Shallow water marine habitat mapping of Kaledupa Island using integrating tradisional ecological knowledge and multispectral image classification

2nd International Conference on Integrated Coastal Management and Marine Biotechnology,

Azhar, Al^1^2 ; Damar, Ario^3^5 ; Bengen, Dietriech G.^4 ; Atmadipoera, Agus S.^4

Graduate School of Coastal and Marine Resources Management, Faculty of Fisheries and Marine Sciences, Bogor Agricultural University, Indonesia^1

Lppptk Kptk, Ministry of Education and Culture, Indonesia^2

Department of Aquatic Resources Management, Faculty of Fisheries and Marine Sciences, Bogor Agricultural University, Indonesia^3

Department of Marine Sciences and Technology, Faculty of Fisheries and Marine Sciences, Bogor Agricultural University, Indonesia^4

Center for Coastal and Marine Resources Studies, LPPM IPB (CCMRS LPPM IPB), Indonesia^5

关键词: Marine habitat mapping;    Marine protected area;    Maximum likelihood analysis;    Multispectral image classification;    Operational land imager;    Remote sensing applications;    Supervised classification;    Traditional ecological knowledge;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/241/1/012038/pdf
DOI  :  10.1088/1755-1315/241/1/012038

来源: IOP

【 摘 要 】

Marine scientists had applied many remote sensing methods to shallow-water marine habitat mapping. However, integrating traditional ecological knowledge and remote sensing application to produce marine habitat map was still very limited. The aims of this study are to try to implement the integration of traditional ecological knowledge and multispectral image classification for shallow-water marine habitat mapping in the marine protected area (MPA) of Kaledupa Island, Wakatobi National Park (WNP). Imagery data used was Landsat 8 Operational Land Imager (2016). Fishers from Kaledupa Island visually interpreted Landsat 8 OLI multispectral image to identify sangnga (live coral), sangnga mate (dead coral), rondo (lamun), and one (sand). Their interpretations were used as input to image supervised classification with Mahalanobis and Maximum Likelihood. Then, habitat mapping with four classes could be produced. Results indicate that marine habitat mapping can be generated well by combining traditional ecological knowledge and multi-spectral image classification, Mahalanobis with the overall accuracy 71.67% and kappa statistic 0.62%; Maximum Likelihood Analysis with the overall accuracy 73.33% and kappa statistic 0.65%. This hybrid method is useful to marine scientists and coastal resource managers in producing swallow-water marine habitat map and management planning of MPA.