Mapping Seagrass Biodiversity Indicators of Pari Island using Multiple WorldView-2 Bands Derivatives
Abstract
Comprehensive information on seagrass biodiversity indicators, such as species composition, percentage cover, and biomass carbon stock, remains limited across various regions globally. Mapping these indicators using remote sensing images requires extracting maximum information from the input images to achieve effective results. This study aims to map seagrass distribution, percent cover (PC), and aboveground carbon stock (AGC) as biodiversity indicators in the optically shallow waters surrounding Pari Island. We integrate WorldView-2 (WV2) derivatives, field seagrass data, and RF classification and regression algorithms to accomplish this objective. The WV2 image derivatives encompass surface reflectance bands, band ratios, mean and variance co-occurrence texture bands, and principle component bands. These inputs are used individually and collectively for mapping, employing a random forest algorithm trained with field seagrass data. Our results demonstrate that the most accurate benthic habitat map achieves an overall accuracy (OA) of 65.2%, with a user's accuracy of 65.2% and a producer's accuracy of 72.8% for the seagrass-dominated class. Seagrass PC mapping yields a root mean square error (RMSE) of 17.1%, with an average PC of 47.4 ± 9.9%. Seagrass AGC mapping achieves an RMSE of 5.0 g C m-2, with an average AGC range of 6.2 – 29.1 g C m-2, estimating the study area's aboveground biomass carbon stock at 27.9 tons C. Combined inputs produce the most accurate results for all biodiversity indicators, emphasizing the importance of utilizing combined bands from SR band derivatives to maximize information input for training mapping algorithms, instead of using derivative bands individually or as replacements for the initial SR bands.
Keywords : Seagrass; Biodiversity; Mapping; WorldView-2; Pari Island
Copyright (c) 2023 Geosfera Indonesia and Department of Geography Education, University of Jember
This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
References
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Wicaksono, P. & Hafizt, M. (2013). Mapping seagrass from space: addressing the complexity of seagrass LAI Mapping. European Journal of Remote Sensing, 46(1). 18-39. https://doi.org/10.5721/EuJRS20134602.
Wicaksono, P., & Lazuardi, W. (2018). Assessment of PlanetScope Images for Benthic Habitat and Seagrass Species Mapping in A Complex Optically Shallow Water Environment. International Journal of Remote Sensing, 39(17), 5739-5765. https://doi.org/10.1080/01431161.2018.1506951.
Wicaksono, P., & Lazuardi, W. (2019). Random forest classification scenarios for benthic habitat mapping using planetscope image. 2019 IEEE International Geoscience and Remote Sensing Symposium (8245-8248). Yokohama: IEEE IGARSS 2019. https://doi.org/10.1109/IGARSS.2019.8899825.
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Wicaksono, P., Maishella, A., Arjasakusuma, S., Lazuardi, W. & Harahap, S.D. (2022a). Assessment of WorldView-2 images for aboveground seagrass carbon stock mapping in patchy and continuous seagrass meadows. International Journal of Remote Sensing, 43(8). 2915-2941. https://doi.org/10.1080/01431161.2022.2074809.
Wicaksono, P., Maishella, A., Lazuardi, W., & Muhammad, F.H. (2022b). Wicaksono, P., Maishella, A., Lazuardi, W., & Muhammad, F. H. (2022). Consistency assessment of multi-date PlanetScope imagery for seagrass percent cover mapping in different seagrass meadows. Geocarto International, 37(27), 15161-15186. https://doi.org/10.1080/10106049.2022.2096122.
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Wicaksono, P., Wulandari, S.A., Lazuardi, W., & Munir, M. (2021b). Sentinel-2 images deliver possibilities for accurate and consistent multi-temporal benthic habitat maps in optically shallow water. Remote Sensing Applications: Society and Environment, 23. 100572. https://doi.org/10.1016/j.rsase.2021.100572.
Widisanto, H., Pranowo, W.S., Simanjuntak, S.M., & Setiadi, H. (2022). Studi konstanta harmonik pasang surut terhadap data suhu permukaan laut di perairan pulau pari: study of tidal harmonic constants on sea surface temperature data in pari island waters. Jurnal Chart Datum. 2(2).139-50. https://doi.org/10.37875/chartdatum.v2i2.100.
Zhang, C., Selch, D., Xie, Z., Roberts, C., Cooper, H., & Chen, G. (2013). Object-based Benthic Habitat Mapping in the Florida Keys from Hyperspectral Imagery. Estuarine, Coastal and Shelf Science, 134, 88-97. https://doi.org/10.1016/j.ecss.2013.09.018.
Bhatia, N., Tolpekin, V. A., Stein, A., & Reusen, I. (2018). Estimation of AOD Under uncertainty: an approach for hyperspectral airborne data. Remote Sensing, 10(947), 1-30. https://doi.org/10.3390/rs10060947.
Congalton, R. G., & Green, K. (2019). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices (3rd ed.). Boca Raton: CRC Press, Taylor & Francis Group, LLC.
Fauzan, M.A., Kumara, I.S.W., Yigyantoro, R., Suwaedana, S., Fadhilah, N., Nurmalasari, I., Apriyani, S., & Wicaksono, P. (2017). Assessing the capability of Sentinel-2A data for mapping seagrass percent cover in Jerowaru, East Lombok. Indonesian Journal of Geography, 49(2). 195-203. Doi: http://dx.doi.org/10.22146/ijg.28407.
Fauzan, M.A., Wicaksono, M.A., & Hartono. (2021). Characterizing Derawan seagrass cover change with time-series Sentinel-2 images. Regional Studies in Marine Science. 48. 102048 Doi: https://doi.org/10.1016/j.rsma.2021.102048.
Fourqurean, J., Duarte, C., Kennedy, H. et al. (2012). Seagrass ecosystems as a globally significant carbon stock. Nature Geosci. 5. 505–509. https://doi.org/10.1038/ngeo1477.
Ginting, D.N.B., Wicaksono, P., & Farda, N.M. (2022). Mapping Benthic Habitat from WorldView-3 Image using Random Forest Case Study: Nusa Lembongan, Bali, Indonesia. In: Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XLVIII-4/W6-2022. ISPRS. Johor Baru, Malaysia. 123-129.
Green E, Mumby P, Edwards A, Clark C. (2000). Remote Sensing Handbook for Tropical Coastal Management. Edwards, A.J., (Editor). Paris: The United Nations Educational, Scientific and Cultural Organization; 348 p.
Hochberg, E. J., Andrefouet, S., & Tyler, M. R. (2003). Sea surface correction of high spatial resolution ikonos images to improve bottom mapping in near-shore environments. IEEE Transactions on Geoscience and Remote Sensing, 47(1). https://doi.org/10.1109/TGRS.2003.815408.
Hossain, M.S., Bujang, J.S., Zakaria, M.H., & Hashim, M. (2015). The application of remote sensing to seagrass ecosystems: an overview and future research prospects. International Journal of Remote Sensing, 36(1). 61-113. https://doi.org/10.1080/01431161.2014.990649.
Kohler, K.E. & Gill, S.M. (2006). Coral Point Count with Excel extensions (CPCe): a Visual Basic program for the determination of coral and substrate coverage using random point count methodology. Comput. Geosci. 32 (9). 1259–1269. DOI: https://doi.org/10.1016/J.CAGEO.2005.11.009.
Mishra, D., Narumalani, S., Rundquist, D., & Lawson, M. (2006). Benthic habitat mapping in tropical marine environments using quickbird multispectral data. Photogrammetric Engineering & Remote Sensing. 72(9). 1037–1048.
Nordlund, L.M., Koch, E.W., Barbier, E.B., & Creed, J.C. (2016). Seagrass Ecosystem Servicesand Their Variability across Genera and Geographical Regions. PLoS ONE. 11(10): e0163091. https://doi.org/10.1371/journal.pone.0163091.
Phinn, S., Roelfsema, C., Dekker, A., Brando, V., Anstee, J.. (2008). Mapping seagrass species, cover and biomass in shallow waters: An assessment of satellite multi-spectral and airborne hyper-spectral imaging systems in Moreton Bay (Australia). Remote Sens Environ. 112(8). 3413–3425. https://doi.org/10.1016/j.rse.2007.09.017.
Pittman, S.J., Roelfsema, C., Say, C., Swanborn, D., Thapa, B., Jensen, K., Baez, S. (2021). Outlining a methodological pathway to improve the global seagrass map. The Pew Charitable Trusts.
Roelfsema, C. M., & Phinn, S. R. (2009). A Manual for Conducting Georeferenced Photo Transects Surveys to Assess the Benthos of Coral Reef and Seagrass Habitats version 3.0. Brisbane, Australia: Centre for Remote Sensing and Spatial Information Science, The University of Queensland.
Roelfsema, C., & Phinn, S. (2010). Integrating field data with high spatial resolution multispectral satellite imagery for calibration and validation of coral reef benthic community maps. Journal of Applied Remote Sensing, 4(1). https://doi.org/10.1117/1.3430107.
Roelfsema, C.M., Lyons, M., Kovacs, E.M., Maxwell, P., Saunders, M.I., Samper-Villarreal, J., & Phinn, S.R. (2014). Multi-temporal mapping of seagrass cover, species and biomass: A semi-automated object based image analysis approach. Remote Sensing of Environment, 150. 172–187. https://doi.org/10.1016/j.rse.2014.05.001.
United Nations Environment Programme (UNEP). (2020). Out of the blue: The value of seagrasses to the environment and to people. UNEP, Nairobi.
Updike, T., & Comp, C. (2010). Radiometric Use of WorldView-2 Imagery. Longmont, Colorado: DigitalGlobe®.
Wicaksono, P. & Hafizt, M. (2013). Mapping seagrass from space: addressing the complexity of seagrass LAI Mapping. European Journal of Remote Sensing, 46(1). 18-39. https://doi.org/10.5721/EuJRS20134602.
Wicaksono, P., & Lazuardi, W. (2018). Assessment of PlanetScope Images for Benthic Habitat and Seagrass Species Mapping in A Complex Optically Shallow Water Environment. International Journal of Remote Sensing, 39(17), 5739-5765. https://doi.org/10.1080/01431161.2018.1506951.
Wicaksono, P., & Lazuardi, W. (2019). Random forest classification scenarios for benthic habitat mapping using planetscope image. 2019 IEEE International Geoscience and Remote Sensing Symposium (8245-8248). Yokohama: IEEE IGARSS 2019. https://doi.org/10.1109/IGARSS.2019.8899825.
Wicaksono, P. (2016). Improving the accuracy of multispectral-based benthic habitats mapping using image rotations: the application of principle component analysis and independent component analysis. European Journal of Remote Sensing, 49(1). 433-463. https://doi.org/10.5721/EuJRS20164924.
Wicaksono, P., Danoedoro, P., Hartono, Nehren, U., Maishella, A., Hafizt, M., Arjasakusuma, S., & Harahap, S.D. (2021a). Analysis of field seagrass percent cover and aboveground carbon stock data for non-destructive aboveground seagrass carbon stock mapping using WorldView-2 image. In: Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci, XLVI-4/W6–2021. ISPRS, Manila. 321–327.
Wicaksono, P., Fauzan, M. A., Kumara, I. S. W., Yogyantoro, R. N., Lazuardi, W., & Zhafarina, Z. (2019). Analysis of reflectance spectra of tropical seagrass species and their value for mapping using multispectral satellite images. International Journal of Remote Sensing, 40(23), 8955-8978. https://doi.org/10.1080/01431161.2019.1624866.
Wicaksono, P., Maishella, A., Arjasakusuma, S., Lazuardi, W. & Harahap, S.D. (2022a). Assessment of WorldView-2 images for aboveground seagrass carbon stock mapping in patchy and continuous seagrass meadows. International Journal of Remote Sensing, 43(8). 2915-2941. https://doi.org/10.1080/01431161.2022.2074809.
Wicaksono, P., Maishella, A., Lazuardi, W., & Muhammad, F.H. (2022b). Wicaksono, P., Maishella, A., Lazuardi, W., & Muhammad, F. H. (2022). Consistency assessment of multi-date PlanetScope imagery for seagrass percent cover mapping in different seagrass meadows. Geocarto International, 37(27), 15161-15186. https://doi.org/10.1080/10106049.2022.2096122.
Wicaksono, P., Maishella, A., Wahyudi, A.J., & Hafizt, M. (2022c). Multitemporal seagrass carbon assimilation and aboveground carbon stock mapping using Sentinel-2 in Labuan Bajo 2019–2020. Remote Sensing Applications: Society and Environment, 27. 100803. DOI: https://doi.org/10.1016/j.rsase.2022.100803.
Wicaksono, P., Wulandari, S.A., Lazuardi, W., & Munir, M. (2021b). Sentinel-2 images deliver possibilities for accurate and consistent multi-temporal benthic habitat maps in optically shallow water. Remote Sensing Applications: Society and Environment, 23. 100572. https://doi.org/10.1016/j.rsase.2021.100572.
Widisanto, H., Pranowo, W.S., Simanjuntak, S.M., & Setiadi, H. (2022). Studi konstanta harmonik pasang surut terhadap data suhu permukaan laut di perairan pulau pari: study of tidal harmonic constants on sea surface temperature data in pari island waters. Jurnal Chart Datum. 2(2).139-50. https://doi.org/10.37875/chartdatum.v2i2.100.
Zhang, C., Selch, D., Xie, Z., Roberts, C., Cooper, H., & Chen, G. (2013). Object-based Benthic Habitat Mapping in the Florida Keys from Hyperspectral Imagery. Estuarine, Coastal and Shelf Science, 134, 88-97. https://doi.org/10.1016/j.ecss.2013.09.018.
Published
2023-08-29
How to Cite
WICAKSONO, Pramaditya; HARAHAP, Setiawan Djody.
Mapping Seagrass Biodiversity Indicators of Pari Island using Multiple WorldView-2 Bands Derivatives.
Geosfera Indonesia, [S.l.], v. 8, n. 2, p. 189-205, aug. 2023.
ISSN 2614-8528.
Available at: <https://jurnal.unej.ac.id/index.php/GEOSI/article/view/41214>. Date accessed: 24 nov. 2024.
doi: https://doi.org/10.19184/geosi.v8i2.41214.
Section
Original Research Articles
