Mapping Aboveground Carbon in Oil Palm Plantations Using LiDAR: A Comparison of Tree-Centric versus Area-Based Approaches
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Coomes, D., Nunes, M., Ewers, R., & Turner, E. (2017). Mapping Aboveground Carbon in Oil Palm Plantations Using LiDAR: A Comparison of Tree-Centric versus Area-Based Approaches. Remote Sensing, 9 (8. 816)https://doi.org/10.3390/rs9080816
Southeast Asia is the epicentre of world palm oil production. Plantations in Malaysia have increased 150% in area within the last decade, mostly at the expense of tropical forests. Maps of the aboveground carbon density (ACD) of vegetation generated by remote sensing technologies, such as airborne LiDAR, are vital for quantifying the effects of land use change for greenhouse gas emissions, and many papers have developed methods for mapping forests. However, nobody has yet mapped oil palm ACD from LiDAR. The development of carbon prediction models would open doors to remote monitoring of plantations as part of efforts to make the industry more environmentally sustainable. This paper compares the performance of tree-centric and area-based approaches to mapping ACD in oil palm plantations. We find that an area-based approach gave more accurate estimates of carbon density than tree-centric methods and that the most accurate estimation model includes LiDAR measurements of top-of-canopy height and canopy cover. We show that tree crown segmentation is sensitive to crown density, resulting in less accurate tree density and ACD predictions, but argue that tree-centric approach can nevertheless be useful for monitoring purposes, providing a method to detect, extract and count oil palm trees automatically from images.
oil palm plantation, aboveground carbon density, laser scanning, LiDAR, crown segmentation, canopy cover, top of canopy height
Airborne laser scanning data were collected by NERC ARF as part of NERC’s Human-modified Tropical Forests Programme (Biodiversity And Land-use Impacts on tropical ecosystem function (BALI) project; grant number: NE/K016377/1). We are grateful to the flight team and the data analysts at NERC’s data analysis node. Marion Pfeifer kindly provided us access to Pléiades imagery. We thank the Sime Darby Foundation, the Sabah Foundation, Benta Wawasan and the Sabah Forestry Department for their support of the SAFE Project. We thank the Royal Society South East Asia Rainforest Research Partnership for logistical support in the field.
NERC (via University of Aberdeen) (NE/K016253/1)
External DOI: https://doi.org/10.3390/rs9080816
This record's URL: https://www.repository.cam.ac.uk/handle/1810/266188
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