Above-Ground Biomass Prediction Dataset for Northeastern India Satellite-Based Pixel-Level Estimates for Assam and Mizoram Forests This dataset contains satellite-based above-ground biomass (AGB) predictions for two locations in northeastern Indian forests. It is specifically optimized for the forest regions in Assam and Mizoram. The predictions are generated using a deep learning model trained on ecologically similar forest sites across South and Southeast Asia. This work was developed by vertify.earth as part of the digital Monitoring, Reporting, and Verification (dMRV) for Himalayasproject, funded by Lacuna. Dataset Structure The dataset contains two locations. Each location includes four files. File Descriptions 1. Input Images (input_image.tif) Multi-sensor satellite data used for biomass prediction Includes Sentinel-1 (SAR), Sentinel-2 (optical), and DEM data Spatial resolution: 10 to 40 meters, depending on the sensor Format: Multi-band GeoTIFF with geospatial referencing 2. Predicted Biomass Maps (predicted_biomass.tif) Pixel-level biomass estimates in Mg/ha (megagrams per hectare) Generated using StableResNet deep learning architecture Model performance: R² = 0.87 RMSE = 28.7 Mg/ha MAE = 19.5 Mg/ha Format: Single-band GeoTIFF Typical value range: 40 to 460 Mg/ha 3. Visualization Maps (visualization.png) Color-coded biomass maps for visual reference PNG format, suitable for reports and presentations Optimized color scales and legends for biomass density 4. Summary Statistics (statistics.txt) Summary statistics of predicted biomass values Includes mean, median, standard deviation, and min/max Includes spatial distribution analysis Model Information Training Data The model was trained on forests in: India: Yellapur, Betul, Achanakmar Thailand: Khaoyai Sites were chosen for ecological similarity to northeastern India: Monsoon-influenced climate Comparable forest types (evergreen, semi-evergreen, moist deciduous) Hilly terrain and similar biomass density range Model Architecture Custom StableResNet with: Residual connections Layer normalization Designed for pixel-level regression stability Feature Engineering Features used include: Spectral indices (NDVI, EVI, NDWI) Texture features (LBP, GLCM) Spatial gradients PCA components Regional Applicability This dataset is tailored for: Primary Use: Biomass mapping in Assam and Mizoram Also Applicable To: Other northeastern Indian states Forest Types: Tropical and subtropical, monsoon-affected Terrain: Hilly and mountainous forest regions Project Context Organization: vertify.earth Project: Digital Monitoring, Reporting, and Verification (dMRV) for Himalayas Funding: Lacuna Fund Purpose: Forest carbon monitoring and REDD+ support Technical Specifications Item Description Number of Locations 2 Spatial Coverage Assam and Mizoram, India Temporal Coverage 2024–2025 Resolution 10–40 meters File Formats GeoTIFF (raster), PNG (visualization), CSV/JSON (statistics) Packaging ZIP folder per location Usage Applications Forest carbon stock assessments REDD+ monitoring and reporting Forest management planning Climate change and biodiversity studies Research on tropical forest dynamics Data Quality and Validation Validated against LiDAR-derived biomass data Cross-validation across multiple forest sites Performance Metrics: R² = 0.87 RMSE = 28.7 Mg/ha MAE = 19.5 Mg/ha Includes quality flags for limited satellite coverage areas Citation If you use this dataset, please cite: CopyEdit vertify.earth (2025). Above-Ground Biomass Prediction Dataset for Northeastern India: Satellite-Based Pixel-Level Estimates for Assam and Mizoram Forests. dMRV for Himalayas Project. Zenodo. https://doi.org/10.5281/zenodo.16536024