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Forest Ecosystem (KLHK) - Plantation Forest
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All plantation forest that already planted include reboisation plantation. Identification also obtained from distribution of Planttaion Forest. (Perdirjen Planologi Kehutanan Nomor: P.1/VII-IPSDH/2015)
Ministry of Environment and Forestry
(2000, 2003, 2006, 2009, 2011, 2012, 2013, 2016)
Data continuity was inconsistent (2000, 2003, 2006, 2009, 2011, 2012, 2013, 2016).
Limitations (accuracy, data collection method, etc.)
Data continuity was inconsistent (2000, 2003, 2006, 2009, 2011, 2012, 2013, 2016).Using 3 different spatial resolution (Landsat 7-8 as the Primary Data, SPOT 1000m, SPOT 250m)
http://webgis.menlhk.go.id:8080/pl/pl.htm
Forest Ecosystem (KLHK) - Primary Dryland Forest
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Dryland forest such as lowland forest, mountain forest, or highland tropical forest which have not intervened by human or logging activity (SNI 7645-2010)
Ministry of Environment and Forestry
(2000, 2003, 2006, 2009, 2011, 2012, 2013, 2016)
Data continuity was inconsistent (2000, 2003, 2006)
Limitations (accuracy, data collection method, etc.)
Data continuity was inconsistent (2000, 2003, 2006, 2009, 2011, 2012, 2013, 2016). Using 3 different spatial resolution (Landsat 7-8 as the Primary Data, SPOT 1000m, SPOT 250m)
http://webgis.menlhk.go.id:8080/pl/pl.htm
Forest Ecosystem (KLHK) - Primary Mangrove Forest
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Part of Wetland Forest (SNI 7645 - 2010).Mangrove forest, nipah (Nypa sp.), nibung (Oncosperma sp.) located near from coastline which have not intervened by human or logging activity. (Perdirjen Planologi Kehutanan Nomor: P.1/VII-IPSDH/2015)
Ministry of Environment and Forestry
(2000, 2003, 2006, 2009, 2011, 2012, 2013, 2016)
Data continuity was inconsistent (2000, 2003, 2006, 2009, 2011, 2012, 2013, 2016).
Limitations (accuracy, data collection method, etc.)
Data continuity was inconsistent (2000, 2003, 2006, 2009, 2011, 2012, 2013, 2016).Using 3 different spatial resolution (Landsat 7-8 as the Primary Data, SPOT 1000m, SPOT 250m)
http://webgis.menlhk.go.id:8080/pl/pl.htm
Forest Ecosystem (KLHK) - Primary Swamp Forest
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Part of Wetland Forest (SNI 7645 - 2010).All forest cover that laid on swamps location such as peatland (include sago forest) which have not intervened by human or logging activity. (Perdirjen Planologi Kehutanan Nomor: P.1/VII-IPSDH/2015)
Ministry of Environment and Forestry
(2000, 2003, 2006, 2009, 2011, 2012, 2013, 2016)
Data continuity was inconsistent (2000, 2003, 2006, 2009, 2011, 2012, 2013, 2016).
Limitations (accuracy, data collection method, etc.)
Data continuity was inconsistent (2000, 2003, 2006, 2009, 2011, 2012, 2013, 2016).Using 3 different spatial resolution (Landsat 7-8 as the Primary Data, SPOT 1000m, SPOT 250m)
http://webgis.menlhk.go.id:8080/pl/pl.htm
Forest Ecosystem (KLHK) - Secondary Dryland Forest
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Dryland forest such as lowland forest, mountain forest, or highland tropical forest which already intervened by human or logging activity (SNI 7645-2010)
Ministry of Environment and Forestry
(2000, 2003, 2006, 2009, 2011, 2012, 2013, 2016)
Data continuity was inconsistent (2000, 2003, 2006, 2009, 2011, 2012, 2013, 2016).
Limitations (accuracy, data collection method, etc.)
Data continuity was inconsistent (2000, 2003, 2006, 2009, 2011, 2012, 2013, 2016). Using 3 different spatial resolution (Landsat 7-8 as the Primary Data, SPOT 1000m, SPOT 250m)
http://webgis.menlhk.go.id:8080/pl/pl.htm
Forest Ecosystem (KLHK) - Secondary Mangrove Forest
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Part of Wetland Forest (SNI 7645 - 2010).Mangrove forest, nipah (Nypa sp.), nibung (Oncosperma sp.) located near from coastline which already disturbed by reflecting logged pattern (path or spot), water bodies or burn marks. Especially for disturbed area that converted into pond/paddy field classified as pond/paddy field and if reflecting water bodies classified as water bodies or swamp. (Perdirjen Planologi Kehutanan Nomor: P.1/VII-IPSDH/2015)
Ministry of Environment and Forestry
(2000, 2003, 2006, 2009, 2011, 2012, 2013, 2016)
Data continuity was inconsistent (2000, 2003, 2006
Limitations (accuracy, data collection method, etc.)
Data continuity was inconsistent (2000, 2003, 2006, 2009, 2011, 2012, 2013, 2016).Using 3 different spatial resolution (Landsat 7-8 as the Primary Data, SPOT 1000m, SPOT 250m)
http://webgis.menlhk.go.id:8080/pl/pl.htm
Forest Ecosystem (KLHK) - Secondary Swamp Forest
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Part of Wetland Forest (SNI 7645 - 2010)All forest cover that laid on swamps location such as peatland (include sago forest & ex fired forest) which already disturbed by reflecting logged pattern (path or spot), bare land, or burn marks. However, if relfecting water bodies classified as water bodies or swamp. (Perdirjen Planologi Kehutanan Nomor: P.1/VII-IPSDH/2015)
Ministry of Environment and Forestry
(2000, 2003, 2006, 2009, 2011, 2012, 2013, 2016)
Data continuity was inconsistent (2000, 2003, 2006, 2009, 2011, 2012, 2013, 2016).
Limitations (accuracy, data collection method, etc.)
Data continuity was inconsistent (2000, 2003, 2006, 2009, 2011, 2012, 2013, 2016).Using 3 different spatial resolution (Landsat 7-8 as the Primary Data, SPOT 1000m, SPOT 250m)
http://webgis.menlhk.go.id:8080/pl/pl.htm
Forest cover change 2000-2016 - Tree Cover Gain
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Identifies areas of tree cover gain
Global land area (excluding Antarctica and other Arctic islands)
Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available from: earthenginepartners.appspot.com/science-2013-global-forest.
Limitations (accuracy, data collection method, etc.)
When zoomed out (< zoom level 13), pixels of gain are shaded according to the density of gain at the
Use the following credit when these data are displayed: Source: Hansen/UMD/Google/USGS/NASA, accessed through Global Forest Watch Use the following credit when these data are cited: Hansen, M. C., P.
Forest cover change 2000-2016 - Tree Cover Loss
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Identifies areas of gross tree cover loss
Global land area (excluding Antarctica and other Arctic islands)
Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available from: earthenginepartners.appspot.com/science-2013-global-forest.
Limitations (accuracy, data collection method, etc.)
When zoomed out (< zoom level 13), pixels of loss are shaded according to the density of loss at the 30 x 30 meter scale. Pixels with darker shading represent areas with a higher concentration of tree cover loss, whereas pixels with lighter shading indicate a lower concentration of tree cover loss. There is no variation in pixel shading when the data is at full resolution (= zoom level 13). The tr
Use the following credit when these data are displayed: Source: Hansen/UMD/Google/USGS/NASA, accessed through Global Forest Watch Use the following credit when these data are cited: Hansen, M. C., P.
State Forest Zone/Kawasan Hutan (KLHK) - Conservation Area
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Area that stated by the government either partially or within the province with a Decree of the Minister of Forestry as forest area with certain main functions. Consist of Protection Area, Conservation Area, Production Area, Limited Production Area, Conversion Production Area and Other Land Use
Ministry of Environment and Forestry
Every change of forest area that stated by government (Ministry of environment and Forestry)
Limitations (accuracy, data collection method, etc.)
Changes of status area occur frequently
State Forest Zone/Kawasan Hutan (KLHK) - Conversion Production Forest
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Area that stated by the government either partially or within the province with a Decree of the Minister of Forestry as forest area with certain main functions. Consist of Protection Area, Conservation Area, Production Area, Limited Production Area, Conversion Production Area and Other Land Use
Ministry of Environment and Forestry
Every change of forest area that stated by government (Ministry of environment and Forestry)
Limitations (accuracy, data collection method, etc.)
Changes of status area occur frequently
State Forest Zone/Kawasan Hutan (KLHK) - Limited Production Forest
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Area that stated by the government either partially or within the province with a Decree of the Minister of Forestry as forest area with certain main functions. Consist of Protection Area, Conservation Area, Production Area, Limited Production Area, Conversion Production Area and Other Land Use
Ministry of Environment and Forestry
Every change of forest area that stated by government (Ministry of environment and Forestry)
Limitations (accuracy, data collection method, etc.)
Changes of status area occur frequently
State Forest Zone/Kawasan Hutan (KLHK) - Other Land Use
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Area that stated by the government either partially or within the province with a Decree of the Minister of Forestry as forest area with certain main functions. Consist of Protection Area, Conservation Area, Production Area, Limited Production Area, Conversion Production Area and Other Land Use
Ministry of Environment and Forestry
Every change of forest area that stated by government (Ministry of environment and Forestry)
Limitations (accuracy, data collection method, etc.)
Changes of status area occur frequently
State Forest Zone/Kawasan Hutan (KLHK) - Production Forest
×
Area that stated by the government either partially or within the province with a Decree of the Minister of Forestry as forest area with certain main functions. Consist of Protection Area, Conservation Area, Production Area, Limited Production Area, Conversion Production Area and Other Land Use
Ministry of Environment and Forestry
Every change of forest area that stated by government (Ministry of environment and Forestry)
Limitations (accuracy, data collection method, etc.)
Changes of status area occur frequently
State Forest Zone/Kawasan Hutan (KLHK) - Protected Area
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Area that stated by the government either partially or within the province with a Decree of the Minister of Forestry as forest area with certain main functions. Consist of Protection Area, Conservation Area, Production Area, Limited Production Area, Conversion Production Area and Other Land Use
Ministry of Environment and Forestry
Every change of forest area that stated by government (Ministry of environment and Forestry)
Limitations (accuracy, data collection method, etc.)
Changes of status area occur frequently
Light detection and ranging (LiDAR) provides geospatial information with massiveamounts of data to use in a variety of applications. Canopy cover percentage of tree crown is one application that is estimated through LiDAR data. This study aims to estimate a canopy cover derived from LiDAR used as reference data with multispectral Landsat 8 OLI. Statistical or machine learning approaches can be used to estimate a canopy cover. In statistical inference, the form of distribution chosen by the analyst and parameter is estimated from the data. This will be a problem in the construction function for the National scale, due to many ecosystems and then will have many functions for each ecosystem. Machine learning (ML) methods, in contrast, are various kinds of algorithms that are used to learn the mapping function or classification rules inductively, directly from the training data. ML can solve problems related to big data and global method and it will be used as a methodical approach to estimate canopy cover on the National scale. In this study, we use a support vector regression (SVR) as ML method to estimate the canopy cover. The estimated percentage of canopy cover will be used as one of the parameters to classify a forest.
Limitations (accuracy, data collection method, etc.)
Canopy cover estimation with Landsat 8 data and LiDAR data derived the percentage of canopy cover as reference data used to develop the estimation model. The model was developed in this study uses the SVR algorithm to estimate canopy cover in various ecosystems. The results of analysis in this study revealed some conclusions which are the model quietly good for estimationof canopy cover in various ecosystems. Based on validation with RMSE calculations for the ecosystems: agroforestry obtained RMSE value of 0.214, mangrove of 0.271, and mountainous of 0.275.
Hudjimartsu, S., Tampinongkol, F., Rudianto, Y., Setiawan, Y., & Budi Prasetyo, L. (2019). CANOPY COVER ESTIMATION BASED ON SUPPORT VECTOR REGRESSION DERIVED FROM LIDAR & LANDSAT 8 OLI. In The 40th Asian Conference on Remote Sensing (ACRS 2019). Daejon. Retrieved from https://a-a-r-s.org/proceeding/ACRS2019
Oil Palm - Agro-Climatic Suitability for Oil Palm
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Agro-Climatic Suitability for Oil Palm
Land suitability of main commodities – retrieved from climatic, topographic, and soil properties parameters (temperature, available water, rooting zone, peat information, and risk of erosion) based on technical report of land suitability assessment for agricultural commodity (Ministry of Agriculture, 2011). The output was classified to 4 main-classes – i.e. S1: Very suitable, S2: suitable, S3: Marginal suitable, N: unsuitable.
SPT from BBSDLP; Land System from RePPRot
Limitations (accuracy, data collection method, etc.)
Unit analysis of land suitability of commodity was defined by feature of land system or SPT. For Sumatera and Java region, the data were derived from SPT data. For the rest of it, the data were derived from Land System. The climatic parameters was defined from WorldClim v2.0 data and we performed zonal statistics to extract the data.
Ritung et al. 2011. Petunjuk Teknis Evaluasi Lahan untuk Komoditas Pertanian. Bogor (ID): Balai Besar Penelitian dan Pengembangan Sumberdaya Lahan Pertanian
Oil Palm - Oil Palm Distribution 2018 (ver 1.0)
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Oil Palm Distribution 2018 (ver 1.0)
The data represents distribution of oil palm data in Indonesia 2018

WMS : https://forests2020.ipb.ac.id/arcgis/services/Ecosystem_OriginalDistribution/OilPalm_Original_Distribution/MapServer/WMSServer?request=GetCapabilities&service=WMS
Query GeoJSON : https://forests2020.ipb.ac.id/arcgis/rest/services/Ecosystem_DistComm4326/Dist_Comm4326_2018/MapServer/2/query
pH in H2O , Kation Exchange Capacity, C org (all soil properties in 60cm soil depth) : Soilgrids.org Temperature, Precipitation, Wet/Dry season frequency : Worldclim.org DEM : DEMNAS (Indonesia Geospatial Agency) : http://tides.big.go.id/DEMNAS/
Limitations (accuracy, data collection method, etc.)
1. not yet published in scientific paper2. Data obtained from differences source that has difference spatial resoultion (data resampled to 1 x 1 km sq based on climate data that has the biggest spatial resolution)
Djaenudin, D., Marwan, H., Subagjo, H., dan A. Hidayat. 2011. Petunjuk Teknis Evaluasi Lahan Untuk Komoditas Pertanian. Balai Besar Litbang Sumberdaya Lahan Pertanian, Badan Litbang Pertanian, Bogor. 36pSNI 7898 : 2014 about Prosedur Pemetaan Tingkat Kesesuaian AgroklimatFick, S.E. and R.J. Hijmans, 2017. Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology.[BIG] Indonesia Geospatial Agency. http://tides.big.go.id/DEMNAS/#Info[ISRIC] International Soil Reference and Information Centre. https://soilgrids.org/#!/?layer=ORCDRC_M_sl2_250m&vector=1
Oil Palm - Oil Palm Distribution 2019 (ver.2.0)
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Oil Palm Distribution 2019 (ver.2.0)
Commodities classification prediction of the main commodities – Predicted using machine learning classifier from 36 predictors (multispectral bands, EVI, SAVI, IBI, Covariates of each multispectral bands, Topographical parameters, and Surface water information from JRC Global Surface Water) .

WMS : https://forests2020.ipb.ac.id/arcgis/services/Ecosystem_OriginalDistribution_2019/OilPalm_Original_Distribution_2019/MapServer/WMSServer?request=GetCapabilities&service=WMS
Query GeoJSON : https://forests2020.ipb.ac.id/arcgis/rest/services/Ecosystem_DistComm4326/Dist_Commodity_2019/MapServer/2/query
Limitations (accuracy, data collection method, etc.)
The results suggest that the model was relatively have a good fit with the references data (OA: 92%, kappa: 84%).
Oil Palm - Oil Palm Distribution 2021
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Commodities classification prediction of the main commodities – Predicted using machine learning classifier from 36 predictors (multispectral bands, EVI, SAVI, IBI, Covariates of each multispectral bands, Topographical parameters, and Surface water information from JRC Global Surface Water) .
Limitations (accuracy, data collection method, etc.)
The results suggest that the model was relatively have a good fit with the references data (OA: 92%, kappa: 84%).
Oil Palm - Oil Palm Expansion in Forest Cover
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The data represents overlayed forest cover data (retrieved from MoEF land cover data) with commodity cover - e.g. oil palm (retrieved from UNDP-IPB commodity data) that indicates forest altered by the commodity
MoEF land cover data and UNDP-IPB commodity
Limitations (accuracy, data collection method, etc.)
The commodity data was estimated using machine learning technique with a relatively good accuracy. see http://webgis.menlhk.go.id:8080/pl/pl.htm for the land cover methods
Oil Palm - Oil Palm Plantation (Austin et al. 2017)
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Oil Palm Plantation (Austin et al. 2017)
Limitations (accuracy, data collection method, etc.)
Paddy - Agro-Climatic Suitability for Paddy
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Agro-Climatic Suitability for Paddy
Land suitability of main commodities – retrieved from climatic, topographic, and soil properties parameters (temperature, available water, rooting zone, peat information, and risk of erosion) based on technical report of land suitability assessment for agricultural commodity (Ministry of Agriculture, 2011). The output was classified to 4 main-classes – i.e. S1: Very suitable, S2: suitable, S3: Marginal suitable, N: unsuitable.
Limitations (accuracy, data collection method, etc.)
Unit analysis of land suitability of commodity was defined by feature of land system or SPT. For Sumatera and Java region, the data were derived from SPT data. For the rest of it, the data were derived from Land System. The climatic parameters was defined from WorldClim v2.0 data and we performed zonal statistics to extract the data.
Ritung et al. 2011. Petunjuk Teknis Evaluasi Lahan untuk Komoditas Pertanian. Bogor (ID): Balai Besar Penelitian dan Pengembangan Sumberdaya Lahan Pertanian
Paddy - Paddy Distribution 2018 (ver 1.0)
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The data represents distribution of paddy in Indonesia 2018.


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