DOI: 10.11648/j.ajrs.20261402.13 ISSN: 2328-580X

Bayesian-Enhanced Deep Learning and Multi-Sensor Fusion for Spatiotemporal Analysis of Deforestation Frontiers and Landscape Structural Dynamics

Lukman Isiaka, Festus Seyi, Adebayo Ojo, Babatunde Salu, Kayode Olorunyomi, Jamiu Aileru, Adeniyi Oluwagbohunmi
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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.

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