Estimating mangrove above-ground biomass in Sabah, Malaysia using field measurements, shuttle radar topography mission and landsat data

Mangroves are one of the most productive forest ecosystems and play an important role in carbon storage. We examined the use of Shuttle Radar Topography Mission (SRTM) data to estimate mangrove Above-ground Biomass (AGB) in Sabah, Malaysia. SRTM-DEM can be considered as Canopy Height Model (CHM) bec...

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Main Authors: Charissa J. Wong, Daniel James, Normah A. Besar, Mui-How Phua
Format: Article
Language:English
English
Published: 2020
Subjects:
Online Access:https://eprints.ums.edu.my/id/eprint/26791/1/Estimating%20mangrove%20above-ground%20biomass%20in%20Sabah%2C%20Malaysia%20using%20field%20measurements%2C%20shuttle%20radar%20topography%20mission%20and%20landsat%20data.pdf
https://eprints.ums.edu.my/id/eprint/26791/2/Estimating%20Mangrove%20Above-ground%20Biomass%20in%20Sabah%2C%20Malaysia%20Using%20Field%20Measurements%2C%20Shuttle%20Radar%20Topography%20Mission%20and%20Landsat%20Data.pdf
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author Charissa J. Wong
Daniel James
Normah A. Besar
Mui-How Phua
author_facet Charissa J. Wong
Daniel James
Normah A. Besar
Mui-How Phua
author_sort Charissa J. Wong
collection UMS
description Mangroves are one of the most productive forest ecosystems and play an important role in carbon storage. We examined the use of Shuttle Radar Topography Mission (SRTM) data to estimate mangrove Above-ground Biomass (AGB) in Sabah, Malaysia. SRTM-DEM can be considered as Canopy Height Model (CHM) because of the flat coastal topography. Nevertheless, we also introduced ground elevation correction using a Digital Terrain Model (DTM) generated with GIS and coastal profile data. We mapped the mangrove forest cover using Landsat imagery acquired in 2015 with the supervised classification method (Kappa coefficient of 0.81). Regression analyses of field AGB and the CHMs resulted in an estimation model with the corrected CHM as the best predictor (R2: 0.73) and cross-validated Root Mean Square Error (RMSE) was 19.70 Mg ha-1 (RMSE%: 11.60). Our study showed Sabah has a mangrove cover of 268,631.91 ha with a total AGB of 44,163,207.07 Mg in 2015. This substantial amount of carbon storage should be monitored over time and managed as part of the climate change mitigation strategy.
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spelling ums.eprints-267912021-04-19T23:54:31Z https://eprints.ums.edu.my/id/eprint/26791/ Estimating mangrove above-ground biomass in Sabah, Malaysia using field measurements, shuttle radar topography mission and landsat data Charissa J. Wong Daniel James Normah A. Besar Mui-How Phua S Agriculture (General) T Technology (General) Mangroves are one of the most productive forest ecosystems and play an important role in carbon storage. We examined the use of Shuttle Radar Topography Mission (SRTM) data to estimate mangrove Above-ground Biomass (AGB) in Sabah, Malaysia. SRTM-DEM can be considered as Canopy Height Model (CHM) because of the flat coastal topography. Nevertheless, we also introduced ground elevation correction using a Digital Terrain Model (DTM) generated with GIS and coastal profile data. We mapped the mangrove forest cover using Landsat imagery acquired in 2015 with the supervised classification method (Kappa coefficient of 0.81). Regression analyses of field AGB and the CHMs resulted in an estimation model with the corrected CHM as the best predictor (R2: 0.73) and cross-validated Root Mean Square Error (RMSE) was 19.70 Mg ha-1 (RMSE%: 11.60). Our study showed Sabah has a mangrove cover of 268,631.91 ha with a total AGB of 44,163,207.07 Mg in 2015. This substantial amount of carbon storage should be monitored over time and managed as part of the climate change mitigation strategy. 2020-09 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/26791/1/Estimating%20mangrove%20above-ground%20biomass%20in%20Sabah%2C%20Malaysia%20using%20field%20measurements%2C%20shuttle%20radar%20topography%20mission%20and%20landsat%20data.pdf text en https://eprints.ums.edu.my/id/eprint/26791/2/Estimating%20Mangrove%20Above-ground%20Biomass%20in%20Sabah%2C%20Malaysia%20Using%20Field%20Measurements%2C%20Shuttle%20Radar%20Topography%20Mission%20and%20Landsat%20Data.pdf Charissa J. Wong and Daniel James and Normah A. Besar and Mui-How Phua (2020) Estimating mangrove above-ground biomass in Sabah, Malaysia using field measurements, shuttle radar topography mission and landsat data. Borneo Science (The Journal of Science and Technology), 41 (2). pp. 22-29.
spellingShingle S Agriculture (General)
T Technology (General)
Charissa J. Wong
Daniel James
Normah A. Besar
Mui-How Phua
Estimating mangrove above-ground biomass in Sabah, Malaysia using field measurements, shuttle radar topography mission and landsat data
title Estimating mangrove above-ground biomass in Sabah, Malaysia using field measurements, shuttle radar topography mission and landsat data
title_full Estimating mangrove above-ground biomass in Sabah, Malaysia using field measurements, shuttle radar topography mission and landsat data
title_fullStr Estimating mangrove above-ground biomass in Sabah, Malaysia using field measurements, shuttle radar topography mission and landsat data
title_full_unstemmed Estimating mangrove above-ground biomass in Sabah, Malaysia using field measurements, shuttle radar topography mission and landsat data
title_short Estimating mangrove above-ground biomass in Sabah, Malaysia using field measurements, shuttle radar topography mission and landsat data
title_sort estimating mangrove above ground biomass in sabah malaysia using field measurements shuttle radar topography mission and landsat data
topic S Agriculture (General)
T Technology (General)
url https://eprints.ums.edu.my/id/eprint/26791/1/Estimating%20mangrove%20above-ground%20biomass%20in%20Sabah%2C%20Malaysia%20using%20field%20measurements%2C%20shuttle%20radar%20topography%20mission%20and%20landsat%20data.pdf
https://eprints.ums.edu.my/id/eprint/26791/2/Estimating%20Mangrove%20Above-ground%20Biomass%20in%20Sabah%2C%20Malaysia%20Using%20Field%20Measurements%2C%20Shuttle%20Radar%20Topography%20Mission%20and%20Landsat%20Data.pdf
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