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Native vegetation across the Brazilian Cerrado is highly heterogeneous and biodiverse and provides important ecosystem services, including carbon and water balance regulation, however, land-use changes have been extensive. Conservation and restoration of native vegetation is essential and could be facilitated by detailed landcover maps. Here, across a large case study region in Goiás State, Brazil (1.1 Mha), we produced physiognomy level maps of native vegetation (n = 8) and other landcover types (n = 5). Seven different classification schemes using different combinations of input satellite imagery were used, with a Random Forest classifier and 2-stage approach implemented within Google Earth Engine. Overall classification accuracies ranged from 88.6–92.6% for native and non-native vegetation at the formation level (stage-1), and 70.7–77.9% for native vegetation at the physiognomy level (stage-2), across the seven different classifications schemes. The differences in classification accuracy resulting from varying the input imagery combination and quality control procedures used were small. However, a combination of seasonal Sentinel-1 (C-band synthetic aperture radar) and Sentinel-2 (surface reflectance) imagery resulted in the most accurate classification at a spatial resolution of 20 m. Classification accuracies when using Landsat-8 imagery were marginally lower, but still reasonable. Quality control procedures that account for vegetation burning when selecting vegetation reference data may also improve classification accuracy for some native vegetation types. Detailed landcover maps, produced using freely available satellite imagery and upscalable techniques, will be important tools for understanding vegetation functioning at the landscape scale and for implementing restoration projects. [ABSTRACT FROM AUTHOR]

The objective of the study was to determine the percentage of green areas in the Northern, Northwestern, and Meia Ponte zones of the city of Goiânia-Goiás, Brazil. The Green Area Index (gai) was applied, in conformity with the methodological principles used by indicators that depend on demographics, expressed by the ratio of green area surfaces per inhabitant. The gai was quantified by interpreting remote sensing images to draw the vegetation cover map, and dasymetric mapping was used for demographic density. Results made it possible to evaluate the relation among spatial distribution of the gai, population quantity, and urban environmental quality. It has been observed an unbalanced correlation between the quantitative-qualitative levels of green areas, which are subordinate to its greenery maintenance and real estate speculation. [ABSTRACT FROM AUTHOR]