PREDIKSI KONVERSI LAHAN PERTANIAN BERBASIS ARTIFICIAL NEURAL NETWORK-CELLULAR AUTOMATA (ANN-CA) DI KAWASAN SLEMAN BARAT

Jurnal Tanah dan Sumberdaya Lahan Pub Date : 2023-07-01 DOI:10.21776/ub.jtsl.2023.010.2.30

Tiara Sarastika, Y. Susena, Dwi Kurniawan

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Abstract

Analysis and prediction of land conversion using spatial-temporal data are essential for environmental monitoring and better land use planning and management. The West Sleman area has the potential to experience land use changes due to anthropogenic factors. This study aimed to determine the spatial-temporal dynamics of land use change in 2012-2022 and predict future land use change using the ANN-CA model for 20 years (2022-2042). Analyzed the spatial-temporal dynamics of land use change based on land use data derived from SPOT imagery, then predicted future land use change with the ANN-CA model using the MOLUSCE plugin on QGIS Desktop 2.18.11. The simulation results showed an accuracy of 86.66% and an overall Kappa value of 83% obtained by comparing the actual data in 2022 with the simulated data on land use change in the same year. The irrigated paddy fields decreased by 6.39% (685.22 ha) due to conversion to settlements. The area of residential buildings increased by 4.65% (498.49 ha) during 2012- 2017. Predictions of land use change in 2022-2042 show that the reduction of irrigated paddy fields will continue, and the number of residential buildings tend to increase.

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利用时空数据分析和预测土地转化对环境监测和更好的土地利用规划和管理至关重要。由于人为因素，西斯莱曼地区有可能经历土地利用的变化。本研究旨在确定2012-2022年土地利用变化的时空动态，并利用ANN-CA模型预测未来20年(2022-2042)的土地利用变化。基于SPOT影像获取的土地利用数据，分析土地利用变化的时空动态特征，利用QGIS Desktop 2.18.11上的MOLUSCE插件，利用ANN-CA模型对未来土地利用变化进行预测。将2022年土地利用变化的实际数据与模拟数据进行对比，模拟结果显示，准确度为86.66%，总体Kappa值为83%。水田面积减少6.39% (685.22 ha)。2012年至2017年，住宅建筑面积增加了4.65%(498.49公顷)。对2022-2042年土地利用变化的预测表明，灌溉水田数量将继续减少，住宅建筑数量有增加的趋势。

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Jurnal Tanah dan Sumberdaya Lahan

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