT value for each forest type in the big islands is obtained by finding the average BAPT for all plots in each forest type: BAPT BAPT where BAPTj = average BAPT forest type-j, BAPTPi = BAPT plot-i, n= number of plots 2 Wood density after applying the correction factor using the Reyes et al. (1992): Y = 0.0134 + 0.8X to match the dry weight of aboveground biomass. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 7 Machine Translated by Google AGR of trees (DBH < 5 cm; height > 1.5 m) In inventory plots with unmeasured DBH trees < 5 cm, the proportion is obtained from research plots or vegetation monitoring plots that have a complete aboveground tree component, then the average proportion of the unmeasured components in the plot is applied. In swamp forest, the average proportion of AGR of trees with DBH < 5 cm; height > 1.5 m to AGR for trees with DBH 5 cm (obtained from peat swamp forest vegetation monitoring plots (Graham, 2013)) was used to calculate the immeasurable component of aboveground tree biomass. The resulting proportions were 11.4% for primary swamp forest and 11.1% for secondary swamp forest, respectively. In primary and secondary dryland forest, the proportion of 0.2% for primary dryland forest and 1.1% for secondary dryland forest was adopted from previous research conducted in dryland protected forest (Krisnawati et al., 2013). BAPT of lower plants (height < 1.5 m) The entire inventory plot only provides data for the tree component above ground level. Understorey plants (including seedlings, shrubs, ferns, herbaceous plants, etc.), which are part of the aboveground biomass in forest ecosystems are generally excluded from inventories. Consequently, aboveground biomass for understorey vegetation is estimated by using the proportions based on the results of previous studies according to the type of forest ecosystem. In swamp forest, the average proportion used was obtained from several studies conducted by Jaya et al. (2007) and Dharmawan (2012), yielded estimates of understorey vegetation biomass of 2.4% of aboveground tree biomass for primary swamp forest and 3.8% for secondary swamp forest. In secondary dryland forest, the proportion of 2.7% of the above-ground tree biomass was obtained from the results of research by Junaedi (2007) and Hardiansyah (2011). In primary dryland forest, the proportion of 0.5% of aboveground tree biomass is based on the results of previous studies in dryland protected forest (Krisnawati et al., 2013). 2.3.2 Estimation of belowground biomass (roots) Estimation of belowground biomass (roots) can be done using allometric models or using the aboveground biomass proportion, and is expressed as the ratio of root biomass to aboveground biomass (root-shoot ratio) (IPCC, 2002). The standard value of the root:shoot ratio of tree biomass has been published in the LULUCF Practical Guide (Land Use, Land Use Change and Forestry) and in the REDD Source Book, which is 0.24 (0.22- 8 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google 0.33) (IPCC, 2003; GOFC-GOLD, 2009). However, the ratio may vary according to species, ecosystem type, soil conditions and climate. The root:shoot ratio of 0.29 was obtained from the research results of Moser et al. (2011) for tropical dryland forest can be used for this type of forest. For swamp forest, the allometric model of Niiyama et al. (2005) was first applied to estimate the belowground biomass in plots with a complete measurement of the aboveground tree component and then obtained the average proportion of belowground biomass to aboveground biomass, the result was the root ratio :shoot of 0.22. 2.3.3 Estimation of litter Litter includes dead tree residue (fruit, leaves, flowers) on the forest surface. This carbon source is reported to vary from 1.3% to 23% of the aboveground tree biomass (derived from various sources documented in Krisnawati et al., 2014). 3.0% proportion of BAPT is used for primary dryland forest; 2.7% is used for secondary dryland forest (Brown et al., 1995; Hardiansyah, 2011); 1.6% is used for primary swamp forest; and 2.3% is used for secondary swamp forest (Jaya et al., 2007; Dharmawan, 2012). 2.3.4 Estimation of dead wood Dead wood includes all dead wood material including dead trees that are still standing, fallen trees and tree parts (trunks, branches, twigs) above the ground. This carbon source is equivalent to 10-40% of the aboveground biomass (Uhl and Kauffman, 1990; Verwer and Van der Meer, 2010). This biomass includes dead wood which is thought to account for 18% of aboveground tree biomass in primary dryland forest and 33% in secondary dryland forest (obtained from various sources documented in Krisnawati et al. (2014)). In peat swamp forest, the proportion of 18.5% AGR was used to estimate dead wood biomass in primary swamp forest (Dharmawan, 2012) and 23.9% for secondary swamp forest (Ludang and Jaya, 2007; Dharmawan 2.4 QUALITY CONTROL AND QUALITY ASSURANCE Inventory plots obtained from various sources may be constructed for different purposes, there is no standard protocol for data collection (eg sampling design, plot size, data measurement scope, etc.). As a consequence, the measurement data varies in quality and scope, both spatially and temporally. However, all inventory plots used for analysis followed the same measurement standards: (1) located in forest with a total measurement area of 0.1 ha; (2) all trees with a diameter of 5 cm at chest height (DBH) were measured for their DBH; and (3) the species being measured were identified. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 9 Machine Translated by Google Inspection of the quality of measurement data from the inventory plot is mainly carried out to see if there are errors in the measurement and recording data. This process includes: (i) checking plot locations, such as administrative locations (province, district, sub-district), geographical position (longitudinal and latitudinal coordinates), forest type, soil type by overlapping related maps, (ii) checking recording unit number (sub- plot) in each plot, (iii) checking DBH measurement data, species name and condition of each tree in the plot, (iv) checking plot information such as base plane, stand density, volume, aboveground biomass. 2.5 RESULTS AND UNCERTAINTY ANALYSIS The quantity of biomass (stored in aboveground trees, understorey vegetation, dead wood and belowground biomass) in each biomass class for each region is used as input for the initial conditions for modeling GHG emissions and removals from activities that take place on forest land. , where changes in carbon stock are calculated based on the impact of certain events. The results of the analysis applied in this standard method are expressed in dmt ha-1 (tonnes per hectare dry weight) for each component of the carbon source (aboveground biomass includes stems, branches, bark and leaves; while belowground biomass includes roots). coarse and fine) and in tC ha-1 (tonnes of carbon per hectare) for dead organic matter sources (dead wood and litter). The output is written in the format required for the method input as described in Standard methods – Data Integration and Reporting (Chapter 8 of this Annex). The output of this analysis is briefly presented in Table 2-2. Statistical analysis was performed to determine the range of conjectures (lower limit and upper limit) at the 95% confidence interval level. 10 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google Table 2-2. Initial above ground biomass (DBH 5 cm) for each forest type and area of analysis in Indonesia. Rate-rate 95% confidence interval (dmt ha-1) Biomass class N plot of Main island (dmt ha-1) (forest type) measurement Rate-rate Down Above INDONESIA 874 266,0 259,5 272,5 Bali and Nusa 52 274,4 247,4 301,3 Southeast Java nd nd nd nd land forest Borneo 333 269,4 258,2 280,6 primary dry Maluku 14 301,4 220,3 382,5 Papua 162 239,1 227,5 250,6 Sulawesi 221 275,2 262,4 288,1 Sumatra 92 268,6 247,1 290,1 INDONESIA 1299 197,7 192,9 202,5 Bali and Nusa 69 162,7 140,6 184,9 Southeast Java 1 170,5 already already land forest Borneo 608 203,3 196,3 210,3 secondary dry Maluku 99 222,2 204,5 239,8 Papua 60 180,4 158,5 202,4 Sulawesi 197 206,5 194,3 218,7 Sumatra 265 182,2 172,1 192,4 INDONESIA 95 192,7 174,6 210,8 Bali and Nusa already already already already Southeast Java already already already already Swamp forest Borneo 3 275,5 269,2 281,9 first Maluku already already already already Papua 67 178,8 160,0 197,5 Sulawesi 3 214,4 -256,4 685,2 Sumatra 22 220,8 174,7 266,9 INDONESIA 354 159,3 151,4 167,3 Bali and Nusa already already already already Southeast Java already already already already Secondary Borneo 166 170,5 158,6 182,5 swamp forest Maluku already already already already Papua 16 145,7 106,7 184,7 Sulawesi 12 128,3 74,5 182,1 Sumatra 160 151,4 140,2 162,6 Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 11 Machine Translated by Google Rate-rate 95% confidence interval (dmt Biomass class N plot of Main island (dmt ha-1) ha-1) (forest type) measurement Rate-rate Down Above Primary mangrove forest Borneo 9 237,2 184,7 298,6 Secondary mangrove Borneo 11 108,0 70,6 152,5 forestb Notes: a BAPT of primary mangrove forest is suspected based on the research results of Murdiyarso et al. (2009); Donato et al. ÿ (2011); and Krisnawati et al. (2014) b BAPT secondary mangrove forest is suspected from the results of research Krisnawati et al. (2012), as reported in ÿ Krisnawati et al. (2014) nd = no data na = not applicable From the estimated aboveground biomass values (Table 2-2), the proportion of other unmeasured carbon sources to aboveground biomass is then derived for each biomass class (forest type) using the proportion values specified in Section 2.3. The results of the estimated unmeasured carbon sources based on their proportions are presented in Table 2-3. Table 2-3. The estimated unmeasured carbon source biomass is based on the relative proportion to aboveground biomass. Class BAPT BAPT BBPT litter Dead wood biomass Main island <5cm understory (in ha-1) (dmt ha-1) (dmt ha-1) (dmt ha-1) (forest type) (dmt ha-1) INDONESIA 0,5 1,2 77,6 8,1 48,1 Bali and Nusa 0,5 1,2 80,1 8,3 49,7 Southeast Java nd nd nd nd nd land forest Borneo 0,5 1,2 78,6 8,2 48,8 primary dry Maluku 0,6 1,4 88,0 9,2 54,5 Papua 0,5 1,1 69,8 7,3 43,3 Sulawesi 0,6 1,2 80,3 8,4 49,8 Sumatra 0,5 1,2 78,4 8,2 48,6 INDONESIA 2,2 5,5 59,5 5,5 65,9 Bali and Nusa 1,8 4,5 49,0 4,5 54,3 Southeast Java 1,9 4,7 51,4 4,7 56,9 land forest Borneo 2,2 5,6 61,2 5,6 67,8 secondary dry Maluku 2,4 6,1 66,9 6,1 74,1 Papua 2,0 5,0 54,3 5,0 60,2 Sulawesi 2,3 5,7 62,2 5,7 68,9 Sumatra 2,0 5,0 54,9 5,0 60,8 12 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google BAPT BAPT BBPT litter Dead wood Biomass class Main island <5cm of lower plants (dmt ha-1) (dmt ha-1) (dmt ha-1) (forest type) (dmt ha-1) (dmt ha-1) INDONESIA 22,0 5,0 48,4 3,4 39,7 Bali and Nusa already already already already already Southeast Java already already already already already Swamp forest Borneo 31,5 7,2 69,1 4,9 56,8 first Maluku already already already already already Papua 20,5 4,7 44,9 3,2 36,9 Sulawesi 24,5 5,6 53,8 3,8 44,2 Sumatra 25,3 5,8 55,4 3,9 45,5 INDONESIA 17,7 6,8 40,4 4,1 42,3 Bali and Nusa already already already already already Southeast Java already already already already already Swamp forest Borneo 19,0 7,3 43,3 4,4 45,3 seconds Maluku already already already already already Papua 16,2 6,2 37,0 3,8 38,7 Sulawesi 14,3 5,5 32,6 3,3 34,1 Sumatra 16,9 6,4 38,4 3,9 40,2 Forest mangrove Borneo nd nd 15,1 nd 99,7 primer Forest secondary Borneo nd nd 14,8 nd 93,3 mangrove Notes: nd = no data na = not applicable The biomass estimates for each component of the carbon source (as presented in Tables 2-2 and 2-3) are used as initial values at the start of the simulation of GHG emissions and removals. For simulation purposes, aboveground biomass sources are divided into stem, branch, bark and leaf components. Sources of below-ground biomass are divided into fine roots and coarse roots. The carbon sources of litter and dead wood are divided into decomposed and resistant components. More detailed information on values and their sources is documented in the INCAS FullCAM Database (see Database description in Appendix 1). Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 13 Machine Translated by Google 2.6 LIMITATIONS The INCAS framework is designed to use the best available data, and assumptions are used to fill in data gaps. The limitations identified in this standard method include: • Only forest lands are included in the initial condition determination during the simulation period and are described in this standard method. There may be several other landforms at the beginning of the simulation period that require establishing initial conditions. • Only forest ecosystem types are used as the basis for forest biomass classification. • Estimates of aboveground biomass, both in primary and secondary mangrove forests, were based solely on the research plots constructed in Kalimantan. • Data for biomass components are not equally available for all region. 2.7 IMPROVEMENT PLAN Improvement plans for this standard method are outlined below: • Land outside forest land needs to be included in the calculation of future land-based sector GHG emissions and removals, and designated as a new/additional biomass class (eg large plantations such as oil palm and rubber). These types of land can be estimated separately. • Factors other than forest ecosystem type that can affect the amount of biomass need to be analyzed, eg soil type, altitude, rainfall, etc. The initial analysis to get the biomass class based on these biophysical factors did not show a strong enough relationship. However, this process needs to be retried when more complete data are available. • More inventory plots are needed in Indonesia's mangrove forests to increase the accuracy of biomass estimates for this forest type. Research recently published by Murdiyarso et al. (2015) need to be included in future calculations as part of updating or improving estimates of mangrove forest biomass. • The proportion of biomass per carbon source for each biomass class needs to be updated if data or research is available showing differences between regions. 14 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google STANDARD METHOD – GROWTH AND FOREST TRANSITION 3.1 PURPOSE This standard method describes the process used by INCAS in determining forest growth and transition that will be used as input for calculating GHG emissions and removals from activities occurring on forest land, including: deforestation, forest degradation, sustainable forest management and enhancement of forest carbon stocks in Indonesia. . This process includes data collection, data analysis, quality control and quality assurance. INCAS adopts an event-driven modeling approach (see Chapter 7, Standard Methods – Data Integration and Reporting) in calculating changes in forest carbon stocks, which include ongoing processes (e.g. growth or production, transition, decomposition/decay) and events that occur over time. occur periodically (eg logging, fires) which usually have a direct impact on carbon flows thereby impacting biomass and carbon stocks at any point in time. The total biomass and carbon stock at a point in time is the result of a series of events in the initial biomass and carbon stock prior to a disturbance process or management event, influenced by growth (production), transition and decomposition processes after a disturbance or management event. The impact of disturbances or management events on forest conditions where GHG emissions and removals are generated, needs to be calculated so that GHG emissions and removals can be estimated accurately. The purpose of this standard method is to describe the methodology used to determine growth rates, aboveground and belowground biomass transfer, and dead organic matter decomposition rates, for each component of each biomass class. The output of this standard method will be used as input in calculating emissions and removals for the production, transfer and decomposition processes for each biomass class (documented in Chapter 7, Standard Methods – Data Integration and Reporting. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 15 Machine Translated by Google 3.2 DATA COLLECTION The data used to determine forest growth are collected from a variety of sources. This includes gathering information from permanent plot measurement data (PUP) built in logged-over natural forest and other forest inventory data, such as permanent sample plots specially constructed for long-term research in order to monitor forest growth/ increment and stand dynamics, as well as data and information available in various publications, including research reports. The PUP, known as the Permanent Plot, is part of a national program initiated by the Minister of Forestry in 1995 through Ministerial Regulation no. 237/Kpts II/1995. The purpose of this regulation is to require all forest concession holders in Indonesia to establish PUPs to monitor growth and post-harvest yields in managed forest areas. The Forestry Research and Development Agency (FORDA) issued guidelines for the creation and measurement of permanent plots through the Decree of the Head of the Forestry Research and Development Agency No. 38/KPTS/VIII-HM.3/93. PUP is classified into two main forest types, namely dry land forest and swamp forest. The plots are built on logged-over areas, 1 to 3 years after felling and are periodically measured/monitored. Each forest management unit (FMU) needs to establish at least 6 plots in dry land forest and 16 plots in swamp forest. Each PUP contains observation plots measuring 100m x 100m, the DBH of all trees 10cm is measured and the species identified. The measurement results are used as information on forest growth and above-ground biomass productivity (DBH 10cm). Another periodic measurement data used is the STREK plot (silvicultural regeneration technique for logged-over forest in East Kalimantan). The plot is considered to be one of, if not the only, relatively good dipterocarp forest PUP in the world (Priyadi et al., 2005). The plot was developed in logged-over forest in East Kalimantan by the Forestry Research and Development Agency, in collaboration with CIRAD-foret and PT Inhutani I in 1989/1990. The plots were constructed to represent three different logging or silvicultural techniques, namely low impact logging with a diameter limit of 50 cm (RIL 50); RIL 60 and conventional logging. PUP was also established in primary forest as a control. The total area of permanent plots is about 48 ha and is measured periodically every 2 years until 2010. Measurements are carried out for all species with a diameter limit of 10 cm. A more detailed description of this plot can be found in Bertault and Kadir (1998) and Siran (2005). Information available in proceedings, journals, student theses, research reports in Indonesia or neighboring countries with similar ecosystem conditions (eg Putz and Chan, 1986; Nguyen The et al. 1998; Inoue et al., 1999; Simbolon, 2003; Hashimoto et al., 2004; Hiratsuka, 2006; Limbong, 2009; Meunpong et al., 2010; Krisnawati et al., 2011; Saharjo, 2011; Susilowati, 2011; Yuniawati et al., 2011; Dharmawan, 2012; Purba et al., 2012) were used. In addition, tables of stand yields for the main industrial plant species in 16 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google Indonesia (Suharlan et al., 1975) was also included. This source of information is used as a reference for the quantification approach in modeling GHG emissions and removals with INCAS to determine growth rates, aboveground and belowground biomass turnover and dead organic matter decomposition rates, for each component of the biomass class. 3.3 ANALYSIS The methodology used to determine forest growth in this standard method includes the development and analysis of growth and increment curves from data and information collected from various sources as described in Section 3.2. All data from the inventory plots and research plots as well as information from various publications are reviewed through a quality control process to ensure that only valid data is used. For each data set, sample locations, forest conditions and parameters affecting growth yields were recorded. Some data and information from publications (eg Putz and Chan, 1986; Nguyen The et al. (1998); Inoue et al., 1999; Simbolon, 2003; Hashimoto et al., 2004; Hiratsuka, 2006; Limbong, 2009; Meunpong et al., 2010; Krisnawati et al., 2011; Saharjo, 2011; Susilowati, 2011; Yuniawati et al., 2011; Dharmawan, 2012; Purba et al., 2012) were further analyzed and transformed to prepare data on forest growth rates and transition rates in the required format. INCAS. Timescale data from permanent plots established in logged-over forest and STREK plots were analyzed to calculate the growth of aboveground biomass after logging. The above-ground biomass calculation is carried out using the approach described in the monograph and the Allometric Model guide for estimating tree biomass in various types and types of ecosystems in Indonesia (Krisnawati et al., 2012; FORDA, 2013). Information from the stand yield table (Suharlan et al. 1975) covering 10 main types of plantation forest (Teak, Rasamala, Damar, Pinus, Sonokeling, Mahogany, Acacia, Sengon, Balsa and Jabon) was re-analyzed to obtain average growth curves for various site index classes. each type of plant. In analyzing growth, three growth phases that occur in stands need to be considered: (1) young phase with high growth rate, (2) full mature phase with constant growth rate, and (3) old phase, with decreased growth rate. The three growth phases, in general, will form a sigmoid curve (Figure 3-1). Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 17 Machine Translated by Google Slowing growth rate Cumulative growth curve Fast, constant growth rate Fast growth rate Time Figure 3-1. Stages of growth speed. Several other regression models that form a sigmoid curve or growth curve (Weibull, root, modified exponential, logistic, logistic power, Gompertz, two-exponential association, triple exponential association), were tested to construct a suitable growth curve, model selection was based on a combination of criteria. statistics and logic. The analysis is documented in the INCAS growth database. Two types of growth curves are considered (Figure 3-2): • Current Annual Increment (CAI ), defined as growth over a one-year period at each stage of forest life. • Average Annual Increment (MAI = Mean Annual Increment), defined as forest growth rate to a certain age. For modeling purposes in INCAS, CAI data are required to calculate annual biomass or carbon stock (this can be developed from both biomass and volume values). Age (yrs) Current annual increment (CAI) Average annual increment (MAI) Figure 3-2. An example of a growth curve of volume growth. 18 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google 3.4 QUALITY CONTROL AND QUALITY ASSURANCE The quality control process is implemented by checking that the methods used in data collection and data analysis meet the minimum standards of feasibility and completeness. This includes checking the quality of measurement data from inventory or monitoring plots to see if there are recording and measurement errors. The accuracy of the data is further checked by overlapping the related maps to check that the forest types match the recorded tree species. Some information such as stand density and base plane were used to check the quality of the data. The procedure for checking data quality was carried out following the procedure described in Krisnawati et al. (2014) and is applied in Chapter 2 of this Annex. 3.5 RESULTS AND UNCERTAINTY ANALYSIS In terms of growth rate, the annual change in biomass carbon stock can be estimated using the addition-loss method, which combines the annual increase in carbon stock from biomass growth with biomass loss due to shifts and management events. The addition of biomass used in INCAS is characterized as plantation growth or natural growth. Plant growth is defined as plant growth that is intentionally planted. Natural growth is defined as growth that occurs as a result of natural succession processes following disturbances in natural forests, e.g. fire, logging. The assumptions, data sources and analysis results that form the growth curves and growth tables for each type of plantation forest and each type of natural forest are documented in the INCAS Growth Database (see database description in Appendix 1). The growth includes: • Plant growth ÿ Agathis (Agathis sp.) ÿ Kemiri (Aleurites moluccana) Acacia (Acacia sp.) Rehabilitation plants (various types) ÿ Balsa (Ochroma bicolor) ÿ Jabon (Anthocephalus cadamba) ÿ Teak (Tectona grandis) ÿ Mahogany (Swietenia sp.) ÿ Pine (Pinus sp.) Rasamala (Altingia excelsa) Sengon (Albizia falcataria) Sonokeling (Dalbergia latifolia) Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 19 Machine Translated by Google • Natural growth Post fire Post felling INCAS assumes no net growth in primary forest, where biomass stocks are assumed to be at equilibrium as before human disturbance (growth equivalent to transition and decomposition). In natural forests that have been disturbed and then left undisturbed over the long term, natural growth may compensate for the loss of biomass due to previous disturbances; and may get the same biomass stock as the initial condition of the forest, although it has a different structure and composition of forest types. Quality control procedures are used to select the best available data for inclusion in the analysis. Statistical analysis was then performed on the selected models to obtain growth curves for plantations and natural forests after disturbance. An example of the output from an analysis of secondary swamp forest growth after fire disturbance is presented below (Figure 3-3). CAI Parameter Standard Year Model Equality R-sq (tons/ha/yr) a b c d error 0 0,0 1 Log_power a/(1+((x/b)^c)) 237,01 15,03 -1,9 9,35 0,98 1,6 2 7,6 Weibull a-b*e^(-c*(x^d)) a*e^(-e^(b-cx)) 222,38 226,33 0,015 1,43 10,2 0,98 3 8,1 216,84 1,45 0,12 11,3 0,98 4 8,5 Gompertz 5 8,7 250 6 8,8 7 8,8 8 8,8 9 8,7 10 8,5 200 11 8,3 12 8,0 13 7,7 14 7,4 150 15 7,1 16 6,8 17 6,4 18 6,1 100 19 5,8 20 5,4 21 5,1 22 4,8 23 4,5 50 24 4,2 25 3,9 26 3,6 27 3,4 0 28 1,5 29 0,9 0 20 40 60 80 100 30 0,5 31 0,3 Years after the fire incident 32 0,2 33 0,2 data Log_power Weibull Gompertz 34 0,2 35 0,0 Figure 3-3. An example of the analysis of the growth of secondary swamp forest after a fire. 20 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google 3.6 LIMITATIONS Some of the limitations of this standard method are outlined below: • Some types of plantation forest and natural forest conditions do not have permanent sample plots with long-term/periodic measurements to be able to explain the long-term impact of management/events on growth. • The same growth curve is applied to all plantation rotations and to each natural forest biomass class because the current approach does not differentiate between site conditions or the more detailed management scale. Initial attempts to generate biomass classes based on site biophysical characteristics did not produce a strong enough relationship. This effort should be retried when more data becomes available. • Transition and decay rates of dead organic matter are not yet available in Indonesia, therefore standard turnover and decay rates were adopted from Australian tropical rainforests as an interim measure, as these forests were seen to have similar transition and decay characteristics and detailed data were available. 3.7 IMPROVEMENT PLAN An outline of the improvement plan for this standard method is described below: • Growth data on plantations and natural forests can be improved by expanding access to additional data currently available and through research designed to fill information/knowledge gaps. • The plantation growth curve could be refined to include additional information on the site's biophysical characteristics and the impact of management practices on growth, particularly on site nutritional conditions and water level management on peatlands. • The secondary natural forest growth curve could be refined by including additional information on the biophysical characteristics of the site and the impact of management practices on subsequent growth processes. • Research on the rate of transition and decomposition in Indonesia needs to be carried out to better understand the rate of transition and decomposition under different conditions of natural forest and plantation forest. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 21 Machine Translated by Google STANDARD METHOD – EVENT AND REGIME FOREST MANAGEMENT 4.1 PURPOSE This standard method describes the process used by INCAS in determining forest management events and regimes that can occur and needs to be modeled for all biomass classes to calculate GHG emissions and removals from activities that take place on forest land, including: deforestation, forest degradation, sustainable forest management and increase in forest carbon stocks in Indonesia. This process includes data collection, data analysis, quality control and quality assurance. For this purpose, an event-based model (as described in Chapter 8, Standard Method – Data Integration and Reporting) is used to track changes in carbon stocks and GHG emissions related to land use and events under management. The model also accounts for major GHG changes and human-induced land use practices. The sub-models used in this model can be integrated into various combinations to suit the available data and required outputs. This model can be used to track carbon stocks and flows under various forest system conditions. There are many possible forest management events and regimes in Indonesia. Forest types and conditions, other land uses, types of events and existing management activity regimes need to be defined so that detailed modeling of GHG emissions and removals can be carried out. A forest management event, as defined in this standard method, is a specific forest management action that takes place at a certain time or on a regular basis and is usually caused by humans. Forest management regimes describe the combination of forest management practices or events applied to land management and the timing of events occurring in a location. The purpose of this standard method is to describe the methods used in determining forest management events and regimes that will be used as inputs in GHG emission modeling and reporting within the INCAS framework. 22 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google 4.2 DATA COLLECTION Various sources of data and information collected from various organizations and government agencies in Indonesia were used for analysis. Spatial data were obtained from the Ministry of Environment and Forestry, the Ministry of Agriculture and the National Institute of Aeronautics and Space (LAPAN). Some relevant data and information were also collected from various forestry offices at the provincial level within the Ministry of Environment and Forestry. This includes the agencies responsible for monitoring and utilizing production forests, watershed management, forest area stabilization, and conservation of natural resources and national parks. Information was also collected from forest concession owners (IUPHHK). In determining management events and regimes, discussions and consultations were carried out with relevant forestry stakeholders and national experts. Prior to the discussion, potential events and management regimes that might be applied in Indonesian forests were identified based on existing knowledge and field experience. Discussions and consultations were carried out to verify the interim analysis/ preliminary, identifying available data and obtaining more detailed data and classifying information related to forest disturbances and types of management that can affect the addition or loss of forest biomass. Four main forest management events leading to forest change were identified: land clearing, logging, burning and planting. a. Land clearing is defined as the conversion of forest areas, either primary or secondary forest, into other land uses (eg settlements, mining, agriculture, etc.) and conversion of natural forests into plantation forests. This event removes all aboveground biomass from the site and transfers some of the live biomass to dead organic matter. b. Logging includes both legal and illegal logging. A logging event is considered legal if the activity is carried out in a plantation/production forest (forest concession area) that has a harvesting permit. Several logging techniques that can have an impact on biomass loss were identified, namely clear logging, selective logging using conventional logging techniques, and selective logging using reduced impact logging (RIL). Logging activities that occur in forests other than production forests (eg protected forests, conservation areas, national parks) are considered illegal logging. This event removes some or all of the aboveground biomass from the site and transfers some of the live biomass into dead organic matter. c. Burning (forest fires) is categorized into moderate and severe fires. This event releases carbon (such as CO2 , CO and CH4 ) and nitrogen (N2 O and NOx ) into the atmosphere and transfers some of the carbon into dead organic matter and soil. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 23 Machine Translated by Google d. Planting includes reforestation, rehabilitation and plant enrichment programs. This event creates new forest in non-forested areas and increases the stock of biomass. All of these events were identified during the analysis and modeling time period. The spatial data used for the analysis in this standard method are presented in Table 4-1. Table 4-1. Sources of data used to define forest management events and regimes. Data Description Source Annual forest/non-forest data analyzed from data Area and Landsat and the incidence of forest loss and gain obtained EIGHT forest change by comparing the annual forest area Primary and secondary dryland forest; primary and secondary swamp forest; primary and secondary mangrove KLHK Land cover forests; and plantations (and all other land cover classes) Production forest (production, limited production and Forest function conversion); KLHK Conservation and protected forest Ministry Soil type Types of organic (peat) and mineral soil Agriculture; IPCC Plantation Palm and rubber KLHK The area and year of operation of the forest concession Forest concessions including the logging system applied (RIL or conventional) KLHK Burned area Annual fire area analysis INCAS (KLHK) 4.3 ANALYSIS The data collected is reviewed as part of the INCAS quality control process in determining its quality and use for modeling GHG emissions and removals. The unique combination of biophysical conditions, management functions and forest management activities are identified and utilized to construct the “suite” within the INCAS. A “suite” describes a site-specific combination and management category, including forest type, forest function, soil type, logging system, plantation, fire, forest transition/ non-forest, subsequent land use categories, management activities and events. The conditions (from each category) used as the basis for determining the “suite” and management regime are presented in Table 4-2. 24 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google Table 4-2. The possible conditions of each category are used in determining the management regime or suite. No. Category Condition Primary dryland forest Primary mangrove forest Primary swamp forest 1. forest type Secondary dryland forest Secondary mangrove forest Secondary swamp forest Plantation forest 2. Conservation forest, protected forest Forest function Production forest Mineral 3. Soil type Organic There isn't any 4. Logging system Conventional RIL There isn't any 5. Plantation Palm oil Rubber Fire 6. Fire No fire Land clearing Land clearing, temporary standing, revegetation 7. Transition There isn't any revegetation Revegetation, Land clearing Deforestation 8. Activity Forest degradation Sustainable forest management Reforestation (Increased forest carbon stock) Other land use Next land use category forest land 9. Agricultural land Plantation forest High intensity fire Medium intensity fire Land clearing Illegal clear-cutting Conventional selective logging 10. Genesis 1 RIL . selective logging Fast-growing dry land Mangrove type planting Planting fast-growing swamp species Cultivation of dry land forest types Swamp forest planting Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 25 Machine Translated by Google No. Category Condition Palm planting Rubber planting Cultivation of dry land types 11. Genesis 2 Swamp type cultivation Fast-growing dry land Planting fast-growing swamp species Mangrove type planting The description of the suite covering the management regimes and events is documented in the INCAS Suite Regime Database (see description in Appendix 1). The number of suites that were recorded after quality control and validation were carried out by reviewing and checking activities was 1,152 suites. INCAS can model many more management regimes, but requires more detailed spatial data and management information. To determine forest management events and regimes, the analysis includes two main procedural steps: 1. All forest land areas were allocated to the management regime based on the suite characteristics, starting in the first year of the simulation period and repeated for each year during the simulation period (2001 to 2012). 2. Areas monitored for changes (changes detected from LAPAN forest cover change analysis) were regrouped under other regimes based on location, time of day, type of change (loss or gain of forest) and suite characteristics. Management regimes are then associated with REDD+ activities (deforestation, forest degradation, sustainable forest management and enhancement of forest carbon stocks) based on the following criteria: • Deforestation occurs when forest cover is monitored in primary and secondary forest cover classes and there is no additional forest cover in the same pixel (area) in subsequent years during the simulation period (land remains non-forest). This represents the 'permanent loss' of forest land. • Forest degradation occurs when the forest land cover class changes from primary forest to secondary forest, or natural forest changes to plantation forest even though forest cover loss is not observed. Forest degradation also occurs when forest cover loss is detected in primary or secondary forest and then forest cover gain is observed at the same pixel (area) in subsequent years in the simulation period. This can also occur when forest cover loss is detected in primary or secondary forest cover classes but concession data indicate harvesting by conventional logging techniques. It may also occur when forest cover loss is not detected in a primary or secondary forest land cover class but fire data indicate that a fire has occurred. 26 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google • Sustainable forest management (SMF) occurs when forest cover is not detected in primary or secondary forest cover classes even though concession data show RIL logging is taking place. This category may include 'temporarily unstocked' forest lands that can grow back to their original forest state. • An increase in forest carbon stock occurs when a plantation forest cover class is observed where it did not exist in the previous year or where revegetation or additional forest cover is observed on non-forest land. The impact of each carbon stock management event is calculated based on available research and measurement data, or according to management prescriptions and expert justification when data is not available. The parameters of each management event are available in the INCAS Event Database (this description is available in Appendix 1). 4.4 QUALITY CONTROL AND QUALITY ASSURANCE The process of quality control and quality assurance is carried out as follows: Quality control – Data checking and validation is carried out by the INCAS team for all data that has been collected. This is done to ensure that the data used is suitable for use and consistent with other data sets. Quality assurance – The following quality assurance steps are performed by members of the INCAS team who are not involved in data analysis and external technical advisors: • review the methodology used to ensure that there are no errors when combining data to generate management regimes and events; • inspect and validate management suites and regimes to ensure consistency and expected accuracy; • reviewing final results to check that they are verifiable and can be compared. 4.5 RESULT The results of this standard method are recorded in the INCAS Suite Regime database . Summary descriptions of regime types, occurrences and suite characteristics are presented in Table 4-3 and Table 4-4. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 27 Machine Translated by Google Table 4-3. Summary description of the regime. No. Description of the regime 1 Conversion of primary dryland forest to other land (settlement, mining etc.) 2 Conversion of primary dryland forest to agriculture 3 Conversion of secondary dryland forest to agriculture 4 Conversion of secondary dryland forest to other land (settlement, mining, etc.) 5 Conversion of secondary dryland forest to plantation forest 6 Conversion of primary mangrove forest to other land (ponds, etc.) 7 Conversion of secondary mangrove forest to other land (ponds, etc.) 8 Conversion of primary swamp forest to agriculture 9 Conversion of primary swamp forest to other land (settlement, mining etc.) 10 Conversion of secondary swamp forest to agriculture 11 Conversion of secondary swamp forest to other land (settlement, mining etc.) 12 Conversion of secondary swamp forest to plantation forest 13 Forest disturbance (fire) followed by natural growth 14 Forest disturbance (illegal logging) followed by natural growth 15 Forest management in dryland forest 16 Forest management in swamp forest 17 Cultivation 18 Environmental rehabilitation/planting 19 Plantation forest management 28 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google Table 4-4. Summary description of the incident. No. Description of the incident 1 Illegal clear-cutting 2 Selective logging of conventional dryland forest 3 Selective logging of dryland forest RIL 4 High intensity fire 5 Land clearing 6 Medium intensity fire 7 Cultivation of dry land types 8 Fast-growing dry land 9 Planting fast-growing swamp species 10 Mangrove type planting 11 Palm planting 12 Rubber planting 13 Swamp type cultivation 4.6 LIMITATIONS Some of the limitations identified in this standard method are: 1. Analysis of events and management regimes is only carried out on forest land (primary dry land forest, primary mangrove forest, primary swamp forest, secondary dry land forest, secondary mangrove forest, secondary swamp forest and plantation forest). Other non-forested lands may experience events and management regimes that should be considered as part of an ongoing improvement plan. 2. Forest functions (production forest, limited production forest, convertible production forest, conservation forest and protection forest) are only categorized into two main functions in this standard method, namely production forest and conservation/ protection forest. Getting more details on the practice of managing all forest functions can improve the accuracy of GHG emission estimates. 3. Given the lack of detailed information regarding the spatial location of the silvicultural system, it is assumed that all forest areas managed in each concession are managed using conventional logging or RIL. In fact, forest concession management can use a combination of the two silvicultural systems. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 29 Machine Translated by Google 4. The condition of the plantation forest in the area detected as a plantation forest was analyzed without considering the actual age of the plant due to the lack of data on the age class of the plant. This affects the spatial allocation of plantation forest management regimes, especially for plantations that have existed since the beginning of the simulation period, so the assumption of distribution in normal age classes is used. This will affect the timing of GHG removals and emissions due to logging. 5. Changes in the silvicultural system within a certain time frame are not included. For example, the silvicultural system used before the start of the simulation period was not considered. This activity may result in forest conditions and biomass quantity as well as the presence of dead organic matter present in different locations from the modeled management regime. This will affect GHG uptake through growth and emissions through decay of dead organic matter during the simulation period. 4.7 IMPROVEMENT PLAN Further information on forest management regimes and their effects on biomass and dead organic matter, as well as carbon stocks should be obtained and analyzed to refine estimates of GHG emissions and removals. This requires more in-depth research into the impact of forest management events and regimes on lagging carbon stocks. 30 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google STANDARD METHOD – FOREST COVER CHANGES 5.1 PURPOSE This standard method describes the process used by the INCAS remote sensing program, known as the Land Cover Change Analysis (LCCA) program, in monitoring forest cover change in Indonesia. The LCCA is designed to provide detailed monitoring between images, and spatial monitoring of changes in Indonesia's forest cover over time. The aim of the LCCA is to produce annual maps of the extent and change of national forests from Landsat images over time. The original goal of the LCCA was to produce maps of annual forest area and change over a 13-year period from 2000 to 2012, and serve as inputs for carbon accounting. The LCCA is based on a regional analysis, namely Kalimantan, Sumatra, Papua, Sulawesi, Java, Nusa Tenggara, and the Maluku Islands, using a nationally consistent methodology. Sources of data and methods used to analyze and obtain annual forest area and change in the LCCA program in Indonesia are described in the Remote Sensing Monitoring Program of Indonesia's National Carbon Accounting System: Methodology and Results, Version 1 LAPAN (2014). The following is a brief explanation. 5.2 COLLECTION OF DATA It is important to get access to data from various international archives covering all of Indonesia. The policy need for national cover, sub-hectare spatial resolution, historical and current periods means that Landsat is the only data available for operational programs. The Landsat images used are sourced from the archives of GISTDA (Thailand), GeoScience Australia, USGS and LAPAN (Indonesia). Landsat images LS-5 and LS-7 were chosen as data sources in providing information on monitoring the implementation of LCCA. The LS-5 became the reference source for most of the period due to technical issues with the line scan corrector ('SLC-off') that affected the LS-7 since mid 2003. Both instruments regularly collect recurring regular cover every 16 days in a period, however not everything that goes through is received and archived. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 31 Machine Translated by Google The most complete archive of LS-5 imagery for western Indonesia during that period was owned by the Thai GISTDA receiving station; Australian archives stored at Geoscience Australia (GA) cover the far east of Indonesia (Papua to the eastern part of Nusa Tenggara) with LS-5 and LS-7 images. LAPAN's receiving station in Parepare covers all of Indonesia, except for the westernmost tip of Sumatra, although only limited images can be archived. The primary data source for the central region is the USGS archive, which is far from complete for LS-5 as it contains sample images selected for satellite storage and downloaded in the US. GA coordinated the acquisition of Landsat images from the international data agency. All selected data is sent to Indonesia for processing in the LCCA program. Samples obtained from high-resolution satellite imagery (eg GeoEye, Ikonos, Quickbird and WorldView2) are used as an accurate reference for interpretation of land cover classes. Such image resolution is able to estimate tree density and tree height from the shadows. 5.3 ANALYSIS The general processing steps in producing forest area and change maps are presented in Figure 5-1. Image selection Pre-Processing Ortho-rectification Image Quality assurance Radiometric correction Cloud cover analysis mosaic Creating a forest base based on the selected year's image Mapping forest and Image processing for other years based on forest base The changes Multi-temporal processing to monitor changes Review results Figure 5-1. Flowchart of steps in the INCAS-LCCA processing sequence (LAPAN, 2014). 32 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google The main steps in pre-processing and mapping forest area from Landsat imagery are described below. All stages of image processing must pass a documented quality assurance test. Cloud cover is a major limitation in the use of optical imagery in many parts of Indonesia. Annual Landsat mosaic timescales with minimal cloud cover were created for each area of the various images prior to classification. Images from all archives are compiled and images with minimal cloud cover are selected. Each year, traces/rows are collected to obtain five images (usually 2-3) to increase the cloud-free area of land. High resolution image samples were used for field testing in mapping forest area from Landsat. Experts from forestry institutions in the regions provide interpretations of land cover in the forest area mapping stage. All satellite images are oktorectified into a general spatial reference (USGS GLS 2000) available from USGS or locally processed on images from Thai and Australian sources and calibrated via radiometric correction procedures. Ground luminance correction was then applied using the Shuttle Radar Topography Mission (SRTM) DEM and the C- correction method. Cloud cover processing is carried out using a semi-automated approach that has been developed in this program, then applied to remove cloud and fog before merging each image from each year into a regional mosaic. Usually, even using multiple images, each mosaic has area data lost due to heavy clouds. Mapping of forest area for each region was carried out by classifying the selected Landsat mosaic 'base year'. For training and validation, experts with knowledge of land cover and forest type played an active role in conducting the basic classification. High- resolution satellite imagery samples are used in stratification and analysis as well as in the optimization of the classification basis according to the local optimal index and threshold. The result is a forest/non-forest probability base map for the selected base year. Automatic matching is performed for the entire year to produce the annual forest probability in the time range. The ability to produce accurate and consistent change maps over time is critical; post- classification distinction for primary naming in a given time cannot be applied, as it will result in an unacceptable error rate. A multi-temporal probabilistic framework is applied over time to generate the final set of forest/non-forest classifications within the period of identifiable area change. In addition to the input of forest probabilities in timescales, accuracy of classification estimation and temporary transition probabilities is required. This approach can use all available data to address input uncertainty and predict missing observations in a given year. The effect is to minimize Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 33 Machine Translated by Google errors that arise from the classification of a particular year and provide consistent information on temporary changes. In addition, cover is predicted in the cloud affected area of the surrounding years. In the end, a manual checking process was carried out on the results of cover and change, with the participation of local experts. This process avoids errors that arise due to spectral overlap and the naming of certain classes for the purpose of providing input for carbon accounting. 5.4 QUALITY CONTROL AND QUALITY ASSURANCE As shown in Figure 5-1, after each image processing step, a quality assurance (QA) process is carried out to check that the method is applied correctly and the results meet the required accuracy standards. If an image does not meet the standards, the cause is investigated and the image is reprocessed, corrected for the problem and re-examined. The next step will not be performed until the previous step is successfully completed. Quality assurance checks also ensure consistency between data processed by different operators and at different times during an activity. 5.5 RESULTS AND UNCERTAINTY ANALYSIS The main result compiled is a map of forest area each year (2000, 2001, 2002, 2003, ...). These results are produced in both local NUTM projections and geodetic projections. The title in the geodetic projection made a mosaic of the entire territory of the island. The resolution of all LCCA products in NUTM is 25 m, in geodetic it is 0.000025 degrees. An example product is shown in Figure 5-2 which shows the area of Indonesian forests in 2009 and Kalimantan as a regional example. 34 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google Figure 5-2. Examples of forest area products (2009) at national, regional and local scales. Local scale includes comparison with Landsat and high-resolution imagery (LAPAN, 2014). The ability to produce change maps that are accurate and consistent over time is very important. INCAS applies a multi-temporal probabilistic framework over time to generate a final set of forest/ non-forest classifications over the period of identifiable area change. In addition to forest probability time range input, classification accuracy estimates and temporary transition probabilities can be constructed. This approach can use all available data to overcome uncertainty in input data and predict missing observations in a given year. This is to minimize errors that arise from the classification of a particular year and provide information on changes that are consistent on a temporary basis. In addition, cover can be predicted in the cloud affected area from the surrounding years. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 35 Machine Translated by Google 5.6 LIMITATIONS The original aim of the LCCA was to produce maps of annual forest area and change from 2000 to 2012. The integration of the LCCA analysis and other spatial analyzes used by INCAS needs to improve efficiency in both processes. 5.7 IMPROVEMENT PLAN Indonesia's satellite reception and archiving capabilities improved during the INCAS development period; the program is scheduled to continue using Landsat 8 and other optical data streams. It is important to expand the scope of historical monitoring to include the period 1990 to 1999 using Landsat imagery and processing methods. The 1990s were a year of major policy-driven land use changes in several provinces in Indonesia and consistent information on historical changes from this period is important. In addition, there is a greater need to integrate LCCA analysis with other spatial analyzes that INCAS uses to improve the efficiency of these two processes. 36 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google STANDARD METHOD – REGIMAL SPATIAL ALLOCATION 6.1 PURPOSE This standard method describes the process used by INCAS to determine the area used by each management regime in modeling GHG emissions and removals from activities taking place on forest land (including natural forests, plantations and certain plantations (oil and rubber) on former forest lands). This process includes data collection, data analysis, quality control and quality assurance. There are several important factors in determining the variation in emissions from various REDD+ activities in Indonesia. The types and conditions of forest and land where REDD+ activities take place, in addition to the types of management activities carried out, need to be spatially identified for detailed modeling of GHG emissions and removals. Available spatial data, which can provide information on the areas where the activity is most likely to occur, are identified and their source documented (see Table 6-1). These spatial data sets are generally not prepared specifically for GHG emission MRV activities. Consequently, the quality of the data spatially and temporally varies. This results in inconsistencies between data sets, which in turn requires decisions about how each data set is used for carbon accounting purposes. The purpose of this standard method is to explain how the available spatial data can be used consistently to allocate the area of each management regime and to derive annual area statistics for use in INCAS modeling. 6.2 DATA COLLECTION Data were collected from various national and provincial government agencies, as well as organizations involved in land management. Spatial data on forest cover and forest cover change were developed by LAPAN as part of the INCAS program. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 37 Machine Translated by Google Spatial data Spatial datasets are used to provide information on the areas where REDD+ activities occur, as shown in Table 6-1. The data is used to create a series of suites, which describe the conditions under which land management regimes can occur. By using biophysical and management data in the identification of each suite, it is possible to allocate land use areas and land use changes for modeling their impact on GHG emissions and removals. Table 6-1. Spatial data sources. Data Description Source Primary or secondary dryland forest, primary or secondary swamp forest or primary or secondary mangrove forest, KLHK Land cover plantation forest (and all other types of land cover classes) Annual forest/non-forest data obtained from Landsat data, the incidence of forest National Institute of Forest area and change loss and addition is obtained by comparing Aeronautics and Space the annual forest area (EIGHT) Fire area Annual burned area INCAS (KLHK) Soil type Organic (peat) Ministry of Agriculture IPCC soil grade IPCC mineral soil grade Digital land map of the world (FAO) Production forest, protection forest, or KLHK Forest function conservation forest Forest utilization Forest concession area KLHK Plantation Oil palm, rubber and other commodity KLHK plantation areas The method used to obtain data on forest area and change is described in Chapter 5 (Standard Method – Forest Cover Change). A spatial layer showing the geographic area of each suite was created for each simulation year (from 2001 to 2012) using the data in Table 4-1. Each suite is allocated using a unique identifier ( suite code) that relates the spatial data to the management regime as output from the Standard Method – Management Events and Regimes (Chapter 4). The suite code is a common attribute for each area locating each regime in each year. The area of each suite will vary over time as the forest transitions from primary forest to secondary forest and to non-forest conditions. 38 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google Management regime Management regimes describe the types and combinations of management events that apply to a particular land use and the time of occurrence. Once assigned to a management regime, an area continues to be managed according to that regime, on an ongoing basis, until a subsequent event is observed that causes a change in the management regime. The suites for forest and agricultural land assessed under this standard method were generated applying the Standard Method – Forest Management Events and Regimes (Chapter 4). The management regime for calculating peat emissions is described in the Standard Method – Peatland GHG Emissions (Chapter 7). All data described in this standard method were compiled from sources originating from Government agencies and other organizations, with the exception of fire areas and mineral soil maps from the IPCC. The fire area data was processed by the INCAS team using the approach developed by Balhorn et al. (2014) for the province of Central Kalimantan. 6.3 ANALYSIS The purpose of this analysis is to calculate the area of land that is managed by each management regime every year in the simulation period. This broad data is an input in the model for estimating GHG emissions and removals and is described in the Standard Method – Data Integration and Reporting (Chapter 8). Areas allocated to a management regime are based on suite characteristics and are repeated for each year during the simulation period (2001 to 2012). Areas monitored for change (change detected from LAPAN forest cover change analysis) were assigned to regimes based on location, timing and direction of change in relation to other suite characteristics . In order to be allocated to a forest management regime, the area must meet the minimum forest area definition for Indonesia of 0.25 ha (as per Minister of Forestry Regulation No. P.14/2004). Since the analysis is based on a change (activity) database, and not on forest area, the area threshold is applied to the aggregate of all years of change. This allows the calculation of annual area changes of less than 0.25 ha, while ensuring that the cleared land meets the definition of forest. All spatial data related to the location and area of the regime in the carbon calculation were collected, converted in the form of shp files and projected in a consistent coordinate system. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 39 Machine Translated by Google Each REDD+ activity reported by INCAS is modeled as a separate estate – that is, a file with the area and time of each regime defined by this standard method. The REDD+ activities modeled in the current INCAS are deforestation, forest degradation, carbon stock enhancement and sustainable forest management. The criteria used to define each activity are presented in Table 4-5 Standard Method – Forest Management Events and Regimes (Chapter 4). Each regime can be defined from the unique combination of spatial and area data values available for modeling directly from GIS results. There are a total of 1,152 different regimes with different areas. Deforestation and reforestation activities can be identified directly from a combination of data sources. Meanwhile for the SMF and forest degradation model some additional processing is required to produce the correct area. Sustainable forest management (SMF): This REDD+ activity fits under the 'forest land remains forest land' category under the UNFCCC. This occurs when forest cover loss is not observed in the primary or secondary forest shown in the land cover map, even though forest concession data show logging using RIL techniques. It is assumed that in SMF there will be growth back to the initial forest condition. Annual forest change data are only relevant to forest management activities that show changes in the forest canopy (clearing does not result in the loss of enough trees to reduce the forest canopy to the 30% threshold of a forest definition). The process for allocating area to these activities depends on forest type, forest function, concession boundaries, absence of forest change and the proportion of forest available for harvesting, as described below. • If the forest type is mapped as dryland forest, SMF's practice is assumed to harvest 40.6% of forest in a 30 year period. This figure is calculated assuming that the effective logging area for each forest concession is 70%, corrected by the actual annual average timber production of 0.58. • If the forest type is mapped as swamp forest, SMF's practice is assumed to harvest 52.2% of forest over a 40 year period. This figure is calculated assuming the effective logging area in each forest concession is 90%, corrected by the average annual actual timber production of 0.58. Forest degradation: determining areas to be assigned to forest degradation activities requires the compilation of a unique combination of all forest change data. 40 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google The year of forest loss and the year of forest addition are determined for each polygon. Polygons that record losses as well as gains are the subject of statistical analysis. If the non-forest intervention period is three years or more, this satisfies the criteria for temporary unstand forest, which in the polygon is assigned to forest degradation events. This analysis was carried out for areas of forest that were logged, then regrown and allocated to the year the forest was lost ('clearreveg'). In contrast, polygons with multiple changes where the first occurrence is forest addition are identified as degradation events in the year of first forest addition ('revegclear'). The assumption is that the land was temporarily unstand before the first year of data on forest area (2000) became available. Forest degradation occurs when: • There is a change from primary forest to secondary forest on the cover map land; • There is a change from primary or secondary forest to plantation forest on the land cover map; • Forest cover loss was not observed in secondary forest as shown by land cover maps, but forest concession data showed that logging was taking place using conventional techniques. 6.4 QUALITY CONTROL AND QUALITY ASSURANCE All data is assumed to be correct from the data provider/source. All data are spatially complete for all of Indonesia (each province). All data is combined into a single polygon data set for each year. The results of the GIS table are exported in Excel format and each regime is assigned according to the selection of the relevant attributes in each input data. All polygons that have an area of less than 0.25 ha are deleted. The small polygons were visually inspected and decided to be the result of accidental overlapping areas due to the different sources of spatial data obtained. Therefore, filtering thousands of database rows is used as a proxy in cleaning the input data to ensure that each combination of land use activities and land use functions is logical. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 41 Machine Translated by Google Another major logical screening, which has been carried out as part of statistical analysis (as a proxy for careful spatial analysis screening) to ensure that clearing and revegetation events are separated by time intervals defined according to the particular regime description. This analysis relies on an in-depth understanding of statistics, spatial analysis, vegetation dynamics, forest management practices and the impact of timescales of events on the resulting carbon calculations. 6.5 RESULT The output of this standard method is expressed in hectares, by regime, in years and is documented in the INCAS Regime Area Database (see description in Appendix 1). The Unique Feature Identifier (FID) of the GIS data is maintained throughout this process so that the newly calculated area of the suite each year can be recombined to the spatial data. The QA/QC process and the application of the rules regarding the minimum area will have an impact on reducing area uncertainty where each data input source is set on the same land area. In the uncertainty analysis, the area varies by a value of about +/- 10%. 6.6 LIMITATIONS Since there are no clear differences between forest and plantation types in the regrowth data based on remote sensing, it is likely that there will be some areas of forest loss and gain containing errors. Spatial analysis tools were not fully developed when this analysis was carried out, so large amounts of data were processed manually. The efficiency of this process should be improved to reduce the potential for errors and reduce processing time, particularly for the spatial allocation of regimes across Indonesia. 6.7 IMPROVEMENT PLAN All input data can be characterized as the best available data. For continuous improvement plans, it is recommended that each data set supplied for this analysis be subjected to more careful pre-processing and standardization. When new versions and updates of each input data are made, the modeling team will need to have access, permission and resources to repeat the methodology in updating the area for subsequent modeling of emissions and removals. 42 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google Improvement plans related to developing modeling and reporting requirements will also require iterating over spatial allocations to fit the new suite . Activities that depend on detecting non-forest to forest conversion (afforestation, reforestation and revegetation) cannot be determined using satellite data without additional interpretation of the results. For example, oil palms meet the parameters of canopy cover, height and area in the definition of “forest”, but policy parameters require them to be identified as a plantation type. The detailed spatial analysis of land cover change from LAPAN combined with spatial data on forest types and management practices has greatly improved the identification of forest change events. This can be further refined by stronger collaboration between the forest cover mapping process and the spatial analysis process. The development of spatial analysis tools will increase the efficiency of spatial allocation of the regimes described in this standard method. Outputs can be sharpened through a more detailed understanding of land management events prior to 2000, as these affect the modeled estimates of forest biomass and peat degradation rates for the period 2001 to 2012. This can be achieved by extending the analysis of annual forest cover change to 1990 and extracting information more detailed than historical land management records. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 43 Machine Translated by Google STANDARD METHOD – PEATLAND GHG EMISSIONS 7.1 PURPOSE This standard method describes the process used by INCAS in modeling GHG emissions from peatlands in Indonesia. This process includes data collection, data analysis, quality control, quality assurance, modeling and reporting. In this standard method, peatland is defined as land containing organic soil. This land is an area with an accumulation of partially decomposed organic matter, ash content equal to or less than 35%, peat depth equal to or more than 50cm and organic carbon content (weight) of at least 12%. Peatland GHG emissions are estimated annually based on the following sources and gases: • biological oxidation due to peat drainage: CO2 -C, CO2 -e • peat fires:3 CO2 -C, CO2 , CO, CH4 • direct emissions from draining organic soils: N2O , CH4 The output of this standard method can be expressed in tonnes for each GHG or expressed in tonnes of CO2 -e GHG emissions. The reporting time period can be specified to meet the reporting requirements. 7.2 DATA COLLECTION The spatial data used in this method is presented in Table 7-1. Spatial data collection methods are described in the Standard Method – Regime Spatial Allocation. 3 Note: N2 O and NOx peat fire emission factors are not available at IPCC Tier 1 due to limited data on N2 O and NOx emissions from organic soil fires. 44 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google Table 7-1. Spatial data sources used. Data Description Source Primary or secondary dryland forest, primary or secondary swamp forest, primary or secondary mangrove forest, KLHK Land cover plantation forest, plantations, rice fields (and all other types of land cover) Ministry Soil type Types of organic (peat) and mineral soil Agriculture; IPCC Big plantation Palm oil KLHK Fire area Annual burned area (spatial) INCAS (KLHK) Annual forest/non-forest data is obtained from Landsat Forest area and change data, and the incidence of forest loss and addition is EIGHT obtained by comparing the annual forest area. The input data for GHG estimation from peat decomposition is shown in Table 7-2. Table 7-2. Source of data for modeling input. Data Description Source Emission factor Peat biological oxidation emission factors and IPCC (2013); Hooijer et al. peat fire emission factors (2014) Fire emissions (CO2 -C, CO and CH4 ), direct Emission factor emissions of nitrogen oxides from draining organic Tier 1 standard IPCC (2013) soils, CH4 emissions from draining organic soils Land area drained peat Annual dry peatland area according to land cover INCAS Standard Method – conditions Regime Spatial Allocation Fire area The area of peatland burned annually in INCAS Indonesia 2001 to 2012 The emission factors used to calculate CO2, DOC and CH4 emissions in the national GHG inventory are shown in Table 7-3. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 45 Machine Translated by Google Table 7-3. Emission factors for peat biological oxidation in Indonesia. FE CO2 -C4 FE DOC5 FE CH4 IPCC land use categories (t C ha-1 th1 ) (t C ha-1 th-1) (kg CH4 ha-1 th-1) Forest land and forest land that have been cleared (shrubs), are 5,3 0,82 4,9 drying up Plantation forest, drained, oil palm plantation6 11,0 0,82 0,0 Plantation, drying, long rotation or unknown 15,0 0,82 4,9 20,0 0,82 4,9 Plantation forest, experiencing drying, rapid rotation, eg: acacia Plantation, drying up, rice field 9,4 0,82 143,5 Source: IPCC (2013)456 Peat biological oxidation emission factors from the IPCC Wetlands Supplement 2013 provide separate emission factors for CO2 , DOC have and CH4 been. developed Alternative from emission research factors in Central Kalimantan, although there is debate among peat scientists as to which emission factors best represent Indonesia's emissions profile. This study of emission factors must be carried out as part of the INCAS continuous improvement plan to integrate the results of peat GHG research. The emission factor for peat fires was developed through the KFCP project in Central Kalimantan. Hooijer et al. (2014) view that the emission factors for fires generated from the KFCP work area reflect normal fire conditions in Indonesia compared to the emission factors contained in the IPCC 2013, because the emission values are overestimated (because it is only based on a small number of studies that were influenced by extreme conditions in 1997/ 98). INCAS adopted the baseline fire emission factor data for the KFCP project site from Page et al. (2014), but re-adapted these emission factors to meet international reporting requirements so that the estimated GHG emissions from organic soil fires are expressed in tonnes per GHG emitted. The method used to determine country-specific emission factors for Indonesia follows the approach outlined in the IPCC 2013, using Equation 2.8 as shown in the box below. 4 See Table 2.1 in IPCC 2013 5 See Table 2.2 in IPCC 2013 6 The majority of identified plantation and agricultural areas are oil palm. Therefore, this FE is used for plantation and agricultural calculations based on the IPCC FE. 46 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google EQUATION 2.8 ANNUAL CO2 AND NON-CO2 EMISSIONS FROM ORGANIC SOIL FIRE Lfire = A MB Cf Gef 10-3 Where: Lfire = amount of CO2 or non-CO2 emissions, e.g. CH4 from fire, ton A = total area burnt annually, ha MB = mass od fuel available for combustion, ton ha-1 (i.e. mass of dry organic spil fuel) (default values in Table 2.6; units differ by gas spacies) Cf = combustion factor, dimensionless Gef = emission factor for each gas, g kg-1 dry matter burnt (default values in Table 2.7) Mass of fuel available for combustion = area (m2 ) * depth of fire (m) * fill weight (t m–3). Table 7-4 shows the input values, mass of fuel available for combustion and CO2 -C, CO and CH4 emissions produced in tonnes per gas per ha for the three types of fires. Total annual emissions are calculated by multiplying the annual burned area by the mass of emissions released by each gas. Table 7-4. Input parameters and CO2 -C, CO and CH4 emissions per ha for fires in organic soils. Fire Fire Third fire and so on Peat fire EF calculation first second Burning depth (cm) 18 11 4 Area (ha) 1 1 1 Filling weight (g cm-3) 0,121 0,121 0,121 1 1 1 Combustion factor FE CO2 -C (g kg-1) 464 464 464 FE CO (g kg-1) 210 210 210 21 21 21 FE CH4 (g kg-1) Mass of fuel available for combustion (t dm 217,8 133,1 48,4 ha-1) CO emissions (tCO ha-1) 45,7 28,0 10,2 CH4 emissions (t CH4 ha-1) 4,6 2,8 1.0 Emitted CO2 -C (t C ha-1) 101,1 61,8 22,5 Emisi CO-C (t C ha-1) 19,6 12,0 4,4 Emitted CH4 -C (t C ha-1) 3,4 2,1 0,8 Total C emissions (t C ha-1) 124,1 75,8 27,6 Source of CO2 -C, CO and CH4 emission factors : Table 2.7, IPCC (2013) Source factors for depth of burn, bulk weight and combustion: Page et al. (2014) Note: N2O and NOx emission factors are not included in the IPCC Tier 1 due to limited data on N2O and NOx emissions from organic soil fires. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 47 Machine Translated by Google Emissions of nitrogen oxides due to soil drying Annual nitrogen oxide emissions from organic soils were calculated by multiplying the annual area of drained peat in the land use category by the 2013 IPCC Tier 1 standard emission factor (Table 7-3). In the national GHG inventory, the 'plantation: oil palm' emission factor is applied to all agricultural land and plantations on peat, as oil palm is the majority of plantation types on peatland. The emission factor of 'forest land and forest land that has been cleared (shrub), and is drained' is used for all lands other than plantation forest, plantations and rice fields. Table 7-5. Standard nitrogen oxide emission factors from organic soils. Emission factor Land use category (kg N2 O-N ha-1 th-1) Forest land, forest land that has been cleared (shrubs), dry land 2,4 Oil palm plantation 1,2 Plantation: sago 3,3 Agricultural crops except rice 5,0 Ricefield 0,4 Meadow 5,0 7.3 ANALYSIS An outline of this approach is shown in Figure 7-1. Total annual GHG emissions are estimated by multiplying the area affected by drying or fire by a specific emission factor. Specific emission factors are used for peat biological oxidation and peat fires. Emissions in the year of fire include biological oxidation emissions and peat fire emissions. 48 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google Land Cover Peat Area Land Cover Peat Area Peatland Primary Dryland Forest Secondary Dryland Forest Burnt Area Primary Mangrove Forest 2001 2007 Secondary Mangrove Forest 2002 2008 Primary Swamp Forest 2003 2009 Secondary Swamp Forest 2004 2010 Timber Plantation 2005 2011 Non Forest 2006 2012 Peat Biological Oxidation Emission Factors, Peat Area by Cover Annual Fire Area CH4, and N2O Cover Class FE Primary Forest Forests are experiencing light drying Forest is experiencing moderate drying heavily degraded forest Plantations and plantations Annual Burned Peat Area Fire Emission Factor Peat M FE Fire Class M O 1 fire O D fire 2 D AND Fire 3 AND L L Annual Peat Emissions from Fire Emissions Biological Oxidation, CH4, and N2O Annual Peat Figure 7-1. INCAS peat GHG emission estimation approach. The approach to estimating peat GHG emissions is consistent with the approach used by INCAS in modeling GHG emissions and removals from biomass and dead organic matter. Both approaches are event-based, where emissions are triggered by events in land management. 7.4 QUALITY CONTROL AND QUALITY ASSURANCE Quality control and quality assurance from emission factor input data and area were carried out by the report authors Hooijer et al. (2014), Ballhorn et al. (2014) and IPCC (2013). Quality assurance of area and emission calculations is carried out by INCAS technical advisors. 7.5 RESULTS AND UNCERTAINTY ANALYSIS Peatland GHG emissions are reported in the source gas and where possible as CO2 -e emissions, as shown in Table 7-6. Carbon emissions from biological oxidation of peat and peat fires are calculated as changes in peat carbon stock in t C ha-1, converted to CO2 -e emissions by multiplying 44/12 (molecular weight ratio of CO2 to carbon). Non-CO2 emissions from peat fires are calculated directly in t CO ha-1 and t CH4 ha-1. Methane emissions are converted to CO2 -e emissions. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 49 Machine Translated by Google Emissions of methane (CH4 ) and nitrogen oxides (N2O ) are converted to CO2 -e emissions by multiplying the 100-year global warming potential for each gas, which is 28 and 265, respectively (Myhre et al., 2013). Table 7-6. Modeling output and reporting units. Unit Unit Output Factor Unit Source initial GWP8 reporting model conversion reporting output general Biological oxidation of drained CO2 -C t C ha-1 44/12 1 peat CO2 -C t C ha-1 44/12 t CO2 1 t CO2 -e Fire CH4 t CH4 ha-1 1 t CH4 28 t CO2 -e peat CO t CO ha-1 1 t CO THAT THAT Direct emission CH4 t CH4 ha-1 1 t CH4 28 t CO2 -e from the ground dried DOC t C ha-1 44/12 t CO2 1 t CO2 -e organic N2 O t N2 O ha-1 1 t N2 O 265 t CO2 -e The adoption of Indonesia-specific emission factors developed from research and the IPCC 2013 Wetlands Update , which uses Indonesian data for tropical soils, reduces the level of uncertainty in emission factors, although there is still debate among peat scientists regarding the accuracy of the resulting emission factors. Further research is needed to expand the land types and management activities covered by emission factors, which will reduce the uncertainty associated with these emission factors. Uncertainty with respect to spatial data also varies considerably between different data sets. This is discussed in the Standard Method – Regime Spatial Allocation. The INCAS program has identified the spatial data required for analysis. The improvement of the data will reduce the uncertainty of GHG emission estimation. 7.6 LIMITATIONS In the national GHG inventory, the main limitation of the approach to estimating peatland GHG emissions is related to the availability and quality of data. • Consistency among spatial data is very important. Some data overlap or have inconsistent information for the same area between data. 50 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google • The spatial extent of the annual burnt area is important. Further research is needed to accurately determine the burned area and historical fire intensity. • Methane emissions from draining canals recorded in the IPCC 2013 are potentially significant sources of emissions, however not enough information is available on the location and size of canals to be included in the national GHG inventory. Further efforts are needed to provide more comprehensive data on the location, size, condition of drained canals and distance from drained canals. • Peat mapping, including peat boundaries and depth, needs to be improved. • Information on peatland management, particularly land use and management intensity after forest clearing, is still limited and needs to be improved. • Water table depth data in disturbed and managed peatland is not available for all peatland regions in Indonesia. Further research needs to be done to develop the relationship between land management, canal management (including canal damming) and the depth of the water table as well as the GHG emissions produced. • Limited research shows that the biological emission factor in the first five years after land clearing is very significant. Further research needs to be done to refine the estimates in terms of quantity and timing of emissions. 7.7 IMPROVEMENT PLAN GHG emissions from organic soils are substantially higher than net emissions from other carbon sources related to deforestation, forest degradation, sustainable forest management and forest carbon stock enhancement modeled using higher tier methods. This indicates that further work is needed to reduce the uncertainty of peat GHG emission estimates. Further research will help reduce some of the sources of uncertainty. Closer collaboration between data holders on peat and peatland management and further analysis of these data can improve the substance of peatland GHG emissions estimates. Detailed, prioritized and continuous improvement plans need to be developed for peat activities. A coordinating agency needs to be appointed to manage its implementation. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 51 Machine Translated by Google STANDARD METHOD – DATA INTEGRATION AND REPORTING 8.1 PURPOSE This standard method describes the process used by INCAS for modeling GHG emissions and removals from activities taking place on forest land, including deforestation, forest degradation, the role of conservation, sustainable forest management and enhancement of forest carbon stocks in Indonesia. This process includes data collection, data analysis, quality control, quality assurance, modeling and reporting. The following modeling of carbon sources and GHG emissions is carried out using a mass balance, based on a triggered-event approach where changes in carbon stocks at each carbon source and carbon flows between sources are quantified: • aboveground biomass • below ground biomass • dead organic matter (dead wood, litter) • carbon emissions from fires. From the modeling, GHG emissions and removals are obtained for the analyzed period. This approach is used for natural forests, plantations, oil palm and rubber plantations. Modeling of other carbon sources and their resulting GHG emissions is also described in this standard method, which includes: • carbon emissions from mineral soils, calculated using standard factor values IPCC emission and activity data; • non-CO2 emissions from fires, calculated using the IPCC standard value of the N:C ratio and the emission factor multiplied by the carbon released from the fire. Carbon emissions and non-CO2 emissions from organic soils (peat) were modeled using the standard method for peatland GHG emissions (described in Chapter 7 of this document). 52 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google The data used as input for modeling GHG emissions and removals are compiled from the outputs of the INCAS standard method and other documents as follows: • Standard Method – Initial Conditions • Standard Method – Growth and Transition • Standard Method – Forest Management Events and Regimes • Standard Method – Forest Cover Change (LAPAN, 2014) • Standard Method – Regime Spatial Allocation • Standard Method – Peatland GHG Emissions • IPCC (2003). Good Practice Guidance for Land Use, Land Use Change and Forestry • IPCC (2006). IPCC Guidelines for National Greenhouse Gas Inventories • IPCC (2014). 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands The output of this standard method is expressed in tonnes per GHG and CO2-equivalent GHG emissions and removals. The reporting time period can be adjusted to meet reporting requirements. 8.2 DATA COLLECTION The input data for modeling GHG emissions and removals are collected from various sources through a series of standard INCAS methods as presented in Table 8.1. Each standard method is specifically designed to produce credible and verifiable input data to obtain an estimate of the net GHG emissions of Indonesia's forests. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 53 Machine Translated by Google Table 8-1. Source of data for modeling input. Standard method Data for modeling input Carbon stock of each aboveground and belowground biomass component and dead wood organic matter for each biomass class at the beginning of the Initial conditions simulation. Values, assumptions and data sources are documented in the fullCAM INCAS Database Growth rate, aboveground and belowground biomass transfer and decomposition rate of dead organic matter, for each component of the Growth and biomass class. forest transition Values, assumptions and data sources are documented in the INCAS Growth Database. The impact of forest management events on carbon stocks for each component of aboveground and belowground biomass and dead organic matter, for each biomass class and time of occurrence allocated to specific Forest management management regimes and forest areas. events and regimes Values, assumptions and data sources are documented in the Database INCAS Incidence and INCAS Regime Group Database Area by year and modeled forest management regime. Data is documented in the INCAS Regime spatial allocation Regime Area Database Peatland GHG emissions are calculated according to the method described in the Standard Method – Peatland GHG Emissions. The results are added to the modeling output of this standard method to calculate the total annual GHG emissions and removals. 8.3 ANALYSIS The methodology and emission factors used to estimate GHG emissions and removals are presented in Table 8-2. The methodology used includes a combination of Tier 2, Approach 2 and Tier 3 methods (models), Approach 2 uses a combination of Indonesia-specific data and other relevant standard values. The standard Tier 1 method is applied when Indonesia- specific data are not available. 54 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google Table 8-2. Summary of methodology and emission factors: land use sector, land use change and forestry.78 CO2 CH4 N2 O NOx , CO Sources and absorption of greenhouse gases Method FE Method FE Method FE Method FE A. Forest Land 1. Forest land remains forest land Managed natural forest (SMF) T3 M Managed natural forest (forest T3 M degradation) Burning biomass7 Emissions IE8 T2 D T2 D T2 D from draining organic soil Peat fires 2. T1/T2 D/CS T1 D T1 D Land conversion to forest land Increase in forest carbon stocks T1 CS T1 CS NO T1 CS T3 M B. Agricultural land 1. Agricultural land remains agricultural NO land 2. Conversion of land into agricultural land (deforestation) Oil palm plantations T3 M Rubber plantations T3 M Other plantations T1 D Biomass burning Emissions IE T2 D T2 D T2 D from draining organic soils Peat fires T1/T2 D/CS T1 D T1 D Emissions from mineral soils T1 CS T1 CS NO T1 CS T1 D T1 D C. Grasslands 1. Grasslands remain grasslands 2. NO Conversion of land into grasslands IE D. Wetlands 1. Wetlands remain wetlands 2. Conversion NO of land to wetlands NO E. Settlements 1. Permanent settlements 2. Conversion of NO land into settlements IE F. Other lands 1. Other lands remain other lands 2. NO Land conversion to other lands Mining IE EF = emission factor, CS = country specific value, D = IPCC standard value, M = , NA = not applicable, NE = not model9 can be estimated, NO = does not occur, IE = covered elsewhere10 , T1 = Tier 1, T2 = Tier 2 dan T3 = Tier 3 7 Biomass burning means burning of aboveground biomass and dead organic matter on site. 8 CO2 emissions from biomass burning are included in the calculations for SMF, forest degradation and deforestation using the T3 model. 9 Models are used (not using single emission factor values) to simulate forest dynamics such as growth, transition and decomposition processes and the impact of management events on carbon stocks and flows. 10 All land converted from forest land to grasslands, wetlands, settlements and other land is included in forest land converted to agricultural land (other plantation crops). Net emissions and removals are assumed to be zero after conversion, except for oil palm and rubber plantations. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 55 Machine Translated by Google GHG emission estimates are prepared for each period when activity data is available and required for reporting. In this national GHG inventory, the period analyzed is 2001 to 2012. Changes in area represent changes in forest area from the previous year. For example, the area reported in 2001 shows the change in forest area from 2000 to 2011. When new activity data becomes available (eg data on forest cover changes in a recently processed year or a new forest map becomes available) the entire time series needs to be reprocessed. This needs to be done to ensure data consistency across timescales and ensure that transitions that take place over multiple years are correctly identified (eg land clearing followed by temporary unstocked forest and then revegetation). 8.3.1 Forest land Carbon emissions and sequestration from above and below ground biomass, dead organic matter and fires The provisional tool adopted in this standard method to calculate GHG emissions and removals from deforestation, forest degradation, sustainable forest management and forest carbon stock enhancement is the Full Carbon Accounting Model (FullCAM). FullCAM is a flexible integration tool, which allows spatial or non-spatial Tier 2 or Tier 3 GHG emissions estimation for agriculture, forestry and other land uses. This toolkit has gone through an extensive review process and is included in the UNFCCC review process as part of the national inventory. Indonesian data can be entered directly or using standard assumptions if Indonesia-specific data is not available. A full description of the design and application of FullCAM can be found in Richards (2001, 2005). Figure 8-1 describes the components and carbon flows simulated in FullCAM for the national GHG inventory. Other process-based tools may become available in the future and need to be evaluated for suitability as a Tier 3 method for estimating GHG emissions and removals from Indonesia's forests. FullCAM calculates changes in tree components as a result of production (growth) and transition (loss of material, eg leaf and stem fall, loss of roots). FullCAM models changes in the source of dead organic matter through inputs from transitions and losses to damage (decomposition) using decay curves. Each component of the carbon source aboveground biomass, belowground biomass and dead organic matter was tracked throughout the simulation period. 56 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google Atmosphere CO2 Initial Removals Conditions Carbon stock Debris Dec. Standing deadwood Res. Aboveground biomass Downed Dec. Stemwood deadwood Res. Branches Increment Dec. Increment (yield tables) Fine wood (yield tables) Res. Bark Natural Dec. Natural Leaves Processes Bark Processes Res. Turnover Turnover Belowground Dec. (% of pool) Leaves (% of pool) Res. Coarse roots Decomposition Decomposition (decay rates) (decay rates) Fine roots Dead coarse Dec. roots Res. Dead fine Dec. roots Res. CO2 -e Events Emissions Clearing/Harvest CO2 -e (% of pool & destination) Fire Atmosphere (% of pool & destination) Planting (% of pool and destination) Figure 8-1. FullCAM components and carbon flows for tree carbon sources and dead organic matter. Changes in the carbon stocks of forest products (eg sawn wood, plywood, pulpwood) can be tracked in the INCAS, but for the current national GHG inventory, they are not tracked due to insufficient data on the quantity of wood products and the rate of decay of wood products used. However, carbon flows into forest product carbon sources are used to indicate whether carbon stocks Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 57 Machine Translated by Google affected by the management event remain on site (a source of dead organic matter carbon) or leave the site (in the form of a source of forest products). Carbon that becomes dead organic matter will decompose over time, enter the soil or leave the site as emissions into the atmosphere. Carbon that enters the source of forest products is assumed to leave the location during logging and become emissions into the atmosphere. The soil model integrated in FullCAM, RothC, has not been calibrated to quantify soil organic carbon changes in Indonesia. Therefore, the change in soil organic carbon in mineral soils is calculated using the other methods described in this standard method. Meanwhile changes in soil organic carbon in organic soils are described in the Standard Method – Peatland GHG Emissions. FullCAM is a process-based and event-driven integration tool, meaning that ongoing changes in carbon stocks are the result of continuous processes (e.g. production, transition, decay) and periodic events (e.g. logging, fires), usually impacting directly on the carbon stream. Process The main process of calculating using INCAS is: • Production – transfer of carbon from the atmosphere to plant sources. Production is a combination of photosynthesis which transfers carbon from the atmosphere to plant sources and respiration, which transfers some material from plants to the atmosphere. The net result is biomass growth. • Shift – transfer of carbon from biomass to dead organic matter when a dead material. • Decomposition/decay – removal of carbon from dead organic matter. Data for production, turnover and decomposition processes are input to FullCAM for each biomass class based on data from the Standard Method – Growth and Transition. Incident Events modify the amount of carbon at each carbon source and the destination for which the carbon is removed. Event types include: • Thinning – harvesting events that remove some or all of the aboveground biomass from the site and transfer some of the live biomass to a dead organic matter source; • Tree planting – the event that tree planting creates new forest in an area where there is no forest, or when primary forest is replaced by secondary forest that has growth characteristics different from primary forest; 58 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google • Forest treatment – forest treatment events (eg application of fertilizers) that change the rate of forest change when the tree yield formula is used in production modeling (this is not used by INCAS for the national forest inventory); • Forest fires – forest fires that release carbon (such as CO2 and CH4 ) and nitrogen (N2 O) into the atmosphere and transfer some of the carbon to dead organic matter sources and soil. The total carbon stock at a given point in time is the result of a series of events applied to the initial carbon stock, then influenced by production, transition and decomposition processes. Data on forest management events and regimes is the input for FullCAM based on data output from the application of the Standard Method – Forest Management Events and Regimes. File plot FullCAM The data is the input to the FullCAM plot file which is 'operated' to produce the output. A plot file shows the unique combination of biomass classes and management regimes that impact on carbon stocks over time. A management regime has a series of events that take place at a certain time. Plot files can be 'operated' individually to calculate changes in carbon stocks in a particular biomass class managed under a particular management regime and the results are expressed on a per hectare basis. Alternatively, plot files can show areas and be combined with other plot files in an estate file to calculate changes in carbon stocks within a forest group (estate). Main plot files Individual plot files are compiled for each combination of potential events and biomass class for analysis. For national GHG inventories, suites, management regimes and plot files are recorded in the INCAS Suite Database . In plot files consisting of primary natural forest or secondary forest, all tree carbon sources and dead organic matter are assumed to be in equilibrium before the first occurrence. In secondary forest this assumption is oversimplified because it seems that growth will still take place in the tree biomass and some of the dead organic matter from previous logging is still undergoing a process of decay. However, due to lack of data, the equilibrium state is used as a conservative assumption. Figures 8ÿ2 to Figure 8ÿ6 provide examples of plot files of deforestation, forest degradation, sustainable forest management and enhancement of forest carbon stocks. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 59 Machine Translated by Google 200 180 160 140 120 100 80 60 40 20 0 0 10 20 30 40 50 Year Total C mass C mass of aboveground tree components C mass of belowground tree components C mass of forest litter C mass of forest deadwood Figure 8-2.Examples of output of changes in carbon mass by carbon source from deforestation. 300 250 200 150 100 50 0 0 10 20 30 40 50 Year Total C mass C mass of aboveground tree components C mass of belowground tree components C mass of forest litter C mass of forest deadwood Figure 8-3. An example of the output of changes in carbon mass by carbon source from forest degradation. 60 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google 200 180 160 140 120 100 80 60 40 20 0 0 10 20 30 40 50 Year Total C mass C mass of aboveground tree components C mass of belowground tree components C mass of forest litter C mass of forest deadwood Figure 8-4. An example of the output of changes in carbon mass by carbon source from sustainable forest management. 200 180 160 140 120 100 80 60 40 20 0 0 10 20 30 40 50 Year Total C mass C mass of aboveground tree components C mass of belowground tree components C mass of forest litter C mass of forest deadwood Figure 8ÿ5. An example of the output of changes in carbon mass by carbon source from an increase in forest carbon stocks. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 61 Machine Translated by Google 200 180 160 140 120 100 80 60 40 20 0 0 10 20 30 40 50 Year Total C mass C mass of aboveground tree components C mass of belowground tree components C mass of forest litter C mass of forest deadwood Figure 8-6.Examples of output of changes in carbon mass by carbon source from forest conversion to plantations. File estate An estate file allows multiple files to be placed in an area each year and modeled as a single forest group. Areas were obtained from the regime spatial allocation process described in the Standard Method – Regime Spatial Allocation. Depending on the level of reporting Indonesia adopts, separate estate files may be processed for specific locations (eg national, provincial, district, project) or reporting purposes (eg REDD+, BUR and national communications to UNFCCC, domestic land use planning). For example, in REDD+ reporting, each report on REDD+ activities is modeled as a separate estate : deforestation, forest carbon stock enhancement, sustainable forest management, and forest degradation (note this definition has not been officially agreed upon by the Government). A description of each REDD+ category is provided below. Similarly, if more detailed spatial data for non-forest land uses are available, separate estates can be modeled for each UNFCCC reporting category. For the national GHG inventory, a simplified assumption is applied that results in all deforestation causing a transition from forest land to agricultural land. 62 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google The area for each plot file in an estate is allocated according to the Standard Method – Regime Spatial Allocation. For national GHG inventories, areas are documented in the INCAS Regime Area database. Deforestation estate files Deforestation estate files contain plot files that model changes in forest carbon stocks and GHG emissions from clear-cutting and fire events that cause deforestation. The process of decaying dead organic matter sources can continue for several years after the initial event. For the national GHG inventory, ongoing agricultural management events are only modeled as plantations. All other plantations are assumed to have a zero emission factor11. More detailed plantation management events need to be included in the deforestation estate file when area and emissions data are available as part of an ongoing improvement plan. Forest degradation estate files Forest degradation estate files contain plot files that model changes in on-site carbon stocks resulting from events that cause primary natural forest to become secondary natural forest (eg through selective logging, human fires or logging followed by natural regeneration). 8.3.2 Plantations and other agricultural land Emissions from oil palm and rubber plantations from agricultural land converted from forest land in the modeling period, were modeled using the process-based model described earlier. All oil palm and rubber plantations that existed at the beginning of the modeling period or in the development process outside forest land were not included in the national GHG inventory. This approach to calculating area net emissions needs to be developed as part of the agricultural land use inventory within the framework of a continuous improvement plan. Other areas that experience changes in land use from forest to non-forest are assumed to have the same emission profile (same emission factors) from the year the deforestation took place. To simplify calculations, it is assumed that on these lands all of the increased biomass is absorbed in the same year as yields harvested (no net annual change in biomass in non-forested areas, therefore no net emissions of biomass in non-agricultural lands) . This is equivalent to applying an emission factor of 0 t C ha-1. 11 Plants other than plantation crops are assumed to emit all carbon sequestered annually through growth due to harvesting cycles of less than a year. All sequestered carbon is emitted at harvest. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 63 Machine Translated by Google 8.3.3 Carbon emissions from mineral soil Annual carbon emissions from disturbed mineral soils were calculated using the Tier 1 method described in the IPCC (IPCC, 2003) in Section 3.3.2.2 for conversion of forest land to plantations and in 3.4.2.2. for conversion to pasture in the estimation of deforestation impacts, as recommended by the Global Forest Observation Initiative Methods and Guidance Document (GFOI, 2013). The majority of deforestation events are conversion of forest land into plantations, and the rest is conversion of forest land to other land uses. Detailed information on land use change after deforestation is not yet available. Therefore, all deforested areas are assumed to be conversion of forest land to agricultural land. Some of the deforested areas may be land clearing for oil palm plantations that were misidentified in the analysis of forest cover change. Therefore, the calculation is based on the conversion of forest land into agricultural land using Equation 3.3.3 in IPCC 2003. Calculations were performed using a simple Excel-based model based on the cumulative annual area of land converted from forest to non-forest. A single emission factor is applied to mineral soils across all non-forest lands based on the assumption that all mineral soils are soils with low activity clay minerals (LAC) (see Table 3.3.3 in IPCC 2003). This could be refined in the future using more detailed soil information. No carbon emissions are assumed to occur on mineral soils for forest lands that remain forest lands, in accordance with IPCC guidelines which state: In Tier 1, it is assumed that when forest remains forest, carbon stock in soil organic matter does not change, despite changes in forest management, type and regime of disturbance ... in other words carbon stock in mineral soil remains constant as long as land remains forest (IPCC, 2003). 8.3.4 N2O emissions from mineral soil Annual N2O emissions from disturbed mineral soils were calculated using the Tier 1 method described in the IPCC (IPCC, 2003) in Section 3.3.2.3 for conversion from forest land to agricultural land. This calculation uses the same area data and the carbon stock change data calculated above for the annual carbon emissions of disturbed mineral soils. Calculations are carried out in a simple way using an Excel- based model as described above. 64 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google 8.3.5 Non-CO2 emissions from surface fires The non-CO2 emissions of burned biomass in surface fires are calculated using the carbon released from the fire event modeled in FullCAM multiplied by the IPCC standard N/C ratio values and the emission ratios described in Section 3.2.1.4 (IPCC, 2003) and using Equation 3.2. 19. The required emission ratios are listed in Table 3A.1.15 and the N/C ratio of the combustibles is set at 0.01 (IPCC, 2003). Calculation of non-CO2 emissions of burned biomass in the national GHG inventory is carried out in a simple Excel-based model. Emissions are reported in total tonnes of CH4 , CO, N2 O and NOx . Methane (CH4 ) and N2O emissions were converted to CO2-e emissions using global warming potentials, 28 and 265, respectively. Considering CO and NOx are secondary GHGs, neither of them were converted to CO2- e. Non-CO2 emissions from peatland fires are discussed in the Standard Method – Peatland GHG Emissions. 8.4 QUALITY CONTROL AND QUALITY ASSURANCE Quality control focuses on ensuring that data obtained from standard methods and other sources uses a format suitable for modeling and meets the requirements of accuracy, consistency, comparability and completeness. This process includes checking that the data input parameter requirements are available, the correct units are used, the geographic and temporal coverage for the region and time period being modeled is fully covered and the data sources are clearly documented. If inconsistencies are found, they must be addressed immediately before continuing with modeling. Resolutions may need to review standard methods or other document sources, and/or seek clarification from the authors of the analysis sources. Quality control needs to be carried out by the team responsible for modeling. Quality assurance needs to be carried out at each step of modeling and reporting, including: • inspection of all steps of the modeling process to ensure all steps are followed; • assurance that the data output of each step is calculated correctly (check sample manual for each calculation); • confirm the correctness of the units used and the conversion between units is calculated accurately; • assurance that results are copied correctly from the model to the report. Quality assurance needs to be carried out by an independent group that is not directly involved in carrying out the calculations. For example, in the national GHG inventory this is done by members of the INCAS team who are not directly responsible for modeling, and is carried out by an external technical advisor. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 65 Machine Translated by Google Quality assurance can identify errors in data, methods, calculations or reporting that need to be corrected prior to finalization of the report. 8.5 RESULT 8.5.1 Reporting year GHG emissions and removals can be estimated at any time period using the INCAS approach, either for historical or future predictions using activity plan data or assumptions (eg through scenarios). The reporting period needs to be selected to meet the reporting requirements. Emissions and removals from land use are defined in the year the activity takes place, or the year when there are lag emissions from events in the previous year (eg decay of dead organic matter in the forest due to logging in the previous year). In some data, the exact time of the activity is unknown, although the year of activity can be estimated. For example, if forest cover was detected at a certain location in 2000 but not in 2001, then forest loss occurred in 2001. If forest was not detected in 2000 but was detected in 2001, then additional forest occurred in 2001. 8.5.2 Land use transition matrix The annual area of land use classes and changes from one class to another are reported according to the land use transition matrix. A separate table is required for each year in the inventory period. The area reported in the final area column is the land area by category at the end of each year. Forests located on peatlands are included in the forest land class, not in the wetland class. All non-forest land is assumed to be agricultural land or other land use due to lack of data available at reporting time to differentiate it into other land use categories. As a result, the land use transition matrix is not included. Land use transition matrices (eg Table 8.3) need to be developed when better spatial data are available to allow differentiation between non-forest land uses. 66 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google Table 8-3. Land use transition matrix. Land Field Land Other Final Year Settlement Forest Agricultural land Grass Wet land area forest land Land agriculture Meadow Wetlands Settlement Other land Initial area Net change 8.5.3 Reporting unit Outputs for each carbon source are converted into common reporting units as shown in Table 8-4. Carbon stocks ( tC ha-1) by carbon source were calculated in FullCAM at each time step in the simulation. Changes in carbon stock between time points are calculated outside of FullCAM by exporting to Excel and calculating time step differences as needed. For INCAS changes in carbon stock are calculated annually, measured in units of t C ha-1 yr-1. Non-CO2 emissions from burning forest biomass were calculated by exporting data to Excel from FullCAM annual calculations of mass C emitted due to tree fires and forest dead organic matter, then converting them to CH4 , N2 O, CO and NOx emissions using standard emission ratios and N/N ratios. C (IPCC, 2003). Emissions from disturbed mineral soil organic matter were calculated as annual changes in carbon stock in tC ha -1, where annual N2O emissions were calculated (in tN2O ha-1). Both changes in carbon stock and N2O emissions are converted to CO2- e emissions. Carbon emissions from biological oxidation of peat and peat fires are calculated as changes in peat carbon stock in t C ha-1, converted to CO2 emissions -e. Non-CO2 emissions from forest fires are calculated directly in t CO ha-1 and t CH4 ha-1. Emissions of methane (CH4 ) are converted to CO2 -e emissions. Changes in carbon stock are converted to CO2 -e emissions by multiplying 44/12 (ratio of molecular weight of carbon dioxide to carbon). Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 67 Machine Translated by Google Emissions of methane (CH4 ) and nitrogen oxides (N2O ) are converted to CO2 -e emissions by multiplying these by the 100-year global warming potential of each gas, which is 28 and 265, respectively (Myhre et al., 2013). Table 8-4. Output models and reporting units. Unit Output Initial output unit Factor Unit Source GWP12 reporting model conversion reporting general Biomass and dead CO2 -C ton C ha-1 44/12 ton CO2 1 ton CO2 -e wood CH4 ton CH4 ha-1 1 ton CH4 28 ton CO2 -e N2 O ton N2 O ha-1 1 ton N2 O 265 ton CO2 -e Biomass burning CO tons of CO ha-1 1 tons of CO NA NA NOx ton NOx ha-1 1 ton NOx NA NA CO2 -C ton C ha-1 44/12 ton CO2 1 ton CO2 -e Mineral soil N2 O ton N2 O ha-1 1 ton N2 O 265 ton CO2 -e Biological oxidation of CO2 -C ton C ha-1 44/12 ton CO2 1 ton CO2 -e draining peat CO2 -C ton C ha-1 44/12 ton CO2 1 ton CO2 -e Fire CH4 ton CH4 ha-1 1 ton CH4 28 ton CO2 -e peat CO tons of CO ha-1 1 tons of CO NA NA Emission directly from drying 265 N2 O ton N2 O ha-1 1 ton N2 O ton CO2 -e organic soil 8.5.4 Reporting categories Reporting categories need to be determined by the Government and may change as domestic and international reporting commitments change. Annual GHG emissions can be reported according to the UNFCCC reporting category (as shown in Table 8-5) or the REDD+ category. The relationship between the REDD+ and UNFCCC categories adopted for the national GHG inventory is presented in Table 8-5. 12 GWP – 100-year Global Warming Potential (Myhre et al., 2013). CO and NOx are secondary greenhouse gases and the GWP value has not been determined. 68 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google Table 8-5. Comparison between UNFCCC reporting categories and REDD+ activities included in national GHG inventories. REDD+ Activities UNFCCC reporting categories Sustainable forest management Forest land remains forest land Forest degradation The role of conservation Deforestation Forest land is converted to agricultural land or grasslands or wetlands or settlements or other land Increase in forest carbon Agricultural land or grasslands or wetlands or settlements or other land stocks converted to forest land REDD+ The reported REDD+ category includes total annual net GHG emissions from above-ground activities related to forestry and forest land use change between 2001 and 2012. Deforestation Deforestation calculations show annual GHG emissions and removals resulting from deforestation- related events on forest land in the time period of analysis and reporting. Net emissions from subsequent land use are included where known (eg plantation development on forest land is included in deforestation calculations). In the absence of more detailed data on subsequent land use on agricultural land (non-large plantations), it is assumed that the next land use will be annual agricultural crops with additional and equivalent biomass loss, resulting in zero annual net emissions in the year following deforestation. Emissions from decay of forest dead organic matter arising from deforestation events are also included, generating emissions for several years after the deforestation event. It also includes ongoing emissions from deforestation events prior to 2000. Forest degradation The calculation of forest degradation is the sum of annual GHG emissions and removals from events that cause primary natural forest to become secondary natural forest (eg through human fires or land clearing followed by natural regeneration) and selective logging using conventional techniques that take place in secondary forest13. Emissions from decay of forest dead organic matter due to forest degradation events are also included, resulting in emissions for several years after the event 13 Logging that takes place using low impact logging (RIL) techniques is included in sustainable forest management. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 69 Machine Translated by Google forest degradation. It also includes ongoing emissions from forest degradation events prior to 2000. Conversion of natural forests to plantations falls into this category. The role of conservation There is no agreed definition of how to calculate the role of conservation. For the purpose of the GHG inventory, the role of conservation can indicate the amount of GHG emissions avoided due to the implementation (or enforcement of) management practices in conservation or protected forests. This includes measures to avoid illegal logging or encroachment in conservation or protected forests. INCAS was designed to model the impact of these activities. However, the role of conservation is not included in the national GHG inventory due to the lack of clear land management activities to model. Further analysis of the types of conservation activities and their impact on GHG emissions needs to be included in the INCAS improvement plan. Sustainable forest management The calculation of sustainable forest management is the annual amount of GHG emissions and removals resulting from ongoing management using RIL techniques on land classified as secondary forest at the beginning of the reporting period (forest land remains forest land14. The results show changes in carbon stock at the site as a result of a series of forest management events in managed natural forests on long-term logging cycles with planning and management methods that have minimal net impact on on-site carbon stocks in the long term (emissions and removals are equivalent but time-separated). while 15. Harvesting and planting operations that take place in industrial plantations are also included in this category. Increase in forest carbon stocks The increase in forest carbon stock is the sum of annual GHG emissions and removals resulting from replanting deforested forest covered in the national inventory (conversion of non-forest land to forest land). 14 Selective logging using conventional techniques in secondary forest is included in forest degradation due to the higher impact on forest carbon stocks. 15 Temporary unstand forest is land that meets the definition of forest when the forest reaches a mature stage, but because of the disturbance event it does not contain forest at a given point in time. Land is expected to re-grow and meet the definition of forest in the future. 70 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google 8.6 UNCERTAINTY ANALYSIS Estimated uncertainty is an important component of national inventories. Its purpose is to help improve inventory accuracy over time by helping to guide decisions regarding methodological choices and priorities for inventory improvement. Uncertainty estimates are not intended to question the validity of emission estimates in national inventories. This assessment is made through review of the technical assessment process. The estimation and communication of uncertainty must be practical and scientifically defensible. For example, the practical approach recognizes that generating quantitative uncertainty estimates will depend on statistically quantified uncertainties in addition to expert judgment. Identifying sources of uncertainty is the first step in estimating uncertainty. The sources of uncertainty are generally separate related to emissions and removals (eg initial biomass, growth, turnover and decomposition rates) and uncertainties related to activity data (areas where emissions occur). The INCAS framework is designed to use the best available data for each input. All efforts are made to reduce the uncertainty of each input variable and modeling step through the process of quality control and quality assurance. To demonstrate a general approach to estimating uncertainty in national inventories, a quantitative analysis of uncertainty was carried out on the deforestation component of the national GHG inventory using Monte Carlo analysis (IPCC Tier 2). Uncertainty analysis is performed for: • demonstrates the use of the Monte Carlo method to assess uncertainty in National level; • gives an indication of the uncertainty of the national GHG inventory estimation on the event deforestation; • identify the main parameters that lead to emission estimates to encourage more focused research in continuous improvement plans. The uncertainty analysis is based solely on the statistical range of data used in FullCAM. This analysis is not related to the assumptions used in this system. The main assumptions contained in the general method include: • the average carbon stock of a forest type is equal to the carbon stock of the forest undergoing change; • the methods used to calculate inputs are unbiased, in particular the use of allometric models to convert baseline measurements to biomass. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 71 Machine Translated by Google 8.6.1 Method Uncertainty analysis was carried out using the Excel version of the Carbon Accounting Model for Forests (CAMFor) program. This program was chosen because the FullCAM uncertainty analysis was designed to operate at this level, not at the large levels used in INCAS. CAMFor is a module within FullCAM as described in Richards and Evan (2000). CAMFor only counts forest land and is the component of FullCAM used by INCAS to calculate forest carbon stocks. Therefore, the calculation of GHG emissions and removals in this uncertainty analysis is very consistent with estimates using FullCAM. The Excel version of CAMFor transparently shows all inputs and calculations, allowing risk analysis software (Pallisade@ Risk) is carried out to perform a quantitative uncertainty analysis. The first step in this method is to confirm that CAMFor accurately reproduces FullCAM output at the estate level. To do this, FullCAM national plot files that include deforestation events are rearranged in CAMFor. Quality assurance is carried out to confirm that the output of CAMFor accurately represents the FullCAM output for each plot file. There is a time difference in how CAMFor calculates for events (only at the end of the year) compared to FullCAM (every day of the year). Given that most of the deforestation in the INCAS occurred mid-period, this made little difference in years of land clearing (<2%) (Figure 8-7). When the FullCAM time is set at the end of the year, the output of CAMFor exactly matches that of FullCAM, so it is concluded that the FullCAM and CAMFor calculations are compatible. Uncertainty analysis was then operated for each CAMFor file using Pallisade@Risk for Monte Carlo analysis. To do this, CAMFor was operated a thousand times for each plot. For each round, the parameters are varied within a set range (defined by the user) and the results (both input and output) are uploaded to Pallisade@Risk. Results are produced to demonstrate the impact of parameter variations on total annual emissions for the INCAS simulation period 2001 to 2012. The key parameter inputs from the INCAS analysis (tree mass and dead organic matter) varied within the 95% confidence interval range of the mean as shown in Table 2-3. Since the biomass data is based on thousands of plots, the confidence interval is very strong. It shows the average of the entire forest area (estate) rather than a single forest plot. 72 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google Comparison of emissions estimates from FullCAM and CAMFOR 14 12 10 8 6 FullCAM 4 Camphor 2 0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Year Figure 8-7. Comparison of annual emissions from deforestation events as estimated by FullCAM and CAMFor, shows very little variation between the two modeling tools. Given that Indonesia-specific data on the rate of decomposition, the fraction of decomposing matter and the rate of decomposition are not available, the parameters vary by around +/-50%. Estimated area and carbon fraction vary around +/- 10%. This seems to be an overestimate for some parameters, but without further information it is acceptable. Further assessments will be carried out as part of an ongoing improvement plan as data becomes available. Indonesia's National Forest Inventory data, supplemented by research plot data, were used to generate estimates of the total aboveground biomass. To be used in FullCAM/CAMFor, the data needs to be divided into components (stems, branches, bark and leaves). Each of these components is subject to a Monte Carlo analysis, although it varies depending on each other (eg all component masses increase or decrease in the same proportion) when the input data are based on aboveground biomass. If the components are measured separately, it is more appropriate to distinguish between the components individually. However, in this case, the method will underestimate the uncertainty. 8.6.2 Uncertainty analysis results – Plot level uncertainty Figures 8-8 through 8-11 provide examples of uncertainty analysis to show the impact of parameter variations on total emissions in year 1 and a simulation of year 10 to assess the impact of parameters on delayed emissions using risk analysis software (Palisade@Risk). Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 73 Machine Translated by Google 2001 / C emissions from dead trees and organic matter -121.75 -92.18 2.5% 95.0% 2.5% 0.07 0.06 0.05 2001 / C emissions from dead 0.04 trees and organic matter Minimum -129.098 Maximum -87.113 Rate-rate -106.486 0.03 Standard deviation 7.354 Score 1000 0.02 0.01 0.00 Figure 8-8. Mass distribution of net carbon emitted in secondary swamp forest due to deforestation in the first year of the simulation. 2001 / C emissions from dead trees and organic matter Correlation Coefficient (Spearman Rank) AreaMod -0.61 CFFRac -0.59 BiomassError -0.49 BreakFrac -0.16 DecFrac -0.01 TOFrac -0.01 Coefficient Value Figure 8ÿ9. Regression sensitivity of net carbon mass emitted in secondary swamp forest due to deforestation in the first year of simulation. 74 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google 2011 / C emissions from dead trees and organic matter -148.2 -112.8 2.5% 95.0% 2.5% 0.050 0.045 0.040 0.035 0.030 2011 / C emissions from dead trees and organic matter Minimum -156.846 0.025 Maximum -106.072 Rate-rate -129.851 0.020 Standard deviation 8.719 Score 1000 0.015 0.010 0.005 0.000 Figure 8ÿ10. Mass distribution of net carbon emitted in secondary swamp forest 10 years after deforestation. 2011 / C emissions from dead trees and organic matter Correlation Coefficient (Spearman Rank) AreaMod -0.63 CFFRac -0.53 BiomassError -0.50 BreakFrac -0.23 DecFrac -0.02 TOFrac 0.00 Coefficient Value Figure 8-11. Regression sensitivity for mass net carbon emitted in secondary swamp forest 10 years after deforestation. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 75 Machine Translated by Google 8.6.3 Results of uncertainty analysis – uncertainty at national level16 Figure 8-12 shows the aggregate of estimated annual emissions and the resulting uncertainty (error bars) for land clearing and fire events that lead to deforestation in Indonesia. These results indicate that it is possible to combine estimation uncertainty to produce a total uncertainty estimate at the national level. Although it shows overall uncertainty, it is less valuable for identifying opportunities to reduce uncertainty than using the results of plot-level uncertainty analysis. Annual emissions from deforestation events 60 50 40 30 20 10 0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year Figure 8-12. National level uncertainty results for deforestation events caused by land clearing and forest fires. 8.6.4 Discussion of uncertainty analysis and improvement plans The approach to determining estimation-related uncertainty should take into account the individual uncertainties of the inputs used in obtaining the estimates, and the uncertainty aggregation process. In estimating emissions from deforestation events, inputs are required for the mass of tree components (eg trunks, branches etc.), the carbon fraction of the forest components, the decomposition rate of each component, the biomass turnover rate, the decomposition rate of dead organic matter and the area of each event. Using CAMFor and risk analysis software (such as Palisade@Risk) it is possible to calculate these individual uncertainties and aggregate them into a single uncertainty estimate. Furthermore, it is possible to determine the contribution of the input values into the overall uncertainty estimate at the plot level. The analysis identified some limitations in the uncertainty analysis, such as gaps in the decomposition and transition rate data specific to Indonesia, as well as better estimates of uncertainty related to area. Though 16 Note: The total emission values differ from the deforestation results presented in this report because the uncertainty analysis only includes deforestation events due to land clearing and fires, while the deforestation calculation covers all activities on deforested land, including delayed emissions from deforestation that occurred prior to the simulation period. 76 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google however, the overall analysis indicates that a similar approach can be taken to account for all incident and reporting processes within the INCAS framework. Tier 2 analysis is available to estimate the significance of individual input values on the overall uncertainty estimate. This contrasts with Tier 1 (aggregation) analysis, which provides estimates of overall emissions but is less important for estimates of individual components. By identifying the input values with the most significant uncertainty, research can be appropriately prioritized to reduce the overall uncertainty of the inventory. This is in line with the objective of uncertainty analysis, which is to help refine inventories over time, not to validate emissions estimates. Therefore, Tier 2 analysis has more clarity of benefits compared to Tier 1 analysis. 8.7 LIMITATIONS Data limitations are presented in each of the standard methods that generate input. The INCAS framework is designed to allow simulations to be carried out using the best available data, assuming they are used to fill data gaps. When better data becomes available, the system can be re-run for the entire span of time, producing consistent output across years. Examples of limited national GHG inventory data are briefly described below. • Broad grouping of biomass was carried out due to limited data that did not allows for more detailed forest stratification. • Not all existing spatial data can be used. This limits the achievement of a detailed level of acreage data, which reduces the potential for model output accuracy, because activity data (acreage) is one of the main factors influencing GHG emission estimates. • Forests located outside forest land according to the Ministry of Environment's classification Living and Forestry as one of the six classes of natural forest or plantation forest were not included in the analysis because data on forest type, condition or management were not available; • Spatial and temporal accuracy of fire area data has high uncertainty; • Lack of clear definition of forest degradation and sustainable forest management requires the assumption of activities for each activity. Different assumptions will make different land allocations for each REDD+ activity. The limitations of the analysis arise due to the characteristics of forest management systems, forest types, soil types and data availability in Indonesia; this means that some processes and events in Indonesia are not easily quantifiable using FullCAM, which was originally designed to meet Australia's national GHG emissions inventory reporting requirements. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 77 Machine Translated by Google • FullCAM cannot model planting events if forest already exists. This means that enrichment planting cannot be modeled as a single event. • FullCAM was unable to model the thinning response when using yield tables (eg when primary forest is selectively logged to become secondary forest) as is the case in Indonesia, due to the unavailability of the data required to use the tree yield formula. This means that when calculating emissions from selective logging in primary forest (to secondary forest) it is necessary to first know the condition of the existing forest, then place new secondary forest with initial biomass equivalent to the stock of old secondary forest biomass. • The soil model in FullCAM is not suitable for mineral soil types in Indonesia. • FullCAM does not include organic soil (peat) as a modelable carbon source. • Some of the data required by FullCAM is not available in Indonesia, thus requiring the adoption of standard values or assumptions (eg decay rates of dead organic matter are not available for Indonesia, therefore decay rates are taken from available tropical rainforests in Australia). 8.8 IMPROVEMENT PLAN • Parameterization of the model and running the analysis requires an iteration process so that the limitations of the data can be identified and directed. For example, the iterative process between spatial analysis and suite and regime compilation will provide the basis for a more efficient and comprehensive regime spatial allocation. • Regulating the spatial allocation of regimes will significantly increase the efficiency of the process and allow for better utilization of the available spatial data. The development of spatial analysis tools needs to be prioritized. • Improvement of the method in determining the burned area, time and frequency fires need to be developed. • Management events on farms need to be included in the deforestation estate file , when area and emission data are available. • An approach to calculating net emissions from oil palm and rubber plantations needs to be developed as part of the agricultural land use inventory component of future GHG inventories. • More detailed information on land needs to be developed. • A more comprehensive uncertainty analysis needs to be carried out. 78 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google REFERENCE Abdurrochim, S., Mandang, Y.I. dan Uhaedi, S. 2004. Indonesian Wood Atlas, 3rd edition (in Indonesian). Research and Development Center for Forest Product Technology. Bogor. Ballhorn, U., Navratil, P. dan Siegert, F. 2014. 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INCAS Database List Base Name No Description Data 1 Basis Data This database contains information on parameters, values, assumptions and FullCAM references/data sources used as input in FullCAM, including: • Initial condition of the tree (above ground biomass, trunk, branches, bark, leaves, coarse roots, fine roots) • Initial conditions of dead organic matter (decomposable dead wood, resistant dead wood, decomposable bark litter, resistant bark litter, decomposable leaf litter, resistant leaf litter, decomposed dead coarse roots, roots resistant coarse dead roots, decomposed dead fine roots, resistant dead fine roots) • Percentage (%) of carbon (dry weight) of the tree (stems, branches, bark, leaves, coarse roots, fine roots) • Wood density (kgdm m-3) • Percentage (%) transition of branches, bark, coarse roots, fine roots) • Percentage (%) of tree resistance (stems, branches, bark, leaves, rough roots, fine root) • Percentage (%) of decomposition of dead organic matter (dead wood that can be decomposed, resistant dead wood, decomposed tree bark litter, resistant bark litter, decomposable leaf litter, resistant leaf litter, decomposed dead coarse roots, resistant dead coarse roots, decomposed dead fine roots, resistant fine roots) • Percentage (%) of atmospheric decomposition of dead organic matter (dead wood decomposable, resistant dead wood, decomposable bark litter, resistant bark litter, decomposable leaf litter, resistant leaf litter, decomposed dead coarse root, resistant dead coarse root, dead wood fine root decomposable, resistant deadwood fine roots) 86 | Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) Machine Translated by Google Base Name No Description Data 2 Basis Data This database contains information on events modeled in each forest type (eg event_ land clearing, illegal logging, selective logging using conventional techniques, FullCAM selective logging using RIL techniques, large fires, moderate fires, planting). Information is available for each event in each forest type including the parameters used by FullCAM, values, assumptions and references used. 3 Basis Data This database contains information on the growth of 48 species and forest conditions Growth including the data and references used, the process applied in model development to obtain CAI values as input for FullCAM. 4 Basis Data This database contains information on 1,152 regimes (land management) in Rejim Suite certain groups which were developed based on the combination of Forest/Non-Forest class, Initial Land Category, Initial Forest Type, Forest Function, Soil Type, Logging system, Plantation, Fire, Forest Transition/ Non-Forest and Land Category Change. Suite is a specific area with a certain regime that will be modeled in FullCAM. 5 Basis Data A regime-wide database was developed for each province in Indonesia (34 Wide Regime provinces). The regime area shows the results of the spatial allocation of the regime to be modeled. This database contains information on regime codes, parameters used in compiling the suite, area, time of occurrence and regime plot files. 6 Basis Data This database contains information on emission calculations and modeled Results areas on various carbon sources (above ground biomass, below ground biomass, litter, dead wood, soil) and other emission sources (burnt peat, biological oxidation of peat) by type. forest, forest function, soil type, activity and province for ease of reporting. Standard Methods for Estimating Greenhouse Gas Emissions from Forests and Peatlands in Indonesia (Version 2) | 87 Machine Translated by Google This publication describes in detail the standard methods of the Indonesian National Carbon Accounting System in calculating net greenhouse gas (GHG) emissions from forests and peatlands in Indonesia in a transparent, accurate, complete, consistent and comparable manner. These methods describe the approaches and methods used in data collection, data analysis, quality control, quality assurance, modeling and reporting. These methods include (i) Initial Conditions, (ii) Forest Growth and Transition, (iii) Forest Management Events and Regimes, (iv) Forest Cover Change, (v) Regime Spatial Allocation, (vi) Peatland GHG Emissions, and ( vii) Data Integration and Reporting. The second version of this standard method includes improvements to the first implemented method in preparing a comprehensive national GHG inventory for forests and peatlands. The results are presented in the 'National Inventory of Greenhouse Gas Emissions and Removals in Indonesia's Forests and Peatlands'. This publication was prepared and published by the Research, Development and Innovation Agency of the Indonesian Ministry of Environment and Forestry. MINISTRY OF ENVIRONMENT AND FORESTRY RESEARCH, DEVELOPMENT AND INNOVATION AGENCY © 2015