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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 · 142 co-authorship links · 27 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Hasanuddin University
  • Universitas Halu Oleo
  • East–West Center
  • Diponegoro University
  • Universitas Gadjah Mada
  • Ministry of Environment and Forestry, Republic of Indonesia
  • 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 NgakanHasanuddin University9
Putu Supadma PutraHasanuddin University8
Risma Illa MaulanyHasanuddin University5
Syamsu RijalHasanuddin University2
Hasnah NatsirHasanuddin University2
Rinaldi SjahrilAffiliation unknown2
Feranita HaringAffiliation unknown2
Muhammad Alif K. SahideHasanuddin University2
Fatwa FaturachmatHasanuddin University2
Muh Nur IhsanAffiliation unknown2
Azril ChairilHasanuddin University2
Fadly SuhatiAffiliation unknown2
Nurul HasfiDiponegoro University1
Ahsan YunusHasanuddin University1
Micah R. FisherEast–West Center1
Emban Ibnurusyd Mas’udAffiliation unknown1
Ahmad MaryudiUniversitas Gadjah Mada1
Hamirul HadiniUniversitas Halu Oleo1
MIRZA ARSIATY ARSYADUniversitas Halu Oleo1
Waode Siti Anima HiseinUniversitas Halu Oleo1
Dirvamena BoerUniversitas Halu Oleo1
NAWIR NAWIRUniversitas Halu Oleo1
SUAIB SUAIBUniversitas Halu Oleo1
Leny RachmawatiMinistry of Environment and Forestry, Republic of Indonesia1
S A PaembonanHasanuddin University1
Andang Suryana SomaHasanuddin University1
Syamsuddin MillangHasanuddin University1
A MujetahidHasanuddin University1
Fardi Ali SyahdarAffiliation unknown1
Arif KoesharyadiAffiliation unknown1
Riswal KarammaHasanuddin University1

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 8 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>