CPGIS Tree Canopy Report

Memphis Regional 2014 Urban Tree Canopy Assessment

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Contents Introduction 4 Land Cover Classification and Potential UTC

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Methods 6

Results

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Canopy Height and Extent

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Priority Planting Areas

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Conclusions

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References

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Introduction Urban forests provide many benefits to residents: improving air quality, reducing stormwater runoff, improving health, and reducing peak summer temperatures, to name just a few (USDA Forest Service). In addition to increasing the previously mentioned benefits, healthy urban forests help attract businesses and residents to the community. For these reasons, it is important to establish a baseline for urban tree canopy cover and identify areas where canopy cover can be improved.

The University of Memphis Center for Partnerships in GIS (CPGIS) partnered with the Wolf River Conservancy to perform a Memphis Regional Urban Tree Canopy (UTC) assessment for Shelby County and the municipalities of Munford and Atoka in Tipton County, Tennessee. Using the four-band National Agricultural Imagery Program’s (NAIP) 1-meter spatial resolution imagery captured in 2012, a five-class land cover assessment was created for the study area. The land cover classes include water, tree canopy, other vegetation, bare soil, and impervious surface.

Based on the land cover classification and parcel data, potential Urban Tree Canopy was also evaluated and incorporated into the priority planting analysis. Land cover metrics and potential UTC were summarized for the following areas: • the study area (Shelby County, Atoka, Munford, and their urban growth areas) • the municipalities of Arlington, Atoka, Bartlett, Collierville, Germantown, Lakeland, Memphis, Millington, and Munford • Memphis city council districts • Munford and Atoka urban growth boundaries • unincorporated Shelby County

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In addition to the land cover classification, the height and extent of the existing tree canopy was extracted using leaf-off LiDAR from December 2011 for Shelby County. Finally, priority planting areas were identified using the land cover classification, potential UTC area, parcel attributes, and a number of environmental and socioeconomic factors. Based on the existing canopy, 41 percent canopy cover is an initial estimate for the attainable canopy cover for each summary area based on remote sensing and spatial analyses. Further on the ground studies to determine the health of existing canopy would allow a more precise canopy goal to be set.

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Land Cover Classification and Potential UTC 2. Land Cover Classification and Potential UTC 2.1 Methods 2.1.1 Classification USDA NAIP 1-meter resolution imagery was used to create a land cover classification map of the study area with five classes: water, tree canopy, other vegetation, bare soil, and impervious surface (Figure 2.1). GRASS GIS (GRASS Development Team, 2014) was used to perform the initial image classification using sequential maximum a posteriori (SMAP) estimation. Unlike traditional pixel-based classification techniques, the SMAP algorithm segments neighboring pixels with similar spectral signatures together at various scales and classifies the pixel groups. Because it functions on groups of similar pixels instead of individual pixels, the overall classification is more contiguous (Figure 2.2).

Figure 2.1: Land Cover Classification for the study area.

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As a final step, the hues, or colors, of the land cover classification were merged with the intensity of the aerial imagery, producing a land cover classification image that is textured, and therefore more visually appealing than the original land cover classification (Figure 2.3).

(a) Maximum Likelihood Classification

(b) SMAP Classification

Figure 2.2: Comparison of Maximum Likelihood Classification and SMAP Classification results

(a) Original land cover classification

(b) Textured land cover classification

Figure 2.3: Comparison between (a) original land cover classification and (b) a fusion of the land cover classification and aerial imagery that produces a textured effect, adding additional context which makes the land cover classification appear more natural.

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2.1.2 Error Correction After completion of the land cover classification, the most common errors were identifies as: • impervious classified as water • impervious classified as bare soil • agricultural fields under cultivation classified as tree canopy In addition to these common errors, because a shadow class was not created, areas in shadow were classified as either impervious or water. To correct these two kinds of errors, LiDAR intensity images were used. LiDAR intensity is a measure of the strength of the LiDAR pulse when it returns to the sensor. The strength of the intensity varies by land cover type, effectively allowing us to ``see through’’ the shadowed areas. Using the uncorrected land cover and the intensity values, shadowed areas were reclassified to their correct land cover class (Figure 2.4). Following the intensity corrections, a manual review of the classification was performed.

Misclassified areas were identified and corrected to reach the final result (Figure 2.1).

Figure 2.4: The image classification process. The uncorrected classification is created from the 1-meter resolution imagery. Misclassified areas are corrected based on the LiDAR intensity values.

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2.1.3 Accuracy Assessment When assessing the accuracy of a remote sensing classification, there are generally three types of accuracy levels reported: overall accuracy, producer’s accuracy and user’s accuracy (sometimes referred to as consumer’s accuracy). Overall accuracy is simply the number of correctly classified points divided by the total number of points sampled. The producer’s accuracy is an indication of the percentage of each land cover class that was correctly classified and can be calculated by dividing the number of correctly classified pixels of a particular land cover type by the row total in the error matrix. User’s accuracy provides an indication of the predictive reliability of the map by giving the percentage of correctly classified pixels of each land cover type.

User’s accuracy can be calculated by dividing the number of correctly classified pixels by the column total.

Another measure of accuracy is called training accuracy, which is calculated by using the training pixels as the reference pixels in the accuracy assessment calculation. Using the training areas as the reference for the final accuracy assessment tends to result in inflated accuracies. However, it is a good measure of the quality of the training pixels. The training accuracy for the land cover classification is 99.1 percent.

To assess the accuracy of the final land cover classification, 1,000 points were selected at random across the study area. The aerial imagery was inspected and a class was assigned to each point. If the accuracy was not to the requested standard, corrections were made to the classification and the process was repeated.

An overall accuracy of 94 percent was achieved. The Producer’s Accuracy of the tree canopy and impervious surface classes is 98 percent and 92 percent, respectively, while the user’s accuracy of the tree canopy and impervious surface classes is 95 percent and 90 percent, respectively. The results of the accuracy assessment can be seen in Table 2.1.

Table 2.1: Accuracy assessment of the land cover classification.

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2.1.4 Potential UTC Potential UTC, or land cover types that can be converted to UTC, was calculated for the study area and used in the priority planting analysis. Potential UTC was defined for this study as areas of bare soil or other vegetation that meet the following criteria: • not used for agriculture according to parcel data • not used as a golf course according to parcel data • not an athletic field • outside of major electrical transmission line easements Shelby and Tipton County parcels with land use codes corresponding to agriculture or golf courses were extracted from the parcels data. Some golf courses were not identified in the parcel data and were manually digitized. Major electrical transmission lines were acquired from OpenStreetMap (http://www.openstreetmap. org) and buffered to determine the right of way area for exclusion. Other vegetation and bare soil land cover classes that did not fall within the bounds of parcels with these land use codes were identified as potential UTC and summarized for each of the boundaries.

2.2 Results 2.2.1 Study Area The study area as a whole is dominated by the other vegetation and tree canopy classes, at 40.4 percent and 36.8 percent, respectively (Figure 2.5). Much of the Other Vegetation class is made up of agricultural land, reducing the amount of land area available for potential UTC.

Figure 2.5: Land cover metrics for the study area.

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Figure 2.6: Land cover metrics for unincorporated Shelby County.

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Figure 2.7. Land cover metrics for Atoka and Munford urban growth boundaries.

2.2.2 Unincorporated Shelby County A large portion of unincorporated Shelby County is agricultural land, which is included in the other vegetation classification and makes up the largest land cover type ( 44.1 percent) followed closely by tree canopy (42.1 percent). Less than five percent is impervious. Figure 2.6 contains the full land cover breakdown for unincorporated Shelby County.

2.2.3 Munford and Atoka Urban Growth Boundaries The Munford and Atoka urban growth boundaries consist primarily of agricultural land and forested area, and these two classes make up the majority of land area for each boundary, with less than three percent impervious surface. Figure 2.7 has the full land cover information.

2.2.4 Municipalities Of the nine municipalities in the study area, Munford has the most existing UTC (46.6 percent), closely followed by Lakeland (45.8 percent). Millington has the lowest existing UTC at 22.3 percent. Memphis has the highest impervious surface area (26.9 percent), while Munford has the least (7.7 percent) See Figure 2.8 for the remaining land cover breakdowns.

2.2.5 Memphis City Council Districts Land cover metrics were summarized for the seven Memphis city council districts as well as the two super districts. Districts five and six have the highest existing UTC, with 40.3 percent and 29.6 percent, respectively.

District three has the lowest existing UTC (21.8 percent) and the highest impervious surface area (43.5 percent). See Figure 2.9 for full breakdown for each district.

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Municipalities

Figure 2.8: Land cover metrics for the municipalities within the study area.

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Memphis City Council Districts

Figure 2.9: Land cover metrics for Memphis City Council Districts.

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Canopy Height and Extent

3. Canopy Height and Extent LiDAR, or Light Detection and Ranging, uses an infrared laser that is cycled, or pulsed, at a very high rate to measure distance and other properties by analyzing the time it takes for the laser to reflect off of a surface and return to the scanner. LiDAR data is represented as a point cloud--each file consists of millions of points that contain information about the pulse. The properties of interest for extracting the canopy height and extent include values for the pulse’s coordinates, elevation, and number of returns. The x, y and z properties represent the spatial location and elevation of the surface feature from which the LiDAR pulse was reflected. Because the LiDAR pulse disperses slightly, each pulse can have multiple returns. For example, in a forested area, the first return might represent the top of the tree canopy followed by multiple returns from the mid-canopy layers, and finally a last return that represents the ground (Newcomb et al., 2013). Returns representing tree canopy can be filtered by applying the following processes: 1. select pulses that have at least two returns 2. calculate their relative height above the ground surface 3. select points with a z value greater than 12 feet The result of following these steps is a point cloud containing only points representing the tree canopy. The tree canopy height and extent raster is created by gridding the points into 1-meter squares and selecting the maximum value in the cell. The canopy height layer is only for Shelby County; LiDAR data for Tipton County was not available during the analysis.

While the steps are straightforward, The LiDAR data for Shelby County comes as 1,233 separate files, each containing millions of points. Processing such a large quantity of data quickly becomes unmanageable without tools to automate the process. Taking advantage of tools from the GDAL (GDAL Development Team, 2013) and LibLAS (Butler et al., 2011) software packages, a script was implemented with Python (http://www.python.org) to automate the extraction of the tree canopy height and extent. Python’s multiprocessing library was used to process many point cloud files concurrently, decreasing the overall processing time required.

The final step in the process is to remove non-tree canopy areas from the output. While the majority of the points in the selection criteria above represent canopy, those properties also represent objects such as utility poles, billboards, and building facades. To remove these features, The canopy height and extent layer was first clumped, which assigns a unique value to each contiguous group of pixels. Clumped areas below a certain area threshold were eliminated from the layer, providing a county-wide canopy height and extent raster. Figure 3.1 provides an example of the canopy height and extent layer at a small scale. Figure 3.2 shows the canopy height layer for the study area.

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Figure 3.1: Example of the canopy height and extent layer overlaid on 1-meter imagery.

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Figure 3.2: The canopy height and extent layer for the study area.

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Priority Planting Areas

4. Priority Planting Areas According to the U.S. Forest Service, the goal of an UTC assessment is to provide information to planners and resource managers that will allow them to set reasonable and attainable goals for maintaining and increasing urban forests. They identify two important questions that UTC assessments should help answer (USDA Forest Service): 1. Where is it socially desirable to plant trees?

2. Where is it financially efficient to plant trees?

To answer these questions, CPGIS implemented a model based on Locke et al. (2010) in which a number of socioeconomic and environmental factors are compiled, standardized, and averaged to arrive at a prioritization score. Factors were collected at the Census Block Group level because they represent the smallest boundary for which the socioeconomic variables are available. The following attributes were summarized for each block group in the study area: 1. the percentage of people at or below poverty level 2. the percentage of the block group that is within the 100 or 500 year flood zone 3. percent impervious land cover 4. percent existing tree canopy 5. percent other vegetation 6. percent bare soil 7. average daily traffic counts 8. average maximum annual temperature After prioritizing block groups based on the eight factors listed above, parcels within high priority block groups were then identified and ranked based on the following criteria: • Highest: publicly owned parcels that are exempt from property taxes and are vacant, a community center, school, or used for recreation • High: privately owned parcels that are exempt from property taxes and are vacant, a community center, school, or used for recreation • Medium: all other parcels within the high priority block groups Refining the prioritization based on parcel boundaries allows priority planting areas to be calculated at a finer spatial scale than the block groups provide. Finally, the plantable area within the prioritized parcels was extracted using the Potential UTC layer so that priority planting areas can also be ranked and sorted based on available planting area (Figure 4.1).

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Figure 4.1: Final prioritized planting areas ranking according to the block groups with the highest need and ease of access to property, showing only the areas within each parcel that is available for increased canopy based on the potential UTC layer.

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Wolf River oxbow lake, near Covington Pike in Memphis

Conclusions It is difficult to directly compare the canopy status of the study area as a whole due to differences in environment and because most UTC studies are summarized at the municipality level. However, with 36.8 percent canopy cover, Shelby County (including Munford, Atoka, and their urban growth areas in Tipton County) has approximately 10 percent less existing canopy than Metropolitan Nashville and Davidson County (Graham and Hanou, 2010). Comparing the municipalities in the study area to other cities around the nation, all of the municipalities in the study area except Memphis fall within the third or fourth quartiles, while Memphis just missed inclusion in the third quartile by 0.3 percent (Figure 5.1).

Figure 5.1: Comparison of municipalites within the study area to other cities around the nation. Municipalities within the study area are shown in green. Data for other cities from Graham and Hanou (2010); Plan-It Geo(2013b,a); American Forests (2013).

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Poracsky and Lackner (2004) recommend setting the canopy goal high, but caution that it should also be attainable. The recommendation is that canopy goal should equal the value of the 75th percentile, which would put the canopy in the above average range. The 75th percentile goals were calculated separately for the Memphis City Council Districts and the municipalities.

The target canopy goal for each Memphis City Council District is 37 percent. The canopy goals for each district are summarized in Table 5.1. The super districts were not calculated since their percentages would be made up of a combination of the council districts within them. Most of the districts have good existing canopy cover, requiring less than a 10 percent increase to reach the target canopy goal. The districts in the southern half of Memphis, which contain more commercial/industrial land use parcels, need the most additional canopy. The priority planting areas reflect this as well, with large areas in the southern half of Memphis that are identified as priority areas.

The target canopy goal for each municipality, excluding Memphis, is 42.39 percent. Memphis was excluded because its canopy goal was calculated separately based on the city council districts. The canopy goals for each municipality are summarized in Table 5.2.

Table 5.1: Additional canopy cover needed to reach the target canopy goal of 37.05 percent in each Memphis city council district.

Table 5.2: Additional canopy cover needed to reach the target canopy goal of 42.39 percent in each municipality, excluding Memphis.

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References American Forests. 10 Best Cities for Urban Forests, 2013.

URL http://www.americanforests.org/our-programs/urbanforests/10-best-cities-for-urban-forests.

Butler, H., Loskot, M., Vachon, P., Vales, M., and Warmerdam, F. libLAS: ASPRS LAS LiDAR Data Toolkit, 2011. URL http://www.liblas.org.

GDAL Development Team. GDAL - Geospatial Data Abstraction Library, Version 1.10.1. Open Source Geospatial Foundation, 2013. URL http://www.gdal.org.

Graham, J. and Hanou, I. Metropolitan Nashville and Davidson County. AMEC Earth & Environmental, April 2010. URL http://www.treesnashville.org/images/tca/pdfs/tcareport.pdf.

GRASS Development Team. Geographic Resources Analysis Support System (GRASS GIS) Software.

Open Source Geospatial Foundation, USA, 2014. URL http://grass.osgeo.org.

Locke, D. H., Grove, J. M., Lu, J. W., Troy, A., O’Neil-Dunne, J. P., and Beck, B. D. Prioritizing preferable locations for increasing urban tree canopy in New York City. Cities and the Environment, 3 (1): 18, 2010.

Newcomb, D., Hargrove, W., Hoffman, F., and Kumar, J. Forest Structure and Bird Nesting Habitat Derived from LiDAR Data. In North Carolina GIS Conference, February 2013.

URL http://www.ncgisconference.com/2013/documents/pdfs/Newcomb_Fri_1030.pdf.

Plan-It Geo. An Assessment of Urban Tree Canopy in Horn Lake, Southaven, and Olive Branch, Mississippi, September 2013a. URL http://issuu.com/planitgeoissuu/docs/desoto_county_ms_ urban_tree_canopy_.

Plan-It Geo. An Assessment of Urban Tree Canopy in Jackson, Mississippi, September 2013b.

URL http://issuu.com/planitgeoissuu/docs/jackson_ms_urban_tree_canopy_assess.

Poracsky, J. and Lackner, M. URBAN FOREST CANOPY COVER IN PORTLAND, OREGON, 1972-2002: Final Report, April 2004. URL http://web.pdx.edu/~poracskj/Cart%20Center/psucc200404-047. pdf.

R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2013. URL http://www.R-project.org. ISBN 3-900051-07-0.

USDA Forest Service. Urban Tree Canopy Assessment, 2013. URL http://www.nrs.fs.fed.us/urban/utc/.

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Memphis Regional Urban Tree Canopy Assessment

2014

A Tennessee Board of Regents Institution An Equal Opportunity/Affirmative Action University • UOM029-FY1415/5C PEERLESS PRINTING