![]() ![]() At the category level, IA results showed that the area of agricultural land experienced net losses in both periods, with net loss in 2013–2020 being 2.3 times greater than that in 2003–2013 (∼1,850 ha per year). IA outputs at interval levels for all categories showed that the annual change-of-area rate was higher during 2013–2020 than during 2003–2013. The overall accuracy of Landsat image classification results for 2003, 2013, and 2020 were 88%, 87%, and 88%, respectively. Monitoring LULC change using GEE and IA has demonstrated reliable findings. Landsat data and a robust random forest (RF) classifier available in GEE were chosen for producing LULC maps. Therefore, this study aims to monitor urban penetration to agricultural land in the north coastal region of West Java Province by applying both methods to two time intervals: 2003–20–2020. As yet, however, no study of land conversion from agriculture to urban areas in Indonesia has adopted GEE and IA approaches simultaneously. ![]() Intensity analysis (IA) is increasingly being used to systematically and substantially analyze land-use/land-cover (LULC) change. Remote sensing technologies have developed rapidly in recent years, including the creation of Google Earth Engine (GEE). ![]() This situation is being experienced by the densely populated and fertile island Java in Indonesia. Uncontrolled urban expansion resulting from urbanization has a disastrous impact on agricultural land. ![]()
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