Machine Learning-Based Spatial Prediction of Nepal’s Forest Biomass Stocks | Data-Driven Bioregionalisation Study

 

1. Introduction

Forest biomass estimation plays a crucial role in understanding carbon sequestration potential, ecosystem productivity, and climate regulation. Nepal’s diverse topography and ecological gradients require spatially explicit modeling approaches to capture regional biomass variability. This research introduces a data-driven bioregionalisation framework combined with machine learning algorithms to enhance prediction accuracy across forest types. By integrating ecological zoning with predictive modeling, the study supports national climate commitments and sustainable forest governance strategies.

2. Data-Driven Bioregionalisation Framework

This topic explores the methodological foundation of dividing Nepal into ecologically homogeneous bioregions using environmental variables such as elevation, precipitation, soil type, and vegetation indices. Bioregionalisation improves predictive consistency by reducing spatial heterogeneity and strengthening model generalization across ecological zones.

3. Machine Learning Algorithms for Biomass Prediction

The research evaluates advanced machine learning models including Random Forest, Support Vector Machines, and Gradient Boosting for estimating forest biomass. These algorithms capture nonlinear relationships between environmental predictors and biomass density, improving predictive performance compared to traditional statistical approaches.

4. Integration of Remote Sensing and GIS Technologies

Satellite imagery, LiDAR datasets, and geospatial layers provide high-resolution environmental inputs for spatial modeling. This section examines how GIS-based data integration enhances biomass mapping accuracy and supports transparent carbon accounting systems.

5. Carbon Accounting and Climate Policy Applications

Accurate biomass stock assessment strengthens national greenhouse gas inventories and REDD+ monitoring systems. This topic discusses how spatial prediction models contribute to evidence-based policymaking, climate finance mechanisms, and sustainable forest resource allocation.

6. Future Directions in AI-Driven Environmental Monitoring

Emerging technologies such as deep learning, drone-based data acquisition, and real-time environmental sensing are transforming forest monitoring systems. This research outlines future opportunities for integrating AI-driven analytics into long-term climate resilience and biodiversity conservation strategies.
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