Jason A. Tullis

TL;DR: This study introduces change detection based on object/neighbourhood correlation image analysis and image segmentation techniques and found that object‐based change classifications incorporating the OCIs or the NCIs produced more accurate change detection classes than other change detection results.

TL;DR: In this paper, the imperviousness of land parcels was mapped and evaluated using high spatial resolution digitized color orthophotography and surface-cover height extracted from multiple-return lidar data.

TL;DR: In this paper, the effects of land cover and surface slope on lidar-derived elevation data were examined for a watershed in the piedmont of North Carolina, and the results showed that on average, the lidarderived elevation under-predicted true elevation regardless of the land cover category.

TL;DR: In this article, the authors present methods for establishing a relationship between nominal post-spacing and its effects on hydraulic modeling for flood zone delineation, and the results indicate that base flood elevation does not statistically change over the postspacing values tested.

TL;DR: In this article, a combination of remotely sensed and in situ variables is used to predict WNV incidence with a correlation coefficient as high as 0.86 and a consistent spatial pattern of model errors is identified, indicating the chosen variables are capable of predicting WNV disease risk across most of the United States, but are inadequate in the northern Great Plains region of the US.