首页|Studies from University Gadjah Mada in the Area of Machine Learning Described (Machine Learning-Based Rice Field Mapping in Kulon Progo using a Fusion of Multispectral and SAR Imageries)
Studies from University Gadjah Mada in the Area of Machine Learning Described (Machine Learning-Based Rice Field Mapping in Kulon Progo using a Fusion of Multispectral and SAR Imageries)
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Investigators publish new report on artificial intelligence. According to news originating from the University Gadjah Mada by NewsRx editors, the research stated, "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." The news journalists obtained a quote from the research from University Gadjah Mada: "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."
University Gadjah MadaCyborgsEmerging TechnologiesMachine Learning
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