| Autor: |
Hidayatullah, M. F., Abdurrahman, U., Pratyaksa, I. F., Badriana, M. R., Nur, A. A., Hidayatullah, A. I., Jeon, C. K., Putri, M. R., Radjawane, I. M., Park, H. S. |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2023, Vol. 2941 Issue 1, p1-10, 10p |
| Abstrakt: |
The Cirebon Coastal Area (CCA) has the potential for the growth of mangrove forests and their rehabilitation efforts. Therefore, to support the rehabilitation efforts, an inventory of the existing mangrove forest is needed. It is to facilitate the management and monitoring of mangrove areas. One of the methods is by investigating and mapping the existing mangrove forest area. Remote sensing data Sentinel-2A imagery (10 m) and UAV imagery were used in this study. The purpose of this study is to map the spatial distribution of mangrove forests in the CCA. The method used is the supervised classification for Sentinel-2A with the Mahalanobis Distance, Maximum Likelihood, Minimum Distance, and Parallelepiped algorithms. We compared the four algorithms. Visual interpretation method for UAV images that will be used as reference data. So that the results of the visual interpretation of the UAV image are used as reference data to test the accuracy of the classification results from the sentinel-2A image. The accuracy test method used is area-based accuracy assessment. The results showed that the use of the supervised classification algorithm on sentinel-2A images was the most effective as the highest accuracy value, namely maximum likelihood, was 85.68%. Based on the classification results, the existing area of mangrove forest in CCA is 317.81 Ha. This research contributes to assessing the most effective supervised classification algorithm for mapping the spatial distribution of mangrove forests in the Cirebon coastal area which is integrated with UAV imagery as a data reference for accuracy assessment. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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