Open AccessA Diversity Index Model based on Spatial Analysis to Estimate High Conservation Value in a Mining Area
Author(s) -
Siti Halimah Larekeng,
Munajat Nursaputra,
Nasri Nasri,
Andi Siady Hamzah,
Andi Subhan Mustari,
Abdur Rahman Arif,
Aris Prio Ambodo,
Yohan Lawang,
Andri Ardiansyah
Publication year - 2022
Publication title -
forest and society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.623
H-Index - 8
eISSN - 2549-4724
pISSN - 2549-4333
DOI - 10.24259/fs.v6i1.12919
Subject(s) - normalized difference vegetation index , biodiversity , vegetation (pathology) , species richness , diversity index , index (typography) , geography , environmental science , sampling (signal processing) , enhanced vegetation index , regression analysis , environmental resource management , ecology , statistics , vegetation index , leaf area index , mathematics , computer science , medicine , filter (signal processing) , pathology , world wide web , computer vision , biology
Large scale land-based investments have a significant impact on natural resources and environmental conditions. It is necessary to protect areas of high conservation value (HCV) within land management investments, such as the mining sector, to minimise this impact. The existence of high conservation value sites in locations with activities related to the mining sector is intended to maintain the ecological and conservation value of a mining investment area. We demonstrate a model that can identify potential high conservation value sites in mining areas using remote sensing data and spatial analysis compiled with field observation data. The research was conducted in one of the largest nickel mining areas (71,047 ha) in South Sulawesi, Indonesia. We mapped vegetation density using the normalized difference vegetation index (NDVI), calculated from Sentinel-2 imagery. We also collected biodiversity data in predetermined inventory sampling plots, which we then used to estimate species richness using the Shannon-Wiener diversity index. Using a linear regression model to compare the normalized difference vegetation index value in each sampling plot with the biodiversity value of flora and fauna, we then estimated biodiversity distribution patterns for the entire study area. We found that potential high conservation value areas (areas likely to have high biodiversity based on our regression model) covered 40,000 ha, more than half of the total concession area.