Multi-Source Remote Sensing Data Product Analysis: Investigating Anthropogenic and Naturogenic Impacts on Mangroves in Southeast Asia

• Main Authors: Anjar Dimara Sakti, Adam Irwansyah Fauzi, Felia Niwan Wilwatikta, Yoki Sepwanto Rajagukguk, Sonny Adhitya Sudhana, Lissa Fajri Yayusman, Luri Nurlaila Syahid, Tanakorn Sritarapipat, Jeark A. Principe, Nguyen Thi Quynh Trang, Endah Sulistyawati, Inggita Utami, Candra Wirawan Arief, Ketut Wikantika
• Format: Article
• Language: English
• Published: MDPI AG 2020-08-01
• Series: Remote Sensing
• Subjects: mangrove sustainability; deforestation depletion; anthropogenic; natural water balance; Southeast Asia
• Online Access: https://www.mdpi.com/2072-4292/12/17/2720

This study investigated the drivers of degradation in Southeast Asian mangroves through multi-source remote sensing data products. The degradation drivers that affect approximately half of this area are unidentified; therefore, naturogenic and anthropogenic impacts on these mangroves were studied. Various global land cover (GLC) products were harmonized and examined to identify major anthropogenic changes affecting mangrove habitats. To investigate the naturogenic factors, the impact of the water balance was evaluated using the Normalized Difference Vegetation Index (NDVI), and evapotranspiration and precipitation data. Vegetation indices’ response in deforested mangrove regions depends significantly on the type of drivers. A trend analysis and break point detection of percentage of tree cover (PTC), percentage of non-tree vegetation (PNTV), and percentage of non-vegetation (PNV) datasets can aid in measuring, estimating, and tracing the drivers of change. The assimilation of GLC products suggests that agriculture and fisheries are the predominant drivers of mangrove degradation. The relationship between water balance and degradation shows that naturogenic drivers have a wider impact than anthropogenic drivers, and degradation in particular regions is likely to be a result of the accumulation of various drivers. In large-scale studies, remote sensing data products could be integrated as a remarkably powerful instrument in assisting evidence-based policy making.