Sustainable Development Goal (SDG) number 14 pertains to the preservation of sustainable marine ecosystems by establishing marine protected areas (MPAs). However, studies have reported massive damage to Indonesian marine ecosystems due to shipping pollution, anchors, and fishing nets. Thus, this study estimated the potential loss of ecosystem service value due to vessel activity expansion in the MPAs of Indonesia. This study was divided into three stages. The first stage is vessel activity expansion zone modeling based on kernel density. The second stage is marine ecosystem service value modeling through semantic harmonization, reclassification, and spatial harmonization. The last stage is the overlay of the vessel expansion zone model, marine ecosystem service value model, and the MPA of Indonesia. The results of this study indicate that the marine neritic zone of Indonesia has an ecosystem service value of USD 814.23 billion, of which USD 159.87 billion (19.63%) are in the MPA. However, the increase in vessel activity that occurred in 2013–2018 could potentially lead to the loss of the ecosystem service value of USD 27.63 billion in 14 protected areas. These results can assist policymakers in determining priority conservation areas based on the threat of vessel activity and value of ecosystem services.

Coronavirus disease-2019 (COVID-19) in Indonesia began to appear on March 2, 2020 and led to a number of fatalities. Spatial analysis is important to study the spatio-temporal trend of COVID-19 cases and fatalities to get a better understanding of the spread as well as to mitigate it. However, such a comprehensive study at national level is not to be seen in Indonesia with limited health infrastructure. This study aims to analyse the spatio-temporal distribution and clusters of COVID-19 in Indonesia for a year period. COVID-19 cases, as well as the fatalities as a consequence of this disease, were collected from the government through publicly shared data. A geographic information system (GIS) was used to manage and analyse the data on demographics, cases, and fatalities. The case fatality rate (CFR) was produced based on the number of cases and deaths per province weekly. The spatio-temporal data of both cases and fatalities were generated from the data. Finally, K-means clustering was employed to classify the cluster of Indonesia based on the proportion of vulnerable age groups, cases, and CFR. The results show that most of the provinces in Indonesia are affected by COVID-19, but the fatalities are not distributed evenly throughout the country. Based on the K-means clustering, two provinces are classified as moderate, namely the Province of East Kalimantan and North Kalimantan. The Province of Jakarta is classified as high, because the vulnerable age group there is highly correlated with the number of cases and deaths.

The Pacitan Regency is at risk of megathrust earthquakes and tsunamis due to the seismic gap along the southern region of Java Island, making risk-reduction efforts crucial. This research aims to analyse the tsunami risk associated with a potential megathrust earthquake scenario in Pacitan’s coastal areas and develop sustainable mitigation strategies. The research employs spatial analysis to evaluate the risk and subsequently formulate strategies for long-term mitigation. A weighted overlay method was utilised to integrate hazard (H) and vulnerability (V) datasets to produce a tsunami risk map (R). The hazard component was modelled using a tsunami propagation simulation based on the Shallow Water Equations in the Delft3D-Flow software, incorporating an earthquake scenario of Mw 8.8 and H-loss calculations in ArcGIS Pro 10.3. The vulnerability assessment was conducted by overlaying population density, land use, and building footprint from the Global Human Settlement Layer (GHSL) datasets. Finally, sustainable strategies were proposed to mitigate the tsunami risk effectively. The results show that Pacitan faces significant tsunami disaster risk, with tsunami waves at the coast reaching 16.6 m. Because the coast of Pacitan is densely populated, mitigation strategies are necessary, and in the present contribution, the authors developed holistic spatial planning, which prioritise the preservation and restoration of natural barriers, such as mangroves and coastal forests.

The process to alleviate flood risk, especially flood from a river that occurs excessively in Indonesia, requires a new approach. The attempt to reduce the risk along with the development of technology is by utilising commercial drones and rapid mapping methods for mapping flood plain area. With the rapid mapping method, the flood mitigation process in the village area can be done quickly. The activity carried out was to map the location of possible flooding in the Way Galih Village and case analysis to determine the location of the biogas digester together with the village government authority. The results of the study show that the data acquisition process and aerial photo processing can be carried out within one day and the village policymaker can quickly make policies and decisions about where to place the biogas digester.

Indonesia is experiencing a rise in natural disasters due to its geographical position within a tropical region, with the Upper Solo River watershed exhibiting a heightened risk of flooding. This region has already suffered numerous floods due to excessive precipitation and insufficient drainage. Susceptibility, hazard, and risk studies have been conducted to investigate this phenomenon but have been limited to specific regions within the catchment area. This study aims to construct a GIS-based flood risk model using Open-Access Spatial Data (OASD) based on diverse physical characteristics, urbanization levels, and population. We used several OASD, including SRTM, Sentinel 2 MSI, GPM v6, NASA-USDA Enhanced SMAP Global Soil Moisture Data, GHS-SMOD R2023A - Global Human Settlement Layers, and GHSL: Global Population Surfaces 1975-2030 (P2023A). The model integrates the risk parameters to identify flood risk using a weighted overlay in ArcGIS. The results demonstrate spatial heterogeneity in flood risk throughout the watershed. The result also reveals that Surakarta City, with a high proportion of its area in the 'High' (57.3%) and 'Very High' (29.54%) risk categories, is at the highest risk of flooding within the watershed. The study enhances understanding of this topic by comprehensively evaluating flood hazards, vulnerabilities, and risks. It highlights the significance of utilizing low-cost OASD to improve flood preparedness and response strategies.

Urban flooding in low-lying coastal zones like Way Lunik, Bandar Lampung, presents a growing challenge driven by rapid land use change, degraded infrastructure, and limited hydrological planning. This study combines high-resolution UAV-derived Digital Surface Models (DSM), national elevation data (DEMNAS), satellite imagery, and field-based drainage surveys to analyze flood risk in the Way Lunik watershed. Hydrological modeling shows that the watershed acts as a terminal catchment with low slopes and only two main discharge points, making it prone to runoff accumulation and tidal backflow. Significant mismatches between modeled and observed drainage patterns stem from blocked channels, informal settlements, and outdated infrastructure. Land cover analysis (2017–2023) reveals substantial growth in impervious surfaces, especially over former green or agricultural areas, increasing runoff and decreasing infiltration. Field surveys confirm drainage blind spots, unplanned flow paths, and encroached outlets in industrial and residential zones. The study also maps building types and clusters of flood-prone areas, often overlapping with marginalized neighborhoods and public facilities. By integrating elevation data, UAV imagery, and ground mapping, this research provides a spatially detailed assessment of urban flood hazards, emphasizing the importance of watershed-scale planning, infrastructure renewal, and the role of UAVs in adaptive flood risk management.