Satellitelnventory of MinnesotaForest Resources

Marvin E. Bauer, ThomasE. Burk, Alan R. Ek, Pol R. Coppin,Stephen D. Lime, TereseA. Walsh,David K. Walters, WilliamBefort, and DavidF. Heinzen

Abstract conditions(Ek, 1983),such updatesare limited becauseof plots. Total forest The methodsand resultsof using LandsatThematic Mapper their concentrationon existing forested areaand areaby cover type changebecause of cropland (rM) doto to classify and estimate the acreageof forest cover types in northeasternMinnesota are described.Portions of abandonment,harvesting, and urban development.Such rea- six TMscenes covering counties with a total area of changesare extremelydifficult to model; that is a major five areas. 14,679 square miles were classified into six and son for wanting to use satellitedata to determineforest forest five rela- nonforest c/osses.The approach involved the integration of Becausethe mean characteristicsof forest strata change sampling, image processing,and estimation. Using two-stage tively little, the major inventory problem is to estimatethe sampling, 343 primary samp)ing units (PSu),each BB acres amount and location of the strata. aerial in size, were photo interpreted and mapped os d source For many yearsforesters have effectivelyutilized field re- of referencedata classifier training and calibration of the photographyas a tool to help monitor and manageforest for part most ru data classifications. sources,and aerial photographsare an integral of 7972 Classification accuracies of up to 75 percent were forest inventory procedures.The launch of Landsat-1in achieved;most misclassification was betweensimilar or re- added an entirely new dimension to the capability to acquire inter- Iated classes.An inversemethod of calibration,based on the Earth resourcesinformation, and there has been much est in the potential of satellitedata and computer-aided error retesobtained from the classificationsof the esu plots, analysistechniques to identify and map forest resources.AI- was used to adjust the TM classificationproportions for clas- though it has generallynot been possiblewith LandsatMSS sification errors. The resulting area estimatefor total forest- data to achievesatisfactory classification accuracy for any land in the five-county area was within 3 percent of the estimatemade independently by the USDaForest Service. but the most generalclassifications in the GreatLakes States, content Area estimates conifer and hardwood types were a number of studieshave shown that the information for forest (rv) higher within 0.8 and 6.0 percent, respectively,of the ForestSewice of LandsatThematic Mapper data is considerably than that of t'lss data (Price,1984; DeGloria, 1984), and that estimates.A study of the useof multidate tw data for the additional spectralbands and finer spatial and radiomet- change detectionshowed that forest canopy depletion, can- opy increment, and no change could be identified with ric resolution of tv data result in significant improvements greaterthan 90 percentaccuracy. The project resultshave in classificationaccuracy for more specific information (Horler been the basis the MinnesotaDepartment of Natural Re- classesdescribing forest species and Ahern, 1986; for (Pe- sourcesand the ForestService to define and begin to imple- Moore and Bauer, 1990Jand forest stand characteristics tersonef o1.,t986; Williamsand Nelson,19s6). The results ment an annual systemof forest inventory which utilizes of Mooreand Bauer(1s90), which providedmuch of the im- Landsat ru data to detect changesin forest cover, petus for this research,showed a 15 to 20 percentincrease in classificationaccuracy of tv data over trls6data, with TM ac- Introduction curaciesof greaterthan B0 percentfor sevenclasses. Forestscovering 16.7 million acres,or nearly a third of the Iand areaof Minnesota,are a significant componentof the state'snatural resourcebase and a significant contributor to Objectives its economy.In spite of growing demandsfor information The overall objectiveof the researchwas to developand test about the state'sforest resources, statewide inventories are proceduresfor using multispectral satellitedata to inventory conductedonly at about 1S-yearintervals. Although forest forestresources in the stateof Minnesota.Specific objectives stand growth models have becomeincreasingly important for were to updating inventory information and projectingfuture forest r Developa methodologyto use digital satellitedata and com- puter-aidedpattern recognitionto classifyforest cover types which will be compatiblewith and complementaryto the conductedby the MinnesotaDepartment of M.E.Bauer, T.E. Burk, A,R.Ek, P.R.Coppin, S.D. Lime, other surveys Natural Resourcesand the U.S. ForestService; T.A. Walsh, and D.K. Waltersare with the Departmentof ForestResources, University of Minnesota,St, Paul, MN 5510 B. PhotogrammetricEngineering & RemoteSensing, W. Befort and D.F. Heinzen are with the Division of Forestry, Vol. 60, No. 3, March 1994,pp. 287-298. MinnesotaDepartment of Natural Resources,Grand Rapids, MN 55744 oosg-l172 IS 4/6003-28 7$03. 00/0 P.R.Coppin is now with the Departmentof Forestryand Nat- O1994 American Societyfor Photogrammetry ural Resources,Purdue University, West Lafayette,IN 47907 and RemoteSensinq

PE&RS proach-is that improved cover type estimation techniques would lead to more efficient and timelv forest descripiions for a variety of purposes, . Multispectral Landsat TM data, together with ground- basedforest inventory data were usedlo produce in inven- tory of Minnesota forest lands, along with a methodology for frequent rFdates.The followilg seclionsdescribe the sihple design and survey model, satellite data classification methl ods and results, calibration and evaluation of Landsat area estimates,change detection using multidate Landsat data, and, lastly, the use of satellite remote sensing for operational ,\' forest inventories in Minnesota. The emphasls is on describ- ing new approachesto forest inventory using satellitedata. Background I ( StudyArea I \ The study areafor the project consistedof five forested countiesin northeasternMinnesota (Figure1), totaling some 9.4 million acres.The region stretchesnearly 230 mil6s easV Figure1. Locationof project five-county studyarea in west and 170 miles north/south.The study irea couesponds noftheasternMinnesota (FtA aspen-birchunit) and Landsat to the USDAForest Service's Forest Inventory and Analysis TMscenes. (na) survey unit one, commonly referredto as the nspen/ Birch unit (Miles and Chen, 1992).The study areais Lom- prised.of a-variety of-foresttypes, with primiry types being aspen-birch,spruce-fir, and pine. . Estimateforest areas and producedigital mapsof the state,s geologyof this region is largely the result of glacia- forest . Ihr resourcesby speciesgroup at County,iegion, and state tion (Wright, 7972), The northeastern portion levels,and determine of the area. the aicuracyand p.Lcisi6nof the forest Lake and Cook counties,is marked areaestimates and mapsderived from wiih an abundanceof Ca- satellitedata, com- nadian Shield lakes, paredto traditionalforest inventory estimates; and displays relatively large topo-- graphic . Investigatealternative innovative approaches to satellitedata variations. The western half of the region, classification,sampling, and estimation and determine to Koochichingcounty, is a vast lowland with iitermittent mo- whatdegree satellite data classifications can be used to ob- raines.The centralportion of the region is characterizedby a tain additionalinformation such as stand density, size class, variety of geologicformations, and is heavily forested.The anddisturbance; and portion of Louis county is dominited by granite . 99ni1al .St. Integratethe satellite data classification, sampling, and esti- highland termed the "Iron Range,;'and is home to mation numerous designsand procedures into theForest Resource As- mining operations.In southern St. sessment Louis and Carlton coun- and AnalysisProgram of the MinnesotaDepartment ties, agricultureand of Natural (nr.rR) other non-forestIand usesare more Resources and ForestInventory and-Analysis prevalent. (rn) of theU.S. Forest Service, Even so, these areasare still predominatelv for- ested. The goal of the project was to developand implement a methodologyto provide cover type areaestimates of * S per- Hardwareand Sottware Environment cent at the g5 percentconfidence level at the statelevel ind Imageprocessing was performed -f on SUNSparcstations and 10 percentwith 9Opercent confidence at the county level, 386 microcomputers,Raster-based image processing and cIS Other performance goals of the final inventory procedure procedureswere completedusing worlistalion ERDTS(Earth were (1Jcost $0.01 to $0.02 per forestedacre, (2) one year to ResourcesData Andysis System).Vector GISand data devel- acquire and analyzethe data, (3) proceduresthat can be im- gp-r{lentprocedures were completedusing pc ArcAnfo 3.4D. plemented by the DNnwith reasonablepersonnel and capital Additional routines were developedin-h6use using C and costs,and (4) enoughflexibility to meet changingconditions other script languages. or requirements. Two important underlying premisesof the objectives hndsat Data and approachtested in the investigationare (1) that the syn- The image data for the project consistedof portions of six optic view of Landsatprovides the opportunity to obtain for- LandsatTM scenes(Figure 1). Data were collectedbetween e-stinventory information over large areas(i.e., state) and (2), 29 May 19BBand 14 June 1988,with scenesalong -were the same that_byusing _computerdata analysismethods to classifypix- path being collectedon the sameday. All images vir- els distributed over countiesand unique sampling designi, it tual]y cloud free,with any clouds oicurring oirtsideof the is also possibleto make accurateand preciseistimatesfor study area; however, haze was observedover water bodies local areas(i.e., counties). This approachoffers a meansto withil the path 26, row 26 scenecovering the northeastpor- improve upon the samplingmethods now used for making tion of the study area. areaestimates from ground-based,two-phase surveys. At the All sceneswere rectified to the UTM(zone 1S)projection sametime, the ground data (which are also used to estimate and coordinatesystem using a nearest-neighborresimpling other parameterssuch as forest stand characteristics;there- schemeto preservethe original digital numbers.Rectification fore, its collection cannot be abandoned)will be used to re- allowed for relatively easybverlayof referencedata setsfor move the bias from (i.e.,to correct)the satellite-based training and accuracyassessment. Because of the inherent estimates.An important considerationin the proposedap- problemsof working acrossscenes of differing acquisition

2aa PE&RS dates(changes in atmosphere,sun angle,and phenology), TneLe1. Hrenancnvor lruroRuertoruCusses FoR PHoro INTERPRETATIoN AND processingwas limited to within scene(or path) processing. CLnssrncmoruoF LANDSATTM DATA.THE FoREsr Coven Tvpes Wene Funrnen Consequently,three separateimage datasetswere processed; SunorvrogorNTo THREE Cnowr't Ct-osune AND THREE StzE Cusses. they were mergedonly as classified(cts) files. PhotoInterpretation Classes SatelliteClasses Code Ash Lowland Hardwoods LH RelerenceData -E,lm Referencedata setswere generatedusing prints (scale AsperVBirch A cncn/Rireh NB 1:9,400)and transparenciesof 3S-mmcolor infrared aerial Aspen photography.Seven Iines of photographywere gatheredat PaperBirch equal intervals acrossthe study area (Plate1A). Interpreta- Northern Hardwoods Northern Hardwoods NH UC tion units were defined as B8-acre(approximately !a- by Upland Conifers Upland Conifers 112-mile)primary samplingunits (PsUs)spaced roughly one Red Pine White Pine mile apart over the entire length of the flightlines fPlate 1B). psus /ack Pine A total of 3+3 were stereoscopicallyinterpreted into ap- BalsamFirAVhite Spruce BalsamFirMhite BF/lVS proximately 100 cover type, size,and density classes(Table BalsamFir Spruce 1). Subsequentto photo interpretation,all psus were visited White Spruce on the ground, or viewed from the air if ground accesswas Lowland Conifers Lowland Confiers LC limited. Covertype designationsand boundarieswere veri- Lowland Black Spruce fied or, if necessary,corrected, either for errors in photo Tamarack interpretationor for chansesthat had occurredbetween the Northern White Cedar S/C/G Landsatand aerial photog"raphyacquisition. The PSUswere CutoverArea Shrub/Cutover/Grass Lowland Grass Iocatedon uscs 7.S-minutequadrangles, and the PSUand pc Upland Grass cover type boundarieswere digitized using ArcAnfo. Ref- Brush erencedatasets were then rasterizedand linked to attribute Cropland,Pasture a."i^,,1r,,"6 Ag data (i.e.,type, size, and density) and registeredto the Land- Urban and Industrial Developed Dev sat image data. RecreationalDevelopment Water Water w Marsh Marsh MA4 SurueyDesign Muskeg Given the emphasison forest areaestimation and satellite data, there is much potential for gains from refinementsin survey design.Designs using large PSUshave been shown to type boundariesand attribute data were approximately$100 an effectivemeans of forest inventory informa- be collecting per Psu. The photographywas essentialfor Iocatingthe Psus tion (Scottef o1.,1983). In particular,Benesslah (1985) has and delineatingcover type boundaries.Its use also expedited shown that such layouts have decidedadvantages for ground the cover type identification. checkingof remotely senseddata. Advantages include ease of field work, variancereduction, and the provision of area data as proportions (fractionsof area)rather than binary (0-1) LandsatTM Classification Methodology and Results counts, Proportion data facilitate the use of remote sensing Although more than 100 land-coverclasses resuited from data becauseit is relatively insensitiveto scaleproblems. interpretationof the aerial photography,it was apparentthat The sampling designused was a single-phasedesign involv- such a detailedclassification was not possiblewith the satel- ing Landsatclassifications of the entire areaand ground lite data. In many casesthere were not enough pixels in the checking of a sampleof large PSUs.This exploited the synop- referencedata for training and classification.In other cases, tic coverageof satellite-acquireddigital remote sensingdata, initial testsshowed no promise in separatingcertain classes with the advantageof using all of the pixels in the popula- (e.g.,aspen versus birch). The reference(ground) data classes tion for making areaestimates. lvere then condensedinto 11 (six forest,five non-forest) The psus were photo interpreted, as well as observedon cover types basedon spectraland forest managementconsid- the ground, to assessforest type. The PSUsmight be 10 to erationsas Iisted in Table 1. 100 or more acresin size;however, the size used here was a In addition to the six reflectiverM bands, a set of six consistentBB acres (equivalent Io 17 by 23 TM pixels). The vegetationindices (vts) were added to form 12 featuresets ground sampling (field visit) establishedan accuratetype used for classification.The vls were chosento be a repre- map for eachPSU, i.e., polygonsin the PSUwere identified sentativesample of the possiblesuch indices. The first set of and labeledas to cover type. This meant usageof cover types indices consistedof the^firstthree TasseledCap components and boundariesaccording to standardsof interest to forest - greenness,brightness, and wetness - with coefficients management(Table 1). Psuswere distributed systematically given by Crist and Cicone (1984).While greenndssis the (with a random start) acrossthe survey unit (Piate1BJ. The most indicative of vegetationcover, brightness is also related ground samplePSUs were then used to train the classifier, to vegetationcover. Wetness is relatedto moisture content and subsequentlythe classificationsbecame the dependent and may be useful for wetland delineationand/or upland variablesfor regressionestimates of the population propor- versuslowland forest types.The secondgroup of vls were ra- tion of the PSUsin the various cover types. tios that have been found to intensify forest canopy charac- The ground PSUswere very inexpensivelymapped and teristics.This group consistsof rv+/rug, TM4|TM2,and rlts/ field checkedusing large-scalecolor infrared aerial photogra- TM4ratios. Jensen (1S83), among others, states that TM4/TM3 phy. Total costsof photographyacquistion, photo interpreta- provides information with respectto vegetationand canopy tion and preliminary cover type of the PSUs,field verification condition and that TM4lTMzmay be a promising featurefor of cover types and boundaries,and digitizing Psu and cover wetland identification. The TMs/TM+ratio has been used in

PE&RS Plate 2. Finalclassiflcation of LandsatTM data of five- county study area.

resentedclasses. In most cases,less than 50 percent of unsu- pervisedclasses could be named,and in no casescould classesbe developedfor all targetclasses. An alternative to the supervised and unsupervised ap- proaches is what we have called "guided" clustering. The proceduremakes use of both supervisedand unsupervised techniques, but avoids many of the problems associatedwith each individually. The method makes use of analyst defined training data, in our case, digitized cover type polygons from the interpreted photo plots (esus), to identify image pixels of a single class that are clustered into spectrally homogenous sub-classes.An example of two PSUSwith cover type poly- gons delineated is shown in Plate 2. The processing stream was follows: (1) Delineateimage pixels for targetclass A; (2) IsoDATA,cluster class A pixelsinto sub-classesA1, (A) Mosaicof LandsatTM images with overlaysof Using Plate 1. 42, ...,An; aerialphotography flightlines (N-S lines) and boundariesof {3) RepeatSteps 1 and 2 for all 1t targetclasses; physiographicstrata. (B) Overlaysof forest type polygons (4) Performmaxirnum-likelihood classification using all sub- on LandsatTM imagery for two sample units (left). Loca- classeson the entireimage; tions of aerialphotography flightlines and sampleunits are (5) Collapse (REcoDE) subclasses back to the original 11 target shownon right handimage. classesi and (6) Perform post-processing procedures (e.g., majority filtering). Guided clusteringprovided consistentlysuperior results to any of the other methodstested. The processis highly auto- studies related to conifer canopy structure [Petersonet o.1., mated, so was ideal for large areaapplication. The approach 1986). combinesthe training and classificationapproach with sta- A number of classification processing approacheswere tistical information from the primary samplingunits. evaluated for their utility in large area classification. Both The sheersize of the areato be classifiedcreated many supervised and unsupervised, and a combination of super- obstaclesthat had to be overcome.It was clear from visua] vised and unsupervised, approacheswere examined. Super- assessmentof the Landsatimagery that land-covergradients vised techniques were tietermined to be inadequate for a efsted within the scenes.In addition, atmosphericand number of reasons:extreme forest complexity, narrow cover phenologicdifferences existed from north to south and, to a type spectral separability, and limited potential for auto- lesser extent, from east to west through the study area. To mited processing. Several types of clustering methodologies, compensatefor such differences, physiographic regions de- from standard lsooata clustering (ERDAS,1991) to hierar- lineatedby Wright (7972) wete used to segmentthe study chical strategiesdefining more than 1,000 classes,were area into eight sub-regions (Plate 1A). TM images for paths 26 tested. In all cases,the ability to name the resulting classes and 28 were not segmentedinto sub-regions becausethere was lirnited by the within-class variability to a few well rep- was not sufficient training data for all of the physiographic

290 PE&RS TeeLE2. DrsrRrBunoN(%) or CovenTypEs FoR Ercx PnysroompxrcReeroru ls DerERr.,rtNEoFRoM REFERENCE Drn. SeeTaaLE 1 ron DescRtmoruor CovenTvpe Cooes. Cover Type Class Region LH A/B NH UC BFAVS LC s/G/c Ag D w lwM Agassiz 2.O 44.O 0.0 2.3 3.0 13.3 6.0 3.6 1.0 20.3 4.5 Border Lakes 0.0 22.5 0.0 29 2.O 72.6 1.1 0.0 o.2 31.1 1.1 Laurentian 0.0 40.4 0.7 19.1 4.3 26.5 5.3 0.0 0.0 1.5 2.2 Mille Lacs 4.7 23.9 7.O 0 0.4 13.3 13.4 25.2 1.9 3.0 7.8 Iron Range 1.0 41.2 1.9 2.6 9.8 8.6 2.8 18.8 5.2 o.7 Tamarack 4,7 22.2 4.O 0.0 3.7 34.5 78.2 3.9 4.9 0.1 4.4 LS Highlands 1.1 39.S 11.8 3.0 3.6 10.2 9.4 7.7 5.1 6.4 2.4 Tamarack II 2.7 39.9 1.3 3.3 1.3 19.8 12.4 2.9 1.9 t.J 7.6 regions falling within each image. The resulting cover type ing the referencedata, often does not relate to or capture the distributions for the reference data are shown in Table 2. The variability that is present in satellite imagery. Forest stands distribution of cover tJpes across physiographic regions is tend to be defined by managementconsiderations, whereas not constant, and is actually quite varied, a good indication the multispectral radiances measuredby the TM data are de- that the segmentationscheme accomplished what we had termined by the biophysical properties of the cover type(s) hoped. Each physiographic region was classified using the within each pixel. guided clustering approach described above. The use of the Two additional observationscan be made about classifi- physiographic stratification prior to classification increased cation accuracy. First, while satellite imagery is typically re- the overall classification accuracy by 10 to 15 percent. Once ferred to as having low resolution (at least in comparison to classified, the subregion GIs files were re-assembledinto the aerial photography), it actually has much higher resolution county and suwey unit files. than forest cover t1pe maps. The minimum mapping unit for After classification, the images were majority filtered to our reference data was 2.5 acres, compared to the 1/4-acre remove "salt-and-pepper" artifacts in an attempt to re-create pixel size of the wt data. Many inclusions of spatially small the forest stand (polygon) structure inhspsa[ in the reference iover types were not mapped in the reference data, and, data. Tests against reference data indicated an optimal win- even if correctly classified in the TM data, would show up as dow size between 4 and 5 pixels square. A 5 by 5 window classification errors. Second, at the pixel level the accuracy was chosen for easeof application and to minimize bias. of the reference data is probably no better than 75 to 80 per- The final classifications for the 11 target classesfor the cent. Therefore, it is impossible to achieve measured classifi- entire study area are shown in Plate 2. Overall classification cation accuraciesof more than that. It also can be noted that accuraciesranged from 64 to B0 percent, with averageclass large area classification is a more difficult problem, likely to accuraciesfrom 63 to 76 percent (Table 3). The Kappa statis- result in lower classification accuracy, than found in pre- tic (Congalton and Mead, 1986), which removes the contribu- vious studies which were typically classifications of small tion of correct classification due to chance, ranged from 0.56 areas. to 0.76. Example enor matrices are given in Tables 4 and 5. Overall accuracy of classification of forest, nonforest, and water for these two examples was BOpercent for Koochich- CalibrationofLandsat fuea Estimates ing County and 85 percent for Lake Superior Highlands. The The satellite imagery provided estimates of type acreagesfor overall accuracy of classifying conifer versus hardwood for- the area as a whole (Table 6). However, it is probable that est, along with nonforest land and water, was 79 percent in those estimateshave a bias associatedwith them. The Koochiching County and77 percent for the Lake Superior ground survey Psus provided observationson how the satel- Highlands. Becauseof the necessity to use all of the available lite data classifications compared to ground classifications reference data for classifier training, all of the reported clas- sification accuraciesare for training data. All pixels within the primary sampling units werc included in the determina- Tnet:3. NuMaenor PntuanySnupunc UNns (N) Useo ron CusstnER tion of classification accuracy. Tnerrrr.rclr.ro Suuulnv Evlumott or CusstnclttoltAccuucv. The majority of classification errors for forest classesoc- Average cuned in adjacent classessuch as lowland hardwood and as- Overall Class pen&irch, lowland conifer and balsam fir/white spruce, and Countyor Kappa Accuracy Accuracy northern hardwood and aspen/birch. The classesof agricul- PhysiographicRegion N Coefficient (%t (%) ture, developed,water, and marsh were classified relatively Koochiching County 67 0.61 73.1 63.8 accurately, generally 75 percent or higher. The shrub/grass- Iake and Cook Counties 68 0.60 68.2 68.8 land/cutover class was the most prone to misclassification; it Agassiz 43 0.62 69.2 74.6 was confused with all classes,especially other types of forest Border lakes 38 0.64 72.9 72.O and non-forest vegetation. In general, similar or related Laurentian 22 0.67 75.8 74.9 classeswere more l&ely to be confused with each other than Mille lacs 19 0.76 80.2 75.9 with different classes. Iron Range 31 0.58 67.6 65.6 Part of the misclassification results from the considera- Tamarack 26 0.60 67.5 68.5 Iake ble difficulty in assigning a unique label to each polygon in Superior Highlands 42 0.64 70.3 73.3 the referencedata and pixel in the Landsat data. The tradi- Tamarack II 27 0.56 64.0 73.7 tional concept of a forest stand, which we used in develop-

PE&RS TeeLE4. Cl.cssrRilror.l ERnon Mlrnrx FoR KoocHtcHtNGCoumy. LandsatTM Classes Ground Classes LH BF/1/VS LC S/C/G Ag Water WM LH 789 727 80 138 244 to 7 27 A/B 747 2364 7LL 220 332 59 1B 51 0 BF/'WS 86 287 801 307 78 1 0 LC 16 626 AO 7L477 522 10 18 52 S/C/G ZJ 309 dI 838 4J o I ,a Ag 737 JV J5 169 700 26 0 0 Dev 6 b4 0 I 188 E 0 Water 30 728 25 28 1 z 403 0 MM I 85 25 J+Z 26 1 0 530 lo Cort. 69.9 50.0 63.8 87.4 J/.J / o.J 78.3 78.7 89.1 Overall accuracy : 73.1%,Average class accuracy = 63.8%,Kappa coefficient = 0.61. TeeLE5. Cr-rssrncerrorEnnonMernx FoR LAKE SupeRroR HrcHr-ruo PnysroeRepnrc Recror.r. LandsatTM Classes Ground Classes LH A/B NH UC BF/1/VS LC S/C/G '_oAo Dev Water M/M LH 772 77 ( 0 0 0 22 0 0 0 0 A/B J 4329 OJ 70 LZb JJI 25 AJ L5Z 54 NH ll 370 r667 20 0 23 103 eo 29 0 0 UC 0 777 J90 5 IJ 27 2 7 8 2 BF/\iVS 0 238 404 27 61 J 1/ 2 o LC 0 86 0 7449 76 0 29 3S 11 S/C/G J 20 18 6 727 38 JU c 7 Ao 0 304 77 18 l3 13S 1050 119 1 0 1rt Dev 0 JI 7 68 34 :)u/ 6 0 Water 0 76 0 5 0 0 0 0 CJ 844 19 lv{/M 0 b5 0 8 20 to 0 10 36 289

%6Corr. 91.0 64.7 84.1 68.4 ot.l 84.5 qo.a 88.2 J6. / 78.7 74.5 Overall accuracy = 7O.3%,Average class accuracy = 73.3/o,Kappa coefficient, = 0.64.

for the sametypes, with the ground classificationsassumed proportions,with the number of elementsof eachvector to be truth. Observationsfrom the pSUscan thus be used to being the total number of types being classified(each vector adjust the satellite-data-basedestimates, a procedurewe may have severalzero elements).The elementsof the vectors hope will result in a reduction or elimination of bias. This are proportionsof the total pSUarea ciassified as belonging adjustmentis commonly referredto as calibration. to a particular type. The smallestpopulation (area)of interest As part of the project,Walsh and Burk (1993)compared in our application is the county, with each county having a the classicaland inversemethods of calibratine satelliteclas- number of ground PSuson which classificationresults were sificationswhere the ground samplingunits w6re pixels. recorded.The total population of interest,an FIA survey unit, They found, through extensivesimulations, that the inverse is a set of counties (five in the presentcase) that are contig- method was superior.The choice of the inversemethod of uous and of similar forest composition. calibration was basedon that oreviousresult and more theo- Eleventypes {classesJwere used in classification.If retical reasons.With the inverie method, the satelliteclassi- there are n, ground PSUSpresent in county 1,we can specify fied proportionsin the various types are used as an n1 x 1.1.matrix X for the county that contains the satellite "independent" fwithout error) variablesin the regression- classifiedproportions for the pSUslocated in the county. basedcalibrator, while the ground classifiedproportions act This provides a systemof L1 equationsrelating the true as "dependent" (with error) variables.These are reasonable (ground)type classifiedproportions(Y) to X: roles for thesevariables as the satelliteclassified proportions are fixed once the classifieris trained (resultsare conditional Y,:XP'*e, on that training) and interestIies directly in predicting Y, : XP, * e, ground classifiedproportions (i.e.,truth). Studiesby the f1l USDAStatistical Reporting Service substantiate the use of this regressionmethod over the traditionai direct expansionesti- mator approach(Hanusch ak et aL, 19s2).Findiirgs by Chhi- kara ef o/, (1986)strongly support the use of the regression Y":XP"*e" estimatorover the direct expansionor stratified ratio esti- mators exceptin casesof very poor classificationaccuracies where Y; : vector with nl elementswhere the i'h elementis in the ground sampleunits, the ground classifiedproportion of type i in The unit of observationfor the calibration problem was PSU,; the ground psu. For each psu, we have a vectoi of satellite X : nr X 11 matrix where elementX,1 is the satel- data classifiedproportions and a vector of ground classified lite classifiedproportion of type k in psu;;

292 PE&RS TneLe6. Ur.rceueRerEoLeruosnr ESTMATES or Anen (1ru THousANDs or Acnes)or SrxFonesr eruo Fve Noru-FonesrCusses. County Class Carlton Cook Koochiching Lake St. Louis RegionTotal Lowland Hardwood 6.1 0.0 226.5 0.0 93.6 326.5 AspenrBirch 272.4 439.8 472.3 407.0 7,377.9 2,843.4 Northern Hardwood 61.3 90.4 0.0 47.5 81.0 280.2 Upland Conifer 6.7 140.9 0.0 343.8 365.5 856.0 BalsamFirMhite Spruce 5.1 788.7 107.4 21.4.7 629.7 Lowland Conifer 75.O 811.1 274.6 888.0 2,726.7 Shrub/Grass/Cutover 65.2 18.3 728.6 25.9 399.0 637.0 Cropland/?asture 82.O 0.0 105.6 0.0 20I.7 388.6 Developed./Other 9.4 0.0 7.9 0.0 767.4 784.7 Water 13.5 r23.6 83.9 207.2 433.4 85s.6 Marsh/\r[uskeg t1 1 30.6 qaa b5.z ocn zrrJ.t) Total s60.4 1,033.1 2,078.5 1,466.6 4,316.0 9,394.6

TneLE7. CauaRAreoLeruosnr Esllaares or Ana (rnTsousulos or Acnes)oF Srx FoREsreruo Ftve Noru-Fonesr Cuqsses. County Class Carlton Cook Koochiching Lake St. Louis RegionTotal Lowland Hardwood ,1 0.0 131.6 0.0 34.3 169.0 AspenlBirch 209.3 474.9 548.3 547.5 1,667.4 3,447.4 Northern Hardwood 50.7 67.8 0.0 78.8 J O.+ .JJ./ Upland Conifer 9.1 IJJ./ 3.8 269.2 418.6 836.4 Balsam FirMhite Spruce 2.7 83.9 !44.5 44' t72.9 388.2 Lowland Conifer / q.J 104.1 789.6 306.4 777.7 2,046.3 naan Shrub/Grass/Cutover 83.4 Ll ,O 42.6 415.1 778.7 CroplandlPasture ol,J 0.0 81.1 0.0 724.7 287.1 Developed/Other 13.1 J.D s.3 13.4 202.8 242.2 Water lo.J I IC.J oJ.c 149.3 {JJ.J 799.8 Marsh/\4uskeg 18.0 30.1 27.6 IJ.J 58.6 taJ.o Total 560.4 1,033.1 2,018.5 1,466.6 4,316.0 9,394.6

F; : vector whose 11 elementsare the coefficients are met in this application;thus, ordinary least squaresis an from the regressionof ground classifiedpropor- appropriatefitting criterion. tions in type i on satelliteclassified proportions For notational convenience,we stack the systemof 11. in all 11 types;and equations(Equation 1) for a particular county and write them G1 : vector with n, elementsrepresenting the error as in predicting ground classifiedproportions : from the satelliteclassified proportions. Y, X,P, * e, (2) This systemof equationswas fit separatelyto each of the where the subscriptI denotesthe county. Here Y, and e, are five countiesin the survey unit. Ordinary Ieastsquares was vectorswith 11n1elements, X1 is a matrix with blocks of X used.The results showedthat 85 percent of the variation, on along the diagonal,and p' is a vector of length 121 (the coef- average,in the ground classifiedproportions was explained ficients are allowed to vary by county). by X. With few exceptions,each regression was dominated While, overall, the results of fitting Equation 2 were sat- by the classifiedproportion correspondingto the satellite isfactory,individual equations,where there were few non- type being predicted.However, all elementsof X were re- zero observationsof ground type in a county, had high stan- tained in each equationto insure additivity of the predicted dard errors of prediction, often exceeding100 percent of the proportions.Individual residual plots gaveno indication of estimatedproportion. As an alternativeto calibrating coun- heterogeneousvariance within any particular equation. ties separately,data acrosscounties (within the survey unit) The systemof equations(Equation 1) would appearto be can be combinedto estimatea pooled regressionequation. a seeminglyunrelated regressions problem. That is, it seems The assumptionneeded to justify such an approachis that likely that errorsin predicting the ground classifiedpropor- the coefficienfsrelating ground classifiedproportions to sat- tions are correlated(cross-equation correlations), While ordi- ellite classifiedproportions are similar acrosscounties nary Ieastsquares provides unbiased estimates of p for within a survey unit (nof that the classifiedproportions of seeminglyunrelated regression problems, accounting for the various types are themselvessimilar acrosscounties). cross-equationcorrelations can result in a more efficient esti- Under this assumption,the calibration equationbecomes mate of p. However,Ericksson (1989, p. 45Jhas shown that (1) when X is identical for each equationin the systemand Yr : XrO* vr (3) (2) errors within an equationare homogeneous,seemingly unrelatedregressions is equivalentto applying ordinary least where the d are pooled or survey unit wide coefficients.Each squaresto each equation separately.Both theserequirements type has a separateset of calibration coefficients,but the

PE&RS 293 Tnele8. Covpentsottor FIAxlo CeuaRAteoLenoser TM EsrMArEs(rr.r Txousenos oF AcREs)or CorurreRs,HARDwooDs, eno Tour Fonesrhno. County

Class Estimate Cook Carlton Koochiching Lake St. Louis Region Total

Conifer 9IA 79.6 376.9 Y/J.] s59.5 7,253.1 3,244.2 TM 86.3 323.7 937.9 61S.8 1,303.2 3,270.9 Hardwood FIA 273.7 478.O 639.5 1,970.6 4,118.9 TM 263.7 542.7 679.9 626.3 1,758.1 3,870.1 Total Forest FIA 352.7 854.9 7,732.8 1,199.0 3,223.7 7,363.1 TM 349.4 866.4 7,677.8 7,246.7 3,061.3 7,I47.7 coefficientsfor a type are constantacross counties. Equation the ForestService FIA estimates range from 0.BSto 2.39 per- 3 was also fit to the PsUdata. cent at the county level and 0.57 percentat the region level. If x, is the vector of satelliteclassified proportions for There are severalsources or causesfor differencesin the county / (classifying the entire land area in the county), we two estimates.The first is that, in three cases,the available have two calibrated estimatesof percent land area by type: referencedata did not include samplesof all rn classes;in other words n, as reportedin Table 3, was too small. A l,: xfi,and,y,* : y,$ larger, more fundamental problem is that the forest lands in northern Minnesota are very complex ecosystemswith a The two calibrated estimatesyr and.y/ can be combined wide range of variability in composition and uniformity. to produce an estimate that should have lower error than This heterogeneitylimits the acCuracyof the photo intirpre- either of the two individually (Burk and Ek, 1982): tation and field checking and, in turn, the reference data. i:wy,+(1 - (4) This, in turn, affects the accuracy of class labeling. In many respectswe are attempting to classify continuous data into Bothyl andyl* are additive; the predicted type propor- discrete classes;doing so results in errors in the areas of tions add to 1. For the combined estimate to be additive re- transition from one type to another. The Forest Service rec- quires that w be a constant acrosstypes. The optimal razfor a ognizes the complexity of the forests and the presenceof type is a function of the ratio of the prediction variances of mixtures in its definitions of classes.For example, the de- y1 andy,* (Burk and Ek, 1982).Computation of estimatesof scription of red pine is, "forests in which red pine comprises those variances indicated that their ratios were relativelv a plurality of the stocking; common associatesinclude east- constantacross t5pe. An averagevalue (0.65)was usedio ob- ern white pine, jack pine, aspen,birch, and maple." tain final estimates:this gives weights 0.606 and 0.394 f.ory1 and yy*, respectively.The final calibratedacreages of each cover type are given in Table 7. PSU-BasedEstimation of FIA Cover Type Areas While the satellite data classifications weie limited to six Evaluationof landsat Area Estimates forest types, forest managementneeds are often more spe- By aggregatingthe rn statistics of cover type acreagesfrom cific. However, the Y, need not be constrained to the same Miles and Chen (1992),we are able to make a rough compar- classesused for the imageclassification; instead, they can be ison of estimatesfrom the calibrated Landsat classifications defined as any ground truth variable. For example, the pSUs and the FIA statistics for conifer, hardwood, and total forest- used in this study were originally typed as 14 forest types. land at the county and region (survey unit) levels (Table B). To estimate FIA cover type acreagedirectly, we define Y;, i Becauseof differences in definition of classesin the two in- : 1, ..., 14, as the prof6rtion of-a psu in-rrn, type i, The ventories, it is difficult to make comparisons of more specific estimationmodel is then the sameas Equation 1, Again, this cover tJpes. The problem is that, on the one hand, the nA system of equations is additive. cover t1pe area statistics are typically reported only for com- A preliminary trial of the psu-basedapproach on the mercial forests or "timberland" (defined as forest land capa- five-countyAspen/Birch FIA survey unit led to comparisons ble of producing 20 cubic feet per acre of industrial wood of total forest area and area by cover type with FrA ;tatistics. crops ...) and, therefore,do not include detailedbreakdowns Study estimatesfor conifer, hardwood, and total forest cover of the cover types for non-commercial, as well as reserved, type aggregationswere -8.4, -0.5, and -3.0 percentdiffer- forest land (reservedland includes state parks and the ent from FIA values. However, further cover type breakdowns BoundaryWater CanoeArea Wilderness).On the other hand, often showedmuch larger differences.Inspeclion of results the Landsatclassifications are for all forest lands. further suggestsdifferences in definition and procedure for In comparing the results of the two surveys, we have as- identifying cover types as a major factor in the lack of agree- sumed the same proportions of cover types for unproductive ment. The cover tJpes on pSUsin this study were identified and reserved lands as for the timberland. However, it is well by photointerpretationfollowed by field checking.However, understoodthat much of the unproductive land is in low- FIA cover types are determined by an algorithm applied to land conifer types such as black spruce.We have, therefore, ground plot tree data (Hansenand Hahn, 1982).Studies by restrictedthe comparisonto conifer, hardwood, and total for- JaakkoPdyry Consulting,Inc. (1992)have noted that changes est.At the region ievel, the differencesin the two estimates in the algorithm applied to this plot tree data can lead to ma- (with FlAas the standardor base)are *0.8, -6.0, and -3.0 ior changesin estimatedacreage. Consequently, the ability to percentfor conifer, hardwood,and total forest,respectively. compareresults to the FIAacreage for the 14 cover types is Differencesin total forest areaestimates at the county level limited in this caseby definitional and proceduralfaitors. range from -5.0 percentto +3.9 percent.The samplinger- Analysesto sharpenthese comparisons are part of a continu- rors for FIAestimates of timberland (not total forestland)for ing study.

294 PE&RS pling error (or variance) as a function of the cover type pro- 0.0400 portion and the area or size of a PSUfor any given classifier

0.03s and costs. Additional important aspectsof the psu-baseddesign are 0.0t00 its statistical simplicity and its potential utility for monitor- p o.ort ing landscape patterns. The simplicity comes from observa- IJJ tion of operational sized land units and the fact that it f; o.ozoo requires only single phase estimation and ordinary least- E c squaresprocedures for areaestimates, The power of the ap- g 0.01J0 U) proach is that it uses as covariates o1l of the pixels in the 0.01m county or areaof interest,yet it doesn'tnecessarily require high accuracy in the satellite classification. Additionally, one 0.0050 could improve estimatesby the methods described in the

0.m earlier calibration section.Statistically, one may view the 0.0m0 0.1m0 0.20m 0.1m0 0.{n0 0.5000 0.6(m PSU as simply a large number of large imageclassification Proportionol Area plots. The precision of psus overall will be less than the sameacreage of small plots distributed randomly over the Figure2. Standarderror comparisons for estimatesof pro- survey unit. However,the cost of assessingthe PsUson the portions of 74 nA covertypes, plus non-forestcategory for ground is modest,and the realism that step adds to classifier 163 psus in St. LouisCounty. n (163) plots,----- training can provide significant gains for the survey design. : n x acresof PSU.i : actualPsus. ChangeDetection Using Multidate Landsat Data Renewablenatural resourcessuch as forestsare continually The approach is described further for St. Louis County. changing.Some forest cover modifications are human-in- Figure 2 shows the standard errors of the mean proportions duced, such as harvest,while others have natural causes, for 14 standard FIA cover types, plus an other/non-forest cate- such as insect or diseasedamage. The rate of changemay be gory, as estimated from the 88-acre PSUs,in comparison with abrupt (e.g.,logging) or subtle/gradual (e.g.,growth). The po- theoretical standard errors. The theoretical standard errors tential of using satellite data to detect and characterize were developedin two ways: (1) assumingeach Psu location changesin forest cover depends on the ability to quantify was instead the location of a standard rn plot (covering ap- temporal effects using multitemporal data sets. As a part of proximately one-acre)and (2) a lower bound assumingthe the project research,we investigatedthe potential of multi- PSUswere broken up and distributed as n x BBone-acre FIA temporal LandsatTM for forest cover changedetection (Cop- plots. The fact that the standard errors for the PSUsample lie pin, 1991). ipproximately midway between the two theoretical cuives rM data, along with detailedground referencedata, for suggeststhat the PSUsale far more effective at error reduc- three different years (19s4, 1986, and 1990)covering a 400 tion than a sample of n FIA plots and substantially less effec- km, (five townships) test site in Beltrami County were ac- tive than the much larger number of plots that might have quired. To minimize sensor calibration effects and standard- been distributed at random had the PSUsbeen broken up and ize data acquisition effects, the TM data were calibrated to checkedas one-acrecomponents. exoatmospheric reflectance following the algorithms of Mark- Ultimately, the choice of survey designmust be consid- ham and Barker (1986).After geometricrectification and reg- ered on a cost basis.While the PSUsare clearly less efficient istration, an atmosphericcorrection routine was applied, than an equivalentacreage of random plots, the travel costs combining two major componentstatmospheric normaliza- are dramaticallyreduced (fewerPSUs than plots), and the tion and transformationto ground reflectance.The normali- cost of a PSUneed not be much if any more expensivethan zation consistedof a statisticalregression over time, based the current rn plots. The latter typically cost $150 to $300 on five spatially well defined landscapefeatures with un- each and involve one to two people and approximatelyone changingspectral-radiometric characteristics. Subsequently, a day of time, including travel. Of that day, much of the effort dark object subtractiontechnique for atmosphericscattering goesinto establishingand measuringa ten-point pSUof small plots on an acre,We proposeinstead that those small plots be spreadacross the Psu and be used to verify the cover type coefficientsfor all bitemporalband pairs rangedfrom 0.9884 of the polygonson the Psu. Use of large PSUsis not unlike to 0.9998;an examplefor tv+ for 1986-90is shown in Fig- what has been done in Scandinavia(Kuusela, 1978; Svenson, ure 3. 1980) and what was found as an optimal "super PsU"or For each time interval (two, four, and six) years,14 cluster plot by Scott et o/. (1984). changefeatures were determined.The changefeatures in- A spreadsheetanalysis of alternative forest inventory de- volved sevenvegetation indices and two changedetection al- signsis currently being developed,including this Psu design, gorithms (standardizeddifferencing and pairwise principal the current multiphaseFIA procedures, and other designs. components).The best four featuresfor classificationwere That effort will also consider optimal PSUsize. However, the selectedbased on J-M distancecalculations of the best mini- optimal Psu size will also dependon practical concernsfor mum separabilitybetween change signatures. A maximum- being able to locate it and potential data analysisas de- likelihood classifier was used for the final classification. scribedbelow. For analysis,precision of this approachwill Classification accuracy and areal correspondencewere evalu- be developedempirically from theseresults and additional ated from contingency matrices and Kappa coefficients of PSUsizes to be tested.It is probablethat a planning model agreement. useful to inventory design and analysis will express sam- The results (Figure 4) demonstrated that disturbances

PE&RS preprocessingsequence summarized above is critical to the forest cover monitoring; similar preprocessingand calibra- tion proceduresare being used in the large scaleapplication 3eo of this technologyby the MinnesotaoNR describecl below. o

o o F60 OperationalUse of LandsatData for ForestInventory in go Minnesota o (E40 As a result of the researchdescribed above, the Minnesota DNRand the U.S. ForestService have iointlv undertakento E Y=-8.9555+1.1013x 6 developan Annual ForestInventory System(nrts) basedon o 20 Re= 0.996 annual samplingof existing ForestInventory and Analysis (rn) ground plots in Minnesota (Befortand Heinzen, 1992; Hahn ef al., 7992).Satellite remote sensingplays an impor- tan-trole in the .a-r'tsplan, and initial Landsatdata analysisis Band 4 Refloctance,1990 well under way. Beciuse of its annual schedule,the nirs pla_nrequires current, inexpensive,large-area imagery to- Figure3. Exampleof the relationshipbetween getherwith low-cost,but robust, interpretationmethbds for band-specificscaled reflectances of calibration both stratification and disturbance detection. The research by sitesover time. Coppin (1991)has indicated that computer analysisof multi- date Landsatdata (summarizedabovei offers a cbst-effective alternativeto relianceon aerial photography. The objectiveof arIS is to createand maintain a current and other changescan be detected very accurately if catego- and continuously updatedrn database.Under the proposed rized in classesthat relate to their effect on the forest can- system,a relatively small proportion of nrRplots will be cho- opy, and if their size exceedsone hectare.Pixel-based sen eachyear for field remeasurement;information on other classificationaccuracies are shown in Table g for thematic plots will be updatedby use of forest growth models.Selec- classificationof pure pixels and for classificationof all pix- tion of plots for measurementis to be basedon (1) likelihood els including mixed or boundary pixels. Foreststands as the of plot disturbancesince the last field measurement,and (2) classicalmanagement units were ascertainedto be too spec- requirementsof a 2O-yearsampling rotation in which all trally heterogeneousto have the change phenomena differen- plots are ultimately field-visited.Satellite remote sensinghas tiated at that level. However,for the three classes,canopy two roles: first, to stratifv a statewidearrav of some45.000 decrease,canopy increase,and no change,the methodology establishedFIA plot locaiions as a meansio reduce the vari- correctly identified 774 out of 759 stands(94 percent)re- ance of areaand volume estimates,and second,to estimate ported as disturbed over the six-year interval, indicating that the likelihood of changeor disturbanceon each plot in order the changeevent was portrayedin a majority of the stand's to prioritize plots for field measurements.Aerial photogra- pixels, A detailedanalysis of the classificationserror struc- plry has been customarilyused for both thesepuiposes-, but ture at the pixel level, togetherwith a post-classificationas- ottaining and interpreting statewide airphoto ioverage on sessmentof a large sampleof commissionerrors and the schedulerequired by arIS is impraciicable.The use of omission errors,indicated that a largemajority of the classi- satelliteimagery is expectedto reduce costsand allow an in- fication errors might not have been errors at all, but instead creasein the frequencyof inventory updates, emanated generalization from the of pixels to the stand level _ The generalremote sensingapproach is to move through in the reference data generation. The results show that the the stateon a four-yearrotation, coveringone of the four rna-

til;;;;;.g, -#- r^.c"*.. T lE" ## i*

Reference Map Figure4. Comparisonof Landsatclassification of changeevents between 1984 and 1990 to the referencemap of JonesTownship.

296 PE&RS TnerE9. Suvvenv oF SrArsrcs EvrLuntrncCnaruce De-recrroru on the geographicaidistribution of the cover types that is not Cl.lssrRcnttotrtAccunncv. availablefrom the conventionalFIA survey. On the other on timber volume Cartographic hand, the FIAsurvey providesinformation Thematic Accuracy* Accuracy** which was not obtainedat leastwith this classificationof Landsatdata, Thus, the two approachesto inventory are Time Interval Overall Kappa Overall Kappa complimentary,with each providing information not avail- (Years) Correct(%) Statistic Correct(%J Statistic able from the other. 2 97 .76 93 .oa A trial of estimatingthe areasof t+ traditional forest 96 .oJ 89 .68 cover types as determinedfrom sample B8-acrePSUS as a o g4 .82 o/ .6S function of the six Landsatforest classesusing a systemof additive linear equationswas also conducted.The results in- *Classificationof purepixels; **areal correspondence of all pixels. dicated that the PSU-basedapproach can provide gains in precision,but comparisonswith FIA statisticswere hindered jor year, geo- forestinventory regionseach For each region, by differencesin definition of cover types by the FIA and this late referencedLandsat data of the most recent summer date siudy. This type of psu-basedestimation is potentially very (Work is obtained. on the Aspen-Birch{unit 1Jregion is cur- cost-effectiveand provides data of increasinginterest to the rently underway using Landsatdata acquiredin 19BBand assessmentof cover type and land use patternsover land- 1992.)After preprocessing,including rectification,radiomet- scaDes.^ ric calibration,and atmosphericnormalization, a general Changedetection or disturbanceclassification involving performed Iand-coverclassification (stratification) is across multidate imageryresulted in overall classificationaccura- multi-county survey units, Becauseof the difficulties in cies of greaterthan 90 percentfor the time intervals of two, forest achievingaccurate Landsat classifications of cover four, and six yearsfor the classescanopy decrease,canopy (stratal types,only broad categories of water, agriculture, increase,and no change.The successrate for the detectionof (e.g., etc.), other nonforest developed/urban,clouds, conifer stand-basedcanopy changeevents over the six year interval The im- forest,and hardwood forest will be mapped. new was 94 percent,The key to obtainingthese results was a rig- previous ageryis then registeredto that of the iteration and orous approachto reflectancecalibration and normaiization vegetation analyzedfor change.Based on changesin the in- for atmosphericeffects. probability gen- dex (e.g.,greenness), a changeranking or is The project results have provided the basis for the Min- eratedfor the pixels in the two forest classes.Digitized nesotaDeoartment of Natural Resourcesand the USDAForest queried Iocationsof the rn plots are then for stratum iden- Serviceto define and begin to implement an annually up- tity and changeranking, Thesetwo data elements,together dated statewideinventory systemwhich utilizes multidate with areaexpansion factors for all cover classes,are entered LandsatTM data to detectchanges in forest cover.Landsat into the plot databasefor use in an algorithm that selects TM imageryacquired at four-yearintervals will be used to plots for field measurementsin the following seasonor for detectmajor changesin forestinventory plot characteristics. projection forward by a forest growth model. Annual field The likelihood of changeas determinedfrom the satellite imageprocessing ac- measurementswill serveas a check on data will be used to determinewhich plots should be revis- curacy. ited for field measurement,The Seneralapproach will be to classify one of the four major forest inventory regionsof the stateeach year. Forestgrowth models will be used to project Summaryand Conclusions in a given year. The objectiveof this researchwas to developand test the use the growth of plots which are not measured part of the system, of multispectralsatellite data togetherwith improved classi- Satellite-acquireddata are an integral and datebasetech- fication and sampling designsto inventory the forest re- along with model predictions,sampling, first stateto incor- sourcesof northeasternMinnesota. Two designalternatives niques.We believethat Minnesotais the inventory were considered:one basedon PSUsampling conceptsand a porate satelliteremote sensinginto its forest could easily be modi- secondthat considereddisturbance classification as the basis system;if successful,the techniques for stratified,two-phase sampling. Classification accuracies fied for implementationin other states. of up to 75 percentfor six forest classesand five nonforest classeswere achieved.Misclassification tended to be be- Acknowledgments tween similar-relatedclasses. A major contributing factor to The support of the National Aeronauticsand SpaceAdminis- the difficulty in classificationis the fact that the majority of tration, NRA-87-106(Grant NAGW-1431; NRA 87-106)' is grate- forest standsare complex mixtures of two or more species fully acknowledged,along with that of the University of which may also differ in size, density, crown closure,and MinnesotaAgricultural Experiment Station and Department a8e' of ForestResources, and the MinnesotaDepartment of Natu- An inversemethod of calibration was used to adjust the ral Resources,Division of Forestry.The contribution of the classificationsfor classificationbias. At the survey unit level, Beltrami County Land Departmentto developmentof refer- the resulting estimatesof forest land areawere 3 percentless ence data setswas indispensablefor the changedetection than comparableForest Service estimates. Agreement be- work. Mark Carterprovided the photo interpretation,and tween the two surveysat the county level rangedfrom -5.0 field checkingwas conductedby Dean Halvorsonand Mi- to *3.9 percent.The differencein estimatesis attributed to chael Smith. Forestcover type maps used in the initial de- differencesin definitions and approachesused in the two velopment of training and classificationprocedures were surveys,as well as to the complexity and variability of the prorridedby the MinnesotaDepartment of Natural Resources, forestlandscape. Aithough the forest cover type estimates usnn Foreit Service,and St. Louis County Land Department. were somewhatless accuratethan hoped for, the LandsatTM Publishedas MinnesotaAgricultural ExperimentStation, classificationshave the advantageof providing information ScienceJournal Series Paper No. 20,961.

PE&RS References Hanuschak, G.A., R.D. Allen, and W.H. Wigton, 1982. Integration of landsat data into tle crop estimation program of USDA,s Statis- Befort, W., and D.F. Heinzen, 7992. tlse of Satellite RemoteSensing tical Reporting Service, Proceedings, Machine processing ol Re- for ForestInventory by the Minnesota Department of Nafural motely SensedData Symposium, Purdue University, W.- Resources,Technical Report, lvlinnsss[a DNR, Division of For- Iafayette, Indiana, pp. 45-56. estrjr, ResourceAssessment Unit, Grand Rapids, Minnesota, 28 Horler, D.N.H., and F.f. Ahern, 1986. Forestrv information content p. of thematic mapper data, IntL l. of RemoteSensing, T:4OS-428. Benesslah,D., 1985. ForcstArca Estimation tlsing Cluster Sampling faakko Priyry Consulting, Inc., 1992. 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