Estimating above-ground biomass of subtropical forest using airborne LiDAR in Hong Kong

Abstract Seventy-percent of the terrestrial area of Hong Kong is covered by vegetation and 40% is protected as the Country Park. The above-ground biomass (AGB) acts as reliable source of carbon sink and while Hong Kong has recognized the importance of carbon sink in forest and urged for forest prote...

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Main Authors: Evian Pui Yan Chan, Tung Fung, Frankie Kwan Kit Wong
Format: Article
Language:English
Published: Nature Portfolio 2021-01-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-021-81267-8
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author Evian Pui Yan Chan
Tung Fung
Frankie Kwan Kit Wong
author_facet Evian Pui Yan Chan
Tung Fung
Frankie Kwan Kit Wong
author_sort Evian Pui Yan Chan
collection DOAJ
description Abstract Seventy-percent of the terrestrial area of Hong Kong is covered by vegetation and 40% is protected as the Country Park. The above-ground biomass (AGB) acts as reliable source of carbon sink and while Hong Kong has recognized the importance of carbon sink in forest and urged for forest protection in the latest strategic plan, yet no study has been conducted on assessing the baseline of terrestrial AGB and its carbon storage. This study compared and estimated the AGB by the traditional allometric modeling and the Light Detection and Ranging (LiDAR) plot metrics at plot-level in a subtropical forest of Hong Kong. The study has tested five allometric models which were developed from pantropical regions, subtropical areas and locally. The best model was then selected as the dependent variable to develop the LiDAR-derived AGB model. The raw LiDAR point cloud was pre-processed to normalized height point cloud and hence generating the LiDAR metric as independent variables for the model development. Regression models were used to estimate AGB at various plot sizes (i.e., in 10-m, 5-m and 2.5-m radius). The models were then evaluated statistically and validated by bootstrapping and leave-one-out cross validation (LOOCV). The results indicated the LiDAR metric derived from larger plot size outperformed the smaller plot size, with model R2 of 0.864 and root-mean-square-error (RMSE) of 37.75 kg/ha. It also found that pantropical model was comparable to a site-specific model when including the bioclimatic variable in subtropical forests. This study provides the approach for delineating the baseline of terrestrial above-ground biomass and carbon stock in subtropical forests upon an appropriate plot size is being deployed.
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spelling doaj.art-2b0c8ec328244e7ebe2659a2f49029562022-12-21T21:32:49ZengNature PortfolioScientific Reports2045-23222021-01-0111111410.1038/s41598-021-81267-8Estimating above-ground biomass of subtropical forest using airborne LiDAR in Hong KongEvian Pui Yan Chan0Tung Fung1Frankie Kwan Kit Wong2Institute of Future Cities, The Chinese University of Hong KongInstitute of Future Cities, The Chinese University of Hong KongDepartment of Geography and Resource Management, The Chinese University of Hong KongAbstract Seventy-percent of the terrestrial area of Hong Kong is covered by vegetation and 40% is protected as the Country Park. The above-ground biomass (AGB) acts as reliable source of carbon sink and while Hong Kong has recognized the importance of carbon sink in forest and urged for forest protection in the latest strategic plan, yet no study has been conducted on assessing the baseline of terrestrial AGB and its carbon storage. This study compared and estimated the AGB by the traditional allometric modeling and the Light Detection and Ranging (LiDAR) plot metrics at plot-level in a subtropical forest of Hong Kong. The study has tested five allometric models which were developed from pantropical regions, subtropical areas and locally. The best model was then selected as the dependent variable to develop the LiDAR-derived AGB model. The raw LiDAR point cloud was pre-processed to normalized height point cloud and hence generating the LiDAR metric as independent variables for the model development. Regression models were used to estimate AGB at various plot sizes (i.e., in 10-m, 5-m and 2.5-m radius). The models were then evaluated statistically and validated by bootstrapping and leave-one-out cross validation (LOOCV). The results indicated the LiDAR metric derived from larger plot size outperformed the smaller plot size, with model R2 of 0.864 and root-mean-square-error (RMSE) of 37.75 kg/ha. It also found that pantropical model was comparable to a site-specific model when including the bioclimatic variable in subtropical forests. This study provides the approach for delineating the baseline of terrestrial above-ground biomass and carbon stock in subtropical forests upon an appropriate plot size is being deployed.https://doi.org/10.1038/s41598-021-81267-8
spellingShingle Evian Pui Yan Chan
Tung Fung
Frankie Kwan Kit Wong
Estimating above-ground biomass of subtropical forest using airborne LiDAR in Hong Kong
Scientific Reports
title Estimating above-ground biomass of subtropical forest using airborne LiDAR in Hong Kong
title_full Estimating above-ground biomass of subtropical forest using airborne LiDAR in Hong Kong
title_fullStr Estimating above-ground biomass of subtropical forest using airborne LiDAR in Hong Kong
title_full_unstemmed Estimating above-ground biomass of subtropical forest using airborne LiDAR in Hong Kong
title_short Estimating above-ground biomass of subtropical forest using airborne LiDAR in Hong Kong
title_sort estimating above ground biomass of subtropical forest using airborne lidar in hong kong
url https://doi.org/10.1038/s41598-021-81267-8
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AT frankiekwankitwong estimatingabovegroundbiomassofsubtropicalforestusingairbornelidarinhongkong