Module 2 - Corridor & Least-Cost Path Analysis
The ultimate goal in modeling corridor analysis is to locate the best path with the least cost. Cost, however, is measured in different ways (usually in the form of distance, money, and/or time), and thus trade-offs are likely to occur. Although there are many variables to be taken into consideration, a particular process is used to reclassify data, applying an overlay, create cost surfaces, and cost distance and/or corridor analysis to achieve a functional design.
For our lab assignment, we used ArcGIS Desktop and the Spatial Analyst Extension to model potential corridor movement and connectivity for black bears between two areas that make up the Coronado National Forest in Tuscon, Arizona.
The variables we considered in justifying a potential corridor included distance to roads, elevation, and land cover. To prepare this data, I first used the Euclidean Distance tool on the roads shapefile. Then I used the Reclassify tool to reclassify the roads shapefile, and then the land cover and elevation shapefiles. After they were all reclassified, I used the Weighted Overlay tool to combine all 3 reclassified rasters into one cost surface. I then ‘inverted’ the output using the Raster Calculator to imply that higher habitat suitability is the lowered (preferred) travel cost. After this part, I ran into challenges in using the Cost Distance tool. Nevertheless, the following steps are what were needed to accurately complete the assignment: use the Cost Distance tool to calculate the least accumulative cost distance for each cell using each Coronado 1 and 2 shapefiles as the sources, and the inverted output from the Weighted Overlay tool as the cost surface. And finally, after creating the 2 outputs from the Cost Distance tool, I know that I was supposed to then use the Corridor tool to mesh-up the results into a suitable corridor analysis.
In our lab report, we were asked to think about other important factors that can be considered to best model this scenario between the two protected areas. I stated that the following would also assist in creating a pattern recognition of suitable corridor connectivity:
For our lab assignment, we used ArcGIS Desktop and the Spatial Analyst Extension to model potential corridor movement and connectivity for black bears between two areas that make up the Coronado National Forest in Tuscon, Arizona.
The variables we considered in justifying a potential corridor included distance to roads, elevation, and land cover. To prepare this data, I first used the Euclidean Distance tool on the roads shapefile. Then I used the Reclassify tool to reclassify the roads shapefile, and then the land cover and elevation shapefiles. After they were all reclassified, I used the Weighted Overlay tool to combine all 3 reclassified rasters into one cost surface. I then ‘inverted’ the output using the Raster Calculator to imply that higher habitat suitability is the lowered (preferred) travel cost. After this part, I ran into challenges in using the Cost Distance tool. Nevertheless, the following steps are what were needed to accurately complete the assignment: use the Cost Distance tool to calculate the least accumulative cost distance for each cell using each Coronado 1 and 2 shapefiles as the sources, and the inverted output from the Weighted Overlay tool as the cost surface. And finally, after creating the 2 outputs from the Cost Distance tool, I know that I was supposed to then use the Corridor tool to mesh-up the results into a suitable corridor analysis.
In our lab report, we were asked to think about other important factors that can be considered to best model this scenario between the two protected areas. I stated that the following would also assist in creating a pattern recognition of suitable corridor connectivity:
- (residential, planned development, construction sites data) to consider the safety of residential families from wild animals, as well as the safety of wild animals from exposure to humans; knowing this data, we can discourage the movement towards these areas so that there is less mortality of wild species, and thus avoiding imbalance to the ecosystem; and
- (water bodies data) research shows that species tend to move alongside natural habitats as oppose to diffusing through them. Water bodies (such as streams, lakes, and rivers) are an integral part to survival (for anyone) when traveling. This data would assist in predicting which routes species might use to move.
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