- Geography 5222 -
Project 7: Introduction
to Raster GIS Analysis
Delineating
Vineyards
Objective:
become
proficient
with basic raster analysis techniques such as data display,
hill shading, surface interpolation, reclassification and overlay
In
this lesson, the purpose was to select land areas meeting specific criteria
for wine production.
The criteria included the following:
More than 100 meters from a stream or floodplain
Landuse type of agriculture or undeveloped
Aspect (slope orientation) between 112 - 337 degrees
Average maximum wind speed less than 25 mph
Average minimun temperature greater than 35 degrees
Soil depth between 31 and 72 inches
Medium - to highly drained soils ( values of 1.5 - 3)
The following screen shots show the different stages of the process.
The
above screenshot demonstrates the different landuse types (agricultural (1),
forest (2), undeveloped (3), and urban (4) of the area. This was done making
a dissolve. A unique value that was located in different raster cells now show
one feature, rather than individually highlighting rows in an attribute table.
The soils symbols locate sample points which will later be interpolated over
the entire surface to show both depth and drainage.
The
screenshot above shows the hydro vector shapefile converted into raster format.
The above
screenshot demonstrates the hillshading function from the elevation grid. The
hillshading function hypothetically illuminates the surface of the land and
gives each raster cell a value from 0 to 255 (increasing from black to white).
This demonstrates which cells fall in a shadow or not.
The screenshot
above denotes the aspect of a particular location. Aspect refers to the down-slope
direction from each cell to the neighboring cell. The different values in each
cell denote a compass direction (0º = north, 180º = south, 90º
= east, and 270º = west). The aspect grid, like the hillshade grid, also
utilizes the elevation theme in its construction.
The
two screenshots above show both the soil depth and soil drainage (respectively
from top to bottom). These grids were created by interpolating values from the
known soil values (shown as a small diamond). The interpolated values in this
case were found using "inverse distance weighted" (IDW) interpolation.
This means that the closer a point is to a known location, the more influence
it has on what the value will be.
The two screenshots immediately above show minimum temperature ranges and maximum wind values, which were both found using IDW interpolation.
The above screenshot demonstrates the distance ranges (in miles) away from the
hydrography layer.
The above screenshot is one of the final products for this task. It shows the distance hydrography layer reclassified into a binary classification scheme (0 being an undesirable location for a vineyard and 1 being a desirable location) - now being names Hdyro_buffer. This same reclassification function was also completed on the landuse grid, the aspect grid, the maximum wind grid, the minimum temperature grid, the soil depth grid, and the soil drainage grid (see the aspect grid and the soil depth grid below).
The reclassified soil aspect grid is shown above.
The
reclassified soil depth grid is shown above.
The layer
above is a calculated layer multiplying both the hydro_buffer layer and flood_grid
layer together. This calculation results in the flood_hydro2 layer shown above.
Again the values of 0 (shown in blue) are undesirable, while the values of 1
are desirable areas for vineyards.
To obtain this final map above, the multiplication of flood_hydro2 layer with the RC grids for landuse, aspect, maximum wind, minimum temperature, soil depth, and soild drainage took place. The calculation resulted in the visualization of the suitable sites for vineyard development which are shown in pink. Below is the same map with both the undesired floodplain areas shown in light blue, the non-suitable areas not meeting the requirements shown in darker blue, the hydrography of the area shown, as well as the suitable land shown in pink.
