RMRS Forest Inventory and Analysis
User Group Meeting
2010 April 13
Why is tree canopy cover important?
 Percent tree canopy cover is an integral part of both
international forest land definitions and U.S.
definitions of forest.
 Percent tree canopy cover is an important variable
both within forest land areas and in areas not
traditionally considered forest.
 Knowing where trees are is an important first step in
quantifying carbon and managing tree resources.
What is NLCD?
National Land Cover Database (NLCD)
NLCD 1992:
Originated from Multi-Resolution Land Characteristics (MRLC)
consortium, a multi-agency program formed to acquire Landsat
data across the conterminous U.S. and generate 30m-pixel land
cover map.
NLCD 2001:
2nd generation land cover map. In addition, 30m- pixel maps of
imperviousness and % tree canopy.
NLCD 2011:
3rd generation land cover, imperviousness, and %tree canopy maps.
NLCD 2001
Percent Tree Canopy Cover
Definition of Tree Canopy:
A layer or multiple layers of branches and foliage at the top
or crown of a forest’s tree.
 From FY07 through FY09 the canopy cover layer was
downloaded from MRLC 400 times per month on average
(not including ftp downloads).
 Input for Landfire modeling.
 Potential source for updating the 2000 assessment of urban
tree cover as part of the Resource Planning Act Assessment.
Example of the NLCD 2001
Percent Tree Canopy Layer
2001 General Modeling Approach
=
Response developed by an
automated classification of tree
crown cover on 1-m DOQs (with
extensive post-processing hand
editing).
Fig. from Homer et al. 2007
Model developed using regression tree (random forest) algorithm
2001 NLCD Zone Map
Preliminary Accuracy Assessments
NLCD 2001 % Tree Canopy Cover
 Homer et al. 2004 reported mean absolute error rates
of 9.9%, 14.1%, and 8.4% in zones 16, 41, and 60
respectively.
 Greenfield et al. 2009 reported under estimation of
percent tree canopy cover.
 An external review of the Landfire canopy cover data
suggests that pixel level values are too high
(http://www.landfire.gov/notifications16.php).
Rationale for using FIA data
 FIA is a fundamental component of Forest Service research.
 FIA is a data rich program:
 % tree canopy cover estimates will be made on all FIA plots (~340,000 in
conterminous US).. including sampling locations that are not consider
forest land (e.g. urban, agricultural).
 Consistency between map based and plot based estimates:
 Adopting the FIA survey design and using FIA data for developing %
tree canopy models will likely provide more consistent estimates of
percent canopy cover.
 The FIA survey design is easily intensified:
 FIA will use NAIP photography to estimate percent tree canopy on all
non-visited plots as well as a portion of visited plots.
 The photo interpretation techniques can be used on an intensified FIA
sample.
Rationale for using
photo interpretation
National Agriculture Imagery Program (NAIP)
1-m resolution, ortho-rectified, natural color, or near Infrared
imagery for the conterminous U.S. .. and free.
 Can use directly in models, with no post-processing editing.
 Can test the robustness of the models using different data
intensities.
 Can test other photo-based responses using tree/no tree
classifications, such as: classification trees or segmentation.
 Can leverage information that will be collected as part of the
FIA program.
General Approach: Proof of Concept
 In general, the proof of concept research will rely on
many of the same explanatory variables used in the
2001 product (e.g. leaf-on and leaf-off Landsat imagery,
DEM derivatives).
 The response variable will be developed from manual
photo interpretation, modeled NAIP Imagery, and FIA
plot modeling (e.g. Toney et al. 2009).
 The type of model will be determined by examining
various quality assessments.
 There are 5 proof of concept study areas.
2011 General Modelling Approach
=
Response developed by photo
interpreting tree crown cover on
NAIP Imagery, modeled NAIP
imagery, and FIA plot modeling
Fig. from Homer et al. 2007
Example modelling
techniques
Random forest algorithm
K nearest neighbor imputation
Support vector machines . . .
Key Research Questions
 Alternative pixel-level modeling techniques.
 Alternative stratification/grouping strategies (i.e.




alternatives to mapping zones).
Using ordinal data for developing model.
Model stability under different sampling intensification
levels.
Impact of scale of response on tree canopy cover estimates
(30m vs 90m).
Relationship among plot-based, PI-based, and modeled
estimates (Toney et al. 2009).
Proof of Concept Study Areas
Study Area Characteristics
1. Approximately one
Landsat scene in area
2. Cover multiple scenes
3. Cover multiple
gradients
I. urban area
II. different vegetation
types
FIA Grid and Photo Intensification
Standard Intensity
FIA grid (6000 acres):
Intensified 4x grid
(1500 acres):
• Modeled % canopy
cover estimates
• Photo interpreted
estimates
• Photo interpreted
% canopy cover
Modeled canopy cover from field data
based on stem mapping and crown
diameter models from Coulston et al.
2010
105 photo points per sampling
location within a 90m2 area
Photo Sample
Repeatability Component
 Between 2 and 5 interpreters are working in each study
area.
 5% of the sample locations are interpreted by all
interpreters
 Comparison will be conducted among interpreters,
and by different landscapes (e.g. urban, heavily
forested, agricultural).
Compare and Contrast:
2001 to 2011 Approaches
 Response data developed from data with similar grain size (1m)
 2001 product developed from fewer (3-4) large (1-4 km2) sample




locations. 2011 proof of concept approach relies on many (~4100)
small (0.81 ha) sample locations.
Sample locations were purposive for 2001. Sample locations
drawn randomly 2011.
Explanatory variables are similar.
Modelling approaches will be similar although additional
approaches will be examined as part of the 2011 proof of concept.
Cost is likely higher using 2011 approach but some of the work is
part of the core FIA program.
Status
 Proof of concept work funded
 Photo interpretation 90% completed
 Explanatory data stacks in place
 Main research components to be completed FY10
 Internal Forest Service funding proposal to be
submitted March 2010
 Prototyping work 2011 (dependent upon additional
funding)
 Production work 2012-2013 (dependent upon
additional funding)
Team Members
1
1,2
1,2
1,2
1
1,2
1
1
1,2
Ken Brewer, Quantitative Sciences Staff WO
Warren Cohen, Pacific Northwest Research Station
John Coulston, Southern Research Station
Mark Finco, Remote Sensing Applications Center
Everett Hinkley, Remote Sensing National Coordinator
Gretchen Moisen, Rocky Mountain Research Station
Frank Sapio, Forest Health Technology Enterprise Team
Brian Schwind, Remote Sensing Applications Center
Ty Wilson, Northern Research Station
1Leadership
Team
2Science Team
Questions?
4x Intensity Photo-based
Sample Locations
105 photo points to estimate
% tree canopy cover for model
development
E.G. NLCD 2001
% Tree Canopy
cover
References
 Homer, C., Haung, C., Yang, L., Wylie, B., Coan, M. 2004. Development of a
2001 national land-cover database for the United States. Photogrammetric
Engineering and Remote Sensing 70, 829-840.
 Greenfield E.J., Nowak D.J., Walton J.T. 2009. Assessment of 2001 NLCD
Percent Tree and Impervious Cover Estimates. PHOTOGRAMMETRIC
ENGINEERING AND REMOTE SENSING. 75(11). 1279-1286.
 Toney, C., Shaw, J.D., Nelson, M.D. 2009. A stem-map model for predicting
tree canopy cover of forest inventory and analysis (FIA) plots. In: McWilliams,
W., Moisen, G., Czaplewski, R. comps. 2009. 2008 Forest Inventory and
Analysis (FIA) symposium. October 21-23, 2008. Park City, UT. Proc. RMRSP-56CD. Fort Collins, CO: U.S. Department of Agriculture, Forest Service,
Rocky Mountain Research Station. 1 CD.
 Coulston, J.W., Oswalt, S.N., Carraway, A.B., Smith, W.B. 2010. Assessing forest
land area based on canopy cover in a semi-arid region: a case study. Forestry,
doi:10.1093/forestry/cpp039