Winarso, Gathot, Rosid, Mohammad Syamsu, Kamal, Muhammad, Asriningrum, Wikanti, Margules, Chris, and Supriatna, Jatna (2023) Comparison of Mangrove Index (MI) and Normalized Difference Vegetation Index (NDVI) for the detection of degraded mangroves in Alas Purwo Banyuwangi and Segara Anakan Cilacap, Indonesia. Ecological Engineering, 197. 107119.

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Abstract

Deterioration of the environmental quality of mangrove ecosystem must be identified as early as possible for management and threat mitigation. Degraded mangrove areas have to be distinguished from healthy ones. Low value of Mangrove Index (MI) corresponded to the degraded mangrove area where dominated by two species (Derris trifoliata and Acanthus ilicifolius), which are indicators of mangrove degradation at Segara Anakan mangrove area. The Normalized Difference Vegetation Index (NDVI) failed to detect the degaradation due to existing of these species by detecting as high index value. The study was designed to (1) test the performance of MI at Alas Purwo mangrove area which categorized as a non-degraded mangrove forest, (2) compare the spatial pattern of MI images with that of NDVI images, and (3) further investigate the ability of MI to detect areas of degraded mangrove when it occurs in only small areas. We used Landsat-8 OLI imagery because it includes the short-wave infrared (SWIR) bands required to generate MI. Both direct visual comparison and correlation analysis of the resulting maps were conducted to evaluate the ability of these indices to detect degraded and non-degraded areas of mangroves. In general, both indices detected a similar pattern, which is contrary to the results from Segara Anakan images. However, a difference value between MI and NDVI of >0.2 indicated the presence of small areas of vegetation with a spectral reflectance similar to that of species indicating degradation at Segara Anakan. This finding was confirmed by field observation of mangrove species. A difference of >0.2 between MI and NDVI indicates mangrove degradation for small areas. This study provides a new tool to determine the condition of mangrove forest remotely, allowing for rapid and targeted management action to tackle degradation.

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