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Raster Analysis

Least Cost Path Analysis, using Model Builder

Purpose - To create the 'best route' for the construction of a new trail to minimize cost and promote, but not interfere with, wildlife and habitat through a series of cost surfaces and a final best cost pathway. Route selection criteria is based on four parameters of slope, Land Cover, River & Road buffers. The most desirable construction parameters are 0 - 4 degree of slope, coniferous open, coniferous sparse, grassland, herb, low shrub, 20 - 200 meters distance from rivers and 0 - 200 metesr from roads.

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Method - A Digital Elevation Model (DEM), Land Cover data set, River and Road feature classes were all re projected to 10TM AEP Forest, using NAD 83 and a cell size of 75 meters. The re projected files were then either masked or clipped to the Waterton Lakes study area.

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The Source data set included the creation of both a Slope and Hillshade model. The Distance and Direction rasters were created using the Euclidean Distance tool for the River and Road clipped feature classes.

 

Both Slope and Land Cover rasters were reclassified to a common measurement scale of one to three, where one is most desirable. Slope was reclassified to three discrete classes of integer values. Where the Land Cover raster data set was reclassified to the same, as 1 indicates the desirable parameters of open, sparse coniferous and grasslands. All other land covers were assigned one discrete integer value based on their desirability for the analysis.

 

To create the Cost Surface, the Weighted Overlay tool was used for the Slope, Land Cover, River & Road buffer data sets. Each input was weighted by assigning a 25% equal percentage of influence.

 

Lastly, the Cost Distance tool was used to create the Cost path. The final path was then converted into a poly line.

Least Cost Path Analysis, written in Python

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