Space-temporal detection of environmental changes in the Brazilian semiarid through Google Earth Engine and GIS

Abstract

Land Use and Land Cover Changes (LULCC) are the leading cause of worldwide land degradation. However, efficient means of detecting and monitoring these environmental changes improve the understanding of this process, especially in developing more effective policies to combat land degradation. This study evaluated the space-temporal trends of ecological changes in the Cabrobó’s Desertification Nucleus (CDN), Pernambuco, Brazilian semiarid region, between 2001 and 2021. For this, we manipulated CHIRPS data, MODIS products (MOD13A1, MOD11A2, MCD43A3, and MOD16A2), and MapBiomas data in the Google Earth Engine (GEE) digital platform and the QGIS software (version 3.10.9). First, we calculated surface albedo and Vegetation Health Index (VHI). After that, we performed a space-temporal trend assessment for all biophysical parameters via non-parametric tests Mann-Kendall (MK) and Sen's Slope Estimator (SSE). In addition, we analyzed LULCC over the last 21 years using MapBiomas. The results obtained showed the effectiveness of using these orbital products for long-term environmental analysis, as well as highlighting the municipalities of Belém do São Francisco, Itacuruba, and Floresta with significant trends at the 1% probability level of the biophysical indices (from decrease to potential evapotranspiration, NDVI and VHI, and addition to albedo and surface temperature) due to a more change in land use and cover. This methodology proved effective for studies on the discrimination of space-temporal trends in the CDN region. It will apply in other Caatinga areas after the appropriate methodological adjustments. All scripts generated in this work can be accessed and manipulated through the LDA Tool repository in the GEE platform.