Classe em Python para cálculo de índices vegetativos a partir de imagens de satélite

Abstract

The growing demand for more efficient and sustainable agricultural practices has driven the need for advanced tools for vegetation monitoring and analysis. In this context, this study focused on the integration of satellite images, with emphasis on the SENTINEL-2 program, and the application of algorithms for calculating vegetation indices such as NDVI, RECI, NDRE, and MSAVI. The main challenge lies in the effective acquisition and processing of this data for application in Precision Agriculture. This study aims to offer a structured and reproducible methodology to overcome this barrier. The primary objective is to develop an approach that allows for the precise acquisition and analysis of satellite images, providing a solid foundation for future research in Precision Agriculture. The final result is a methodology that integrates libraries such as Sentinelsat and Rasterio, enabling the efficient acquisition, processing, and analysis of satellite data.