Monitoring vegetation greenness with satellite data

18 Pages·1993·2.4 MB·English

by  Burgan, Robert E

Historic, Archive Document
Do not assume content reflects current
scientific knowledge, policies, or practices.

United States Monitoring Vegetation
Department
ofAgriculture
Forest Service Greenness With
Intermountain
Research Station
Data
General Technical Satellite
Report INT-297
May 1993
Robert E. Burgan
Roberta A. Hartford
AVHRR
THE AUTHORS ROBERTA A. HARTFORD began working at the
ROBERT BURGAN Intermountain Fire Sciences Laboratory In Missoula,
E. received his bachelor's degree
MT, in 1968 assisting with research of chemical and
in forest engineering in 1963 and his master's degree
physical properties of forest and range fuels. In the
in forest fire control in 1966 from the University of
early 1970's she taught high school sciences and did
Montana. From 1963 to 1969, he served on the timber
management staff of the Union and Bear-Sleds seasonal work on the Lolo National Forest in fuel
inventory. Since 1976 she has remained at the Fire
Districts, Wallowa-Whitman National Forest. From
1969 to 1975, he was a research forester on the staff Lab involved in analysis of fuel and fuel bed proper-
ties, smoldering combustion, and fire behavior of
of the Institute of Pacific Islands Forestry, Honolulu,
laboratory and wildland fires. Recent and current work
HI. From 1975 to 1987, he was at the Intermountain
include studies in the use of satellite remote sensing to
Fire Sciences Laboratory, Missoula, MT, first as a
member of the National Fire-Danger Rating Research assess fire potential in wildland vegetation, collecting
Work Unit, and then as a research forester in the Fire fiinrfeobremhataivoinorsydosctuemmetnetcahtnioolno,gayntdo udsoicnugmgeenotgrwailpdhfiicrael
Behavior Research Work Unit. From 1987 to 1989 he
growth. Roberta received her undergraduate degree
was in Macon, GA, atthe Forest Meteorology and
in zoology in 1970 and a master's degree in forestry
Eastern Fire Management Research Work Unit, a part with soils and fire management emphasis in 1993 from
of the Southeastern Forest Experiment Station. In
the University of Montana.
1989 he returned to the Fire Behavior Research Work
Unit in Missoula.
The use oftrade orfirm names in thispublication is forreaderinformation anddoes not
implyendorsementbythe U.S. DepartmentofAgriculture ofanyproductorservice
Intermountain Research Station
324 25th Street
Ogden, UT84401
Greenness
Monitoring Vegetation
Data
Witli Satellite
Robert E. Burgan
Roberta A. Hartford
INTRODUCTION While we are notready to provide live vegetation
moisture content assessments, we have usedthis in-
Currentassessmentoflivingvegetationcondition dexto develop two vegetation greenness measures
relies onvarious methods ofmanual sampling. While usefiil to fire managers both as an aidto estimating
suchmeasurements canbequite accurate,theyare broad areafire potential andformanaging pre-
difficultto obtainover abroadarea, sotheyfailtopor- scribed fires. We also expectthese greenness meas-
traychanges inthe patternofvegetationgreenness ures to be useful to land managers ofotherdisci-
acrossthelandscape. Thetechnology—discussedinthis plines. Forexample, watershed managers could
reportprovides severalimprovements ^itcoverslarge obtainbasicinformation ontiming and extent of
geographic areas, the assessmentisupdatedweekly, snow cover. Range managers could make weeklyob-
itis easily obtained, anditisinexpensive. servations ofvegetationgreenness at 1-km resolu-
The technology needs to be incorporatedinto an tion. Pest managers findthis informationusefiil
integrated fire danger/behavior system, andthat because insect activityis tiedto vegetationflush,
system is currentlybeingdevelopedbythe Fire Be- whichcanbe observedwiththis technology. While
havior Research WorkUnit ofthe Intermountain we won't address the subjectinthis paper, geogra-
Fire Sciences Laboratoryin cooperationwith other phers have used NDVI datato develop a map that
researchers andfire managers. The proposed sys- portrays vegetation patterns across the United
tem will use new satellite andweathertechnologies. States (Loveland and others 1991). Additional uses
These technologies and data include improved are likelyto be identified as this technology emerges
weather information resultingfrom the National from the testingphase into more general application
Weather Service's modernization program, geo- by awider audience.
graphic information systems, digital terrain data, This reportdiscusses the concepts, interpretation,
andincreased reliance on satellite observations of use, and acquisition ofbroad-scale vegetationgreen-
seasonal changes inlive vegetation condition. ness images usefulto the landmanager. We also
This reportlooks atthe use ofsatellite data within provide information on obtainingthe necessary soft-
the larger fire danger/behavior system. We present ware andhardware.
it separately at this time to give land managers an
opportunityto become familiarwithit. HOW THE IMAGES ARE PRODUCED
Fire managers need direct observationofvegeta-
tion greenness because Uvingvegetationhas a TheTIROS-N series ofpolar-orbitingweather sat-
strongeffect onthe propagation and severityofwild- ellites from NOAAprovide daily global observations
landfires. The 1988 revisionto the 1978 National ofEarth's siuface. The datafrom afternoon satelUte
Fire Danger Rating System (Burgan 1988) requires overpasses ofthe United States are received daily at
ocular estimates ofvegetationgreenness, but these the Earth Resources Observation Systems (EROS)
are difficultto obtain, especially forlarge areas. Im- Data Center(EDC)in SiouxFalls, SD.
provedobservations can be obtainedfi'equently on a The spatial resolution oftheAVHRRis 1.1 km
continental scale fi'om theAdvancedVery High whenthe satellite is directly overhead. Thus, a
ResolutionRadiometer (AVHRR) onboardthe Na- square, 1.1 km on a side, is the ground arearepre-
tional Oceanic andAtmosphericAdministration's sentedbyeach pictiu-e element, or pixel. The
(NOAA) polar orbitingweather satellites (Kidwell AVHRR sensor onboardthe NOAAafternoon satel-
1991). The remote sensingcommunityhas used lite (in 1993, NOAA-11) collects reflectance datain
AVHRR
datato develop a Normalized Difference five spectral channels. Foreach pixel, aniuneric
Vegetation Index(NDVI) (Goward and others 1991; value is recorded, representingthe amount oflight
Spanner and others 1990; Tucker 1977; Tucker and reflectedfrom Earth's surface, in each channel's
Choudhury 1987; Tucker and Sellers 1986). The in- range. However, channel 1 (red, 0.58to 0.68 mi-
dexis sensitive to the quantity ofactively photosyn- crons) and channel 2 (near-infrared, 0.725 to 1.10
thesizingbiomass onthe landscape.
1
microns) are the most useful for monitoring vegeta- Yellow and light green indicate moderate quantities
tion and are used to calculate the NDVI. ofgreen vegetation, while darker green tones repre-
The NDVI is the difference ofnear-infrared and sent more luxuriant vegetation. Bare soil, snow,
visible red reflectance values normalized overtotal and clouds are white. Water is blue.
reflectance. That is, Sample NDVI values are also presented graphi-
cally with numbers that range from 0.0 to 0.66; 0.66
NDVI = NearIR (Channel 2) - Red (Channel 1) is the approximate maximum NDVI value obtained
NearIR (Channel 2) + Red (Channel 1) from observing dense, green vegetation ofthe con-
This equation produces NDVI values in the range of terminous United States.
-1.0 to 1.0, where negative values generallyrepre- Graphic comparisons ofNDVI values show that
sent clouds, snow, water, and other nonvegetated grass, shrub, and forested pixels trend differently '
surfaces, and positive values represent vegetated (fig. 2A). High NDVI values indicate complete or
surfaces. The NDVI relates to photosynthetic activ- nearly complete coverage by green vegetation. Low
ity ofliving plants. The higher the NDVI value, the values indicate cured or sparse vegetation.
more "green" the cover type (Deering and others Differences in timing and extent ofgreenness
1975). That is, the NDVI increases as the quantity within avegetationtype can be observed at specific
ofgreen biomass increases. sites across differentyears (fig. 2B, C, D). Finally,
Cloud-free observations ofthe land surface are the NDVI allows observation ofdifferences in the
necessary for monitoring vegetation with satellites. timing ofgreenup as a function ofelevation (fig. 2E).
The likelihood ofa singleAVHRR overpass being To interpret the NDVI values for field use, we
completely cloud free is minimal. Holben (1986) have devised methods to convert the NDVI data into
showed that compositingAVHRR data acquired over more easily understandable representations ofveg-
several days produces spatially continuous cloud- etation greenness. These are called "visual green-
free imagery over large areas with sufficient tempo- ness" and "relative greenness."
ral resolution to study green vegetation dynamics. Visual greenness (VG) indicates how green each
The duration ofconsecutive daily observations is re- pixel is in relation to a standard reference such as a
ferredto as the compositingperiod. The compositing highly green and densely vegetated agricultural
process requires each daily overpass to be precisely field. It is calculated as:
registered to a common map projection to ensure VG =NDoio.mnoo
that each pixel represents the same ground location
each day. where
The method for determining which portion ofeach NDq= observedNDVIvalue for agiven 2-week
overpass to include in the composite is based on the period.
maximum NDVI decision rule. For each pixel, the
highest NDVI value inthe compositing period is re- An image is produced that portrays vegetation
tained. Thisreducesthenvimberofdoud-contaminated greenness as you would expect to see it ifyou were
pixels because cloud and cloud shadow values are flying over the landscape. In this context, normally
generally negative, while clear day observations of dry, sparsely vegetated areas, such as in Nevada,
vegetated surfaces are positive. The resulting maxi- will look cured compared to normally wet, fully veg-
mum NDVI composite is a neeir cloud-free image etatedareas suchasthe coastal forests ofWashington
that depicts the maximum vegetative greenness for and Oregon.
the compositing period. The EDC has been produc- Because the visual greenness images may indicate
ing such biweekly NDVI composites ofthe contermi- ratherlimited changes over time, a second measure
nous United States since 1990 (Eidenshink 1992). ofvegetation greenness is useful. Relative green-
In addition, 1989 data have recently been made ness (RG) is also a percentage value, but it expresses
available by EDC. how green each pixel currently is in relation to the
Operationally, it is desirable to have a new assess- range ofgreenness observations for that pixel since
ment ofthe vegetation condition more than once ev- January 1, 1989. It is calculated as:
ery 2 weeks. Therefore, the biweekly NDVI compos- RG = (NDo-ND^^)/{ND^ -ND^n)*lOO
ite image is updated every week. A new biweekly where
image is produced each week by droppingthe oldest
week's data and adding the newest week's data. NDq= observed NDVI value for a given 2-week
Figure 1 presents selected biweekly images for 1992. period
These show the capability ofthe NDVI to portray ND^„ = minimum NDVI value observed histori-
seasonal change in vegetation greenness. cally forthat pixel
An intuitive color palette contains red through tan ND^ = maximum NDVI value observed histori-
tones thatindicate mostly curedorsparse vegetation. cally forthat pixel.
2
3
E
0.60
0.54
0.48 /'"A / V
0.42 /J i: /'//' *VI.\-.V/;.'i1 •.*\\\
0) //// II ^\-\VX,
>>Q 00..3360 "4,00.\\0ftJ//—/111/;
r"1 :
:
0.24 -6,000ft/ / Figure2—(A) Example Montana forest, Colorado
0.18 shrub, and California grass sites showdifferences in
seasonal greenness trends. (B,C,D) Annual differ-
0.12
8,000ft ences in timing and amount ofgreenup can be ob-
0.06 served within grass, shrub, and forestvegetation at
specific individual locations. (E) Elevational differ-
0.00 1 1 1 1 1
Apr May Jun Jul Aug Sep Oct ences in the timing and amountof greenness can be
Elevation Influence observed.
4
Figure 3 shows the difference betweenthe visual site, the assumedNDVI value of0.25 is at 80 per-
andrelative greenness calculations. The left verti- cent ofthe range between the minimum and maxi-
cal bar, labeled "Raw NDVI Scale," represents the mum (0.05 and 0.30) values recorded historically for
maximum likelyrange (0.00 to 0.66) ofNDVI values that site. Thatis, relative greenness = (0.25 - 0.05)/
thatwill be encounteredin anyvegetation type. The (0.30- 0.05)*100, or 80 percent. This site would ap-
next vertical barto the right, labeled"Visual Green- pear fairly green onthe relative greenness map be-
ness Scale," is simply the NDVI range convertedto cause itis about 80 percent as green as it has ever
a percentage scale. Assuming arawNDVI value of been historically. Forthe wet site, an actual NDVE
0.25, the visual greenness value wouldbe 38 percent value of0.25 is at about 12 percent ofthe range be-
(rounded) because 0.25 is about 38 percent of0.66, tween its minimum and maximum values (0.20 and
the maximum Ukely NDVI value. Because visual 0.60), so its relative greenness = (0.25 - 0.20)7(0.60 -
greenness is calculated strictly as apercentage of 0.20)*100, or about 12 percent. This site would ap-
the maximum NDVI (0.66), all vegetationtypes are pear quite dry onthe relative greenness map be-
referenced to a single scale. Therefore, drylandveg- cause its NDVI value of0.25 is still farbelow the
etation may never produce anNDVI value much historical maximum of0.60 forthis site. In other
greater than 0.25, so it may never show as being words, this site is much less green than its historical
much more than 38 percent green on the visual maximvim.
greenness map. But awet site would mostlikely Historical maximum andminimum NDVI maps
shownearly 100 percent green onthis map some- for the entire United States are produced by search-
time d;iringthe growing season. ing allthe biweeklyNDVI values recorded forthe
The nexttwo verticalbars to the rightrepresent periodofrecord and savingthe largest and smallest
the relative greenness concept. Two examples are values observedfor each pixel. Pixels affectedby
given, one labeled"Dry Site" andthe other"Wet clouds and snow are excluded. These NDVI values
Site." The dry site NDVI range goes from 0.05 are then compositedinto maximum and minimum
to 0.30 andthe wet site ranges from 0.20 to 0.60. maps andusedwith currentbiweeklyNDVI maps
While the ranges givenhere arejust examples, ac- to perform the visual andrelative greenness calcula-
tual values have been determined forevery square tions (fig. 4). The historical NDVI data base is up-
kilometer ofthe United States from 4years ofhis- dated annually.
torical data, as ofDecember 31, 1992. Forthe dry
Relative Greenness Scale
Visual
Greenness
Raw NDVI Scale
Scale Percent Percent
0.66 100 NDVI Green
Range Range
0.60 -r 100
Percent
NDVI Green
Range Range
0.30 -r 100
Actual NDVI 0.25 12 Percent
38 Percent 80 Percent
0.20 0
A Sample
Wet Site
0.05 0
0.00 0 A Sample
All Sites Dry Site
Figure3—Anygiven NDVI valuewill produce differentpercentagevalues of
visual and relativegreenness.
5
myj -,,J.UD.a,4. .19.92.,, Minimum NQ-v! Mao Maxirorn NDV( Mod
,
V i sua) Greenness i....... ...,..R.e...l..a.t.)...y.e.,...b.r.e&ri,ness
—
Figure4 Visual and relative greenness maps are produced by processing current
and historical NDVI data differently.
6