Muhammad Rokhis Khomarudin

This paper provide an explanation about environment problems, conservation attempts, and integrated environment planning approach in the frame of ecosystem services conservation. The literature exploration is used to develop analysis on environmental management, conservation, coastal environment problems, evaluation approach, and environment planning. Whilst, the society’s perceptional approach is used to analyze the socio cultural discussion. The result shows that better environment quality will keep the ecosystem services quality maintained well. The environment valuation is needed to identify the conditional changes, thus the conservation approach and planning can be selected appropriately. Kulon Progo Coastal Area is specific because there has been an environment change because of the building of a big capacity international airport. There are many impacts on cultural ecosystem services in the future, otherwise some environment managements are applied. This paper recommends a coastal area management approach comprehensively. The recommendation is the the PCRM_PI ((Protect, Connect, Restore, Manage, Partner, Inform) and integrated coastal spatial planning using Marine Spatial Planning (MSP).

Water-level monitoring in rice paddies supports sustainable farming, responsible water management, and greenhouse gas emission mitigation. SAR-based remote sensing is an effective alternative for estimating water levels, especially in regions where optical observations are limited. This study evaluates ten ALOS-2/PALSAR-2 L-band SAR-derived polarimetric parameters for their contribution and effectiveness in water-level estimation across rice-growing phases using random forest regression in the Subang District, which is one of the largest rice-yield areas in West Java, Indonesia. Overall, L-band polarimetric information is clearly related to water-level dynamics throughout the rice-growing cycle, confirming its strong potential for quantitative water-level retrieval. The highest estimation accuracy was achieved by integrating all polarimetric parameter groups (MAE = 1.37 cm, RMSE = 1.79 cm, R² = 0.52, r = 0.73), indicating that no single group can adequately represent the complex scattering mechanisms governing water-level variability across an entire cropping season. Variable importance analysis shows a relatively uniform contribution (7.63–12.90%), suggesting synergies across parameters in water-level estimation. Phase-specific evaluation further reveals that Phase 2, corresponding to the vegetative-to-generative transition, is the optimal temporal window for L-band SAR-based water-level retrieval due to enhanced double-bounce scattering and reduced signal saturation. While Phase 2 data maximizes physical sensitivity and correlation, whole-phase modeling provides greater robustness and lower absolute errors, making it more suitable for L-band SAR-based operational water-level monitoring applications.

Jakarta faces a critical challenge of extensive land subsidence, ranking prominently globally. This research employs a combined technical and policy evaluation approach to analyze the issue, incorporating sustainability considerations to assess the efficacy of Governor Regulation of Jakarta Number 93 of 2021, focusing on how the groundwater-free zone relates to land subsidence in the city. We processed 81 ALOS-2 PALSAR-2 synthetic aperture radar (SAR) data using persistent scatterer interferometric synthetic aperture radar (PS-InSAR) with HH polarization from 2017 to 2022 and ground truthing with 255 global positioning system (GPS) real-time kinematic (RTK) validation points. Our findings reveal a significant misalignment in the designated groundwater-free zone in the central part of Jakarta. At the same time, severe land subsidence primarily affects northern and northwestern Jakarta, with an average land subsidence rate of 5–6 cm/year. We strongly advocate for a thorough evaluation to rectify and redefine the boundaries of groundwater-free zones, improve regulatory frameworks, and effectively address land subsidence mitigation in the study area. The impact of domestic water needs on land subsidence highlights the urgency of action. Based on a combination of land subsidence velocity rates and domestic water demand, we have classified the cities in Jakarta into three levels of recommendations for groundwater-free zones. The cities are ranked in order of priority from highest to lowest: (1) West Jakarta, (2) North Jakarta, (3) South Jakarta, (4) East Jakarta, and (5) Central Jakarta, which holds the lowest priority.

The North Java coastal area, known as the Pantura region, is experiencing significant land subsidence, with certain areas sinking up to 10 cm per year. Pekalongan is among the most affected, with subsidence rates between 10 and 19 cm annually, mainly due to groundwater extraction, sediment compaction, and coastal erosion. Other coastal cities, like Semarang and Demak, show rates averaging 4 to 10 cm per year. This rapid subsidence is due to favorable geological conditions and ongoing urban development. This study investigates land subsidence in Pekalongan using the PS-InSAR method and dynamic visualization of time-series land cover data. PS-InSAR was applied to 45 scenes from ALOS-2 PALSAR-2 to monitor subsidence from 2014 to 2022. The results were validated with in situ subsidence benchmarks. Urban development dynamics were analyzed through land cover and land use change (LULC) and population density over the same period, using the GLC_FCS30D dataset in the GEE to detect non-natural LULC. The PS-InSAR results indicated that over 60.9% of investigation points experienced subsidence, up to 100 cm between 2014 and 2022. Ground validation showed an 83% agreement with PS-InSAR results. A statistical analysis of LULC from 2014 to 2022 did not show significant built-up area development, but the extension of salt marshes and water bodies indicated subsidence expansion. The population density reached 6873 people per square km by 2022, causing extensive groundwater use for domestic and industrial purposes, further aggravating the subsidence.

The disposal of waste materials such as plastic, foam, cloth, paper, rubber, and wood in river areas significantly impacts local environments, affecting regions several kilometers away and even reaching the open sea. This study utilized data from the Sentinel-1 C-SAR spanning 2017–2024 to detect water bodies. Additionally, an adaptive decision tree algorithm method with the Otsu threshold was applied to the Sentinel-2 optical data of March 19, 2024, to identify aquatic vegetation and floating waste (non-vegetative objects) within the water body. The study focused on the inlet of the Saguling Reservoir, where the Citarum River flows near the Bridge of Babakan Sapan (BBS) in West Java Province. This location is known for frequent waste accumulation. The methodology was then employed to monitor accumulated waste along the Citarum River on June 17, 22, and July 12, 2024. The model demonstrated an overall accuracy of 86% and a Kappa coefficient of 79% in distinguishing waste, water, and vegetation, with most errors occurring in the vegetation and waste classes. Additionally, the model's effectiveness in detecting waste in narrow water bodies with densely populated buildings on both sides, as observed in a narrow river along slum areas in Cirebon City, was comparatively lower.

Rapid urban population growth in Bandung City has led to the development of slums due to inadequate housing facilities and urban planning. However, it remains unclear how these slums are distributed and evolve spatially and temporally. Therefore, it is necessary to map their distribution and trends effectively. This study aimed to classify slum areas in Bandung City using a machine learning-based local knowledge approach; this classification exercise contributes towards Sustainable Development Goal 11 related to sustainable cities and communities. The methods included settlement and commercial/industrial classification from 2021 SPOT-6 satellite data by the Random Forest classifier. A knowledge-based classifier was used to derive slum and non-slum settlements from the settlement and commercial/industrial classification, as well as railway, river, and road buffering. Our findings indicate that these methods achieved an overall accuracy of 82%. The producer’s accuracy for slum areas was 70%, while the associated user’s accuracy was 92%. Meanwhile, the Kappa coefficient was 0.63. These findings suggest that local knowledge could be a potent option in the machine learning algorithm.