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MAPPING MANGROVE SURFACE CARBON STOCKS USING MULTISENSOR IMAGERY IN CLUNGUP MANGROVE CONSERVATION (CMC) MALANG REGENCY

Jurnal Geografi · Vol. 14 No. 2 (2022) · hlm. 192–201

DOI
10.24114/jg.v14i2.33575
ISSN
2549-7057, 2085-8167
Terbit
2022-08-09
Penerbit
Universitas Negeri Medan
Bahasa
en

Abstrak

Inggris

Mangroves can store carbon effectively with a value of about 1,023 Mg C/Ha and become one of the richest forests that store 4-20 billion tons of blue carbon globally. Remote sensing imagery can be used to map mangrove surface carbon stocks using radar and optical image sensors. Generally, forest carbon on earth is stored in two places, namely above the surface (Above Ground Carbon, AGC) and below the surface (Below Ground Carbon, BGC). This study aims to estimate the surface carbon stock of mangroves using multisensory imagery using the Random Forest method in the Clungup Mangrove Conservation (CMC) area, Malang Regency, East Java. Four vegetation indices (IRECI, NDI45, NDVI, SAVI), single band, and VV VH polarization were used as predictive variables. Estimating the carbon stock mangrove value using Sentinel-1 imagery produced 2,126 tons of C with R² 0.11. Meanwhile, Sentinel-2 produces an estimated carbon value of 2,025 tons C with an R² of 0.22. The estimation model using Sentinel-2 shows a better evaluation value with a Root Mean Squared Error (RMSE) of 0.89 and a Mean Absolute Error (MAE) of 0.75. The IRECI vegetation index is the most important variable in estimating carbon stocks. The results of the mapping accuracy of the Sentinel-1 model show a value of 34.73% and Sentinel-2 35.03%.Keywords: Mangrove, Carbon, Sentinel-1, Sentinel-2, Random Forest

Inggris

Mangroves can store carbon effectively with a value of about 1,023 Mg C/Ha and become one of the richest forests that store 4-20 billion tons of blue carbon globally. Remote sensing imagery can be used to map mangrove surface carbon stocks using radar and optical image sensors. Generally, forest carbon on earth is stored in two places, namely above the surface (Above Ground Carbon, AGC) and below the surface (Below Ground Carbon, BGC). This study aims to estimate the surface carbon stock of mangroves using multisensory imagery using the Random Forest method in the Clungup Mangrove Conservation (CMC) area, Malang Regency, East Java. Four vegetation indices (IRECI, NDI45, NDVI, SAVI), single band, and VV VH polarization were used as predictive variables. Estimating the carbon stock mangrove value using Sentinel-1 imagery produced 2,126 tons of C with R² 0.11. Meanwhile, Sentinel-2 produces an estimated carbon value of 2,025 tons C with an R² of 0.22. The estimation model using Sentinel-2 shows a better evaluation value with a Root Mean Squared Error (RMSE) of 0.89 and a Mean Absolute Error (MAE) of 0.75. The IRECI vegetation index is the most important variable in estimating carbon stocks. The results of the mapping accuracy of the Sentinel-1 model show a value of 34.73% and Sentinel-2 35.03%.Keywords: Mangrove, Carbon, Sentinel-1, Sentinel-2, Random Forest

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  • Universitas Gadjah Mada
  • Universitas Esa Unggul
  • Ministry of Marine Affairs and Fisheries
  • Center for International Forestry Research
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Rekan penulisOrganisasiArtikel bersama
Projo DanoedoroUniversitas Gadjah Mada2
Prima WidayaniUniversitas Gadjah Mada2
Muhammad Sufwandika WijayaUniversitas Gadjah Mada2
Hartono HartonoUniversitas Gadjah Mada2
Pramaditya WicaksonoUniversitas Gadjah Mada2
Novi Susetyo AdiMinistry of Marine Affairs and Fisheries2
Nur Mohammad FardaUniversitas Gadjah Mada1
Hartono HartonoUniversitas Gadjah Mada1
Barano Ss MeterayAfiliasi tidak diketahui1
Sigit HerumurtiAfiliasi tidak diketahui1
Prama Ardha AryagunaUniversitas Esa Unggul1
Yanuar Adji NugrohoUniversitas Gadjah Mada1
Nguyễn Thanh HùngAfiliasi tidak diketahui1
Hoàng Công TríAfiliasi tidak diketahui1
Uji Astrono PribadiCenter for International Forestry Research1
Gilang Aria SetaAfiliasi tidak diketahui1
Abimanyu Putra PratamaUniversitas Gadjah Mada1
Intan LestariUniversitas Gadjah Mada1
Mulyanto MUniversitas Gadjah Mada1
Yusri KhoirurrizqiUniversitas Gadjah Mada1
Rohmad SasongkoUniversitas Gadjah Mada1
Nur Laila Eka UtamiUniversitas Gadjah Mada1
Amanda IrbahUniversitas Gadjah Mada1
Erny PoedjirahajoeUniversitas Gadjah Mada1
Much. Taufik Tri HermawanUniversitas Gadjah Mada1
Frita Kusuma WardhaniUniversitas Gadjah Mada1
Siti ZahrotunisaUniversitas Gadjah Mada1

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Sumber metadata: OAI-PMH (metadata jurnal) http://jurnal.unimed.ac.id/2012/index.php/geo/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">MAPPING MANGROVE SURFACE CARBON STOCKS USING MULTISENSOR IMAGERY IN CLUNGUP MANGROVE CONSERVATION (CMC) MALANG REGENCY</dc:title>
	<dc:title xml:lang="en-US">MAPPING MANGROVE SURFACE CARBON STOCKS USING MULTISENSOR IMAGERY IN CLUNGUP MANGROVE CONSERVATION (CMC) MALANG REGENCY</dc:title>
	<dc:creator xml:lang="en">Hakim, Maulana Abdul</dc:creator>
	<dc:creator xml:lang="en">Kamal, Muhammad</dc:creator>
	<dc:creator xml:lang="en">Arjasakusuma, Sanjiwana</dc:creator>
	<dc:description xml:lang="en">Mangroves can store carbon effectively with a value of about 1,023 Mg C/Ha and become one of the richest forests that store 4-20 billion tons of blue carbon globally. Remote sensing imagery can be used to map mangrove surface carbon stocks using radar and optical image sensors. Generally, forest carbon on earth is stored in two places, namely above the surface (Above Ground Carbon, AGC) and below the surface (Below Ground Carbon, BGC). This study aims to estimate the surface carbon stock of mangroves using multisensory imagery using the Random Forest method in the Clungup Mangrove Conservation (CMC) area, Malang Regency, East Java. Four vegetation indices (IRECI, NDI45, NDVI, SAVI), single band, and VV VH polarization were used as predictive variables. Estimating the carbon stock mangrove value using Sentinel-1 imagery produced 2,126 tons of C with R² 0.11. Meanwhile, Sentinel-2 produces an estimated carbon value of 2,025 tons C with an R² of 0.22. The estimation model using Sentinel-2 shows a better evaluation value with a Root Mean Squared Error (RMSE) of 0.89 and a Mean Absolute Error (MAE) of 0.75. The IRECI vegetation index is the most important variable in estimating carbon stocks. The results of the mapping accuracy of the Sentinel-1 model show a value of 34.73% and Sentinel-2 35.03%.Keywords: Mangrove, Carbon, Sentinel-1, Sentinel-2, Random Forest</dc:description>
	<dc:description xml:lang="en-US">Mangroves can store carbon effectively with a value of about 1,023 Mg C/Ha and become one of the richest forests that store 4-20 billion tons of blue carbon globally. Remote sensing imagery can be used to map mangrove surface carbon stocks using radar and optical image sensors. Generally, forest carbon on earth is stored in two places, namely above the surface (Above Ground Carbon, AGC) and below the surface (Below Ground Carbon, BGC). This study aims to estimate the surface carbon stock of mangroves using multisensory imagery using the Random Forest method in the Clungup Mangrove Conservation (CMC) area, Malang Regency, East Java. Four vegetation indices (IRECI, NDI45, NDVI, SAVI), single band, and VV VH polarization were used as predictive variables. Estimating the carbon stock mangrove value using Sentinel-1 imagery produced 2,126 tons of C with R² 0.11. Meanwhile, Sentinel-2 produces an estimated carbon value of 2,025 tons C with an R² of 0.22. The estimation model using Sentinel-2 shows a better evaluation value with a Root Mean Squared Error (RMSE) of 0.89 and a Mean Absolute Error (MAE) of 0.75. The IRECI vegetation index is the most important variable in estimating carbon stocks. The results of the mapping accuracy of the Sentinel-1 model show a value of 34.73% and Sentinel-2 35.03%.Keywords: Mangrove, Carbon, Sentinel-1, Sentinel-2, Random Forest</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Medan</dc:publisher>
	<dc:date>2022-08-09</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jurnal.unimed.ac.id/2012/index.php/geo/article/view/33575</dc:identifier>
	<dc:identifier>10.24114/jg.v14i2.33575</dc:identifier>
	<dc:source xml:lang="en">JURNAL GEOGRAFI; Vol. 14 No. 2 (2022): JURNAL GEOGRAFI; 192-201</dc:source>
	<dc:source>2549-7057</dc:source>
	<dc:source>2085-8167</dc:source>
	<dc:source>10.24114/jg.v14i1</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://jurnal.unimed.ac.id/2012/index.php/geo/article/view/33575/19190</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2022 JURNAL GEOGRAFI</dc:rights>
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