habitat corridor modeling process

8/14/2019 Habitat Corridor Modeling Process

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Geomatics 2022Cartography: Digital Mapping

February 15, 2009Joe Fraser

Grid Modeling and Map Algebra:

Calculating a Habitat Corridor


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Description of the Exercise

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Figure 1: select command

Figure 2: unpaved_rd1

Part 1: Building a Deer Suitability Grid

Proximity to Resource Road Grid

Step 1: Selecting Unpaved Roads

Because deer are drawn to the edges of forest clearings,low-traffic roads provide two areas where they are likelyto be found. To include unpaved roads in the deersuitability grid, select them from the q_rd file. Shownbelow at left is the resultant unpaved roads grid, atright the unpaved roads grid is compared to the pavedroads proximity grid created later on, to make sure thata paved road and an unpaved road do not overlap. Inthis case two roads were removed because of thiscomparison.

Figure 3: unpaved_rd1 vs. proximity to paved roa

Step 2: Buffer the Unpaved Roads

A temporary grid is needed to show the graduateddistance from the paved roads. This grid will be re-classed to be used as the resource road proximitysuitability grid.

Figure 5: road_suit_tmFigure 4: eucdistance command

Calculating a habitat corridor requires anumber of variables. First, two areas of suitablehabitat must be determined. Secondly, a path

between these two areas must be mapped,connecting the two in a corridor. The suitablehabitat grid is made up of four variables in thisexercise: proximity to dirt roads, water, and forestedge, and the fourth variable is percentage of slope.Each of these is ranked from 1 to 10, with lowernumbers assigned to the cells most favourable to

the habitat of deer.Corridor impedance is a grid made from

variables detrimental to the habitat of deer. Paved

roads serve vehicle traffic, and deer need to be acertain distance from a source of water theprotection of the forest.

The overall goal of the exercise is to find thebest path according to the variables of suitabilityand impedance to connect two existing habitats.


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Figure 8: road_suit

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Figure 9 eucdistance command

Figure 6: reclass command Figure 7: unpavedroad_suit.txt remap table

Step 3: Reclass Using a Remap Table

Using the remap table, the proximity to the center of thedirt road is given a ranking, with 1 as the most suitable,10 as the least. In this remap table, the actual road isgiven a value of 100 to discount that area. The best

areas assigned in the remap table are from 1m to 75m,with up to 150m as preferred. Further than that thearea is less valuable, and from 250m to the end of theeucdistance at 1205 are set to 10. Below is the grid.

Step 4: Proximity to Riparian Areas Suitability

The process of determining suitable locations accordingto proximity to watercourses begins with a eucdistanceon a grid with the streams and rivers.

Figure 10 water_tem


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Figure 14: nested commandsselect and eucdistance

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Figure 11 water_suit.txt remap table

Figure 13 water_suit

Step 5: Using the Remap Table to Reclass

Similar to the unpaved road remap table, water_suit.txtis used to classify the distance away from the stream. Avalue of 10 is given to anything beyond 250m, assuming

that deer need to stay relatively close to a source ofwater. The stream itself is given 100 as well, and thearea within 100m is the most suitable. The gridwater_suit will be used as a variable in the deersuitability grid.

Figure 12 reclass comma

Step 6: Field Edge Proximity Suitability

Here is an example of a nested command. Using theselect function, a eucdistance of 10 is created on onlythe value of 10 from the previously created field grid.

The value of 10 represents the edges of the field.

Figure 15 water_su


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Figure 10: streams_ord


Figure 20: field_suit

Figure 16 eucdistance command

Step 7: Buffering the Edges of the Fields

Eucdistance provides a buffer from the edge of thefields. Again, this grid will be reclassed to create aranked suitability grid.

Step 8: Creating a Field_suit Grid

Field_suit.txt is used to reclassify the values in the

field_buf grid. Areas further than 250 metres areclassified as being undesirable. This way, land close toorchards is weighted higher, assuming that the fooddeer like to eat best grows within a short range of theclearing edge. Below is field_suit, the grid that will bethe third variable in the habitat suitability grid.

Figure 17 f_edge_b

Figure 18 field_suit.txt remap table

Figure 19 reclass command using a remap ta


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Figure 23: dem


Step 10: Reclassing the Slope Percentage Grid

Using the assumption that deer are fairly sure-footedanimals, the slope_suit grid was created. Grades of 0 to10% were given a value of 1, and up to 50% slope wascharacterized as acceptable. Values of 50 to 75% arentprohibitive if other values in the deer suitability grid arefavourable, but grades greater than 75% were ruled out.Slope is an important factor to consider when terrain isvaried, as a chasm or rock outcrop can interrupt acorridor and render calculations incorrect if forgotten.

Figure 21: Spatial Analyst Menu

Figure 22: Spatial Analyst Dialogue

Figure 24: Slope_Percen

Figure 25: slope_suit.txt

Figure 26: reclass command

Figure 27: slope_suit

Step 9: Slope Suitability

Using the dem1 grid, Spatial Analyst can create a slopegrid with percentages.


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Step 11: Creating the Deer Suitability Grid

The deer suitability grid is made up of the four abovegrids: road_suit, water_suit, field_suit, and slope_suit.At right, the map algebra formula shows the weighting

of these variables, with water and field proximity being 3and 2 times more important, respectively, than road andslope suitability. The resultant grid is shown below. Figure 28: adding weighted grids


Figure 29: deer_suit

Part 2: Creating Hexagon Patches

Step 1: Create a Point Grid

The first step in creating the hexagon patches is tomake the point grid with the queens_clip boundaryextent. The distance between the triangulated points is537.29, to create a final output of 25 hectares.

Figure 30a: GeoWizards menu Figure 30b: GeoWizards menu

Figure 30c: GeoWizards menu Figure 30d: GeoWizards menu

Figure 31: point_537


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Step 2: Build Thiessen Polygons

Based on the points grid created in step 1, Thiessenpolygons can be made. These are the hexagonal patchesthat will be filled with an average value using the zonal

mean command.

Figure 32a: Build Thiessen Polygons Menu

Figure 32b: Build Polygons Me

Figure 32c: Build Polygons Me

Step 3: Cleaning the Polygons

The polygons need to be cleaned using the ETGeoWizards add-on before they are converted to a grid.

Figure 34a: Clean Polygons Menu Figure 34b: Clean Polygons Me

Figure 33: hex_537


Figure 34c: Clean Polygons Menu Figure 34d: Clean Polygons Me

Figure 35: hex_537c

Step 4: Converting Polygons

Finally, the polygons can beconverted to grid using the

ET_ID field. This ensures eachhexagon has a uniqueidentifier. The reason astandardized hexagon patch isused is because of its shape -unlike a square, the hexagonis

, making it ideal forcostpath analysis or directiongrids. Using a square shapefavours the squares above,below and to the side.

equidistant each adjacenthexagon

Figure 36: Convert Features to Raster

Figure 37: hex_537c


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Figure 39: Patchmean1

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Figure 38: zonalmean command

Part 3: Prepare a Zonal Mean Grid

Create a Zonal Mean Grid

Using the deer suitability grid, a zonal mean commandfills in the hexagon patches with the average value.What is created is in essence an average of the areasdeer are likely to prosper, ranked from low (mostsuitable, 2.73) to high (unsuitable, 47).

Part 4: Selecting the Largest Habitats

Step 1: Recoding the Patchmean1 Grid

Patchmean1 is now recodedto show the groups ofhabitats within 5.5 metresof the best habitats. Thearea is small and precisebecause all of the rankingvalues were kept relatively close together.

Figure 40: recoding Patchmean1

Figure 41: hab_patch

Step 2: Assign a Unique Number

The regiongroup commandcan assign each habitatcluster a unique number.

The grid is shown at left.Figure 42: regiongroup comma

Figure 43: hab_clust1


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Figure 44a: select command Figure 44b: select comma

Figure 45: Clusters 3 and 17

Step 3: Selecting the Largest Habitat Clusters

The two largest habitat clusters are cluster 3 andcluster 17. Using the select command, two new grids arecreated containing only the values of 3 and 17,

respectively. Below, these two clusters are show abovethe rest of the clusters.

Corridor Impedance

The easiest way to figureout the barriers to aclear corridor. In thisequation, the mean isfound from the grids road_cor_suit, water_suit, anddeer_suit. Road_cor_suit was made from a reclass of abuffer of all the paved roads, shown on page 2. Thisensures that paved roads are given a low suitabilityranking. This grid will be used to figure out thecostdistance paths between the two habitat areas.

Part 5: Creating a Corridor Impedance Grid

Figure 46: mean command

Figure 46: cor_imp g


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Part 6: Cost Distance Grids

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Figure 47a: costdistance command

Cost3 and Cost17 Grids

Using the cor_imp grid, a costdistance commandprovides a way to show the path of least resistance forgrids cost3 and cost17. The results are shown below Figure 47b: costdistance command

Figure 48: cost17 grid Figure 49: cost3 g

Cost3 and Cost17 Grids

Once the costdistance gridshave been created, thecorridor command cancreate the best pathbetween the two clusters.Below is the final corridorgrid cor3-17 shown with the best 5% of the contoured

results highlighted.

Figure 50: corridor command

Figure 51: cor_3-17 g

Figure 52: cor_3-17 with contours


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Model Builder

Figure 53: Model Builder Diagra

Sources and Software

Software:- ArcGIS 9.3 with Spatial Analyst- CorelDRAW X4- CorelCAPTURE X4

Source Data:

- Terrain Data DEM from NSCC AVC LAN- NSGC Contours and Hydrography- Department of Natural Resources: Forcov.shp and Biosys.shp- LANDSAT DATA 7TM for deriving Orchard.shp

habitat corridor modeling process

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