Global peatland extent

Distribution of peatland that covers the tropics and sub tropics, excluding small islands. It was mapped in 231 meters spatial resolution. Peat is here defined as any soil having at least 30cm of decomposed or semi-decomposed organic material with at least 50% of organic matter. This corresponds to 29% of carbon content using 1.72 as the transformation factor. The peatland map is produced by adding the peat forming wetlands: mangrove (20), swamp/bog (30), Fen (40), riverine (50), and floodswamps (60) (note: the number in parentheses refer to pixel code of each class in Wetlands dataset). Our map of peatlands was contrasted against n=275 geo-positioned soil profiles containing peat, with 65% of agreement. Further fieldwork is however needed to validate our map. Mangroves are here considered to host the thresholds of depth and organic matter content needed for peat definition, although mineral soil may prevail. Mangroves contribute with ca. 180,000 km2 to the 1.7 million km2 of peatlands (11%), which would need further ground validation (i.e. in areas like Indonesian Papua have large extents of mangrove that contribute to peat, which would need ground-truthing to validate if they contain peat as defined here).

Model outputs were updated on Dec 20, 2017. This project used a machine learning data-driven model to predict the distribution of soil carbon under mangrove forests globally. Specifically this dataset contains: 1) a compilation of georeferenced and harmonized soil profile data under mangroves compiled from literature, reports and unpublished contributions 2) global mosaics of soil carbon stocks to 1m and 2m depths produced at 100 m resolution 3) tiled predictions of soil carbon stocks produced at 30 m resolution 4) shape file containing the tiling system 5) shape file containing country boundaries used for calculating national level statistics. For detailed methodologies, please see the scientific paper (https://doi.org/10.1088/1748-9326/aabe1c).

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

Cummulative probability of organic soil based on the TAXOUSDA and TAXNWRB predicted using the global compilation of soil ground observations. Accuracy assessement of the maps is availble in Hengl et at. (2017) DOI: 10.1371/journal.pone.0169748. Data provided as GeoTIFFs with internal compression (co='COMPRESS=DEFLATE'). Measurement units: probability.

Soil organic carbon stock in tons per ha for 6 standard depth intervals predicted using the global compilation of soil ground observations. Accuracy assessement of the maps is availble in Hengl et at. (2017) DOI: 10.1371/journal.pone.0169748. Data provided as GeoTIFFs with internal compression (co='COMPRESS=DEFLATE'). Measurement units: t / ha.

The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program. The GFCC Tree Cover Multi-Year Global dataset is available for four epochs centered on the years 2000, 2005, 2010, and 2015. The dataset is derived from the GFCC Surface Reflectance product (GFCC30SR), 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+) images at 30 meter resolution. GFCC30TC provides tree canopy information and can be used to understand forest changes. Each tree cover product features four files associated with it; a tree cover layer with an embedded color map, a tree cover error (uncertainty) file, and an index (provenance) file, plus a list of path/rows that relate to the Surface Reflectance input files. Note that the index file and file list were not generated for the 2015 epoch. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD).