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Satellite Imagery for Classification of Rice Growth Phase Using Freeman Decomposition in Indramayu, West Java, Indonesia

HAYATI Journal of Biosciences · Vol. 25 No. 3 (2018)

DOI
10.4308/hjb.25.3.126
ISSN
2086-4094, 1978-3019
Terbit
2018-10-24
Penerbit
Bogor Agricultural University, Indonesia
Bahasa
eng

Abstrak

Monitoring at every growth of rice plants is an important information for determining the grain pro-duction estimation of rice. Monitoring must to be have timely work on the rice plant development. However, timely monitoring and the high accuracy of information is a challenge in remote sensing based on rice agriculture monitoring and observation. With increased quality of synthetic aperture radar (SAR) systems utilizing polarimetric information recently, the development and applications of polarimetric SAR (PolSAR) are one of the current major topics in radar remote sensing. The ad-vantages provided by PolSAR data for agricultural monitoring have been extensively studied for applications such as crop type classification and mapping, crop phenology monitoring, productivity assessment based on the sensitivity of polarimetric parameters to indicators of crop conditions. Freeman and Durden successfully decomposed fully PolSAR data into three components: Single bounce, double bounce, and volume scattering. The three-component scattering provide features for distinguishing between different surface cover types. These sensitivities assist in the identification of growing phase. The observed growing phase development in time series, reflected in the consistent temporal trends in scattering, was generally in agreement with crop phenological development stages. Supervised classification was performed on repeat-pass Radarsat-2 images, with an overall classification accuracy of 77.27% achieved using time series Fine beam data. The study demonstrated that Radarsat-2 Fine mode data provide useful information for crop monitoring and classification of rice plants.

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  • Bandung Institute of Technology
  • National Research and Innovation Agency
  • Sumatera Institute of Technology
  • Lampung University
  • Udayana University
  • Indonesian National Institute of Aeronautics and Space
  • Chiba University
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Rekan penulisOrganisasiArtikel bersama
Tri Muji SusantoroNational Research and Innovation Agency11
Deni SuwardhiBandung Institute of Technology7
Mochamad Firman GhazaliLampung University5
Fenny Martha DwivanyBandung Institute of Technology4
Riantini VirtrianaBandung Institute of Technology4
Sukristiyanti SukristiyantiBandung Institute of Technology4
Sayidah SulmaIndonesian National Institute of Aeronautics and Space4
Suliantara SuliantaraNational Research and Innovation Agency4
Herru Lastiadi SetiawanNational Research and Innovation Agency4
Adis JayatiNational Research and Innovation Agency4
Kurdianto KurdiantoNational Research and Innovation Agency4
Soni DarmawanBandung Institute of Technology3
Dudung Muhally HakimBandung Institute of Technology3
I Wayan NuarsaUdayana University3
Shafarina Wahyu TrisyantiBandung Institute of Technology3
Ishak Hanafiah IsmullahBandung Institute of Technology3
I NyomanUdayana University2
Agus SutantoAndalas University2
Husna NugrahaprajaBandung Institute of Technology2
Anggun TridawatiBandung Institute of Technology1
Mamad SugandiLampung University1
Aqilla Fitdhea AnestaLampung University1
Laode M Golok JayaAfiliasi tidak diketahui1
Armi SusandiBandung Institute of Technology1
Katmoko Ari SambodoIndonesian National Institute of Aeronautics and Space1
Muhammad Ulin NuhaSumatera Institute of Technology1
Arif RohmanSumatera Institute of Technology1
Adam Irwansyah FauziSumatera Institute of Technology1
Nesya Hafiza ArdaniSumatera Institute of Technology1
Redho Surya PerdanaSumatera Institute of Technology1
Aynaz LotfataUniversity of California, Davis1
M. Angga Hadi PratamaAfiliasi tidak diketahui1
Hasan Adi NugrahaBandung Institute of Technology1
Anjar Dimara SaktiBandung Institute of Technology1
Muhammad Alsamtu Tita Sabila Pratama SuhartonoAkita University1

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Sumber metadata: OAI-PMH (metadata jurnal) http://journal.ipb.ac.id/index.php/hayati/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">Satellite Imagery for Classification of Rice Growth Phase Using Freeman Decomposition in Indramayu, West Java, Indonesia</dc:title>
	<dc:creator xml:lang="en">Nurtyawan, Rian</dc:creator>
	<dc:creator xml:lang="en">Saepuloh, Asep</dc:creator>
	<dc:creator xml:lang="en">Harto, Agung Budi</dc:creator>
	<dc:creator xml:lang="en">Wikantika, Ketut</dc:creator>
	<dc:creator xml:lang="en">Kondoh, Akihiko</dc:creator>
	<dc:subject xml:lang="en">Growing phase of rice plants</dc:subject>
	<dc:subject xml:lang="en">Freeman-Durden Decomposition Model</dc:subject>
	<dc:subject xml:lang="en">Classification of Freeman-Durden Decomposition</dc:subject>
	<dc:description xml:lang="en">  Monitoring at every growth of rice plants is an important information for determining the grain pro-duction estimation of rice. Monitoring must to be have timely work on the rice plant development. However, timely monitoring and the high accuracy of information is a challenge in remote sensing based on rice agriculture monitoring and observation. With increased quality of synthetic aperture radar (SAR) systems utilizing polarimetric information recently, the development and applications of polarimetric SAR (PolSAR) are one of the current major topics in radar remote sensing. The ad-vantages provided by PolSAR data for agricultural monitoring have been extensively studied for applications such as crop type classification and mapping, crop phenology monitoring, productivity assessment based on the sensitivity of polarimetric parameters to indicators of crop conditions. Freeman and Durden successfully decomposed fully PolSAR data into three components: Single bounce, double bounce, and volume scattering. The three-component scattering provide features for distinguishing between different surface cover types. These sensitivities assist in the identification of growing phase. The observed growing phase development in time series, reflected in the consistent temporal trends in scattering, was generally in agreement with crop phenological development stages. Supervised classification was performed on repeat-pass Radarsat-2 images, with an overall classification accuracy of 77.27% achieved using time series Fine beam data. The study demonstrated that Radarsat-2 Fine mode data provide useful information for crop monitoring and classification of rice plants.</dc:description>
	<dc:publisher xml:lang="en">Bogor Agricultural University, Indonesia</dc:publisher>
	<dc:date>2018-10-24</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>http://journal.ipb.ac.id/hayati/article/view/23825</dc:identifier>
	<dc:identifier>10.4308/hjb.25.3.126</dc:identifier>
	<dc:source xml:lang="en">HAYATI Journal of Biosciences; Vol. 25 No. 3 (2018): July 2018; 126</dc:source>
	<dc:source>2086-4094</dc:source>
	<dc:source>1978-3019</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>http://journal.ipb.ac.id/hayati/article/view/23825/15501</dc:relation>
</oai_dc:dc>