The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLVI-4/W6-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W6-2021, 321–327, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W6-2021-321-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W6-2021, 321–327, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W6-2021-321-2021

  18 Nov 2021

18 Nov 2021

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

P. Wicaksono1, P. Danoedoro1, Hartono1, U. Nehren2, A. Maishella3, M. Hafizt4, S. Arjasakusuma1, and S. D. Harahap3 P. Wicaksono et al.
  • 1Department of Geographic Information Science, Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia
  • 2Institute for Technology and Resources Management in the Tropics and Subtropics (ITT) University of Applied Sciences, Cologne, Germany
  • 3Cartography and Remote Sensing, Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia
  • 4Research Center for Oceanography, Indonesian Research and Innovation Agency, Jalan Pasir Putih I, Ancol Timur 14430, Jakarta, Indonesia

Keywords: Seagrass, Percent Cover, Above-Ground Carbon Stock, Mapping, WorldView-2

Abstract. Remote sensing can make seagrass aboveground carbon stock (AGCseagrass) information spatially extensive and widely available. Therefore, it is necessary to develop a rapid approach to estimate AGCseagrass 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)-AGCseagrass relationship in seagrass at the species and community levels to estimate AGCseagrass from PCv and (2) perform AGCseagrass 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 AGCseagrass using deglint, water column corrected, and principle component bands. The results were a rapid AGCseagrass estimation using an easily measured parameter, the seagrass PCv. At the community level, the AGCseagrass map had 58.79% accuracy (SEE = 5.41 g C m−2), whereas at the species level, the accuracy increased for the class Ea (64.73%, SEE = 6.86 g C m−2) and EaThCr (70.02%, SEE = 4.32 g C m−2) but decreased for ThCr (55.08%, SEE = 2.55 g C m−2). The results indicate that WorldView-2 image reflectance can accurately map AGCseagrass in the study area in the range of 15–20 g C m−2 for Ea, 10–15 g C m−2 for EaThCr, and 4–8 g C m−2 for ThCr. Based on our model, the AGCseagrass in the study area was estimated at 13.39 t C.