The application of remote sensing data has been significant in modeling soil erosion. However, previous studies have fallen short in elucidating and lacked an understanding of the multifactor influencing erosion. This study addresses these limitations by employing the InVEST and the Geodetector models. Specifically, it aims (1) to delineate both spatial and temporal variations in soil erosion within the Citarum watershed from 2010 to 2020, (2) to identify the key drivers of soil erosion and unravel the underlying mechanisms, and (3) to identify the high-risk zones for soil erosion. Both models consider a range of natural predictors, including topography (slope factor), climate (precipitation factor), and vegetation cover (vegetation factor). In addition, they incorporate social parameters such as income per capita and population density, which interact with the watersheds position in the downstream, middle, and upper streams. The results reveal that, over a decade, the average soil erosion increased by 15.50×10⁶ tons, marking a 16.65 surge. The impact of factors varies significantly across different subwatershed areas. For example, fraction vegetation cover interactions influence upper- and middle-stream regions, while the downstream area is notably affected by precipitation interactions. The high-risk erosion areas in the watershed are primarily influenced by slope, precipitation, and fractional vegetation cover. In these areas, factors causing high erosion risks include slope, precipitation, and other environmental variables categorized into strata. The study highlights the varying influential factors in different watershed areas.

This study investigates the application of remote sensing technologies to identify the biophysical characteristics of marine ecosystems for spatial planning, focusing on optimal conservation scenarios within the Raja Ampat Marine Protected Area (MPA) on Arefi Island, Southwest Papua, Indonesia. Indigenous communities manage this area. WorldView-3 satellite imagery, combined with an object-based image analysis (OBIA) approach, was used to classify and map coastal ecosystems. A Marine Reserve Design using the Spatially Explicit Annealing (Marxan) model was applied to delineate conservation areas and propose zoning strategies. Based on ecological values (EV), three scenarios were tested to prioritize conservation features while ensuring sustainable ecosystem use. Image analysis revealed that Arefi Island’s coastal ecosystems cover 64.78 hectares, consisting of seagrass beds (45.41%), coral reefs (36.35%), and mangroves (18.24%), with a kappa accuracy of 0.82. Results indicate that EV3 selects the highest number of planning units, ensuring broader conservation coverage, while EV1 selects the fewest. EV2 is the most budget-friendly option with the lowest cost, whereas EV3 is the most expensive. Ecological Scenario II provided a balanced approach, allocating larger areas for local community use while preserving conservation integrity. Moreover, sensitivity analysis confirmed that a conservation objective targeting 40% of the total area (EV II) is the most effective model for Arefi Island. The zoning breakdown under this scenario includes a core zone of 19.53 hectares, a utilization zone of 15.96 hectares, a sustainable fisheries zone of 15.67 hectares, and other zones covering 92.89 hectares. This study highlights the effectiveness of remote sensing and spatial planning tools, such as Marxan, in marine conservation within indigenously managed areas, emphasizing the importance of balancing conservation efforts with sustainable community use for future planning.

Rapid land use and land cover (LULC) changes in densely populated watersheds pose serious challenges to the sustainability of ecosystem services (ES), yet their spatially explicit economic consequences remain insufficiently understood. This study analyzes the spatio-temporal dynamics of LULC and ecosystem service values (ESVs) in the Citarum Watershed, Indonesia, one of the country’s most critical and intensively transformed watersheds. Multi-temporal Landsat imagery from 2003, 2013, and 2023 was classified using a Random Forest algorithm, while future LULC conditions for 2043 were projected using a Multi-layer Perceptron–Markov Chain (MLP–MC) model under three scenarios: Business-as-Usual (BAU), Protecting Paddy Field (PPF), and Protecting Forest Area (PFA). ESVs were quantified at multiple spatial scales (county, 250 m grids, and 100 m grids) using both the Traditional Benefit Transfer (TBT) method and a Spatial Benefit Transfer (SBT) approach that integrates biophysical indicators with socio-economic variables. The contribution of LULC transitions to ESV dynamics was further assessed using the Ecosystem Service Change Intensity (ESCI) index. The results reveal substantial historical forest and shrubland losses, alongside rapid expansion of settlements and dryland agriculture, indicating intensifying anthropogenic pressure on watershed functions. Scenario analysis shows continued degradation under BAU, limited mitigation under PPF, and improved forest retention under PFA; although settlement expansion persists across all scenarios. Total ESV declined from USD 2641.33 million in 2003 to USD 1585.01 million in 2023, representing a cumulative loss of 46.13%. Projections indicate severe ESV losses under BAU and PPF by 2043, while PFA substantially reduces, but does not eliminate economic degradation. ESCI results identify forest and shrubland conversion to settlements and dryland agriculture as the dominant drivers of ESV decline. These findings demonstrate that integrating multi-scenario LULC modeling with spatially explicit ESV assessment provides a more robust basis for ecosystem-based spatial planning and supports sustainable watershed management under increasing development pressure.

This is a preliminary result study on biodiversity index evaluation based on satellite analysis in shallow water areas managed by the community. This study explores high-resolution satellite image analysis methods for assessing biophysics parameters in shallow water areas, installed sensors for monitoring water quality indexes, and GIS analysis for marine zonation mapping. This initial study review highlights the potential of satellite imagery for biodiversity assessment and emphasizes the importance of integrating remote sensing data with ground-based observations for accurate monitoring. A simple NDVI and lyzenga of Plannet high-resolution satellite imagery is used to assess coastal habitat cover, focussing on coral reefs, seagrasses, and mangroves. Marxan, a systematic conservation planning model, designs conservation zones based on ecological and socio-economic criteria. The results reveal coral reefs as dominant habitats, highlighting the importance of protecting these ecosystems. The study also measured water quality index and socio-economic factors such as ports and settlements for Marxan analysis. Core zones resulting from Marxan analysis are recommended for marine protected area implementation within Community-based and ecosystem-based management approaches. This preliminary result showed that remote sensing data plays a crucial role in informing decision-making for marine conservation, facilitating more effective and detailed conservation planning in the region. Overall, this research provides valuable insights for marine shallow water planning and conservation effort.

Water conservation (WC) is a vital ecosystem service (ES) that plays an essential role in the sustainable management of water resources and ensures ecological security. This research examines the WC capacity of the Citarum watershed in West Java Province, Indonesia, from 2010 to 2020. The specific objectives of this research are as follows: (1) to assess the 10-year temporal and spatial variations of WC using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) and topographic index model; (2) to analyze the temporal interchange between WC and its influencing factors through the Spatial Elastic Coefficient Trajectory Model (SECTM); and (3) to determine the driving factors (DFs) affecting WC by applying Multiscale Geographically Weighted Regression (MGWR). Key findings of this study reveal significant trends in WC from 2010 to 2020: the average WC in the Citarum watershed decreased from 513.96 mm/ha to 453.64 mm/ha (11.74%), indicating a concerning decline in ES capacity. This study also noted that regions implementing comprehensive regulations dominated the study area, covering approximately 72.70% of the total area (502,295 hectares). This illustrates that the implementation of rules plays a crucial role in the WC. Lastly, the MGWR analysis found that WC in the Citarum watershed positively correlated with topography, climate, and vegetation while negatively correlating with socioeconomic factors. This indicates that WC levels are generally lower in areas with higher human activity and economic growth, highlighting the impact of anthropogenic pressures on natural resources. This framework helps stakeholders plan to ensure sustainable development in the area, as it provides valuable insights into the interactions between the ecological and socioeconomic factors affecting WC.

Coastal flooding driven by sea-level rise and land subsidence poses severe risks to low-lying communities. This study evaluates the causes and impacts of coastal sinking in Sayung, Demak, Central Java, using multi-temporal Landsat imagery (1977, 2024), tidal gauge data, and GPS measurements. A set of spectral indices—Normalized Difference Vegetation Index (NDVI), Weighted Modified Normalized Difference Water Index (WMNDWI), Land Surface Water Index (LSWI), and Normalized Difference Built-up Index (NDBI)—were calculated and integrated as input features for a Random Forest machine learning model to detect and classify environmental changes. Results indicated an average land subsidence rate of approximately 6 cm/year ± 0.8 cm/year, validated against InSAR-based measurements, and a classification accuracy of 91% (RMSE of 0.8 cm/year). A substantial decline in vegetation indices was observed, reflecting the conversion of agricultural land into built-up areas and water bodies. Extensive flooding and shoreline retreat were documented, with high-risk zones concentrated along densely developed coastlines. These findings highlight the urgent need for integrated management strategies, including stricter groundwater regulation, continuous remote-sensing-based monitoring, and large-scale mangrove restoration, to safeguard ecological functions and enhance the socio-economic resilience of coastal communities in the face of accelerating climate change impacts.