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Machine Learning-Based Rice Field Mapping in Kulon Progo using a Fusion of Multispectral and SAR Imageries

Forum Geografi · Vol. 37 No. 2 (2023) · hlm. 134–148

DOI
10.23917/forgeo.v37i2.20304
ISSN
2460-3945, 0852-0682
Terbit
2023-12-29
Penerbit
Universitas Muhammadiyah Surakarta
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-nc-nd/4.0

Abstrak

The land-conversion of rice fields can reduce rice production and negatively impact food security. Consequently, monitoring is essential to prevent the loss of productive agricultural land. This study uses a combination of Sentinel-2 MSI, Sentinel-1 SAR, along with SRTM (elevation and slope data) to monitor rice fields land-conversion. NDVI, NDBI and NDWI indices are transformed from the annual median composite Sentinel-2 MSI images used to identify different rice fields with another object. A monthly median composite of SAR images from Sentinel-1 data are used to identify cropping patterns of rice fields in the inundation phase. The classification is performed by using the Random Forest machine learning algorithm in the Google Earth Engine (GEE) platform. Random Forest classification is run using 1000 trees, with a 70:30 ratio of training and testing data from sample features extracted by visual interpretation of high resolution Google Earth imagery. In this study, Random Forest classification is effective in computing a high amount of multi-temporal and multi-sensory data to map rice-field land conversion with an accuracy rate of 96.16% (2021) and 95.95% (2017) for mapping paddy fields. From the multitemporal rice field maps in 2017—2021, a conversion of 826.66 hectares of rice-fields to non-rice fields was identified. Based on the spatial distribution, the conversion from rice-field to non-rice field is higher at the area near the roads, built area and Yogyakarta International Airport. Therefore, it is important to assess and ensure that National Strategic Projects are managed with due regard to environmental impacts and food security.

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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
Muhammad KamalUniversitas Gadjah Mada3
Projo DanoedoroUniversitas Gadjah Mada2
Sigit HerumurtiAfiliasi tidak diketahui1
Prama Ardha AryagunaUniversitas Esa Unggul1
Yanuar Adji NugrohoUniversitas Gadjah Mada1
Prima WidayaniUniversitas Gadjah Mada1
Muhammad Sufwandika WijayaUniversitas Gadjah Mada1
Hartono HartonoUniversitas Gadjah Mada1
Pramaditya WicaksonoUniversitas Gadjah Mada1
Novi Susetyo AdiMinistry of Marine Affairs and Fisheries1
Uji Astrono PribadiCenter for International Forestry Research1
Gilang Aria SetaAfiliasi tidak diketahui1
Abimanyu Putra PratamaUniversitas Gadjah Mada1
Intan LestariUniversitas Gadjah Mada1
Siti ZahrotunisaUniversitas Gadjah Mada1
Maulana Abdul HakimUniversitas Gadjah Mada1

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Sumber metadata: OAI-PMH (metadata jurnal) http://journals.ums.ac.id/index.php/fg/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">Machine Learning-Based Rice Field Mapping in Kulon Progo using a Fusion of Multispectral and SAR Imageries</dc:title>
	<dc:creator>Khoirurrizqi, Yusri</dc:creator>
	<dc:creator>Sasongko, Rohmad</dc:creator>
	<dc:creator>Utami, Nur Laila Eka</dc:creator>
	<dc:creator>Irbah, Amanda</dc:creator>
	<dc:creator>Arjasakusuma, Sanjiwana</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Rice field;Land Conversion;Remote sensing;Multi-Sensor;Machine Learning</dc:subject>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:description xml:lang="en-US">The land-conversion of rice fields can reduce rice production and negatively impact food security. Consequently, monitoring is essential to prevent the loss of productive agricultural land. This study uses a combination of Sentinel-2 MSI, Sentinel-1 SAR, along with SRTM (elevation and slope data) to monitor rice fields land-conversion. NDVI, NDBI and NDWI indices are transformed from the annual median composite Sentinel-2 MSI images used to identify different rice fields with another object. A monthly median composite of SAR images from Sentinel-1 data are used to identify cropping patterns of rice fields in the inundation phase. The classification is performed by using the Random Forest machine learning algorithm in the Google Earth Engine (GEE) platform. Random Forest classification is run using 1000 trees, with a 70:30 ratio of training and testing data from sample features extracted by visual interpretation of high resolution Google Earth imagery. In this study, Random Forest classification is effective in computing a high amount of multi-temporal and multi-sensory data to map rice-field land conversion with an accuracy rate of 96.16% (2021) and 95.95% (2017) for mapping paddy fields. From the multitemporal rice field maps in 2017—2021, a conversion of 826.66 hectares of rice-fields to non-rice fields was identified. Based on the spatial distribution, the conversion from rice-field to non-rice field is higher at the area near the roads, built area and Yogyakarta International Airport. Therefore, it is important to assess and ensure that National Strategic Projects are managed with due regard to environmental impacts and food security. </dc:description>
	<dc:publisher xml:lang="en-US">Universitas Muhammadiyah Surakarta</dc:publisher>
	<dc:contributor xml:lang="en-US">Pusat Studi Lingkungan Hidup Universitas Gadjah Mada</dc:contributor>
	<dc:date>2023-12-29</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journals.ums.ac.id/index.php/fg/article/view/20304</dc:identifier>
	<dc:identifier>10.23917/forgeo.v37i2.20304</dc:identifier>
	<dc:source xml:lang="en-US">Forum Geografi; Vol 37, No 2 (2023): December 2023; 134-148</dc:source>
	<dc:source>2460-3945</dc:source>
	<dc:source>0852-0682</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.ums.ac.id/index.php/fg/article/view/20304/8853</dc:relation>
	<dc:relation>https://journals.ums.ac.id/index.php/fg/article/view/20304/8930</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) 2023 Yusri Khoirurrizqi, Rohmad Sasongko, Nur Laila Eka Utami, Amanda Irbah, Sanjiwana Arjasakusuma</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-nc-nd/4.0</dc:rights>
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