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Evaluating Seasonal Variablity As an Aid to Cover-Type Mapping From PEER.REVIEWED ARIICTE EvaluatingSeasonal Variability as an Aid to Gover-TypeMapping from Landsat Thematic MapperData in the Noftheast JamesR. Schrieverand RussellG. Congalton Abstract rate classifications for the Northeast (Nelson et ol., 1g94i Hopkins ef 01.,19BB). However, despite these advances, spe- Classification of forest cover types in the Northeast is a diffi- cult task. The conplexity and variability in species contposi- cific hardwood forest types have not been reliably classified tion makes various cover types arduous to define and in the Northeast. Developments within the remote sensing identify. This project entployed recent advances in spatial community have shown promise for classification of forest and spectral properties of satellite data, and the speed and cover types throughout the world, These developments indi- potN/er of computers to evaluate seasonol varictbility os an aid cate that, by combining supervised and unsupervised classifi- cation techniques, increases in the accuracy of forest to cover-type mapping from Landsat Thematic Mapper (ru) classifications can be expected (Fleming, 1975; Lyon, 1978; dato in New Hampshire. Dato fron May (bud break), Sep- the su- tember (leaf on), and October (senescence) were used to ex- Chuvieco and Congaiton, 19BBJ.By combining both plore whether different lea.f phenology would improve our pervised and unsupervised processes,a set of spectrally and informationally unique training statistics can be generated. ability to generate forest-cover-type maps. The study area covers three counties in the southeastern corner ol New' This approach resr-rltsin improved classification accuracv (Green Hampshire. A modified supervised/unsupervised approach due to the improved grouping of training statistics was used to classify the cover types. A detailed accuracv os- and Teply, 1991). This studv will utilize tlvt satellite data sessment was perfornted to evaluate the clossification. The and a combined classification approach to help determine if results indicate that specific northeast hardwood species con it is possible to discriminate between specific northeastern be identified ond that tinte of the yeor can significanily offect hardwood forest types. the cover-type classification accuracy. In addition, few studies to date have employed satellite imagery taken during autumnal senescence.Autumn data sets have been shown to increase accuracies in hardwood Introduction forest type delineations when applied to aerial photography A relevant and accurate fbrest cover-tvoe and/or land-classi- (Ecler, 19Sg). It is also possible that imagery acquired in the fication system is essential to providing information for effec- spring, at or shortly after bud break, may provide advan- tive management of natural resources.Research aimed at tages for specific hardwood species delineation. Therefore, developing methods for reliably classifying forest cover and/ this study compared classification accuracies for three tem- or habitat types dates back many decades and continues to poral data sets (autumn, spring, and summer) to determine (Heimburger, this day 1934; Westveld, 1952; Damman, 1964; if seasonal variabilitl' significantlv affects classification ac- Pfisteret aL.,1977:Eyre, 1980; Leak, lgaz; Smalley, 1986). curacY. Three approaches to the classification of habitat types have been outlined: biophvsical, forest type, and forest type/forest ClassificationSystem soils classifications (Leak, 1982). The single factor important For information generated bv satellite imagerv to be useful, a to all three approaches is the classification of forestland into classification svstem which utilizes this information must be specific cover types. Satellite imagery has been demonstrated developed. If the results of a classification are to be of value to be a cost effective method fbr classifying forest cover to potential users, it is also important that the classification types throughout the world (e.g.,|oyce, 1978; Kushwaha, scheme be well defined, reievant, understood, and accepted. 1990; Green, 1990; Schardl et aL.,1990;Congalton et o1., To deveiop a relevant and useftii classification, project objec- 1993t. tives and data limitations must be determined. In the United States,remote sensing projects involving One limitation of tM satellite imagery when compared to the classification of forest types have typically focused on aerial photographv is that spatial resolution cannot be con- forest tvoes in the South or West. Recent advances in tech- trolled. To identify individr,ral tree species r-rsingaerial pho- nology *hich have improvecl the spatial, spectral, and radio- tography, a scale of 1:8,000 or larger seems appropriate metric properties of Landsat Thematic Mapper (rv) .satellite (Ciesla, 19sg). The small scale associated rvith Tu satellite imagery have shown promise for increased success in accu- Photogrammetric Engineering & Remote Sensing, Department of Natural Resourc;es,University of New Hamp- Vol. or, No. 3, March 1995, pp. 321,-327. shire, 215 JamesHall, Durham, NH 03824. 0099-11 1219s/6013-321s3.00/0 f.R. Schriever is presentlv with Pacific Meridian Resources, O 19S5 American Societv for Photogrammetry 5200 S.W. Macadam Avenue, Suite 570, Portland, OR 97201. and Remote Sensrnu PE&RS PEER REVIEWED ARIICTE imagery limits its usefulness to classification of forest stands can be termed a purely single- or multi-factor system.AI- with a minimum of several acres rather than individual trees. though site factorsare not consideredin defining or identify- Therefore, forest cover type classification systems employed ing the types, the descriptions"give recognition to the in studies utilizing satellite imagery must define cover types ecologicalfactors that helped to createthe types and will at the siand level. continue to influence their development" (Eyre, 1980).Com- An important consideration in choosing a forest-type bining theseclassification schemes (see below) should facili- classification svstem is if existing or potential (climax) vege- tate applications which utilize satellite data for forest tation t1,pes are to be described. Methods for gathering infor- cover-typemapping in the forestscommon to the Northeast. mation to determine potential vegetation types typically In addition, forest-typerecognition coupled with soil maps involve examination of understorv vegetation, regenerating can provide information which indicates likely habitats,cli- tree species, and/or examination of soil conditions (Leak, max species,and martagementIimitations and/or potential 1982). Befort (ts8o) was able to utilize understory vegetation (Leak,1982). This informationwill be valuableto foresters, and regeneration for aerial identification of habitat types in wildlife ecologists,and other natural resourcemanagers. northern Idaho and eastern Washington using very large scale photography. Dense canopy stlucture typical of the Forest Types Northeast, and the small scale of satellite imagery, limits the Cover usefulness of this technique for classification of northeastern Coniferous cover tvoes. WP - Easternwhite pine comprisesa maiorityof the stocking Two approaches that describe the existing vegetation are (>70 percent) and characteristically occurs in pure stands. single-factor (Bailey et al,, "lg7B) or dominance type, and On lighter textured soil its'associates include red pine, pitch multi-factor classifications. The single-factor classification pine, quaking and bigtooth aspen, red maple, pin cherry, and white oak. On heavier soils associates are birches(paper, method is based on a single measure, for example, dominant sweet, and gray), black cherry, white ash, northern red oak, vegetation. The mr-rlti-factorclassification takes into account sugar maple, basswood, hemlock, red spruce, balsam fir, several distinguishing characteristics.Habitat types, for ex- white soruce. and northern white cedar. ample, can be identified by soils, landforms, and chronose- . WH - Eistern white pine and eastern hemlock, in combina- quences of vegetation (Leak, 1gB2). tion, comprise the largest proportion of the stocking, but nei- Multi-factor classifications can result in the proliferation ther species alone represents more than half of the total. The of categories.For example, five elevation classes,five soil- combination rarely exists without associates and red maple is tvpe classes,and five cover-types classeswill result in 125 a very common one. Other common associates include paper possibie classifications. Congalton (1991) has stressed the im- birch, northern red oak, beech, sugar maple, yellow birch, gray birch, red spruce, white ash, and balsam fir. portance of employing a mutually exclusive and totally ex- . HE - Eastern hemlock is pure or provides a maiority of the the haustive classification approach for determining accuracy stocking (>70 percent). Common associatesare eastern white of remotelv sensed classified data. For the multi-factor classi- pine, balsam fir, red spruce, sugar maple, beech, yellow fication syitem to approach a mutuallv exclusive and totally birch, northern red oak, white oak, yel1ow poplar, basswood, exhaustive condition, it must be subjectively simplified into black cherry, red maple, and white ash. categorieswhich have only a few distinguishing characteris- o OC - Other conifer species comprise a majority (>70 percent] oI the stocking.ln lhe strrdyarea lhe most common speciesis By way of corrtrast,a single-factor ciassification system red pine. However, red spruce and any other conifer species
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