2-Vegetation Input

2. Vegetation Input Derivation


Figure 5 shows the LANDFIRE land cover classification by ungulate forage preferences established in the literature.

Task 1 : Classify the LANDFIRE land cover data

  • Step 1: Load LANDFIRE land cover type raster: Us_110evt.img (existing land cover type)
  • Step 2: Export the Existing LANDFIRE Raster as Grazing_Veg_Capacity.img
  • Step 3: Add a new field to Grazing_Veg_Capacity.img and name it “Code” to represent ungulate grazing preferences (0-4) (Table 4).
  • Step 4: Assign the ungulate land cover preferences (0-4) to the field named “Code” based on Table 2 using field SAF_SRM in Us_110evt.img

Table 4. Suitability of LANDFIRE land cover classes for ungulate grazing

O - Unsuitable - LANDFIRE land cover = Cropland, developed, roads, barren, or water.

1 - Barely Suitable - LANDFIRE land cover = sparsely vegetated.

2 - Moderately Suitable - LANDFIRE land cover = conifer forest.

3 - Suitable - LANDFIRE land cover = woodland or evergreen shrubland.

4 - Preferred - LANDFIRE land cover = grasslands, scrubland steep or riparian.

  • Step 5: Use the raster to ASCII command to convert the .img raster to an ASCII raster for use in MATLAB.


Figure 6 - Your classified output should look something like the above for the Escalante (click on image for larger view).

Output file:

  • Grazing_Veg_Capacity.img
  • Grazing_Veg_Capacity.asc

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