ANALYSIS OF FIELD SEAGRASS PERCENT COVER AND ABOVEGROUND CARBON STOCK DATA FOR NON-DESTRUCTIVE ABOVEGROUND SEAGRASS CARBON STOCK MAPPING USING WORLDVIEW-2 IMAGE

Remote sensing can make seagrass aboveground carbon stock (AGC<sub>seagrass</sub>) information spatially extensive and widely available. Therefore, it is necessary to develop a rapid approach to estimate AGC<sub>seagrass</sub> in the field to train and assess its remote sensi...

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Autores principales:	P. Wicaksono, P. Danoedoro, Hartono, U. Nehren, A. Maishella, M. Hafizt, S. Arjasakusuma, S. D. Harahap
Formato:	article
Lenguaje:	EN
Publicado:	Copernicus Publications 2021
Materias:	Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820
Acceso en línea:	https://doaj.org/article/840ca51e9c5a4347ab4beabc34c60bb4
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spelling	oai:doaj.org-article:840ca51e9c5a4347ab4beabc34c60bb42021-11-19T01:55:11ZANALYSIS OF FIELD SEAGRASS PERCENT COVER AND ABOVEGROUND CARBON STOCK DATA FOR NON-DESTRUCTIVE ABOVEGROUND SEAGRASS CARBON STOCK MAPPING USING WORLDVIEW-2 IMAGE10.5194/isprs-archives-XLVI-4-W6-2021-321-20211682-17502194-9034https://doaj.org/article/840ca51e9c5a4347ab4beabc34c60bb42021-11-01T00:00:00Zhttps://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVI-4-W6-2021/321/2021/isprs-archives-XLVI-4-W6-2021-321-2021.pdfhttps://doaj.org/toc/1682-1750https://doaj.org/toc/2194-9034Remote sensing can make seagrass aboveground carbon stock (AGC<sub>seagrass</sub>) information spatially extensive and widely available. Therefore, it is necessary to develop a rapid approach to estimate AGC<sub>seagrass</sub> in the field to train and assess its remote sensing-based mapping. The aim of this research is to (1) analyze the Percent Cover (PCv)-AGC<sub>seagrass</sub> relationship in seagrass at the species and community levels to estimate AGC<sub>seagrass</sub> from PCv and (2) perform AGC<sub>seagrass</sub> mapping at both levels using WorldView-2 image and assess the accuracy of the resulting map. This research was conducted in Karimunjawa and Kemujan Islands, Indonesia. Support Vector Machine (SVM) classification was used to map seagrass species composition, and stepwise regression was used to model AGC<sub>seagrass</sub> using deglint, water column corrected, and principle component bands. The results were a rapid AGC<sub>seagrass</sub> estimation using an easily measured parameter, the seagrass PCv. At the community level, the AGC<sub>seagrass</sub> map had 58.79% accuracy (SEE = 5.41 g C m<sup>−2</sup>), whereas at the species level, the accuracy increased for the class Ea (64.73%, SEE = 6.86 g C m<sup>−2</sup>) and EaThCr (70.02%, SEE = 4.32 g C m<sup>−2</sup>) but decreased for ThCr (55.08%, SEE = 2.55 g C m<sup>−2</sup>). The results indicate that WorldView-2 image reflectance can accurately map AGC<sub>seagrass</sub> in the study area in the range of 15–20 g C m<sup>−2</sup> for Ea, 10–15 g C m<sup>−2</sup> for EaThCr, and 4–8 g C m<sup>−2</sup> for ThCr. Based on our model, the AGC<sub>seagrass</sub> in the study area was estimated at 13.39 t C.P. WicaksonoP. DanoedoroHartonoU. NehrenA. MaishellaM. HafiztS. ArjasakusumaS. D. HarahapCopernicus PublicationsarticleTechnologyTEngineering (General). Civil engineering (General)TA1-2040Applied optics. PhotonicsTA1501-1820ENThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVI-4-W6-2021, Pp 321-327 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820
spellingShingle	Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820 P. Wicaksono P. Danoedoro Hartono U. Nehren A. Maishella M. Hafizt S. Arjasakusuma S. D. Harahap ANALYSIS OF FIELD SEAGRASS PERCENT COVER AND ABOVEGROUND CARBON STOCK DATA FOR NON-DESTRUCTIVE ABOVEGROUND SEAGRASS CARBON STOCK MAPPING USING WORLDVIEW-2 IMAGE
description	Remote sensing can make seagrass aboveground carbon stock (AGC<sub>seagrass</sub>) information spatially extensive and widely available. Therefore, it is necessary to develop a rapid approach to estimate AGC<sub>seagrass</sub> in the field to train and assess its remote sensing-based mapping. The aim of this research is to (1) analyze the Percent Cover (PCv)-AGC<sub>seagrass</sub> relationship in seagrass at the species and community levels to estimate AGC<sub>seagrass</sub> from PCv and (2) perform AGC<sub>seagrass</sub> mapping at both levels using WorldView-2 image and assess the accuracy of the resulting map. This research was conducted in Karimunjawa and Kemujan Islands, Indonesia. Support Vector Machine (SVM) classification was used to map seagrass species composition, and stepwise regression was used to model AGC<sub>seagrass</sub> using deglint, water column corrected, and principle component bands. The results were a rapid AGC<sub>seagrass</sub> estimation using an easily measured parameter, the seagrass PCv. At the community level, the AGC<sub>seagrass</sub> map had 58.79% accuracy (SEE = 5.41 g C m<sup>−2</sup>), whereas at the species level, the accuracy increased for the class Ea (64.73%, SEE = 6.86 g C m<sup>−2</sup>) and EaThCr (70.02%, SEE = 4.32 g C m<sup>−2</sup>) but decreased for ThCr (55.08%, SEE = 2.55 g C m<sup>−2</sup>). The results indicate that WorldView-2 image reflectance can accurately map AGC<sub>seagrass</sub> in the study area in the range of 15–20 g C m<sup>−2</sup> for Ea, 10–15 g C m<sup>−2</sup> for EaThCr, and 4–8 g C m<sup>−2</sup> for ThCr. Based on our model, the AGC<sub>seagrass</sub> in the study area was estimated at 13.39 t C.
format	article
author	P. Wicaksono P. Danoedoro Hartono U. Nehren A. Maishella M. Hafizt S. Arjasakusuma S. D. Harahap
author_facet	P. Wicaksono P. Danoedoro Hartono U. Nehren A. Maishella M. Hafizt S. Arjasakusuma S. D. Harahap
author_sort	P. Wicaksono
title	ANALYSIS OF FIELD SEAGRASS PERCENT COVER AND ABOVEGROUND CARBON STOCK DATA FOR NON-DESTRUCTIVE ABOVEGROUND SEAGRASS CARBON STOCK MAPPING USING WORLDVIEW-2 IMAGE
title_short	ANALYSIS OF FIELD SEAGRASS PERCENT COVER AND ABOVEGROUND CARBON STOCK DATA FOR NON-DESTRUCTIVE ABOVEGROUND SEAGRASS CARBON STOCK MAPPING USING WORLDVIEW-2 IMAGE
title_full	ANALYSIS OF FIELD SEAGRASS PERCENT COVER AND ABOVEGROUND CARBON STOCK DATA FOR NON-DESTRUCTIVE ABOVEGROUND SEAGRASS CARBON STOCK MAPPING USING WORLDVIEW-2 IMAGE
title_fullStr	ANALYSIS OF FIELD SEAGRASS PERCENT COVER AND ABOVEGROUND CARBON STOCK DATA FOR NON-DESTRUCTIVE ABOVEGROUND SEAGRASS CARBON STOCK MAPPING USING WORLDVIEW-2 IMAGE
title_full_unstemmed	ANALYSIS OF FIELD SEAGRASS PERCENT COVER AND ABOVEGROUND CARBON STOCK DATA FOR NON-DESTRUCTIVE ABOVEGROUND SEAGRASS CARBON STOCK MAPPING USING WORLDVIEW-2 IMAGE
title_sort	analysis of field seagrass percent cover and aboveground carbon stock data for non-destructive aboveground seagrass carbon stock mapping using worldview-2 image
publisher	Copernicus Publications
publishDate	2021
url	https://doaj.org/article/840ca51e9c5a4347ab4beabc34c60bb4
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ANALYSIS OF FIELD SEAGRASS PERCENT COVER AND ABOVEGROUND CARBON STOCK DATA FOR NON-DESTRUCTIVE ABOVEGROUND SEAGRASS CARBON STOCK MAPPING USING WORLDVIEW-2 IMAGE

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