Fadhlullah Ramadhani

Palm Oil Mill Effluent (POME) treatment ponds play a dual role in pollutant removal and greenhouse gas (GHG) emissions. This study examines the relationship between water quality parameters and methane (CH₄) fluxes across a tropical multi-pond POME treatment system operating under continuous...

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.

Reliable estimation of oil palm carbon stock is essential for climate mitigation, concession management, and sustainability certification. While satellite-based approaches offer scalable solutions, redundancy among spectral indices and inter-sensor variability complicate model development. This study evaluates machine learning regressors for predicting oil palm carbon stock at tree (CO_tree, kg C tree⁻¹) and hectare (CO_ha, Mg C ha⁻¹) scales using spectral indices derived from Landsat-8, Landsat-9, and Sentinel-2. Fourteen vegetation indices were screened for multicollinearity, resulting in a lean feature set dominated by NDMI, EVI, MSI, NDWI, and sensor-specific indices such as NBR2 and ARVI. Ten regression algorithms were benchmarked through cross-validation. Ensemble models, particularly Random Forest, Gradient Boosting, and XGBoost, outperformed linear and kernel methods, achieving R² values of 0.86–0.88 and RMSE of 59–64 kg tree⁻¹ or 8–9 Mg ha⁻¹. Feature importance analysis consistently identified NDMI as the strongest predictor of standing carbon. Spatial predictions showed stable carbon patterns across sensors, with CO_tree ranging from 200–500 kg C tree⁻¹ and CO_ha from 20–70 Mg C ha⁻¹, consistent with published values for mature plantations. The study demonstrates that ensemble learning with sensor-specific index sets provides accurate, dual-scale carbon monitoring for oil palm. Limitations include geographic scope, dependence on allometric equations, and omission of belowground carbon. Future work should integrate age dynamics, multi-year composites, and deep learning approaches for operational carbon accounting.

Agricultural drought poses a major threat to rice production in Indonesia, highlighting the need for dynamic prediction to support timely and effective management strategies. This study aims to develop a new approach for predicting rice drought stress that incorporates the characteristics of SPI3, emphasizing onset and trends, and to evaluate the model’s accuracy in predicting rice drought. The onset of SPI3 denotes conditions at the start of the planting season, while the SPI3 trend represents the four-month gradient from planting to harvest. The Normalized Difference Vegetation Index (NDVI) derived from MODIS was utilized to validate the spatial and temporal predictions of rice drought using the Proportion Correct (PC) method. The model performs most reliably in capturing severe droughts during the dry season, with accuracies in the very high drought category ranging from 60% to 85%. Performance declines in March and August, highlighting challenges during the transitions between wet and dry seasons. During the El Niño year, predictions aligned with observed very high drought (PC: 59–77%), whereas in the La Niña year, they matched the low drought category (PC: 72–76%). Comparable prediction accuracies in Indramayu and Bone indicate the feasibility of developing a generalized model for Indonesia’s diverse rice-producing areas. Future improvements should integrate higher-resolution data and machine learning, account for local irrigation practices, and expand validation across regions to enhance model transferability and comprehensively assess its performance.

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.