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Spatial modeling of deforestation susceptibility in the Tepo Asa Aroa Forest Management Unit, Central Sulawesi Province, Indonesia

Journal of Degraded and Mining Lands Management · Vol. 13 No. 4 (2026) · hlm. 11131–11145

DOI
10.15243/jdmlm.2026.134.11131
ISSN
2502-2458, 2339-076X
Terbit
2026-10-01
Penerbit
Brawijaya University
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-nc/4.0

Abstrak

Spatial information on deforestation is essential for supporting site-level forest management in areas experiencing land-cover change. This study aimed to detect deforestation in the Tepo Asa Aroa Forest Management Unit and to model its spatial probability and driving factors. Spectral changes between 2015 and 2026 were quantified using Change Vector Analysis, in which magnitude and direction were derived from multispectral bands and vegetation-related indices. These variables, together with spectral and index differences, were used as predictors in a Random Forest algorithm to detect deforestation. Binary logistic regression was then applied to model deforestation probability using distance from roads, elevation, slope, distance from settlements, distance from forest utilization permits, distance from rivers, population density, and distance from agricultural areas. The results showed that deforestation covered 1,956.23 ha during 2015-2026, with an average annual rate of 177.84 ha. All driving factors were statistically significant. Distance from roads, elevation, slope, distance from forest utilization permits, distance from rivers, and distance from agricultural areas reduced the odds of deforestation, whereas distance from settlements and population density increased them. The model showed good discrimination, with an area under the receiver operating characteristic curve of 0.889. The predicted probabilities were classified into low, moderate, and high deforestation susceptibility classes. These findings provide spatial evidence to support monitoring, field verification, rehabilitation planning, and sustainable forest management.

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  • Tadulako University
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Rekan penulisOrganisasiArtikel bersama
Hasriani MuisTadulako University6
Sudirman Daeng MassiriTadulako University5
Arman MaiwaTadulako University5
Misrah MisrahTadulako University4
Rhamdhani Fitrah BaharuddinTadulako University4
Nurul Fajriah IstiqamahTadulako University4
Siti SusiantiTadulako University2
Akhbar AkhbarTadulako University2
Abdul RahmanUniversitas Halu Oleo2
Naharuddin NaharuddinTadulako University2
Hendra PribadiTadulako University2
Muhammad Adam SuniTadulako University2
Rhmadhani Fitrah BaharuddinAfiliasi tidak diketahui2
Abdul RahmanTadulako University2
Rizky PurnamaTadulako University2
Madina Dwi PanuntunTadulako University2
Istiqomah Shariati ZamaniTadulako University2
Dwi Kartika Asih HasibuanTadulako University2
Abdul RahmanUniversitas Halu Oleo1
AkhbarAfiliasi tidak diketahui1
GolarAfiliasi tidak diketahui1
NaharuddinAfiliasi tidak diketahui1
Hendra PribadiAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) http://jdmlm.ub.ac.id/index.php/jdmlm/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Spatial modeling of deforestation susceptibility in the Tepo Asa Aroa Forest Management Unit, Central Sulawesi Province, Indonesia</dc:title>
	<dc:creator>Arianingsih, Ida</dc:creator>
	<dc:creator>Somba, Bunga Elim</dc:creator>
	<dc:creator>Malik, Adam</dc:creator>
	<dc:creator>Golar, Golar</dc:creator>
	<dc:creator>Hulu, Amati Eltriman</dc:creator>
	<dc:creator>Adriansyah R, Dandy</dc:creator>
	<dc:creator>Toknok, Bau</dc:creator>
	<dc:subject xml:lang="en-US">binary logistic regression</dc:subject>
	<dc:subject xml:lang="en-US">change vector analysis </dc:subject>
	<dc:subject xml:lang="en-US">deforestation susceptibility</dc:subject>
	<dc:subject xml:lang="en-US">forest management unit</dc:subject>
	<dc:subject xml:lang="en-US">spatial modeling</dc:subject>
	<dc:description xml:lang="en-US">Spatial information on deforestation is essential for supporting site-level forest management in areas experiencing land-cover change. This study aimed to detect deforestation in the Tepo Asa Aroa Forest Management Unit and to model its spatial probability and driving factors. Spectral changes between 2015 and 2026 were quantified using Change Vector Analysis, in which magnitude and direction were derived from multispectral bands and vegetation-related indices. These variables, together with spectral and index differences, were used as predictors in a Random Forest algorithm to detect deforestation. Binary logistic regression was then applied to model deforestation probability using distance from roads, elevation, slope, distance from settlements, distance from forest utilization permits, distance from rivers, population density, and distance from agricultural areas. The results showed that deforestation covered 1,956.23 ha during 2015-2026, with an average annual rate of 177.84 ha. All driving factors were statistically significant. Distance from roads, elevation, slope, distance from forest utilization permits, distance from rivers, and distance from agricultural areas reduced the odds of deforestation, whereas distance from settlements and population density increased them. The model showed good discrimination, with an area under the receiver operating characteristic curve of 0.889. The predicted probabilities were classified into low, moderate, and high deforestation susceptibility classes. These findings provide spatial evidence to support monitoring, field verification, rehabilitation planning, and sustainable forest management.</dc:description>
	<dc:publisher xml:lang="en-US">Brawijaya University</dc:publisher>
	<dc:date>2026-10-01</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://jdmlm.ub.ac.id/index.php/jdmlm/article/view/17904</dc:identifier>
	<dc:identifier>10.15243/jdmlm.2026.134.11131</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Degraded and Mining Lands Management; Vol. 13 No. 4 (2026); 11131–11145</dc:source>
	<dc:source>2502-2458</dc:source>
	<dc:source>2339-076X</dc:source>
	<dc:source>10.15243/jdmlm.2026.134</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jdmlm.ub.ac.id/index.php/jdmlm/article/view/17904/2150</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Journal of Degraded and Mining Lands Management</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-nc/4.0</dc:rights>
</oai_dc:dc>