Center for Urban Forest Research Newsletter, Winter 2005

8/9/2019 Center for Urban Forest Research Newsletter, Winter 2005

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Urban ForestWinter 2005

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ResearchCenter for Urban Forest Research Pacifc Southwest Research Station USDA Forest Service

Replace eld surveys with AVIRIS infrared imagery. Can we?Imagine ying over your com-

munity forest and taking a picturethat allows you to identify and mapspecies. No more eld surveys.Then imagine the possibilities andcost savings.

AVIRISAirborne VisibleInfrared Imaging Spectrometer

Tree type and species informa-tion are critical parameters forurban forest management, benet-cost analysis, and urban planning.Traditionally, urban forest manag-

ers have obtained these parametersfrom an analysis of eld surveys.However, our Centers recent workwithAVIRIS, under the leadershipof Dr. Qingfu Xiao, suggests that inthe future, there may be a muchcheaper, and just as effective, alter-native.

Understanding the Urban Forest

To understand how urbanforests function and to estimate

the value of their environmentalservices we must rst be able toidentify properties related to urbanforest structure and composition(McPherson et al. 1997). Also, agood understanding of the structureof the urban forest provides otherinformation useful to urban manag-ers, such as planning tree pruning,removal, and insect or disease con-trol activities.

Basic information required todescribe urban forest structureincludes tree numbers, spatial dis-tributions, species composition,

dimensions, and growing condi-tions. Traditionally, this informa-tion has been collected in eld

Illustrations courtesy Jet Propulsion Lab

AVIRIS is an acronym for theAirborne Visible InfraRed ImagingSpectrometer. AVIRIS is a world class instrument in the realm of EarthRemote Sensing. It is a unique optical sensor that delivers calibrated imagesof the upwelling spectral radiance in 224 contiguous spectral channels(also called bands) with wavelengths from 400 to 2500 nanometers (nm).

AVIRIS has been own on two aircraft platforms: a NASA ER-2 jet and theTwin Otter turboprop. The ER-2 is a U2 aircraft modied for increasedperformance which ies at approximately 20 km above sea level, at about730 km/hr. The Twin Otter aircraft ies at 4km above ground level at130km/hr. AVIRIS has own all across the US, plus Canada and Europe.
http://cufr.ucdavis.edu/products/cufr564_Qingfu_AVRIS_Paper.pdfhttp://aviris.jpl.nasa.gov/http://cufr.ucdavis.edu/products/cufr_55_EM97_26.PDFhttp://aviris.jpl.nasa.gov/http://aviris.jpl.nasa.gov/http://aviris.jpl.nasa.gov/http://aviris.jpl.nasa.gov/http://cufr.ucdavis.edu/products/cufr_55_EM97_26.PDFhttp://aviris.jpl.nasa.gov/http://cufr.ucdavis.edu/products/cufr564_Qingfu_AVRIS_Paper.pdf


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Urban Forest Research Winter 2005

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Urban Forest Research

is a publication of the Center

for Urban Forest Research,

Pacic Southwest Research

Station, USDA Forest Service.

For more information, contactthe 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 andemployer, and prohibits discrimination in allprograms and activities.

Editor: Jim Geiger

Production: Laurie Litman, InfoWright

(continued from previous page)


surveys. However, such surveys areexpensive and time consuming, andrequire periodic updates to remainvalid. Aerial photograph interpreta-

tion has been used successfully, butis slow and expensive to conductthe mapping on a large scale.

What About Infrared Imagery?

Vegetation has unique spectral-reectance characteristics, whichmakes infrared imagery so attrac-tive. Vegetation has a high absorp-tion rate in red wavelengths anda strong reectance rate in near-infrared wavelengths. This allowsus to separate plants from other

ground-surface covers becausenon-plant covers absorb and reectinfrared at a different rate.

Differences in foliage, branch-es, and architecture among treespecies provides informationto uniquely identify them with

AVIRIS. Differences in canopyarchitecture, such as leaf areadensity, leaf and branch angles,leaf shape, internal anatomy, andleaf and branch surface roughness,cause individual tree species to re-

ect differently.

Other Methods

The Normalized Difference Veg-etation Index, red-edge, and otherband ratio methods are currentlybeing used to separate vegetationtypes. However, these simple meth-ods cannot be used to identify treespecies because they do not cap-ture the unique spectral character-istics of each tree species. Anothermethod, texture analysis, works

well in natural forest mapping toidentify species, but it doesnt workwell in the urban forest because ur-ban tree species are too similar intexture.

Remote Sensing

Remotely sensed data havebeen used for quite a few years to

Comparison of variousinstruments ability to seetrees.

Channels Spatial Resolution

AVIRIS 224 4m

Landsat 7 30m

SPOT 4 30m

IKONOS 4 4m

Tree type spatial distribution of study area. (a) Colour-infrared

Airborne Visible Infrared Imaging Spectrometer (AVIRIS) image (R~850nm, G~650 nm, B~550 nm). (b) Conifer classied pixels in red, (c)broadleaf evergreen tree pixels in green, and (d ) broadleaf deciduoustree pixels in blue.

identify and map vegetation, landuse, and land cover in regional orsub-regional assessments. LandsatThematic Mapper(TM) seven-band,30m, data, and four-band, 20m,Satellite pour lObservation de la

Costs

Desired Parameters: speciesidentication, tree health (stress

and vigor), leaf area, and canopycover area.

Urban forest size: 25,000 trees

AVIRIS: Images = $3,000 5,000.Remote sensing specialist for 4months = approximately $15,000.Total cost = under $20,000.

Typical inventory: $1 to $5 per tree.
http://edc.usgs.gov/products/satellite/tm.htmlhttp://edc.usgs.gov/products/satellite/tm.htmlhttp://telsat.belspo.be/beo/en/satellites/spot.htmhttp://telsat.belspo.be/beo/en/satellites/spot.htmhttp://edc.usgs.gov/products/satellite/tm.htmlhttp://edc.usgs.gov/products/satellite/tm.html


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Terre (SPOT) data, and four-band,4m, IKONOS data have signi-cantly improved the accuracy ofidentifying vegetation, especiallyestimates of dominant tree species.However, the accuracy in urbansettings becomes a problem be-cause urban areas are a mosaic ofmany different species, land uses,and man-made structures, each ofwhich has different spectral reec-

tance characteristics.Unlike trees in rural forests,

which tend to form continuous can-opies, trees in urban settings are of-ten single trees or isolated groups.The inuence of background, suchas soil and shadow, makes theproblem of characterizing treesby remote sensing even more dif-

cult. In such cases, high spatialresolution of remotely sensed datais important for mapping individualtrees (Avery and Berlin 1992).

Why AVIRIS?

AVIRIS compensates for the va-riety of backgrounds in urban areasby delivering calibrated images in224 contiguous spectral channelswith wavelengths from 400nm to

2500 nm. This enriched spatialand spectral data reduces the reso-lution problems associated withbroad-band low-spatial resolutionsensors, such as Landsat with just7 channels, and SPOT and IKONOSwith just 4, thus giving AVIRIS theability to see trees 30 to 70 timesbetter than other methods.

Tree species spatial distribution of a selected area of Modesto. (a) Colourinfrared AVIRIS image, (b) tree species identication from the GISdatabase, and (c) tree species identication derived from AVIRIS data.


AVIRIS: Description of SensorSystem

Scanner type: nadir-viewing,whiskbroom

Image width (swath): 11 km(high altitude), 1.9 km (lowaltitude)

Typical image length: 10 - 100km

Spatial response: 1.0 mrad,corresponding to a pixel 20mx 20m (high altitude) or 4m x4m (low altitude) on the ground

Spectral response: visible tonear-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 kmof ground track data, per ight

Adding GIS

Combing AVIRIS with GIS sig-nicantly improves the accuracyof the AVIRIS results. The spatiallocation ability of GIS is a standardmethod for registering images tobase maps, as shown in a recentreport (Shao et al. 1998). This abil-ity to accurately locate individualtrees using GIS, combined with the

AVIRIS analysis, makes it relativelyeasy to conrm the AVIRIS results.Plus, it signicantly raises the con-dence level when replicating theprocedure in other city areas ornearby regions.

Study Objectives

There were three objectives forthis study. The intent being thatthe results would provide tree can-opy information to urban planningand projects related to analysis ofregional urban energy budgets, air
http://telsat.belspo.be/beo/en/satellites/spot.htmhttp://www.spaceimaging.com/products/ikonos/http://www.clarebooks.co.uk/item3916.htmhttp://apt.allenpress.com/aptonline/?request=get-abstract&issn=0749-0208&volume=014&issue=01&page=0299http://cufr.ucdavis.edu/products/cufr564_Qingfu_AVRIS_Paper.pdfhttp://cufr.ucdavis.edu/products/cufr564_Qingfu_AVRIS_Paper.pdfhttp://apt.allenpress.com/aptonline/?request=get-abstract&issn=0749-0208&volume=014&issue=01&page=0299http://www.clarebooks.co.uk/item3916.htmhttp://www.spaceimaging.com/products/ikonos/http://telsat.belspo.be/beo/en/satellites/spot.htm


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pollution, and hydrology.

1. identify urban tree species byphysiognomic type based ontheir spectral character as de-tected by the AVIRIS sensor,that is, whether they are broad-leaf deciduous, broadleaf ever-green, or conifer types.

2. identify urban trees by speciesbased on their canopy reec-tance characteristics.

3. map these urban trees.

Findings

We checked our results againstground reference data and by com-parison to tree information in an

existing GIS database. We found atthe tree type level, mapping was ac-complished with 94% accuracy. Atthe tree species level, the averageaccuracy was 70% but this variedwith both tree type and species.Of the four evergreen tree species,the average accuracy was 69%. Forthe 12 deciduous tree species, theaverage accuracy was 70%. Therelatively low accuracy for severaldeciduous species was due to smalltree size and overlapping among

tree crowns at the 3.5m spatialresolution of the AVIRIS data.

SoConclusions

What this means is that we cannow identify individual tree spe-cies with fairly high accuracy us-ing high spatial resolution (3.5m)

AVIRIS data. Therefore, the answerto our question isyes, we canreplace eld surveys with AVIRIS.

And when combined with GIS, it

adds the ability to validate the nalmaps.The potential value of these

data for urban forest applications,besides species identication,includes estimating tree health(stress and vigor), leaf area, andcanopy cover. In addition to treecharacterization, AVIRIS can be

used for characterizing land cover.For example, we can now separateman-made structures, such asbuildings or type of pavement (po-rous, concrete, asphalt, gravel), bythe materials that are used.

AVIRIS data acquired in spring

or summer rather than Octobermight provide better identica-tion of some species or additionalinformation about tree condition.For example, data acquired in bothsummer and winter seasons couldbe used to easily identify deciduousand evergreen trees.

The mix of land cover for streettrees also plays an important partin the outcome. You can expectpixels of most street trees in resi-dential areas to be mixed with road

and/or turf grass. Street trees willalso be mixed with bare soil and/orroad in median strips and in somecommercial areas. This mixingreduces the number of possiblecombinations and is the greatestreason that accuracy increased inthis study compared to our earlier

results with less sophisticated tech-niques. Because most trees will stillbe within mixed pixels at this scale(3.5m), increasing spatial resolu-tion of the hyperspectral datasetcould improve the accuracy of treeidentication.

Caveats

This urban forest tree speciesmapping method has the potentialto improve our ability to more ac-curately map urban trees whilereducing costs compared to eldsampling or other traditional meth-ods. However, what we also foundwas that it is not fully transferablefrom one city to another withoutsome calibration from groundtruthing. We also found that using

this method to identify trees in lo-cations other than along the streetmay not yield the same results dueto the potential for more complexmixing combinations off street.

Jim Geiger

Your city on AVIRIS. Aerial photo, left, AVIRIS, right


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Winter 2005 Urban Forest Research

Fitting Trees into the Planning ProcessAnother Look at How to do Parking Lots Right

In urban areas, perhaps the

greatest benet from trees is therole they play in reducing the im-pacts of parking lots. Our Centers2001 study found that parking lotsoccupy about 10 percent of theland in our cities. They act as min-iature heat islands and are sourcesof motor vehicle pollutants. Byshading cars and lowering parkinglot temperatures, trees can reduceevaporative emissions of hydrocar-bons (HC) that leak from fuel tanksand hoses (Scott et al. 1999). HC

emissions are involved in O3 for-mation; parked cars contribute 15to 20 percent of total motor vehicleHC emissions. Parking lot treeplanting is one practical strategycommunities can use to meet andsustain mandated air quality stan-dards.

Many parking lot ordinances

specify one tree for a certain num-

ber of parking spaces or a certainamount of planted area per space.However, under these ordinances,trees can be clustered in islandsor along the lot perimeter, oftenresulting in large areas of unshadedpavement.

To obtain more extensive shadeit is necessary to increase treenumbers and provide more soil vol-ume for tree roots, approximately200 cubic feet (2.5 feet deep) fora 4-inch diameter tree, and about

1,500 cubic feet for a 24-inch diam-eter tree (see gure 1, Urban 1992).

After the trees are installed, it isimportant that the new trees arepruned early to train their growth,the crowns are allowed to reachtheir full potential (no drastic prun-ing that disgures the tree), andany dead trees are replaced.

Figure 1. Developed from several sources by Urban (1992), this graphshows the relationship between tree size and required soil volume. Forexample, a 16-inch DBH tree with 640 ft2 of crown projection area underthe dripline requires 1,000 ft3 of soil.

Keys to Successful Parking Lot

ShadingPerhaps most important, make keyplanning decisions prior to startingthe project:

1. Provide adequate time to reviewshade plans and parking lotratios.

2. Certify that parking spaces andtrees are located as per theordinance.

3. Inspect, inspect, inspect.

Parking Lot Tree Planting Rulesof Thumb

Where appropriate, consult withyour local city forester/arborist orother tree expert:

Ensure adequate soil volume fortree roots by specifying a mini-mum planter width of six feet(see gure 2).

Encourage the use ofstructuralsoils, a designed medium thatcan meet or exceed pavementdesign and installation require-ments while remaining rootpenetrable and supportive of treegrowth. An additional referencefor structural soils is ReducingInfrastructure Damage by TreeRoots (Costello and Jones 2003).

Develop tree planting details (seegure 3) and specications thatrequire loosening a large volumeof soil where the tree will beplanted. Because soils are heav-

ily compacted prior to paving thelot, planting trees in small holesreduces root extension and poordrainage can kill the trees.

Install parking lot lighting thatdoes not conict with requiredshade tree locations or growth.Light standards no greater than


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http://cufr.ucdavis.edu/products/cufr_74_EM01_62.PDFhttp://cufr.ucdavis.edu/products/cufr_7_KS99_76.PDFhttp://www.mntca.org/Reference_manual/soils_and_fertility.htmhttp://www.hort.cornell.edu/uhi/outreach/csc/article.htmlhttp://www.hort.cornell.edu/uhi/outreach/csc/article.htmlhttp://www.wcisa.net/pbItem.asp?PubID=5http://www.wcisa.net/pbItem.asp?PubID=5http://www.wcisa.net/pbItem.asp?PubID=5http://www.wcisa.net/pbItem.asp?PubID=5http://www.wcisa.net/pbItem.asp?PubID=5http://www.wcisa.net/pbItem.asp?PubID=5http://www.hort.cornell.edu/uhi/outreach/csc/article.htmlhttp://www.hort.cornell.edu/uhi/outreach/csc/article.htmlhttp://www.mntca.org/Reference_manual/soils_and_fertility.htmhttp://cufr.ucdavis.edu/products/cufr_7_KS99_76.PDFhttp://cufr.ucdavis.edu/products/cufr_74_EM01_62.PDF


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Figure 3. Because soils are heavily compactedprior to paving the lot, planting trees in small holesreduces root extension and poor drainage can kill thetrees. Excavating the native soil and planting intothe loosened backll will improve tree growth andestablishment (City of Sacramento 2002).

Figure 2. Two feet of vehicle overhang into planter areais allowed, provided the planter is the correct minimumwidth. Vehicle overhang is not allowed into requiredsetback areas (City of Sacramento 2002).

Sign up for UrbanForest Research

NOTE: This newsletter is only

available in electronic format

To sign up for Urban ForestResearch, please visit our websiteat http://cufr.ucdavis.edu/newsletter.asp

Send comments or suggestionsto Jim Geiger, Center forUrban Forest Research, PacicSouthwest Research Station,USDA Forest Service, c/oDepartment of Plant Sciences,Mail Stop 6, University ofCalifornia, 1 Shields Avenue,

Suite 1103, Davis, CA 95616-

8780 or contact [email protected].

16 feet in height are strongly en-

couraged.

Do not allow planting of treesnot on a communitys Recom-mended Tree List (developed bythe city forester or arborist). Re-vise the tree list if necessary.

Consult the Recommended TreeList to identify tree species suit-able for parking lots.

Be sure crown diameters onparking lot plans correctly re-ect crown diameters specied

in the tree list. Correct diam-eters in the tree list if necessary.

Be sure crown diameters formature trees are not overstatedin the tree list, thus allowingparking lot plans to reect moreshade than they can actuallyachieve. Correct if necessary.

Follow up to ensure trees are ac-

tually planted and not removed

shortly after planting.

Pay particular attention to treesplanted near store fronts, tomake sure trees will not obstructsigns.

Do not allow small-stature treesto be substituted for large-stat-ure trees after the plans havebeen approved.

Increase use of one-way aisles,angled parking spaces, andshared parking to reduce overall

imperviousness (CRCOG 2002;Center for Watershed Protection,1998).

For more information, seeWhere are all the cool parking lots?In addition, Sacramentos parkinglot ordinance: environmental andeconomic costs of compliance pro-vides further background.

Illustrations courtesy City of Sacramento.

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http://www.cityofsacramento.org/planning/longrange/shading_guide.pdfhttp://www.cityofsacramento.org/planning/longrange/shading_guide.pdfhttp://www.crcog.org/Publications/TCSP/Ch08_Fact%20Sheet_Parking.pdfhttp://www.cwp.org/22_principles.htmhttp://cufr.ucdavis.edu/products/3/cufr_151.pdfhttp://cufr.ucdavis.edu/products/cufr_74_EM01_62.PDFhttp://cufr.ucdavis.edu/products/cufr_74_EM01_62.PDFhttp://cufr.ucdavis.edu/products/cufr_74_EM01_62.PDFhttp://cufr.ucdavis.edu/products/cufr_74_EM01_62.PDFhttp://cufr.ucdavis.edu/products/cufr_74_EM01_62.PDFhttp://cufr.ucdavis.edu/products/cufr_74_EM01_62.PDFhttp://cufr.ucdavis.edu/products/3/cufr_151.pdfhttp://www.cwp.org/22_principles.htmhttp://www.crcog.org/Publications/TCSP/Ch08_Fact%20Sheet_Parking.pdfhttp://www.cityofsacramento.org/planning/longrange/shading_guide.pdfhttp://www.cityofsacramento.org/planning/longrange/shading_guide.pdf

Documents

Center for Urban Forest Research Newsletter, Winter 2005