Abstract:This paper attempts to unveil the hidden potential of the local food through local food mapping, drawing local food potential based on the “triple burden” theory from Professor Moerdijati Gardjito. An index, called “index of food relocalisation” is adopted and then modified into different name called local food index due to data availability, which is expected to provide a geographical location of the local food potential by proposing a research questions: where do the local food potentials distribute in Yogya… Show more
“…11c). The observed conditions from both figures, we can see that the croplands are attributed to the physical conditions, for example the topography, water, and agroecology characteristic (Widiyanto, 2019). Implementation of RF classification with Sentinel-1 and Sentinel-2 dataset resulted a land cover map for Boyolali Regency (Fig.…”
Mapping agricultural land cover data is important as an effort to support national food security, especially in Boyolali, Central Java Province, Indonesia which is one of the national rice granaries. However, mapping in the mountain slope area using optical data only is challenging due to cloud cover. The development of remote sensing technology has encouraged the possibility to integrate data with different sensors. This data integration is needed to optimize the ability to detect and map cropland that has a variety of characteristics. Therefore, this study aims to identify the cropland through the integration of time-series optical and Synthetic Aperture Radar (SAR) data. Detection of cropland was carried out using 2021 data. Polarisation of VV, VH, and ratio of VV/VH data was derived from the Sentinel-1, whereas image indices of Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Soil Adjusted Vegetation Index (SAVI) data were obtained from Sentinel-2. Data of Sentinel-1 and Sentinel-2 was combined and several features were selected based on their importance score. Random Forest (RF) classification was then performed. The result show that the mapping using integrated data could improve the accuracy. This indicates the possibility of data to be implemented in further studies such as the cropland type mapping and the estimation of food productivity.
“…11c). The observed conditions from both figures, we can see that the croplands are attributed to the physical conditions, for example the topography, water, and agroecology characteristic (Widiyanto, 2019). Implementation of RF classification with Sentinel-1 and Sentinel-2 dataset resulted a land cover map for Boyolali Regency (Fig.…”
Mapping agricultural land cover data is important as an effort to support national food security, especially in Boyolali, Central Java Province, Indonesia which is one of the national rice granaries. However, mapping in the mountain slope area using optical data only is challenging due to cloud cover. The development of remote sensing technology has encouraged the possibility to integrate data with different sensors. This data integration is needed to optimize the ability to detect and map cropland that has a variety of characteristics. Therefore, this study aims to identify the cropland through the integration of time-series optical and Synthetic Aperture Radar (SAR) data. Detection of cropland was carried out using 2021 data. Polarisation of VV, VH, and ratio of VV/VH data was derived from the Sentinel-1, whereas image indices of Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Soil Adjusted Vegetation Index (SAVI) data were obtained from Sentinel-2. Data of Sentinel-1 and Sentinel-2 was combined and several features were selected based on their importance score. Random Forest (RF) classification was then performed. The result show that the mapping using integrated data could improve the accuracy. This indicates the possibility of data to be implemented in further studies such as the cropland type mapping and the estimation of food productivity.
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