Drones and machine learning for estimating forest carbon storage

• Published in: Carbon Research Vol. 1; no. 1
• Main Authors: Sharma, Sadikshya, Dhal, Sambandh, Rout, Tapas, Acharya, Bharat Sharma
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 14-10-2022
• Subjects: Ceramics; Composites; Earth and Environmental Science; Environment; Environmental Engineering/Biotechnology; Fossil Fuels (incl. Carbon Capture); Glass; Natural Materials; Perspective; Renewable and Green Energy; Climate change; Satellites; Carbon; Machine learning; Drones
• ISSN: 2731-6696; 2731-6696
• DOI: 10.1007/s44246-022-00021-5

Estimating forest carbon storage is crucial for understanding sink capacities to facilitate carbon crediting and mitigate climate change. Images captured with RGB or LiDAR cameras, mounted on drones, could be used to derive forest structural parameters such as canopy area, height, and tree diameter. Further, these data could be used in Machine Learning models and allometric equations to rapidly and precisely estimate and model carbon storage in their living biomass.
Graphical Abstract
Highlights
1. Forests capture and store several tons of carbon in their living biomass, and are therefore, a useful option for carbon sequestration.
2. Drone imagery-derived forest structural parameters could be used in Machine Learning (ML) models and allometric eqs.
3. Integrating drone data with ML models provide a rapid, accurate, and cost-effective approach to estimate and model carbon capture and storage.