Purpose. Determining the Acreage of Different Land use Categories

Transcription

1 Purpose A water agency knows the amount of water used for each parcel, but they cannot distinguish from indoor uses and outdoor uses, such as landscaping and irrigation. This study is a solution to measuring irrigation and landscaping. Determining the Acreage of Different Land use Categories By knowing the total acreage of a particular land use, calculating a percentage of irrigated land will be more accurate. Setting up preliminary statistics will make many of the later process faster and easier. Keep in mind this example is only dealing with Western Municipal Water District s Updated Retail Service. Here are the steps used to find the total acreage of the different land uses; Layers used: UpdatedRetailService (this is like a city boundary) LU_AREAS Satellite Imagery (not needed for this process) Steps: 1. Load the above layers or open WDF.MXD if you have it. 2. Symbolize the lu_area in an understandable way (not necessary) 3. Go to Selection and Select by Location. 4. The dropdown box should be select features from. 5. Place a check mark by lu_areas 6. Under that: dropdown, select, have their centroid in. 7. The features in this layer: should be UpdatedRetailService. 8. Press OK

2 Your map should now look like this: From this selection we need to make another selection, but by attribute. Go back to Selection and choose Select by attribute. The window should be setup as follows. 1. Layer : lu_areas 2. Method: Select from current selection 3. Double Click "WDFEXTLUCO" (this is the existing landuse codes) 4. Click = 5. Click Get Unique Values 6. Double Click the top code 1122

3 Your map should now look like: If successfully done, you should have all of the 1122 (Residential Single Family < 1 dwelling/acre) selected within the UpdatedRetailSerive boundary. Now get the statistics. 1. Go to Selection 2. Select Statistics 3. Layer: lu-areas 4. Field: ACRES According to the statistics, the sum of all 1122 residential single family homes with 1 or less dwellings per acre occupy 7, acres within the Updated Retail Service area. This same process can be repeated for all landuse codes, and the furthermore, in all the other service areas, such as Corona, Norco, etc.

4 Now the measurements of irrigated land can be properly divided from total acreage to get an accurate percentage. Parcel Samples: 10 percent of parcels within a land-use category are used to get an average percent of irrigated land. Provided satellite image is required here forth. After selecting the land-use code: 1. Zoom into a parcel. 2. Use the identify tool and right click on the parcel. Statistics for that parcel are displayed. Within the identify window, you can find and copy the APN number and the total acres for this parcel. 3. Copy and paste these numbers into an excel document that has all of the land use categories separated by sheets. The example above is for land use 1221 Commercial Low. The APN number is The total acres are Select the measure tool. 5. Select polygon. 6. Select area acres units. 7. Use the measure tool to trace the irrigated portions within the parcel. 8. Enter the irrigated amount into the spread sheet along with the total acres and APN. 9. In excel, use another column for this calculation; =sum(cell# for irrigated acres/cell# for total acres) This process should be repeated for 10 percent of parcels within a land use category. At the bottom of each sheet in excel for the land use categories there should be more calculations for totals that display an average irrigated land per land use category.

5 Example of 10 percent calculations for land use Condo: This method required tedious tasks that in the end may not have been accurate due to inaccuracies of guess what is actually being irrigated. Just because something appears to be green on an image, does not mean that it is being irrigated. There are many cases in which green vegetation is naturally occurring. I have chosen to use remote sensing to determine where healthy vegetation (irrigation and landscaping) is taking place. The small percentage of samples on a photo was not as effective as satellite images that contain a collection of electromagnetic signatures from healthy vegetation. Remote sensing also reduces human error and bias. Below is a cut-out from a Landsat image that shows where evapotranspiration is being emitted from recently watered vegetation either naturally or

6 irrigated. The United State Geological Survey (USGS) recently released Landsat 7 (remote sensing satellite) images free to the public. We acquired the scene that contains Western Municipal Water District. The scene came with 8 total images. Each image is a representation of a group of electromagnetic radiation (EMR) wavelengths known as bands. The values of the scene range from 0 to 255. Healthy vegetation is a range within Human eyes perceive only a small portion of the EMR spectrum so these values were converted to color. This is done by choosing 3 bands (images within the scene) and assigning colors to them. Healthy vegetation best shows up between visible red and near infra-red (not visible to the eye) in which we chose bands 4, 3, and 2. False Color Infra-red over Retail Service Area

7 A tool from ERDAS that is incorporated into ArcGIS called Image Analysis was used to stack (merge) the 3 bands into one image. The result is a classic false infra-red image that shows healthy vegetation. Image Analysis was used to further highlight the vegetation by some of its built in tools. Spatial Analysis in ArcGIS converts the image into a feature that can then be combined with the land use shapefile. The land use shapefile that has a healthy vegetation index is used to calculate the healthy vegetation acreage within each land use. The entirety of Western can be analyzed, and the process can be repeated for each city boundary. Healthy Vegetation Intensity: Red being most intense, green is less intense.

8 Not only does remote sensing show where land has healthy vegetation, but it shows water intensity of healthy vegetation. This can be interpreted as how much water is being used for irrigation and landscaping. A model can be assembled that shows water intensity on vegetation within a parcel. For example, within Western, one of the highest water intensity of vegetation is on a golf course. The following is from a presentation given at a Water Resource Institute meeting:

9 Green: Least likely ET Yellow: Natural Red: Most Likely ET Images must be assembled following a period of no natural precipitation so that man made irrigation stands out. The distinguishable difference between irrigated features from their environment is more evident on a larger scale. The next step is to define the boundary (threshold) between what is irrigated and what is not irrigated. Note: After the threshold, the values now represent:

10 Green: least ET Yellow: medium ET Red: high ET Non-irrigated values have been removed so what is left is what is suspect of irrigation. Choosing the threshold is subjective, but with informed discretion from the ET values captured from satellite. The next image is the same extent but without the ET values. As you can see, the ET values are justly placed over areas of obvious irrigation, such as farms and lawns.

11 The threshold was used to convert the raster image of ET to a shapefile. The existing shapefile was a Land Use Parcel Map (LU). The LU shapefile has two new feature categories; Irrigated and Not-irrigated. Use these codes to understand the next few figures.

12 A percentage of an irrigated LU type is calculated by dividing the sum of irrigated and not-irrigated by the Irrigated as you can see in the spreadsheet which is taken directly from the attribute table of the newly created shapefile. The colors of the rows represent the colors on the shapefile for irrigated or notirrigated. (Intentionally left blank)

13 Here is a cut-out from the shapefile of Lee Lake Water District within Western s boundary. Here are the attributes from the Lee Lake Cut-out: The General Plan (GP) dictates the zoning of a particular LU. This method of measuring irrigation has revealed that what was originally thought to be a higher use of irrigation is less. So the first method described results in much more irrigation percentages than what is found in this method.

14 Here is a cut-out for Riverside: Here are the attributes for Riverside: What makes this method better is that nothing was sampled as in the first method, where it was heavily reliant on a small percentage of samples and samples that may have not been correct due to human error. This method of remote sensing can be repeated as long as satellites are still capturing scenes.

15 There is always room for improvement. The scenes from Landsat 7 can only get down to 15 meter resolution. There are much higher resolution images from other scene providers. Here is an IKONOS image at 1 meter resolution: Thanks to the Water Resources Institute at CSUSB To Susan Longville for this continued opportunity To Lisa Pierce for her knowledge and support To the Professors in Geography Send questions to Aaron Fregeolle frgll23@hotmail.com

16 WDFEXTLUCO Irr Acres Sum Acres Irrigated Percent 1122 n 24, y 4, , % 1132 n 32, y 6, , % 1162 n 13, y 1, , % 1183 n 1, y , % 1184 n y % 1186 n y , % 1211 n 6, y , % 1221 n 11, y 1, , % 1231 n 1, y , % 1261 n y % 1901 n 158, y 16, , % 2111 n 1, y 1, , % 2121 n 10, y 8, , % 2131 n 1, y 2, , %