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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Autores principales:	Evian Pui Yan Chan, Tung Fung, Frankie Kwan Kit Wong
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2b0c8ec328244e7ebe2659a2f4902956
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spelling	oai:doaj.org-article:2b0c8ec328244e7ebe2659a2f49029562021-12-02T13:48:41ZEstimating above-ground biomass of subtropical forest using airborne LiDAR in Hong Kong10.1038/s41598-021-81267-82045-2322https://doaj.org/article/2b0c8ec328244e7ebe2659a2f49029562021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81267-8https://doaj.org/toc/2045-2322Abstract 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.Evian Pui Yan ChanTung FungFrankie Kwan Kit WongNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Evian Pui Yan Chan Tung Fung Frankie Kwan Kit Wong Estimating above-ground biomass of subtropical forest using airborne LiDAR in Hong Kong
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.
format	article
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
title	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_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_sort	estimating above-ground biomass of subtropical forest using airborne lidar in hong kong
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2b0c8ec328244e7ebe2659a2f4902956
work_keys_str_mv	AT evianpuiyanchan estimatingabovegroundbiomassofsubtropicalforestusingairbornelidarinhongkong AT tungfung estimatingabovegroundbiomassofsubtropicalforestusingairbornelidarinhongkong AT frankiekwankitwong estimatingabovegroundbiomassofsubtropicalforestusingairbornelidarinhongkong
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Estimating above-ground biomass of subtropical forest using airborne LiDAR in Hong Kong

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