Recognition of Agricultural Land-Use Change with Machine Learning-Based for Regional Food Security Assessment in Kulon Progo Plains Area

Zulfa Khoirun Nisa, Ansita Gupitakingkin Pradipta, Liana Ni'mathus Sholikah, Bangkit Fatwa Pratama, Akram Sripandam Prihanantya, - Ngadisih, Sahid Susanto, Sigit Supadmo Arif

Abstract


High conversion of agricultural land in Kulon Progo Regency, as such the construction of the Yogyakarta International Airport (YIA) and the Bedah Menoreh road, has resulted in food production and impacted food security, including Kulon Progo plains area. This study aimed to calculate the conversion rate of agricultural land and analyze its impact on food security in the Kulon Progo plains area from 2005 to 2020. The primary materials needed are Kulon Progo administrative maps, Landsat 7 and 8 images, land productivity data, population data, and consumption per capita data. With tools used is Google Earth Engine (GEE), SPSS 25, Google Earth Pro, and ArcGIS 10.3. The method used is calculating the Normalized Difference Vegetation Index (NDVI) and machine learning-based classification through GEE to identify land-use change and analyze the state of food security. The study proved that between 2015 and 2020, there was a conversion of paddy fields, with an average rate of 126 ha/year. The existence of new paddy fields influenced this land increase. However, in 2020 there is still food insecurity in Pengasih District, thus caused by the new paddy fields not being optimally used for rice growth. The productivity of the land produced is not optimal. With the availability of agricultural land in 2020 (1382.85 ha), food self-sufficiency will be limited for the next 24.75 years if there is no effort to increase paddy fields.

Keywords


Land-use change; agricultural; machine learning; GEE; food security.

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References


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DOI: http://dx.doi.org/10.18517/ijaseit.13.1.16550

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