skip to main content

Uji Akurasi Metode Berbasis Citra Satelit untuk Ekstraksi Data Batimetri

*Ayu Nur Safi'i  -  Badan Informasi Geospasial, Indonesia
Ratna Sari Dewi  -  Badan Informasi Geospasial, Indonesia
Open Access Copyright (c) 2020 TEKNIK

Citation Format:
Abstract

Citra penginderaan jauh memungkinkan pengumpulan data batimetri dengan cakupan luas, sehingga dapat diintegrasikan dengan informasi terestrial untuk pemodelan wilayah pesisir dan pemodelan garis pantai. Data batimetri berbasis citra satelit cukup menjanjikan karena kemampuannya untuk mengisi kesenjangan data kedalaman yang diperoleh dari survei hidrografi. Tujuan dari penelitian ini difokuskan untuk mengevaluasi tiga model dalam memperoleh informasi kedalaman. Model-model tersebut dibuat dengan mengintegrasikan data citra penginderaan jauh dan data pengukuran in-situ (pemeruman) untuk menyediakan dan mengisi data kedalaman di perairan dangkal kawasan pesisir antara permukaan laut dan batas awal data pemeruman. Tiga model yang dievaluasi yaitu: Random Forest (RF), Multi Linear Regression (MLR) dan Generalized Additive Model (GAM). Secara statistik (hasil RMSE), GAM lebih unggul dibandingkan MLR dan RF dalam memperoleh informasi kedalaman. Nilai RMSE masing-masing adalah 0,16, 0,32 dan 0,64 untuk GAM, MLR dan RF. Namun, dari hasil visualisasi, model SDB dengan menggunakan GAM sangat ‘smooth’. Sementara jika dilihat dari penggunaan data, RF sangat tergantung pada jumlah data training. Dalam hal ini, MLR cukup menjanjikan untuk digunakan dalam memperoleh informasi kedalaman karena memiliki akurasi model SDB yang baik dan memiliki pola kedalaman yang lebih andal/reliable. Selain itu, MLR tidak terlalu tergantung pada jumlah data training.

Fulltext View|Download
Keywords: kedalaman; batimetri; batimetri berbasis citra; perairan dangkal, penginderaan jauh, SBES

Article Metrics:

  1. Abdallah, H., Bailly, J. S., Baghdadi, N. N., Saint-Geours, N., & Fabre, F. (2013). Potential of space-borne LiDAR sensors for global bathymetry in coastal and inland waters. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/JSTARS.2012.2209864
  2. Arya, A., Winarso, G., & Santoso, A. I. (2016). Evaluasi Akurasi Ekstrasi Kedalaman Laut dengan Metode Lyzenga dan Modifikasinya Menggunakan Data SPOT-7 di Teluk Belagbelang Mamuju. Jurnal Ilmiah Geomatika, 22(1), 9–19. https://doi.org/10.24895/JIG.2016.22-1.423
  3. Astrium Services. (2013). SPOT 6 & SPOT 7 imagery user guide. In Astrium Services
  4. Bramante, J. F., Raju, D. K., & Sin, T. M. (2013). Multispectral derivation of bathymetry in Singapore’s shallow, turbid waters. International Journal of Remote Sensing, 34(6), 2070–2088. https://doi.org/10.1080/01431161.2012.734934
  5. Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
  6. Cahalane, C., Magee, A., Monteys, X., Casal, G., Hanafin, J., & Harris, P. (2019). A comparison of Landsat 8, RapidEye and Pleiades products for improving empirical predictions of satellite-derived bathymetry. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2019.111414
  7. Casal, G., Monteys, X., Hedley, J., Harris, P., Cahalane, C., & McCarthy, T. (2019). Assessment of empirical algorithms for bathymetry extraction using Sentinel-2 data. International Journal of Remote Sensing. https://doi.org/10.1080/01431161.2018.1533660
  8. Chénier, R., Faucher, M.-A., & Ahola, R. (2018). Satellite-Derived Bathymetry for Improving Canadian Hydrographic Service Charts. In ISPRS International Journal of Geo-Information (Vol. 7, Issue 8). https://doi.org/10.3390/ijgi7080306
  9. Clark, R. K., Fay, T. H., & Walker, C. L. (1987). Bathymetry calculations with Landsat 4 TM imagery under a generalized ratio assumption. Applied Optics, 26(19), 4036_1-4038. https://doi.org/10.1364/AO.26.4036_1
  10. Dewi, R. S., Lumban-Gaol, Y., Safi’I, A. N., Rizaldy, A., Syetiawan, A., & Rahadiati, A. (2020). Assessing the effect of various training and testing set ratios to model the satellite derived bathymetry. IOP Conference Series: Earth and Environmental Science. https://doi.org/10.1088/1755-1315/500/1/012032
  11. Dierssen, H. M., & Theberge, A. E. (2016). Bathymetry: Assessment. In Encyclopedia of Natural Resources: Water (pp. 629–636). Taylor & Francis. https://doi.org/10.1081/e-enrw-120048588
  12. Dwi, M., Manessa, M., Haidar, M., Hastuti, M., Kresnawati, D. K., Division, T. M., & Sensing, R. (2017). Determination Of The Best Methodology For Bathymetry Mapping Using Spot 6 Imagery : A Study Of 12 Empirical. International Journal of Remote Sensing and Earth Sciences, 14(2), 127–136
  13. Geyman, E. C., & Maloof, A. C. (2019). A Simple Method for Extracting Water Depth From Multispectral Satellite Imagery in Regions of Variable Bottom Type. Earth and Space Science. https://doi.org/10.1029/2018EA000539
  14. Green, E., Mumby, P., Edwards, A., & Clark, C. (2000). Remote Sensing Handbook for Tropical Coastal Management
  15. Hamilton, M. K., Davis, C. O., Rhea, W. J., Pilorz, S. H., & Carder, K. L. (1993). Estimating chlorophyll content and bathymetry of Lake Tahoe using AVIRIS data. Remote Sensing of Environment, 44(2–3), 217–230
  16. Ismunarti, D. H., Zainuri, M., Sugianto, D. N., & Saputra, S. W. (2020). Pengujian Reliabilitas Instrumen Terhadap Variabel Kontinu Untuk Pengukuran. Buletin Osenaografi Marina, 9(1), 1–8. https://doi.org/10.14710/buloma.v9i1.23924
  17. Jawak, S. D., & Luis, A. J. (2015). Spectral information analysis for the semiautomatic derivation of shallow lake bathymetry using high-resolution multispectral imagery : A case study of Antarctic coastal oasis. Aquatic Procedia, 4(Icwrcoe), 1331–1338. https://doi.org/10.1016/j.aqpro.2015.02.173
  18. Kanno, A., Koibuchi, Y., & Isobe, M. (2011a). Statistical Combination of Spatial Interpolation and Multispectral Remote Sensing for Shallow Water Bathymetry. IEEE Geoscience and Remote Sensing Letters, 8(1), 64–67. https://doi.org/10.1109/LGRS.2010.2051658
  19. Kanno, A., Koibuchi, Y., & Isobe, M. (2011b). Shallow Water Bathymetry from Multispectral Satellite Images: Extensions of Lyzenga’s Method for Improving Accuracy. Coastal Engineering Journal, 53(4), 431–450. https://doi.org/10.1142/S0578563411002410
  20. Kanno, A., & Tanaka, Y. (2012). Modified lyzenga’s method for estimating generalized coefficients of satellite-based predictor of shallow water depth. IEEE Geoscience and Remote Sensing Letters, 9(4), 715–719. https://doi.org/10.1109/LGRS.2011.2179517
  21. Kanno, A., Tanaka, Y., Kurosawa, A., & Sekine, M. (2013). Generalized Lyzenga’s Predictor of Shallow Water Depth for Multispectral Satellite Imagery. Marine Geodesy, 36(4), 365–376. https://doi.org/10.1080/01490419.2013.839974
  22. Lyzenga, D. R. (1978a). Passive remote sensing techniques for mapping water depth and bottom features. Applied Optics, 17(3), 379–383. https://doi.org/10.1364/AO.17.000379
  23. Lyzenga, D. R. (1978b). Passive remote sensing techniques for mapping water depth and bottom features. Applied Optics, 17(3), 379. https://doi.org/10.1364/ao.17.000379
  24. Manessa, M. D. M., Kanno, A., Sekine, M., Haidar, M., Yamamoto, K., Imai, T., & Higuchi, T. (2016). Satellite-Derived Bathymetry using Random Forest Algorithm and Worldview-2 Imagery. Geoplanning: Journal of Geomatics and Planning; Vol 3, No 2 (2016): (October 2016)DO - 10.14710/Geoplanning.3.2.117-126. https://ejournal.undip.ac.id/index.php/geoplanning/article/view/12047
  25. Manessa, M., Kanno, A., Sekine, M., Haidar, M., Yamamoto, K., Imai, T., & Higuchi, T. (2016). Satellite Derived Bathymetry Using Random Forest Algorithm and Worldview-2 Imagery. Geoplanning, 3(2), 117–126. https://doi.org/10.14710/geoplanning.3.2.117-126
  26. Mavraeidopoulos, A., Navy, H., Pallikaris, A., Academy, H. N., & Oikonomou, E. (2017). Satellite Derived Bathymetry (SDB) and Safety of Navigation. May
  27. Mishra, D., Narumalani, S., Rundquist, D., & Lawson, M. (2006). Benthic habitat mapping in tropical marine environments using quickbird multispectral data. Photogrammetric Engineering and Remote Sensing. https://doi.org/10.14358/PERS.72.9.1037
  28. Misra, A., Vojinovic, Z., Ramakrishnan, B., Luijendijk, A., & Ranasinghe, R. (2018). Shallow water bathymetry mapping using Support Vector Machine (SVM) technique and multispectral imagery. International Journal of Remote Sensing, 39(13), 4431–4450. https://doi.org/10.1080/01431161.2017.1421796
  29. Neill, S. P., & Hashemi, M. R. (2018). Ocean Modelling for Resource Characterization. In Fundamentals of Ocean Renewable Energy. https://doi.org/10.1016/b978-0-12-810448-4.00008-2
  30. Nurkhayati, R. (2013). Pemteaan Batimetri Perairan Dangkal Menggunakan Citra Quickbird di Perairan Taman Nasional Karimun Jawa, Kabupaten Jepara, Jawa Tengah. Jurnal Bumi Indonesia, 2(2), 140–148
  31. Pattanaik, A., Sahu, K., & Bhutiyani, M. R. (2015). Estimation of Shallow Water Bathymetry Using IRS-Multispectral Imagery of Odisha Coast, India. Aquatic Procedia, 4, 173–181. https://doi.org/https://doi.org/10.1016/j.aqpro.2015.02.024
  32. Philpot, W. D. (1989). Bathymetric mapping with passive multispectral imagery. Applied Optics, 28(8), 1569–1578. https://doi.org/10.1364/AO.28.001569
  33. Ratna Sari Dewi, Hartanto, P., Oktaviani, N., Pujawati, I., Nursugi, & Aditya, S. (2019). Satellite-Derived Bathymetry to Improve Bathymetric Map of Indonesia. Proceeding of LISAT Conference 2019
  34. Ravindra, K., Rattan, P., Mor, S., & Aggarwal, A. N. (2019). Generalized additive models: Building evidence of air pollution, climate change and human health. In Environment International. https://doi.org/10.1016/j.envint.2019.104987
  35. Sagawa, T., Yamashita, Y., Okumura, T., & Yamanokuchi, T. (2019). Satellite derived bathymetry using machine learning and multi-temporal satellite images. Remote Sensing. https://doi.org/10.3390/rs11101155
  36. Setiawan, K. T., Dwi, M., Manessa, M., Winarso, G., & Anggraini, N. (2018). Estimasi Batimetri dari Data Spot 7 Studi Kasus Perairan Gili Matra Nusa Tenggara Barat. Jurnal Penginderaan Jauh Dan Pengelolaan Data Citra Digital, 15(2), 69–82
  37. Stumpf, R., Holderied, K., & Sinclair, M. (2003). Determination of Water Depth with High-Resolution Satellite Imagery over Variable Bottom Types. Limnol. Oceanogr, 48, 547–556. https://doi.org/10.4319/lo.2003.48.1_part_2.0547
  38. Van Hengel, W., & Spitzer, D. (1991). Multi-temporal water depth mapping by means of Landsat TM. International Journal of Remote Sensing, 12(4), 703–712
  39. Vinayaraj, P. (2017). Development of Algorithms for Near-shore Satellite Derived Bathymetry Using Multispectral Remote Sensing Images. 1
  40. Vinayaraj, P., Raghavan, V., & Masumoto, S. (2016). Satellite-Derived Bathymetry using Adaptive Geographically Weighted Regression Model. Marine Geodesy, 39(6), 458–478. https://doi.org/10.1080/01490419.2016.1245227
  41. Wood, S. (2019). Package ‘ mgcv .’ https://doi.org/10.1201/9781315370279>
  42. Yeu, Y., Yee, J. J., Yun, H. S., & Kim, K. B. (2018). Evaluation of the accuracy of bathymetry on the nearshore coastlines of western korea from satellite altimetry, multi-beam, and airborne bathymetric LiDAR. Sensors (Switzerland). https://doi.org/10.3390/s18092926
  43. Yuwono, & Sidad, B. F. (2017). Studi Tentang Pembangunan Pelabuhan Cilamaya ditinjau dari Aspek Teknis (Studi Kasus : Pelabuhan Cilamaya Karawang). Geoid, 12(2), 173–180

Last update:

No citation recorded.

Last update:

No citation recorded.