Forests are critical ecosystems that regulate climate, preserve biodiversity, and support human livelihoods by providing essential resources. However, they are increasingly vulnerable due to the growing impacts of deforestation and habitat fragmentation, which endanger their value and long-term sustainability. Assessing forest and deforestation fragmentation is vital for promoting sustainable logging, guiding ecosystem restoration, and biodiversity conservation. This study introduces an advanced approach that integrates the Local Connected Fractal Dimension (LCFD) with near real-time (NRT) land use and land cover (LULC) data from the Dynamic World dataset (2017–2024) to enhance deforestation monitoring and landscape analysis. By leveraging high-frequency, high-resolution satellite imagery and advanced imaging techniques, this method employs two fractal indices, namely the Fractal Fragmentation Index (FFI) and the Fractal Fragmentation and Disorder Index (FFDI), to analyze spatiotemporal changes in the forest landscape and enhance deforestation monitoring, providing a dynamic, quantitative method for assessing forest fragmentation and connectivity in real time. LCFD provides a refined assessment of spatial complexity, localized connectivity, and self-similarity in fragmented landscapes, improving the understanding of deforestation dynamics. Applied to Nigeria’s Okomu Forest, the analysis revealed significant landscape transformations, with peak fragmentation observed in 2018 and substantial recovery in 2019. FFI and FFDI metrics indicated heightened disturbances in 2018, with FFDI increasing by 75.2% in non-deforested areas and 61.1% in deforested areas before experiencing rapid declines in 2019 (82.6% and 87%, respectively), suggesting improved landscape connectivity. Despite minor fluctuations, cumulative deforestation trends showed a 160.5% rise in FFDI from 2017 to 2024, reflecting long-term stabilization. LCFD patterns highlighted persistent variability, with non-deforested areas recovering 12% connectivity by 2024 after a 38% reduction in 2019. These findings reveal the complex interplay between deforestation and landscape recovery, emphasizing the need for targeted conservation strategies to enhance ecological resilience and connectivity. Fractal indices offer significant potential to generate valuable insights across multiple spatial scales, thereby informing strategies for biodiversity preservation and adaptive landscape management.

Industrial activities are among the major sources of heavy metal accumulation in soils, posing significant threats to environmental quality, ecosystem stability, and public health due to the persistence and bioaccumulative nature of toxic metals. Among these pollutants, lead (Pb) and cadmium (Cd) are widely recognized as critical indicators of industrial contamination because of their potential mobility through soil–water–plant systems and subsequent entry into the food chain. In response to increasing industrialization around the Asa Dam area in Ilorin, Kwara State, Nigeria, this study evaluated the spatial distribution and concentration of Pb and Cd in soils within selected industrial zones. A total of ten soil samples were systematically collected from each industrial site at a depth of 20 cm during the wet season, and sampling locations were georeferenced using a Garmin Global Positioning System (GPS) receiver. The concentrations of Pb and Cd were determined using Atomic Absorption Spectrophotometry (AAS), while spatial distribution patterns were analyzed and mapped using the Inverse Distance Weighting (IDW) interpolation technique within a Geographic Information System (GIS) environment. The results showed that the concentrations of Pb and Cd across all sampled locations remained below the permissible limits recommended by the World Health Organization (WHO). Comparatively higher concentrations were observed around KAM Industries relative to Doyin Investments, although these values were still within acceptable environmental thresholds. The spatial distribution maps further indicated localized variations in heavy metal concentrations without evidence of severe contamination hotspots. Overall, the findings suggest that soils within the Asa Dam industrial corridor are presently not significantly contaminated by Pb and Cd and therefore pose minimal immediate ecological and human health risks. Nevertheless, continuous environmental monitoring is recommended to ensure early detection of potential future contamination associated with ongoing industrial expansion in the area.

Monitoring deforestation and forest fragmentation in tropical ecosystems remains challenging due to rapid anthropogenic pressures, understory disturbances, and the temporal limitations of conventional remote-sensing approaches dominated by annual, reflectance-based observations. This study presents an integrated spatiotemporal framework for biannual deforestation mapping in the Akure Forest Reserve (2020–2023), combining high-resolution Planet NICFI optical imagery with Sentinel-1 SAR data to enhance fine-scale temporal detection and reduce classification uncertainty. Three U-Net architectures with ResNet18, ResNet34, and ResNet50 backbones were evaluated across eight biannual datasets and benchmarked against traditional machine learning classifiers. To improve temporal coherence in SAR-derived predictions, a Bayesian updating strategy was applied. The resulting biannual maps enabled a detailed analysis of deforestation frontier dynamics through patch size distribution, patch formation speed, and spatial configuration. To characterize multiscale degradation patterns, conventional landscape metrics (Number of Patches, Patch Density, Mean Patch Size, Edge Density, Aggregation Index, and Forest Fragmentation Index) were integrated with fractal-based indicators, including Fractal Dimension and Local Connected Fractal Dimension. Results indicate that the U-Net model with a ResNet34 backbone achieved the highest classification performance (Precision = 0.9342, IoU = 0.9086), while Bayesian temporal updating further enhanced temporal stability (Precision = 0.9663, IoU = 0.9470), revealing pronounced clustering of deforestation in late 2023 (Coefficient Variation (CV) = 1.939). Fragmentation analysis reveals progressive micro-fragmentation characterized by increasing patch number and density, declining mean patch size, and persistently high local fractal connectivity, indicating intense internal forest erosion despite apparent structural stability. This structural–functional decoupling suggests that forests may remain spatially intact while undergoing substantial functional degradation. By integrating deep learning, Bayesian inference, and multiscale spatial metrics, this study provides a more sensitive and spatially explicit characterization of deforestation dynamics, offering valuable insights for geospatial analysis, conservation planning, and sustainable forest management.

Land degradation poses a significant threat to environmental sustainability, agricultural productivity, and socio-economic stability, particularly in cities and villages where mining activities are very rampant. This study aims to assess land degradation vulnerability in Atakunmosa Area, Osun State using Geospatial techniques. By leveraging multi-temporal satellite imagery and spatial analysis techniques, the study identifies and maps degradation-prone areas based on a combination of biophysical and anthropogenic indicators, including land use/land cover (LULC), slope, modified soil adjusted vegetation indices (MSAVI), topographic wetness index (TWI), geology, soil types, soil acidity, soil texture, soil depth and drainage density. The SRTM DEM, Landsat 8 OLI, soil map, and geology map were used to create these thematic maps. Each class of these parameters was given appropriate weighting factors. Using the analytical hierarchical process, weighting factors were assigned to the different themes according to their influence to land degradation. GIS tools were utilized to integrate these spatial layers using weighted overlay analysis to produce the Land Degradation Vulnerability Index (LDVI) for the study area. The results indicated that 36.6 % (297.19 km²) of the total area was prone to high degradation risks, 17.1% (138.85 km²) was prone to moderate risks, 14.2% (115.30 km²) was prone to low risks, while 32.1% (260.65 km²) was prone to very low risks. The consistency ratio (CR) for the study was less than 0.1, which is an indication of the acceptability of the pairwise comparism. The study highlights the critical role of geospatial technologies in environmental assessment and decision-making processes.