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.
View Article and Find Full Text PDFUnderstanding the dynamics of urban landscapes and their impacts on ecological well-being is crucial for developing sustainable urban management strategies in times of rapid urbanisation. This study assesses the nature and drivers of the changing urban landscape and ecosystem services in cities located in the rainforest (Akure and Owerri) and guinea savannah (Makurdi and Minna) of Nigeria using a combination of remote sensing and socioeconomic techniques. Landsat 8 datasets provided spatial patterns of the normalised difference vegetation index (NDVI) and normalised difference built-up index (NDBI).
View Article and Find Full Text PDFRapid urbanization is having a considerable impact on various aspects of living, thereby altering the biophysical environment. This study adopted the use of remote sensing technique and geographical information system (GIS) to analyse the relationship between changing land use/land cover and land surface temperature in a rapidly urbanizing tropical city of Ibadan between 1984 and 2019. Landsat series TM, ETM+, and OLI satellite imageries of Ibadan region city for 1984, 2002, and 2019, respectively, were obtained from the US Geological Survey (USGS) Landsat series of Earth Observation satellites accessible on the Google earth engine (GEE) platform.
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