a look at the remote sensing applications …...ai/en: the remote sensing applications program...

A Look at the Remote Sensing ApplicationsProgram of the National Agricultural

Statistics ServiceJ. Donald Alieni

It

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Abstract: This paper presents a summary ofthe procedures used by the National Agri-cultural Statistics Service (NASS) in its croparea estimation program during the years1980-1987. It includes briefly some of theresults with more detailed information pro-vided by Allen and Hanuschak (1988). Inits application program, NASS formed aregression estimator for crop acreage byusing satellite data in conjunction withground data which were collected during theannual June Enumerative Survey. The track

I Research and Applications Division. National Agri-cultural Statistics Service, Washington. D.C. 20250.U.S.A.

Acknowledgements: The author wishes to thank all ofthe NASS staff (headquarters and field offices) whoworked in the area of remote sensing applications andresearch during the 1972-1987 period. A special thanksgoes not only to those that are referenced in this paperbut also to those who left files. notes, and letters behindas a trail for others to follow. This especially includesthose managers involved in remote sensing applicationsand research who provided support over the years aswell as costs summaries and documented project plans.We also wish to extend our appreciation to SherriFinks, Suzy Babb and Amy Curkendall who diligentlydid the formatting and word processing of this paper.Our appreciation is also extended to the staffs of otherparticipating organizations:

National Aeronatics and Space AdministrationHeadquarters - Washington. D.C. (Pill Thome -now with Destek Inc.)Ames Research Center (Buzz Slyc)National Space Technology LahoratoryJohnson Space Center (Gene Rice - now at NASAlIeadquarters - Space Station Project)

record shows that the Landsat based croparea estimates for major producing regionsof the U.S. were closer than the JuneEnumerative Survey (JES) direct expansionestimates to the Agricultural StatisticsBoard final estimates most of the time. Thebasic methodology, data processing tech-niques, and concepts used in the Landsatestimating program were developed throughvarious research projects during the 1972-1979 period and are introduced briefly here.The timing of this description is appropriate

Boeing Corporation

Martin Mariella Data Systems (MMDS)

Bolt. Beranek and Newman Corporation (BBN)

U.S. Department of Agriculture'sSoil Conservation ServiceForest ServiceAgricultural Stabilization and ConservationService (George Melvin)World Agricultural Outlook Board(Dick McArdle)Agricultural Research Service (Galen lIart)

California Department of Water Resources(Jay Baggell)

University of Houston (Dr. Raj Chhikara)Purdue University (Dr. Marv Bauer - now withUniversity of Minnesota)University of California at Berkeley (Dr. RandyThomas)French Ministry of Agriculture (Jean Meyer-Roux -now with European Economic Community)EOSAT Corporation (Dick Kott)National Oceanic and Atmospheric Administration(NOAA)U.S. Geological Survey (USGS)EROS Data Center (Paul Severson)

with 1987 being the final year of operationalcrop estimation using data from the Land-sat Multispectral Scanner Sensor (MSS). Inthe future there will most likely be a returnto the use of satellite data in the estimatingprogram, but for now NASS will no longerbe using this data to produce timely cropestimates. The priwary reason for this dis-continuation is the uncertain status ofthe current Landsat satellites which havealready outlived their expected design lives.In addition new satellite technology in the

l. Introduction

The National Agricultural Statistics Service(NASS), an agency within the United StatesDepartment of Agriculture (USDA), hasthe primary responsibility of providingstatistics for domestic crop and livestockproduction. In particular, for major crops,estimates are made for planted and harvestedacreage, yields, prices received, and, in someinstances, stocks and disposition. For themost part, the statistics are derived fromdata collected through a variety of samplesurveys. For principal crops, the majorsurveys include a national area frame surveyin June; a quarterly multiple frame survey(list and area) in June, September, December.and March; and during the growing season,monthly objective yield surveys using actualfield plots for yield forecasts. The areasampling frame used by the agency has beenconstructed and stratified based on landusage (primarily percent cultivated). NASSfirst began using remotely sensed data in the1950s to aid in the construction of state areasampling frames; at that time, it was in theform of aerial photography. The use ofearth resource satellite data from the U.S.Landsat was a natural extension in this pro-cess. In 1977, the value of photo-interpretingLandsat imagery in area frame constructionwas demonstrated (Hanuschak and Morrissey]977).

Landsat's value as a digital input in the

United States, France, Japan, India, and theUSSR has produced data far superior tothat yielded by Landsat's MSS. However, inorder for NASS to take advantage of thenew data, more research is required to assessthe feasibility of its use so that when anew program is implemented, the anticipatedimprovement in the accuracy of the resultswill be cost effective.

Kc)' words: Landsat; crop area estimates:satellite data.

development of a viable crop estimator alsobecame recognized. In concert with thelaunch of Landsat I in 1972, NASS statisti-cians were selected by the National Aero-nautics and Space Administration (NASA)to be principal investigators on the use ofLandsat data for agricultural statistics.With a small research staff, they proceededto conduct a pilot test of combining conven-tiona] ground-gathered data with Landsatdigital data to form a crop area estimator.The pilot was considered worth pursuingfurther with several years of research. Fullstate tests were conducted in Illinois in 1975(Gleason, Hanuschak, Starbuck, and Sigman1977) and Kansas in 1976 (Craig, Cardenas,and Sigman 1978). The first timely (i.e., endof season) crop area estimate was calculatedfor Iowa in 1978 (Hanuschak et al. 1979).This experience and NASS's participationand evaluation role in the governmentalLACIE (Large Area Crop Inventory Exper-iment) project in the mid-] 970s led NASS tothe point of large scale applications.

The progression of Landsat data usage inthe crop estimating program of NASS wasgiven additional impetus by the initiationof the AgRISTARS (Agriculture andResource Inventory through AerospaceRemote Sensing) program which began onOctober I. 1979. Initially, this was to be asix-year project set to end September 30,1985, but was later extended to October I,

AI/en: The Remote Sensing Applications Program of' NASS

..

1986. It was an interagency program involv-ing not only the USDA, but also theNational Aeronautics and Space Adminis-tration, the National Oceanic and Atmos-pheric Administration, the U.S. Departmentof Interior, and the Agency for InternationalDevelopment. Its focus was on the possibleuses of aerospace remotely sensed data toanswer agricultural resource questions aswell as to meet identified information needsof the USDA. Moreover, it was to deter-mine the usefulness, cost, and extent towhich these data could be integrated intoexisting or future USDA systems so as toimprove the objectivity, reliability, time-liness, and adequacy of information requiredto carry out USDA missions (NASA 1981).

One of the major initiatives underAgRIST ARS was to apply the above objec-tives toward estimating domestic crops andland covers. It was in this area that theNASS research staff took the leadership role(NASA 1981). Plans were to make cropestimates in two states in 1980 using theLandsat data, and each succeeding year,two more states would be added with thegoal of having ten states in the estimatingprogram by 1984. The crops to be estimatedwere to be large area crops in homogeneousregions. In addition, the intent was forresearch to continue in the area of determin-ing land covers. The ultimate goal of theLandsat estimation program was to providetimely estimates of crop acreages whichwould have significantly smaller samplingerrors than the estimates that were then inuse.

In summary, NASS employed a four-prong approach in using the satellite data:(I) remote sensing was to be viewed asjust another method of data collection,(2) remote sensing would be used to supple-ment existing efforts, (3) integration intothe estimation program would be foundedon strong statistical procedures, and (4)

resource effective techniques would have tobe developed for the program to be success-ful. It was realized from the start that therewould be both challenges and some con-straints in using satellite data. First, itwas understood that it would be used asauxiliary data since statistical defensibilitywould require some source of ground datato be used to insure proper categorizationof the satellite information. Also, it wasrealized that cloud cover problems couldprevent some of the data from being avail-able during the times when it might beneeded. There would also be massiveamounts of data to be processed whichmight limit the rate of the program's expan-sion. Additionally, completion of the cropacreage estimates would come at the end ofthe crop year because of the time needed toperform the analysis; this meant that thefigures could not be used in early seasonforecasts. Finally, the resolution (i.e., degreeof interpretability) of the MSS data wouldbe such that some crops and some stateswith small field sizes could not be includedin the estimation program.

2. The Landsat Space Program

NASA's Landsat satellite series began withthe launch of Landsat I in July 1972. Thiswas followed by Landsat II in January 1975and Landsat III in March 1978. The firsttwo spacecrafts were equipped with ReturnBeam Vidicon (RBV) cameras and Multi-spectral Scanners (MSS) while Landsat IIIprovided data from an High ResolutionPanchromatic (also referred to as RBV)camera as well as from an MSS. The decisionwas made to use the MSS data as opposedto the RBV data since MSS was in a formthat was more adaptable for computer pro-cessing. It supplied four bands of data foranalysis and a spatial resolution of eightysquare meters (later sixty meters). Resolu-

tion in this context can be thought of as theability of the imaging system to distinguishclosely spaced objects in the subject area; inessence, this means that the spatial resolu-tion can be thought of as the minimalground area in which the sensor is sensitiveto radiation. A more detailed discussionof the satellite data used can be found inAppendix A. The point to point fly overperiod for these crafts was eighteen days.Landsat IV was launched in J u]y ]982 andLandsat V in March ]984. The point topoint fly over period was sixteen days forthese two satellites. They were equippedwith the Multispectral Scanners as well asThematic Mappers (TM). The TM data wasconsidered superior since it provided sevenbands of data as well as thirty-meter reso]u-tion. However, all previous research byNASS had been directed as MSS data; inaddition, the TM data was roughly fivetimes more expensive to buy than MSS andeven more expensive to process. These fac-tors influenced the agency's decision to con-tinue with MSS data until the benefits of theThematic Mapper could be investigatedfurther.

Due to vastly improving computer tech-nology, the agency is currently in the pro-cess of researching the use of TM data.In addition, data from the French SPOTsatellite which was launched in 1986 is alsobeing examined. The French spacecraft pro-vides three bands of MSS type data but withtwenty-meter resolution and a ten-meterpanchromatic band. The French SPOT satel-lite is also pointab]e which is a definiteadvantage for maximizing the probability ofcloud free coverage. There are possibilitiesfor other data since Japan and India alsohave recently launched satellites with capa-bilities similar to those of the Landsat series.In addition, there are plans at the present tointroduce Landsat VI in ]991. The intentionsare that this Landsat will be equipped with

a Thematic Mapper but not a MultispectralScanner. As a result, data in the formatcurrently used by NASS will no longer beavailable from sources in the United Statesonce Landsat IV and V fail. At this time,both are deteriorating with only one satelliteproviding MSS data and the other provid-ing only TM data.

There are normally only three differentLandsat products used by NASS's statisti-cians in their analysis process. The first ofthese are I : ] ,noo,ooo scale transparencies.These are used to evaluate cloud coverageand, in turn, to decide what combinations ofimagery dates can be used to provide thebest data. Secondly, there are ]: 250,000scale black and white paper products whichare actually photographic interpretations ofthe scenes. These are used in the registrationprocess (see Section 3). Lastly, there are thedata tapes themselves. The costs of theseproducts remained relatively stable up to1983 when a large price increase occurred.Expenditures in this area have rangedannually from 10 to ]5 percent of the totalproject costs since 1983.

3, Methodology

The methodology used by NASS in its cropestimating programs is best described inU.S. Department of Agricu]ture (1983).Currently, major surveys use multiplesampling frames. More precisely, a listframe is used from which samples are drawnwith an area frame used to account for theincompleteness which is inherent in the list.It should be noted that the area frame canstand alone as a complete frame coveringthe entire population and, as a result, is usedto provide indications for crop acreages aswell as being a complementary part of amultiple frame indication. The area frameestimator works well for major crops andlivestock inventories, but not so well with

Allen: The Remote Sensing Applications Program of N ASS

minor or rare items such as specializedcrops. Area frame procedures have beenmost recently described by Cotter andNealon (1987).

A major source of crop data for NASS isthe June Enumerative Survey (JES) which isconducted nationwide each year. This par-ticular survey relies only on the area framewhich is itself a stratified population. Fromthe frame, a sample of approximately ]6,000land segments is randomly selected withthe segment size ranging from 40 to 2400acres and averaging 450 acres. The grounddata are expanded to state, regional, andnational totals using methods similar tothose outlined in Cochran (1977) for strati-fied sampling. At the national level, samplingerrors are normally less than two percent formajor crops such as corn, soybeans, andwinter wheat. Survey training programs,efforts at standardization, and edits andanalysis are used to reduce and control thenonsamp]ing errors. Crop acreage estimatesbased on the JES are published around July]0 each year. Final year end estimates arepublished around January 15 of the follow-Ing year.

In crop acreage estimation, the Landsatdata are used in conjunction with the JESdata in the form of a regression estimator.The exact nature of this estimator as used byNASS was initially described by Von Steenand Wigton (1976). The estimator also hasbeen recounted in a number of other NASSresearch reports, and most recently it wasdescribed in detail by Holko and Sigman(1984). Studies have cited two technicalproblems with the regression estimator: (I) abias may exist in the regression estimatorwhen sample sizes are small (Chhikara andMcKeon 1985; Lundgren 1984) and (2) abias may exist as a result of using the samearea frame segments to estimate both theparameters of the discriminant functionsand the regression equation (Jones 1987;

Holko ]984; Zuttermeister ]985; Gleasonet al. ]977). Current and future researchprojects will address these issues.

Normally, it takes approximately three tofour months to obtain and analyze fully theLandsat data for a state. Also, it is desirablethat the data being used pertain to the opti-mum growing period for the crop beingestimated. For winter wheat in the centralpart of the United States, this means thatLandsat data would normally be at theirbest if they related to the period betweenmid-April and the end of May. The opti-mum for the spring crops being estimatedwould ideally relate to the period betweenmid-July and the end of August. Because ofthis timing, the Landsat indications are usedonly in setting end of the season acreageestimates.

Briefly, the process begins with the cali-bration of the JES land segments to a mapbase; that is, the exact location of a segmentis translated into a set of latitudinal andlongitudinal coordinates. Then, the grounddata are collected through the JES, edited,and put into machine language. The fieldboundaries that were indicated during theJES are then digitized along with the seg-ment boundaries. Each frame or scene ofLandsat data, each covering an area of 170kilometers by 185 kilometers, must also beregistered or assigned latitudes and ]ongi-tudes. The next step requires that thesegments and fields be mapped on to theLandsat scenes using the coordinate systemthat was derived during calibration andregistration. Each pixel (a square areacovering 0.8 acres of a scene) that overlapsa JES segment is assigned a crop or covertype based on the corresponding JESground data. In addition. each pixel also hasa set of MSS measurements. In the ensuingphase, a clustering algorithm is applied tothe set of pixels representing each crop.Each of the resulting clusters has associated

with it a mean vector and a covanancematrix (i.e., a signature).

All the segment data are then classifIedinto crop types based on clustering results.Next a regression relationship is developedbetween the ground data and the classifiedpixels. The entire Landsat scene is thenclassified based on the clustering of thesample ground data. In the succeeding step,the regression relationship is applied to thefull scenes. All the data are finally aggre-gated across scenes from the Landsat esti-mate. The fInal estimate will also includeJES expansions for areas not covered byLandsat scenes. Domain estimators are usedin these situations (Cochran ]977).

In more specific terms. the JES yields adirect expansion estimate for crop acreagewhich is based solely on the JES data whilethe regression estimator uses the JES dataand the Landsat data.

J .1. JES direct expallsioll

For a given state. let h = 1. 2..... Ldenote the land use strata. Within eachstratum, the total land area contains N"primary sampling units from which 11" units(segments) are selected. Using only the areaframe data collected during the JES, thedirect expansion estimator for the totalacreage of a particular crop in any givenstate can be expressed as

,Y = L N".I'"

11=1

where .i\ = L;'I~I .\'",/n" and .\'111 is the reportedacreage of the specified crop in segmentj instratum h. The corresponding variance esti-mate is given by

L' Nil' N n,,- hVar(Y) = ----

h~1 n"(II,, - I) N"11"

X L (.1'", - .l'h)",~ I

3.2. ReXl'e.l'siolle.l'timatioll

The total based on the regression estimatoris given by

I.

Y = L N".i·hl,cg)" I

-. -., h (X - \' ) and h iswhere .1"I"'g) - ,I" + "I, . I, I,

the estimated regression coefficient for landuse stratum h based on regressing groundreported acres on classified pixels in the Ill,

sampled segments.Here X" is the average number of pixels

classified to the specified crop in each PSU;that is, all frame units are included in thecalculation and X" = L'~"I~II/N" where X",is the number of pixels in the ith frame unitof stratum h. Similarly. ,\'" is the averagenumber of pixels classified to the crop ineach of the sampled segments in stratum h;that is, only the sampled frame units areincluded and ,\'" = L?~ I xhdn" where XI,I isthe number of pixels in the jth sample unitof stratum h. The corresponding varianceestimate is given by

Var(Y) =

x (I + _I )11" - 3

where R~is the coefficient of determinationbetween the reported acreage for the specifiedcrop and the corresponding pixels classifiedto the crop. Note that the variance of theregression estimator approaches zero as Rl,approaches unity for fixed n". In otherwords, as the correlation increases betweenthe ground data and the classified Landsatdata, the variance in the regression esti-ma tor decreases.

Since Landsat data cannot be obtainedfor an entire state on any given date, states

Allen: The Remote Sensing Applications Program of NASS

are partitioned into separate analysis areasor districts for each date for which Landsatdata are obtained. The regression estimate isdeveloped within each analysis district. Asreferred to earlier, the statistical character-istics for the set of pixels in the sample mustfirst be developed. Initially, all pixels of aknown crop type are grouped together andthen clusters are formed. Pixels formingfield or segment boundaries as well as thosefor which ground data are felt to be inade-quate are excluded. Again each analysisdistrict is processed separately. There aretwo clustering algorithms which can beused. The first algorithm relies on theISODA T A algorithm and is referred to as"ordinary clustering" (Ball and Hall 1967);it works suitably for small data sets but theprocessing costs incurred with its use makeit less suitable for large data sets. Thesecond is Classy which is a maximum Iike]i-hood clustering algorithm developed atJohnson Space Center in Houston, Texas(Lennington and Rassbach ]979); it func-tions best if there are a large number ofpixels (greater than 500). Additionally,Classy relies more on normality assump-tions than does ordinary clustering. One ormore clusters result for each land cover.Statistics surrounding the resulting clustersare examined which in effect allows theanalyst to perform editing functions toimprove the accuracy of the classification.Among the statistics provided are twomeasures of separability: Swain-Fu (Swain1972) and transformed divergence (Swainand Davis 1978).

Once the signatures for the clustershave been determined, all the pixels in thesampled segments within an analysis districtare classified using this information. Thisis performed separately for each analysisdistrict. Multivariate normality is assumedwhich seems to be justifiable for mostremote sensing applications. Additionally,

classification accuracy seems to be robust toviolations of the assumption (Swain andDavis 1978). In particular, quadratic discri-minant scores based on multivariate nor-ma]ity are calculated for each pixel in eachanalysis district

dF(x) = -0.5In (detS,}

0.5(x - X, )TSi I (x - x,)

+ In p,

where i = I, 2, ... , g represents the indi-vidual clusters. In the formulation, p, repre-sents the prior probability that a pixelbelongs to population i and S, is thesample covariance matrix. So for each pixel,g discriminant scores are computed. A pixelthen would be assigned to population k if

dF(x) = largest of dP(x), ... , dKQ(x).

The classification is performed with equalprior probabilities as well as with distinctpriors. There are several ways to derive thevalues for unequal probabilities. The mostcommon procedure is to assume that theground data are completely "true" and thento compute the number of pixels corre-sponding to each ground cover; a weightreflecting the resulting proportions is subse-quently assigned to each land cover category.Since each land cover category may consistof severa] clusters, this weight is further pro-portioned to reflect the percentage of pixelsthat are in each cluster within a category.Severa] sets of classifiers are used up to thepoint of the final accumulation of analysisdistrict estimates to a state level estimate.That way, the analyst has the opportunity tocontinue the assessment of each set's perfor-mance up to the final step of the process.

Each set of classifiers is applied separatelyto all the pixels in each analysis district. Theresults of this full frame classification as wellas the results from the sampled segmentclassification are then used as inputs into

Here the variance for the JES direct expan-sion is based on the June survey alone anddocs not reflect any updates in the data thatresulted from fol\ow-up contacts neces-sitated by the remote sensing project. Ingeneral. the relative efficiency can bethought of as the factor by which the JESsample size would have to be increased inorder to yield a direct expansion with avariance eq ual to that obtained using theLandsat data. The approximate break evenRE in terms of cost effectiveness was cal-culated to be approximately in the 2.5 to 3.5range based on 1981 data (Hanuschak,Al\en, and Wigton 1982). That is, an REabove that range would indicate that thesame precision could be obtained at less costif the Landsat-JES approach was used asopposed to expanding the JES sample size.

cover. Each year every effort was made toobtain cloud free imagery within the opti-mum period, but experience showed this tobe an impossible task. Therefore, it becamenecessary to address the issue in the acreageestimates. A post stratifIcation approach islIsed for this purpose. Areas for whichLandsat data are available compose onepost stratum; here the regression estimatoras outlined earlier is used. For areas notcovered by Landsat data, the direct expan-sion estimator is applied. This approach ispossible since the total number of frameunits as wel\ as the sampled units withinareas covered and not covered are known.

In order to determine the success associ-ated with the regression estimator, its rela-tive efficiency (RE) is calculated. The RE isa measurement of the gain in precision ofthe regression estimator as compared to theJES direct expansion

the regression estimator that was outlinedearlier. The final step of the process calls forcombining all the estimate~ which at thispoint are at an analysis district level.

Typically, a classifier is evaluated byexamining its confusion matrix. However, ifa classifier is to be used to estimate cropacreage, its performance should be assessedon that basis. This means that the varianceof the regression estimate can be thought ofas the measure of the classification's ade-quacy (Gleason et a!. 1977). The best esti-mate in this sense is obtained by maximizingthe correlation between the ground data andsatellite data. The relationship between thetwo can be affected by numerous factors.Among these are the region of interest, thedata of the imagery (since there exists anoptimum time frame for a given crop in agiven region). and the number of pixels usedin developing the clusters. The correlationsare also affected by the prior probabilitiesused, the number of clusters decided uponas well as the number of land cover cate-gories, and whether or not some land usestrata are pooled or excluded during theanalysis. The decision to exclude segments isusually made in instances where there areless than five segments within a given stratawithin a particular analysis district whilepooling usually is done only for strata whichhave the same expansion factors. The aver-age field size was also found to have an effectwith results better for those crops grown inlarger fields (Cardenas and Hanuschak1978); for this reason, the methodology isbest suited for major crops in the principalproducing areas. Additionally, the satellitedata themselves are assigned a quality levelby the data providers; obviously, the betterthe quality of the data, the higher one'sexpectations would be for the accuracy ofthe classification process.

One of the major problems which must bedealt with in using Landsat data is cloud

REVariance (JES direct

area expansion)Variance (Landsat/JES combined'

regression estimator)


The cost-benefit ratio is certainly notan exact measure and is subject to manyassumptions and questions, such as:

I. Would or would not the agency con-tinue a general purpose JES?

2. What would be the financial benefit tothe agricultural sector of the economyif the corn acreage variance estimatewas cut by a factor of two to three?

3. What would be the cost of a groundsurvey for only crop and land useinformation for remote sensing analy-sis and not a general purpose JES?

4. What is the value of county levelremote sensing estimates that the JESdoes not provide?

5. What would be the magnitude of thegains in estimating other items if thesample size of the JES were to bedoubled or tripled?

6. Could the general purpose JES bedoubled or tripled in terms of overallresponse burden and implementation(enumerator and state office workload,potential nonsampling errors, etc.)?

Over time, the costs of the JES haveincreased while the remote sensing costshave decreased. As a result, the breakevenpoint for cost effectiveness has declined con-siderably. In 1987, by the same criterion,relative efficiencies exceeding the 1.5 to 2.5range would be considered an indicationof cost effectiveness. A graphical represen-tation of the total Landsat costs and the JEScosts follow in Figure I. The Landsat costsare all costs associated with the Landsatestimate above and beyond the operationalJES costs.

4, Program Coverage and Results

From 1980 to 1987, the program expandedfrom two states (Iowa and Kansas) andthree crops (corn, soybeans, and wheat) toeight states (Arkansas, Colorado, Illinois,Indiana, Iowa, Kansas, Missouri, andOklahoma) and three additional crops(cotton, rice, and sorghum). This alone wasa significant accomplishment with nearlythe same personnel resources. The growth of

200

180-'".. 160••"0"0... 1400

'""0C 120••'"=0:: 100-'"'" 800u

60

40

LEGEND

JES Cosls

Landsal Cosls

1981 1982 1983 1984 1985 1986 1987

Year1982: Extensive doud coverage in Missouri lowered Landsat costs.

Fig. 1. A verage costs per state in Landsat program Landsat versus JES

60

50

40"••'""~ 30...,

""20

10

o1980 1981 1982 1983 1984 1985 1986 1987

Year

- Corn

Soybeans

Wheal

Fig. 2. Percent or United States planted acreage accountedj()r h.l' states in Landsat program

the project is reflected in Figure 2. Across allcrops and all years there were thirty-nineregional estimates calculated. Out of thesethirty-nine estimates, the Landsat basedestimator was closer to the AgriculturalStatistics Board's final estimate twenty-one times (see Table I). While this is not astatistically significant difference, it is still ofinterest to see such a new and complex tech-nology outperform the June EnumerativeSurvey. in terms of estimated accuracy. Thecomparison assumes the Board's final esti-mate to be the best approximation to "truevalues." Comparisons of state estimates canbe found in Allen and Hanuschak (1988).

5, Project Cost

Project cost data were diligently maintainedduring the eight-year stint of the remotesensing estimation program. One's perspec-tive of these costs can be enhanced some-what by reviewing the historical cost datafor various rcmote sensi ng research effortsconducted prior to ]980. When the firstfull state project (Illinois 1975 data)was conducted. thc total cost was $750,000.

The project encompassed two years andincluded much of the developmental phaseof EDITOR (the computer software usedfor the analysis). The first real time full stateapplication project (Iowa - 1978 data) bycomparison cost $300,000. The first actualoperational year (1980) costs were $200,000per state; this was reduced to an average ofapproximately $140,000 per state for the1982-1986 period and ended with an aver-age of $129.000 per state for 1987. Noneof the cost data have been adjusted forinflation which would show an even moredramatic cost reduction over time. The twomajor reasons for the sharp drop in costswere (I) the ellicient use of computerresources with a range of applicationsinvolving supercomputers. mainframe. mini-.and microcomputers, and (2) the inclusionof more states in the project while maintain-ing approximately the same level of per-sonnel. Additionally. the proper mix ofhardware and optimized software tended tomaximize the gains in cost effIciency. Thegraph that follows (Figure 3) shows detailedcosts for 1981 1987.

Allen: The Remote Sensing Applications Program o{ NASS

Tahle J. Comparison offinal Agricultural Statistics Board's ( A S8) .final estimate with JuneEnumerative Survey (JES) and Landsat hased estimates

Year CORN PLANTED ACRES SOYBEAN PLANTED ACRES

JES Estimate Landsat Estimate JES Estimate Landsat Estimateas percent of as percent of as percent of as percent ofASB Final ASB Final ASB Final ASB Final

1980 98.4 99.5* 100.8* 98.01981 100.3* 99.8 104.0 97.9*1982 IO\.I 99.5* 103.3 100.4 *1983 103.4 99.8* 100.7* 97.91984 99.8* 97.1 103.4 99.3*1985 100.1 * 99.4 100.9* 98.51986 98.6 99.1 * 103.7 101.3*]987 ]00.0* 97.6 ]02.6 101.2*-- --

Average 100.2* 98.9 102.4 99.3*

Year WHEAT HARVESTED ACRES RICE PLANTED ACRES


1980 107.4 ]04.0*1981 107.7 103.9* 150.6 ]00.0*]982 106.4 101.4*1983 ]04.0 100.9* 117.4 109.2*]984 10\.0* 98.1 97.0* 96.7]985 103.0 100.0* 102.7* 109.71986 102.\ * 95.7 9\.2 94.3*\987 100.6* 96.0 90.3* 88.1

Average 104.\ 100.0* 108.2 99.7*

Year COTTON PLANTED ACRES SORGHUM PLANTED ACRES


\983 98.4* 76.91984 89.0 104.4 * 105.8 95.0*1985 93.0* 109.4 102.4* 89.91986 133.9 120.1 * 96.3* 86.31987 122.1 108.1* 99.4* 93.1

Average 107.3 103.8* 10\.0* 91.2

*Most accurate result compared to the ASB final.

200 LEGEND

• Salary180

El Tra\'f'1160;;,'"...: 0 Equipment i-l-l 140 F.xpf'ns~0Q...,.., E2]en 120 DataQ7....:en;..;

~ Computer0 100 Proces..'i:in~:c~en,...en 800

-l~,...0

60,...

40

20

o19RI \982 19R.\ 19R4 19R5 19R6 1987

\'ear

FiK.3. AveraKe costs pl.'f .\·tate!()r Landsat estimation proKram: lWll-1987

6. Recent Developments

At the end of 1987. the decision was madeto rechannel some of the resources of theremote sensing applications group back intoa research program. There were severalreasons for this. Foremost was the concernover the anticipated failure of the currentLandsat satellites. At the present time.Landsat IV and V are still functioning.However. the life expectancies of these satel-lites are elapsing. The failure of either wouldcreate obstacles for NASS. Past studieshave shown that two MSS satellites arcneeded in order to provide coverage ade-

quate to overcome cloud coverage (Winings1982), and currently, all of the method-ology is geared toward the use of the MSSdata so the use of other types of data is notpossible at this time. This is further com-pounded by the 1~lct that future Landsatsatellites will not carry multispectral scan-ners. In addition. there is a need to study thenew high resolution sensors on the pointableFrench Spot satellite as well as Landsat'sThematic Mapper to evaluate their suitabil-ity for use in an operational program. Thisall translates into the need for more verywell targeted research. Another factor influ-


encing the decision to defer the applicationsprogram was the reduction in availablefunding. Federal budget cuts have demandedthat some projects be curtailed, and remotesensing applications with expendituresexceeding one million dollars per year wasmanagement's choice when coupled withthe above considerations. On a positivenote, the cessation of the remote sensingapplications program is being viewed asonly a transition period that will lead to aneven better course of action in the use ofadvanced higher resolution satellite data.The advanced sensor data from the com-mercial systems of the 1990s will containimproved spatial, spectral, and temporalinformation in the data. To NASS, thisshould translate into more accurate acreageestimates and perhaps also crop conditionor yield assessments if expenses can be con-trolled through cost effective methodology.Additionally, the program change also hasallowed for more resources to be applied toresearch on computer assisted area frameconstruction. In connection with this pro-ject, NASS has recently received a grantfrom NASA headquarters in support of thisendeavor.

7. Conclusions and Observations

During the eight-year project, there werethree major findings. First, crop area esti-mates using Landsat data for large areascould be produced in a timely fashion withrelatively small additional staffing. Secondly,the Landsat based estimates had consider-ably lower sampling errors than the JESground survey, and the regional Landsatestimates tended to be closer to the Agricul-tural Statistics Board final estimates. Third,based on internal agency costs and benefits,the extra cost of processing the Landsatdata were near a breakeven point for corn,soybeans, wheat, and sorghum and abovebreakeven for cotton and rice. Cloud cover,

technical problems with the satellites andground systems, and the limits on theamount of information contained in Land-sat's MSS data seemed to be the majorproblems encountered that prevented morepositive comparisons. Additionally, thereare many other considerations, findings,and benefits from this eight-year project. Itis not feasible to recall or list each andeveryone of them but some of those whichpotentially could be termed as having majorsignificance include the following:

I. The agency research staff gainedconsiderable experience in the use ofsupercomputers.

2. The agency research staff gainedconsiderable experience in the use ofspecialized hardware for digitization,both vector and video. The knowledgegained in vector digitization and inthe visual interpretation of Landsatimagery in terms of land use was con-veyed to the area sampling frame con-struction staff and has subsequentlypaid large dividends in the efficiency ofarea frame construction as well as inrelated quality control.

3. The agency now has a small highlytrained staff to evaluate the moreadvanced satellite sensors of today andthe future.

4. The agency staff gained an interna-tional reputation for its Landsatmethodology and large scale inventorycapabilities as well as its efficient use ofsupercomputers.

5. Statistical formulas were developed forsmall area (county level) crop acreageestimates (Amis, Lennington, Martin,McGuire, and Shen 1982; Battese andFuller 198 I; Cardenas, Blanchard, andCraig 1978; Huddleston and Ray 1976;Walker and Sigman 1982; Chhikaraand McKeon 1987).

6. The research staff of N ASS, along with

the USDA's Soil Conservation Serviceand severa] other federal and stategovernment agencies. demonstratedthat land cover inventories in additionto crop acreage inventories could besuccessfully estimated using the Land-sat regression procedures.

7. The use of Landsat data for yield fore-casting and estimation was examinedbut it was determined that any increasein information on crop yields was notcost effective compared to NASS'sconventional yield surveys.

H. To the author's knowledge. economicbenefit studies to determine the "valueof the agricultural economy" of moreaccurate crop area estimates have notbeen cond ucted for the ]980-1987period by professional economists.Therefore. thorough results from acost-benefit analysis (considering bothinternal and external factors) are notavailable.

Appendix A: Discussion of Satellite Data

The Landsat data used in NASS's remotesensing program during the 1980-19H7period consisted of a set of measurementsmade by the satellite's multispectral scanner(MSS). Each measurement for the first threesatellites in the series encompassed an areaof 1.0 acres while the area was 0.8 acres forthe other two. This measurement area isreferred to as a pixel. Each of the satellites inthe Landsat series was designed to travel ina nearly circular polar orbit with ]4 orbitsper day. Repeated coverage occurred everyIH days for Landsat I. II. and III while therepetition was every 16 days for the laterversions. The orhiting paths covered widthsof IH5 kilometers and provided cross sec-tions that were 170 kilometers in length. Thescanner itself is a camera like device thatdivides the image heing received into pixelsand then measures the hrightness of each

pixel along the electromagnetic spectrum.Specifically. the total radiance of an object ismeasured in four bands of the spectrum. Twoare in the visible portion of the spectrum(0.5-0.6 micrometers and 0.6-0.7 micro-meters) while the other two are in the nearinfrared portion (0.7-0.8 micrometers and0.8-].1 micrometers). These four measure-ments provide a spectral "signature" for anobject. The differences in these signaturesallow for the classification of the pixels.The spatial resolution in the early satelliteversions was HO meters square hut this wasreduced to 60 meters for Landsat IV and V.

Thematic Mappers (TM) were providedon Landsats IV and V and were advancedsensors compared to the Multispectra]Scanners. These devices operate in sevenspectral hands with 30 meter resolution.Three of the bands are visible with anoverall range of 0.45 micrometers to 0.69micrometers. There is one near infrared(0.76-0.90 micrometers) and two shortwaveinfrared bands (1.55-1.75 micrometers and2.0H-2.35 micrometers). The final band isthermal infrared (] 0.50-] 2.50 micrometers).Measures for all but the latter are providedwith 30 meter spatia] resolution with thethermal band having 120 meter resolution.Pixel size for TM data is 0.2 acres (U .S.Geological Survey and National Oceanicand Atmospheric Administration 1984).

The first SPOT (Satellite Pour (,Ohser-vation de la Terre) satellite which waslaunched in 1986 follows a near polar orbitsimilar to the Landsat satellites. There aretwo sensors on hoard known as HRVs (highresolution visible). They produce mu]ti-spectral images with 20 meter spatial resolu-tion with measurements on two visiblehands (0.50-0.59 micrometers and 0.61-0.68 micrometers) as well as a single nearinfrared hand (0.79-0.89 micrometers).Additionally. hlack and white images, usingthe spectral hand ranging from 0.5] to 0.73


micrometers, are produced with 10 meterresolution. The resulting pixel sizes are0.098 acres and 0.025 acres respectively.Orbits repeat every 26 days. However, sincethe sensors are pointable, the same groundarea can be observed several days in a row.The path width is 60 kilometers which canbe adjusted to 81 kilometers. The cross sec-tional lengths are a constant 60 kilometers.Plans call for launching SPOT-2 some timein 1989 and SPOT -3 ill the early 1990s(SPOT Image Corporation 1988).

8. References

Allen, J.D. and Hanuschak, G.A. (1988).The Remote Sensing Applications Pro-gram of the National Agricultural Stat-istics Service 1980-1987. National Agri-cultural Statistics Service staff report no.SRB-88-08, United States Department ofAgriculture, National Agricultural Stat-istics Service.

Amis, M.L., Lennington, R.K., Martin,M.V., McGuire, W.G., and Shen, S. S.(1982). Evaluation of Small Area CropEstimation Techniques Using Landsatand Ground-Derived Data. Report no.LEMSCO-17597, Lockheed Engineeringand Management Services Company, Inc.

Ball, G.H. and Hall, D.J. (1967). A Cluster-ing Technique for Summarizing Multi-variate Data. Behavioral Science, 12,153-155.

Battese, G.E. and Fuller, W.A. (1981).Prediction of County Crop Areas UsingSatellite Data. Proceedings of the Sectionon Survey Research Methods, AmericanStatistical Association, 500-505.

Cardenas, M., Blanchard, M., and Craig, M.(1978). On the Development of SmallArea Estimators Using Landsat Dataas Auxiliary Information. Staff report,United States Department of Agriculture;Economics, Statistics, and CooperativeService.

Cardenas, M. and Hanuschak, G.A. (1978).An Exploratory Study on the Effectsof Field Size and Boundary Pixels onCrop Signatures. Internal working paper,United States Department of Agriculture;Economics, Statistics, and CooperativeService.

Chhikara, R.S. and McKeon, U. (1987).Estimation of County Crop AcreagesUsing Landsat Data as Auxiliary Infor-mation. Houston, Texas: University ofHouston.

Chhikara, R.S. and McKeon, U. (1990).Estimation of Finite Population Under aCalibration Model. Journal of OfficialStatistics, 6, 295-310.

Cochran, W.G. (1977). Sampling Tech-niques. New York: John Wiley.

Cotter, J. and Nealon, J. (1987). AreaFrame Design for Agricultural Surveys.Staff report, United States Departmentof Agriculture, National AgriculturalStatistics Service.

Craig, M.E., Cardenas, M., and Sigman,S.R. (1978). Area Estimates by LandsatKansas 1976 Winter Wheat. Staff report,United States Department of Agriculture,Economics Statistical Service.

Gleason, c.P., Hanuschak, G.A., Starbuck,R. R., and Sigman, R.S. (1977). StratifiedAcreage Estimates in the Illinois Crop-Acreage Experiment. Staff report, UnitedStates Department of Agriculture, Stat-istical Research Service.

Hanuschak, G.A. and Morrissey, K.M.(1977). Pilot Study of the Potential Con-tributions of Landsat Data in the Con-struction of Area Sampling Frames. Staffreport, United States Department ofAgriculture, Statistical Research Service.

Hanuschak, G.A., Craig, M., Ozga, M.,Luebbe, R., Cook, P., Kleweno, D., andMiller, C. (1979). Obtaining Timely CropArea Estimates Using Ground-Gatheredand Landsat Data. Technical bulletin no.

1609, United States Department of Agri-culture; Economics, Statistical, andCooperative Service.

Hanuschak, G.A., Allen, R.D., and Wigton,W.H. (1982). Integration of LandsatData Into the Crop Estimation Programof United States Department of Agricul-ture's Statistical Reporting Service 1972-1982. Paper presented at the 1982 MachineProcessing of Remote Sensed Data Sym-posium, West Lafayette, Indiana.

Holko, M.L. and Sigman, R.S. (1984). TheRole of Landsat Data in Improving UnitedStates Crop Statistics. Paper presented atthe Eighteenth International Symposiumon Remote Sensing of Environment,Paris, France, October 1-5, 1984.

Holko, M. (1984). A Remote Sensing Appli-cation Methodology and Results. Unpub-lished working paper, United StatesDepartment of Agriculture, StatisticalResearch Service.

Huddleston, H.F. and Ray, R. (1976). ANew Approach to Small Area CropAcreage Estimation. Paper presented atthe Annual Meeting of the AmericanAgricultural Economics Association.State College, Pennsylvania.

Jones, E.M. J r. (1987). Classifier Study.Unpublished manuscript. United StatesDepartment of Agriculture, NationalAgricultural Statistics Service.

Lennington, R.K. and Rassbach, M.E.(1979). Mathematical Description andProgram Documentation for CLASSY- An Adaptive Maximum LikelihoodClustering Method. Report no. LEC-12177 (JSC-14621), Lockheed ElectronicsCompany, Inc.

Lundgren, J.c. (1984). A Study of Bias andVariance in Landsat Data Based Regres-sion Estimates for Crop Surveys UsingSimulated Data. Report no. LEMSCO-20239, Lockheed Engineering and Manage-ment Services Company, Inc.

NASA (1981). AgRISTARS, Annual ReportFiscal Year 1980. AP-JO-04111,

Houston, Texas: NASA.SPOT Image Corporation (1988). SPOT

User's Handbook (vol. I). Reston,Virginia: Spot Image.

Swain. P.H. (1972). Pattern Recognition. ABasis for Remote Sensing Analysis.LARS information note 11572, WestLafayette, Indiana, Purdue University.

Swain, P.H. and Davis, S.M. (1978).Remote Sensing. The QuantitativeApproach. New York: McGraw-Hill.

United States Department of Agriculture,Statistical Reporting Service (1983).Scope and Methods of the StatisticalReporting Service. Miscellaneous pub-lication no. 1308, Washington, D.C.:United States Department of Agriculture.

United States Geological Survey andNational Oceanic and AtmosphericAdministration (1984). Landsat 4 DataUsers Handbook. Alexandria, Virginia:United States Geological Survey andNational Oceanic and AtmosphericAdministration.

Von Steen, D. and Wigton, W. (1976). CropIdentification and Acreage MeasurementUtilizing Landsat Imagery. United StatesDepartment of Agriculture, StatisticalResearch Service.

Walker. G. and Sigman, R. (1982). The Useof Landsat for County Estimates of CropAreas. Evaluation of the Huddleston-Rayand Battese- Fuller Estimators. StatisticalResearch Service Staff report no. AGES820909. United States Department ofAgriculture. Statistical Research Service.

Winings, S. B. (1982). Landsat Image A vail-ability for Crop Area Estimation. Paperpresented at the Eighth InternationalMachine Processing of Remotely SensedData Symposium for Applications ofRemote Sensing, West Lafayette,Indiana.

Allen: The Remote Sensing Applications Program (~rNASS

Zuttermeister, J.P. (1985). Evaluating TMData for Statistical Research ServiceCrop Acreage and Production Estimates.Staff report no. RSB-85-02, United States

Department of Agriculture, StatisticalResearch Service.

Received November 1988Revised June 1990

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