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 - 4 of 4
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    Land Cover Shapefiles for Minneapolis and St. Paul, Minnesota
    (2018-10-26) Anderson, Abigail W; McLachlan, K; awoodsanderson at g m a i l (dot) com; Anderson, Abigail W
    This project’s aim was to produce a land cover model of downtown Minneapolis and St. Paul, Minnesota. Our purpose was to discriminate features that offer potential cover and foraging habitat for birds (i.e. trees, shrubs, turf grass, water) from features that are less suitable for birds (i.e. impervious surfaces and buildings). Though we had birds in mind, the models we produced have broad utility in many contexts. To achieve our objectives, we integrated a variety of freely available spatial data. Object-based Image Analysis (OBIA) was the primary methodology we used to generate thematic land cover models.
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    Minnesota Land Cover Classification and Impervious Surface Area by Landsat and Lidar: 2013-14 Update
    (2016-08-03) Rampi, Lian P; Knight, Joe F; Bauer, Marvin; jknight@umn.edu; Knight, Joe F; Remote Sensing and Geospatial Analysis Laboratory, University of Minnesota
    This is a 15-meter raster dataset of a land cover and impervious surface classification for 2013-14, level two classification. The classification was created using a combination of multitemporal Landsat 8 data and LiDAR data with Object-based image analysis. By using objects instead of pixels we were able to utilize multispectral data along with spatial and contextual information of objects such as shape, size, texture and LiDAR-derived metrics to distinguish different land cover types. While OBIA has become the standard procedure for classification of high resolution imagery we found that it works equally well with Landsat imagery. For the objects classified as urban or developed, a regression model relating the Landsat greenness variable to percent impervious was developed to estimate and map the percent impervious surface area at the pixel level.
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    Photochemical Data of Stormflow Samples Collected Near Minneapolis and St. Paul, Minnesota from 2014-September to 2015-October
    (2017-08-24) McCabe, Andrew J; Arnold, William A; arnol032@umn.edu; Arnold, William A
    Stormflow samples were collected from 31 sites near Minneapolis and St. Paul, Minnesota between 2014-September and 2015-October. Optical and photochemical parameters of the samples were measured under controlled laboratory conditions. The data were collected to better understand the way in which land cover with variable levels of human impact influence the formation rate and yield of triplet excited states of dissolved natural organic matter (3DOM*). Rates of formation (Rf,T) and apparent quantum yields (AQYT) were measured for 3DOM* using the chemical probe, 2,4,6-trimethylphenol, under a broadband xenon-arc lamp with a 290-nm wavelength filter.