Geomatics and Environmental Engineering, vol. 17, no. 2

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Vol. 17 No. 2 (2023): Geomatics and Environmental Engineering

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Submitted
Feb 14, 2022
Accepted
Nov 29, 2022
Published
Jan 30, 2023
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This work is licensed under a Creative Commons Attribution 4.0 International License.

Assessing the Shallow Water Habitat Mapping Extracted from High-Resolution Satellite Image with Multi Classification Algorithms

https://doi.org/10.7494/geom.2023.17.2.69
Muhammad Rizki Nandika
National Research and Innovation Agency, Research Center for Oceanography, Jakarta, Indonesia
https://orcid.org/0000-0003-2514-4927
Azura Ulfa
National Research and Innovation Agency, Research Center for Remote Sensing, Jakarta, Indonesia
https://orcid.org/0000-0002-2229-9045
Andi Ibrahim
National Research and Innovation Agency, Research Center for Remote Sensing, Jakarta, Indonesia; University of Twente, Faculty of Geo-Information Science and Earth Observation, Enschede, Netherlands
https://orcid.org/0000-0002-6561-2978
Anang Dwi Purwanto
National Research and Innovation Agency, Research Center for Remote Sensing, Jakarta, Indonesia; Bandung Institute of Technology, Geodesy and Geomatics Engineering Department, Bandung, Indonesia
https://orcid.org/0000-0003-1055-9302

Corresponding Author(s) : Muhammad Rizki Nandika

rizki.nandika@gmail.com

Geomatics and Environmental Engineering, Vol. 17 No. 2 (2023): Geomatics and Environmental Engineering

Abstract

Remote sensing technology is reliable in identifying the distribution of seabed cover yet there are still challenges in retrieving the data collection of shallow water habitats than with other objects on land. Classification algorithms based on remote sensing technology have been developed for application to map benthic habitats, such as Maximum Likelihood, Minimum Distance, and Support Vector Machine. This study focuses on examining those three classification algorithms to retrieve information on the benthic habitat in Pari Island, Jakarta using visual interpretation data for classification, and data field measurements for accuracy testing. This study used five classes of benthic objects, namely sand, sand-seagrass, rubble, seagrass, and coral. The results show how the proposed approach in this study provides an overall good classification of marine habitat with an accuracy produced 63.89–81.95%. The Support Vector Machine algorithm produced the highest accuracy rate of about 81.95%. The Support Vector Machine algorithm at a very high spatial resolution is considered to be capable of identifying, monitoring, and performing the rapid assessment of benthic habitat objects.

Keywords

accuracy coral seagrass Maximum Likelihood Minimum Distance Support Vector Machine remote sensing
Nandika, M. R., Ulfa, A., Ibrahim, A., & Purwanto, A. D. (2023). Assessing the Shallow Water Habitat Mapping Extracted from High-Resolution Satellite Image with Multi Classification Algorithms. Geomatics and Environmental Engineering, 17(2), 69–87. https://doi.org/10.7494/geom.2023.17.2.69
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References
  1. Supriharyono: Pengelolaan Ekosistem Terumbu Karang. Djambatan, Jakarta 2000.
  2. Sembiring I., Wantasen A., Ngangi E.L.A.: Manfaat Langsung Terumbu Karang di Desa Tumbak Kabupaten Minahasa Tenggara [Direct benefit value of coral reefs in the Tumbak Village Southeast Minahasa Regency]. Jurnal Perikanan dan Kelautan Tropis, vol. 8(2), 2012, pp. 58–63. https://doi.org/10.35800/jpkt.8.2.2012.409.
  3. Purwanto A.D., Prayogo T., Marpaung S., Suhadha A.G.: Analysis of potential fishing zones in coastal waters: A case study of Nias Island waters. International Journal of Remote Sensing and Earth Sciences, vol. 17(1), 2020, pp. 9–24. https://doi.org/10.30536/j.ijreses.2020.v17.a3298.
  4. Aldilla A.: Analisis Kondisi Habitat Karang di Pulau Rimaubalak, Kandangbalak, dan Panjurit, Lampung Selatan. Institut Pertanian Bogor, Bogor 2014 [M.Sc. thesis].
  5. Maulana H., Anggoro S., Yulianto B.: Kajian Kondisi dan Nilai Ekonomi Manfaat Ekosistem Terumbu Karang di Pantai Wediombo, Kabupaten Gunung Kidul, Daerah Istimewa Yogyakarta. Jurnal Ilmu Lingkungan, vol. 14(2), 2016, pp. 82–87. https://doi.org/10.14710/jil.14.2.82-87.
  6. Buhl-Mortensen P., Buhl-Mortensen L.: Impacts of bottom trawling and litter on the seabed in Norwegian waters. Frontiers in Marine Science, vol. 5(42), 2018, pp. 1–9. https://doi.org/10.3389/fmars.2018.00042.
  7. Hall-Spencer J.M., Harvey B.P.: Ocean acidification impacts on coastal ecosystem services due to habitat degradation. Emerging Topics in Life Science, vol. 3(2), 2019, pp. 197–206. https://doi.org/10.1042/etls20180117.
  8. Ban N.C., Bax N.J., Gjerde K.M., Devillers R., Dunn D.C., Dunstan P.K., Hobday A.J. et al.: Systematic conservation planning: A better recipe for managing the high seas for biodiversity conservation and sustainable use. Conservation Letters, vol. 7(1), 2013, pp. 41–54. https://doi.org/10.1111/conl.12010.
  9. Rice J., Arvanitidis C., Borja A., Frid C., Hiddink J., Krause J., Lorance P. et al.: Marine Strategy Framework Directive – Task Group 6 Report Seafloor integrity. EUR 24334 EN – Joint Research Centre. Office for Official Publications of the European Communities, Luxembourg 2010.
  10. Hoegh-Guldberg O., Hoegh-Guldberg H., Veron J.E.N., Green A., Gomez E.D., Lough J., King M. et al.: The Coral Triangle and Climate Change: Ecosystems, People and Societies at Risk. WWF Australia, Brisbane 2009.
  11. Alvarez-Filip L., Estrada Saldivar N., Pérez-Cervantes E., González-Barrios F.J., Secaira Fajardo F.: Comparative analysis of risks faced by the world’s coral reefs. UNAM-The Nature Conservancy, 2021. https://doi.org/10.13140/RG.2.2.33912.37125.
  12. Wicaksono P., Lazuardi W.: Assessment of PlanetScope images for benthic habitat and seagrass species mapping in a complex optically shallow water environment. International Journal of Remote Sensing, vol. 39(17), 2018, pp. 5739–5765. https://doi.org/10.1080/01431161.2018.1506951.
  13. Butler J.D., Purkis S.J., Yousif R., Al-Shaikh I., Warren C.: A high-resolution remotely sensed benthic habitat map of the Qatari coastal zone. Marine Pollution Bulletin, vol. 160, 2020, 111634. https://doi.org/10.1016/j.marpolbul.2020.111634.
  14. Chegoonian A.M., Mokhtarzade M., Valadan Zoej M.J.: A comprehensive evaluation of classification algorithms for coral reef habitat mapping: challenges related to quantity, quality, and impurity of training samples. International Journal of Remote Sensing, vol. 38(14), 2017, pp. 4224–4243. https://doi.org/10.1080/01431161.2017.1317934.
  15. Mumby P.J., Green E.P., Edwards A.J., Clark C.D.: Coral reef habitat mapping: how much detail can remote sensing provide? Marine Biology, vol. 130, 1997, pp. 193–202. https://doi.org/10.1007/s002270050238.
  16. Mishra D., Sunil N., Donald R., Merlin L.: Benthic habitat mapping in tropical marine environments using QuickBird multispectral data. Photogrammetric Engineering & Remote Sensing, vol. 72(9), 2006, pp. 1037–1048. https://doi.org/10.14358/PERS.72.9.1037.
  17. Asmala A., Shaun Q.: Analysis of maximum likelihood classification on multispectral data. Applied Mathematical Sciences, vol. 6(129), 2012, pp. 6425–6436.
  18. Abinaya V., Poonkuntran S.: Classification of satellite image using Minimum Distance classification algorithm. International Journal of Computer Science and Engineering, vol. 6(3), 2019, pp. 15–18.
  19. Muslim A.M., Komatsu T., Dianacia D.: Evaluation of classification techniques for benthic habitat mapping. International Society for Optics and Photonics. Remote Sensing of the Marine Environment II, vol. 8525, 2012. https://doi.org/10.1117/12.999305.
  20. Sukarno: Panen kecepatan pemulihan (recovery rate) terumbu karang di Indonesia. Program COREMAP LIPI. Jakarta, 2008.
  21. Direktorat Pendayagunaan Pulau-Pulau Kecil Indonesia: Pari. http://www.ppk-kp3k.kkp.go.id/direktori-pulau/index.php/public_c/pulau_info/370 [access: 20.05.2022].
  22. Planet Labs: Planet Imagery Product Specifications. https://assets.planet.com/docs/Combined-Imagery-Product-Spec-Dec-2018.pdf [access: 20.05.2022].
  23. Vrigazova B.: The proportion for splitting data into training and test set for the bootstrap in classification problems. Business Systems Research, vol. 12(1), 2021, pp. 228–242. https://doi.org/10.2478/bsrj-2021-0015.
  24. Aggarwal S.: Principles of remote sensing. [in:] Sivakumar M.V.K., Roy P.S., Harmsen K., Saha S.K. (eds.), Satellite Remote Sensing and GIS Applications in Agricultural Meteorology: Proceedings of the Training Workshop, 7–11 July, 2003, Dehra Dun, India, World Meteorological Organisation, Geneva 2004, pp. 23–38.
  25. Mondejar J.P., Tongco A.F.: Near infrared band of Landsat 8 as water index: a case study around Cordova and Lapu-Lapu City, Cebu, Philippines. Sustainable Environment Research, vol. 29, 2019, 16. https://doi.org/10.1186/s42834-019-0016-5.
  26. Mumby P.J., Skirving W., Strong A.E., Hardy J.T., LeDrew E.F., Hochberg E.J., Stumpf R.P., David L.T.: Remote sensing of coral reefs and their physical environment. Marine Pollution Bulletin, vol. 48(3–4), 2004, pp. 219–228. https://doi.org/10.1016/j.marpolbul.2003.10.031.
  27. Lyzenga D.R.: Passive remote sensing techniques for mapping water depth and bottom features. Applied Optics, vol. 17(3), 1978, pp. 379–383. https://doi.org/10.1364/ao.17.000379.
  28. Lyzenga D.R.: Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data. International Journal of Remote Sensing, vol. 2(1), 1981, pp. 71–82. https://doi.org/10.1080/01431168108948342.
  29. Green E.P., Mumby P.J., Edwards A.J., Clark Ch.D.: Remote Sensing Handbook for Tropical Coastal Management. UNESCO, Paris 2000.
  30. Shenai H., Gala J., Kekre K., Chitale P., Karani R.: Combating COVID-19 using object detection techniques for next-generation autonomous systems. [in:] Poonia R.Ch., Agarwal B., Kumar S., Khan M., Marques G., Nayak J. (eds.), Cyber-Physical Systems: AI and COVID-19, Elsevier Inc., 2022, pp. 55–73. https://doi.org/10.1016/B978-0-12-824557-6.00007-8.
  31. Ponnusamy R., Sathiamoorthy S., Kaliyamoorthi M.: A review of image classification approaches and techniques. International Journal of Recent Trends in Engineering & Research, vol. 3(3), 2017, pp. 1–5. https://doi.org/10.23883/IJRTER.2017.3033.XTS7Z.
  32. Richards J.A.: Supervised Classification Techniques. [in:] Richards J.A., Remote Sensing Digital Image Analysis: An Introduction, Springer, Berlin, Heidelberg 1986, pp. 173–189. https://doi.org/10.1007/978-3-662-02462-1_8.
  33. Prabowo N.W., Siregar V.P., Agus S.B.: Klasifikasi Habitat Bentik Berbasis Objek Dengan Algoritma Support Vector Machines Dan Decision Tree Menggunakan Citra Multispektral Spot-7 Di Pulau Harapan Dan Pulau Kelapa. Jurnal Ilmu Teknologi Kelautan Tropis, vol. 10(1), 2018, pp. 123–134. https://doi.org/10.29244/jitkt.v10i1.21670.
  34. Marapareddy R., Aanstoos J.V., Younan N.H.: Accuracy analysis comparison of supervised classification methods for anomaly detection on levees using SAR imagery. Electronics, vol. 6(4), 2017, 83. https://doi.org/10.3390/electronics6040083.
  35. Sergioli G., Russo G., Santucci E., Stefano A., Torrisi S.E., Palmucci S., Vancheri C., Giuntini R.: Quantum-inspired minimum distance classification in biomedical context. International Journal of Quantum Information, vol. 16(8), 2018, 1840011. https://doi.org/10.1142/S0219749918400117.
  36. Congalton R.G.: A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, vol. 37(1), 1991, pp. 35–46. https://doi.org/10.1016/0034-4257(91)90048-B.
  37. Landis J.R., Koch G.G.: The measurement of observer agreement for categorial data. Biometrics, vol. 33(1), 1977, pp. 159–174. https://doi.org/10.2307/2529310.
  38. Zoffoli M.L., Frouin R., Kampel M.: Water column correction for coral reef studies by remote sensing. Sensors, vol. 14, 2014, pp. 16881–16931. https://doi.org/10.3390/s140916881.
  39. Manessa M.D.M., Haidar M., Budhiman S., Winarso G., Kanno A., Sagawa T., Sekine M.: Evaluating the performance of Lyzenga’s water column correction in case-1 coral reef water using a simulated Wolrdview-2 imagery. IOP Conference Series: Earth and Environmental Science, vol. 47, 2016, 012018. https://doi.org/10.1088/1755-1315/47/1/012018.
  40. Wouthuyzen S., Abrar M. (eds.): Gugusan Pulau Pari, Kepulauan Seribu: Tinjauan Aspek Bio-Ekologi, Sosial-Ekonomi-Budaya, dan Pengelolaan Berkelanjutan. LIPI Press, Jakarta, Indonesia 2020.
  41. Smithers S.: Fringing Reefs. [in:] Hopley D. (ed.), Encyclopedia of Modern Coral Reefs: Structure, Form and Process, Encyclopedia of Earth Sciences Series, Springer, Dordrecht 2011, pp. 430–446. https://doi.org/10.1007/978-90-481-2639-2_15.
  42. Ceccarelli D.M., McLeod I.M., Boström-Einarsson L., Bryan S.E., Chartrand K.M., Emslie M.J., Gibbs M.T. et al.: Substrate stabilisation and small structures in coral restoration: State of knowledge, and considerations for management and implementation. PLoS ONE, vol. 15(10), 2020, e0240846. https://doi.org/10.1371/journal.pone.0240846.
  43. Kutser T., Jupp D.L.B.: On the possibility of mapping living corals to the species level based on their optical signatures. Estuarine, Coastal and Shelf Science, vol. 69, 2006, pp. 607–614. https://doi.org/10.1016/j.ecss.2006.05.026.
  44. Hochberg E.J., Atkinson M.J., Apprill A., Andréfouët Se.: Spectral reflectance of coral. Coral Reefs, vol. 23, 2004, pp. 84–95. https://doi.org/10.1007/s00338-003-0350-1.
  45. Hollnagel E.: Human Reliability Analysis: Context and Control. Academic Press, London 1993.
  46. Maclure M., Willett W.C.: Misinterpretation and misuse of the kappa statistic. American Journal of Epidemiology, vol. 126(2), 1987, pp. 161–169. https://doi.org/10.1093/aje/126.2.161.
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