Land‐cover change and environmental impact analysis in the Greater Mankato area of Minnesota using remote sensing and GIS modelling

Land use and land-cover (LULC) data provide essential information for environmental management and planning. This research evaluates the land-cover change dynamics and their effects for the Greater Mankato Area of Minnesota using image classification and Geographic Information Systems (GIS) modelling in high-resolution aerial photography and QuickBird imagery. Results show that from 1971 to 2003, urban impervious surfaces increased from 18.3% to 32.6%, while cropland and grassland decreased from 54.2% to 39.1%. The dramatic urbanization caused evident environmental impacts in terms of runoff and water quality, whereas the annual air pollution removal rate and carbon storage/sequestration remained consistent since urban forests were steady over the 32-year span. The results also indicate that highly accurate land-cover features can be extracted effectively from high-resolution imagery by incorporating both spectral and spatial information, applying an image-fusion technique, and utilizing the hierarchical machine-learning Feature Analyst classifier. This research fills the high-resolution LULC data gap for the Greater Mankato Area. The findings of the study also provide valuable inputs for local decision-makers and urban planners.