Apar Mangrove Density Analysis, Pariaman City, West Sumatra
Downloads
Coastal areas are very vulnerable to climate change, especially due to rising sea levels that have a major effect on coastal ecosystems, including mangrove forests. Mangroves play an important ecological and economic role, but are degraded by various factors. Indonesia, which has the largest mangrove forest in the world, faces a significant decline in mangrove forest area every year, including in West Sumatra with a damage rate of 22.67%. In the city of Pariaman, which is directly opposite the Indian Ocean, there is a Mangrove Forest Park in Desa Apar that is important for the local ecosystem. This study aims to measure the density of mangroves in Apar Village using Landsat 8 satellite images and the NDVI (Normalized Difference Vegetation Index) method. The results of the analysis showed that out of a total of 10 hectares of mangrove area, 8.88 ha had a very high density, 0.55 ha had a high density, and 0.11 ha had a medium density. This data is important for the planning of mangrove protection policies and rehabilitation strategies, as well as supporting the development of sustainable ecotourism in the region. The use of technologies such as ArcGIS in NDVI analysis enables accurate vegetation monitoring, providing a solid foundation for effective mangrove management and conservation.
Affandi, O., Zaitunah, A., & Batubara, R. (2017). Potential economic and development prospects of non timber forest products in community agroforestry land around Sibolangit Tourism Park. Forest and Society, 1(1), 68-77.
Annisa, Amin Yunita Nur, Rudhi Pribadi, and Ibn Pratikto. "Analysis Of Changes In Mangrove Forest Area In Brebes And Wanasari Districts, Brebes Regency Using Landsat Satellite Images In 2008, 2013 And 2018."Journal of Marine Research 8.1 (2019): 27-35.
Fahreza, F. D., Aulia, A., Fauzan, F. S., Somantri, L., & Ridwana, R. (2022). Utilization of sentinel-2 image with ndvi method for mangrove vegetation density change in Indramayu Regency. Undiksha Journal Of Geographical Education, 10 (2), 155-165.
Jault, P., Leclerc, T., Jennes, S., Pirnay, J. P., Que, Y. A., Resch, G., ... & Gabard, J. (2019). Efficacy and tolerability of a cocktail of bacteriophages to treat burn wounds infected by Pseudomonas aeruginosa (PhagoBurn): a randomised, controlled, double-blind phase 1/2 trial. The Lancet Infectious Diseases, 19(1), 35-45.
Prasetio, R. T., & Ripandi, E. (2019). Optimization of forest type classification using deep learning based on optimize selection. Journal Of Informatics, 6 (1), 100-106.
Philiani, I. The Son, L., Harvianto, L., & Muzaki, A. A. (2016). Mangrove forest vegetation mapping using normalized difference vegetation index (NDVI) method in Arakan Village, South Minahasa, North Sulawesi. Solar Octagon Interdisciplinary Journal of Technology, 1(2), 211-222.
Ramayanti, Lorenzia Anggi, Bambang Darmo Yuwono, and Moehammad Awaluddin. "Mapping of critical land level using remote sensing and Geographic Information System (Case Study: Blora Regency)."Journal Of Geodesy Undip 4.2 (2015): 200-207.
Rositasari R, Setiawan WB, Supriadi IH, Hasanuddin H, Prayuda B. 2011. Coastal vulnerability prediction to climate change: Study case in Cirebon coastal land. Jurnal Ilmu dan Teknologi Kelautan Tropis, 3(1).
Susanto AH, Soedarti T, Purnobasuki H. (2013). Mangrove community structure around Suramadu Bridge Surabaya side. Bioscientiae, 10 (1), 1-10
Setiawan, H. (2013). Ecological Status of mangrove forests at various thickness levels. Wallacea Journal Of Forest Research, 2 (2), 104-120.
Saleh, M. S., Althaibani, A., Esa, Y., Mhandi, Y., & Mohamed, A. A. (2015, October). Impact of clustering microgrids on their stability and resilience during blackouts. In 2015 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE) (pp. 195-200). IEEE