Version 1
: Received: 10 June 2024 / Approved: 10 June 2024 / Online: 11 June 2024 (10:14:03 CEST)
How to cite:
Chamberlin, A. J.; Liu, Z. Y.-C.; Cross, C. G.; Pourtois, J.; Siregar, I. Z.; Nurrochmat, D. R.; Setiawan, Y.; Webb, K.; Hopkins, S.; Sokolow, S. H.; De Leo, G. A. Improving Remote Monitoring of Carbon Stock in Tropical Forests Using Machine Learning: A Case Study in Indonesian Borneo. Preprints2024, 2024060671. https://doi.org/10.20944/preprints202406.0671.v1
Chamberlin, A. J.; Liu, Z. Y.-C.; Cross, C. G.; Pourtois, J.; Siregar, I. Z.; Nurrochmat, D. R.; Setiawan, Y.; Webb, K.; Hopkins, S.; Sokolow, S. H.; De Leo, G. A. Improving Remote Monitoring of Carbon Stock in Tropical Forests Using Machine Learning: A Case Study in Indonesian Borneo. Preprints 2024, 2024060671. https://doi.org/10.20944/preprints202406.0671.v1
Chamberlin, A. J.; Liu, Z. Y.-C.; Cross, C. G.; Pourtois, J.; Siregar, I. Z.; Nurrochmat, D. R.; Setiawan, Y.; Webb, K.; Hopkins, S.; Sokolow, S. H.; De Leo, G. A. Improving Remote Monitoring of Carbon Stock in Tropical Forests Using Machine Learning: A Case Study in Indonesian Borneo. Preprints2024, 2024060671. https://doi.org/10.20944/preprints202406.0671.v1
APA Style
Chamberlin, A. J., Liu, Z. Y. C., Cross, C. G., Pourtois, J., Siregar, I. Z., Nurrochmat, D. R., Setiawan, Y., Webb, K., Hopkins, S., Sokolow, S. H., & De Leo, G. A. (2024). Improving Remote Monitoring of Carbon Stock in Tropical Forests Using Machine Learning: A Case Study in Indonesian Borneo. Preprints. https://doi.org/10.20944/preprints202406.0671.v1
Chicago/Turabian Style
Chamberlin, A. J., Susanne H. Sokolow and Giulio A. De Leo. 2024 "Improving Remote Monitoring of Carbon Stock in Tropical Forests Using Machine Learning: A Case Study in Indonesian Borneo" Preprints. https://doi.org/10.20944/preprints202406.0671.v1
Abstract
Deforestation through land-use conversion, illegal logging, and timber trafficking is believed to cause ~10% of the annual human carbon dioxide emissions at the global level. Given the large contribution, local and national policies have been set in place in the effort to reduce deforestation and support reforestation. However, accurate assessment of forest carbon stock is expensive and challenging in remote areas and on large geographical scales. To improve carbon stock monitoring and evaluation of fine-scale forest loss, we developed a rapid, automatic, scalable, and cost-efficient generalized deep learning framework that uses diverse remote sensing data and satellite imagery to derive aboveground carbon density from accurate estimates of tree canopy heights at fine-grained resolution (30x30 meters) in remote tropical rainforests. The remote sensing data is composed of Landsat-8, Sentinel-1, land cover, digital elevation model, and NASA CMS airborne LiDAR, as well as vegetation indices, texture metrics, and climatic data. Data sources were compiled into a data pipeline which produced >300 features and 2 million observations over forests in Indonesian Borneo. Using LiDAR swath data on canopy forest high for ~100,000 hectares in Indonesian Borneo to create a training and validation datasets, our neural network model produced aboveground carbon density estimates with R2 of 0.82, which is a significant improvement from comparable works by using Random Forest (R2 of 0.3-0.5). This deep learning framework can be used to facilitate further carbon stock modeling in other forest regions (e.g., Brazil) as well as for the general purpose of climate change mitigation.
Keywords
Carbon Stock Estimation; Deep Learning; Deforestation
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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