AI-driven approaches for forest growth assessment and management

Abstract

The advent of digital data has markedly increased the utilization of Artificial Intelligence (AI) in forestry, significantly enhancing the precision and efficiency of forest monitoring. This chapter explores the transformative impact of AI on forest management, tracing the evolution of AI from its foundational concepts to its wide-ranging applications in diverse sectors. It highlights AI’s ability to replicate human cognitive functions, such as learning and problem-solving, emphasizing its crucial role in improving the accuracy and effectiveness of forest monitoring systems. The discussion extends to the integration of AI with cutting-edge technologies such as machine learning, deep learning, and remote sensing. A detailed description of various algorithms, including the Generalized Linear Model, Generalized Additive Model, Partial Least Squares Regression, Gradient Boost Machine, Support Vector Machines, Random Forests, and Neural Networks, is provided and their applications in forest growth assessment, change detection, and the analysis of disease and fire risks, both globally and within the Indian context, are meticulously discussed. © 2026 Elsevier Inc. All rights reserved..