Published: 8 July 2023| Version 1 | DOI: 10.17632/nmsf2b9tr7.1


Land degradation is the phenomenon that negatively impacts human populations and ecosystems around the world, resulting in a reduction or loss of the biological or economic productivity and complexity of land. In order to mitigate this phenomenon, countries of the United Nations have proposed Target 15.3 of the Sustainable Development Goal 15, aiming to combat desertification, restore degraded lands and soils, and strive for a land degradation-neutral world by the year 2030. The raster data provided here represents areas where there has been a loss of biological or economic productivity between the years 2001 and 2020, contributing to the measurement of land degradation in the country. The methodology is described in "Steps to reproduce" below. The following data is made available here: Product 1: Raster data with the degradation class with clipping for the five administrative regions of Brazil. 30-meter spatial resolution, geographic coordinate system, WGS84 datum. Representation of values in Int16 with compression (reducing its size); Product 2: degraded area calculated by the plugin for each administrative region in Brazil in a spreadsheet (xlsx); Product 3: Vector data (shapefile format) with the calculation of degraded areas in the attribute table for the study areas of the research linked to the Nexus project (Cerrado and Caatinga biomes, São Francisco river basin and transpositions, and some municipalities in the Queimadas region, Petrolina and Barreiras). Data in the plane coordinate system, Albers equivalent conic projection, SIRGAS 2000 datum. We thanks FAPESP (funding of Projeto Nexus) and CNPQ (funding of research fellowship PIBIC and PCI)


Steps to reproduce

The data was extracted from a land degradation indicator based on the methodology presented on the Trends.Earth platform (2023) and Good Practice Guidance of SDG Indicator 15.3.1 of United Nations Convention to Combat Desertification (Sims et al., 2021). QGIS 3.16 and Trends.Earth plugin was used to obtain the data. The indicator was generated through three subindicators: biological productivity, soil organic carbon (SOC) and land use and land cover transition. The standard datasets provided by the Trends.Earth plugin were used for biological productivity and SOC, based on MODIS NDVI data for biological productivity and SoilGrids and CCI-LC European Space Agency for SOC. For the land use and land cover transitions sub-indicator, land use and cover classification data from the MapBiomas Project (2020) were used, with a pixel resolution of 30 meters. To generate this last indicator, a land use and land cover transition matrix had to be manually filled. The following transitions were considered indicative of productivity decline (degradation): from forest vegetation to anthropized areas, from shrub/herbaceous vegetation, wetlands, and other types of natural cover to agriculture and artificial areas, and from agriculture to artificial areas. To generate the final indicator, the One Out All Out (1OAO) principle from Trends.Earth (2023) was employed. According to this principle, if a pixel is classified, e.g., as degradation in at least one of the three sub-indicators, the pixel in the final indicator will be classified as degradation. The processing outcome resulted in a raster dataset depicting the classes degradation, improvement, s stability and without data, for each administrative region of Brazil. The dataset compared data from 2001 and 2020, with a pixel size of 30 meters. Additionally, the area calculation for each class was performed using the plugin Trends.Earth. In support of the objectives of the Nexus Project (Fapesp, 2017/22269-2), the degraded areas (in km²) of the biomes Cerrado, Caatinga, the São Francisco river basin, and selected municipalities in the Queimadas, Petrolina, and Barreiras region were calculated. This calculation was carried out using the zonal statistics and field calculator tools in QGIS. In this case, the raster data was reprojected to the Albers projection based on IBGE parameters (2023). Bibliographical references: IBGE (2023), link: MapBiomas Project, link: NEXUS project, link: Sims et al (2021), link: Trends.Earth, link: United Nations - SDG 15, link:


Universidade Federal de Sao Paulo, Instituto Nacional de Pesquisas Espaciais


Environmental Science, Geoinformatics, Environmental Geoscience


Fundação de Amparo à Pesquisa do Estado de São Paulo

Conselho Nacional de Desenvolvimento Científico e Tecnológico