Fundamentals of Environmental Geomatics Lab 8
11:372:233, Fall 2011
Raster Data Analysis II: Overlays
Today we will use ArcMap to combine and manipulate multiple raster-based geographic data layers.
The results of these simple analyses will allow us to visualize complex spatial relationships.
Learning Objectives:
•
Raster data structure
•
Basic raster analysis
Introduction
Common vector-based overlay analyses such as union and intersection have analogous functions for use
with raster-based data. Logical operators such as “and” and “or” are used to perform these functions. They
result in additional grids that contain values of 1 (True) and 0 (False) in answer to the questions we pose.
Manipulating Raster Data
Performing Raster Unions and Intersections
1. Launch ArcMap and add “wetgrid” and “hydsoilgrid” from the location listed below.
\\ad-rsc\data\teach\envgeolab\class_data\hopewell\
These grids show the extent of wetlands and hydric soil varieties in Hopewell Township, respectively.
Wetland vegetation and hydric soils commonly co-occur. The grids should overlap to a large extent.
The areas in which they do not overlap are a bit of a curiosity, and may warrant further investigation.
2. Make sure the “Spatial Analyst” extension is turned on. (Select “Extensions…” from the “Customize”
drop-down on the main menu, and make sure that the box next to “Spatial Analyst” is checked.)
3. Begin by to performing the raster equivalent of a union analysis. Select “Raster Calculator” from the
“Map Algebra” toolbox under “Spatial Analyst Tools”. Create the following expression with the
calculator, set the output raster as “hopeunion” in your student folder, and click OK. Note: The vertical
line appears when you choose “OR” as your logical operator. The statement below is therefore read,
“The output grid is either ‘wetgrid’ or ‘hydsoilgrid’.”
“wetgrid” | “hydsoilgrid”
The grid that results is the union of both input grids. Areas where either wetlands or hydric soils occur
have a value of 1 (true) and areas where neither occurs have a value of 0 (false). Set the zero values in
all three grids to no fill in order to view the overlapping areas for yourself. This calculation is quick and
easy compared to a vector union. Now, deactivate the “union” layer.
4. The union equivalent is a handy way to examine all the features of two or more layers, but it is often
more useful to isolate and examine areas of overlap and non-overlap. Such grids are the product of a
raster “intersection.” Create the following expression with the “Raster Calculator,” set the output raster
as “hopeintersect” in your student folder, and click OK.
“wetgrid” & “hydsoilgrid”
Fundamentals of Environmental Geomatics - Lab 8
11:372:233:04, Fall 2011
Notice this time only the areas that are both wetlands and hydric soils have a value of one. Set the zero
values of the union and intersection grids to no fill in order to visualize the areas in which overlapping
occurs, and those where it does not occur.
5. There is one raster overlay operation that has no direct vector equivalent. It is called the “exclusive or.”
The output of this function contains only areas in which one or another of the input features occur.
Areas where either both input features or neither input feature occur are excluded. Create the following
expression using the “Raster Calculator” “XOR” operator, set the output raster as “hopexor” in your
student folder, and click OK.
“wetgrid” != “hydsoilgrid”
6. Examine the differences between each of the grids you have created, and then remove them from
the data frame.
Quick Review:
Symbol Function Output
|
OR
Combined extent of input layers (like Union)
&
AND
Extent of overlap of input layers (like Intersect)
!=
XOR
Combined extent of input layers, except where overlap occurs
(like OR minus AND)
Simple Map Arithmetic
Recall the exercise you performed last week. The Expand command was used to buffer the forested
areas of Middlesex County. The resulting grid, however, contained both buffered and forested areas. We will
use simple arithmetic in order to generate a layer containing only the 90m buffer.
1. Load “forbuffer” and “forest” into your data frame from the following location.
\\ad-rsc\data\teach\envgeolab\class_data\mid
2. Input grids must cover the same extent and have a common cell resolution when
performing arithmetic functions on multiple layers. The value of each cell in an output
grid is dependent upon the values of the cells at corresponding locations in the input
grids. The cell boundaries of these grids must, therefore, be aligned. The following
illustration is an example that shows what happens when we add two grids together:
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Fundamentals of Environmental Geomatics - Lab 8
11:372:233:04, Fall 2011
If we take a look at the forest and expanded forest layers, we can see that both have
values of 1 where there is forest or buffered forest, respectively (and 0 in non-forest and
non-buffered areas respectively) . What arithmetical operator (+, -, * or /) do we need to
use to create an output grid that has a value of 1 for the 90 m buffers and a value of zero
for everything else?
3. Use the “Raster Calculator” to create an equation that subtracts the forest grid from the
expanded forest (set the output raster to “forbuffonly” in your student folder). Evaluate
this expression, and review the results.
4. Now, instead of subtracting the grids, add them together (set the output raster to
“forbuffboth” in your student folder). The output of this addition allows us to determine
where the buffers are, but also has the advantage of differentiating between the forests
and the buffers.
Weighting and Rating
We are not limited to performing these simple operations on only 2 layers. We can string
together equations that combine numerous grids in various ways. We can add two grids and then
divide by a third grid, for example. One of the most common uses of multiple-expression map
arithmetic is “weight-and-rate” analysis. Today, we will employ a weight-and-rate scenario for
the purpose of a suitability analysis. This is an analysis in which we try to determine the
suitability of an area for a particular use or application (i.e. apple orchard).
The first step is to prepare input layers that represent criteria important to the issue at
hand (i.e. appropriate soil, cheap land, market access and gentle slopes in the case of siting an
apple orchard). While our inputs may start out as complex (like a soils map) we usually simplify
them into a ranking based on their suitability for the project (for example, a scale of 1 to 5 where
1 means bad soil for apple trees and 5 means great soil for apple trees).
Once we rank our input data layers, we then combine them to create a layer that shows
where the best and worst locations for our project are. If we had 5 input layers, each ranked
from 1 to 5, and we simply added them together, our output would be a map with values ranging
from 5 to 25 (theoretically). If 1 represented the worst areas for each input and 5 the best, it’s
obvious that the higher the value of the output area, the better it is for our purpose. The
reclassification and raster calculator functions make these operations quite simple.
The above is an example of “rating.” What about “weighting?” Imagine that soil quality
and cheap land are more important to us than market access. We can weight the soil and land
value layers more heavily by multiplying them by a number (greater than 1) representing their
importance (2, 5, 1.5, etc.) BEFORE combining them with the other layers. We might, instead,
divide the market access layer (by a weight greater than 1) to reduce its importance.
As you may imagine, the final outcome can vary tremendously depending on how the
input grids are ranked and weighted. If you’re ever called upon to do such an analysis, it is your
responsibility to clearly explain your ranking and weighting scheme. If you ever need to judge
the results of such an analysis, perhaps in order to help make a decision at a planning board
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Fundamentals of Environmental Geomatics - Lab 8
11:372:233:04, Fall 2011
meeting, be sure the person who presents the results clearly explains the ranking and weighting.
Weight-and-rate analyses are quite useful, but their results are easily misinterpreted.
1.
The Center for Remote Sensing and Spatial Analysis recently completed an
assessment of the Highlands of New York and New Jersey. One of the products of
the project was the creation of five raster data layers representing areas ranked
according to their importance for biodiversity, agriculture, recreation, water supply
and forest cover. Each of these layers was ranked from 0 to 5, with 5 representing the
highest importance. Load the 5 raster layers located in the following location.
\\ad-rsc\data\teach\envgeolab\class_data\highlands
2.
Perform a simple addition of all 5 layers to generate a composite layer. Note the
highest and lowest values (set the output raster to “highnoweight” in your student
folder). How might you simplify the output to make it easier to understand? You
could classify the scores using the Symbology properties (i.e. values 5,10, 15, 20, 25),
or you could divide the combination layer by 5 to rescale back to the original scale of
the inputs (i.e. values 1-5). Try both methods, and compare the results. (Hint: you’ll
probably want to use “Classified” symbology.)
3.
Now explore the impact of weighting. Imagine you work at the New Jersey
Department of Environmental Protection. You have been informed that you will be
working in cooperation with the Department of Agriculture to identify places where
important agricultural areas coincide with areas of import for biodiversity, forest
resources, water supply and recreational areas. Use the “Raster Calculator” to weight
the agricultural input layer with a value of 3. Then add the weighted layer to the
other four layers. Visually compare the results with the straight addition performed
above.
Assignment:
Using the Highlands data, devise your own weight-and-rate suitability analysis. First, determine
what you would want your output layer to display (such as important forest areas that are also
important for water resource conservation). Then, weight the appropriate layers and combine
them. There are two requirements for your output: It must combine a minimum of three
layers, at least two of which must be weighted. You may not use the agricultural example
above. After performing the analysis, create a map showing your output. Remember to choose
appropriate symbology (i.e. one that implies your ranking varies from low importance to high).
Also include an explanation of what you wanted your output layer to display, what your input
layers are and how you rated and weighted them. Include all map elements.
Due the beginning of next class.
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