The Step-by-Step Activity for Lesson 6 is divided into two parts. In Part I, we will create a point shapefile from our starting data tables. We will then use the field calculator to calculate the carbon sequestration for each tree and create totals by plot. In Part II, we will use the Spatial Analyst extension tools to interpolate the plot data to a raster grid covering the entire study area. We will use the results to calculate the total carbon for the study forest. During interpolation, we will experiment with several different toolbar settings to see how they affect the results.
Note: You should not complete this step until you have read through all of the pages in Lesson 6. See the Lesson 6 Checklist for further information.
In Part I, we will create the shapefile we will use to interpolate our data (a point shapefile of plots with the total carbon as an attribute). To create this, we start with the two CSV files "GPS.csv" and "Tree _Measurements.csv".
How far is the study forest from the city of Ann Arbor, MI or State College, PA? What is the surrounding land used for (commercial, agriculture, residential, etc.)?
Make sure you have the correct answer before moving on to the next step.
Check the Properties > Source Tab > Spatial Reference to make sure the Plot shapefile was projected correctly to NAD 183 UTM Zone 16N. If your projection doesn’t match, make sure you remove the base maps, and choose the coordinate system of the Map.
Make sure you have the correct answer before moving on to the next step.
Check the location of your plots by comparing your plot shapefile to the map below. Note: Your map will not look exactly like this by default. I changed the symbology of the points, added labels of Plot ID's, and added the Imagery layer in the background to make it easier to compare your data to the example. If you add the imagery base map again, make sure you remove it from your map and Save before moving on to the next step.
We are going to use a somewhat general set of equations to estimate the carbon stored in each tree. For this lesson, we do not need a high level of accuracy. The important part is to demonstrate the concept of how one can calculate carbon credits using GIS. You can read more about the method we will use at: How to calculate the amount of CO2 sequestered in a tree per year [2].
There are more sophisticated methods you can use that take into account the tree species, age, climate, and other factors. The paper, “Methods for Calculating Forest Ecosystem and Harvested Carbon with Standard Estimates for Forest Types of the United States [3]” highlights an example of a more complex methodology. An example of a simpler method is highlighted in the “Landowner’s Guide to Determining Weight and Value of Standing Pine Trees [4]”.
Variable | Description | Units | Equation |
---|---|---|---|
D | Measured tree diameter (DBH) | Inches | See Tree Measurements Table (be careful with your units here). |
H | Measured tree height | Feet | See Tree Measurements Table (be careful with your units here). |
Wa | Total above-ground weight of the tree (w/o roots) | Pounds | Wa = 0.15D2 *H |
Wt | Total weight of the tree and roots | Pounds | Wt = 1.2 Wa |
Wd | Dry weight of the tree | Pounds | Wd = 0.725Wt |
Wc | Weight of carbon in the tree | Pounds | Wc = 0.5Wd |
Ws | Weight of carbon dioxide sequestered in the tree | Pounds | Ws=3.6663Wc |
Make sure you have the correct answer before moving on to the next step.
Compare your data with the summary statistics below for the "Ws" variable.
Mean | 801.0505393089 |
---|---|
Median | 337.511190219 |
Std. Dev. | 1,171.7755087661 |
Count | 278 |
Min | 0 |
Max | 6,748.03406916 |
Sum | 222,692.04992788 |
Nulls | 0 |
Skewness | 2.6994064237 |
Kurtosis | 10.3340354971 |
If your data does not match this, go back and redo your calculations. Pay special attention to unit conversations (make sure to round to the nearest 4 decimal places), data types of the fields you used, and typos in equations.
Make sure you have the correct answer before moving on to the next step.
Compare your data with the summary statistics below for the "c_lbsqm" variable.
Mean | 157.6023000198 |
---|---|
Median | 109.55277128 |
Std. Dev. | 162.8283983546 |
Count | 18 |
Min | 6.420046445 |
Max | 646.682030534 |
Sum | 2,836.8414003566 |
Nulls | 0 |
Skewness | 1.5234771786 |
Kurtosis | 5.4280093282 |
If your data does not match this, go back and redo your calculations.
In Part II, we will use the Spatial Analyst extension tools to interpolate the carbon sequestration data we calculated for each plot to the entire forest. We will run the same interpolation tool several times to see how altering the extent, mask, and cell size settings affect the results. We will start by accepting all default settings. Then we will change the settings one at a time to see how each one affects the results.
Do some plots have more trees than others? Is there a lot of variation in the total amount of carbon or carbon per square meter value? If so, why do you think this may occur? Hint: Look at an aerial image basemap.
Do you see any spatial patterns in the data? For example, do some areas of the forest have higher values than others? If so, why do you think this may occur?
Remember from the Background Information section that the Spatial Analyst tools are governed by user-specified settings. Two of the most common errors when using Spatial Analyst tools are to either completely ignore these settings, or to set them improperly. Let’s try to interpolate our data using all of the defaults and see what our results look like.
Make sure you double-check ALL environment settings before running ANY tools in Spatial Analyst! The program often resets your cell size, extent, and mask to program or data layer defaults.
Click the Show Help >> button to help define particular input parameters.
You can review the specific input and environment settings you used in the Analysis tab, Geoprocessing group > History. This can be helpful if you are not sure if you made a mistake somewhere along the way during a complex workflow.
Make sure you have the correct answer before moving on to the next step.
If your map does not match the example below, go back and redo the previous step.
What is the default setting for analysis extent?
What is the cell size of the "default" raster we created? Why?
Raster Attribute Tables
You may notice that the option to open the attribute table of the "default" raster is grayed out. ArcGIS Pro only builds raster attribute tables if certain conditions are met. One of the conditions is that the values in the raster have to be integers. Since the values in our raster have decimals, it is not possible to view the attribute table.
Now that we’ve explored the default settings, let’s see what happens if we alter just the extent settings. Unlike vector files, rasters will always have the shape of a perfect rectangle. The size and location of the rectangle is defined by its extent.
Make sure you have the correct answer before moving on to the next step.
If your map does not match the example below, go back and redo the previous step.
How do the extents of the "parcel_extent," "forest_extent" and "default" rasters compare?
Now, let’s see what happens if we alter the mask and extent settings. Even though all rasters are defined as perfect rectangles, you can still represent your data as a sinuous shape. The computer creates this illusion by assigning cells outside the sinuous shape values of "NoData." There is not a direct equivalent to this concept in vector files.
What would the output raster look like in the following scenario?
In Step 3, we learned that the default cell size will depend on the input data. If you are using one or more rasters as inputs, the cell size will default to the coarsest raster resolution. If you are using a vector file, it will calculate the cell size based on the extent of the file to create 250 cells. The default for rasters seems appropriate since GIS best practices dictate that you should always go with the cell size of your coarsest input dataset. However, the default for vector files is quite arbitrary.
How do we choose a more meaningful cell size for our analysis? One rule of thumb is that you don’t want to "create" higher resolution data than what exists in your measured values. We know that the tree data was collected by measuring trees that fell within 10 m diameter circular plots. A cell size of 1 cm would not be appropriate, because we do not know how the data varies at that scale. A cell size of 1,000 m would be too large, since it is larger than our study area. For this project, we will use a cell size of 1 m, since our carbon values are in pounds per square meter.
Now that we have an understanding of how the spatial analyst environment settings function, we can return to our original question. We want to figure out how much carbon the study forest sequesters. To accomplish this, we will use the "Zonal Statistics" tool in Spatial Analyst. This tool allows us to calculate statistics of the cell values of one raster (e.g., carbon1m) within zones specified by another file (e.g., forest boundary). We will use it to sum the carbon values in each cell to create a total for the entire forest.
ArcGIS may not show all the digits in a table by default. If your numbers do not match the numbers in the quiz, expand the columns in your table to display all the digits.
The monetary value of each carbon credit fluctuates based on the current market conditions. Check out the more information about the California’s cap and trade system at Center for Climate and Energy Solutions(C2ES) [6].
One of the main take away points from this lesson is that Spatial Analyst is a modeling tool. Models don’t give exact final answers; rather they give you estimates of reasonable answers based on a set of assumptions.
Environments settings allow you to easily alter the underlying assumptions of your model (cell size, mask, extent) and then quickly recalculate your results.
Selecting environment settings in Spatial Analyst tools can be confusing and seem somewhat arbitrary. If you don’t know which Environments setting you should use for a particular scenario, you can try experimenting with a variety of options. This type of sensitivity analysis will help you understand how changing model assumptions affect your final results.
That’s it for the required portion of the Lesson 6 Step-by-Step Activity. Please consult the Lesson Checklist for instructions on what to do next.
Try one or more of the activities listed below:
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.
Links
[1] https://www.e-education.psu.edu/geog487/sites/www.e-education.psu.edu.geog487/files/image/lesson05/Pro/Lesson5ExportTable.png
[2] https://www.e-education.psu.edu/geog487/sites/www.e-education.psu.edu.geog487/files/activities/lesson05/Calculating_CO2_Sequestration_by_Trees.pdf
[3] https://www.fs.usda.gov/ecosystemservices/pdf/estimates-forest-types.pdf
[4] https://www.uaex.uada.edu/publications/pdf/FSA-5017.pdf
[5] http://www.onlineconversion.com
[6] https://www.c2es.org/content/california-cap-and-trade/