Service Description: I&M Staff in conjunction with a University of Tennessee graduate student developed maximum entropy (specifically the program MaxEnt) distribution models to predict wetland occurrence. Because MaxEnt is able to account for interactions among landscape features and climate variables, it is an ideal modeling approach for predicting the probability of finding a wetland across the park’s complex topography. Model outputs were based on 24 environmental variables (e.g., slope, microtopography, rainfall, etc.) at a 30x30 m grid cell resolution.
Service ItemId: 6247eda1c2054325bab7647ee20ee126
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Max Record Count: 2000
Supported query Formats: JSON
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Description: I&M Staff in conjunction with a University of Tennessee graduate student developed maximum entropy (specifically the program MaxEnt) distribution models to predict wetland occurrence. Because MaxEnt is able to account for interactions among landscape features and climate variables, it is an ideal modeling approach for predicting the probability of finding a wetland across the park’s complex topography. Model outputs were based on 24 environmental variables (e.g., slope, microtopography, rainfall, etc.) at a 30x30 m grid cell resolution.
Copyright Text: Great Smoky Mountains National Park,
Resource Management & Science,
Inventory & Monitoring Branch
Spatial Reference: 4269 (4269)
Initial Extent:
XMin: -84.0444181237
YMin: 35.255901321943
XMax: -82.9953255362999
YMax: 35.964841474057
Spatial Reference: 4269 (4269)
Full Extent:
XMin: -83.9967320969999
YMin: 35.434090703
XMax: -83.0430115629999
YMax: 35.786652093
Spatial Reference: 4269 (4269)
Units: esriDecimalDegrees
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