Land-Use and Land-Cover Dynamics in the Brazilian Caatinga Dry Tropical Forest

Abstract

The use of remote sensing to determine land-use and land-cover (LULC) dynamics is often applied to assess the levels of natural forest conservation and monitor deforestation worldwide. This study examines the loss of native vegetation in the Campo Maior Complex (CMC), in the Brazilian Caatinga dry tropical forest, from 2016 to 2020, considering the temporal distribution of rainfall and discussing the trends and impacts of forest-degradation vectors. The Google Earth Engine (GEE) platform is used to obtain the rainfall data from the CHIRPS collection and to create the LULC maps. The random forest classifier is used and applied to the Landsat 8 collection. The QGIS open software and its SPC plugin are used to visualize the LULC dynamics. The results show that the months from June to October have the lowest average rainfall, and that 2019 is the year with the highest number of consecutive rainy days below 5 mm. The LULC maps show that deforestation was higher in 2018, representing 20.19%. In 2020, the proportion of deforestation was the lowest (11.95%), while regeneration was the highest (20.33%). Thus, the characterization of the rainfall regime is essential for more accurate results in LULC maps across the seasonally dry tropical forests (SDTF).

Keywords

• remote sensing
• precipitation
• deforestation
• Google Earth Engine
• biodiversity loss