Benthic habitat mapping using high-resolution UAV imagery: A case study of Mantanani Besar Island, Sabah

Wei Sheng Chong; Izz Khalisah Nashir; Imelus Nius; Pei Zhao Liew; Fikri Akmal Khodzori; Muhammad Aiman Mohd Azseri; Muhammad Dawood Shah.

Precise benthic habitat mapping is essential for the efficient management and preservation of tropical marine ecosystems. This study assesses the effectiveness of Unmanned Aerial Vehicle (UAV) technology for high-resolution mapping of the benthic habitats adjacent to Mantanani Besar Island, Malaysia. Employing a Support Vector Machine (SVM) algorithm, high-spatial-fidelity imagery (GSD = 3.72 cm/pixel) was classified into two hierarchical tiers: a binary Level 1 (L1) scheme (live coral and non-living substrate) and a multi-class Level 2 (L2) scheme consisting of six distinct classes (branching coral, massive coral, patch coral, sand, coral rubble, and submerged rock). The UAV-derived classification attained an Overall Accuracy (OA) of 88.60% (κ = 0.8625), illustrating the efficacy of this platform for detailed habitat description. The findings reveal that live coral occupies roughly 37.55 ha (30%) of the examined area, whilst non-coral substrates, mainly sand and rubble, constitute the remaining 88.86 ha (70%). The occurrence of fragmented benthic substrates, especially in settlement locales, indicates considerable past reef degradation possibly caused by sedimentation and human activities. This study highlights the use of UAVs as a precise instrument for marine surveillance, delivering essential spatial data for the conservation of Mantanani's biodiversity.

KEYWORDS: Benthic habitat mapping; Drone; Pulau Mantanani Classification; Borneo.



Download this PDF file

REFERENCES

1. Andréfouët, S., Muller-Karger, F. E., Hochberg, E. J., Hu, C. & Carder, K. L. 2001. Change detection in shallow coral reef environments using Landsat 7 ETM + data. Remote Sensing of Environment, 78(1–2), 150–162.
2. Burdziakowski, P. & Bobkowska, K. 2021. UAV photogrammetry under poor lighting conditions—Accuracy considerations. Sensors, 21(10), 3531.
3. Casella, E., Rovere, A., Pedroncini, A., Stark, C. P., Casella, M., Ferrari, M. & Firpo, M. 2016. Drones as tools for monitoring beach topography changes in the Ligurian Sea (NW Mediterranean). Geo-Marine Letters, 36(2), 151–163.
4. Casella, E., Collin, A., Harris, D., Ferse, S., Bejarano, S., Parravicini, V., Hench, J. L. & Rovere, A. 2017. Mapping coral reefs using consumer-grade drones and structure from motion photogrammetry techniques. Coral Reefs, 36(1), 269–275.
5. Castellanos-Galindo, G. A., Casella, E., Mejía-Rentería, J. C. & Rovere, A. 2019. Habitat mapping of remote coasts: Evaluating the usefulness of lightweight unmanned aerial vehicles for conservation and monitoring. Biological Conservation, 239, 108282.
6. Chong, W. S., Zaki, N. H. M., Hossain, M. S., Muslim, A. M. & Pour, A. B. 2021. Introducing Theil-Sen estimator for sun glint correction of UAV data for coral mapping. Geocarto International, 37(15), 4527-4556.
7. Chong, W. S., Khodzori, F. A. & Shah, M. D. 2023. The Synergy of Remote Sensing in Marine Invasion Science. In: Shah, M. D., Ransangan, J. & Venmathi Maran, B. A. (Eds.). Marine Biotechnology: Applications in Food, Drugs and Energy. Singapore: Springer Nature Singapore. pp. 299–313.
8. Colomina, I. & Molina, P. 2014. Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 79–97.
9. Green, E. P., Mumby, P. J., Edwards, A. J. & Clark, C. D. 2000. Remote Sensing Handbook for Tropical Coastal Management. Coastal Management Sourcebooks 3.
10. Hamylton, S. 2011. Estimating the coverage of coral reef benthic communities from airborne hyperspectral remote sensing data: Multiple discriminant function analysis and linear spectral unmixing. International Journal of Remote Sensing, 32(24), 9673–9690.
11. Hughes, T. P., Kerry, J. T., Baird, A. H., Connolly, S. R., Dietzel, A., Eakin, C. M., Heron, S. F., Hoey, A. S., Hoogenboom, M. O., Liu, G., Stella, J. S. & Torda, G. 2018a. Global warming transforms coral reef assemblages. Nature, 556(7702), 492–496.
12. Hughes, T. P., Anderson, K. D., Connolly, S. R., Heron, S. F., Kerry, J. T., Lough, J. M., Baird, A. H., Baum, J. K., Berumen, M. L., Bridge, T. C., Torda, G. & Wilson, S. K. 2018b. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science, 359(6371), 80–83.
13. Meister, M. & Qu, J. J. 2024. Quantifying Seagrass Density Using Sentinel-2 Data and Machine Learning. Remote Sensing, 16(7), 1165.
14. Mumby, P. J., Green, E. P., Edwards, A. J. & Clark, C. D. 1999. The cost-effectiveness of remote sensing for tropical coastal resources assessment and management. Journal of Environmental Management, 55(3), 157–166.
15. Muslim, A. M., Chong, W. S., Safuan, C. D. M., Khalil, I. & Mohammad Shawkat Hossain. 2019. Coral reef mapping of UAV: A comparison of sun glint correction methods. Remote Sensing, 11(20), 2422.
16. Pajares, G. 2015. Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs). Photogrammetric Engineering & Remote Sensing, 81(4), 281–330.
17. Stone, A., Hickey, S., Radford, B. & Wakeford, M. 2024. Mapping emergent coral reefs: a comparison of pixel‐and object‐based methods. Remote Sensing in Ecology and Conservation, 11, 20-39.
18. Tuttle, L. J. & Donahue, M. J. 2022. Effects of sediment exposure on corals: a systematic review of experimental studies. Environmental Evidence, 11(1), 4.
19. Ventura, D., Grosso, L., Pensa, D., Casoli, E., Mancini, G., Valente, T., Scardi, M. & Rakaj, A. 2023. Coastal benthic habitat mapping and monitoring by integrating aerial and water surface low-cost drones. Frontiers in Marine Science, 9, 1096594.
20. Wilkinson, C. R. 2008. Status of coral reefs of the world. Global Coral Reef Monitoring Network and Reef and Rainforest Research Center.
21. Wu, M. T. 2022. Confusion matrix and minimum cross-entropy metrics based motion recognition system in the classroom. Scientific Reports, 12(1), 3095.