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Object-Based Mangrove Mapping Comparison on Visible and NIR UAV Sensor

Indonesian Journal of Geography · Vol. 53 No. 2 (2021) · hlm. 165–169

DOI
10.22146/ijg.50861
ISSN
2354-9114, 0024-9521
Terbit
2021-08-28
Penerbit
Faculty of Geography, Universitas Gadjah Mada
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-nc/4.0

Abstrak

Mangrove ecosystems are natural resources that have potential value for development due to their high productivity. Mapping and identification of mangroves have always played a crucial role in mangrove ecosystem conservation efforts, especially to support the sustainable development goal of coastal resources and climate change issues. Several attempts have been made using Unmanned Aerial Vehicle (UAV) techniques acquisition of high spatial resolution aerial images data with various sensors and object-based classification for image processing with various levels of success. This study aims to identify mangrove objects using UAV with true color and NIR false-color sensors using the OBIA approach. The UAV used in this study was DJI Phantom 3 Pro with a true-color sensor (default) and NIR false-color (modified Canon IXUS 160 cameras). The comparison between the two types of sensor of aerial photographs as a source for mangrove mapping proved that the latter performed better than the former because of the near-infrared band can optimally discriminate between mangrove and non-mangrove objects. This will assist future research directions in the mangrove ecosystems mapping method.

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  • University of Indonesia
  • Universitas Gadjah Mada
  • Indonesian National Institute of Aeronautics and Space
  • IPB University
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  • Organisasi berbeda
  • Organisasi sama
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Rekan penulisOrganisasiArtikel bersama
Ratih Dewanti DimyatiUniversitas Gadjah Mada3
Faris ZulkarnainUniversity of Indonesia3
Kustiyo KustiyoIndonesian National Institute of Aeronautics and Space2
Nurul Sri RahatiningtyasUniversity of Indonesia2
Akbar Cahyadhi Pratama PutraAfiliasi tidak diketahui2
Tantri Utami WidhaningtyasAfiliasi tidak diketahui2
Bachtiar W. MutaqinAfiliasi tidak diketahui2
Babag PurbantoroAfiliasi tidak diketahui2
Triarko NurlambangAfiliasi tidak diketahui2
Nur Mohammad FardaAfiliasi tidak diketahui2
Totok Wahyu WibowoAfiliasi tidak diketahui2
Dimas Bayu IchsandyaUniversity of Indonesia1
Iqbal Putut Ash ShidiqUniversity of Indonesia1
Riza Putera SyamsuddinUniversity of Indonesia1
Farhan Makarim ZeinUniversity of Indonesia1
I Wayan Gede Krisna ArimjayaUniversity of Indonesia1
Ananda FitrianiAfiliasi tidak diketahui1
Ahmad MukhlishinAfiliasi tidak diketahui1
Aan SuhendriAfiliasi tidak diketahui1
S SunartoAfiliasi tidak diketahui1
Marimin MariminIPB University1
Etty RianiAfiliasi tidak diketahui1
Uju UjuAfiliasi tidak diketahui1
Rodiah Nurbaya SariAfiliasi tidak diketahui1
Sri Suryo SukoraharjoAfiliasi tidak diketahui1
Ema HastariniAfiliasi tidak diketahui1
Franciscus Edi PriyonoAfiliasi tidak diketahui1
Sunarto ZulkifliAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) http://jurnal.ugm.ac.id/ijg/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Object-Based Mangrove Mapping Comparison on Visible and NIR UAV Sensor</dc:title>
	<dc:creator>Khakhim, Nurul</dc:creator>
	<dc:creator>Marfai, Muh Aris</dc:creator>
	<dc:creator>Putri, Ratih Fitria</dc:creator>
	<dc:creator>Dimyati, Muhammad</dc:creator>
	<dc:creator>Untoro, Muhammad Adnan Shafry</dc:creator>
	<dc:creator>Adiwijaya, Raden Ramadhani Yudha</dc:creator>
	<dc:creator>Walinono, Taufik</dc:creator>
	<dc:creator>Lazuardi, Wahyu</dc:creator>
	<dc:creator>Pratama, Dimas Novandias Damar</dc:creator>
	<dc:creator>Wicaksono, Arief</dc:creator>
	<dc:creator>Musthofa, Azis</dc:creator>
	<dc:creator>Isnaen, Zulfikri</dc:creator>
	<dc:subject xml:lang="en-US">Geography; Mapping; Remote Sensing; UAV; Sensor; Mangrove</dc:subject>
	<dc:subject xml:lang="en-US">NIR-sensor; GEOBIA; Baros Mangrove Conservation Area; Bantul</dc:subject>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:description xml:lang="en-US">Mangrove ecosystems are natural resources that have potential value for development due to their high productivity. Mapping and identification of mangroves have always played a crucial role in mangrove ecosystem conservation efforts, especially to support the sustainable development goal of coastal resources and climate change issues. Several attempts have been made using Unmanned Aerial Vehicle (UAV) techniques acquisition of high spatial resolution aerial images data with various sensors and object-based classification for image processing with various levels of success. This study aims to identify mangrove objects using UAV with true color and NIR false-color sensors using the OBIA approach. The UAV used in this study was DJI Phantom 3 Pro with a true-color sensor (default) and NIR false-color (modified Canon IXUS 160 cameras). The comparison between the two types of sensor of aerial photographs as a source for mangrove mapping proved that the latter performed better than the former because of the near-infrared band can optimally discriminate between mangrove and non-mangrove objects. This will assist future research directions in the mangrove ecosystems mapping method.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Geography, Universitas Gadjah Mada</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2021-08-28</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-US">Peer-reviewed Article</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijg/article/view/50861</dc:identifier>
	<dc:identifier>10.22146/ijg.50861</dc:identifier>
	<dc:source xml:lang="en-US">Indonesian Journal of Geography; Vol 53, No 2 (2021): Indonesian Journal of Geography; 165-169</dc:source>
	<dc:source>2354-9114</dc:source>
	<dc:source>0024-9521</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijg/article/view/50861/pdf</dc:relation>
	<dc:coverage xml:lang="en-US"></dc:coverage>
	<dc:coverage xml:lang="en-US"></dc:coverage>
	<dc:coverage xml:lang="en-US"></dc:coverage>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Authors</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-nc/4.0</dc:rights>
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