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Dataset contains locations of drainage tile permits in North Dakota obtained from the State Water Commission as of October 2013. This dataset provides the latitude and longitude of the location given on the permit, which is not provided by the State Water Commission database. Additionally, overlap of permit polygons (if depicted in a geospatial application) is possible from two permits that share the same area. Permit applications that were marked as denied or that did not require a permit as determined by the State Water Commission were removed from this database.
Categories: Data,
Publication;
Types: Citation,
Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: EARTH SCIENCE AGRICULTURE AGRICULTURAL ENGINEERING AGRICULTURAL EQUIPMENT,
EARTH SCIENCE TERRESTRIAL HYDROSPHERE SURFACE WATER DRAINAGE,
North Dakota,
agricultural drainage,
agricultural water use,
Dataset contains locations of drainage tile permits in South Dakota obtained from County and Local Water Board records. The counties included are: Brown, Deuel, Grant, Hamlin, Hanson, Kingsbury, Lake, McCook, Minnehaha, Moody, Roberts, Spink, and Turner. The date permits records were collected vary by county. A majority of the permits from counties were collected as of January 2012. Two counties (Hanson and Minnehaha) were updated in December,2013. In addition to the location (as a TRS) and available (if given)pipe information recorded on the permit, this dataset provides the latitude and longitude of the location given on the permit, which is not typically provided on the permit application or record. Additionally,...
Categories: Data,
Publication;
Types: Citation,
Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: EARTH SCIENCE AGRICULTURE AGRICULTURAL ENGINEERING AGRICULTURAL EQUIPMENT,
EARTH SCIENCE TERRESTRIAL HYDROSPHERE SURFACE WATER DRAINAGE,
South Dakota,
agricultural drainage,
agricultural water use,
Brief updates from the project website.
Categories: Data,
Publication;
Types: Citation;
Tags: Academics & scientific researchers,
Academics & scientific researchers,
Completed,
Data Acquisition and Development,
Document,
We developed a screening system to identify introduced plant species that are likely to increase wildfire risk, using the Hawaiian Islands to test the system and illustrate how the system can be applied to inform management decisions. Expert-based fire risk scores derived from field experiences with 49 invasive species in Hawai′i were used to train a machine learning model that predicts expert fire risk scores from among 21 plant traits obtained from literature and databases. The model revealed that just four variables can identify species categorized as higher fire risk by experts with 90% accuracy, while low risk species were identified with 79% accuracy. We then used the predictive model to screen 365 naturalized...
Categories: Data,
Publication;
Types: Citation,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Hawaii,
farming,
fire,
fire risk,
flammability,
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