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

Abstract

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.