Theoretical foundations of the NDVI index and its application in remote sensing

The Normalized Difference Vegetation Index (NDVI) is a widely used spectral index in remote sensing for assessing vegetation presence, health, and density. This thesis explores the theoretical underpinnings of NDVI, focusing on the distinct spectral characteristics of healthy vegetation in the red and near-infrared (NIR) regions of the electromagnetic spectrum. It delves into the biophysical principles governing chlorophyll absorption and mesophyll scattering, which form the basis of the NDVI calculation. Furthermore, the thesis examines the diverse applications of NDVI across various fields of remote sensing, including agriculture, forestry, environmental monitoring, and climate change research. Methodological considerations, such as sensor characteristics, atmospheric correction, and data processing techniques, are discussed to ensure accurate and reliable NDVI estimations. The advantages and limitations of NDVI, along with its evolving role in synergistic approaches with other remote sensing indices and machine learning algorithms, are also addressed. This work aims to provide a comprehensive understanding of NDVI's theoretical framework and its practical utility in contemporary remote sensing applications.