DOI: 10.5281/zenodo.16536023
title: Above-Ground Biomass Prediction Dataset for Northeastern India: Satellite-Based Pixel-Level Estimates for Assam and Mizoram Forests

description: 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:







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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