LAND COVER PREDICTION OF INDRAMAYU REGENCY USING THE CELLULLAR AUTOMATA METHOD AND ITS RELATION TO THE REBANA PRIORITY AREA DEVELOPMENT PLAN - Anggraini Leksono, Ayubella
Industrial Estate (KPI) have the potential to trigger land conversion and threaten Food Agricultural Land (LP2B). An accurate land cover prediction method is needed to control land conversion. The study aims is to predict the land cover of Indramayu Regency in 2031 using the Cellular Automata (CA) method and to analyze its relation to the Rebana KPI development plan. Land cover maps is produced based on Landsat satellite imagery with hybrid classification (supervised and unsupervised) using training sample images of NDBI, NDVI, NDWI, Google Earth, and field surveys. The CA method is carried out using an Artificial Neural Network algorithm based on the business as usual scenario and the KPI scenario built in 2026 and 2031. The variables of distance to roads, to existing settlements, to coastlines and population density, are used as driving factors of land cover change. This study also analyzes the suitability of 2031 land cover against LP2B and KPI. A land cover map is produced with a good accuracy of 64.45% (overall accuracy). The 2011-2016 land cover image is used as a transition rule for prediction of 2031 land cover. Land change in 2011-2016 and prediction of 2031 land cover (in all scenarios) show a significant increase in built-up area and decrease in agricultural land. There was an increase in built-up land of 34,212.71 hectares (business as usual), 45,116.35 hectares (2026 KPI scenario), and 51,915.26 hectares (2031 KPI scenario) or respectively 517.97%, 683.08%, and 786.02% of land built up by 2021. There has been a decline in agricultural land of 31,121.28 hectares (business as usual), 38,087.00 hectares (2026 KPI scenario), 42,879.49 hectares (2031 KPI scenario) or a row of 21.74% , 25.21%, and 29.96% of the agricultural land area in 2021. Analysis on LP2B land shows a decrease in agricultural land of 21.06% of the agricultural land area in 2021 (business as usual scenario), 26.73% (KPI scenario 2026), and 29.01% (2031 KPI scenario). On KPI land, it shows that KPI has the potential to cause conversion of agricultural land area of 12,282.62 hectares or 61.86% of the total KPI or 9.76% of the total LP2B. In addition, 7,176.45 hectares (36.14% of the total KPI) are located in water body which is prone to flooding and can threaten the preservation of water resources. Policies are needed to control land conversion and land use management to optimize LP2B and KPI. The data and methods developed in this study can be used to develop policies for controlling land conversion and spatial planning in Indramayu Regency