Mapping native and non-native vegetation in the Brazilian Cerrado using freely available satellite products

Abstract

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.

An Ultra-Resolution Features Extraction Suite for Community-Level Vegetation Differentiation and Mapping at a Sub-Meter Resolution

“…High-spatial-resolution images from earth-observing satellites have been effective for acquiring detailed information on land cover and vegetation types [89][90][91]. Previous studies have utilized high-spatial-resolution images for discriminating land cover and vegetation physiognomic types such as deciduous broad-leaved forests and evergreen broad-leaved forests [92][93][94][95][96]. Though some researchers have tried unsupervised segmentation and labeling methods such as k-means and hierarchical clustering techniques for land cover and vegetation classification and mapping [97,98], supervised classification of land cover and vegetation types by employing machine learning classifiers has been frequently utilized in recent studies [99,100].…”