Spatiotemporal Variation of the Vegetation Index by Normalized Difference in the Hydrographic Basin of Pernambuco Semiarid, Brazil

Abstract

Remote sensing is an important mechanism for spatiotemporal monitoring of vegetation. Vegetation indexes have been widely used in monitoring vegetated areas, to determine and estimate the index of leaf area, biomass, and photosynthetically active radiation, among others. In these indexes, the Normalized Difference Index Vegetation (NDVI) stands out, which indicates the health status of a vegetative canopy. Considering the proven applicability of this index, in addition to the relevant importance of the Caatinga biome, related to the need for monitoring at the expense of desertification processes, the article aimed to analyze temporal variations of the NDVI in a hydrographic basin of the Brazilian semiarid region, in the municipality of Itacuruba/PE, Brazil. The study area covers a hydrographic basin belonging to the São Francisco River. Remote sensing techniques were used to estimate the NDVI in four orbital images provided by NASA/USGS in the year 2020, with less than 10% clouds. Then, the data were subjected to descriptive statistical analysis to obtain minimum, maximum, mean, standard deviation, and coefficient of variation values. The results showed that the NDVI presented values ranging from -0.30 to 0.57. The means found ranged from 0.16 to 0.23 for the months studied, as well as in the standard deviation values, values between 0.06 and 0.09 were found, corroborating other correlated studies in the region. The coefficient of variation data showed average variability, according to the Warrick & Nielsen classification criteria. It is noteworthy that, in addition to the dry climate of the region, the shrubby vegetation presents physiological defense characteristics such as the fall of foliage. It was concluded that the use of remote sensing was effective in evaluating the spatial-temporal dynamics of the vegetation index in the watershed, managing to measure environmental variations at the expense of climate change.