Insights into Landscape Structure Change in Urbanising Rainforest and Guinea Savanna Ecological Regions of Nigeria.

Abstract

In the face of unabated urban expansion, understanding the intrinsic characteristics of landscape structure is pertinent to preserving ecological diversity and managing the supply of ecosystem services. This study integrates machine-learning-based geospatial and landscape ecological techniques to assess the dynamics of landscape structure in cities of the rainforest (Akure and Owerri) and Guinea savanna (Makurdi and Minna) ecological regions of Nigeria between 1986 and 2022. Supervised classification using the random forest (RF) machine-learning classifier was performed on Landsat images on the Google Earth Engine (GEE) platform, and landscape metrics were calculated with FRAGSTATS to assess landscape composition, configuration, and connectivity. The results reveal a consistent pattern of urban expansion in all four cities at varying intensities. The proportion of the built-up class exhibited positive correlations with the largest patch index (r = 0.86, p < 0.05) and aggregation (r = 0.39, p < 0.05), indicating a concurrent rise in landscape densification as urban expansion persists. For the agricultural and vegetation landscapes, landscape proportion correlates negatively with fragmentation (r = -0.88, p < 0.05) and connectivity (r = -0.77, p < 0.05), but positively with aggregation (r = 0.89, p < 0.05). The increased patch density indicates a rising magnitude of landscape fragmentation and heterogeneity over time with varying implications for ecosystem functioning. These findings demonstrate the complex interplay between urbanisation and ecological processes within and across different ecoregions, highlighting the need for targeted ecological management, sustainable urban planning, and regionally informed landscape conservation strategies.