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Assessing mangrove health index as a basis for degradation mitigation planning

Journal of Degraded and Mining Lands Management · Vol. 13 No. 2 (2026) · hlm. 9953–9962

DOI
10.15243/jdmlm.2026.132.9953
ISSN
2502-2458, 2339-076X
Terbit
2026-04-01
Penerbit
Brawijaya University
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-nc/4.0

Abstrak

This study aimed to detect mangrove cover, analyze spatio-temporal changes, and assess mangrove health conditions in South Banawa District, Donggala Regency, using multi-temporal Sentinel-2A imagery. Mangrove detection was conducted using a machine learning based Decision Tree algorithm, while mangrove health was evaluated using the Mangrove Health Index (MHI). The variables included spectral bands and multiple spectral indices (NDVI, NDBI, MNDWI, CMRI, NBR, GCI, SIPI, and ARVI). The classification model demonstrated very high performance, with Overall Accuracy, Kappa, and F1-Score values exceeding 98%. The results indicated a decline in mangrove area from 123.96 ha to 95.5 ha, equivalent to a loss of 28.46 ha (22.96%) during the observation period. Degradation was spatially concentrated in areas with high accessibility and proximity to shrimp farming activities. Despite this decline, mangrove conditions were predominantly classified as healthy (87.56%), followed by moderate (12.41%) and poor (0.03%) categories. MHI-based mitigation strategies prioritize low-index areas for restoration through hydrological rehabilitation and buffer zone establishment, while healthy areas are primarily focused on conservation and periodic monitoring. This approach supports data-driven conservation planning, restoration prioritization, and sustainable coastal management based on remote sensing and machine learning.

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  • Tadulako University
  • Universitas Islam Negeri Sultan Syarif Kasim Riau
  • Universitas Pattimura
  • Universitas Halu Oleo
  • IPB University
  • University of Malaya
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Rekan penulisOrganisasiArtikel bersama
Hasriani MuisTadulako University5
Sudirman Daeng MassiriTadulako University4
Arman MaiwaTadulako University4
Nurul Fajriah IstiqamahTadulako University4
Misrah MisrahTadulako University3
Ida ArianingsihTadulako University3
Rhamdhani Fitrah BaharuddinTadulako University3
IS ZamaniAfiliasi tidak diketahui2
Nurfiah NurfiahUniversitas Pattimura2
Abdul RahmanUniversitas Halu Oleo2
Naharuddin NaharuddinTadulako University2
Hendra PribadiTadulako University2
Muhammad Adam SuniTadulako University2
Rhmadhani Fitrah BaharuddinAfiliasi tidak diketahui2
Abdul RahmanTadulako University2
Bunga Elim SombaTadulako University2
Adam MalikTadulako University2
Golar GolarTadulako University2
Dandy Adriansyah RIPB University2
Siti SusiantiTadulako University1
Akhbar AkhbarTadulako University1
Fauziah Shahul HamidUniversity of Malaya1
Irwan TaslapratamaUniversitas Islam Negeri Sultan Syarif Kasim Riau1
Ervina AryantiUniversitas Islam Negeri Sultan Syarif Kasim Riau1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

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) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Assessing mangrove health index as a basis for degradation mitigation planning</dc:title>
	<dc:creator>Toknok, Bau</dc:creator>
	<dc:creator>Hulu, Amati Eltriman</dc:creator>
	<dc:creator>Purnama, Rizky</dc:creator>
	<dc:creator>Panuntun, Madina Dwi</dc:creator>
	<dc:creator>Zamani, Istiqomah Shariati</dc:creator>
	<dc:creator>Hasibuan, Dwi Kartika Asih</dc:creator>
	<dc:subject xml:lang="en-US">Machine Learning</dc:subject>
	<dc:subject xml:lang="en-US">mangrove degradation</dc:subject>
	<dc:subject xml:lang="en-US">Mangrove Health Index</dc:subject>
	<dc:subject xml:lang="en-US">Remote Sensing</dc:subject>
	<dc:description xml:lang="en-US">This study aimed to detect mangrove cover, analyze spatio-temporal changes, and assess mangrove health conditions in South Banawa District, Donggala Regency, using multi-temporal Sentinel-2A imagery. Mangrove detection was conducted using a machine learning based Decision Tree algorithm, while mangrove health was evaluated using the Mangrove Health Index (MHI). The variables included spectral bands and multiple spectral indices (NDVI, NDBI, MNDWI, CMRI, NBR, GCI, SIPI, and ARVI). The classification model demonstrated very high performance, with Overall Accuracy, Kappa, and F1-Score values exceeding 98%. The results indicated a decline in mangrove area from 123.96 ha to 95.5 ha, equivalent to a loss of 28.46 ha (22.96%) during the observation period. Degradation was spatially concentrated in areas with high accessibility and proximity to shrimp farming activities. Despite this decline, mangrove conditions were predominantly classified as healthy (87.56%), followed by moderate (12.41%) and poor (0.03%) categories. MHI-based mitigation strategies prioritize low-index areas for restoration through hydrological rehabilitation and buffer zone establishment, while healthy areas are primarily focused on conservation and periodic monitoring. This approach supports data-driven conservation planning, restoration prioritization, and sustainable coastal management based on remote sensing and machine learning.</dc:description>
	<dc:publisher xml:lang="en-US">Brawijaya University</dc:publisher>
	<dc:date>2026-04-01</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/17545</dc:identifier>
	<dc:identifier>10.15243/jdmlm.2026.132.9953</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Degraded and Mining Lands Management; Vol. 13 No. 2 (2026); 9953-9962</dc:source>
	<dc:source>2502-2458</dc:source>
	<dc:source>2339-076X</dc:source>
	<dc:source>10.15243/jdmlm.2026.132</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jdmlm.ub.ac.id/index.php/jdmlm/article/view/17545/2005</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>