In Part I, we will review the historical data and organize the map for analysis. In Part II, we will use the roads dataset to create rasters of habitat quality and forest patches. In Part III, we will generate statistics about the size, shape, and habitat quality of each forest patch. We will also generate statistics of habitat quality by land management type (conservation vs. logging areas). In Part IV, we will share our analysis results using ArcGIS Online.
Note: You should not complete this step until you have read through all of the pages under the Lesson 7 Module. See the Lesson 7 Checklist for further information.
In Part I, we will review the data and organize the map for analysis.
Since all of the datasets used in this lesson have the same projection, we do not have to be concerned with the order in which we load the data.
How many of the management units are used for logging? What about conservation?
Using the "Imagery Hybrid" layer, can you see the approximate extent of the rainforests located in the Congo Basin? What kind of details can you see in the forest if you zoom in very close?
In Part II, we will use the roads dataset to create raster data layers of habitat quality and forest patches. In Part III, we will generate statistics about the size, shape, and habitat quality of each forest patch.
We will use the following coded values:
When you convert a feature layer to a raster, you have to choose a field in the feature layer from which to base the grid cell values on. You often need to create a new dummy field and assign a value that is consistent for all of the records you want to convert (like we did above).
It is also important to note that if there are any selected records in the vector layer, only those records will be converted to a raster layer. Therefore, be sure to clear any selected features before performing the conversion.
The data type of the field you choose is very important. For example, if you choose a numerical field that contains decimal values, the resultant grid will not have an attribute table. However, if you choose an integer field, the resultant raster will have an attribute table. If you choose a text field, ArcGIS will automatically assign each unique text value an integer code in a new field named "VALUE."
The new raster layer will be created based on all defined Spatial Analyst environment settings. Always check these settings before converting features to a raster to avoid potentially undesirable results.
It is important to note that although the extent setting is utilized by Feature to Raster, the mask setting is ignored. Although you will not notice this with the "RoadsGrid.tif" layer, you will see the effects of this when you create a buffered grid later in this lesson.
Make sure you have the correct answer before moving on to the next step.
The "RoadsGrid.tif" raster should have the following information. If your data does not match this, go back and redo the previous step.
Remember from the Background Information section that edge effects can occur up to 2 km from roads. We will consider all areas 2 km from roads as "edge habitat" and areas farther than 2 km from roads as "interior habitat." To do this, we need to create a buffer of the road centerlines.
Make sure you have the correct answer before moving on to the next step.
The "EdgeGrid" raster should have the following information. If your data does not match this, go back and redo the previous step.
Did the Reclassify Tool honor the mask and extent settings?
Hint: Compare the InteriorGrid.tif and EdgeGrid.tif rasters along the study area boundary.
Make sure you have the correct answer before moving on to the next step.
The "InteriorGrid.tif" grid should have the following information. If your data does not match this, go back and redo the previous step.
In steps 1, 2, and 3, we created three individual grids, one for each level of habitat quality. To continue the analysis, we need a way to merge all of the data sets into one grid. The Mosaic to New Raster tool in Toolboxes will allow you to mosaic multiple raster data layers together by stacking them on top of one another. The values in the output raster will be determined based on the order the files are specified during the mosaic. Cells will first be assigned according to the cell values in the first input raster; all remaining null values will be filled in with the middle input raster, and so on. We want the roads to be on top of the stack, the edge habitat in the middle, and the forests on the bottom.
This tool does not honor the Output extent environment settings. If you want a specific extent for your output raster, consider using the Clip tool. You can either clip the input rasters prior to using this tool, or clip the output of this tool.
Make sure you have the correct answer before moving on to the next step.
The "HabMosaic" raster should have the following information. If your data does not match this, go back and redo the previous step.
What value was assigned to areas with roads, since they have data in both the "RoadsGrid" and "EdgeGrid" rasters?
Which habitat type (roads, edge, or interior) covers the majority of the study area?
How can you calculate the area of each habitat type?
The Raster Calculator utilizes all raster environment settings, so it is highly useful when working with raster data. As displayed above, simply selecting a raster layer and running the Raster Calculator will generate a new raster layer based on the current environmental settings. Try changing these settings to see the differences when running the Raster Calculator on a particular raster layer.
Make sure you have the correct answer before moving on to the next step.
The "HabitatGrid" raster should have the following information. If your data does not match this, go back and redo the previous step.
We now have one grid with values showing the range of habitat quality within the study area. The next step is to create a grid of forested areas, which we need to create the forest fragments. We will use the "RoadsGrid.tif" raster we created in Part II Step 1 to create a new grid representing forested areas (cells that are NOT roads).
Make sure you have the correct answer before moving on to the next step.
The "ForestGrid.tif" raster should have the following information. If your data does not match this, go back and redo the previous step. You may need to adjust for the Mask and Processing Extent here as well.
Make sure you have the correct answer before moving on to the next step.
The "ForestPatches.tif" grid should have the following information. If your data does not match this, go back and redo the previous step.
OID | Value | Count | link |
---|---|---|---|
0 | 1 | 64201 | 1 |
1 | 2 | 58 | 1 |
2 | 3 | 122867 | 1 |
3 | 4 | 19 | 1 |
4 | 5 | 30 | 1 |
Why did we use the number "100" to calculate the area?
Make sure you have the correct answer before moving on to the next step.
The "ForestPatches.tif" grid should have the following information. If your data does not match this, go back and redo the previous step.
oid | value | count | forestid | area_sq |
---|---|---|---|---|
0 | 1 | 64201 | 1 | 642010000 |
1 | 2 | 58 | 2 | 580000 |
2 | 3 | 122867 | 3 | 1228670000 |
3 | 4 | 19 | 4 | 190000 |
4 | 5 | 30 | 5 | 300000 |
5 | 6 | 1 | 6 | 10000 |
6 | 7 | 13 | 7 | 130000 |
7 | 8 | 1 | 8 | 10000 |
8 | 9 | 318427 | 9 | 3184270000 |
How many individual forest patches are there? Which forest patch is the largest? Which forest patch is the smallest? Why do you think there are so many patches with an area of exactly 10,000 sq m?
In Part III, we will use two Spatial Analyst tools to bring together the raster layers we created in Part I (habitat quality) and Part II (forest patches). Zonal Geometry calculates several geometry measures, such as area and thickness, for zones in a raster. We will use it to generate a table of statistics about the size and shape of each forest patch. We will also use the Zonal Histogram Tool to tabulate the number of cells of each habitat type within each forest patch and management unit.
Make sure you have the correct answer before moving on to the next step.
The "PatchGeometry" table should have the following information. If your data does not match this, go back and redo the previous step.
OID | Value | Area | Perimeter | Thickness | Xcentroid | ycentroid | Majoraxis | minoraxis | orientation |
---|---|---|---|---|---|---|---|---|---|
0 | 1 | 642010000 | 350600 | 6343.9 | 1688100 | 359206 | 24878.6 | 8214.21 | 81.9311 |
1 | 2 | 580000 | 3800 | 212.1 | 1696040 | 379519 | 631.205 | 292.488 | 84.2196 |
2 | 3 | 1228670000 | 907000 | 6250.3 | 1756350 | 335775 | 36173.2 | 10811.8 | 134.675 |
3 | 4 | 190000 | 2400 | 150 | 1698610 | 378516 | 270.871 | 223.275 | 140.531 |
4 | 5 | 300000 | 2800 | 170.7 | 1699130 | 378353 | 401.382 | 237.911 | 110.363 |
5 | 6 | 10000 | 400 | 50 | 1699560 | 378016 | 56.419 | 56.419 | 90 |
6 | 7 | 130000 | 1600 | 150 | 1700800 | 377131 | 219.193 | 188.785 | 166.224 |
7 | 8 | 10000 | 400 | 50 | 1698360 | 377216 | 56.419 | 56.419 | 90 |
Which field in the "PatchGeometry" table is the equivalent to the "ForestID" field? What are the units of the fields "AREA," "PERIMETER," and "THICKNESS"? What do the values in the fields "XCENTROID," "YCENTROID," "MAJORAXIS," "MINORAXIS", and "ORIENTATION" mean?
The Zonal Histogram tool will create a summary table that contains one row for each unique value in the "Value raster" and one column for each unique value in the "Zone dataset." The tool will calculate the total number of cells for each combination of a unique row and column. The tool can also create a graph based on the output table, which we are going to skip.
Make sure you have the correct answer before moving on to the next step.
The "Habitat_by_Patch" table should have the following information. If your data does not match this, go back and redo the previous step.
oid | Label | Value_2 | Value_3 | FORESTID | EDge_sqm | int_sqM | PCTtotedge | pcttotint |
---|---|---|---|---|---|---|---|---|
0 | 1 | 22207 | 41994 | 1 | 222070000 | 419940000 | 35 | 65 |
1 | 2 | 58 | 0 | 2 | 580000 | 0 | 100 | 0 |
2 | 3 | 74095 | 48772 | 3 | 740950000 | 487720000 | 60 | 40 |
3 | 4 | 19 | 0 | 4 | 190000 | 0 | 100 | 0 |
4 | 5 | 30 | 0 | 5 | 300000 | 0 | 100 | 0 |
What do numbers in the "LABEL" field of the "Habitat_by_MU" mean? Which management unit "use" has the most roads?
Make sure you have the correct answer before moving on to the next step.
The "Habitat_by_MU" table should have the following values. If your data does not match this, go back and redo the previous step.
OID | Label | logging | coservation | habitat | logSqm | conssqm | pcttotlog | pcttotcons |
---|---|---|---|---|---|---|---|---|
0 | 1 | 35322 | 1428 | Low Quality Habitat | 353220000 | 14280000 | 96 | 4 |
1 | 2 | 635978 | 44954 | Medium Quality Habitat | 6359780000 | 449540000 | 93 | 7 |
2 | 3 | 302425 | 167611 | High Quality Habitat | 3024250000 | 1676110000 | 64 | 36 |
Make sure you have the correct answer before moving on to the next step.
The "forestpatchpoly" shapefile should have the following information. If your data does not match this, go back and redo the previous step. Note that this table has been sorted based on "gridcode".
Fid | Shape* | Id | gridcode | ForestID |
---|---|---|---|---|
36 | Polygon | 37 | 1 | 1 |
0 | Polygon | 1 | 2 | 2 |
61 | Polygon | 62 | 3 | 3 |
1 | Polygon | 2 | 4 | 4 |
2 | Polygon | 3 | 5 | 5 |
Why is there such a large range of values for the edge to area ratio results?
How would the results of the analysis change if we used a larger or smaller cell size?
Make sure you have the correct answer before moving on to the next step.
The "Final_Forest_Patches" attribute table should have the following information. If your data does not match this, go back and redo the previous step.
FID | Shape* | Forest ID | Totareasqm | perimeterm | thichnessm | edge_sqm | int_sqm | pcttotedge | pcttotint | edgetoarea |
---|---|---|---|---|---|---|---|---|---|---|
36 | Polygon | 1 | 642010000 | 350600 | 6343.9 | 222070000 | 419940000 | 35 | 65 | 0.05461 |
0 | Polygon | 2 | 580000 | 3800 | 212.1 | 580000 | 0 | 100 | 0 | 0.655172 |
61 | Polygon | 3 | 1228670000 | 90700 | 6250.3 | 740950000 | 487720000 | 60 | 40 | 0.07382 |
1 | Polygon | 4 | 190000 | 2400 | 150 | 190000 | 0 | 100 | 0 | 1.26316 |
2 | Polygon | 5 | 300000 | 2800 | 170.7 | 300000 | 0 | 100 | 0 | 0.933333 |
3 | Polygon | 6 | 10000 | 400 | 50 | 10000 | 0 | 100 | 0 | 4 |
5 | Polygon | 7 | 130000 | 1600 | 150 | 130000 | 0 | 100 | 0 | 1.23077 |
Notice how the default outputs from many of the Spatial Analyst tools are not very easy to understand. It’s worth the time to create more intuitive fields, units, and names while you are doing the analysis. That way you can easily interpret your results later on and share them with others in a meaningful format.
In Part IV, we will finalize our map in ArcGIS, then you will be asked to share your results with the Geog487 AGO group as web maps. As a final step, you will combine the output from the Step-by-Step and Advanced Activity into a web application.
That’s it for the required portion of the Lesson 7 Step-by-Step Activity. Please consult the Lesson Checklist for instructions on what to do next.
Try one or more of the optional activities listed below.
Landsat satellite images were used to digitize the road data we used in this lesson. You can read more about Landsat data on NASA’s website [3]. As of October 2008, Landsat data is available for free to the public. It can be viewed and downloaded from the USGS Earth Explorer Viewer [4].
Note: Try This! Activities are voluntary and are not graded, though I encourage you to complete the activity and share comments about your experience on the lesson discussion board.