Date created: | Last Updated:

Category: Project

Description: Ongoing increases in human and climate pressures, associated with the lack of monitoring initiatives, make the Caatinga one of the most vulnerable biomes in the world. The Caatinga is located in the semi-arid region of Brazil, and its vegetation phenology is highly dependent on precipitation, which has a high spatial and temporal variability. Under these circumstances, satellite image-based methods are valued due to their ability to uncover human-induced changes from climate effects on land cover. In this study, a time series stack of 670 Landsat images over a period of 31 years (1985–2015) was used to investigate spatial and temporal patterns of land-cover clearing (LCC) due to vegetation removal in an area of the Caatinga biome. We compared the performance of surface albedo (SA), the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI) and evaluated their suitability for monitoring LCC in contrast to precipitation-related variations. We applied a residual trend analysis (TSS-RESTREND), with detection of significant structural changes (breakpoints) to monthly Landsat time series. Our results show that SA was able to identify LCC with a higher accuracy (89%) than EVI (44%) and NDVI (46%). The overall outcome of the study shows the benefits of using spectral indices of Landsat time series that incorporate the short-wave infrared region, such as the SA, compared to vegetation indices for the monitoring of land-cover clearing, in seasonal dry forests such as the Caatinga.