Garofalo Costan*
Fractional Vegetation Coverage (FVC) is a critical parameter in ecological and environmental studies. It represents the proportion of ground covered by green vegetation, providing essential information for understanding ecosystem dynamics, monitoring environmental changes, and managing natural resources. Traditionally, FVC estimation relied on field surveys and remote sensing techniques. However, the advent of Machine Learning (ML) has revolutionized this field, offering enhanced accuracy and efficiency in FVC analysis. This essay delves into the integration of machine learning for enhanced fractional vegetation coverage analysis, exploring its methodologies, applications, benefits, and challenges. Before the integration of machine learning, FVC estimation primarily relied on field-based methods and remote sensing techniques. Field-based methods involve direct measurement of vegetation coverage through ground surveys. While these methods are accurate, they are labor-intensive, time-consuming, and limited in spatial coverage. Remote sensing techniques, on the other hand, utilize satellite or aerial imagery to estimate FVC over large areas. These techniques include spectral vegetation indices (such as NDVI), image classification, and regression analysis. Although remote sensing offers broader spatial coverage, it faces challenges like atmospheric interference, sensor limitations, and complex data processing requirements.
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