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A Diversity Index Model based on Spatial Analysis to Estimate High Conservation Value in a Mining Area

Forest and Society · Vol. 6 No. 1 (2022) · pp. 142–156

DOI
10.24259/fs.v6i1.12919
ISSN
2549-4333, 2549-4724
Published
2022-01-17
Publisher
Forestry Faculty, Universitas Hasanuddin
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

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.

Keywords

Collaboration network of the authors

40 authors · 146 co-authorship links · showing the 40 most connected

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  • Universitas Halu Oleo
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Putu Oka NgakanAffiliation unknown9
Putu Supadma PutraAffiliation unknown8
Risma illa MaulanyAffiliation unknown5
Syamsu RijalAffiliation unknown4
Hasnah NatsirAffiliation unknown3
Risye DwiyaniAffiliation unknown3
Lady ChaniaAffiliation unknown3
Ida Bagus Oka AgastyaAffiliation unknown3
Dian Adhetya Arif ArifAffiliation unknown3
Muh. IhsanAffiliation unknown2
Chairil AAffiliation unknown2
Fadly SuhatiAffiliation unknown2
Kaimuddin KaimuddinAffiliation unknown2
Wardiman Mas'udAffiliation unknown2
Hamirul HadiniUniversitas Halu Oleo1
MIRZA ARSIATY ARSYADUniversitas Halu Oleo1
Waode Siti Anima HiseinUniversitas Halu Oleo1
Dirvamena BoerUniversitas Halu Oleo1
Suaib SuaibAffiliation unknown1
NAWIR NAWIRAffiliation unknown1
Samuel A. PaembonanAffiliation unknown1
Syamsuddin MillangAffiliation unknown1
Leny RachmawatiAffiliation unknown1
Andang Suryana SomaAffiliation unknown1
A. MujetahidAffiliation unknown1
Fardi Ali SyahdarAffiliation unknown1
Arif KoesharyadiAffiliation unknown1
Muh. JayadiAffiliation unknown1
Risma NeswatiAffiliation unknown1
Sofyan AbdullahAffiliation unknown1
Erlin HarriAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/fs/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">A Diversity Index Model based on Spatial Analysis to Estimate High Conservation Value in a Mining Area</dc:title>
	<dc:creator xml:lang="en">Larekeng, Siti Halimah</dc:creator>
	<dc:creator xml:lang="en">Nursaputra, Munajat</dc:creator>
	<dc:creator xml:lang="en">Nasri, Nasri</dc:creator>
	<dc:creator xml:lang="en">Hamzah, Andi Siady</dc:creator>
	<dc:creator xml:lang="en">Mustari, Andi Subhan</dc:creator>
	<dc:creator xml:lang="en">Arif, Abdur Rahman </dc:creator>
	<dc:creator xml:lang="en">Ambodo , Aris Prio </dc:creator>
	<dc:creator xml:lang="en">Lawang , Yohan </dc:creator>
	<dc:creator xml:lang="en">Ardiansyah , Andri</dc:creator>
	<dc:subject xml:lang="en">Diversity Index</dc:subject>
	<dc:subject xml:lang="en">GIS Modelling</dc:subject>
	<dc:subject xml:lang="en">HCV</dc:subject>
	<dc:subject xml:lang="en">Mining Area</dc:subject>
	<dc:subject xml:lang="en">South Sulawesi</dc:subject>
	<dc:description xml:lang="en">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.</dc:description>
	<dc:publisher xml:lang="en">Forestry Faculty, Universitas Hasanuddin</dc:publisher>
	<dc:date>2022-01-17</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/fs/article/view/12919</dc:identifier>
	<dc:identifier>10.24259/fs.v6i1.12919</dc:identifier>
	<dc:source xml:lang="en">Forest and Society; Vol. 6 No. 1 (2022): APRIL; 142-156</dc:source>
	<dc:source>2549-4333</dc:source>
	<dc:source>2549-4724</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/fs/article/view/12919/7778</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2022 Forest and Society</dc:rights>
	<dc:rights xml:lang="en">http://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>