Mapping of the Spatio-Spectral Dynamics of Mangrove Chlorophyll Concentrations via Sentinel-2 Satellite Imagery
DOI:
https://doi.org/10.23917/forgeo.v38i2.4518Keywords:
Google Earth Engine, Sentinel 2, Mangroves, Canopy chlorophyll, Vegetation healthAbstract
Mangrove ecosystems play a critical role in maintaining coastal health; however, they are increasingly threatened by anthropogenic activities and climate change. Health assessment is essential for effective conservation efforts. However, traditional remote sensing techniques such as the normalised difference vegetation index (NDVI) may not fully capture the complex physiological processes influencing vegetation health. Therefore, this study investigated chlorophyll (Chl) dynamics in mangroves using remote sensing techniques, including the NDVI and a novel method, the normalised area over reflectance curve (NAOC), via Sentinel-2 satellite imagery during October 2023, and analysed spatial variations in Chl content (CC) via the Google Earth Engine API. NDVI and NAOC-Chl were weakly correlated (0.47), highlighting their complementary roles. The average NDVI and NOAC-Chl values for different species were analysed, and Rhizophora mucronata presented the highest value (NDVI: 0.86 ± 0.08, NOAC: 20.48 ± 4.49.
Downloads
References
Ahmad, S., Pandey, A. C., Kumar, A., Parida, B. R., Lele, N. V., & Bhattacharya, B. K. (2020). Chlorophyll deficiency (chlorosis) detection based on spectral shift and yellowness index using hyperspectral AVIRIS-NG data in Sho-layar reserve forest, Kerala. Remote Sensing Applications: Society and Environment, 19, 100369. doi: 10.1016/j.rsase.2020.100369 [Google Scholar] [CrossRef]
Akram, H., Hussain, S., Mazumdar, P., Chua, K. O., Butt, T. E., & Harikrishna, J. A. (2023). Mangrove Health: A Re-view of Functions, Threats, and Challenges Associated with Mangrove Management Practices. Forests, 14(9), 1698. doi: 10.3390/f14091698 [Google Scholar] [CrossRef]
Alongi, D. M. (2022). Climate Change and Mangroves. In S. C. Das, Pullaiah, & E. C. Ashton. (2022). Mangroves: Bio-diversity, Livelihoods and Conservation. Springer Nature Singapore, 175–19. doi: 10.1007/978-981-19-0519-3_8 [Google Scholar] [CrossRef]
Arifanti, V. B. (2020). Mangrove management and climate change: a review in Indonesia. In IOP conference series: earth and environmental science. IOP Publishing, 487(1), 012022. doi: 10.1088/1755-1315/487/1/012022 [Google Scholar] [CrossRef]
Arifanti, V. B., Novita, N., & Tosiani, A. (2021). Mangrove deforestation and CO2 emissions in Indonesia. IOP Confe-rence Series: Earth and Environmental Science, 874(1), 012006. doi: 10.1088/1755-1315/874/1/012006 [Google Scholar] [CrossRef]
Asner, G. P., Wessman, C. A., & Archer, S. (1998). Scale Dependence Of Absorption Of Photosynthetically Active Ra-diation In Terrestrial Ecosystems. Ecological Applications, 8(4), 1003–1021. doi: 10.1890/1051-0761(1998)008 [1003:SDOAOP]2.0.CO;2 [Google Scholar] [CrossRef]
Ballut-Dajud, G. A., Sandoval Herazo, L. C., Fernández-Lambert, G., Marín-Muñiz, J. L., López Méndez, M. C., & Be-tanzo-Torres, E. A. (2022). Factors affecting wetland loss: A review. Land, 11(3), 434. doi: 10.3390/land11030434 [Google Scholar] [CrossRef]
Barbier, E. B., & Strand, I. (1998). Valuing mangrove-fishery linkages–a case study of Campeche, Mexico. Environ-mental and Resource Economics, 12(2), 151–166. doi: 10.1023/A:1008248003520 [Google Scholar] [CrossRef]
Basheer Ahammed, K. K., & Pandey, A. C. (2021). Characterization and impact assessment of super cyclonic storm AMPHAN in the Indian subcontinent through space borne observations. Ocean & Coastal Management, 205, 105532. doi: 10.1016/j.ocecoaman.2021.105532 [Google Scholar] [CrossRef]
Beck Eichler, P. P., & Barker, C. P. (2020). Sea Level Forecast Indicators: Sea-Level Rise Forecast by “The Moon and Sun” Foraminifera Species Indicators. In P. P. Beck Eichler & C. P. Barker, Benthic Foraminiferal Ecology. Springer International Publishing, 111–132. doi: 10.1007/978-3-030-61463-8_7 [Google Scholar] [CrossRef]
Broge, N. H., & Leblanc, E. (2001). Comparing prediction power and stability of broadband and hyperspectral vege-tation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Envi-ronment, 76(2), 156–172. doi: 10.1016/S0034-4257(00)00197-8 [Google Scholar] [CrossRef]
Carmona, F., Rivas, R., & Fonnegra, D. C. (2015). Vegetation Index to estimate chlorophyll content from multispectral remote sensing data. European Journal of Remote Sensing, 48(1), 319–326. doi: 10.5721/EuJRS20154818 [Google Scholar] [CrossRef]
Cavalcanti, L. F., do N Feitosa, F. A., Cutrim, M. V., de JF Montes, M., Lourenço, C. B., Furtado, J. A., & dos S Sá, A. K. D. (2022). Drivers of phytoplankton biomass and diversity in a macrotidal bay of the Amazon Mangrove Coast, a Ramsar site. Ecohydrology & Hydrobiology, 22(3), 435–453. doi: 10.1016/S0034-4257(00)00197-8 [Google Scholar] [CrossRef]
Çelekli, A., & Zariç, Ö. E. (2023). Hydrobiology and ecology in the context of climate change: The future of aquatic ecosystems. Retrieved from https://aperta.ulakbim.gov.tr/record/263238
Cendrero-Mateoa, M. P., Moranb, M. S., Papugaa, S. A., Laparrad, V., Ponce-Camposb, G., Riverad, J. P., & Wange, G. (2014). Appendix C: Seasonal Variation Of Net Photosynthesis And Chlorophyll Fluorescence Under Nitro-gen Treatments In Wheat. Chlorophyll Fluorescence Response To Water And Nitrogen Deficit, 88. [Google Scholar]
Chakraborty, S. K. (2019). Bioinvasion and Environmental Perturbation: Synergistic Impact on Coastal–Mangrove Ecosystems of West Bengal, India. In C. Makowski & C. W. Finkl (Eds.), Impacts of Invasive Species on Coas-tal Environments Springer International Publishing, 29, 171–245. doi: 10.1007/978-3-319-91382-7_6 [Google Scholar] [CrossRef]
Chandel, S. P. K. (2022). Impacts of Tourism on Environment. Central Asian Journal of Innovations on Tourism Ma-nagement and Finance, 3(10), 90–98. [Google Scholar]
Chang-Hua, J. U., Yong-Chao, T. I. A. N., Xia, Y. A. O., Wei-Xing, C. A. O., Yan, Z. H. U., & Hannaway, D. (2010). Es-timating leaf chlorophyll content using red edge parameters. Pedosphere, 20(5), 633-644. doi: 10.1016/S1002-0160(10)60053-7 [Google Scholar] [CrossRef]
Chang-Hua, J. U., Yong-Chao, T., Xia, Y. A. O., Wei-Xing, C. A. O., Yan, Z. H. U., & Hannaway, D. (2010). Estimating leaf chlorophyll content using red edge parameters. Pedosphere, 20(5), 633–644. [Google Scholar]
Connelly, X. M. (1997). The Use of a chlorophyll meter (SPAD-502) for field determinations of red mangrove (Rhizo-phora Mangle L.) leaf chlorophyll amount. NASA University Research Centers Technical Advances in Educa-tion, Aeronautics, Space, Autonomy, Earth and Environment, 1(URC97032). Retrieved from https://ntrs.nasa.gov/citations/20010000391[Google Scholar]
Croft, H., & Chen, J. M. (2017). Leaf pigment content. Comprehensive Remote Sensing, 3, 117–142. [Google Scholar]
Croft, H., Chen, J. M., Zhang, Y., Simic, A., Noland, T. L., Nesbitt, N., & Arabian, J. (2015). Evaluating leaf chlorophyll content prediction from multispectral remote sensing data within a physically based modelling framework. IS-PRS Journal of Photogrammetry and Remote Sensing, 102, 85–95. doi: 10.1016/j.isprsjprs.2015.01.008 [Google Scholar] [CrossRef]
Czaja, M., Kołton, A., & Muras, P. (2020). The complex issue of urban trees—Stress factor accumulation and ecologi-cal service possibilities. Forests, 11(9), 932. [Google Scholar]
Daughtry, C. S., Walthall, C. L., Kim, M. S., De Colstoun, E. B., & McMurtrey Iii, J. E. (2000). Estimating corn leaf chlo-rophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74(2), 229–239. doi: 10.1016/S0034-4257(00)00113-9 [Google Scholar] [CrossRef]
de Lacerda, L. D., Ward, R. D., Borges, R., & Ferreira, A. C. (2022). Mangrove trace metal biogeochemistry response to global climate change. Frontiers in Forests and Global Change, 5, 47. doi: 10.3389/ffgc.2022.817992 [Google Scholar] [CrossRef]
De, T. K., Raman, M., Sarkar, S. K., & Mukherjee, A. (2021). Ecological assessment of Hooghly River considering a few of the more perturbed sites based on some relevant physico-chemical and biological variables—A part of the AVIRIS-NG (NASA-ISRO) ground truth verification. Regional Studies in Marine Science, 41, 101598. [Google Scholar]
Delegido, J., Alonso, L., Gonzalez, G., & Moreno, J. (2010). Estimating chlorophyll content of crops from hyperspectral data using a normalized area over reflectance curve (NAOC). International Journal of Applied Earth Observa-tion and Geoinformation, 12(3), 165–174. doi: 10.1016/j.jag.2010.02.003 [Google Scholar] [CrossRef]
Dou, Z., Cui, L., Li, J., Zhu, Y., Gao, C., Pan, X., Lei, Y., Zhang, M., Zhao, X., & Li, W. (2018). Hyperspectral estimation of the chlorophyll content in short-term and long-term restorations of mangrove in Quanzhou Bay Estuary, China. Sustainability, 10(4), 1127. doi: 10.3390/su10041127 [Google Scholar] [CrossRef]
Ewel, K., Twilley, R., & Ong, J. I. N. (1998). Different kinds of mangrove forests provide different goods and services. Global Ecology & Biogeography Letters, 7(1), 83–94. doi: 10.1111/j.1466-8238.1998.00275.x [Google Scholar] [CrossRef]
Fauzi, A., Sakti, A., Yayusman, L., Harto, A., Prasetyo, L., Irawan, B., Kamal, M., & Wikantika, K. (2019). Contextuali-zing mangrove forest deforestation in southeast asia using environmental and socioeconomic data products. Forests, 10(11), 952. doi: 10.3390/f10110952 [Google Scholar] [CrossRef]
Fava, F., Colombo, R., Bocchi, S., Meroni, M., Sitzia, M., Fois, N., & Zucca, C. (2009). Identification of hyperspectral vegetation indices for Mediterranean pasture characterization. International Journal of Applied Earth Obser-vation and Geoinformation, 11(4), 233–243. doi: 10.1016/j.jag.2009.02.003 [Google Scholar] [CrossRef]
Fernández-Tejedor, M., Velasco, J. E., & Angelats, E. (2022). Accurate estimation of Chlorophyll-a concentration in the coastal areas of the Ebro Delta (NW Mediterranean) using Sentinel-2 and its application in the selection of areas for mussel aquaculture. Remote Sensing, 14(20), 5235. doi: 10.3390/rs14205235 [Google Scholar] [CrossRef]
Flores-de-Santiago, F., Kovacs, J. M., & Flores-Verdugo, F. (2013a). Assessing the utility of a portable pocket instrument for estimating seasonal mangrove leaf chlorophyll contents. Bulletin of Marine Science, 89(2), 621–633. doi: 10.5343/bms.2012.1032 [Google Scholar] [CrossRef]
Flores-de-Santiago, F., Kovacs, J. M., & Flores-Verdugo, F. (2013b). The influence of seasonality in estimating man-grove leaf chlorophyll-a content from hyperspectral data. Wetlands Ecology and Management, 21(3), 193–207. doi: 10.1007/s11273-013-9290-x [Google Scholar] [CrossRef]
Gafurov, A., Prokhorov, V., Kozhevnikova, M., & Usmanov, B. (2024). Forest Community Spatial Modelling Using Machine Learning and Remote Sensing Data. Remote Sensing, 16(8), 1371. doi: 10.3390/rs16081371 [Google Scholar] [CrossRef]
Glenn, E. P., Huete, A. R., Nagler, P. L., & Nelson, S. G. (2008). Relationship between remotely sensed vegetation in-dices, canopy attributes and plant physiological processes: What vegetation indices can and cannot tell us about the landscape. Sensors, 8(4), 2136–2160. doi: 10.3390/s8042136 [Google Scholar] [CrossRef]
Grove, D. (2021). Ocean acidification and carbon limitation affect photosynthetic capacity of the seagrass (Amphibo-lis antarctica) and its calcifying epiphytes [PhD Thesis, University of Plymouth]. Retrieved from https://pearl.plymouth.ac.uk/handle/10026.1/17162
Guimarães, T. T., Veronez, M. R., Koste, E. C., Gonzaga Jr, L., Bordin, F., Inocencio, L. C., ... & Mauad, F. F. (2017). An alternative method of spatial autocorrelation for chlorophyll detection in water bodies using remote sensing. Sustainability, 9(3), 416. doi: 10.3390/su9030416 [Google Scholar] [CrossRef]
Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002). Integrated narrow-band vege-tation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81(2), 416–426. doi: 10.1016/S0034-4257(02)00018-4 [Google Scholar] [CrossRef]
Hai, P. M., Tinh, P. H., Son, N. P., Thuy, T. V., Hanh, N. T. H., Sharma, S., Hoai, D. T., & Duy, V. C. (2022). Mangrove health assessment using spatial metrics and multi-temporal remote sensing data. Plos one, 17(12), e0275928. doi: 10.1371/journal.pone.0275928 [Google Scholar] [CrossRef]
Hati, J. P., Goswami, S., Samanta, S., Pramanick, N., Majumdar, S. D., Chaube, N. R., Misra, A., & Hazra, S. (2021). Es-timation of vegetation stress in the mangrove forest using AVIRIS-NG airborne hyperspectral data. Modelling Earth Systems and Environment, 7, 1877–1889. [Google Scholar]
Jiang, H., Halverson, J. B., & Simpson, J. (2008). On the differences in storm rainfall from Hurricanes Isidore and Lili. Part I: Satellite observations and rain potential. Weather and Forecasting, 23(1), 29–43. doi: 10.1175/2007WAF2005096.1 [Google Scholar] [CrossRef]
Kamarianakis, Z., & Panagiotakis, S. (2023). Design and Implementation of a Low-Cost Chlorophyll Content Meter. Sensors, 23(5), 2699. doi: 10.3390/s23052699 [Google Scholar] [CrossRef]
Kannankai, M. P., Alex, R. K., Muralidharan, V. V., Nazeerkhan, N. P., Radhakrishnan, A., & Devipriya, S. P. (2022). Urban mangrove ecosystems are under severe threat from microplastic pollution: a case study from Mangala-vanam, Kerala, India. Environmental Science and Pollution Research, 29(53), 80568-80580. [Google Scholar]
Karang, I. W. G. A., Pravitha, N. L. P. R., Nuarsa, I. W., Basheer Ahammed K. K, & WicaksonoPramaditya. (2024). High-resolution seagrass species mapping and propeller scars detection in Tanjung Benoa, Bali through UAV imagery. Journal of Ecological Engineering, 25(1). doi: 10.12911/22998993/174943 [Google Scholar] [CrossRef]
Ku, K.-B., Mansoor, S., Han, G. D., Chung, Y. S., & Tuan, T. T. (2023). Identification of new cold tolerant Zoysia grass species using high-resolution RGB and multispectral imaging. Scientific Reports, 13(1), 13209. [Google Scholar]
Kumar, N., Deepak, P. M., Basheer Ahammed, K. K., Rao, K. N., Gopinath, G., & Dinesan, V. P. (2022). Coastal vulne-rability assessment using Geospatial technologies and a Multi-Criteria Decision Making approach–a case study of Kozhikode District coast, Kerala State, India. Journal of Coastal Conservation, 26(3), 1–14. [Google Scholar]
Kumari, A., & Rathore, M. S. (2021). Roles of Mangroves in Combating the Climate Change. In R. P. Rastogi, M. Phul-waria, & D. K. Gupta (Eds.), Mangroves: Ecology, Biodiversity and Management (pp. 225–255). Retrieved from https://link.springer.com/chapter/10.1007/978-981-16-2494-0_10[Google Scholar] [CrossRef]
Lagomasino, D., Fatoyinbo, T., Lee, S., Feliciano, E., Trettin, C., Shapiro, A., & Mangora, M. M. (2019). Measuring mangrove carbon loss and gain in deltas. Environmental Research Letters, 14(2), 025002. doi: 10.1088/1748-9326/aaf0de [Google Scholar] [CrossRef]
Li, H., Cui, L., Dou, Z., Wang, J., Zhai, X., Li, J., Zhao, X., Lei, Y., Wang, J., & Li, W. (2023). Hyperspectral Analysis and Regression Modelling of SPAD Measurements in Leaves of Three Mangrove Species. Forests, 14(8), 1566. doi: 10.3390/f14081566 [Google Scholar] [CrossRef]
Li, N., Huo, L., & Zhang, X. (2024). Using only the red-edge bands is sufficient to detect tree stress: A case study on the early detection of PWD using hyperspectral drone images. Computers and Electronics in Agriculture, 217, 108665. doi: 10.1016/j.compag.2024.108665 [Google Scholar] [CrossRef]
Liang, L., Qin, Z., Zhao, S., Di, L., Zhang, C., Deng, M., Lin, H., Zhang, L., Wang, L., & Liu, Z. (2016). Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method. International Jour-nal of Remote Sensing, 37(13), 2923–2949. doi: 10.1080/01431161.2016.1186850 [Google Scholar] [CrossRef]
Mahasani, I., Widagti, N., & Karang, I. (2015). Estimasi persentase karbon organik di hutan mangrove bekas tambak, Perancak, Jembrana, Bali. Journal of Marine and Aquatic Sciences, 1(1), 14–18. [Google Scholar]
Matsui, N. (1998). Estimated stocks of organic carbon in mangrove roots and sediments in Hinchinbrook Channel, Aus-tralia. Mangroves and Salt Marshes, 2, 199–204. [Google Scholar]
Minu, A., Routh, J., Machiwa, J., & Pamba, S. (2020). Spatial variation of nutrients and primary productivity in the Ru-fiji Delta mangroves, Tanzania. African Journal of Marine Science, 42(2), 221–232. doi:10.2989/1814232X.2020.1776391 [Google Scholar] [CrossRef]
Misra, G., Cawkwell, F., & Wingler, A. (2020). Status of phenological research using Sentinel-2 data: A review. Remote Sensing, 12(17), 2760. [Google Scholar]
Misra, G., Cawkwell, F., & Wingler, A. (2020). Status of phenological research using Sentinel-2 data: A review. Remote Sensing, 12(17), 2760. doi: 10.3390/rs12172760 [Google Scholar] [CrossRef]
Montagna, P., Palmer, T. A., & Pollack, J. B. (2012). Hydrological changes and estuarine dynamics (Vol. 8). Springer Science & Business Media. Retrived from https://books.google.co.in/books?hl=en&lr=&id=7hymBy82V5EC &oi=fnd&pg=PR3&dq=Coastal+developments+can+disrupt+natural+hydrological+patterns,+affecting+the+intrcate+balance+of+salinity+and+water+flow+crucial+for+mangrove+health.+&ots=dDoWiB6O&sig=4ao9yifjdgFJX0TCKz3aBBH0wFE#v=onepage&q&f=false
Moorhouse, H. L., Roberts, L. R., McGowan, S., Panizzo, V. N., Barker, P., Salehin, M., Do, T. N., Nguyen Thanh, P., Rahman, M. F., Ghosh, T., Das, S., Hackney, C., Salgado, J., Roy, M., Opel, A., Henderson, A. C. G., & Large, A. R. G. (2021). Tropical Asian mega‐delta ponds: Important and threatened socio‐ecological systems. Geo: Geography and Environment, 8(2), e00103. doi:10.1002/geo2.103 [Google Scholar] [CrossRef]
Musacchi, S., Sheick, R., Mia, M. J., & Serra, S. (2023). Studies on physiological and productive effects of multileader training systems and Prohexadione-Ca applications on apple cultivar’WA 38′. Scientia Horticulturae, 312, 111850. [Google Scholar]
Neres, J., Dodonov, P., Mielke, M. S., & Strenzel, G. M. R. (2020). Relationships between portable chlorophyll meter es-timates for the red mangrove tree (Rhizophora mangle L.). Ocean and Coastal Research, 68. Retrieved from https://www.scielo.br/j/ocr/a/jyGNrw5BGcHwsLyjh3NSpSr/?lang=en[Google Scholar]
Newton, A., Icely, J., Cristina, S., Perillo, G. M., Turner, R. E., Ashan, D., Cragg, S., Luo, Y., Tu, C., & Li, Y. (2020). An-thropogenic, direct pressures on coastal wetlands. Frontiers in Ecology and Evolution, 8, 144. [Google Scholar]
Ng, C. K. C., & Ong, R. C. (2022). A review of anthropogenic interaction and impact characteristics of the Sundaic mangroves in Southeast Asia. Estuarine, Coastal and Shelf Science, 267, 107759. [Google Scholar]
Numbere, A. O. (2021). Impact of Urbanization and Crude Oil Exploration in Niger Delta Mangrove Ecosystem and Its Livelihood Opportunities: A Footprint Perspective. In A. Banerjee, R. S. Meena, M. K. Jhariya, & D. K. Yadav (Eds.), Agroecological Footprints Management for Sustainable Food System (pp. 309–344). Springer Singa-pore. doi: 10.1007/978-981-15-9496-0_10 [Google Scholar] [CrossRef]
Odabas, M. S., Senyer, N., Kayhan, G., & Ergun, E. (2017). Estimation of Chlorophyll Concentration Index at Leaves using Artificial Neural Networks. Journal of Circuits, Systems and Computers, 26(02), 1750026. doi: 10.1142/S0218126617500268 [Google Scholar] [CrossRef]
Pastor-Guzman, J., Atkinson, P. M., Dash, J., & Rioja-Nieto, R. (2015). Spatiotemporal variation in mangrove chloro-phyll concentration using Landsat 8. Remote Sensing, 7(11), 14530–14558. [Google Scholar]
Perri, S., Detto, M., Porporato, A., & Molini, A. (2023). Salinity‐induced limits to mangrove canopy height. Global Eco-logy and Biogeography, 32(9), 1561–1574. https://doi.org/10.1111/geb.13720[Google Scholar] [CrossRef]
Pineda, M., & Barón, M. (2022). Health status of oilseed rape plants grown under potential future climatic conditions assessed by invasive and noninvasive techniques. Agronomy, 12(8), 1845. [Google Scholar]
Pu, R., Gong, P., Tian, Y., Miao, X., Carruthers, R. I., & Anderson, G. L. (2008). Using classification and NDVI differen-cing methods for monitoring sparse vegetation coverage: A case study of saltcedar in Nevada, USA. Interna-tional Journal of Remote Sensing, 29(14), 3987–4011. doi: 10.1080/01431160801908095 [Google Scholar] [CrossRef]
Röthig, T., Trevathan‐Tackett, S. M., Voolstra, C. R., Ross, C., Chaffron, S., Durack, P. J., Warmuth, L. M., & Sweet, M. (2023). Human‐induced salinity changes impact marine organisms and ecosystems. Global Change Biology, 29(17), 4731–4749. doi: 10.1111/gcb.16859 [Google Scholar] [CrossRef]
Rugel, E. J., Henderson, S. B., Carpiano, R. M., & Brauer, M. (2017). Beyond the normalized difference vegetation in-dex (NDVI): developing a natural space index for population-level health research. Environmental research, 159, 474-483. [Google Scholar]
Saenger, P., Hegerl, E. J., & Davie, J. D. (1983). Global status of mangrove ecosystems. International Union for Conser-vation. Nature and Natural Resources.
Sentinel, C. (2021). 2.:(processed by ESA), MSI Level-2A BOA Reflectance Product. Collection 1.
Steven, A., Addo, K. A., Llewellyn, G., Ca, V. T., Boateng, I., Bustamante, R., Doropoulos, C., Gillies, C., Hemer, M., & Lopes, P. (2020). Coastal development: Resilience, restoration and infrastructure requirements. World Re-sources Institute, Washington DC. Retrieved from Www. Oceanpanel. Org/b l Ue-Papers/Coastal-Development-Resilience-Restoration-and-Infrastructure-Requi r Ements.
Su, Z., Shen, J., Sun, Y., Hu, R., Zhou, Q., & Yong, B. (2024). Deep Spatio-Temporal Fuzzy Model for NDVI Forecas-ting. IEEE Transactions on Fuzzy Systems. Retrieved from https://smujo.id/biodiv/article/view/11293
Sugiana, I. P., Andiani, A. A. E., Dewi, I. G. A. I. P., Karang, I. W. G. A., As-Syakur, A. R., & Dharmawan, I. W. E. (2022). Spatial distribution of mangrove health index on three genera dominated zones in Benoa Bay, Bali, In-donesia. Biodiversitas Journal of Biological Diversity, 23(7). [Google Scholar]
Sun, Y., Qin, Q., Ren, H., Zhang, T., & Chen, S. (2019). Red-edge band vegetation indices for leaf area index estimation from sentinel-2/msi imagery. IEEE Transactions on Geoscience and Remote Sensing, 58(2), 826–840. [Google Scholar]
Sun, Y., Wang, B., & Zhang, Z. (2023). Improving leaf area index estimation with chlorophyll insensitive multispectral red-edge vegetation indices. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sen-sing, 16, 3568-3582. doi: 10.1109/JSTARS.2023.3262643 [Google Scholar] [CrossRef]
Umprasoet, W., Mu, Y., Somrup, S., Junchompoo, C., Guo, Z., & Zhang, Z. (2023). Assessment of Habitat Risks Caused by Human Activities and Integrated Approach to Marine Spatial Planning: The Case of Sriracha District—Sichang Island. Coasts, 3(3), 190–208. [Google Scholar]
Verrelst, J., Alonso, L., Camps-Valls, G., Delegido, J., & Moreno, J. (2011). Retrieval of vegetation biophysical parame-ters using Gaussian process techniques. IEEE Transactions on Geoscience and Remote Sensing, 50(5), 1832–1843. doi: 10.1109/TGRS.2011.2168962 [Google Scholar] [CrossRef]
Wang, F., Liu, J., Qin, G., Zhang, J., Zhou, J., Wu, J., ... & Ren, H. (2023). Coastal blue carbon in China as a nature-based solution towards carbon neutrality. The Innovation, 4(5), 100481. doi: 10.1016/j.xinn.2023.100481 [Google Scholar] [CrossRef]
Wang, Y.-S., & Gu, J.-D. (2021). Ecological responses, adaptation and mechanisms of mangrove wetland ecosystem to global climate change and anthropogenic activities. International Biodeterioration & Biodegradation, 162, 105248. doi: 10.1016/j.ibiod.2021.105248 [Google Scholar] [CrossRef]
Weier, J., & Herring, D. (2000). Measuring vegetation (ndvi & evi). NASA Earth Observatory, 20(2). [Google Scholar]
White, E., & Kaplan, D. (2017). Restore or retreat? Saltwater intrusion and water management in coastal wetlands. Eco-system Health and Sustainability, 3(1), e01258. doi: 10.1002/ehs2.1258 [Google Scholar] [CrossRef]
Wong, W. Y., Al-Ani, A. K. I., Hasikin, K., Khairuddin, A. S. M., Razak, S. A., Hizaddin, H. F., ... & Azizan, M. M. (2021). Water, soil and air pollutants’ interaction on mangrove ecosystem and corresponding artificial intelli-gence techniques used in decision support systems-a review. Ieee Access, 9, 105532-105563. [Google Scholar]
Xue, H., Xu, X., Zhu, Q., Yang, G., Long, H., Li, H., ... & Li, Y. (2023). Object-oriented crop classification using time se-ries sentinel images from google earth engine. Remote Sensing, 15(5), 1353. doi: 10.3390/rs15051353 [Google Scholar] [CrossRef]
Zhao, W., Li, B., Li, R., Wu, X., Lin, G., & Huang, Y. (2024). Urban mangroves at risk: Understanding organophosphate flame retardant pollution at different urban function zones in shenzhen. Ocean & Coastal Management, 255, 107262. doi: 0.1016/j.ocecoaman.2024.107262 [Google Scholar]
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Basheer Ahammed K. K, I Wayan Gede Astawa Karang, I Wayan Nuarsa, I Gede Surya Indrawan, I Gede Hendrawan, Ni Made Nia Bunga Surya Dewi, Arvind Chandra Pandey
This work is licensed under a Creative Commons Attribution 4.0 International License.
