Analysis of Mangrove Density using NDVI and Macrobenthos Diversity in Ampekale Tourism Village South Sulawesi, Indonesia

Abstract

The mangrove ecosystem faces significant challenges, as its quality and quantity are threatened with decline. This study aimed to analyze the mangrove density and macrobenthos diversity in the Ampekale Ecotourism Village, Indonesia. This research utilizes remote sensing image analysis through the Normalized Difference Vegetation Index (NDVI) method. Complementary ground-check surveys were conducted to ascertain the density and diversity of mangrove and macrobenthos species. The overall mangrove density in the Ampekale area was relatively high, with some areas displaying moderate density. The sequence of mangrove species, from most common to least common, includes Rhizophora mucronata, Avicennia marina, Avicennia alba, Rhizophora apiculata, Sonneratia alba, Bruguiera gymnorhiza, and Acanthus ilicifolius. The most prevalent macrobenthos belong to the Crustacea class, such as Uca sp., Sesarma, Metaplax sp., and Scylla serrata. Moreover, the most diverse macrobenthos belong to the Gastropods class. The distribution of this biodiversity depends on their location (coastal or inland), tidal fluctuations, and river estuaries. Mangrove ecosystems situated in estuaries with high densities exhibited elevated macrobenthos abundance. This correlation suggests that areas characterized by dense mangroves also harbor stable ecosystem conditions with abundant macrobenthos. In contrast, ecosystems featuring lower biodiversity demonstrated reduced stability. These findings contribute valuable insights into the conservation and sustainability of mangrove ecosystems. Keywords: ecotourism, macrobenthos, mangrove density, mangrove ecosystem, NDVI

Integrated Supervised Classification of LULC in Identifying Musang King Durian Illegal Farming Location

“…In previous studies, many researchers have focused on the use of the Normalized Different Vegetation Index (NDVI) to detect vegetation health (Stamford et al 2023), tree species (Ozdarici-Ok and Ok 2023; Sheeren et al 2016;Valderrama-Landeros et al 2018), forest monitoring (Gallardo-Salazar et al 2023;Pesaresi et al 2020;Suwanto et al 2021), agriculture (Ahmed 2016;Gandhi et al 2015) and mangrove density (Arfan et al 2024) but there is limited research on using NDVI to identify potential illegal activities such as illegal mining and logging. This study will contribute to filling this gap in the literature by applying NDVI analysis to identify possible illegal activities related to land use changes.…”