A data-driven approach to forest health assessment through multivariate analysis and machine learning techniques.

backgroundHimalayan 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.

resultsPCA revealed that elevation, disturbance, and regeneration explained 74.3% variance. Forest health varied across sites, with 10 categorized as healthy, 19 as moderate, and 8 as unhealthy. Forest regeneration was highly skewed (2.67) and leptokurtic (9.8), with few sites showing high seedling abundance, while deforestation (mean = 294 stumps ha

conclusionsThis study highlights ML-driven classification as a precise, scalable, and objective tool for large-scale forest health assessments. Conservation efforts should prioritize degraded forests through afforestation, slope stabilization, controlled grazing, erosion management, and continuous ecosystem monitoring. Future studies should integrate high-resolution remote sensing (e.g., Landsat, Sentinel-2) and climate datasets (e.g., temperature, precipitation, and drought indices) to enhance predictive capabilities and support long-term forest management planning. The findings underscore the value of data-driven approaches, establishing machine learning as an effective tool to enhance forest monitoring and support evidence-based forest conservation and management in the Himalayas.