Uncontrolled urban expansion resulting from urbanization has a disastrous impact on agricultural land. This situation is being experienced by the densely populated and fertile island Java in Indonesia. Remote sensing technologies have developed rapidly in recent years, including the creation of Google Earth Engine (GEE). Intensity analysis (IA) is increasingly being used to systematically and substantially analyze land-use/land-cover (LULC) change. As yet, however, no study of land conversion from agriculture to urban areas in Indonesia has adopted GEE and IA approaches simultaneously. 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: 2003–2013 and 2013–2020. Landsat data and a robust random forest (RF) classifier available in GEE were chosen for producing LULC maps. Monitoring LULC change using GEE and IA has demonstrated reliable findings. The overall accuracy of Landsat image classification results for 2003, 2013, and 2020 were 88%, 87%, and 88%, respectively. 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. 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). In contrast, the built-up area made net gains in both periods, reaching almost twice as much in the second period as in the first (∼2,030 ha per year). The transition-level IA performed proved that agricultural land had been the primary target for the expansion of built-up areas. The most extensive spatial distribution of land conversion from agriculture to built-up area was concentrated in the regencies of Bekasi, Karawang, and Cirebon. These findings are intended to provide stakeholders with enrichment in terms of available literature and with valuable inputs useful for identifying better urban and regional planning policies in Indonesia and similar regions.

Indonesia faces significant challenges in meeting food security targets due to rapid agricultural land loss, with approximately 1.22 million hectares of rice fields converted between 1990 and 2022. Therefore, this study developed a prediction model for the loss of rice fields by 2030, incorporating land productivity attributes, specifically rice cropping intensity/RCI, using geospatial technology—a novel method with a resolution of approximately 10 m for quantifying ecosystem service (ES) impacts. Land use/land cover data from Landsat images (2013, 2020, 2024) were classified using the Random Forest algorithm on Google Earth Engine. The prediction model was developed using a Multi-Layer Perceptron Neural Network and Markov Cellular Automata (MLP-NN Markov-CA) algorithms. Additionally, time series Sentinel-1A satellite imagery was processed using K-means and a hierarchical clustering analysis to map rice fields and their RCI. The validation process confirmed high model robustness, with an MLP-NN Markov-CA accuracy and Kappa coefficient of 83.90% and 0.91, respectively. The present study, which was conducted in Indramayu Regency (West Java), predicted that 1602.73 hectares of paddy fields would be lost within 2020–2030, specifically 980.54 hectares (61.18%) and 622.19 hectares (38.82%) with 2 RCI and 1 RCI, respectively. This land conversion directly threatens ES, resulting in a projected loss of 83,697.95 tons of rice production, which indicates a critical degradation of service provisioning. The findings provide actionable insights for land use planning to reduce agricultural land conversion while outlining the urgency of safeguarding ES values. The adopted method is applicable to regions with similar characteristics.

The Cisadane River, Tangerang City, is one of the most important rivers in Indonesia. This river provides raw water for Municipal Waterworks (PDAM) of the Tangerang City and Soekarno-Hatta International Airport, but the water quality tends to deteriorate due to pollution. The government has made...

Payment for ecosystem services is a conservation strategy designed to offer farmers financial incentives for managing land to provide ecological benefits without disturbing livelihoods. However, the distribution of spatial financial feasibility is challenging when implementing this strategy on watershed scale. This study aimed to develop payment for ecosystem services model to improve quality in lake water catchment. The model estimated incentive values based on the costs of farmers’ losses, water yields, and pollution loads. The potential loss was calculated by determining the income of farmers in lake water catchment spent on land conversion from intensive agriculture to agroforestry. Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) modeling tool was used to calculate water yield and pollution load. The model was tested with case study approach at Lake Rawa Pening in Indonesia, consisting of nine sub-basins and 75 village administrations. The results showed that the reference compensation for farmers was 1,255.97 USD/ha/year. Considering the spatial distribution of water yields, the incentive for each village varied widely from 891.54 USD/ha/year to 1,557.06 USD/ha/year, even within the same sub-basin. Ten villages had an incentive above 1,450.00 USD/ha/year. However, considering the water pollution load, 26 villages had an incentive above 1,450.00 USD/ha/year with a maximum of 2,024.17 USD/ha/year. Therefore, village boundary should be an analysis unit for determining spatial incentive feasibility rather than a sub-basin boundary. Moreover, the level of water pollution load can become an additional variable to justify the amount of incentives received by farmers.

Cimanuk river is one of the seven rivers in West Java. Cimanuk river pollution is indicated to have suffered as a result of the activity of domestic waste, industrial and agricultural uncontrolled in the riparian area of Cimanuk river. This research aims to analize water quality of Cimanuk river based on water quality standard on Government Regulation of Republic of Indonesia (IDN), Regulation of the Governor of West Java (WJ), World Health Organization (WHO), Enviromental Standard of United Kingdom (UK), Environmental Protection Agency (EPA), Enviromental Quality Standard (EQS), and Department of Environment (DOE). This research used pollution index method. The result is water quality of Cimanuk river not meet water quality standard where the TSS (94.85±84,60 mg/L), BOD (9.61±3.16 mg/L), COD (37.69±14.01 mg/L), DO (5.12±1.22 mg/L), NH3N (0.25±0.24 mg/L). Degradation of water quality of Cimanuk river from upstream to downstream marked by increased pollution index value annually. Pollution index of Cimanuk river ranging from 1.25 to 20.31. Water quality status of Cimanuk river has been from lightly polluted to heavilypolluted.