Urban Forest
Research
Winter 2005
Center for Urban Forest Research • Pacific Southwest Research Station • USDA Forest Service
Replace field surveys with AVIRIS infrared imagery. Can we?
Imagine flying over your community forest and taking a picture
that allows you to identify and map
species. No more field surveys.
Then imagine the possibilities and
cost savings.
AVIRIS—Airborne Visible
Infrared Imaging Spectrometer
Tree type and species information are critical parameters for
urban forest management, benefitcost analysis, and urban planning.
Traditionally, urban forest managers have obtained these parameters
from an analysis of field surveys.
However, our Center’s recent work
with AVIRIS, under the leadership
of Dr. Qingfu Xiao, suggests that in
the future, there may be a much
cheaper, and just as effective, alternative.
Understanding the Urban Forest
To understand how urban
forests function and to estimate
the value of their environmental
services we must first be able to
identify properties related to urban
forest structure and composition
(McPherson et al. 1997). Also, a
good understanding of the structure
of the urban forest provides other
information useful to urban managers, such as planning tree pruning,
removal, and insect or disease control activities.
Illustrations courtesy Jet Propulsion Lab
AVIRIS is an acronym for the Airborne Visible InfraRed Imaging
Spectrometer. AVIRIS is a world class instrument in the realm of Earth
Remote Sensing. It is a unique optical sensor that delivers calibrated images
of the upwelling spectral radiance in 224 contiguous spectral channels
(also called bands) with wavelengths from 400 to 2500 nanometers (nm).
AVIRIS has been flown on two aircraft platforms: a NASA ER-2 jet and the
Twin Otter turboprop. The ER-2 is a U2 aircraft modified for increased
performance which flies at approximately 20 km above sea level, at about
730 km/hr. The Twin Otter aircraft flies at 4km above ground level at
130km/hr. AVIRIS has flown all across the US, plus Canada and Europe.
Basic information required to
describe urban forest structure
includes tree numbers, spatial distributions, species composition,
dimensions, and growing conditions. Traditionally, this information has been collected in field
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2
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Comparison of various
instrument’s ability to “see”
trees.
Channels Spatial Resolution
AVIRIS
224
4m
Landsat
7
30m
SPOT
4
30m
IKONOS
4
4m
surveys. However, such surveys are
expensive and time consuming, and
require periodic updates to remain
valid. Aerial photograph interpretation has been used successfully, but
is slow and expensive to conduct
the mapping on a large scale.
What About Infrared Imagery?
Vegetation has unique spectralreflectance characteristics, which
makes infrared imagery so attractive. Vegetation has a high absorption rate in red wavelengths and
a strong reflectance rate in nearinfrared wavelengths. This allows
us to separate plants from other
ground-surface covers because
non-plant covers absorb and reflect
infrared at a different rate.
Differences in foliage, branches, and architecture among tree
species provides information
to uniquely identify them with
Costs
Desired Parameters: species
identification, tree health (stress
and vigor), leaf area, and canopy
cover area.
Urban forest size: 25,000 trees
AVIRIS: Images = $3,000 – 5,000.
Remote sensing specialist for 4
months = approximately $15,000.
Total cost = under $20,000.
Typical inventory: $1 to $5 per tree.
Urban Forest Research
Tree type spatial distribution of study area. (a) Colour-infrared
Airborne Visible Infrared Imaging Spectrometer (AVIRIS) image (R~850
nm, G~650 nm, B~550 nm). (b) Conifer classified pixels in red, (c)
broadleaf evergreen tree pixels in green, and (d ) broadleaf deciduous
tree pixels in blue.
AVIRIS. Differences in canopy
architecture, such as leaf area
density, leaf and branch angles,
leaf shape, internal anatomy, and
leaf and branch surface roughness,
cause individual tree species to reflect differently.
identify and map vegetation, land
use, and land cover in regional or
sub-regional assessments. Landsat
Thematic Mapper (TM) seven-band,
30m, data, and four-band, 20m,
Satellite pour l’Observation de la
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Other Methods
The Normalized Difference Vegetation Index, red-edge, and other
band ratio methods are currently
being used to separate vegetation
types. However, these simple methods cannot be used to identify tree
species because they do not capture the unique spectral characteristics of each tree species. Another
method, texture analysis, works
well in natural forest mapping to
identify species, but it doesn’t work
well in the urban forest because urban tree species are too similar in
texture.
Remote Sensing
Remotely sensed data have
been used for quite a few years to
Urban Forest Research
is a publication of the Center
for Urban Forest Research,
Pacific Southwest Research
Station, USDA Forest Service.
For more information, contact
the Center at the Department
of Environmental Horticulture, University
of California, 1 Shields Ave, Suite 1103,
Davis, CA 95616-8587. (530) 752-7636
USDA is an equal opportunity provider and
employer, and prohibits discrimination in all
programs and activities.
Editor: Jim Geiger
Production: Laurie Litman, InfoWright
Winter 2005
3
AVIRIS: Description of Sensor
System
Tree species spatial distribution of a selected area of Modesto. (a) Colour
infrared AVIRIS image, (b) tree species identification from the GIS
database, and (c) tree species identification derived from AVIRIS data.
Terre (SPOT) data, and four-band,
4m, IKONOS data have significantly improved the accuracy of
identifying vegetation, especially
estimates of dominant tree species.
However, the accuracy in urban
settings becomes a problem because urban areas are a mosaic of
many different species, land uses,
and man-made structures, each of
which has different spectral reflectance characteristics.
Unlike trees in rural forests,
which tend to form continuous canopies, trees in urban settings are often single trees or isolated groups.
The influence of background, such
as soil and shadow, makes the
problem of characterizing trees
by remote sensing even more dif-
Winter 2005
ficult. In such cases, high spatial
resolution of remotely sensed data
is important for mapping individual
trees (Avery and Berlin 1992).
Why AVIRIS?
AVIRIS compensates for the variety of backgrounds in urban areas
by delivering calibrated images in
224 contiguous spectral channels
with wavelengths from 400nm to
2500 nm. This enriched spatial
and spectral data reduces the resolution problems associated with
broad-band low-spatial resolution
sensors, such as Landsat with just
7 channels, and SPOT and IKONOS
with just 4, thus giving AVIRIS the
ability to “see” trees 30 to 70 times
better than other methods.
•
Scanner type: nadir-viewing,
whiskbroom
•
Image width (swath): 11 km
(high altitude), 1.9 km (low
altitude)
•
Typical image length: 10 - 100
km
•
Spatial response: 1.0 mrad,
corresponding to a “pixel” 20m
x 20m (high altitude) or 4m x
4m (low altitude) on the ground
•
Spectral response: visible to
near-infrared (400 to 2500 nm),
with 224 contiguous channels,
approximately 10 nm wide
•
Data quantization: 12 bits
•
Data capacity: 10 gigabytes,
corresponding to about 850 km
of ground track data, per flight
Adding GIS
Combing AVIRIS with GIS significantly improves the accuracy
of the AVIRIS results. The spatial
location ability of GIS is a standard
method for registering images to
base maps, as shown in a recent
report (Shao et al. 1998). This ability to accurately locate individual
trees using GIS, combined with the
AVIRIS analysis, makes it relatively
easy to confirm the AVIRIS results.
Plus, it significantly raises the confidence level when replicating the
procedure in other city areas or
nearby regions.
Study Objectives
There were three objectives for
this study. The intent being that
the results would provide tree canopy information to urban planning
and projects related to analysis of
regional urban energy budgets, air
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Urban Forest Research
4
pollution, and hydrology.
1. identify urban tree species by
physiognomic type based on
their spectral character as detected by the AVIRIS sensor,
that is, whether they are broadleaf deciduous, broadleaf evergreen, or conifer types.
2. identify urban trees by species
based on their canopy reflectance characteristics.
3. map these urban trees.
Findings
We checked our results against
ground reference data and by comparison to tree information in an
existing GIS database. We found at
the tree type level, mapping was accomplished with 94% accuracy. At
the tree species level, the average
accuracy was 70% but this varied
with both tree type and species.
Of the four evergreen tree species,
the average accuracy was 69%. For
the 12 deciduous tree species, the
average accuracy was 70%. The
relatively low accuracy for several
deciduous species was due to small
tree size and overlapping among
tree crowns at the 3.5m spatial
resolution of the AVIRIS data.
So…Conclusions
What this means is that we can
now identify individual tree species with fairly high accuracy using high spatial resolution (3.5m)
AVIRIS data. Therefore, the answer
to our question is—yes, we can
replace field surveys with AVIRIS.
And when combined with GIS, it
adds the ability to validate the final
maps.
The potential value of these
data for urban forest applications,
besides species identification,
includes estimating tree health
(stress and vigor), leaf area, and
canopy cover. In addition to tree
characterization, AVIRIS can be
Urban Forest Research
Your city on AVIRIS. Aerial photo, left, AVIRIS, right
used for characterizing land cover.
For example, we can now separate
man-made structures, such as
buildings or type of pavement (porous, concrete, asphalt, gravel), by
the materials that are used.
AVIRIS data acquired in spring
or summer rather than October
might provide better identification of some species or additional
information about tree condition.
For example, data acquired in both
summer and winter seasons could
be used to easily identify deciduous
and evergreen trees.
The mix of land cover for street
trees also plays an important part
in the outcome. You can expect
pixels of most street trees in residential areas to be mixed with road
and/or turf grass. Street trees will
also be mixed with bare soil and/or
road in median strips and in some
commercial areas. This mixing
reduces the number of possible
combinations and is the greatest
reason that accuracy increased in
this study compared to our earlier
results with less sophisticated techniques. Because most trees will still
be within mixed pixels at this scale
(3.5m), increasing spatial resolution of the hyperspectral dataset
could improve the accuracy of tree
identification.
Caveats
This urban forest tree species
mapping method has the potential
to improve our ability to more accurately map urban trees while
reducing costs compared to field
sampling or other traditional methods. However, what we also found
was that it is not fully transferable
from one city to another without
some calibration from ground
truthing. We also found that using
this method to identify trees in locations other than along the street
may not yield the same results due
to the potential for more complex
mixing combinations off street.
—Jim Geiger
Winter 2005
5
Fitting Trees into the Planning Process
Another Look at How to do Parking Lots Right
In urban areas, perhaps the
greatest benefit from trees is the
role they play in reducing the impacts of parking lots. Our Center’s
2001 study found that parking lots
occupy about 10 percent of the
land in our cities. They act as miniature heat islands and are sources
of motor vehicle pollutants. By
shading cars and lowering parking
lot temperatures, trees can reduce
evaporative emissions of hydrocarbons (HC) that leak from fuel tanks
and hoses (Scott et al. 1999). HC
emissions are involved in O3 formation; parked cars contribute 15
to 20 percent of total motor vehicle
HC emissions. Parking lot tree
planting is one practical strategy
communities can use to meet and
sustain mandated air quality standards.
Many parking lot ordinances
specify one tree for a certain number of parking spaces or a certain
amount of planted area per space.
However, under these ordinances,
trees can be clustered in islands
or along the lot perimeter, often
resulting in large areas of unshaded
pavement.
To obtain more extensive shade
it is necessary to increase tree
numbers and provide more soil volume for tree roots, approximately
200 cubic feet (2.5 feet deep) for
a 4-inch diameter tree, and about
1,500 cubic feet for a 24-inch diameter tree (see figure 1, Urban 1992).
After the trees are installed, it is
important that the new trees are
pruned early to train their growth,
the crowns are allowed to reach
their full potential (no drastic pruning that disfigures the tree), and
any dead trees are replaced.
Figure 1. Developed from several sources by Urban (1992), this graph
shows the relationship between tree size and required soil volume. For
example, a 16-inch DBH tree with 640 ft2 of crown projection area under
the dripline requires 1,000 ft3 of soil.
Winter 2005
Keys to Successful Parking Lot
Shading
Perhaps most important, make key
planning decisions prior to starting
the project:
1. Provide adequate time to review
shade plans and parking lot
ratios.
2. Certify that parking spaces and
trees are located as per the
ordinance.
3. Inspect, inspect, inspect.
Parking Lot Tree Planting Rules
of Thumb
Where appropriate, consult with
your local city forester/arborist or
other tree expert:
• Ensure adequate soil volume for
tree roots by specifying a minimum planter width of six feet
(see figure 2).
• Encourage the use of structural
soils, a designed medium that
can meet or exceed pavement
design and installation requirements while remaining root
penetrable and supportive of tree
growth. An additional reference
for structural soils is Reducing
Infrastructure Damage by Tree
Roots (Costello and Jones 2003).
• Develop tree planting details (see
figure 3) and specifications that
require loosening a large volume
of soil where the tree will be
planted. Because soils are heavily compacted prior to paving the
lot, planting trees in small holes
reduces root extension and poor
drainage can kill the trees.
• Install parking lot lighting that
does not conflict with required
shade tree locations or growth.
Light standards no greater than
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Urban Forest Research
6
Figure 2. Two feet of vehicle overhang into planter area
is allowed, provided the planter is the correct minimum
width. Vehicle overhang is not allowed into required
setback areas (City of Sacramento 2002).
Illustrations courtesy City of Sacramento.
•
•
•
•
•
16 feet in height are strongly encouraged.
Do not allow planting of trees
not on a community’s Recommended Tree List (developed by
the city forester or arborist). Revise the tree list if necessary.
Consult the Recommended Tree
List to identify tree species suitable for parking lots.
Be sure crown diameters on
parking lot plans correctly reflect crown diameters specified
in the tree list. Correct diameters in the tree list if necessary.
Be sure crown diameters for
mature trees are not overstated
in the tree list, thus allowing
parking lot plans to reflect more
shade than they can actually
achieve. Correct if necessary.
Follow up to ensure trees are ac-
Urban Forest Research
Figure 3. Because soils are heavily compacted
prior to paving the lot, planting trees in small holes
reduces root extension and poor drainage can kill the
trees. Excavating the native soil and planting into
the loosened backfill will improve tree growth and
establishment (City of Sacramento 2002).
tually planted and not removed
shortly after planting.
• Pay particular attention to trees
planted near store fronts, to
make sure trees will not obstruct
signs.
• Do not allow small-stature trees
to be substituted for large-stature trees after the plans have
been approved.
• Increase use of one-way aisles,
angled parking spaces, and
shared parking to reduce overall
imperviousness (CRCOG 2002;
Center for Watershed Protection,
1998).
For more information, see
Where are all the cool parking lots?
In addition, Sacramento’s parking
lot ordinance: environmental and
economic costs of compliance provides further background.
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Send comments or suggestions
to Jim Geiger, Center for
Urban Forest Research, Pacific
Southwest Research Station,
USDA Forest Service, c/o
Department of Plant Sciences,
Mail Stop 6, University of
California, 1 Shields Avenue,
Suite 1103, Davis, CA 956168780 or contact jgeiger@fs.fed.us.
Winter 2005