The global map of forest types provides a spatially explicit representation of primary forest, naturally regenerating forest and planted forest (including plantation forest) for the year 2020 at 10m spatial resolution. The base layer for mapping these forest types is the extent of forest cover of version 1 of the …

This is a pan-European map of living forest above and below-ground biomass. The map was implemented following IPCC Tier 1 method. Maps of forest biomass stock and carbon are relevant for quantifying terrestrial carbon storage and carbon sinks as well as for estimating potential emissions from land cover changes (afforestation, deforestation, reforestation), forest fragmentation and biotic (pests) and abiotic (e.g. forest fires, windstorms) disturbances.

The morphological spatial pattern analysis derived from the JRC forest map 2006 (FMAP2006) using the MSPA Algorithm. The map product is produced at a spatial resolution of 25-m. Further details available in: Vogt P. (2009): MSPA-Guidos: Innovative Methods In Landscape Pattern Analysis. In J.P. Metzger (ed.) Conference proceedings of the 2009 Latin Amercian IALE conference, Sao Paulo, Brazil, pp. 42.

This is a pan-European map of forest biomass increment. The map was implemented using MODIS GPP data (NASA Product MOD17A3) adjusted with GPP data derived from upscaling FLUXNET observations using the Model Tree Ensemble (MTE) technique (Jung et al., 2011) to derive a 1 km resolution woody biomass increment map. The map was validated using regional information from the most recent publicly available National Forest Inventories data of several European countries.

The dataset provides maps of Managed and Unmanaged Forests at a global scale, with spatial resolutions of 5 km and 50 km. These maps serve as a proxy for countries' managed and non-managed forest areas, produced to support comparability between independent estimates and national Greenhouse Gas (GHG) reporting under the UNFCCC. The methodology uses country maps of managed forests where available, but also makes assumptions where official data is not present, employing independent datasets that may not be fully consistent with national forest definitions when no other options are available.

Here, we present the version 1.0 (2024) of the “Global JRC Proxy Maps of Managed and Non-Managed Forests,” which is used to “translate” Bookkeeping Model results into national inventory definitions in the Global Carbon Budget 2024 (Friedlingstein et al., 2024). Country-level results of this translation are available through the Global Land Use Carbon Fluxes Data Hub: https://forest-observatory.ec.europa.eu/carbon/fluxes. This dataset will be updated as better information becomes available.

The spatial forest coverage is derived from the Copernicus Global Land Cover dataset for the assessment year 2019 (accessed in February 2022). We outline the procedure of pre-processing the original Copernicus data segments into a continuous global forest mask, which is compliant with the FAO forest definition. This forest mask is then analysed for various forest attribute layers summarising the spatial status of forest cover and its degree of forest degradation. The analysis is conducted in a dual approach: first, using the original WGS84 map projection for explanatory and user-friendly visualisation on web portals and second, in equal area projection allowing statistical evaluation of the forest attribute layers. Spatially explicit maps and statistical summaries are derived for a total of 278 reporting units, comprised of 255 countries, 21 global ecological zones, the EU27, and the full global coverage.

The full dataset, described in section 3.5 of the Technical Report (https://doi.org/10.2760/41048) is available at: https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/FOREST/FAL/CGLO/LATEST/

تقدّم الخريطة العالمية لغطاء الغابات تمثيلاً مكانيًا واضحًا لوجود الغابات وعدم وجودها في عام 2020 بدقة مكانية تبلغ 10 أمتار. يتوافق العام 2020 مع تاريخ قطع اللائحة الصادرة عن الاتحاد الأوروبي "بشأن إتاحة بعض السلع والمنتجات المرتبطة بإزالة الغابات وتدهورها في سوق الاتحاد الأوروبي وتصديرها من الاتحاد الأوروبي" (EUDR، اللائحة (الاتحاد الأوروبي) 2023/1115). في سياق قانون إزالة الغابات في الاتحاد الأوروبي، يمكن استخدام خريطة الغطاء الحرجي العالمي كمصدر معلومات غير إلزامي وغير حصري وغير ملزم قانونًا. يمكنك الاطّلاع على مزيد من المعلومات حول الخريطة واستخدامها على مرصد الاتحاد الأوروبي لإزالة الغابات وتدهورها (EUFO)، وتحديدًا في قسم الأسئلة الشائعة. تعني الغابة أرضًا تمتد على مساحة تزيد عن 0.5 هكتار وتضم أشجارًا يزيد ارتفاعها عن 5 أمتار وتغطيها مظلة شجرية بنسبة تزيد عن %10، أو أشجارًا يمكن أن تصل إلى هذه الحدود في الموقع، باستثناء الأراضي التي تُستخدم بشكل أساسي في الزراعة أو الأراضي الحضرية. يشير الاستخدام الزراعي إلى استخدام الأراضي لأغراض زراعية، بما في ذلك المزارع الزراعية (أي مجموعات الأشجار في أنظمة الإنتاج الزراعي مثل مزارع أشجار الفاكهة ومزارع نخيل الزيت وبساتين الزيتون وأنظمة الحراجة الزراعية) والمناطق الزراعية المخصصة، ولتربية الماشية. ويتم استبعاد جميع المزارع التي تنتج سلعًا أساسية ذات صلة غير الخشب، مثل الماشية والكاكاو والبن ونخيل الزيت والمطاط وفول الصويا، من تعريف الغابات. تم إنشاء الخريطة العالمية لغطاء الغابات من خلال الجمع بين الطبقات المكانية العالمية الحالية (التي تغطي مساحة واسعة أو عالمية في نطاقها)، مثل الغطاء الأرضي واستخدام الأراضي وارتفاع الأشجار. تهدف الخريطة إلى تمثيل حالة الغطاء الحرجي بحلول 31 كانون الأول (ديسمبر) 2020. يشكّل الغطاء الأرضي العالمي من مشروع ESA World Cover إحدى الطبقات الأساسية لتحديد مدى تغطية الأشجار لعام 2020 بدقة مكانية تبلغ 10 أمتار. في عام 2024، تم تحسين الخريطة العالمية لغطاء الغابات لعام 2020 من خلال دمج ملاحظات المستخدمين وطبقات بيانات مكانية جديدة أو معدَّلة. أصبحت هذه الخريطة الآن ترصد بشكل أفضل الغابات غير المخزّنة مؤقتًا والغابات الاستوائية المنخفضة الكثافة والغابات الاستوائية الثانوية التي تمت إعادة نموها لمدة خمس سنوات على الأقل. بالإضافة إلى ذلك، تم تحسين معايير الاستبعاد لاستبعاد الأشجار بشكل أكثر فعالية في المناطق الحضرية ومواقع التعدين والأراضي الرطبة والمناطق التي تعتمد على الزراعة المتنقلة ومزارع الأشجار. ويتم تحقيق ذلك من خلال استخدام خرائط عالمية متعددة لمساحة الأشجار المظلِّلة ومساحة المحاصيل وخرائط سلعية خاصة بالمحاصيل، وذلك للتمييز بشكل أكثر دقة بين الغابات والأشجار المستخدمة في الزراعة. للحصول على إمكانية الوصول المباشر إلى البيانات والبيانات الوصفية، يُرجى الرجوع إلى كتالوج بيانات مركز الأبحاث المشتركة (JRC 2024). يتضمّن تقرير فني (Bourgoin et al 2025) وصفًا لأسلوب الربط المستخدَم في الإصدار الثاني. يمكن الاطّلاع على تقييم دقة خريطة الغطاء الحرجي العالمي في تقرير منفصل. قد تتم مراجعة الخريطة العالمية لغطاء الغابات في حال توفُّر معلومات جديدة أو طبقات بيانات إضافية على نطاق واسع أو طبقات بيانات مكانية عالمية معدَّلة لعام 2020. للاطّلاع على قائمة بالمشاكل المعروفة، يُرجى الرجوع إلى هذا الموقع الإلكتروني.

Percent of ecoregion area covered with forest habitat.

We derived the forest map from forest and woodland classes of the Global Land Cover 2000 data set (JRC 2003) with areas of human habitation and infrastructure from the Global Rural-Urban Mapping Project database (CIESIN et al. 2004) removed. We applied a zonal sum procedure to those data to show the amount of forest by ecoregion.

These data were derived by The Nature Conservancy, and were displayed in a map published in The Atlas of Global Conservation (Hoekstra et al., University of California Press, 2010). More information at http://nature.org/atlas.

Data derived from:

Center for International Earth Science Information Network (CIESIN), International Food Policy Research Institute (IFPRI), the World Bank, and Centro Internacional de Agricultura Tropical (CIAT). 2004. Global Rural-Urban Mapping Project (GRUMP): Urban Extents, Columbia University Palisades, New York, USA. Available at http://sedac.ciesin.columbia.edu/gpw/. Digital media.

Joint Research Centre of the European Commission (JRC). 2003. GLC 2000: Global Land Cover Mapping for the Year 2000. Ispra, Italy: European Commission Joint Research Centre, Institute for Environment and Sustainability. Available at www-tem.jrc.it/glc2000. Digital media.

For more about The Atlas of Global Conservation check out the web map (which includes links to download spatial data and view metadata) at http://maps.tnc.org/globalmaps.html. You can also read more detail about the Atlas at http://www.nature.org/science-in-action/leading-with-science/conservation-atlas.xml, or buy the book at http://www.ucpress.edu/book.php?isbn=9780520262560

Forest area map of Europe at 100 m resolution based on the Copernicus 2018 Forest Type map and modified to match the NFI Forest Area Statistics at sub-national level for the year 2020

Pan-European Forest / Non Forest Map with target year 2000, Data Source: Landsat ETM+ and Corine Land Cover 2000, Classes: forest, non-forest, clouds/snow, no data; Method: automatic classification performed with an in-house algorithm; spatial resolution: 25m

Pan-European Forest / Non Forest Map with target year 2006, Data Source: Landsat ETM+ and Corine Land Cover 2006, Classes: forest, non-forest, clouds/snow, no data; Method: automatic classification performed with an in-house algorithm; spatial resolution: 25m. In addition, the forest map 2006 is extended to FTYPE2006 to include forest types (broadleaf, coniferous forest) that are mapped using MODIS composites.

This dataset consists of 3 GIS maps that indicate the soil biomass productivity of grasslands and pasture, of croplands and of forest areas in the European Union (EU27)

Estreguil, C., Caudullo, G., de Rigo,D., 2014. Connectivity of Natura 2000 forest sites in Europe. F1000Posters 2014, 5: 485. doi: 10.6084/m9.figshare.1063300. ArXiv: 1406.1501

CONNECTIVITY OF NATURA 2000 FOREST SITES IN EUROPE

Christine Estreguil ¹, Giovanni Caudullo ¹, Daniele de Rigo ¹ ² ¹ European Commission, Joint Research Centre, Institute for Environment and Sustainability,Via E. Fermi 2749, I-21027 Ispra (VA), Italy ² Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria,Via Ponzio 34/5, I-20133 Milano, Italy

Background/Purpose: In the context of the European Biodiversity policy, the Green Infrastructure Strategy is one supporting tool to mitigate fragmentation, inter-alia to increase the spatial and functional connectivity between protected and unprotected areas. The Joint Research Centre has developed an integrated model to provide a macro-scale set of indices to evaluate the connectivity of the Natura 2000 network, which forms the backbone of a Green Infrastructure for Europe. Themodel allows a wide assessment and comparison to be performed across countries in terms of structural (spatially connected or isolated sites) and functional connectivity (least-cost distances between sites influenced by distribution, distance and land cover).Main conclusion: The Natura 2000 network in Europe shows differences among countries in terms of the sizes and numbers of sites, their distribution as well as distances between sites. Connectivity has been assessed on the basis of a 500 m average inter-site distance, roads and intensive land use as barrier effects as well as the presence of "green" corridors. In all countries the Natura 2000 network is mostly made of sites which are not physically connected. Highest functional connectivity values are found for Spain, Slovakia, Romania and Bulgaria. The more natural landscape in Sweden and Finland does not result in high inter-site network connectivity due to large inter-site distances. The distribution of subnets with respect to roads explains the higher share of isolated subnets in Portugal than in Belgium.

References [1] Bennett, J., 2010. OpenStreetMap. Packt Publishing. ISBN: 978-1-84719-750-4.[2] Bennett, G., Bento Pais, R., Berry, P., Didicescu, P. S., Fichter, M., Hlavác, V., Hoellen, K., Jones-Walters, L., Miko, L., Onida, M., Plesník, J., Smith, D.,Wakenhut, F., 2010. Green Infrastructure Implementation: Proceedings of the European Commission Conference 19 November 2010. (Ed: Karhu, J.). European Commission, 28 pp. http://ec.europa.eu/environment/nature/ecosystems/green_infrastructure.htm[3] de Rigo, D., 2012. Applying semantic constraints to array programming: the module ”check_is” of the Mastrave modelling library. In: Semantic Array Programming with Mastrave - Introduction to Semantic ComputationalModelling. http://mastrave.org/doc/mtv_m/check_is[4] Directorate-General for Environment (DG ENV), 2012. The Multifunctionality of Green Infrastructure. Science for Environment Policy. http://ec.europa.eu/environment/nature/ecosystems/docs/Green_Infrastructure.pdf[5] Directorate-General for Environment (DG ENV), 2012. Natura 2000 data - the European network of protected sites. Temporal coverage: 2011. European Environment Agency web portal. http://www.eea.europa.eu/data-and-maps/data/ds_resolveuid/60860bd4-28d6-44aa-93c7-d9354a8205e3.[6] European Commission, 1992. Council directive 92/43/EEC of 21 may 1992 on the conservation of natural habitats and of wild fauna and flora. Official Journal of the European Union 35 (L 206), 7-50.[7] European Commission, 2010. Directive 2009/147/EC of the European Parliament and of the Council of 30 November 2009 on the conservation of wild birds. Official Journal of the European Union 53 (L 20), 7-25.[8] European Commission, 2011. Our life insurance, our natural capital: an EU biodiversity strategy to 2020. Brussel, COM (2011) 244 final. http://ec.europa.eu/environment/nature/biodiversity/comm2006/pdf/2020/1_EN_ACT_part1_v7%5B1%5D.pdf[9] European Commission, 2013. Green Infrastructure (GI) - Enhancing Europe’s Natural Capital. Brussel, COM (2013) 249 final. http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2013:0249:FIN:EN:PDF[10] European Environment Agency, 2012a. Corine Land Cover 2006 raster data - version 16. European Environment Agency web portal. http://www.eea.europa.eu/data-andmaps/data/ds_resolveuid/ef13cef8-2ef5-49ae-9545-9042457ce4c6[11] Estreguil, C., Caudullo, G., de Rigo, D., Whitmore, C., San-Miguel-Ayanz, J., 2012. Reporting on European forest fragmentation: standardized indices and web map services. IEEE Earthzine 5 (2), 384031+. http://www.earthzine.org/?p=384031 (2nd quarter theme: Forest Resource Information)[12] Estreguil, C., Caudullo, G., San-Miguel-Ayanz, J., 2013. Connectivity of Natura 2000 Forest Sites. EUR26087EN. Luxemburg: Publications Office of the European Union. JRC 83104. DOI:10.2788/95065[13] Estreguil, C., de Rigo, D., Caudullo, G., 2014. A proposal for an integrated modelling framework to characterise habitat pattern. Environmental Modelling & Software 52, 176-191. DOI:10.1016/j.envsoft.2013.10.011[14] Estreguil, C., de Rigo, D., Caudullo, G., 2014. Supplementary materials for: A proposal for an integrated modelling framework to characterise habitat pattern. http://mastrave.org/bib/Estreguil_etal_EMSsuppl_2014.pdf (Extended version of the supplementary materials as published in Environmental Modelling & Software 52, 176-191, DOI:10.1016/j.envsoft.2013.10.011).[15] Haklay,M.,Weber, P., 2008. OpenStreetMap: User-Generated Street Maps. Pervasive Computing 7(4). doi: 10.1109/MPRV.2008.80[16] McHugh, N., Thompson, S., 2011. A rapid ecological network assessment tool and its use in locating habitat extension areas in a changing landscape. Journal for Nature Conservation 19 (2011) 236-244. DOI:10.1016/j.jnc.2011.02.002[17] Saura, S., Torné, J., 2009. Conefor Sensinode 2.2: a software package for quantifying the importance of habitat patches for landscape connectivity. Environmental Modelling & Software 24 (1), 135-139. DOI:10.1016/j.envsoft.2008.05.005[18] Soille, P., Vogt, P., 2009.Morphological segmentation of binary patterns. Pattern Recogn. Lett. 30 (4), 456-459. DOI:10.1016/j.patrec.2008.10.015[19] Van Rossum, G., Drake Jr., F., 2011. The Python Language Reference Manual (version 3.2). Network Theory Limited, ISBN 978-1-906966-14-0.

The objective of the ESA TropForest project was to create a harmonised geo-database of ready-to-use satellite imagery to support 2010 global forest assessment performed by the Joint Research Centre (JRC) of the European Commission and by the Food and Agriculture Organization (FAO). Assessments for year 2010 were essential for building realistic deforestation benchmark rates at global to regional levels. To reach this objective, the project aimed to create a harmonised ortho-rectified/pre-processed imagery geo-database based on satellite data acquisitions (ALOS AVNIR-2, GEOSAT-1 SLIM6, KOMPSAT-2 MSC) performed during year 2009 and 2010, for the Tropical Latin America (excluding Mexico) and for the Tropical South and Southeast Asia (excluding China), resulting in 1971 sites located at 1 deg x 1 deg geographical lat/long intersections. The project finally delivered 1866 sites (94.7% of target) due to cloud coverages too high for missing sites

description: The U.S. Geological Survey (USGS) has a long history of involvement in multi-scale, and multi-temporal land cover characterization and mapping of the world. During the 1970's, the Anderson System for land use and land cover classification system was developed and the conterminous United States (US) was mapped using aerial photographs. During 1980's, 75% of state of Alaska was mapped using Landsat satellite data. During the 1990's, (i) land cover characteristics database concept was demonstrated, (ii) Multi-Resolution Land Characteristics (MRLC) consortium was formed with Environmental Protection Agency (EPA), National Oceanic and Atmospheric Administration (NOAA), and US Forest Service (USFS), (iii) Global Land Cover Characteristics database was completed, and (iv) land cover and vegetation databases of the U.S. using Landsat TM data were completed. During 2000-2002, a forest canopy density map was produced as a part of Forest Resources Assessment 2000 for the Food and Agriculture Organization of the United Nations (FAO), and MRLC-2001 dataset was released. In 2003, land cover mapping of North America was carried out as a contribution to the Global Land Cover 2000 project being implemented by the Joint Research Center (JRC) of European Commission (EC).; abstract: The U.S. Geological Survey (USGS) has a long history of involvement in multi-scale, and multi-temporal land cover characterization and mapping of the world. During the 1970's, the Anderson System for land use and land cover classification system was developed and the conterminous United States (US) was mapped using aerial photographs. During 1980's, 75% of state of Alaska was mapped using Landsat satellite data. During the 1990's, (i) land cover characteristics database concept was demonstrated, (ii) Multi-Resolution Land Characteristics (MRLC) consortium was formed with Environmental Protection Agency (EPA), National Oceanic and Atmospheric Administration (NOAA), and US Forest Service (USFS), (iii) Global Land Cover Characteristics database was completed, and (iv) land cover and vegetation databases of the U.S. using Landsat TM data were completed. During 2000-2002, a forest canopy density map was produced as a part of Forest Resources Assessment 2000 for the Food and Agriculture Organization of the United Nations (FAO), and MRLC-2001 dataset was released. In 2003, land cover mapping of North America was carried out as a contribution to the Global Land Cover 2000 project being implemented by the Joint Research Center (JRC) of European Commission (EC).

Pan-European Forest Type Map with target year 2006, Data Source: Landsat ETM+ and Corine Land Cover 2006 and MODIS composites. Classes: broadleaf, coniferous and mixed class, clouds/snow, no data; Method: automatic classification performed with an in-house algorithm; spatial resolution: 250m.

Digital Terrain Model for Continental Europe based on the three publicly available Digital Surface Models and predicted using an Ensemble Machine Learning (EML). EML was trainined using GEDI level 2B points (Level 2A; "elev_lowestmode") and ICESat-2 (ATL08; "h_te_mean"): about 9 million points were overlaid vs MERITDEM, AW3D30, GLO-30, EU DEM, GLAD canopy height, tree cover and surface water cover maps, then an ensemble prediction model (mlr package in R) was fitted using random forest, Cubist and GLM, and used to predict most probable terrain height (bare earth). Input layers used to train the EML include:

• "lcv_bare.earth_glcf.landsat": UMD GLAD bare earth estimate for year 2010 based on Landsat time series,
• "dtm_elev.dsm_alos.aw3d": Digital Surface Model based on ALOS AW3D,
• "dtm_canopy.height_glad.umd": UMD GLAD canopy height for 2019 based on GEDI data,
• "dtm_elev.dsm_eudem.eea": Copernicus EU DEM based on the SRTM and ASTER DEMs,
• "hyd_surface.water_jrc.gswe": JRC Global Surface Water Explorer surface water probability based on the Landsat time-series,
• "lcv_landcover.12_pflugmacher2019": land cover map of Europe at 30 based on Pflugmacher et al. (2019),
• "lcv_tree.cover_umd.landsat_2000": forest tree cover for year 2000 based on the Global Forest Change data,
• "lcv_tree.cover_umd.landsat_2010": forest tree cover for year 2010 based on the Global Forest Change data,

Detailed processing steps can be found here. Read more about the processing steps here.

Training data set can be obtained in the file "gedi_elev.lowestmode_2019_eumap.RDS". The initial linear model fitted using the four independent Digital Surface / Digital Terrain models shows:

Residuals:
 Min    1Q  Median    3Q   Max 
-124.627  -1.097  0.973  2.544  59.324 
 
Coefficients:
 Estimate Std. Error t value Pr(>|t|)  
(Intercept)     -1.6220640 0.0032415 -500.4  <2e-16 ***
 eu_dem25m_     -0.1092988 0.0005531 -197.6  <2e-16 ***
 eu_AW3Dv2012_30m_  0.0933774 0.0005957  156.7  <2e-16 ***
 eu_GLO30_30m_    0.2637153 0.0006062  435.1  <2e-16 ***
 eu_MERITv1.0.1_30m_ 0.7496494 0.0005009 1496.6  <2e-16 ***
 ---
 Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 7.059 on 9588230 degrees of freedom
(2046196 observations deleted due to missingness)
Multiple R-squared: 0.9996,  Adjusted R-squared: 0.9996 
F-statistic: 5.343e+09 on 4 and 9588230 DF, p-value: < 2.2e-16

Which show that MERIT DEM (Yamazaki et al., 2019) is the most correlated DEM with GEDI and ICESat-2, most likely because it has been systematically post-processed and majority of canopy problems have been removed. Summary results of the model training (mlr::makeStackedLearner) using all covariates (including canopy height, tree cover, bare earth cover) shows:

Variable: elev_lowestmode.f 
R-square: 1 
Fitted values sd: 333 
RMSE: 6.54 

Ensemble model:
Call:
stats::lm(formula = f, data = d)

Residuals:
   Min    1Q  Median    3Q   Max 
-118.788  -0.871  0.569  1.956 165.119 

Coefficients:
       Estimate Std. Error t value Pr(>|t|)  
(Intercept) -0.198402  0.003045 -65.15  <2e-16 ***
regr.ranger 0.452543  0.001117 405.04  <2e-16 ***
regr.cubist 0.527011  0.001516 347.61  <2e-16 ***
regr.glm   0.020033  0.001217  16.47  <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 6.544 on 9588231 degrees of freedom
Multiple R-squared: 0.9996,  Adjusted R-squared: 0.9996 
F-statistic: 8.29e+09 on 3 and 9588231 DF, p-value: < 2.2e-16

Which indicates that the elevation errors are in average (2/3rd of pixels) between +1-2 m. The variable importance based on Random Forest package ranger shows:

Variable importance:
        variable  importance
4  eu_MERITv1.0.1_30m_ 430641370770
1   eu_AW3Dv2012_30m_ 291483345389
2     eu_GLO30_30m_ 201517488587
3      eu_dem25m_ 132742500162
9 eu_canopy_height_30m_  5148617173
7       bare2010_  2087304901
8    treecover2000_  1761597272
6    treecover2010_  141670217

The output predicted terrain model includes the following two layers:

• "dtm_elev.lowestmode_gedi.eml_mf": mean estimate of the terrain elevation in dm (decimeters) filtered using Gaussian filter and 2x pixel window in SAGA GIS,
• "dtm_elev.lowestmode_gedi.eml_md": standard deviation of the independently fitted stacked predictors quantifying the prediction uncertainty in dm (decimeters),

The predicted elevations are based on the GEDI data hence the reference water surface (WGS84 ellipsoid) is about 43 m higher than the sea water surface for a specific EU country. Before modeling, we have corrected the reference elevations to the Earth Gravitational Model 2008 (EGM2008) by using the 5-arcdegree resolution correction surface (Pavlis et al, 2012).

All GeoTIFFs were prepared using Integer format (elevations rounded to 1 m) and have been converted to Cloud Optimized GeoTIFFs using GDAL.

Disclaimer: The output DTM still shows forest canopy (overestimation of the terrain elevation) and has not been hydrologically corrected for spurious sinks and similar. This data set is continuously updated. To report a bug or suggest an improvement, please visit here. To access DTM derivatives at 30-m, 100-m and 250-m please visit here. To register for updates please subscribe to: https://twitter.com/HarmonizerGeo.

In the latest monitoring report presents the latest information about the granted Eenergy Global National Award for Cambodia award of Prey Lang Community Network (PLCN), deforestation and the government crackdown on illegal logging. The report also includes the map of PLCN records linked to near real-time satellite forest monitoring by the European Commission's Joint Research Centre (JRC), other relevant data and recommendation from PLCN.

The morphological spatial pattern analysis derived from the Forest/Non-Forest Map 2000 (FMAP2000) using the MSPA algorithm at a spatial resolution of 25-m. Further details available in: Soille P, Vogt P, 2008. Morphological segmentation of binary patterns. Pattern Recognition Letters 30, 4:456-459, doi: 10.1016/j.patrec.2008.10.015

AbstractForest-savanna mosaics exist across all major tropical regions. Yet, the influence of environmental factors on the distribution of these mosaics is not well explored, limiting our understanding of the environmental constraints on savannas especially in Southeast Asia, where most savannas exist in mosaics. Despite clear structural and functional characteristics indicative of savannas, most SE Asian savannas continue to be classified as forest. This designation is problematic because SE Asian savannas are threatened by both fragmentation and forest-centric management practices. By studying forest-savanna mosaics across SE Asia, we aimed to parse out how landscape mosaics of forest and savanna may be constrained by fire, climate, and soil characteristics. We used remotely sensed data to characterize the distribution of tree cover and forest-savanna mosaics. Using regression models, we quantified the relative effects of precipitation, fire frequency, seasonality, and soil characteristics on average tree cover and landscape patchiness. We found that low tree cover, indicative of savannas, occurs in drier, seasonal subregions that experience frequent fire. Further, our results demonstrate that fire and precipitation strongly shape landscape patchiness. Landscapes were patchiest in subregions with low precipitation and intermediate fire frequency. These results demonstrate that the environmental factors important in delineating the distribution of savannas globally shape the distribution of tree cover and landscape patchiness across SE Asia. Fire especially drives patterns of tree cover across scales. In a region where fire suppression is a common management strategy, our results suggest that further research studying vegetation response to fire and fire suppression is needed to improve management and conservation of these mosaic landscapes. More broadly, this work demonstrates a useful approach for studying the environmental drivers that influence the distribution of forest-savanna mosaics., MethodsFire frequency was derived from the MCD64A1.006 MODIS Burned Area Monthly Global product (500 m resolution) (Giglio et al. 2018) Mean annual precipitation (MAP) was derived from both CHIRPS Daily: Climate Hazards Group InfraRed Precipitation with Station Data from 1981-2020 (0.05° resolution; roughly 5.6 x 5.6 km) (Funk et al. 2015) and TRMM 3B43: Monthly Precipitation Estimates (0.25° resolution; roughly 28 x 28 km) (Huffman et al. 2007). Precipitation seasonality was derived as described by Schwartz et al. (2020) and Feng et al. (2013). Soil sand content was extracted from Harmonized World Soil Database (30 arc seconds resolution) (FAO/IIASA/ISRIC/ISS-CAS/JRC 2012). We characterized tree cover using the Global Forest Change (GFC) v1.7 (2000-2019) product which contains high resolution (30 m) maps of tree cover (%) from the year 2000 (Hansen et al. 2013). Aggregated to 0.05 degrees resolution. For analysis of within landscape patterns, 1,000 grid cells or landscapes were randomly subsampled, and 30 m resolution tree cover was extracted for each. Characterizing landscape mosaics: Within each landscape, raw 30 m tree cover data was classified as either forest (tree cover >65%), savanna (tree cover <65%), or anthropogenic land cover. Landscape metrics: number of patches, mean patch area, Shannon evenness, and landscape shape index.