Impact of Precipitation Fluctuations on Vegetation Dynamics: A Spatiotemporal Remote Sensing Analysis in Pernambuco's Semi-Arid Region, Brazil

Vegetation cover in seasonally dry forests, such as the Caatinga, is intrinsically linked to rainfall. Temporal data from sensors like MODIS TERRA-AQUA, combined with the CHIRPS precipitation product, allow for the monitoring of this relationship. This study aimed to evaluate the spatial dynamics of vegetation cover in the Pajeú River watershed (PE), between 2010 and 2022, through land use and land cover analysis and time series of the NDVI, EVI, and Albedo indices derived from MODIS, correlating these variables with CHIRPS data and quantifying vegetation-precipitation relationships. The results showed significant correlations: precipitation had a direct relationship with the mean values of NDVI (wet season: 0.60; dry season: <0.20) and EVI (wet season: >0.40; dry season: <0.20), and an inverse relationship with Albedo (dry season: >0.16). In 2022, there was a 15% reduction in vegetated areas during the dry season, highlighting the vulnerability of vegetation to hydrological seasonality, resulting in reduced vegetation cover and increased areas of exposed soil. It is concluded that the integration of MODIS and CHIRPS data proved to be an effective tool for monitoring vegetation responses to rainfall seasonality, providing valuable support for water resource management in semi-arid regions.