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🌲 DeepBioFusion: Tree-Level Above Ground Biomass (AGB) Estimation using Multi-Modal Deep Learning

This repository contains the complete code and supporting material for our study on individual tree-level estimation of Above Ground Biomass (AGB) using multi-modal remote sensing data and deep learning. The proposed model does not rely on LiDAR data for prediction, making it suitable for operational scalability across large forested regions.

🔍 Overview

Accurate AGB estimation plays a crucial role in forest carbon accounting and ecological monitoring. Traditional approaches rely heavily on field data or LiDAR, which are costly and difficult to scale. This study introduces DeepBioFusion, a CNN-based architecture that fuses:

  • 🛰️ Optical imagery (e.g., RGB or multispectral)
  • 📡 Synthetic Aperture Radar (SAR) data (X, C, and L bands)
  • 🌳 Tree species maps derived from the Norwegian Forest Inventory

🧠 Key Contributions

  • 🚫 LiDAR-free prediction: Model predicts biomass without needing LiDAR input at inference.
  • 🌐 Scalable design: Adaptable to any region with satellite access.
  • 🌲 Individual tree resolution: Focused on single-tree level predictions.
  • 🔁 Reproducible workflow: Includes complete pipeline for training and evaluation.

🧪 Methodology

The end-to-end pipeline includes segmentation, patch extraction, species labeling, and biomass estimation using a multi-branch CNN.

📌 Methodology Diagram

This diagram summarizes the complete DeepBioFusion pipeline — from LiDAR segmentation to AGB prediction using CNNs and multi-modal fusion.

🧬 Visual Samples from the Study

🗺️ AGB Distribution Map

Estimated AGB values are visualized spatially, reflecting biomass distribution across a Norwegian forested region.

🌳 Tree Species Classification Map

Each individual tree is classified into species groups (e.g., Spruce, Pine, Deciduous), forming the categorical input for our model.

🌲 Digital Terrain Model vs Canopy Height Model

LiDAR-derived terrain and canopy height models used for initial tree crown segmentation and quality assessment.

📊 Actual vs Predicted Biomass

Scatter plot comparing predicted biomass values with field-based measurements. The high correlation supports model validity.

🧰 Setup

📦 Installation

git clone https://github.com/Ci2Lab/DeepBioFusion.git
cd DeepBioFusion
pip install -r requirements.txt