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Recent studies have attempted to extract impervious surfaces from high-resolution satellite imagery such as Ikonos and QuickBird. These images, however, often lack necessary spectral information due to technological limitations. This study integrates spectral information (temperature and moisture) derived from Landsat-7 ETMϩ imagery with Ikonos imagery to derive high-resolution impervious surface information. Furthermore, three popular methods, including linear regression modeling, artificial neural network, and regression tree have been developed and compared using a paired t-test statistic. Analysis of results reveal that Tasseled Cap components particularly greenness and wetness of Ikonos imagery are most important in estimating sub-pixel imperviousness. Also, to some extent the brightness temperature derived from Landsat-7 ETMϩ image helps in better estimation of impervious surfaces. Moreover, a comparative analysis indicates that the non-linear approaches yielded statistically better results. Particularly, the regression tree model generated best results with highest Pearson's r (0.939) and lowest mean absolute error (8.307).

Within a drainage system, drainage ditches are designed to improve existing natural drainage. Although drainage ditches are mostly engineered, they can also be part of natural watercourses. For environmental sustainability, in many places there are guidelines to establish vegetative buffer strips along the boundary of drainage ditches. In this landscape planning study, a geospatial modeling framework was established to identify these drainage system landforms and the boundary that separates these landforms from their surrounding areas across Waseca County in south-central Minnesota. By employing almost 2000 GPS spot elevation measurements from five ditch systems and one-meter Light Detection and Ranging (LiDAR) derived digital elevation model (DEM) data, the drainage ditch berm polygons were delineated. Eight low light angle hillshade rasters at 45-degree azimuth intervals were used to construct the model. These hillshade rasters were combined to form a composite raster so that the effect of multiple azimuths can be captured during ditch berm delineation. The GPS points identified as the top of the berm were used to extract cell values from the combined hillshade. These cell values were modeled further using statistical distribution graphs. The statistical model derived +0.5 and +1 standard deviation values (cell values 812 and 827, respectively) of the combined hillshade raster were utilized to obtain complete berm polygons. In this semi-automated method, between 67.30% to 79.80% of ditch berm lengths were mapped with an average error that is less than the resolution of the DEM. Demarcation of these boundaries are important for local governments in Minnesota and throughout the world, as it could help guide land–water management and aid sustainable agriculture.

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In this paper, the historical trend of urban growth and the associated drivers were examined through econometric analysis for the rapidly growing Grafton area in the State of Wisconsin. Specifically, panel data analysis was carried out to examine the drivers of urban growth such as demographic factors, location of jobs, travel time, housing types, property values, etc. Results reveal that panel data analysis, particularly the random effects model, was successful in analyzing the drivers of urban growth at the census block group level. This study found that population, local jobs, household income, and house price were positively associated with urban growth. The study also found that urban growth in the study area is not decided by the access to the nearest central city, but other factors, such as the rural atmosphere of the region, local jobs, and emerging centers of employment opportunities, have significant influences on urban development.