DOI: 10.5281/zenodo.5063025
title: Multi-decade land cover and land use samples for Brazil based in a stratified sampling design and visual interpretation of Landsat data

description: This dataset is composed by 85,152 random points throughout the Brazilian territory selected according to a stratified sampling design, based in 127 regular regions and six slope classes (SRTM). Each sample was visually inspected by three independent interpreters, which associated all the land use and land cover (LULC) changes between 1985 and 2018, on a <strong>yearly basis</strong>, using as reference two <strong>Landsat</strong> images per year, a <strong>MODIS</strong> NDVI time series and high resolution images from <strong>Google Earth</strong>. This process was guided by a reference labeling protocol which established the follow LULC classes: <strong>Annual crop:</strong> Areas occupied with short to medium-term crops, usually with a vegetative cycle of less than one year, which after harvest needs to be re-planted. <strong>Aquaculture:</strong> Artificial lakes, where aquaculture and/or salt production activities predominate <strong>Beach and dune (Other):</strong> Sandy areas, with bright white color, where there is no vegetation predominance of any kind. <strong>Forest formation:</strong> Vegetation types with predominance of tree species, with continuous canopy formation <strong>Grassland formation:</strong> Grassland formations with predominance of herbaceous stratum <strong>Mangrove (Other):</strong> Dense and Evergreen Forest formations, often flooded by tide and associated with the mangrove coastal ecosystem. <strong>Mining (Other):</strong> Areas where clear signs of extensive mineral extractions are present, shows clear exposure of the soil by the action of heavy machinery. Only regions surrounding the AhkBrasilien (AHK) and the CPRM digital reference data were considered. <strong>Not observed:</strong> Areas blocked by clouds or atmospheric noise, or with absence of ground observation masked out from analysis. <strong>Other non-forest natural formations:</strong> Marshes (with fluvio-marine influence). <strong>Other non-vegetated area (Other):</strong> Non-permeable surface areas (infrastructure, urban expansion or mining) not mapped into their classes <strong>Pasture:</strong> Pasture areas, natural or planted, related with farming activity. In particular in the Pampa and Pantanal biomes part of the area classified as Grassland Formation also includes pasture areas. <strong>Perennial crop:</strong> Areas occupied with crops with a long cycle (more than one year), which allow successive harvests without the need for new crop. <strong>Rocky outcrop (Other)</strong>: Naturally exposed rocks without soil cover, often with the partial presence of rupicolous vegetation and high slope. <strong>Salt flat (Other):</strong> "Apicuns" or Salt flats are formations often without tree vegetation, associated to a higher, hypersaline and less flooded area in the mangrove, generally in the transition between this area and the continent. <strong>Savanna formation:</strong> Savanna formations with defined tree and shrub-herbaceous stratum <strong>Semi-perennial crop:</strong> Cultivated areas with sugar cane <strong>Tree plantation:</strong> Planted tree species for commercial use (e.g. Eucalyptus, Pinus and Araucaria) <strong>Urban infrastructure:</strong> Urban areas with predominance of non-vegetated surfaces, including roads, highways and constructions. <strong>Water:</strong> Rivers, lakes, dams, reservoir and other water bodies <strong>Wetland:</strong> Wetlands with fluvial influence or swampy areas To enable a proper area estimation and accuracy assessment (Stehman, 2014) the dataset is provided with the <strong>sampling probability</strong> for each sample (<em>brazil_lulc_samples_1985_2018</em> and <em>brazil_lulc_samples_1985_2018_row_wise</em>) and the <strong>sampling weight</strong> (<em>brazil_lulc_samples_1985_2018_row_wise</em>), which was adjusted to disregard the "<strong>Not observed" </strong>class. The <strong>number of votes</strong> for the associated LULC class (visual interpretation agreement) and an indication if the sample is <strong>between two different LULC </strong>classes (border flag) are also provided. A publication describing in detail the methodology used to produce this dataset is under preparation.