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

Area of Science:

• Ecology
• Computer Science
• Environmental Science

Background:

• Himalayan forests are biodiverse but threatened by human activities and climate change.
• Assessing forest health is crucial for effective conservation and management.
• This study focuses on the Western Himalayas, a region with unique ecological challenges.

Purpose of the Study:

• To classify forest health using ecological indicators and machine learning (ML).
• To identify the primary drivers influencing forest health in the Western Himalayas.
• To evaluate the performance of different ML models for forest health assessment.

Main Methods:

• Ecological indicators (density, size, regeneration, deforestation, slope, grazing, erosion) were collected from 37 sites.
• Principal Component Analysis (PCA) reduced data dimensionality.
• K-means clustering categorized forests into healthy, moderate, and unhealthy classes.
• Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM) models were trained and validated.

Main Results:

• PCA identified elevation, disturbance, and regeneration as key factors explaining 74.3% of variance.
• Forest health varied, with 10 healthy, 19 moderate, and 8 unhealthy sites.
• Random Forest (RF) demonstrated superior performance (accuracy 0.83, balanced accuracy 0.88) compared to SVM and DT.
• RF analysis highlighted tree DBH, height, regeneration rate, soil erosion, and tree density as critical drivers.

Conclusions:

• ML classification provides a precise, scalable, and objective method for large-scale forest health assessment.
• Conservation should focus on degraded forests, addressing afforestation, slope stabilization, grazing, and erosion.
• Integrating remote sensing and climate data can enhance future predictive models for forest management.