| Mapa | Fuente | Descripción | Link de descarga |
| Forest/non-forest map for Colombia. 2000 | IDEAM | Forest/non-forest map for the Colombian continental area from Landsat images for the year 2000. | Access |
| Forest/non-forest map for Colombia. 2005 | IDEAM | Forest/non-forest map for the Colombian continental area from Landsat images for the year 2005. | Access |
| Forest/non-forest map for Colombia. 2010 | IDEAM | Forest/non-forest map for the Colombian continental area from Landsat images for the year 2010. | Access |
| Forest/non-forest map for Colombia. 2012 | IDEAM | Forest/non-forest map for the Colombian continental area from Landsat images for the year 2012. | Access |
| Forest/non-forest map for Colombia. 2013 | IDEAM | Forest/non-forest map for the Colombian continental area from Landsat images for the year 2013. | Access |
| Forest/non-forest map for Colombia. 2014 | IDEAM | Forest/non-forest map for the Colombian continental area from Landsat images for the year 2014. | Access |
| Map of forest change 2012- 2013 for Colombia. | IDEAM | Forest/non-forest map for the Colombian continental area from Landsat images for the year 2012. | Access |
| CORINE Land Cover Methodology adapted for Colombia. 2018. | IDEAM, SINCHI, PNN | The geographic objective of the Land Cover map period 2018 at scale 1:100,000 corresponds to the reinterpretation and/or visual interpretation of Landsat 8 images for the continental part and Sentinel 2 images for the archipelago of San Andres, Providencia and Santa Catalina, all with capture date within the period (year) 2018. It consists of 54 thematic classes in the level 3 attribute and 130 thematic classes in the legend attribute, according to the National Land Cover legend, ranging up to the maximum interpreted level, thus reaching the third, fourth, fifth and sixth levels in some coverages. | Access |
| Land cover 2000 - 2002. | IDEAM | Land Cover Map Corine Land Cover Methodology Adapted for Colombia Scale 1:100,000 Period 2000 - 2002 | Access |
| Land cover 2005 - 2009. | IDEAM | Land Cover Map Corine Land Cover Methodology Adapted for Colombia Scale 1:100,000 Period 2005 - 2009 | Access |
| Continental, sailor and coastal ecosystems of Colombia. | IDEAM | Map of continental, coastal and marine ecosystems of Colombia, scale 1:100,000 version 2.1 elaborated from base information corresponding to: a) climatic classification of Caldas Lang from IDEAM b) Geopedology map from IGAC c) Land cover map from IDEAM, et al d) Map of biotic units generated by IAvH. | Access |
| Multitemporal evolution of the glacier surface of the Sierra Nevada del Cocuy 1850-2016. | IDEAM | Multitemporal analysis of the change of the glacier coverage area over the Sierra Nevada del Cocuy. | Access |
| Multitemporal evolution of the glacier surface of the Nevado del Huila Volcano 1850-2016. | IDEAM | Multitemporal analysis of the change of the glacier coverage area on the Nevado del Huila Volcano. | Access |
| Multitemporal evolution of the glacier surface of the Nevado del Ruiz Volcano 1850-2016. | IDEAM | Multitemporal analysis of the change of the glacier coverage area on the Nevado del Ruiz volcano. | Access |
| Evolución multitemporal de la superficie glaciar del Volcán Nevado del Tolima de 1850-2016. | IDEAM | Análisis multitemporal del cambio del área de cobertura glaciar sobre el Volcán Nevado del Tolima . | Access |
| Multitemporal evolution of the glacier surface of the Nevado de Santa Isabel Volcano from 1850-2016. | IDEAM | Multitemporal analysis of the change of the glacier coverage area on the Nevado de Santa Isabel Volcano. | Access |
| Multitemporal evolution of the glacier surface of the Sierra Nevada de Santa Marta 1850-2017. | IDEAM | " Multitemporal analysis of the change in the glacier coverage area over the Sierra Nevada de Santa Marta. " | Access |
| Ecosystems map. 2012. | SINCHI | Aquatic and terrestrial ecosystem layer of 2012 of the Amazon Region scale 1:100,000, according to CORINE Land Cover methodology and the delimitation and classification of geoform units, climate (temperatures - climatic floors and precipitation). | Access |
| Land cover in the Colombian Amazon. 2002 | SINCHI | Land cover map of the Colombian Amazon at a scale of 1:100,000 for the year 2002. | Access |
| Land cover in the Colombian Amazon. 2007 | SINCHI | Land cover map of the Colombian Amazon at a scale of 1:100,000 for the year 2007. | Access |
| Land cover in the Colombian Amazon. 2012 | SINCHI | Land cover map of the Colombian Amazon at a scale of 1:100,000 for the year 2012. | Access |
| Land cover in the Colombian Amazon. 2014 | SINCHI | Land cover map of the Colombian Amazon at a scale of 1:100,000 for the year 2014. | Access |
| Agricultural Landscapes of the Colombian Amazon. 2002. | SINCHI | " Map of agricultural and livestock landscapes for the 2002 period at a scale of 1:100,000 corresponding to the area anthropically transformed by agricultural or livestock activities. " | Access |
| "Agricultural landscapes of the Colombian Amazon. 2007. " | SINCHI | Map of agricultural and livestock landscapes for the 2007 period at a scale of 1:100,000 corresponding to the area anthropically transformed by agricultural or livestock activities. | Access |
| " Agricultural landscapes of the Colombian Amazon. 2012. " | SINCHI | " Map of agricultural and livestock landscapes for the 2012 period at a scale of 1:100,000 corresponding to the area anthropically transformed by agricultural or livestock activities. " | Access |
| Agricultural landscapes of the Colombian Amazon. 2014. | SINCHI | Map of agricultural and livestock landscapes for the period 2014 at a scale of 1:100,000 corresponding to the area anthropically transformed by agricultural or livestock activities. | Access |
| National agricultural frontier | UPRA | Map of the national agricultural frontier, natural forests and non-agricultural areas and legal exclusions. | Access |
| Coverage monitoring in National Parks of Colombia. 2019. | PNN | "Satellite monitoring of Earth coverage that cover the Natural National Parks (PNN) of Colombia at 1: 25,000 scale with the aim of analyzing the dynamics of the coverage inside the PNN with a level of detail greater than that made by the scale 1: 100,000 that has been held since 2000. This monitoring makes an annual reading, based on the interpretation of planet Scope satellite images for the 53 continental parks of the Natural National Parks System of Colombia, with an identification and classification of coverage greater than 1 hectare." | Access |
| Coverage monitoring in National Parks of Colombia. 2020. | PNN | Satellite monitoring of the coverage of the Earth that cover the Natural National Parks (PNN) of Colombia at 1: 25,000 scale with the aim of analyzing the dynamics of the coverage inside the PNN. | Access |
| Coverage monitoring in National Parks of Colombia. 2021. | PNN | Satellite monitoring of the coverage of the Earth that cover the Natural National Parks (PNN) of Colombia at 1: 25,000 scale with the aim of analyzing the dynamics of the coverage inside the PNN. | Access |
| " Delimited moorlands. 2020. " | Ministerio de ambiente y desarrollo sostenible | Layer containing the boundaries of the delimited moorlands as of June 2020 (scales 1:25,000 and 1:100,000). | Access |
| Mangroves of Colombia | Instituto de Investigaciones Marinas y Costeras “José Benito Vives de Andreis” (INVEMAR) | " Vector layer of mangroves in Colombia using digital image processing techniques. For the Caribbean and Pacific at a scale of 1:25,000, the methodology was based on a semi-automated process in the Google Earth Engine platform where optical and radar images were used to classify mangrove and other general coverages in a supervised manner. Field points and high resolution imagery were used to train and validate the classification. Different algorithms were used for the Caribbean and Pacific due to the particular conditions of each zone; for the Pacific, images from the years 2019 and 2020 were processed; in the case of the Caribbean, images from the year 2020 were processed. For both cases the minimum mapping unit is 1600 m2. For San Andres, Providencia and Santa Catalina the mapping scale is 1:5,000, and its methodology consisted of processing and visual interpretation of high resolution images. " | Access |
| The Global Mangrove Watch (GMW). | Nathan Thomas et al., s. f.; Pete Bunting et al. | Platform that generates a global mangrove baseline map for 2010 using ALOS PALSAR and Landsat (optical) data, and changes to this baseline for times between 1996 and 2020 derived from JERS-1 SAR, ALOS PALSAR and ALOS-2 PALSAR -2. | Access] |
| Global Mangrove Forests Distribution. 2000. | NASA SEDAC at the Center for International Earth Science Information Network | Database prepared from 2000 Landsat satellite data. More than 1,000 Landsat scenes obtained from the USGS Earth Resources Observation and Science (EROS) Center were classified using hybrid supervised and unsupervised digital image classification techniques with NASA funding. | Access |
| Global USGS mangrove distribution. | The UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC) | Dataset showing the global distribution of mangrove forests, derived from satellite earth observation imagery. The dataset was created using Global Land Survey ( GLS ) data and the Landsat archive. Approximately 1000 Landsat scenes were interpreted using hybrid supervised and unsupervised digital image classification techniques. See Giri et al. (2011) for more details. | Access |
| Global Distribution of Modelled Mangrove Biomass. 2014. | The UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC) | Dataset showing modeled global patterns of aboveground biomass of mangrove forests. Based on a review of 95 field studies worldwide on mangrove carbon storage and fluxes, a climate-based model for potential mangrove above-ground biomass was developed, with nearly four times the explanatory power of the only previously published model. The map highlights the high variability in mangrove aboveground biomass and indicates areas that could be prioritized for mangrove conservation and restoration. | Access |
| Global cropland expansion in the 21st century. | P. Potapov, S. Turubanova, M.C. Hansen, A. Tyukavina, V. Zalles, A. Khan, X.-P. Song, A. Pickens, Q. Shen, J. Cortez | Time series of global-scale cropland extent at a spatial resolution of 30 m was derived from the Landsat satellite data archive with four-year mapping intervals. Cultivated areas are defined as all land used for annual and perennial arable crops for human consumption, fodder (including hay) and biofuels. | Access |
| Global Map of Oil Palm Plantations. 2019. | Descals, Adrià, et al. “High-resolution global map of smallholder and industrial closed-canopy oil palm plantations.” Earth System Science Data 13.3 (2021): 1211-1231 | Study presenting the first global map of oil palm plantations for the year 2019 derived from remotely sensed data with a spatial resolution of 10m. Sentinel-1 and Sentinel-2 data were used for this purpose in a map that discriminates between smallholder and industrial oil palm plantations. | Access |
| ESA WorldCover 10m. 2020-2021. | ESA (Agencia Espacial Europea) | The European Space Agency (ESA) WorldCover 10m product provides a global land cover map at 10 m resolution based on Sentinel-1 and Sentinel-2 data. The WorldCover product comes with 11 land cover classes, aligned with the UN-FAO Land Cover Classification System, and has been generated under the ESA WorldCover project. | Access |
| Rapid Expansion of Human Impact On Natural Land in South America Since 1985. | Global Land Analysis & Discovery | Class codes of the strata are the following classes: Outside the study area, another land use, stable land coverage, stable terrestrial coverage, Amazonas, Rebrote, Tree plantations, cultivation lands 2016-2018, cultivation lands 1985-1994 and water." | Access |
| Global Lakes and Wetlands Database. | World Wild Life (WWF) | Combination of multiple sources available for lakes and wetlands at a global scale (1:1 to 1:3 million resolution) and the application of GIS functionality. This allows the generation of a database that focuses on three coordinated levels on (1) large lakes and reservoirs, (2) smaller water bodies, and (3) wetlands. | https://www.worldwildlife.org/pages/global-lakes-and-wetlands-database |
| Global Wetlands Map | CIFOR, USAID y CGIAR – FTA | The Global Wetland Map covers tropical and subtropical regions (40° N to 60° S; 180° E to -180° W), excluding small islands. This mapping is a hydrogeomorphological model based on an Expert System approach to estimate wetland areas, which is based on three biophysical indices related to wetland and peatland formation: (1) long-term water supply exceeding atmospheric water demand; (2) annually or seasonally waterlogged soils; and (3) a geomorphological position where water is supplied and retained (Gumbricht et al. 2017). | Access |
| Global high-resolution floodplains (GFPLAIN250m). | Nardi, Fernando; Annis, Antonio (2018): GF PLAIN 250 m. figshare. Dataset. https://doi.org/10.6084/m9.figshare.6665165.v1 | The GF PLAIN 250 m includes raster data of the Earth's floodplains identified using a geometric approach presented in Nardi et al. (2006, 2018). The 250 m floodplain dataset is derived from NASA SRTM digital elevation model processing compiled from (http://srtm.csi.cgiar.org/), and in particular the 250 m SRTM version 4.1 DTM. | Access |
| Tensor Flow Hydra Flood Models. | Mayer, T., Poortinga, A., Bhandari, B., Nicolau, A.P., Markert, K., Thwal, N.S., Markert, A., Haag, A., Kilbride, J., Chishtie, F. and Wadhwa, A. | This dataset is a surface water output image from the Hydrologic Remote Sensing Analysis for Floods (HYDRAFloods) system that uses a Deep Learning Tensor Flow approach. Specifically, this Joint Research Center (JRC) binary cross-entropy (BCE) adjusted learning rate data model and methodology are discussed in detail in the recent. | Access |
| GLIMS: Global Land Ice Measurements From Space. | National Snow and Ice Data Center (NSIDC) | "Global Land Ice Measurements From Space (GLMS) is an international initiative with the aim of repeatedly inspecting the approximately 200,000 glaciers in the world.
The project seeks to create a worldwide terrestrial ice inventory, including the measurements of the glacier area, geometry, surface speed and snow line elevation. To perform these analyzes, the GLYMS project uses satellite data, mainly from the advanced thermal emission and spatial reflection (ASTER) and the Landsat Enhanced Thematic Mapper Plus (ETM+), as well as historical information derived from maps and aerial photographs." El proyecto busca crear un inventario completo a nivel mundial del hielo terrestre, incluidas las mediciones del área del glaciar, la geometría, la velocidad de la superficie y la elevación de la línea de nieve. Para realizar estos análisis, el proyecto GLIMS utiliza datos satelitales, principalmente del Radiómetro Avanzado de Emisión Térmica y Reflexión Espacial (ASTER) y del Landsat Enhanced Thematic Mapper Plus (ETM+), así como información histórica derivada de mapas y fotografías aéreas. | Access |
| Global land cover and land use. 2019, v1.0. | M.C. Hansen, P.V. Potapov, A.H. Pickens, A. Tyukavina, A. Hernandez-Serna, V. Zalles, S. Turubanova, I. Kommareddy, S.V. Stehman. | Global land cover and land use map of 2019 derived from Landsat satellite imagery and, from it, where the spatial extent and dispersion of land use disaggregated by climate domain and ecozone is estimated. | Access |
| ESRI Global Land Use Land Cover from Sentinel-2. | ESRI | Global land use/land cover (LULC) map developed with Esri and in collaboration with Microsoft's AI for Earth. This was built using the highest resolution and publicly available satellite data from the European Space Agency. | Access |
| ESA WorldCover 10 m. 2020. | ESA (Agencia Espacial Europea) | The WorldCover 10 m 2020 product of the European Space Agency (ESA) provides a global land coverage map by 2020 with a 10 m resolution based on Sentinel-1 and Sentinel-2 data. The WorldCover product comes with 11 land coverage classes, aligned with the UN-FAO land coverage classification system, and has been generated within the framework of the project that WorldCover. | Access |
| High Resolution Population Density Maps + Demographic Estimates. | Facebook Connectivity Lab and Center for International Earth Science Information Network – CIESIN – Columbia University | Machine learning-based computer vision method for creating population maps from global-scale satellite imagery, with spatial sensitivity corresponding to individual buildings and suitable for global deployment. By combining this settlement data with census data, population maps with a resolution of ~30 meters are created. | Access |
| World Settlement Footprint 2015, 2019 y World Settlement Footprint Evolution (1985-2015) | Marconcini, Mattia; Metz-Marconcini, Annekatrin; Üreyen, Soner; Palacios-Lopez, Daniela; Hanke, Wiebke; Bachofer, Felix; et al | The World Settlement Footprint (WSF) is a 10 m resolution binary mask describing the extent of human settlement globally derived using multi-temporal Landsat-8 and Sentinel-1 imagery (of which ~217,000 and ~107,000 scenes have been processed, respectively). | Access |
| GHS-BUILT | Comisión Europea | " Multitemporal information layer on the presence of built-up area derived from Landsat image collections (GLS1975, GLS1990, GLS2000 and Landsat 8 2013/2014 ad-hoc collection). " | Access |
| Sentinel-2 forest loss alert. | A.H. Pickens, M.C. Hansen, B. Adusei, P. Potapov, University of Maryland | Near real-time mapped primary forest loss at 10 m resolution using Sentinel-2 multispectral data. Clouds, shadows, and water are detected in each new Sentinel-2 image and a forest loss algorithm is applied to all remaining clear terrain observations. The algorithm is based on spectral data from each new image in combination with spectral metrics from a reference period of the previous two years. Confidence is built through repeated observations of loss in the ensuing images. | Access |
| Global-scale data set of mining areas. | FINEPRINT | Based on visual interpretation of satellite imagery with Sentinel-2, the map of mining activities worldwide and the estimated area directly used for mining activities, in particular coal and metallic minerals, is presented. The mining polygons include all satellite-identified features in mining areas, such as open pits, tailings dams, waste rock piles, water ponds and processing infrastructure. | Access. |
| Colombian Mining Monitoring (COMIMO). | Universidad del Rosario | CoMiMo is a free access tool that analyzes the entire country on a monthly basis in search of open pit mines to visualize their location. CoMiMo will make it possible to detect illegal mining and act in a timely manner to counteract its impacts. | Access |
| Global Mining Areas and Validation Datasets. | Maus, Victor; Giljum, Stefan; Gutschlhofer, Jakob; da Silva, Dieison M; Probst, Michael; Gass, Sidnei L B; Luckeneder, Sebastian; Lieber, Mirko; McCallum, Ian | " It contains over 21,000 polygons of mining-related activities, primarily coal and metallic minerals. Various data sources were compiled to identify the approximate location of active mines at any time between the years 2000 and 2017. This dataset does not cover all existing mining locations worldwide. The polygons were expertly delineated using cloud-free Sentinel-2 and very high resolution satellite imagery available from Google Satellite and Bing Imagery. " | Access |
| Hansen Global Forest Change v1.8. | 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, y J. R. G. Townshend | " Results of time series analysis of Landsat imagery to characterize global forest extent and change. " | Access; |
| WWF HydroSHEDS Free Flowing Rivers Network v1. | World Wildlife Fund | HydroSHEDS is a mapping product that provides hydrographic information for regional and global scale applications. | Access |
| Global surface water dynamics. | Pickens, A.H., Hansen, M.C., Hancher, M., Stehman, S.V., Tyukavina, A., Potapov, P., Marroquin, B., y Sherani, Z | Global maps derived from all Landsat 5, 7 and 8 scenes highlight changes in surface water extent during this period, and an assessment based on probabilistic samples provides unbiased estimators of permanent water area, seasonal water, water loss, water gain, temporary land, temporary water and high-frequency change. | Access |
| Copernicus Global Land Cover Layers: CGLS-LC100 Collection 3. | Buchhorn, M. ; Lesiv, M. ; Tsendbazar, N. – E. ; Herold, M. ; Bertels, L. y Smets, B | The 100 m resolution dynamic land cover map (CGLS-LC100) provides a primary land cover outline. In addition to these discrete classes, the product also includes continuous field layers for all basic land cover classes that provide proportional vegetation/land cover estimates for land cover types. E.g. crop cover fraction, grass cover fraction. | Access |
| Global 1-km Consensus Land Cover. | NCEAS, NASA, NSF, y Yale University | The datasets integrate multiple land cover products derived from global remote sensing and provide consensus information on the prevalence of 12 land cover classes at 1 km resolution. | Access |
| GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery. | Liangyun,Liu, Xiao,Zhang, Xidong,Chen, Yuan,Gao, y Jun, Mi | A new global 30 m land cover product with a fine classification system for the year 2015 (GLC_FCS30-2015). The product was produced by combining time series of Landsat imagery and high quality training data from GSPECLib (Global Spatial Spatial Temporal Spectra Library) on the Google Earth Engine computing platform. | Access |
| Global PALSAR-2/PALSAR Forest/Non-Forest Map. | Masanobu Shimada, Takuya Itoh, Takeshi Motooka, Manabu Watanabe, Shiraishi Tomohiro, Rajesh Thapa, y Richard Lucas | The global forest/non-forest (FNF) map is generated by classifying the SAR (backscatter coefficient) image in the 25 m resolution PALSAR-2/PALSAR SAR global mosaic, so that strong and low backscatter pixels are assigned as “forest” and “non-forest”, respectively. | Access |
| MCD12Q1.006 MODIS Land Cover Type Yearly Global 500m. | NASA LP DAAC at the USGS EROS Center | The MCD12Q1 V6 product provides global land cover types at annual intervals (2001-2016) derived from six different classification schemes. It is derived using supervised classifications of MODIS Terra and Aqua reflectance data. | Access |
| Planet & NICFI Basemaps for Tropical Forest Monitoring – Tropical Americas. | Planet Team | Planet Scope image mosaics on a semi-annual or monthly basis. | Access |
| Global Forest Cover Change (GFCC) Tree Cover Multi-Year Global 30m. | Sexton, J. O., Song, X.-P., Feng, M., Noojipady, P., Anand, A., Huang, C., Kim, D.-H., Collins, K.M., Channan, S., DiMiceli, C., y Townshend, J.R.G | Landsat Vegetation Continuous Fields (VCF) tree cover layers contain estimates of the percentage of horizontal ground in each 30 m pixel covered by woody vegetation greater than 5 m in height. The dataset is available for four epochs centered on the years 2000, 2005, 2010, and 2015. The dataset is derived from the GFCC Surface Reflectance (GFCC30SR) product, which is based on enhanced Global Land Survey (GLS) datasets. The GLS datasets are composed of high-resolution Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) imagery with a resolution of 30 meters. | Access |
| Primary Humid Tropical Forests. | Turubanova S., Potapov P., Tyukavina, A., y Hansen M | Primary tropical rainforest map. The extent of the primary forest was mapped for the year 2001 at a spatial resolution of 30 meters using Landsat images acquired worldwide, free of charge and constantly processed. | Access |
| Global 2010 Tree Cover (30 m). | Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., Loveland, T.R., Kommareddy, A., Egorov, A., Chini, L., Justice, C.O., y Townshend, J.R.G | The global tree cover data (treecover2010) are per-pixel estimates of maximum percent tree cover from around 2010 (peak of the growing season) derived from Landsat 7 ETM+ annual cloud-free growing season composite data. A regression tree model estimating percent tree canopy cover per pixel was applied to annual composites from 2000 to 2012 inclusive. Data gaps and noise from individual years were replaced using median values from multiple years. First, a median of annual tree canopy cover values from 2009-2011 was used to estimate 2010 tree cover. For pixels that do not yet have an estimate, the median calculation was expanded to include tree cover values from 2008 to 2011 and then from 2008 to 2012. The resulting layer represents the estimated maximum tree canopy cover per pixel, 1-100% for 2010 in integer values (1-100). | Access |
| Global 2010 Bare Ground (30 m). | Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., Loveland, T.R., Kommareddy, A., Egorov, A., Chini, L., Justice, C.O., y Townshend, J.R.G | Map of global bare ground cover via per-pixel estimates of percent bare ground cover circa 2010, derived from Landsat 7 ETM+ median reflectance values per band of all cloud/shadow-free observations during the growing season. | Access |
| Global Forest Canopy Height. 2019. | P. Potapov, X. Li, A. Hernandez-Serna, A. Tyukavina, M.C. Hansen, A. Kommareddy, A. Pickens, S. Turubanova, H. Tang, C.E. Silva, J. Armston, R. Dubayah, J. B. Blair, M | A new global map of forest canopy height at 30 m spatial resolution was developed by integrating Global Ecosystem Dynamics Investigation (GEDI) lidar forest structure measurements and Landsat analysis-ready time series data. NASA's GEDI is a space-based instrument operating aboard the International Space Station since April 2019. It provides footprint-based measurements of vegetation structure, including forest canopy height between 52 ° N and 52 ° S globally. Landsat multi-temporal metrics representing surface phenology serve as independent variables for global forest height modeling. The locally applied and calibrated bagged regression tree ensemble model of “moving window” regression was implemented to ensure high quality of forest height prediction and consistency of the global map. | Access |
| Tree Cover Height. | World Resources Institute (WRI) | This dataset shows the annual tree canopy height in meters for South America for the period 1985-2016. Tree canopy height is estimated using Landsat satellite imagery calibrated with LIDAR data. | Access |
| Trees in Mosaic Landscapes (beta). | Brandt J., y Stolle F | Tree extent in urban areas, agricultural lands, and in open canopy and dry forest ecosystems. Here we present a globally consistent method for identifying trees with canopy diameters greater than 3 m with medium resolution optical and radar imagery. The 10-m Sentinel-2 optical imagery and Sentinel-1 radar imagery are used every two weeks, cloud-free and with panoramic sharpness, to train a fully convolutional network, consisting of a convolutional gated recurrent unit layer and a feature pyramid attention layer. | Access |
| Global coverage of mangroves for select years from 1996 to 2016. | Bunting P., Rosenqvist A., Lucas R., Rebelo L-M., Hilarides L., Thomas N., Hardy A., Itoh T., Shimada M. y Finlayson C.M | This dataset was generated by Aberystwyth University as part of the Global Mangrove Watch (GMW) project, which is part of the Kyoto & Carbon Initiative of the Japan Aerospace Exploration Agency (JAXA) and the Mangrove Capital Africa Program coordinated by Wetlands International and funded by DOB Ecology. The map (v2.0) shows the global extent of mangrove forests for the year 2010, derived from random forest classification of a combination of L-band radar (ALOS PALSAR) and optical (Landsat-5, -7) satellite data. | Access |
| Crop map. | ESA (Agencia Espacial Europea) | "WorldCereal developed an efficient, agile and robust Earth observation-based system for timely global field-scale crop monitoring. The open source WorldCereal system allows you to create local to global annual cropland extent maps at 10 m resolution, update crop maps seasonally, differentiate between irrigated fields and active rainfed fields, produce global corn and wheat, two of the main staple crops." | Access |
