Urbanization alters land surface characteristics, intensifies urban heat, and degrades air quality, posing significant environmental and public health challenges. This study investigated the relationship between urban land surface temperature (LST) and air pollution in Kano Metropolis, Nigeria, using Earth observation data, geospatial techniques, and correlation analysis. Land use/land cover (LULC) analysis revealed that built-up areas account for 67.4% of the metropolitan area, contributing to elevated land surface temperatures, particularly within the densely urbanized local government areas of Kano Municipal, Gwale, Ungogo, Dala, and Fagge, as demonstrated by the LST and Urban Heat Island (UHI) analyses. To evaluate the relationship between urban heat and air quality, Pearson correlation analysis showed strong and statistically significant positive relationships between LST and particulate matter, with PM2.5 (r = 0.74, p < 0.001) and PM10 (r = 0.61, p < 0.001) exhibiting the strongest associations. In contrast, CO2, TVOC, and HCHO displayed weak and statistically non-significant relationships with LST. Air Quality Index (AQI) assessment further revealed that 53.6% of the metropolitan area experienced unhealthy air quality, with localized very unhealthy conditions concentrated around commercial cooking areas where biomass fuels are extensively used. These findings indicate that particulate pollution is closely associated with urban surface heating, whereas gaseous pollutants are comparatively less influenced by LST. Overall, the results demonstrate that rapid urbanization, increasing impervious surfaces, and anthropogenic emissions collectively contribute to elevated urban temperatures and deteriorating particulate air quality in Kano Metropolis. These findings provide valuable evidence to support sustainable urban planning through urban greening, the use of high-albedo materials, and cleaner cooking technologies to mitigate urban heat, improve air quality, and reduce public health risks in rapidly urbanizing cities.
Surface water quality is a sensitive global environmental issue, as it is important for long-term economic development and environmental sustainability. Due to population growth, urbanization, and the effects of climate change, the degradation of surface water quality cannot be avoided. Therefore, a more accurate, continuous, and operational monitoring of water quality is highly significant. This study aims to systematically review and synthesize existing literature on the technological advancement, challenges, and future directions of Remote Sensing (RS) and Geographic Information System (GIS) techniques in surface water quality monitoring. Following PRISMA guidelines, a structured literature search of published studies was conducted across Science Direct, Web of Science, and Scopus databases between 2012 and 2025. The findings demonstrate that RS data can reliably retrieve key water quality parameters and provide large-scale, repeatable observations across surface water when integrated with GIS-based spatial modeling and in situ validation. This review systematically consolidates RS retrieval methods and GIS-based analytical frameworks, explicitly distinguishing robust retrievals from context-dependent proxy approaches and identifying methodological gaps that constrain operational deployment. The synthesis from the integration of the enhanced RS–GIS results will enable the RS–GIS community to develop a strong, scalable pathway to map spatial heterogeneity and future-ready water quality monitoring systems that support environmental sustainability, resource security across diverse aquatic environments, provide early warning on environmental risks, and inform water governance.
Urban trees are a critical component of green infrastructure in tropical cities, yet city-scale evidence on tree health in Indonesia remains limited. This study assessed urban tree health in Medan City using the Forest Health Monitoring (FHM) protocol, Tree Level Index (TLI), GIS-based spatial analysis, and Normalized Difference Vegetation Index (NDVI) validation to support Eco Forest City planning. A total of 1184 trees, representing a 30% sample from 3947 inventoried trees across six sub-districts, were evaluated based on damage location, type, and severity. Average Nearest Neighbor (ANN) and Kernel Density Estimation (KDE) were applied to examine spatial clustering of health classes, while Sentinel-2 NDVI values were extracted as an indicator of vegetation greenness. Overall, 65.79% of trees were classified as healthy, 30.66% as lightly damaged, 3.04% as moderately damaged, and 0.51% as severely damaged. Tree health differed significantly among sub-districts (Kruskal-Wallis chi(2) = 82.31, p < 0.001), with Medan Tuntungan showing the best condition, whereas Medan Marelan and Medan Amplas showed the poorest profiles. ANN and KDE results indicated that tree health classes were spatially clustered, supporting geographically targeted management. NDVI values differed significantly among health classes (Kruskal-Wallis H = 49.144, p < 0.001), although the weak Spearman correlation suggests that NDVI is more appropriate as supplementary validation than as a substitute for field assessment. These findings support risk-based tree management through routine FHM monitoring, priority inspection in vulnerable sub-districts, spatially explicit maintenance zoning, and gradual species diversification to strengthen Medan's Eco Forest City planning.
Against the backdrop of rapid global urbanization, revealing the indirect ecological effects triggered by urban environmental restructuring and human management interventions carries early-warning significance for preventing cliff-like vegetation degradation driven by unchecked urban expansion. Existing studies predominantly rely on linear assumptions and neglect the temporal lags inherent in ecological responses, making it difficult to capture the long-run and short-run dynamics as well as potential nonlinear thresholds of indirect impacts. Using Nanjing as the study region, this research constructs a spatiotemporal panel dataset at 1 km & times; 1 km resolution based on multi-source remote sensing data from 2001-2018. Building upon the quantification of urbanization's indirect effects on vegetation, we introduce a panel CS-ARDL model (Cross-Sectionally Augmented Autoregressive Distributed Lag model) to decompose short-run dynamic shocks from long-run equilibrium effects. Results reveal that urban construction land continues to expand along the Yangtze River axis, forming a "core-corridor-cluster" spatial pattern. Concurrently, vegetation indirect effects display pronounced north-south spatial differentiation: the Yangtze River's north bank (exemplified by Luhe District) exhibits clustering of negative indirect effects, whereas the core urban area south of the Yangtze generally exhibits positive responses, accompanied by a synchronized evolution from "negative-weakly negative-shifting toward positive" across the study period. ARDL (Augmented Autoregressive Distributed Lag model) results further demonstrate that the short-run scale exhibits stronger spatial heterogeneity and volatility. At the long-run scale, urbanization's indirect effects on vegetation are predominantly positive, displaying a declining gradient of "core urban areas > suburban areas > rural areas"; however, significant clusters of negative long-run effects persist in both core urban zones and far-suburban expansion edges.
Soil degradation in Mediterranean agricultural systems is strongly conditioned by topography, water redistribution and solar exposure, factors that can be effectively studied using very high-resolution remote sensing. This study evaluates the potential of Unmanned Aerial Vehicle (UAV)-derived geomorphometry combined with machine learning techniques to analyse the spatial variability of the Normalized Difference Vegetation Index (NDVI) as a surface spectral response under post-harvest conditions in a Mediterranean cereal field affected by soil degradation and gully erosion, located near Casabermeja (M & aacute;laga, southern Spain). High-resolution RGB and multispectral UAV data were used to generate a Digital Terrain Model (DTM), multi-scale local relief metrics, hydrological indices and curvature derivatives, together with NDVI maps acquired after harvest under dry and compacted soil conditions. A Random Forest regression model was applied using 3000 sampling points to link geomorphometric variables with NDVI spatial patterns. Although predictive performance was moderate (R2 = 0.31; RMSE = 0.04), variable-importance analysis identified the main terrain-related factors associated with spatial variability in the spectral signal, highlighting slope, solar exposure and erosion-and flow-convergence-related indices. The results demonstrate the usefulness of UAV-based geomorphometry and machine learning as diagnostic tools for analysing terrain-controlled surface spectral patterns and identifying areas potentially affected by soil degradation processes in Mediterranean agroecosystems.
