Over the years, pipelines have been the most economic medium for transporting crude oil to production and distribution facilities in the Niger Delta area of Nigeria. However, damages to the pipelines in this area by interdiction have hampered the continuous flow of crude oil to the facilities. Consequently, the revenue of the government dwindles, and the environment is severely degraded. This study assesses the economic and environmental impacts of pipeline interdiction in the Niger Delta region. Data from National oil spills detection and response agency, Nigeria is used to map spatial distribution of oil spills using Kernel Density Estimation with Geographic Information System. Literature was assessed to synthesize the historical, socioeconomic, and environmental impacts of oil spills and pipeline interdiction. Soil samples were collected from study area to determine the types of hydrocarbon pollutants and their concentrations in comparison with uncontaminated sites in the area. Results show that the range of concentrations of total petroleum hydrocarbon (TPH) for the impacted soil (IMP) was 17.27–58.36 mg/kg; remediated soil (RS) was 11.73–50.78 mg/kg which were higher than the concentrations of 0.68 mg/kg in the control samples (CS). Polycyclic aromatic hydrocarbons (PAH) concentrations were in the range of 0.43–77.54 mg/kg for IMP, 0.42–10.65 mg/kg for RS, against CS value of 0.49 mg/kg while BTEX ranged between 0.02 – 0.38 mg/kg for IMP, 0.01–2.7 for RS against CS value of 0.01. The values of the PAH were higher than the limits of the Department of Petroleum Resources, Nigeria. This study also revealed that pipeline interdiction has affected the livelihood of the inhabitants of the study area and the revenue of the Nigerian government. The major hotspots for oil spills in the Niger Delta region are Bayelsa, Rivers and Delta states.

Evaluating water level changes at intertidal zones is complicated because of dynamic tidal inundation. However, water level changes during different tidal phases could be evaluated using a digital surface model (DSM) captured by unmanned aerial vehicle (UAV) with higher vertical accuracy provided by a Global Navigation Satellite System (GNSS). Image acquisition using a multirotor UAV and vertical data collection from GNSS survey were conducted at Kilim River, Langkawi Island, Kedah, Malaysia during two different tidal phases, at high and low tides. Using the Structure from Motion (SFM) algorithm, a DSM and orthomosaics were produced as the main sources of data analysis. GNSS provided horizontal and vertical geo-referencing for both the DSM and orthomosaics during post-processing after field observation at the study area. The DSM vertical accuracy against the tidal data from a tide gauge was about 12.6 cm (0.126 m) for high tide and 34.5 cm (0.345 m) for low tide. Hence, the vertical accuracy of the DSM height is still within a tolerance of ±0.5 m (with GNSS positioning data). These results open new opportunities to explore more validation methods for water level changes using various aerial platforms besides Light Detection and Ranging (LiDAR) and tidal data in the future.

Estimating surface elevation changes in mangrove forests requires a technique to filter the mangrove canopy and quantify the changes underneath. Hence, this study estimates surface elevation changes underneath the mangrove canopy through vegetation filtering and Difference of DEM (DoD) techniques using two epochs of unmanned aerial vehicle (UAV) data carried out during 2016 and 2017. A novel filtering algorithm named Surface estimation from Nearest Elevation and Repetitive Lowering (SNERL) is used to estimate the elevation height underneath the mangrove canopy. Consequently, DoD technique is used to quantify the elevation change rates at the ground surface, which comprise erosion, accretion, and sedimentation. The significant findings showed that region of interest (ROI) 5 experienced the highest volumetric accretion (surface raising) at 0.566 cm³. The most increased erosion (surface lowering) was identified at ROI 8 at −2.469 cm³. In contrast, for vertical change average rates, ROI 6 experienced the highest vertical accretion (surface raising) at 1.281 m. In comparison, the most increased vertical erosion (surface lowering) was spotted at ROI 3 at −0.568 m. The change detection map and the rates of surface elevation changes at Kilim River enabled authorities to understand the situation thoroughly and indicate the future situation, including its interaction with sea-level rise impacts.

Rainfall-triggered landslides pose significant risks in hilly and mountainous areas, necessitating advanced susceptibility and hazard modeling in GIS environments. This review examines research trends, influential authors, and emerging themes in this field through bibliometric analysis and content review, covering publications from 2007 to 2025 with a focus on the last five years. Recent research trends reveal a shift towards complex, physically based models incorporating multiple rainfall variables and their interactions with local conditions. Emerging trends include machine learning applications, increased spatiotemporal analysis, and rising interest in climate change impacts on landslide occurrence. However, there is a lack of attention given to how topography and other factors might explain spatial differences in rainfall. Research output has increased since 2020, driven by initiatives like the Kyoto Landslide Commitment 2020. While progress has been made in landslide susceptibility modeling, several challenges persist, such as developing standardized protocols for case study research to enhance comparability; exploring methods to integrate multiple case studies and improve generalizability; investigating emerging technologies like big data analytics to address sample limitations; improving translation of research findings for practitioners and policymakers; and enhancing methods to incorporate topographic effects on rainfall variability in susceptibility models. Many studies rely on single-site case studies, limiting result generalizability. Better integration of topographic influences on rainfall spatial variability is needed, particularly in mountainous regions. Addressing these challenges will be crucial for advancing landslide risk assessment. Future research should focus on developing robust, transferable models that account for complex environmental interactions and provide practical decision-support tools.

Population explosion in the city of Johor Bahru has led to traffic congestions, which hitherto to that there were better movements and flow of vehicles in and around the city. Road transportation in particular has played a gargantuan role in creating and maintaining easy and quick access to various destinations. Increases in the number of vehicle indicate a higher economic prosperity that contributes to traffic congestion within the city. This can be seen in a number of developments such as increased population density and creation of alternative routes over several years, which has still not totally eradicated the traffic congestion problems of the city. Ideally, a good public transport service would carry the passenger directly from their origin to their destination without having to face traffic congestion. The buses may not be available and accessible from certain origins or destinations of residents or they may need to walk some distances from their residents to the bus stops resulting into discouragement for most of the people using public transport services. This paper aims to utilise geospatial analysis approach to suggest effective bus routing that would be able to increase connectivity to rural areas and boost commercial activities through better transportation. Route selection is the process of finding locations that meet the selection criteria for each parameter using Geographic Information System (GIS). The process employs applied multi-criteria decision analysis and network analysis to generate thpsye highest score areas for bus rapid transit (BRT) route by maximising network and mobility and ensuring the newly suggested routes are connected to the existing networks of BRT to serve the community.