gesjGEOGRAPHY, ENVIRONMENT, SUSTAINABILITYGEOGRAPHY, ENVIRONMENT, SUSTAINABILITY2071-93882542-1565Russian Geographical Society10.24057/2071-9388-2023-2886gesj-3324Research ArticleRESEARCH PAPERСтатьиMapping Seagrass Percent Cover And Biomass In Nusa Lembongan, Bali, IndonesiaBr GintingDevica Natalia

Yogyakarta, 55281

WicaksonoPramaditya

Yogyakarta, 55281

prama.wicaksono@ugm.ac.idFardaNur Mohammad

Yogyakarta, 55281

Faculty of Geography, Gadjah Mada UniversityIndonesia2024030420241711627Copyright © Br Ginting D., Wicaksono P., Farda N., 20242024Br Ginting D., Wicaksono P., Farda N.Br Ginting D., Wicaksono P., Farda N.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ges.rgo.ru/jour/article/view/3324

Seagrass meadow is one of the blue-carbon ecosystems capable of absorbing and storing carbon more effectively in the bodies and sediments than terrestrial ecosystems. However, nationwide data on its carbon stock remains elusive due to limitations and challenges in data collection and mapping. Seagrass percent cover and biomass, which were closely related with above-ground carbon stock, can be effectively mapped and monitored using remote sensing techniques. Therefore, this study aimed to compare the accuracy of 4 scenarios as well as assess the performance of random forest and stepwise regression methods, for mapping seagrass percent cover and biomass in Nusa Lembongan, Bali, Indonesia. The scenarios were experimented using only atmospherically corrected images, sunglint, water, as well as sunglint and water column corrected images. Furthermore, WorldView-3 images and in-situ seagrass data were used, with the image corrected by applying the scenarios. Random forest and stepwise regression methods were adopted for mapping and modelling. The optimum mapping scenario and method were chosen based on R2, RMSE, and seagrass spatial distribution. The results show that the atmospherically corrected image produced the best seagrass percent cover and biomass map. Range of R2 using random forest and stepwise regression model was 0.49–0.64 and 0.50–0.58, with RMSE ranging from 18.50% to 21.41% and 19.36% to 20.72%, respectively. Based on R2, RMSE, and seagrass spatial distribution, it was concluded that the random forest model produced better mapping results, specifically for areas with high seagrass percent cover.

WorldView-3Percent coverBiomassRandom forestStepwiseThe authors are grateful to Ignatius Salivian Wisnu Kumara for providing the insitu data. The authors are also thankful to the National Research and Innovation Agency and the Program “Rekognisi Tugas Akhir” (RTA) UGM 2022, for supporting and facilitating this study.ReferencesAnggoro, A., Siregar, V. P., & Agus, S. B. (2016). The Effect of Sunglint on Benthic Habitats Mapping in Pari Island Using Worldview-2 Imagery. Procedia Environmental Sciences, 33, 487–495. https://doi.org/10.1016/j.proenv.2016.03.101Anggoro, A., Siregar, V. P., & Agus, S. B. (2016). The Effect of Sunglint on Benthic Habitats Mapping in Pari Island Using Worldview-2 Imagery. Procedia Environmental Sciences, 33, 487–495. https://doi.org/10.1016/j.proenv.2016.03.101Ariasari, A., Hartono, & Wicaksono, P. (2019). Random forest classification and regression for seagrass mapping using PlanetScope image in Labuan Bajo, East Nusa Tenggara. 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The authors declare that there are no conflicts of interest present.