Accurately quantifying the above-ground carbon stock of tropical rainforest trees is the core component of “Reduction of Emissions from Deforestation and Forest Degradation-plus” (REDD+) projects and is important for evaluating the effects of anthropogenic global change. We used high-resolution optical imagery (IKONOS-2) to identify individual tree crowns in intact and degraded rainforests in the mountains of Northern Borneo, comparing our results with 50 ground-based plots dispersed in intact and degraded forests, within which all stems > 10 cm in diameter were measured and identified to species or genus. We used the dimensions of tree crowns detected in the imagery to estimate above-ground biomasses (AGBs) of individual trees and plots. To this purpose, preprocessed IKONOS imagery was segmented using a watershed algorithm; stem diameter values were then estimated from the cross-sectional crown areas of these trees using regression relationships obtained from ground-based measurements. Finally, we calculated the biomass of each tree (AGBT, in kg), and the AGB of plots by summation (AGBP, in Mg ha-1). Remotely sensed estimates of mean AGBT were similar to ground-based estimates in intact and degraded forests, even though small trees could not be detected from space-borne sensors. The intact and degraded forests not only had different AGB but were also dissimilar in biodiversity. A tree-centric approach to carbon mapping based on high-resolution optical imagery, could be a cheap alternative to airborne laser-scanning.
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Citation
Phua M-H, Ling Z-Y, Coomes DA, Wong W, Korom A, Tsuyuki S, Ioki K, Hirata Y, Saito H, Takao G (2017). Seeing trees from space: above-ground biomass estimates of intact and degraded montane rainforests from high-resolution optical imagery. iForest 10: 625-634. - doi: 10.3832/ifor2204-010
Academic Editor
Davide Travaglini
Paper history
Received: Aug 22, 2016
Accepted: May 04, 2017
First online: Jun 01, 2017
Publication Date: Jun 30, 2017
Publication Time: 0.93 months
© SISEF - The Italian Society of Silviculture and Forest Ecology 2017
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This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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