Pemanfaatan Teknologi Penginderaan Jauh Pada Geospatial Intelligence Dalam Mencegah Illegal Fishing Untuk Menciptakan Keamanan di Wilayah Perairan Indonesia
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Nov 25, 2024
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Abstract
Technology has become a necessity in the modern era. The use of remote sensing technology in geospatial intelligence can be an effective solution to detect and prevent illegal fishing activities in Indonesia's maritime areas. This study uses a literature review method by gathering in-depth insights from journals and books. The focus is on remote sensing technology in geospatial intelligence to prevent illegal fishing. The purpose of this research is to examine the role of remote sensing in securing and monitoring Indonesia's maritime areas against illegal fishing. A technology-based approach using satellite imagery can be utilized for early detection and surveillance of maritime areas in Indonesia. Satellite imagery provides spatial and temporal data. The results indicate that the integration of remote sensing technology in geospatial intelligence plays a crucial role in enhancing surveillance capabilities in Indonesia's maritime regions.
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Sadewa, A. H., Harsono, G., Prihanto, Y., & Haloho, L. S. (2024). Pemanfaatan Teknologi Penginderaan Jauh Pada Geospatial Intelligence Dalam Mencegah Illegal Fishing Untuk Menciptakan Keamanan di Wilayah Perairan Indonesia. Journal on Education, 7(1), 7371-7382. https://doi.org/10.31004/joe.v7i1.7450
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References
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Massaquoi, B., Roberts, N. J., & Tian, G. (2021). Marine fishing management towards sustainability in Sierra Leone. International Journal of Sustainable Development and Planning, 16(5). https://doi.org/10.18280/ijsdp.160514
McDonald, G., Wilson, M., Veríssimo, D., Twohey, R., Clemence, M., Apistar, D., Box, S., Butler, P., Cadiz, F. C., Campbell, S. J., Cox, C., Effron, M., Gaines, S., Jakub, R., Mancao, R. H., Rojas, P. T., Tirona, R. S., & Vianna, G. (2020). Catalyzing sustainable fisheries management through behavior change interventions. Conservation Biology, 34(5). https://doi.org/10.1111/cobi.13475
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Sivasankari, M., Anandan, R., & Chamato, F. A. (2022). HE-DFNETS: A Novel Hybrid Deep Learning Architecture for the Prediction of Potential Fishing Zone Areas in Indian Ocean Using Remote Sensing Images. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/5081541
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Syahdan, M., Athmadipoera, A. S., Susilo, S. B., & Gaol, J. L. (2023). Determining the Potential Fishing Zone of Small Pelagic Fishes Based on Spatial and Temporal Variability of Remote Sensing Satellite Data. Egyptian Journal of Aquatic Biology and Fisheries, 27(3). https://doi.org/10.21608/ejabf.2023.306903
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