Journal
INTERNATIONAL JOURNAL OF REMOTE SENSING
Volume 29, Issue 4, Pages 1169-1184Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/01431160701294703
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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.
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