High-Resolution Seagrass Species Mapping and Propeller Scars Detection in Tanjung Benoa, Bali through UAV Imagery

Karang, I. Wayan Gede Astawa; Pravitha, Ni Luh Putu Ratih; Nuarsa, I. Wayan; Basheer Ahammed, K. K.; Wicaksono, Pramaditya

doi:10.12911/22998993/174943

Artykuł - szczegóły

Tytuł artykułu

High-Resolution Seagrass Species Mapping and Propeller Scars Detection in Tanjung Benoa, Bali through UAV Imagery

Autorzy

Karang I. Wayan Gede Astawa , Pravitha Ni Luh Putu Ratih , Nuarsa I. Wayan , Basheer Ahammed K. K. , Wicaksono Pramaditya

Treść / Zawartość

Pełne teksty:

15_karang_high_resolution_jee_1_2024.pdf

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Identyfikatory

DOI

10.12911/22998993/174943

Języki publikacji

Abstrakty

As a part of the marine ecosystem, seagrass plays a significant role in the coastal environment. However, due to increased threats from natural causes and anthropogenic pressures, seagrass decline will likely begin in many areas of the world. Therefore, several studies have been carried out to observe seagrass distribution to help resolve the issue. Remote sensing is often used due to its ability to achieve high accuracy when distinguishing seagrass distribution. Still, this method lacks in species classification because not all satellites and similar aerial vehicles have fine spatial resolution to distinguish distinct species of seagrass. In this study, we aim to address the issue by utilizing unmanned aerial vehicles (UAV), which are known for providing finer resolution and better imagery. Samuh Beach at Tanjung Benoa, Bali, Indonesia, was chosen as the study site location because it experiences high levels of marine tourism and anthropogenic activities. From the UAV flight mission, the images obtained were processed. The result’s accuracy was also tested with an error matrix. The species found in this study are Enhalus acoroides, Halodule pinifolia, Thalassia hemprichii, Cymodocea rotundata, and Syringodium isoetifolium, with 65% overall accuracy of the species classification map. This result indicates that UAVs can be a strong option for similar studies in the future. In addition to that, this study was able to observe the scars on the seagrass beds left by boat propeller activities from marine tourism. However, further research is needed to gain a better understanding of these objects.

Słowa kluczowe

seagrass distribution UAV species classification scar boat propeller Tanjung Benoa anthropogenic pressure

Wydawca

Polskie Towarzystwo Inżynierii Ekologicznej
Lublin University of Technology

Czasopismo

Journal of Ecological Engineering

Rocznik

2024

Tom

Vol. 25, nr 1

Strony

161--174

Opis fizyczny

Bibliogr. 48 poz., rys., tab.

Twórcy

autor

Karang I. Wayan Gede Astawa

gedekarang@unud.ac.id

Faculty of Marine Science and Fisheries, Udayana University, Jimbaran, Bali, Indonesia
Center for Remote Sensing and Ocean Sciences (CReSOS), Udayana University, Denpasar, Bali, Indonesia

https://orcid.org/0000-0001-8998-0604

autor

Pravitha Ni Luh Putu Ratih

Faculty of Marine Science and Fisheries, Udayana University, Jimbaran, Bali, Indonesia

https://orcid.org/0000-0002-7462-7100

autor

Nuarsa I. Wayan

Faculty of Marine Science and Fisheries, Udayana University, Jimbaran, Bali, Indonesia

https://orcid.org/0000-0001-8134-2640

autor

Basheer Ahammed K. K.

Department of Geography, Central University of Jharkhand, Cheri Manatu, 835 222, India

https://orcid.org/0000-0001-9003-923X

autor

Wicaksono Pramaditya

Department of Geographic Information Science, Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia

https://orcid.org/0000-0001-5797-0284

Bibliografia

1. Ahmed S., Al-Durgham K., Al-Nahar A. 2020. Multi-source satellite images merging for urban land cover mapping. The Egyptian Journal of Remote Sensing and Space Science, 23(1), 77-85.
2. Azizah N.N., Siregar V.P., Agus S.B. 2016. Penerapan Algoritma Spectral Angle Mapper (Sam) Untuk Klasifikasi Lamun Menggunakan Citra Satelit Worldview-2. Jurnal Penginderaan Jauh, 13(2), 61-72.
3. Bao Y., Chen L., Zhang Z., Zhang J. 2019. An efficient image registration algorithm for multi- source remote sensing images. Remote Sensing, 11(9), 1108.
4. Chayhard S., Manthachitra V., Nualchawee K., Buranapratheprat A. 2018. Multi-temporal mapping of seagrass distribution by using integrated remote sensing data in Kung Kraben Bay (KKB), Chanthaburi province, Thailand. International Journal of Agricultural Technology, 14(2), 161-170.
5. Congalton R.G., Green K. 2008. Assessing the accuracy of remotely sensed data: principles and practices. 2nd edition. CRC Press. https://doi.org/10.1201/9780429052729
6. Congalton R.G., Green K. 2019. Assessing the accuracy of remotely sensed data: principles and practices. 3rd edition. CRC Press, Boca Raton.
7. Dekker A., Brando V., Anstee J., Fyfe S., Malthus T., Karpouzli, E. 2006. Remote sensing of seagrass ecosystems: use of spaceborne and airborne sensors. Seagrasses: Biology, Ecology and Conservation, 347-359.
8. Duarte C.M. Cebrián J. 1996. The fate of marine autotrophic production. Limnology and oceanography, 41(8), 1758-1766.
9. Doukari M., Katsanevakis S., Soulakellis N., Topouzelis K. 2021. The Effect of Environmental Conditions on the Quality of UAS Orthophoto-Maps in the Coastal Environment. ISPRS Int. J. Geo-Inf, 10(18), https://doi.org/10.3390/ijgi10010018.
10. Ginting D.N.B., Wicaksono P., Farda N.M. 2023. Mapping Benthic Habitat from WORLDVIEW-3 Image Using Random Forest Case Study: Nusa Lembongan, Bali, Indonesia. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 123-129.
11. Glasby T.M., West G. 2018. Dragging the chain: Quantifying continued losses of seagrasses from boat moorings. Aquatic Conservation: Marine and Freshwater Ecosystems, 28(2), 383-394.
12. Hallac D.E., Sadle J., Pearlstine L., Herling F.,Shinde D. 2012. Boating impacts to seagrass in Florida Bay, Everglades National Park, Florida, USA: links with physical and visitor-use factors and implications for management. Marine and Freshwater Research, 63(11), 1117-1128.
13. Indayani A.B., Danoedoro P., Wicaksono P., Winarso G., Setiawan K.T. 2020. Analisis spektral dari serapan dan pantulan daun lamun menggunakan spektroradiometer trios-ramses di Nusa Lembongan dan Pemuteran, Bali. Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital, 17(2).
14. Joyce K.E., Duce S., Leahy S.M., Leon J., Maier S.W. 2018. Principles and practice of acquiring drone-based image data in marine environments. Marine and Freshwater Research, 70(7), 952-963.
15. Karang I.W.G.A., Dharma I.G.B.S, Astaman I.D.M.K.P. 2022. Mapping of shallow-water benthic habitats in Nusa Lembongan, Bali using Sentinel-2B and Landsat 8 satellite data. AACL Bioflux, 15(3).
16. Karang I.W.G.A., Nagendra I.W.M.D., Astaman I.D.M.K.P., Hendrawan I.G. 2019. Pemetaan Habitat Perairan Dangkal di Kawasan Padat Wisata Tanjung Benoa Bali Menggunakan Data Remote Sensing. ECOTROPHIC : Jurnal Ilmu Lingkungan (Journal Of Environmental Science), 13(2), 227-237.
17. Khairunnisa, Setyobudiandi I., Boer, M. 2021. Seagrass distribution in the east coast of Bintan. IOP Conference Series: Earth and Environmental Science, 782(4), 042001.
18. Knudby A., Nordlund L. 2011. Remote sensing of seagrasses in a patchy multi-species environment. International Journal of Remote Sensing, 32(8), 2227-2244.
19. Kumara I.S.W. 2018. Pemetaan sepsies lamun melalui integrasi citra multispektral dan pola respon spectral di Nusa Lembongan, Bali. Thesis, Universitas Ghajah Mada. Yogyakarta, Indonesia.
20. Lazuardi W., Ardiyanto R., Marfai M.A., Mutaqin B.W., Kusuma D.W. 2021. Coastal Reef and Seagrass Monitoring for Coastal Ecosystem Management. International Journal of Sustainable Development & Planning.
21. Li, S. 2018. Seagrass Mapping and Human Impact Evaluation Using Remote Sensing Imagery at Core Banks, North Carolina.
22. LIPI, 2014. Technical guide for mapping bottom habitat of shallow marine waters. Pusat Penelitian Oseanografi Lembaga Ilmu Pengetahuan Indonesia, 300, Jakarta, Indonesia.
23. Lyons M., Phinn S., Roelfsema C. 2011. Integrating Quickbird multi-spectral satellite and field data: Mapping bathymetry, seagrass cover, seagrass species and change in Moreton Bay, Australia in 2004 and 2007. Remote Sensing, 3(1), 42-64.
24. Nababan B., Mastu L.O.K., Idris N.H., Panjaitan J.P. 2021. Shallow-water benthic habitat mapping using drone with object based image analyses. Remote Sensing, 13(21), 4452.
25. Nahirnick N.K., Reshitnyk L., Campbell M., Hessing‐Lewis M., Costa M., Yakimishyn J., Lee L. 2019. Mapping with confidence; delineating seagrass habitats using Unoccupied Aerial Systems (UAS). Remote Sensing in Ecology and Conservation, 5(2), 121-135.
26. Oguslu E., Islam K., Perez D., Hill V.J., Bissett W.P., Zimmerman R.C., Li J. 2018. Detection of seagrass scars using sparse coding and morphological filter. Remote Sensing of Environment, 213, 92-103.
27. O’Neill J.D., Costa M. 2013. Mapping eelgrass (Zostera marina) in the Gulf Islands National Park Reserve of Canada using high spatial resolution satellite and airborne imagery. Remote Sensing of Environment, 133, 152-167.
28. 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 sensing of Environment, 112(8), 3413-3425.
29. Riani E., Djuwita I., Budiharsono S., Purbayanto A., Asmus H. 2012. Challenging for seagrass management in Indonesia. Journal of Coastal Development, 15(3), 234-242.
30. Riniatsih I., Ambariyanto A., Yudiati E., Redjeki S., Hartati R., Triaji M.J.R., Siagian H. 2021, April. Spatial assessment of seagrass ecosystem using the Unmanned Aerial Vehicle (UAV) in Teluk Awur, Coastal Water of Jepara. IOP Conference Series: Earth and Environmental Science, 744(1), 012063.
31. 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.
32. Román A., Tovar-Sánchez A., Olivé I., Navarro G. 2021. Using a UAV-Mounted Multispectral Camera for the Monitoring of Marine Macrophytes. Frontiers in Marine Science, 1225.
33. Rosalina D., Irwan K.H R., Jamil K., Surachmat A., Utami E. 2022. Diversity, Ecological Index, and Distribution Pattern of Seagrass in Coastal Waters of North Bali. Journal of Hunan University Natural Sciences, 49(9).
34. Sari K.A.P., Pertami N.D., Pratiwi M.A. 2023. Keanekaragaman dan Asosiasi Antarspesies Lamun di Perairan Pantai Samuh, Nusa Dua, Bali. Current Trends in Aquatic Science, V(1), 64-73.
35. Sondak C.F.A., Kaligis E.Y. 2022. Assessing the seagrasses meadows status and condition: A case study of Wori Seagrass Meadows, North Sulawesi, Indonesia. Biodiversitas Journal of Biological Diversity, 23(4).
36. Sudiarta I.K., Sudiarta I.G. 2011. Status kondisi dan identifikasi permasalahan kerusakan padang lamun di Bali. Jurnal Mitra Bahari, 5(2), 104-126.
37. Suteja Y., Atmadipoera A.S., Riani E., Nurjaya I.W., Nugroho D., Cordova M.R. 2021. Spatial and temporal distribution of microplastic in surface water of tropical estuary: Case study in Benoa Bay, Bali, Indonesia. Marine Pollution Bulletin, 163, 111979.
38. Tahara S., Sudo K., Yamakita T., Nakaoka M. 2022. Species level mapping of a seagrass bed using an unmanned aerial vehicle and deep learning technique. PeerJ, 10, e14017.
39. Théau J. 2008. Temporal Resolution. In: Shekhar, S., Xiong, H. (eds) Encyclopedia of GIS. Springer, Boston, MA.
40. Wang L., Zhao G., Li Y., Zhang Y., Yu W. 2021. Multi-source remote sensing image registration with feature-based method. International Journal of Remote Sensing, 42(7), 2616-2635.
41. Watiniasih N.L., Nuarsa I.W., Merdana I.M., Budiarsa I.N., Dharma A., Antara I.N.G., Poborini M. W. 2019. Organism Associated with Cymodocea Serulata in Different Habitats near Urban Coastal Area. IOP Conference Series: Earth and Environmental Science, 396(1), 012006.
42. 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.
43. 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.
44. Wijantara I.G.A., Karang I.W.G.A., Indrawan G.S. 2022. Pemetaan Distribusi Lamun di Selat Ceningan Menggunakan Drone Komersial. Journal of Marine and Aquatic Sciences, 8(2), 279-287.
45. Yang B., Hawthorne T.L., Hessing-Lewis M., Duffy E. J., Reshitnyk L.Y., Feinman M., Searson H. 2020. Developing an introductory UAV/drone mapping training program for seagrass monitoring and research. Drones, 4(4), 70.
46. Yang D., Huang, D. 2011. Impacts of typhoons Tianying and Dawei on seagrass distribution in Xincun Bay, Hainan province, China. Acta Oceanologica Sinica, 30(1), 32.
47. Zhang C., Xie Z. 2013. Object-based vegetation mapping in the Kissimmee River watershed using HyMap data and machine learning techniques. Wetlands, 33, 233-244.
48. Zhang X., Liu J., Zhang X. 2018. Multi-source remote sensing image registration based on feature extraction and cross correlation coefficient. International Journal of Remote Sensing, 39(12), 3852-3871.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).

Identyfikator YADDA

bwmeta1.element.baztech-4d1da4ca-f42e-4566-af0d-fc991b149051