As the composition and intensity of land use changes, there are impacts on cropland, forests, wetlands and lakes, with accompanying changes in productivity, biodiversity, habitat fragmentation, and water quality. Remote sensing and geospatial analysis tools provide capability needed for mapping, monitoring and analysis of the landscape.
Wetland maps currently used in wetland management and policy efforts are two-dimensional representations of a four-dimensional phenomenon (the three dimensions of space combined with time). Our research focuses on developing novel wetland mapping methods using object-based image analysis, lidar, radar, advanced classifiers, and temporal dynamics. These methods show great potential for monitoring temporal variations in wetland diagnostic parameters such as moisture content and hydrologic cycle.
We have a very active UAS research and teaching program. We have collected hundreds of image datasets applied to research questions in wide-ranging disciplines such as forestry, urbanization, and infrastructure management. Products of our UAS program are used in several UMN courses.
A significant focus of the lab is on developing novel methods to process, classify, and visualize remotely sensed data. Especially important are object-based image analysis (OBIA) methods using multiple data types and ensemble classifiers.
One of the most significant land uses in terms of impact on the environment is the creation of impervious surfaces such as roads and buildings. The amount of impervious surface in an area affects environmental factors such as the amount and chemical composition of runoff, the magnitude of the urban heat island effect, the degree of wildlife habitat fragmentation, and the impact on human-perceived values such as aesthetics. Therefore, mapping and monitoring of impervious areas is an essential component in managing urban growth.