Identification of agricultural land use change based on machine learning for regional food security analysis in the mountainous region of Kulon Progo regency

Abstract

After New Yogyakarta International Airport (NYIA) opening, Kulon Progo continues to develop, one of which is The Bedah Menoreh route project which passes through the mountainous region. The development encourages agricultural land conversion, which impacts food security in Kulon Progo, especially in the mountainous region. This study aims to identify the conversion of agricultural land in the mountainous region of Kulon Progo Regency in 2005 – 2020 and analyze its impact on regional food security. The method used is a Normalized Difference Vegetation Index (NDVI) on Landsat Imagery using Machine Learning through Google Earth Engine (GEE) to identify land-use change and mathematical calculations in analyzing regional food security. The result of the supervised classification is a land cover map of the mountainous region of Kulon Progo Regency, which shows that every year the area of rice fields, in general, continues to decrease until 2020 the total area is 2,102.79 ha with a rate of agricultural land conversion -114.87 ha/year. It causes regional food security to be in a food-insecure condition, even though the availability of rice fields can be used for food self-sufficiency for up to 53 years. Other factors such as climate, rice seeds, soil, and water quality, in this case, are quite influential in rice production, not only productivity and agricultural land area.

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

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