A data-driven approach to forest health assessment through multivariate analysis and machine learning techniques.
Journal:
BMC plant biology
Published Date:
Jul 15, 2025
Abstract
BACKGROUND: Himalayan forests are fragile, rich in biodiversity, and face increasing threats from anthropogenic pressures and climate change. Assessing their health is critical for sustainable forest management. This study integrated ecological indicators (tree density, size, regeneration, deforestation, slope, grazing, and erosion) with machine learning (ML) to classify forest health and identify key drivers across 37 Western Himalayan sites. Principal component analysis (PCA) reduced data dimensionality, highlighting major ecological gradients. K-means clustering was used to group forests into three distinct classes based on ecological characteristics, due to its efficiency in identifying natural patterns within multivariate data. ML models, including Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM) were trained and validated using an 80:20 train-test split and 5-fold cross-validation.