Rapid Bathymetry Mapping Based on Shallow Water Cloud Computing in Small Bay Waters: Pilot Project in Pacitan-Indonesia
Abstract
Mapping coastal areas generally requires large data constellations in time series and requires analysis using complex mathematical and modeling approaches. In shallow-water bathymetric mapping, remote sensing plays an important role in supporting conventional bathymetric mapping, especially in areas that are difficult to access. This method called Satellite Derived Bathymetry (SDB). The cloud computing approach is a solution for mapping shallow water bathymetry rapid and effectively. This study using Google Earth Engine (GEE) to compute remote sensing data for produce near-shore bathymetry. The method of Li et al. (2021) performs bathymetric extraction without using depth samples but uses chlorophyll-A as input for depth extraction parameter calculations. This study examines a small bay in the waters of Pacitan, Anakan Bay, and the waters of Kemujan Island in the Karimunjawa Islands. Within this study area, significant differences in resulting depth are very limited, ranging from 0 to -17.8. The developed model, based on the algorithm proposed by Li et al. (2021), is estimated to be able to provide accurate predictions of up to around 90% in the waters studied, with a root mean error rate (RMSE) of 1.1 meters.
References
[1] Alevizos, E., Le Bas, T. and Alexakis, D. D. 2022. Assessment of PRISMA Level-2 Hyperspectral Imagery for Large Scale Satellite-Derived Bathymetry Retrieval. Mar. Geod., 45(3): 251–273. DOI:10.1080/01490419.2022.2032497
[2] Arief et al. 2013. Pengembangan Metode Pendugaan Kedalaman Perairan Dangkal Menggunakan Data Satelit Spot-4 Studi Kasus: Teluk Ratai, Kabupaten Pesawaran (Methode Development for Shallow Water Depth Bathymetric Estimation Using Spot-4 Satellite Data, a Case Study: Ratai Bay. J. Penginderaan Jauh, 10(1): 1–14. (in Indonesian)
[3] Bhargava, R. Sarkar, D. and Friess, D. A. 2021. A cloud computing-based approach to mapping mangrove erosion and progradation: Case studies from the Sundarbans and French Guiana. Estuar. Coast. Shelf Sci., 248: 106798. DOI: 10.1016/j.ecss.2020.106798
[4] Bierwirth, P. N., Lee, T.J. and Burne, R.V. 1993. Shallow sea-floor reflectance and water depth derived by unmixing multispectral imagery. Photogramm. Eng. Remote Sens., 59(3): 331–338.
[5] Bousquin, J. 2021. Discrete Global Grid Systems as scalable geospatial frameworks for characterizing coastal environments. Environ. Model. Softw., 146: 105210. DOI: 10.1016/j.envsoft.2021.105210
[6] Casal, G. et al. 2020. Understanding satellite-derived bathymetry using Sentinel 2 imagery and spatial prediction models. GIScience Remote Sens., 57(3): 271–286. DOI: 10.1080/15481603.2019.1685198
[7] Chen, A. Ma, Y. and Zhang, J. 2021. Partition satellite derived bathymetry for coral reefs based on spatial residual information. Int. J. Remote Sens., 42(8): 2807–2826. DOI: 10.1080/01431161.2020.1809738
[8] Chybicki, A. 2018. Three-Dimensional Geographically Weighted Inverse Regression (3GWR) Model for Satellite Derived Bathymetry Using Sentinel-2 Observations. Mar. Geod., 41(1): 1–23. DOI:10.1080/01490419.2017.1373173
[9] Gabr, B. Ahmed, M. and Marmoush, Y. 2020. PlanetScope and Landsat 8 Imageries for Bathymetry Mapping. J. Mar. Sci. Eng., 8(143). Available at: www.mdpi.com/journal/jmse
[10] Ghosh, S. and Mistri, B. 2022. Analyzing the multi-hazard coastal vulnerability of Matla–Bidya inter-estuarine area of Indian Sundarbans using analytical hierarchy process and geospatial techniques. Estuar. Coast. Shelf Sci., 279: 108144. DOI: 10.1016/j.ecss.2022.108144
[11] Goldberg, D., Olivares, M., Li, Z. and Klein, A. G. 2014. Maps and GIS data libraries in the era of big data and cloud computing. J. Map Geogr. Libr., 10(1): 100–122. DOI: 10.1080/15420353.2014.893944
[12] Hodúl, M. et al. 2020. Photogrammetric Bathymetry for the Canadian Arctic. Mar. Geod., 43(1): 23–43. DOI:10.1080/01490419.2019.1685030
[13] Jégat, V. et al. 2016. Satellite-Derived Bathymetry: Performance and Production. Can. Hydrogr. Conf., no. May: 1–8.
[14] Knudby, A. Ahmad, S. K. and Ilori, C. 2016. The Potential for Landsat-Based Bathymetry in Canada. Can. J. Remote Sens., 42(4): 367–378. DOI: 10.1080/07038992.2016.1177452
[15] Kurniawan, A. et al. 2021. Challenges of Acquisition Bathymetry Information on PlanetScope Data and Nautical Chart : Experiment Based on IHO S-44 Total Vertical Uncertainty in Multi-Method J. Hunan Univ. Natural Sci., 48(12): 331–345.
[16] Li J. et al. 2021. Automated global shallowwater bathymetry mapping using google earth engine. Remote Sens., 13(8). DOI: 10.3390/rs13081469
[17] Lumban-Gaol, Y. Ohori, K. A. and Peters, R. 2022. Extracting Coastal Water Depths from Multi-Temporal Sentinel-2 Images Using Convolutional Neural Networks. Mar. Geod., 45(6): 615–644. DOI:10.1080/01490419.2022.2091696
[18] Lyzenga, D. R. 1978. Passive remote sensing techniques for mapping water depth and bottom features. Appl. Opt., 17(3): 379. DOI: 10.1364/ao.17.000379
[19] Lyzenga, D. R. 1985. Shallow-water bathymetry using combined lidar and passive multispectral scanner data,” Int. J. Remote Sens., 6(1): 115–125. DOI: 10.1080/01431168508948428
[20] Lyzenga, D. R. Malinas, N. P. and Tanis, F. J. 2006. Multispectral Bathymetry Using a Simple Physically Based Algorithm, 44(8): 2251–2259.
[21] Marcello, J. Eugenio, F. Martín, J. and Marqués, F. 2018. Seabed mapping in coastal shallow waters using high resolution multispectral and hyperspectral imagery. Remote Sens., 10(8). DOI: 10.3390/rs10081208
[22] Mudiyanselage, S. S. J. D., Abd-Elrahman, A. Wilkinson, B. and Lecours, V. 2022. Satellite-derived bathymetry using machine learning and optimal Sentinel-2 imagery in South-West Florida coastal waters. GIScience Remote Sens., 59(1): 1143–1158. DOI: 10.1080/15481603.2022.2100597
[23] Pe’eri, S. et al. 2014. Satellite Remote Sensing as a Reconnaissance Tool for Assessing Nautical Chart Adequacy and Completeness. Mar. Geod., 37(3): 293–314. DOI: 10.1080/01490419.2014.902880
[24] Philpot, W. D. 1989. Bathymetric mapping with passive multispectral imagery. Appl. Opt., 28(8): 1569. DOI:10.1364/ao.28.001569
[25] Putman N. F. et al. 2023. Improving satellite monitoring of coastal inundations of pelagic Sargassum algae with wind and citizen science data. Aquat. Bot., 188: 1–10, 2023. DOI: 10.1016/j.aquabot.2023.103672
[26] Rumson, A. G., Hallett, S. H. and Brewer, T. R. 2017. Coastal risk adaptation: the potential role of accessible geospatial Big Data. Mar. Policy, 83: 100–110. DOI: 10.1016/j.marpol.2017.05.032
[27] Salameh E. et al. 2019. Monitoring Beach Topography and Nearshore Bathymetry Using Spaceborne Remote Sensing: A Review. Remote Sens., 11(19). DOI: 10.3390/rs11192212
[28] Stumpf, R. P. Holderied, K. and Sinclair, M. 2003. Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnol. Oceanogr., 48(1) II: 547–556. DOI:10.4319/lo.2003.48.1_part_2.0547
[29] Susa, T. 2022. Satellite Derived Bathymetry with Sentinel-2 Imagery: Comparing Traditional Techniques with Advanced Methods and Machine Learning Ensemble Models. Mar. Geod., 45(5): 435–461. DOI:10.1080/01490419.2022.2064572
[30] Traganos, D. et at. 2018. Estimating satellite-derived bathymetry (SDB) with the Google Earth Engine and sentinel-2. Remote Sens., 10(6): 1–18. DOI: 10.3390/rs10060859
[31] Van Hengel, W. and Spltzer, D. 1991. Multi-temporal water depth mapping by means of landsat TM. Int. J. Remote Sens., 12(4): 703–712. DOI: 10.1080/01431169108929687
[32] Vinayaraj, P. Raghavan, V. and Masumoto, S. 2016. Satellite-Derived Bathymetry using Adaptive Geographically Weighted Regression Model Satellite-Derived Bathymetry using Adaptive Geographically, Mar. Geod., 39(6): 458–478. DOI: 10.1080/01490419.2016.1245227
[33] Wang, Y. Chen, Y. Feng, Y. Dong, Z. and Liu, X. 2023. Multispectral Satellite-Derived Bathymetry Based on Sparse Prior Measured Data. Mar. Geod., 46(5): 426–440. DOI: 10.1080/01490419.2023.2213840
[34] Wicaksono and Pramaditya 2014.The Use of Image Rotations on Multispectral- Based Benthic Habitats Mapping, no. November, pp. 4–7. DOI: 10.13140/2.1.3877.6006
[35] Wicaksono, P. 2015. Perbandingan Akurasi Metode Band Tunggal Dan Band Rasio Untuk Pemetaan Batimetri Pada Laut Dangkal Optis. Simp. Nas. Sains Geoinformasi, IV(March): 792. DOI:10.13140/RG.2.1.1340.3286
[36] Xu C. et al. 2022. Cloud-based storage and computing for remote sensing big data: a technical review. Int. J. Digit. Earth, 15(1): 1417–1445. DOI: 10.1080/17538947.2022.2115567
[37] Zhang, C. Yin, K. Shi, X. and Yan, X. 2021. Risk assessment for typhoon storm surges using geospatial techniques for the coastal areas of Guangdong, China. Ocean Coast. Manag., 213(238) 105880. DOI:10.1016/j.ocecoaman.2021.105880
[38] Zhang, X. Ma, Y. and Zhang, J. 2020. Shallow water bathymetry based on inherent optical properties using high spatial resolution multispectral imagery. Remote Sens., 12(18). DOI: 10.3390/RS12183027
[2] Arief et al. 2013. Pengembangan Metode Pendugaan Kedalaman Perairan Dangkal Menggunakan Data Satelit Spot-4 Studi Kasus: Teluk Ratai, Kabupaten Pesawaran (Methode Development for Shallow Water Depth Bathymetric Estimation Using Spot-4 Satellite Data, a Case Study: Ratai Bay. J. Penginderaan Jauh, 10(1): 1–14. (in Indonesian)
[3] Bhargava, R. Sarkar, D. and Friess, D. A. 2021. A cloud computing-based approach to mapping mangrove erosion and progradation: Case studies from the Sundarbans and French Guiana. Estuar. Coast. Shelf Sci., 248: 106798. DOI: 10.1016/j.ecss.2020.106798
[4] Bierwirth, P. N., Lee, T.J. and Burne, R.V. 1993. Shallow sea-floor reflectance and water depth derived by unmixing multispectral imagery. Photogramm. Eng. Remote Sens., 59(3): 331–338.
[5] Bousquin, J. 2021. Discrete Global Grid Systems as scalable geospatial frameworks for characterizing coastal environments. Environ. Model. Softw., 146: 105210. DOI: 10.1016/j.envsoft.2021.105210
[6] Casal, G. et al. 2020. Understanding satellite-derived bathymetry using Sentinel 2 imagery and spatial prediction models. GIScience Remote Sens., 57(3): 271–286. DOI: 10.1080/15481603.2019.1685198
[7] Chen, A. Ma, Y. and Zhang, J. 2021. Partition satellite derived bathymetry for coral reefs based on spatial residual information. Int. J. Remote Sens., 42(8): 2807–2826. DOI: 10.1080/01431161.2020.1809738
[8] Chybicki, A. 2018. Three-Dimensional Geographically Weighted Inverse Regression (3GWR) Model for Satellite Derived Bathymetry Using Sentinel-2 Observations. Mar. Geod., 41(1): 1–23. DOI:10.1080/01490419.2017.1373173
[9] Gabr, B. Ahmed, M. and Marmoush, Y. 2020. PlanetScope and Landsat 8 Imageries for Bathymetry Mapping. J. Mar. Sci. Eng., 8(143). Available at: www.mdpi.com/journal/jmse
[10] Ghosh, S. and Mistri, B. 2022. Analyzing the multi-hazard coastal vulnerability of Matla–Bidya inter-estuarine area of Indian Sundarbans using analytical hierarchy process and geospatial techniques. Estuar. Coast. Shelf Sci., 279: 108144. DOI: 10.1016/j.ecss.2022.108144
[11] Goldberg, D., Olivares, M., Li, Z. and Klein, A. G. 2014. Maps and GIS data libraries in the era of big data and cloud computing. J. Map Geogr. Libr., 10(1): 100–122. DOI: 10.1080/15420353.2014.893944
[12] Hodúl, M. et al. 2020. Photogrammetric Bathymetry for the Canadian Arctic. Mar. Geod., 43(1): 23–43. DOI:10.1080/01490419.2019.1685030
[13] Jégat, V. et al. 2016. Satellite-Derived Bathymetry: Performance and Production. Can. Hydrogr. Conf., no. May: 1–8.
[14] Knudby, A. Ahmad, S. K. and Ilori, C. 2016. The Potential for Landsat-Based Bathymetry in Canada. Can. J. Remote Sens., 42(4): 367–378. DOI: 10.1080/07038992.2016.1177452
[15] Kurniawan, A. et al. 2021. Challenges of Acquisition Bathymetry Information on PlanetScope Data and Nautical Chart : Experiment Based on IHO S-44 Total Vertical Uncertainty in Multi-Method J. Hunan Univ. Natural Sci., 48(12): 331–345.
[16] Li J. et al. 2021. Automated global shallowwater bathymetry mapping using google earth engine. Remote Sens., 13(8). DOI: 10.3390/rs13081469
[17] Lumban-Gaol, Y. Ohori, K. A. and Peters, R. 2022. Extracting Coastal Water Depths from Multi-Temporal Sentinel-2 Images Using Convolutional Neural Networks. Mar. Geod., 45(6): 615–644. DOI:10.1080/01490419.2022.2091696
[18] Lyzenga, D. R. 1978. Passive remote sensing techniques for mapping water depth and bottom features. Appl. Opt., 17(3): 379. DOI: 10.1364/ao.17.000379
[19] Lyzenga, D. R. 1985. Shallow-water bathymetry using combined lidar and passive multispectral scanner data,” Int. J. Remote Sens., 6(1): 115–125. DOI: 10.1080/01431168508948428
[20] Lyzenga, D. R. Malinas, N. P. and Tanis, F. J. 2006. Multispectral Bathymetry Using a Simple Physically Based Algorithm, 44(8): 2251–2259.
[21] Marcello, J. Eugenio, F. Martín, J. and Marqués, F. 2018. Seabed mapping in coastal shallow waters using high resolution multispectral and hyperspectral imagery. Remote Sens., 10(8). DOI: 10.3390/rs10081208
[22] Mudiyanselage, S. S. J. D., Abd-Elrahman, A. Wilkinson, B. and Lecours, V. 2022. Satellite-derived bathymetry using machine learning and optimal Sentinel-2 imagery in South-West Florida coastal waters. GIScience Remote Sens., 59(1): 1143–1158. DOI: 10.1080/15481603.2022.2100597
[23] Pe’eri, S. et al. 2014. Satellite Remote Sensing as a Reconnaissance Tool for Assessing Nautical Chart Adequacy and Completeness. Mar. Geod., 37(3): 293–314. DOI: 10.1080/01490419.2014.902880
[24] Philpot, W. D. 1989. Bathymetric mapping with passive multispectral imagery. Appl. Opt., 28(8): 1569. DOI:10.1364/ao.28.001569
[25] Putman N. F. et al. 2023. Improving satellite monitoring of coastal inundations of pelagic Sargassum algae with wind and citizen science data. Aquat. Bot., 188: 1–10, 2023. DOI: 10.1016/j.aquabot.2023.103672
[26] Rumson, A. G., Hallett, S. H. and Brewer, T. R. 2017. Coastal risk adaptation: the potential role of accessible geospatial Big Data. Mar. Policy, 83: 100–110. DOI: 10.1016/j.marpol.2017.05.032
[27] Salameh E. et al. 2019. Monitoring Beach Topography and Nearshore Bathymetry Using Spaceborne Remote Sensing: A Review. Remote Sens., 11(19). DOI: 10.3390/rs11192212
[28] Stumpf, R. P. Holderied, K. and Sinclair, M. 2003. Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnol. Oceanogr., 48(1) II: 547–556. DOI:10.4319/lo.2003.48.1_part_2.0547
[29] Susa, T. 2022. Satellite Derived Bathymetry with Sentinel-2 Imagery: Comparing Traditional Techniques with Advanced Methods and Machine Learning Ensemble Models. Mar. Geod., 45(5): 435–461. DOI:10.1080/01490419.2022.2064572
[30] Traganos, D. et at. 2018. Estimating satellite-derived bathymetry (SDB) with the Google Earth Engine and sentinel-2. Remote Sens., 10(6): 1–18. DOI: 10.3390/rs10060859
[31] Van Hengel, W. and Spltzer, D. 1991. Multi-temporal water depth mapping by means of landsat TM. Int. J. Remote Sens., 12(4): 703–712. DOI: 10.1080/01431169108929687
[32] Vinayaraj, P. Raghavan, V. and Masumoto, S. 2016. Satellite-Derived Bathymetry using Adaptive Geographically Weighted Regression Model Satellite-Derived Bathymetry using Adaptive Geographically, Mar. Geod., 39(6): 458–478. DOI: 10.1080/01490419.2016.1245227
[33] Wang, Y. Chen, Y. Feng, Y. Dong, Z. and Liu, X. 2023. Multispectral Satellite-Derived Bathymetry Based on Sparse Prior Measured Data. Mar. Geod., 46(5): 426–440. DOI: 10.1080/01490419.2023.2213840
[34] Wicaksono and Pramaditya 2014.The Use of Image Rotations on Multispectral- Based Benthic Habitats Mapping, no. November, pp. 4–7. DOI: 10.13140/2.1.3877.6006
[35] Wicaksono, P. 2015. Perbandingan Akurasi Metode Band Tunggal Dan Band Rasio Untuk Pemetaan Batimetri Pada Laut Dangkal Optis. Simp. Nas. Sains Geoinformasi, IV(March): 792. DOI:10.13140/RG.2.1.1340.3286
[36] Xu C. et al. 2022. Cloud-based storage and computing for remote sensing big data: a technical review. Int. J. Digit. Earth, 15(1): 1417–1445. DOI: 10.1080/17538947.2022.2115567
[37] Zhang, C. Yin, K. Shi, X. and Yan, X. 2021. Risk assessment for typhoon storm surges using geospatial techniques for the coastal areas of Guangdong, China. Ocean Coast. Manag., 213(238) 105880. DOI:10.1016/j.ocecoaman.2021.105880
[38] Zhang, X. Ma, Y. and Zhang, J. 2020. Shallow water bathymetry based on inherent optical properties using high spatial resolution multispectral imagery. Remote Sens., 12(18). DOI: 10.3390/RS12183027
Published
2024-02-29
How to Cite
KHAKHIM, Nurul et al.
Rapid Bathymetry Mapping Based on Shallow Water Cloud Computing in Small Bay Waters: Pilot Project in Pacitan-Indonesia.
Journal of Environmental Management and Tourism, [S.l.], v. 15, n. 1, p. 41 - 51, feb. 2024.
ISSN 2068-7729.
Available at: <https://journals.aserspublishing.eu/jemt/article/view/8320>. Date accessed: 21 nov. 2024.
doi: https://doi.org/10.14505/jemt.v15.1(73).04.
Section
Article
Copyright© 2024 The Author(s). Published by ASERS Publishing 2024. This is an open access article distributed under the terms of CC-BY 4.0 license.