Improved Forest Classification in the Northern Lake States Using Multi
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l PEER.REVIEWED ARIICTE lmprovedFotest Glassification in the NorthernLake States UsingMulti-Temporal Landsat lmagery Peter T. Wolter, DavidJ. Mladenoff,George E. Host, and ThomasR. Crow Abstmct to the MSSsensor (Williams et al., 1,984:Toll, 1985),The ad- (band to 1.75pm, Forest classifications using single date Landsat rM data have dition of two middle infraredbands 5, 1..55 contentof been only moderutely successful in separating cover and band 7,2.o8 to 2.35pm),sensitive to moisture forest (Tucker, Hunt ef al,, tgBZ; types in the northern Lake States region. Few regional vegetation 1980; Ripple, 1986; forest to im- classificotions have been presented that achieve genus or Hunt and Rock, 1gB9;Wolter, 1990),has been shown prove (Toll, 1sB5; Bensonand species level accuracy. We developed a more specific forest classificationresults forest L990; Moore and cover classification using rtr't data early summer in con- DeGloria, 1985; Stenbackand Congalton, from of the TM junction with uss dates to capture phenological changes Bauer, 1990).However, the increasedresolution four classificationsof suffi- of different tree species. Among the 22 types classifed, sensorhas not resulted in forest cover forest (i,e., to warrant practical use multi-temporal image analysis aided in separating 1.3types. cient detail Anderson Level III) (Skidmore and Of greatest significance, trembling aspen, sugar maple, nor-th- of this technologyby forestlandmanagers multi-temporal or multi- ern rcd oak, northern pin oak, black ash, and tamarack were Turner, 19BB).Classifications using potential for higher forest classifi- successfully clossified. The overoll classification occuracy phenological imagery have cation precision over single-dateclassifications (Schriever was 83.2 percent and the forest classification accuracy was and Congalton,1993). In this paper we develop an applica- 80.1 percent. This approach may be useful for broad-scale tion of this multi-phenological approachto classify dominanl forest cover monitoring in other areas, particularly where an- cillary dato layers are not available. forest specieswithin northern Lake Statesconditions. 0bjectives Introduction The oblectivesof this study include Forest cover type mapping in the northern Lake States(Min- nesota,Wisconsin, and MichiganJ using spacebornesensors o Developinga forest classificationwith dominant tree species has been a forest managementgoal since the launch of Land- Ievel precision within northern Lake Statesconditions, phenologicalevents sat-1 on 23 1972. Forest classificationsof large regions o Using ltss digital data to capture major July of hardwood forest cover tyPes, with Anderson Level III precision (Anderson et aL,,L976) are o Reassessingthe utility of vss data for multi-temporal or especially neededto assistlandscape-scale analysis and man- multi-phenologicaiforest classifications,and agementobjectives (Mladenoff ef 01.,1993; Mladenoff and o Determiningthe practicality of a layered classificationap- Pastor,1993). Unfortunately, detailed level III forest cover proach utilizing image ratioing and ratio differencingtech- mapping efforts using a single date of LandsatMultispectral niques for multi-temporal image analysis. Scanner(lrss) data have been largely unsuccessful(Mead and Meyer, 1977;Roller and Visser,1980; Downs, 1gB1), Background Moore and Bauer (rggo) concluded that forest heterogeneity There are few accountsof researchwhere TM data have been in northern Minnesota and the suboptimal spectraland radio- used to classify northern mesic and boreal Lake Statesfor- metric resolution of the MSSsensor preclude detailed classifi- ests.Studies that used Tv or Thematic Mapper Simulator cation. The Thematic Mapper (rv) sensorsaboard Landsats4 (rus) data in this region have covered small areas(Shen ef and 5 (launched in 1982 and 1.984,respectively) provide en- o/., tgB5; Hopkins et ol., 1.sBB;Moore and Bauer,1990; Bol- hanced spatial, spectral,and radiometric resolution superior stad and Lillesand, 1992) relative to the 34,225-Wrfcoverage of a full Landsat scene. Using airborne TMSin northern Min- P.T. Wolter and G.E.Host are with the Natural ResourcesRe- nesota,Shen ef o1.(1985) achieved 84.2 percent accuracyfor searchInstitute, University of Minnesota, Duluth, MN 55811. five forest species:red pine (Plnus resinosa),iack pine (P. banksiana),black spruce (Piceamafiana), paper birch (Be- D. f. Mladenoff, formerly with the University of Minnesota, tula papyfifero),papynlera), and tremblitremrtltng aspen lropulu(Populus tremuloides). Duluth, Natural ResourcesResearch Institute, is now with Theii 23-km'zstudy area was ideal as it contained mostly the Wisconsin Departmentof Natural Resources,and Univer- sity of Wisconsin-MadisonDepartment of Forestry,Madison, PhotogrammetricEngineering & RemoteSensing, wI 53706. Vol. ot, No. 9, September1995, pp. 7129-1.1-43. T.R. Crow is with the USDA Forest Service,North Central 0099-111 2/95/6109-1 129$3.00/0 ForestExperiment Station, Forestry SciencesLaboratory, O 1995 American Society for Photogrammetry Rhinelander,WI 54501. and RemoteSensing PE&RS PEER.REVIEWED ARTICTE pure, spatially homogeneouscover types. Furthermore,be- forestedarea near Ottawa, Canada.For discrimination of co- causethe TMSinstrument was flown at an altitude of 72OO niferous forest, deciduous forest,and agricultural land using m, atmosphericaffects may have been negligible. Buchheim a maximum-likelihood decision rule. thev found that three et al. (1985),using simulated spor (SystemeProbatoire d'Ob- dates of MSSimagery from |une. September,and October servation de Ia Terre) data of a 60-km' study area in north- provided the best results (84 percent overall classificationac- western Wisconsin, were able to visually discriminate curacy) over all other single- or multiple-date classifications Anderson level III (species)forest types. However, while tested.The OctoberMSS scene captured peak senescencefor computer-aidedclassification (maximum likelihood) was ex- most hardwoods while the |une MSSscene captured hard- cellent at level I (96 percent overall accuracy)and level II wood leaf flush. Kalensky and Scherk (tgzs) concluded that, (91 percent overall accuracy),level III classificationprecision although the October,fune, and SeptemberMSS scenes indi- (90 percent overall accuracy)was limited to lowland types: vidually produced low overall classificationaccuracies (67 white-cedar-balsamfir (Thuia occidentolis-Abiesbalsamia), percent, 69 percent, and 81 percent, respectively),their col- tamarack (Larix laricino), and black spruce. Hopkins ef 41. lective use mitigated the effectsof individual image noise. (rgaa) used Tru data of a 15-km' study area in northwestern Beaubien (1979)concluded that comparing or superimposing Wisconsin and reported Anderson Level III accuracyfor red MSSimages taken at different seasonsprovides better con- pine and jack pin-ebut only Level II accuracyfor remaining trast among certain types of vegetationin easternQuebec. forestclasses. Hopkins ef o1.,(1988) and Shen ef 01.(1985) Using rrassdata for a classification of the Crater Lake Na- consideredconditions in their respectivestudv areasunre- tional Park region, Walsh (1980)found that Septemberim- presentativeof typical northernLike Statesfoiest cover. agery provided more information than summer MSSdata due In northern Wisconsin, Bolstad and Lillesand (19s2) to the phenological condition of vegetationand the Iower combined a priori information (soil and terrain position) and sun an8le. TM data in a maximum-likelihood classificationof two study Conversely,Kan and Weber (1978) determined that there areas(each 300 km'z).Pooled classificationaccuracy for one was no clear benefi,t of multiple-date classifications over sin- of the areasreached 94 percent for northern hardwoods, red gle-dateclassifications using MSSdata for nine broad vegeta- pine, jack pine, pine/haidwood. upland brush, lowland coni- tion communities acrossthe United States(including central fer, lowland brush, Sphagnum,lowland vegetation,crop/pas- hardwoods, northern hardwoods, northern conifers, and bo- ture, soil/urban, aquatic vegetation,and water, an increaseof real). Nelson et al. (1984)stated that senescentimagery 24 percent compared to the same area classified without o should be avoided for forest classifications in New England. priori knowledge. However, genus or specieslevel discrimi- Their imagery recorded the later stagesof senescencewhere nation was not obtained for most forest types. The ancillary many forest standswere leafless.Similarly, Toll (1985)in a data they used were manually digitized or scannedfrom 1: Maryland study used both November (uss and ru) and luly 24,000-scaleU. S. GeologicalSurvey maps (terrain position) (uss and ru) data in a comparisonbetween classificationpo- and 1:2o,0oo-scaleU. S. Soil ConservationService soil sur- tentials of the two sensors.ToU (1985)concluded that the vey maps. Unfortunately, these types of ancillary data are November TM classificationaccuracy was not significantly not yet conveniently available in contiguous digital form for better than MSSaccuracy. This result was attributed to fall many areas(Mladenoff and Host, 1994).To manually digitize color variability and foliar loss in the November imagery, or scan these data layers for large regional classifications Their November results also suggestthat MSSdata may be would be an enormoustask. Multi-temporal image analysis superior to TM data when analyzing senescentimagery. That provides additional forest cover informition without reiiance