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 Potyondy
12/12/2011
A Comparative Analysis of Urban Tree Canopy Assessment Methods in
Minnesota
Remote Sensing of Natural Resources and Environment | FR 5262 | University of Minnesota
Philip J Potyondy
Project Description / Objectives
The 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 / Concepts
The 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
A Comparative Analysis of Urban Tree Canopy Assessment Methods in
Minnesota
Remote Sensing of Natural Resources and Environment | FR 5262 | University of Minnesota
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 cleanup, 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 areas
ERDAS 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
A Comparative Analysis of Urban Tree Canopy Assessment Methods in
Minnesota
Remote Sensing of Natural Resources and Environment | FR 5262 | University of Minnesota
Philip J Potyondy
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
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.
A Comparative Analysis of Urban Tree Canopy Assessment Methods in
Minnesota
Remote Sensing of Natural Resources and Environment | FR 5262 | University of Minnesota
Philip J Potyondy
Digitize urban forest canopy cover via technician photo interpretation over study area
Technicians 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
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 blocks
Essentially, 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 blocks
Community 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
A Comparative Analysis of Urban Tree Canopy Assessment Methods in
Minnesota
Remote Sensing of Natural Resources and Environment | FR 5262 | University of Minnesota
Philip J Potyondy
extrapolation equation (Appendix 1 – Area Based Geographically Weighted Extrapolation
Equation).
Calculate urban forest canopy using randomly generated points within study area interpreted
by a technician
iTree 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
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.
Results
Percent 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 areas
This 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 area
This method resulted in a Urban Tree Canopy of 18.03%.
Digitize urban forest canopy cover via technician photo interpretation within stratified
random sampled blocks
This 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 blocks
This method resulted in a Urban Tree Canopy of 16.32%.
A Comparative Analysis of Urban Tree Canopy Assessment Methods in
Minnesota
Remote Sensing of Natural Resources and Environment | FR 5262 | University of Minnesota
Philip J Potyondy
Calculate urban forest canopy using randomly generated points within study area interpreted
by a technician
This method resulted in a Urban Tree Canopy of 18.2% ±3.88.
Discussion
While 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
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.
Acknowledgements
Special 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.
A Comparative Analysis of Urban Tree Canopy Assessment Methods in
Minnesota
Remote Sensing of Natural Resources and Environment | FR 5262 | University of Minnesota
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+Landscap
es,+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/agrissearch/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.
A Comparative Analysis of Urban Tree Canopy Assessment Methods in
Minnesota
Remote Sensing of Natural Resources and Environment | FR 5262 | University of Minnesota
Philip J Potyondy
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.
A Comparative Analysis of Urban Tree Canopy Assessment Methods in
Minnesota
Remote Sensing of Natural Resources and Environment | FR 5262 | University of Minnesota
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]
A Comparative Analysis of Urban Tree Canopy Assessment Methods in
Minnesota
Remote Sensing of Natural Resources and Environment | FR 5262 | University of Minnesota
Philip J Potyondy
Appendix 2 – Summation of all Hibbing Canopy Assessment Methods
Method -
Block
Crown Width
Canopy
Crown Width
Zone & Block
Weighted
Digitized
Canopy by
Block
% Canopy by
Zone
Downtown
Residential
Curvilinear
Residential
Curvilinear
Residential
Curvilinear
Residential
Downtown
Residential
Downtown
Residential
Downtown
Residential
Rectilinear
1
Residential
Rectilinear
1
Residential
Rectilinear
1
Residential
Rectilinear
1
Residential
Rectilinear
1
Residential
Rectilinear
2
Residential
Rectilinear
2
10.82627814
8.209346936
2
3
4
20
13
9.555344751
% Canopy by
Block
5
1
18.2
±3.88
Zone
% Crown
Width
Canopy by
Block
0
30
30.4629
Average %
Canopy by
Zone based
on Weighted
Sample
Blocks
0.094364814
32
24.7143
Average %
Canopy by
Zone based
on Weighted
Sample
Blocks
5.746252919
8
iTree
Canopy
18.03025138
Downtown
7
Remote
Sensed
Canopy
- whole
commu
nity
17.27869848
Downtown
25
Remote
Sensed Canopy
- Study Area
16.31701398
6
26
Digitized
Canopy of
entire Study
Area
% Canopy -
31
24
Digitized
Canopy Zone
& Block
Weighted
3.905032037
4.772222482
5.549537349
% Canopy by
Block
4.03660130399
7.937625324
9.34833207
0.01696548694
11.8787133785
0
16.5137132985
0
32.0669019339
0
19.1527013389
0
36.7043184568
0
12.7678010491
0
4.582377608
4.001468783
6.82796135372
11.22509323
19.41402803
10.16218598
25.01122876
30.26071488
15.56629214
15.38741248
21.90806624
25.60545165
13.1415185
18.80349536
25.9797691
28.84238791
19.01707565
13.92479693
9.278947421
14.00892487
17.9198059
18.27680187
35.1592571781
0
9.934912367
7.460594314
17.5005439018
0
3.973239458
4.914606109
7.42321768286
18.2965845
18.22195483
25.6114731796
0
19.30892316
24.48207764
29.1896026273
0
16.91593079
24.86233151
18.00398007
20.54100231
29.684211
19.58868778
20.04814108
25.4379172025
0
34.1718470518
0
A Comparative Analysis of Urban Tree Canopy Assessment Methods in
Minnesota
Remote Sensing of Natural Resources and Environment | FR 5262 | University of Minnesota
Philip J Potyondy
17
14
16
9
22
15
33
21
18
12
10
11
34
35
23
19
29
28
27
Residential
Rectilinear
2
Residential
Rectilinear
2
Residential
Rectilinear
2
Residential
Rectilinear
2
Residential
Rectilinear
2
Residential
Rectilinear
2
Residential
Rectilinear
2
Residential
Rectilinear
2
Residential
Rectilinear
2
Residential
Rectilinear
2
Residential
Rectilinear
2
Residential
Rectilinear
2
Residential
Rectilinear
2
Residential
Rectilinear
2
Residential
Rectilinear
2
Residential
Rectilinear
2
Residential
Rectilinear
3
Residential
Rectilinear
3
Residential
Rectilinear
3
16.31460753
15.93521184
22.6689316110
0
19.93283486
24.06005875
31.7170300909
0
11.54233052
13.4340902
20.8691754065
0
19.16741877
19.67411822
25.6670220737
0
9.640564022
14.55527369
24.2646588134
0
20.26091254
25.61440433
34.2799639426
0
6.369596107
17.25068948
28.5431938617
0
16.81531746
17.90598087
28.8888842873
0
17.73191568
21.67548015
27.0385804020
0
5.978768037
25.05833186
30.3218130578
0
26.53515876
20.98337101
31.6550458813
0
14.43362269
15.86067885
25.8623293783
0
12.98038743
16.64633397
15.6939124199
0
25.85913924
28.95192677
24.5202844990
0
8.23733276
14.86252738
22.5530643762
0
11.56470879
15.26121748
23.2958382653
0
10.92303348
13.5132103
32.05682068
20.92700492
23.49509063
45.3649436310
0
15.49729602
14.37436233
20.8737997134
0
13.09773311
20.18545253
27.7042472665
0
A Comparative Analysis of Urban Tree Canopy Assessment Methods in
Minnesota
Remote Sensing of Natural Resources and Environment | FR 5262 | University of Minnesota
Philip J Potyondy
Appendix 3 – Object Oriented Classification Accuracy Assessment
CLASSIFICATION ACCURACY ASSESSMENT REPORT
----------------------------------------ERROR MATRIX
-------------
Classified Data
--------------Background
Tree
Not Tree
Reference Data
-------------Background Class 1 Class 2 Row Total
------------------- ---------- ---------0
0
0
0
0
25
3
28
0
10
74
84
Column Total
ACCURACY TOTALS
---------------Class
Name
---------Background
Tree
Not Tree
Totals
0
35
77
Reference
Totals
----------
112
Classified Number Producers Users
Totals Correct Accuracy Accuracy
---------- --------------- ----0
0
0 ----35
28
25 71.43% 89.29%
77
84
74 96.10% 88.10%
112
Overall Classification Accuracy = 88.39%
KAPPA (K^) STATISTICS
--------------------Overall Kappa Statistics = 0.7143
Conditional Kappa for each Category.
-----------------------------------Class Name
Kappa
-------------Background
0
Tree
0.8442
Not Tree
0.619
112
99