This study focuses on land cover and land management changes in relation to food security and environmental services in a semi-arid area of East Nusa Tenggara (ENT), Indonesia. The study was conducted in the Central Sumba District of ENT province. A classification and regression tree (CART) for land cover classification was analyzed using machine learning techniques through the implementation of the Google Earth Engine. A Focus Group Discussion (FGD) survey followed by in-depth interviews was conducted for primary data collection, involving a total of 871 respondents. The socio-economic data were statistically analyzed descriptively using non-parametric tests. The study showed that (1) there has been a substantial change in land use during the devolution era that has both positive and negative implications for food security and environmental services; (2) there has been population pressure in fertile and agricultural land as a direct impact of the development of city infrastructure; and (3) national intervention through the Food Estate program has fostered and shaped land use change and land management in the Central Sumba District. The study highlights the importance of the devolution spirit in aiding the management of limited arable/agricultural land in predominantly semi-arid areas to ensure food security and environmental services.

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.

Deep learning-based palm tree detection in unmanned aerial vehicle imagery with Mask R-CNN

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.

Tidal floods are increasingly common along the north coast of Java, significantly impacting coastal areas such as Tangerang Regency. This study evaluates the conditions of flood-prone coastal locations and analyzes resilience to tidal flood disasters in Tangerang Regency, situated adjacent to a metropolitan city. Tidal floods have substantial adverse effects on society and infrastructure, leading to unavoidable damage during occurrences. The research begins with an analysis of land use and land cover (LULC) changes and the pattern of built-up surfaces in Tangerang Regency, utilizing the Random Forest algorithm on Landsat data for classification. Socio-resilience is quantitatively analyzed using a scoring method within a Geographic Information System (GIS), while qualitative analysis employs the Driving Force, Pressure, State, Impact, and Response (DPSIR) framework based on interviews with local inhabitants affected by flooding. The investigation reveals that most subdistricts in Tangerang exhibit low resilience, with only two classified as moderate. Despite this, coastal communities have demonstrated adaptation to flood conditions. The primary objective of this research is to provide valuable insights for residents coping with the challenges posed by tidal floods.