Palm Oil Mills

“Oil palm concession” refers to an area allocated by a government or other body for industrial-scale oil palm plantations. This data set displays oil palm concessions as a single layer assembled by aggregating concession data for multiple countries. The data may come from government agencies, NGOs, or other organizations and varies by date and data sources. Only active or available concessions are included in this layer. Inactive or expired concessions are excluded as defined by the dataset source. As available, additional data source information is linked below: - Cambodia - Cameroon - Democratic Republic of the Congo - Malaysia  If you are aware of concession data for additional countries, please email us [here](mailto:gnw@wri.org). Displays boundaries of areas allocated by governments to companies for oil palm plantations.

Time series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2023. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset, produced by Impact Observatory, Microsoft, and Esri, displays a global map of land use and land cover (LULC) derived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017 - 2023. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, which used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. Each global map was produced by applying this model to the Sentinel-2 annual scene collections from the Mircosoft Planetary Computer. Each of the maps has an assessed average accuracy of over 75%. These maps have been improved from Impact Observatory’s previous release and provide a relative reduction in the amount of anomalous change between classes, particularly between “Bare” and any of the vegetative classes “Trees,” “Crops,” “Flooded Vegetation,” and “Rangeland”. This updated time series of annual global maps is also re-aligned to match the ESA UTM tiling grid for Sentinel-2 imagery. All years are available under a Creative Commons BY-4.0.

The Spatial Database of Planted Trees (SDPT) was compiled by Global Nature Watch using data obtained from national governments, non-governmental organizations and independent researchers. In version 2.0 (v2.0) data were compiled for 158 countries around the world, with most country maps originating from supervised classification or manual polygon delineation of Landsat, SPOT or RapidEye satellite imagery.  “Planted trees” in the SDPT includes planted forests, stand of planted trees - other than tree crops - grown for wood and wood fiber production or for ecosystem protection against wind and/or soil erosion. Planted forests can efficiently produce high quantities of wood products that may alleviate pressure on natural forests, create jobs that support rural development, and/or provide a range of ecosystem services, especially when established on degraded lands. The data set also includes perennial tree crops, such as rubber, oil palm, coffee, coconut, cocoa and orchards. The SDPT makes it possible to identify planted forests and tree crops apart from natural forests and enables changes in these planted areas to be monitored independently from changes in global natural forest cover.  The SDPT v2.0 contains 264 million hectares of planted forest and 65 million hectares of agricultural trees, or approximately 90% of the world’s total planted forest area in 2020 (FAO 2020). The SDPT was compiled through a procedure that included cleaning and processing each individual data set before creating a harmonized attribute table.  For more detailed information, please refer to the Technical Note. Data is available for download in all countries except Papua New Guinea. If you are aware of any additional planted trees data, please let us know by filling out this form.  Identifies planted forests and tree crops on a near-global scale.

This data set estimates agriculture-linked deforestation for oil palm, soy, cattle, cocoa and coffee annually for the years 2001-2015. While agriculture is generally recognized to be a major driver of deforestation, few studies have attempted to estimate the role that particular commodities play in global deforestation, and even fewer have been spatially explicit. In this analysis, we estimate the extent to which these commodities are replacing forests and map their impacts using the best available spatially explicit data. We report results globally at the second administrative level (e.g., county, municipality, or other administrative subdivision, depending on the country). To identify the specific commodities that have replaced forested land, we analyzed the overlap of current commodity extent with global annual tree cover loss from 2001 to 2018. We used recent, detailed crop maps for global oil palm and South American soy and supplemented with coarser resolution global data where needed for the other commodities and regions. Provides annual agriculture-linked deforestation estimates for oil palm, soy, cattle, cocoa and coffee for the years 2001-2015 by administrative boundary

This data set was generated by Aberystwyth University and soloEO within the framework of the Global Mangrove Watch (GMW) project, which is a part of the Japan Aerospace Exploration Agency's (JAXA) Kyoto & Carbon Initiative and the Mangrove Capital Africa Programme coordinated by Wetlands International and financed by DOB Ecology. The map (v2.0) depicts the global extent of mangrove forests for the year 2010, derived by Random Forest Classification of a combination of L-band radar (ALOS PALSAR) and optical (Landsat-5, -7) satellite data. All satellite data and software used to derive the GMW mangrove maps are available in the public domain. Approximately 15,000 Landsat scenes and 1,500 ALOS PALSAR (1 x 1 degree) mosaic tiles were used to create optical and radar image composites covering the coastlines along the tropical and sub-tropical coastlines in the Americas, Africa, Asia and Oceania. The classification was confined using a mangrove habitat mask, which defined regions where mangrove ecosystems can be expected to exist. The mangrove habitat definition was based on geographical parameters such as latitude, elevation and distance from ocean water. Training for the habitat mask and classification of the 2010 mangrove mask was based on randomly sampling 38 million points using the mangrove masks (for the year 2000) of Giri et al. (2011) and Spalding et al. (2010) and the water occurrence layer defined by Pekel et al. (2017). The data set is available for download at http://data.unep-wcmc.org/datasets/45 Global coverage of mangroves for select years from 1996 to 2016

Land cover polygons over Indonesia, classified from Landsat-7 imagery acquired on 26 May 2002. Each feature records the land cover class, sensor, date, area in square metres and hectares, and a confidence rating. Published by Orion Terrametrics.