Forest structure and above-ground biomass estimation in Danum Valley using airbone Lidar Data

Forest structure and above-ground biomass estimation in Danum Valley using airbone Lidar Data

Above-ground Biomass (AGB) estimation in tropical forest is challenging due to the complex forest structure. Evolving technology in remote sensing especially airborne Light Detection and Ranging (LiDAR) is a promising technology that construct a three dimensional model of the complex forest canopy....

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Main Author: Hue, Su Wah
Format: Thesis
Language:English
English
Published: 2017
Subjects:
SD1-669.5 Forestry
Online Access:https://eprints.ums.edu.my/id/eprint/37971/1/24%20PAGES.pdf
https://eprints.ums.edu.my/id/eprint/37971/2/FULLTEXT.pdf
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spelling my-ums-ep.379712024-01-25T08:29:12Z Forest structure and above-ground biomass estimation in Danum Valley using airbone Lidar Data 2017 Hue, Su Wah SD1-669.5 Forestry Above-ground Biomass (AGB) estimation in tropical forest is challenging due to the complex forest structure. Evolving technology in remote sensing especially airborne Light Detection and Ranging (LiDAR) is a promising technology that construct a three dimensional model of the complex forest canopy. The application of airborne LiDAR data was examined to estimate AGB of the primary and the logged over forest near to Danum Valley Conservation Area (DVCA). Field based AGB was calculated using the allometric equations of Yamakura (Yamakura et al. 1986) and Chave (Chave et al., 2014). A total of 50 plots were collected in the primary (n=20) and logged-over (n= 30) forests. The structure of the forests were analyzed using the airborne LiDAR data acquired in October 2013. LiDAR metrics were calculated from the first and last returns of the point clouds at plot level to generate the height metrics and laser penetration (LPs). From the result of the stepwise regression analysis, maximum DBH was explained by a multivariate model with height metric H70 as predictor (R2 = 0.62, RMSE = 16.59 cm). A single predictor model with height metric H90 was effective to estimate maximum tree height (R2 = 0.88, RMSE = 3.6 m) for both forests combined while a multivariate model comprised of H10, H70 and H90 explained 87% of the Lorey's Height (LH) of the combined forest. For AGB estimation, a stepwise multiple regression analysis was used to develop an AGB model by relating the height metrics and LPs. Natural log transformation model of AGB (Chave) for both forests combined improved the AGB estimation with R2 of 0. 70 (RMSEcv=26.16%). The predictors Hmean, LP0, LP1 and LP14 were selected in this regression model. The results suggest that airborne LiDAR is a reliable and accurate technology to estimate AGB in tropical forest in Sabah. 2017 Thesis https://eprints.ums.edu.my/id/eprint/37971/ https://eprints.ums.edu.my/id/eprint/37971/1/24%20PAGES.pdf text en public https://eprints.ums.edu.my/id/eprint/37971/2/FULLTEXT.pdf text en validuser masters Universiti Malaysia Sabah Fakulti Sains dan Sumber Alam
institution Universiti Malaysia Sabah
collection UMS Institutional Repository
language English
English
topic SD1-669.5 Forestry
spellingShingle SD1-669.5 Forestry
Hue, Su Wah
Forest structure and above-ground biomass estimation in Danum Valley using airbone Lidar Data
description Above-ground Biomass (AGB) estimation in tropical forest is challenging due to the complex forest structure. Evolving technology in remote sensing especially airborne Light Detection and Ranging (LiDAR) is a promising technology that construct a three dimensional model of the complex forest canopy. The application of airborne LiDAR data was examined to estimate AGB of the primary and the logged over forest near to Danum Valley Conservation Area (DVCA). Field based AGB was calculated using the allometric equations of Yamakura (Yamakura et al. 1986) and Chave (Chave et al., 2014). A total of 50 plots were collected in the primary (n=20) and logged-over (n= 30) forests. The structure of the forests were analyzed using the airborne LiDAR data acquired in October 2013. LiDAR metrics were calculated from the first and last returns of the point clouds at plot level to generate the height metrics and laser penetration (LPs). From the result of the stepwise regression analysis, maximum DBH was explained by a multivariate model with height metric H70 as predictor (R2 = 0.62, RMSE = 16.59 cm). A single predictor model with height metric H90 was effective to estimate maximum tree height (R2 = 0.88, RMSE = 3.6 m) for both forests combined while a multivariate model comprised of H10, H70 and H90 explained 87% of the Lorey's Height (LH) of the combined forest. For AGB estimation, a stepwise multiple regression analysis was used to develop an AGB model by relating the height metrics and LPs. Natural log transformation model of AGB (Chave) for both forests combined improved the AGB estimation with R2 of 0. 70 (RMSEcv=26.16%). The predictors Hmean, LP0, LP1 and LP14 were selected in this regression model. The results suggest that airborne LiDAR is a reliable and accurate technology to estimate AGB in tropical forest in Sabah.
format Thesis
qualification_level Master's degree
author Hue, Su Wah
author_facet Hue, Su Wah
author_sort Hue, Su Wah
title Forest structure and above-ground biomass estimation in Danum Valley using airbone Lidar Data
title_short Forest structure and above-ground biomass estimation in Danum Valley using airbone Lidar Data
title_full Forest structure and above-ground biomass estimation in Danum Valley using airbone Lidar Data
title_fullStr Forest structure and above-ground biomass estimation in Danum Valley using airbone Lidar Data
title_full_unstemmed Forest structure and above-ground biomass estimation in Danum Valley using airbone Lidar Data
title_sort forest structure and above-ground biomass estimation in danum valley using airbone lidar data
granting_institution Universiti Malaysia Sabah
granting_department Fakulti Sains dan Sumber Alam
publishDate 2017
url https://eprints.ums.edu.my/id/eprint/37971/1/24%20PAGES.pdf
https://eprints.ums.edu.my/id/eprint/37971/2/FULLTEXT.pdf
_version_ 1794022699144577024

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