Using Remote Sensing and Random Forest to Assess the Conservation Status of Critical Cerrado Habitats in Mato Grosso do Sul, Brazil

Abstract

Brazil's Cerrado is a highly diverse ecosystem and it provides critical habitat for many species. Cerrado habitats have suffered significant degradation and decline over the past decades due to expansion of cash crops and livestock farming across South America. Approximately 1,800,000 km 2 of the Cerrado remain in Brazil, but detailed maps and conservation assessments of the Cerrado are lacking. We developed a land cover classification for the Cerrado, focusing on the state of Mato Grosso do Sul, which may also be used to map critical habitat for endangered species. We used a Random Forest algorithm to perform a supervised classification on a set of Landsat 8 images. To determine habitat fragmentation for the Cerrado, we used Fragstats. A habitat connectivity analysis was performed using Linkage Mapper. Our final classification had an overall accuracy of 88%. Our classification produced higher accuracies (72%) in predicting Cerrado than existing government maps. We found that remaining Cerrado habitats were severely fragmented. Four potential corridors were identified in the southwest of Mato Grosso do Sul, where large Cerrado patches are located. Only two large patches remain in Mato Grosso do Sul: one within the Kadiwéu Indian Reserve, and one near the southeastern edge of the Pantanal-dominated landscape. These results are alarming for rare species requiring larger tracts of habitat such as the giant armadillo (Priodontes maximus).

Spatial Evolution of Prosopis Invasion and its Effects on LULC and Livelihoods in Baringo, Kenya

“…We assessed the evolution of Prosopis invasion and the associated LULC changes over time and space by using multi-temporal and multispectral Landsat satellite imagery as well as by verification of mapping outputs with historical Google Earth Imagery [38] and local expert knowledge. Applying Random Forest on the Landsat data provided good estimations and reliable accuracies, which has also been reported in other studies [21,[51][52][53][54]. This provides potential for the long-term monitoring of Prosopis invasion.…”

Spatiotemporal Patterns and Phenology of Tropical Vegetation Solar-Induced Chlorophyll Fluorescence across Brazilian Biomes Using Satellite Observations

“…The Amazon is the largest biome (about 50-60% of all Brazil) with a humid tropical climate, low seasonal temperature variability, approximately 1000-2000 mm of annual precipitation, containing approximately half the world's tropical rainforest, and critical for biodiversity and the global carbon balance [84,104,105]. The Cerrado is second in area to the Amazon (about 22-25%), with about 1200-1800 mm of precipitation, experiences seasonal periods of tropical followed by dry climate, and has a great diversity of vegetation types and canopy covers, with savanna, woody savanna, dry grasslands, wet grasses, and tropical forests, often heavily fragmented for agriculture [96,97,[106][107][108]. The Pantanal is one of the largest wetlands in the world (160,000 km 2 ) with a dynamic yearly flooding regime and is a United Nations Educational, Scientific and Culture Organization (UNESCO) World Heritage site containing tropical wetlands with arid tropical species alongside dry shrubland and grasses as well as xeric species [109][110][111].…”

Best Accuracy Land Use/Land Cover (LULC) Classification to Derive Crop Types Using Multitemporal, Multisensor, and Multi-Polarization SAR Satellite Images

“…According to [52], the classifier has three main advantages for LULC classifications from remote sensing images: (i) It reaches higher accuracies than other machine learning classifiers; (ii) It has the ability to measure the importance level of the input images; (iii) It does not make any assumptions about the distributions assumptions of the input images. Therefore, Random Forest classifications have been successfully applied to crop classification scenarios using remote sensing images, optical [53], and radar [54,55]. Ok et al [56] concluded an accuracy increase using the Random Forest classifier over the Maximum Likelihood classifier of about eight percent to classify crops using one Spot5 satellite image.…”