Between Dec 19, 2024 and Jan 2, 2025, datasets can be submitted to DRUM but will not be processed until after the break. Staff will not be available to answer email during this period, and will not be able to provide DOIs until after Jan 2. If you are in need of a DOI during this period, consider Dryad or OpenICPSR. Submission responses to the UDC may also be delayed during this time.

Browsing by Subject "Land Cover"

Now showing 1 - 3 of 3
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    2015 Twin Cities Metropolitan Area Urban Tree Canopy Assessment
    (2017-01-03) Knight, Joe F; Rampi, Lian P; Host, Trevor K; jknight@umn.edu; Knight, Joseph, F
    A high-resolution (1-meter) tree canopy assessment was completed for the Twin Cities Metropolitan Area. Mapping of existing and potential tree canopy is critical for urban tree management at the landscape level. This classification was created from combined 2015 aerial imagery, LIDAR data, and ancillary thematic layers. These data sets were integrated using an Object-Based Image Analysis (OBIA) approach through multi-resolution image segmentation and an iterative set of classification commands in the form of customized rulesets. eCognition® Developer was used to develop the rulesets and produce raster classification products for TCMA. The results were evaluated using randomly placed and independent verified assessment points. The classification product was analyzed at regional scales to compare distributions of tree canopy spatially and at different resolutions. The combination of spectral data and LiDAR through an OBIA method helped to improve the overall accuracy results providing more aesthetically pleasing maps of tree canopy with highly accurate results.
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    Rochester 1-Meter Land Cover Classification (Impervious Surface Focused)
    (2016-08-01) Host, Trevor K; Rampi, Lian P; Knight, Joe F; jknight@umn.edu; Knight, Joe F; Remote Sensing and Geospatial Analysis Laboratory
    A high-resolution (1-meter) land cover classification raster dataset was completed for three different geographic areas in Minnesota: Duluth, Rochester, and the seven-county Twin Cities Metropolitan area. This classification was created using high-resolution multispectral National Agriculture Imagery Program (NAIP) leaf-on imagery (2015), spring leaf-off imagery (2011- 2014), Multispectral derived indices, LiDAR data, LiDAR derived products, and other thematic ancillary data including the updated National Wetlands Inventory, LiDAR building footprints, airport, OpenStreetMap roads and railroads centerlines. These data sets were integrated using an Object-Based Image Analysis (OBIA) approach to classify 12 land cover classes: Deciduous Tree Canopy, Coniferous Tree Canopy, Buildings, Bare Soil, other Paved surface, Extraction, Row Crop, Grass/Shrub, Lakes, Rivers, Emergent Wetland, Forest and Shrub Wetland. We mapped the 12 classes by using an OBIA approach through the creation of customized rule sets for each area. We used the Cognition Network Language (CNL) within the software eCognition Developer to develop the customized rule sets. The eCognition Server was used to execute a batch and parallel processing which greatly reduced the amount of time to produce the classification. The classification results were evaluated for each area using independent stratified randomly generated points. Accuracy assessment estimators included overall accuracies, producers accuracy, users accuracy, and kappa coefficient. The combination of spectral data and LiDAR through an OBIA method helped to improve the overall accuracy results providing more aesthetically pleasing maps of land cover classes with highly accurate results.
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    Watershed-based Stressors for the Great Lakes Basin
    (2024-01-11) Host, George; Kovalenko, Katya; Brown, Terry; Johnson, Lucinda; Ciborowski, Jan; ljohnson@d.umn.edu; Johnson, Lucinda; Natural Resources Research Institute
    The Watershed-based Stressors for the Great Lakes Basin dataset includes component and aggregated measures of environmental stress to coastal ecosystems from watersheds of the Great Lakes Basin. Stressors include the amount of agricultural and developed land use, as well as road and population density. These summaries are based on a set of 5971 watersheds that cover the US and Canadian Great Lakes basin, derived using methods from Hollenhorst et al. (2007). Indices presented in this dataset include SumRel (Host et al. 2011) and the more recent combined Agriculture and Development - AgDev index (Host et al. 2019). These were developed as part of the Great Lakes Environmental Indicators II (GLEI-II) project, funded through the Great Lakes Restoration Initiative and used to quantify the response of biota (birds, fish, macroinvertebrates, diatoms and wetland vegetation) to varying degrees of watershed stress (Kovalenko et al. 2014). As of 2015, a more recent version of watersheds has been created by the Great Lakes Aquatic Habitat Framework and stressors recalculated based on those watersheds.