A Comparative Analysis of Urban Tree Canopy …knightlab.org/rscc/projects/F11/MN_UTC.docx · Web viewA Comparative Analysis of Urban Tree Canopy Assessment Methods in Minnesota Remote

Remote Sensing of Natural Resources and Environment | FR 5262 | University of Minnesota

A Comparative Analysis of Urban Tree Canopy Assessment Methods in Minnesota

Philip J Potyondy12/12/2011



Philip J Potyondy

Project Description / ObjectivesThe primary objective of this study is to compare a number of Urban Forest Canopy analysis methods. This study will inform and potentially validate canopy assessment methods used by the Emerald Ash Borer Rapid Response Community Preparedness Project that is being conducted throughout Greater Minnesota by a team of researchers at the University of Minnesota in collaboration with community leaders and volunteers.

Urban Forest Canopy Assessment Methods Digitize urban forest canopy cover using image classification remote sensing techniques

and software over study areas. Digitize urban forest canopy cover via technician photo interpretation over study area. Digitize urban forest canopy cover via technician photo interpretation within stratified

random sampled blocks. Calculate urban forest canopy using field collected tree canopy width measurements

within stratified random sampled blocks. Calculate urban forest canopy using randomly generated points within study area

interpreted by a technician.

Each method will result in an estimate of urban forest canopy cover within the included communities. Results will be compared. The results of this study will help shape future urban forest canopy assessment. The timing and financial cost of each method will also be discussed. The field collected tree data is considered existing data since it has already been collected by tree inventory teams within the communities. The primary reason for collecting this data is to determine the composition of the urban forest as communities prepare for the arrival of the emerald ash borer (Johnson, 2011). Urban forest canopy can be used to estimate a myriad of benefits that trees provide to communities (McPherson, 1995, 1996, 1998a, 1998b, 2010; McPherson, Nowak, & Rowntree, 1994; Minnesota Department of Natural Resources, 2000; Piego & Breuste, n.d.; USDA Forest Service, n.d.). The resulting canopy data will also be used to estimate how removal of all ash trees from a community will impact winter home energy consumption.

Materials / Tools / ConceptsThe Minnesota communities of Hibbing, Hutchinson, and Rochester were chosen for this study. Communities selected for this study were chosen from communities that were already participating in the Emerald Ash Borer Rapid Response Community Preparedness Project. The three selected communities range in population size from 13,835 in Hutchinson, 16,237 in Hibbing, and 103,486 in Rochester. The communities are geographically spread across Minnesota north, central, and southern – in order with latitude and longitude: Hibbing



Philip J Potyondy

(47.4273,-92.9376), Hutchinson (44.8884,-94.380), and Rochester (44.024,-92.470). Communities were also selected by virtue of having municipal utility service. We have found it is easier to gather utility data from municipal providers than from commercial energy providers.

Tree inventory data was utilized from data collected by trained volunteer community tree inventory teams from each community as part of the Emerald Ash Borer Rapid Response Community Preparedness Project. Both publicly and privately owned trees were included. Trees over maintained lawn were measured, but trees in natural or densely vegetated areas were not included. Besides gathering data on species, diameter at breast height, and condition rating, the volunteers also collect the average crown width of trees within the sample blocks. Crown width measurements were utilized to estimate the overall canopy within the study area. The tree inventory study was set up as a stratified random sampling method with blocks as the sampling unit stratified by zones. Zones were stratified based in canopy cover, building type, and street type (curvilinear or straight). The tree inventory design is based on A Statistical Method for the Accurate and Rapid Sampling of Urban Street Tree Populations (Jaenson, Bassuk, & Schwager, 1992).

National Agricultural Imagery Program (NAIP) Digital Orthorectified Images from 2010 were used (U.S. Department of Agriculture, Farm Service Agency, 2010). Images of each community were downloaded using the MN Northstar Mapper (Minnesota Geospatial Information Office, 2011). The imagery is summer “leaf-on“ 3-band natural color imagery with 1 meter pixel resolution and is formatted to the UTM coordinate system using NAD83.

Google and Bing imagery was also utilized to guide technician interpretations. Bing Bird’s Eye imagery was especially beneficial, where available. Other leaf off imagery was also referenced where available, but was not used as a primary source of interpretation.

ERDAS Imagine 2010 was utilized for the raster image classification and accuracy assessment. ESRI ArcGIS 10 was used for vector operations including area calculations, classification clean-up, clipping raster images to the study areas, digitizing tree canopies, and all related vector processing. iTree Canopy was utilized for point based classification (USDA Forest Service, 2011).

Procedures

Digitize urban forest canopy cover using image classification remote sensing techniques and software over study areasERDAS Imagine Objective was utilized to conduct an Object Oriented Classification to classify tree canopy from the 2010 NAIP imagery. There are only two classifications as a result of this procedure, tree canopy and not tree canopy. The classification was generated by using the following model processes. A spectral single feature probability (SFP) was used as a Raster Pixel Processor. Threshold and Clump with a probability threshold setting of 0.90 was used as a



Philip J Potyondy

Raster Object Creator. Dilate with a square Kernel Size of 3 and Size Filter with a Minimum Object Size of 30 pixels and no Maximum Object Size were used as Raster Object Operators. Polygon Trace was used for the Raster To Vector Conversion. Island Filter with a Maximum Object Size of 1,000,000,000 square meters, Generalize with a Tolerance of 0.50 meters, and Split with a Probability Threshold of 0.45 and 5 Recursions were used as Vector Object Operators. Geometry and Template Match were used as Vector Object Processors. The Geometry Object Classifier was set to Multi Bayesian Network with a Pixel Probability Weight of 50 and set to Enforce Distribution Bounds. Geometry:Area was set to Type Gaussian and Locked with setting of Minimum 100.90 square feet, Maximum 6035.29 square feet, Mean 2549.07 square feet, and a Standard Deviation of 2710.31 square feet. Geometry:Circularity was set to Type Linear with setting of Minimum 0.60, Maximum 1.00, Mean 0.00, and a Standard Deviation of 0.10. There were also three Vector Cleanup Operators that were used: a Probability Filter with a Minimum Probability of 0.45, a Convex Hull, and a Template Match with Level of Detail set to 16.

The resulting vector shapefile was cleaned up in ArcGIS. Clean-up mostly entailed removing tree canopy polygons from areas that were actually grass. The cleaned-up shapefile was then used to Erase the canopy from the study area shapefile. The cleaned-up canopy shapefile was then Merged with the study area shapefile that had the canopy shaped holes. This erase and then merge method was used to avoid overlap. This resulted in a single shapefile that covers the study area and contains the tree canopy and the space without tree canopy. An attribute was added to the table denoting tree canopy as 1 and areas that are not tree canopy as 2. Zero was avoided because in the next step the shapefile was converted to a raster .img using ERDAS Imagine which uses a minimum bounding rectangle to generate a rectangular raster file. The space outside the study area or area of interest but inside the bounding rectangle by default receives a value of zero; thus we did not want areas without tree canopy that are within the study area to be lumped with the area outside the study area.

The resulting raster file was used to conduct an accuracy assessment in ERDAS Imagine. There was not any available suitable reference imagery other than the imagery that was used for the classification. Acquiring additional imagery was cost prohibitive and not within the budget for this project. The accuracy assessment was conducted without viewing the classification and with the assistance of an additional technician. Reference locations were randomly generated across the image. A total of 112 points were referenced to conduct the accuracy assessment with the study area.

Digitize urban forest canopy cover via technician photo interpretation over study areaTechnicians used ArcGIS to view, interpret, and digitize canopy by hand from the same 2010 NAIP imagery as above across the entire study area.

To prepare for comparison and analysis with the other canopy assessment methods the resulting shapefile was then processed using multiple tools in ArcGIS. First the many polygons



Philip J Potyondy

that made up the tree canopy were Dissolved into a single polygon with no attributes. The dissolved shapefile was then processed with the Identity tool by the study area boundary to ensure canopy outside the study area was not included and attributes of the study area boundary file were applied to the result. The Canopy of Study Area file was retained, canopy area was calculated, and canopy area was divided by area of the Study Area resulting in Percent Canopy of Study Area. The Canopy of Study Area file is then processed with the Identity tool by the Zones that were generated for the tree inventory. Canopy of Zones file is retained, canopy area is calculated by zone, and canopy area by zone is divided by the area of each given zone resulting in Percent Canopy by Zone. Canopy of Zones file is then processed with the Identity tool by the Sampled Blocks that were generated for the tree inventory, where field data was collected. Canopy of Blocks file is retained, canopy area is calculated by block, and canopy area by block is divided by the area of each given block resulting in Percent Canopy by Block.

Digitize urban forest canopy cover via technician photo interpretation within stratified random sampled blocksEssentially, technicians used ArcGIS to view, interpret, and digitize canopy by hand from the same 2010 NAIP imagery as above only within the sampled blocks that were generated for the tree inventory. In actuality we clipped this data from the dataset that was digitized over the entire study area (as described above). Percent canopy by block was calculated by dividing canopy area per block by the area of each given block. Block level canopy data was used to estimate zone and community scale tree canopy by using an area based geographically weighted extrapolation equation (Appendix 1 – Area Based Geographically Weighted Extrapolation Equation).

Calculate urban forest canopy using field collected tree canopy width measurements within stratified random sampled blocksCommunity tree inventory teams collected two canopy width measurements and averaged them to report average canopy width per tree (see above section Materials / Tools / Concepts). These average canopy widths were equated to area by dividing the average canopy widths by 2 to get the radius of the canopy and then the radius was squared and multiplied by π (Area of Circle = πr²). The sum of the areas of all the trees within a given block was used as an estimation of percent canopy for the block. This block level canopy data was used to estimate zone and community scale tree canopy by using an area based geographically weighted extrapolation equation (Appendix 1 – Area Based Geographically Weighted Extrapolation Equation).

Calculate urban forest canopy using randomly generated points within study area interpreted by a technicianiTree Canopy was utilized for this point based classification (USDA Forest Service, 2011). iTree Canopy is a web application that guides a user through an interpretation process to determine a cover classification for a given area. The user sets a study area by uploading a shapefile to the



Philip J Potyondy

software. The user then determined which classes they would like to classify and how many randomly generated points they would like to classify. We chose the following cover classes: tree, non-tree vegetation, impervious building, and impervious ground. iTree Canopy uses Google Maps API to display the points within the study area. The Google provided imagery at the time of running this analysis was very coarse and was nearly impossible to interpret from. Bing however had much better imagery especially the Bird’s Eye View. X, Y coordinates were copied from the Google Map to a Bing Map for classification and the resulting data was collected within iTree Canopy. 100 points were interpreted with the study area. iTree Canopy provides a table and calculates percent cover along with standard error on the fly.

ResultsPercent canopy varies across the various methods (See Appendix 2 – Summation of all Hibbing Canopy Assessment Methods)

Digitize urban forest canopy cover using image classification remote sensing techniques and software over study areasThis method resulted in a Urban Tree Canopy of 24.71%.

Accuracy Assessment on this method resulted in an overall classification accuracy of 88.39% (See Appendix 3 – Object Oriented Classification Accuracy Assessment)

Digitize urban forest canopy cover via technician photo interpretation over study areaThis method resulted in a Urban Tree Canopy of 18.03%.

Digitize urban forest canopy cover via technician photo interpretation within stratified random sampled blocksThis method resulted in a Urban Tree Canopy of 17.28%.

Calculate urban forest canopy using field collected tree canopy width measurements within stratified random sampled blocksThis method resulted in a Urban Tree Canopy of 16.32%.

Calculate urban forest canopy using randomly generated points within study area interpreted by a technicianThis method resulted in a Urban Tree Canopy of 18.2% ±3.88.

DiscussionWhile there is variation within the various canopy percentages the results are reasonably close. At least this is the case with the Hibbing data. Analysis in Hutchinson and Rochester are still in process. If the results for the other communities remain consistent a methodology/model can be adopted for the other 12 communities that are involved in the Emerald Ash Borer Rapid



Philip J Potyondy

Response Community Preparedness Project. Further analysis will also seek to determine a correlation between the results. Perhaps we will find that one method consistently under accounts for urban tree canopy yet do to time and budget constraints is the most reasonable method to use. If we can determine the relationship between the methods the model could be tweaked to account for this consistent difference. Time and resources are definitely worth considering in choosing which method to pursue moving forward with. Extrapolating from existing data would by far be the least expensive choice. Next would be hand digitizing only the randomly stratified blocks. Digitizing a whole community by hand is cost prohibitive with regards to person hours. Object based classification could be a repeatable means to solve the question; however it takes special skills and software to accomplish. This iTree Canopy method was both low cost and not time consuming, however the scale of data returned is course and only reportable at the community scale. Since this data will be used to estimate winter energy use impact from ash tree canopy loss a method that can be at reported at a zone or tighter scale is desired. Another reason why the extrapolating from the existing field collected tree inventory data would be ideal, is that canopy width could be directly linked to a species. None of the other canopy assessment methods has the ability to link the canopy to a given species.

AcknowledgementsSpecial thanks to Steve Ewest for digitizing and providing significant GIS analysis assistance, to Valerie Price and Casey Dabrowski for assistance with digitizing. Also thanks to the Community leaders and volunteers and my advisor Gary Johnson.



Philip J Potyondy

References

Jaenson, R., Bassuk, N., & Schwager, S. (1992). A statistical method for the accurate and rapid sampling of urban street tree populations. J. Arboric, 18(July), 171-183. Retrieved from http://www.hort.cornell.edu/uhi/research/articles/JArb18(4).pdf

Johnson, G. (2011). Emerald Ash Borer Rapid Response Community Preparedness Project. Retrieved November 12, 2011, from http://www.mntreesource.com/

McPherson, E. G. (1995). Net Benefits of Healthy and Productive Urban Forests. In G. A. Bradley (Ed.), Urban Forest Landscapes Integrating Multidisciplinary Perpectives (pp. 180-194). Seattle and London: University of Washington Press.

McPherson, E. G. (1996). Urban forest landscapes, how greenery saves greenbacks. In C. Wagner (Ed.), American Society of Landscape Architects (pp. 27–29). Washington, D.C.: American Society of Landscape Architects. Retrieved from http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Urban+Forest+Landscapes,+How+Greenery+Saves+Greenbacks#0

McPherson, E. G. (1998a). Atmospheric carbon dioxide reduction by Sacramento’s urban forest. Journal of Arboriculture, 24(4). Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.155.764&rep=rep1&type=pdf

McPherson, E. G. (1998b). Structure and sustainability of Sacramento’s urban forest. Journal of Arboriculture, 24(July), 173-190. Retrieved from http://agris.fao.org/agris-search/search/display.do?f=1999/US/US99073.xml;US1999005726

McPherson, E. G. (2010). Energy , Climate Change , Air Quality and Urban Greening. Davis, CA: USDA Forest Service PSW Research Station.

McPherson, E. G., Nowak, D. J., & Rowntree, R. A. (1994). Chicago’s Urban Forest Ecosystem : Results of the Chicago Urban Forest Climate Project.

Minnesota Department of Natural Resources. (2000). Conserving Wooded Areas in Developing Communities Best Management Practices in Minnesota. Minnesota Department of Natural Resources.



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Minnesota Geospatial Information Office. (2011). MN NorthStar Mapper. Retrieved from http://www.mngeo.state.mn.us/chouse/northstarmapper.html

Piego, C., & Breuste, J. (n.d.). Environmental, Social and Economic services of urban trees.

U.S. Department of Agriculture, Farm Service Agency, A. P. F. O. (2010). National Agricultural Imagery Program (NAIP) Digital Orthorectified Images (DOQ), Minnesota, 2010. Retrieved from http://www.mngeo.state.mn.us/chouse/metadata/naip10.html

USDA Forest Service. (2011). i-Tree Canopy. Retrieved from http://www.itreetools.org/canopy/index.php

USDA Forest Service. (n.d.). The Value of Trees. USDA Forest Service NA-IN-02-04.



Philip J Potyondy

Appendices

Appendix 1 – Area Based Geographically Weighted Extrapolation Equation Below are the equations that were used to extrapolate from block level tree canopy data to zone level and then to the community scale. In the equations below community is replaced with village to avoid potential confusion between the variables used to represent canopy and community. Since the Study Blocks were all different sizes, they were weighted by the proportion they contain of the Zone they are contained by. Likewise the Zones were weighted by the proportion they contain of the Study Area.

Definition of variables: Village X (vX); Area of Village X = A vX

Zone Q (zQ); Area of zQ = A zQ Study Block 1 (sb1); Area of sb1 = A sb1 Study Block 2 (sb2); Area of sb2 = A sb2 Study Block 3 (sb3); Area of sb3 = A sb3 Zone R (zR); Area of zR = A zR Study Block 4 (sb4); Area of sb4 = A sb4 Study Block 5 (sb5); Area of sb5 = A sb5 Zone S (zS); Area of zS = A zS Study Block 6 (sb6); Area of sb6 = A sb6 Study Block 7 (sb7); Area of sb7 = A sb7

Equations:Geographic Weight of Study Block 1 = (A sb1 /A zQ)

Percent Canopy of Study Block 1 = C sb1

Estimated Percent Canopy of Zone Q = C zQ = [(A sb1 / A zQ) * C sb1] + [(A sb2 / A zQ) * C sb2] + [(A sb3 / A zQ) * C sb3]

Estimated Percent Canopy of Zone R = C zR = [(A sb4 / A zR) * C sb4] + [(A sb5 / A zR) * C sb5]

Estimated Percent Canopy of Zone S = C zS = [(A sb6 / A zS) * C sb6] + [(A sb7 / A zS) * C sb7]

Estimated Percent Canopy of Village X = C vX = [(A zQ / A vX) * C zQ] + [(A zR / A vX) * C zR] + [(A zS / A vX) * C zS]



Philip J Potyondy

Appendix 2 – Summation of all Hibbing Canopy Assessment Methods

Method - Crown Width Canopy

Crown Width Zone & Block

Weighted

Digitized Canopy by

Block

Digitized Canopy Zone

& Block Weighted

Digitized Canopy of

entire Study Area

Remote Sensed Canopy - Study

Area

Remote Sensed Canopy - whole commu

nity

iTree Canopy

% Canopy - 16.31701398 17.27869848 18.03025138 24.7143 30.4629 18.2 ±3.88

Block Zone

% Crown Width Canopy by Block

Average % Canopy by Zone based on Weighted Sample Blocks

% Canopy by Block

Average % Canopy by Zone based on Weighted Sample Blocks

% Canopy by Zone

% Canopy by Block

6 Downtown 5.746252919 9.555344751 3.905032037 4.772222482 5.549537349 4.03660130399

31 Downtown 0.094364814 0 0.01696548694

5 Downtown 10.82627814 8.20934693611.8787133785

0

24Residential Curvilinear 11.22509323 19.41402803 10.16218598 19.01707565 13.92479693

16.51371329850

26Residential Curvilinear 25.01122876 30.26071488

32.06690193390

25Residential Curvilinear 15.56629214 15.38741248

19.15270133890

7Residential Downtown 21.90806624 13.1415185 25.9797691 9.278947421 14.00892487

36.70431845680

8Residential Downtown 7.937625324 9.34833207

12.76780104910

32Residential Downtown 4.582377608 4.001468783 6.82796135372

30

Residential Rectilinear 1 25.60545165 18.80349536 28.84238791 17.9198059 18.27680187

35.15925717810

1

Residential Rectilinear 1 9.934912367 7.460594314

17.50054390180

2

Residential Rectilinear 1 3.973239458 4.914606109 7.42321768286

3


25.61147317960

4


29.18960262730

20


25.43791720250

13


34.17184705180

17


22.66893161100



Philip J Potyondy

14


31.71703009090

16


20.86917540650

9


25.66702207370

22


24.26465881340

15


34.27996394260

33


28.54319386170

21


28.88888428730

18


27.03858040200

12


30.32181305780

10


31.65504588130

11


25.86232937830

34


15.69391241990

35


24.52028449900

23


22.55306437620

19


23.29583826530

29


45.36494363100

28


20.87379971340

27


27.70424726650



Philip J Potyondy

Appendix 3 – Object Oriented Classification Accuracy Assessment

CLASSIFICATION ACCURACY ASSESSMENT REPORT-----------------------------------------

ERROR MATRIX-------------

Reference Data--------------

Classified Data Background Class 1 Class 2 Row Total--------------- ---------- ---------- ---------- ---------- Background 0 0 0 0 Tree 0 25 3 28 Not Tree 0 10 74 84

Column Total 0 35 77 112

ACCURACY TOTALS---------------- Class Reference Classified Number Producers Users Name Totals Totals Correct Accuracy Accuracy ---------- ---------- ---------- ------- --------- ----- Background 0 0 0 --- --- Tree 35 28 25 71.43% 89.29% Not Tree 77 84 74 96.10% 88.10%

Totals 112 112 99

Overall Classification Accuracy = 88.39%

KAPPA (K^) STATISTICS---------------------Overall Kappa Statistics = 0.7143Conditional Kappa for each Category.------------------------------------

Class Name Kappa ---------- -----

Background 0 Tree 0.8442 Not Tree 0.619

Documents

A Comparative Analysis of Urban Tree Canopy …knightlab.org/rscc/projects/F11/MN_UTC.docx · Web viewA Comparative Analysis of Urban Tree Canopy Assessment Methods in Minnesota Remote