Utilization of Sentinel-2 Imagery in Mapping the Distribution and Estimation of Mangroves' Carbon Stocks in Bengkulu City

  • Ayub Sugara Department of Marine Science, Faculty of Agriculture, Bengkulu University, Jl. WR 7 Supratman, Kandang Limun, Bengkulu, 38371, Indonesia
  • Agung H. Lukman Department of Forestry, Faculty of Agriculture, Bengkulu University, Jl. WR 7 Supratman, Kandang Limun, Bengkulu, 38371, Indonesia
  • Aninda W. Rudiastuti Research Center for Geospatial, National Research and Innovation Agency (BRIN), Cibinong Science Center, Jl. Raya Bogor Km. 46 Cibinong, Bogor, 16911, Indonesia
  • Ari Anggoro Department of Marine Science, Faculty of Agriculture, Bengkulu University, Jl. WR 7 Supratman, Kandang Limun, Bengkulu, 38371, Indonesia
  • Muhammad F. Hidayat Department of Forestry, Faculty of Agriculture, Bengkulu University, Jl. WR 7 Supratman, Kandang Limun, Bengkulu, 38371, Indonesia
  • Feri Nugroho Department of Digital Business, Faculty of Economics and Business, Jakarta Global University, Depok, 16412, Indonesia
  • Ali M. Muslih Department of Forestry, Faculty of Agriculture, Universitas Syiah Kuala, Jl. Teuku Nyak Arief Darussalam-Banda Aceh, 23111, Indonesia
  • An Nisa N. Suci Department of Marine Science, Faculty of Agriculture, Bengkulu University, Jl. WR 7 Supratman, Kandang Limun, Bengkulu, 38371, Indonesia
  • Rifi Zulhendri Lembaga Lestari Alam Laut Untuk Negeri (LATUN), Jl. Bencoolen Kebun Keling, Teluk Segara District, Bengkulu, 38116, Indonesia
  • Marissa Rahmania Department of Information Management, Management, National Chin-Yi University of Technology, No. 11, Lane 243, Section 1, Zhongshan Road, Taiping District, Taichung City, 41171, Taiwan

Abstract

The mangroves' aboveground biomass significantly contributes to the global carbon cycle or economic and ecological values. This makes knowledge about the spatial extent of the mangroves indispensable for policymakers. The sequence of mangroves’ condition range also requires remote sensing data to update the geographical information and synthesize carbon stock in Bengkulu. Therefore, this study aims to create a spatial distrribution of mangroves and evaluate their carbon stock in Bengkulu City using Sentinel-2 imagery. The semi-empirical method uses Sentinel-2 imagery through NDVI to appraise and picture the mangroves' aboveground carbon stock. An allometric equation was used to compute the mangroves' aboveground carbon stock from field measurements. Non-linear regression was used to establish a connection between the NDVI calculated from the Sentinel-2 imagery and the mangroves' aboveground biomass measured in the field, which was subsequently used for aboveground carbon estimation. The results showed that mangroves mapping could derive overall accuracy of 89.09%, where the high-density class existed in 135.12 Ha of total area. It was also discovered that Sentinel-2 imagery could estimate mangroves carbon stock up to 61%. The carbon stock estimation based on the imagery has a value of 16.3992 – 115.134 t C/ha, while that of field survey data ranges from 19.69 to 326.06 t C/ha. These results showed that Sentinel-2B spectral data is functional and has a good chance of being able to predict carbon stock.


 


Keywords : Carbon; mangroves; NDVI; remote sensing; sentinel-2B


 


Copyright (c) 2022 Geosfera Indonesia and Department of Geography Education, University of Jember


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Apriyanto, E., Nugroho, P. B. A., & Siswahyono. (2021). Species composition, diversity and biomass of mangroves forest in Pulau Bai-Pantai Panjang natural conservation park of Bengkulu, Indonesia. AACL Bioflux, 14(4), 2012–2020.

Baloloy, A. B., Blanco, A. C., Candido, C. G., Argamosa, R. J. L., Dumalag, J. B. L. C., Dimapilis, L. L. C., & Paringit, E. C. (2018). Estimation of mangrove forest aboveground biomass using multispectral bands, vegetation indices and biophysical variables derived from optical satellite imageries: rapideye, planetscope and sentinel-2. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 4(3).

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Fathoni, M. N., Chulafak, G. A., & Kushardono, D. (2017). Kajian awal pemanfaatan data radar sentinel-1 untuk pemetaan lahan baku sawah di Kabupaten Indramayu Jawa Barat. Seminar Nasional Penginderaan Jauh Ke-4, (October), 179–186.

Galidaki, G., Zianis, D., Gitas, I., Radoglou, K., Karathanassi, V., Tsakiri–Strati, M., … Mallinis, G. (2017). Vegetation biomass estimation with remote sensing: focus on forest and other wooded land over the Mediterranean ecosystem. International Journal of Remote Sensing, 38(7), 1940–1966. https://doi.org/10.1080/01431161.2016.1266113.

Ghosh, S. M., Behera, M. D., Jagadish, B., Das, A. K., & Mishra, D. R. (2021). A novel approach for estimation of aboveground biomass of a carbon-rich mangrove site in India. Journal of Environmental Management, 292(May), 112816. https://doi.org/10.1016/j.jenvman.2021.112816.

Hastuti, A. W., Suniada, K. I., & Islamy, F. (2017). Carbon stock estimation of mangrove vegetation using remote sensing in Perancak Estuary, Jembrana District , Bali. International Journal of Remote Sensing and Earth Sciences (IJReSES), 14(2), 137–150.

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Jennerjahn, T. C. (2021). Relevance and magnitude of "Blue Carbon" storage in mangrove sediments: Carbon accumulation rates vs. stocks, sources vs. sinks. Estuarine, Coastal and Shelf Science, 248. https://doi.org/10.1016/j.ecss.2020.107156.

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Kauffman, J. B. D. C. D. (2012). Protocols for the measurement, monitoring and reporting of structure, biomass and carbon stocks in mangrove forests. Bogor, Indonesia: CIFOR.

Komiyama, A., Poungparn, S., & Kato, S. (2005). Common allometric equations for estimating the tree weight of mangroves. Journal of Tropical Ecology, 21(4), 471–477. https://doi.org/10.1017/S0266467405002476.

Kusumaningtyas, M. A., Kepel, T. L., Solihuddin, T., Lubis, A. A., Putra, A. D. P., Sugiharto, U., … Rustam, A. (2022). Carbon sequestration potential in the rehabilitated mangroves in Indonesia. Ecological Research, 37(1), 80–91. https://doi.org/10.1111/1440-1703.12279.

Manna, S., Nandy, S., Chanda, A., Akhand, A., Hazra, S., & Dadhwal, V. K. (2014). Estimating aboveground biomass in Avicennia marina plantation in Indian Sundarbans using high-resolution satellite data. Journal of Applied Remote Sensing, 8(1). https://doi.org/10.1117/1.JRS.8.083638.

Mngadi, M., Odindi, J., & Mutanga, O. (2021). The utility of sentinel-2 spectral data in quantifying above-ground carbon stock in an urban reforested landscape. Remote Sensing, 13(21). https://doi.org/10.3390/rs13214281.

Mondal, P., Liu, X., Fatoyinbo, T. E., & Lagomasino, D. (2019). Evaluating combinations of sentinel-2 data and machine-learning algorithms for mangrove mapping in West Africa. Remote Sensing, 11(24). https://doi.org/10.3390/rs11242928.

Myeong, S., Nowak, D. J., & Duggin, M. J. (2006). A temporal analysis of urban forest carbon storage using remote sensing. Remote Sensing of Environment, 101, 277–282. https://doi.org/10.1016/j.rse.2005.12.001.

Noor, Y. R., Khazali, M., & Suryadiputra, I. N. N. (2012). Panduan pengenalan mangrove di Indonesia. Bogor: Ditjen. PHKA.

Nyanga, C. (2020). The role of mangroves forests in decarbonizing the atmosphere. In M. Bartoli, M. Frediani, & L. Rosi (Eds.), Carbon-Based Material for Environmental Protection and Remediation. London, United Kingdom: InTechOpen.

Ormsby, J. P., Choudhury, B. J., & Owe, M. (1987). Vegetation spatial variability and its effect on vegetation indices. International Journal of Remote Sensing, 8(9), 1301–1306. https://doi.org/10.1080/01431168708954775.

Osgouei, P. E., Kaya, S., Sertel, E., & Alganci, U. (2019). Separating built-up areas from bare land in mediterranean cities using Sentinel-2A imagery. Remote Sensing, 11(3), 1–24. https://doi.org/10.3390/rs11030345.

Otukei, J. R., & Blaschke, T. (2010). Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12, S27-S31. https://doi.org/10.1016/j.jag.2009.11.002.

Perry, E., Sheffield, K., Crawford, D., Akpa, S., Clancy, A., & Clark, R. (2022). Spatial and temporal biomass and growth for grain crops using NDVI Time Series. Remote Sensing, 14(13), 3071.

Pham, T. D., Le, N. N., Ha, N. T., Nguyen, L. V., Xia, J., Yokoya, N., … Takeuchi, W. (2020). Estimating mangrove above-ground biomass using extreme gradient boosting decision trees algorithm with fused Sentinel-2 and ALOS-2 PALSAR-2 data in Can Gio Biosphere Reserve, Vietnam. Remote Sensing, 12(777). https://doi.org/10.9930rs/12050777.

Pricillia, C. C., Patria, M. P., & Herdiansyah, H. (2021). Environmental conditions to support blue carbon storage in mangrove forest: A case study in the mangrove forest, nusa lembongan, Bali, Indonesia. Biodiversitas, 22(6), 3304–3314. https://doi.org/10.13057/biodiv/d220636.

Purnamasari, E., Kamal, M., & Wicaksono, P. (2021). Comparison of vegetation indices for estimating above-ground mangrove carbon stocks using PlanetScope image. Regional Studies in Marine Science, 44, 101730. https://doi.org/10.1016/j.rsma.2021.101730.

Ren, Z., Zheng, H., He, X., Zhang, D., Yu, X., & Shen, G. (2015). Spatial estimation of urban forest structures with Landsat TM data and field measurements. Urban Forestry & Urban Greening, 14(2), 336–344. https://doi.org/https://doi.org/10.1016/j.ufug.2015.03.008.

Rudiastuti, A. W., Farda, N. M., & Ramdani, D. (2021). Mapping built-up land and settlements: a comparison of machine learning algorithms in google earth engine. In S. B. Wibowo & P. Wicaksono (Eds.), Seventh Geoinformation Science Symposium 2021 (Vol. 12082, pp. 42–52). SPIE. https://doi.org/10.1117/12.2619493.

Rudiastuti, A. W., Munawaroh, M., Setyawan, I. E., & Pramono, G. H. (2018). Coastal management strategy for small island: Ecotourism potency development in Karimata Island, West Kalimantan. In IOP Conference Series: Earth and Environmental Science (Vol. 148). https://doi.org/10.1088/1755-1315/148/1/012013.

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Published
2022-12-24
How to Cite
SUGARA, Ayub et al. Utilization of Sentinel-2 Imagery in Mapping the Distribution and Estimation of Mangroves' Carbon Stocks in Bengkulu City. Geosfera Indonesia, [S.l.], v. 7, n. 3, p. 219-235, dec. 2022. ISSN 2614-8528. Available at: <https://jurnal.unej.ac.id/index.php/GEOSI/article/view/30294>. Date accessed: 25 feb. 2024. doi: https://doi.org/10.19184/geosi.v7i3.30294.
Section
Original Research Articles