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Mapping the distribution of whitebark pine(Pinus albicaulis) in Waterton Lakes

National Park using logistic regression andclassification tree analysis

G.J. McDermid and I.U. Smith

Abstract. Accurate spatial information on the distribution of whitebark pine, a keystone species in alpine environmentsacross western Canada, is critical for the planning of conservation activities designed to ameliorate the damaging effects ofblister rust, mountain pine beetle, and interspecific competition. We compared classification tree analysis and logisticregression analysis to explore their relative abilities to model whitebark pine presence and absence withmedium-spatial-resolution satellite and topographic variables across a complex study site in Waterton Lakes National Park,Alberta. Both techniques were found to be effective, generating map products of roughly equal thematic quality (91%overall accuracy; kappa = 0.76). However, the logistic model was valuable in its ability to predict ratio-level probabilitysurfaces, whereas the classification tree approach was simpler, faster, and found to generate a slightly more balanced modelfrom an individual class accuracy perspective. End users selecting between the two techniques should make choices thatbalance flexibility with simplicity while always taking care to exercise sound modeling practices.

Rsum. Linformation exacte dans lespace sur la distribution du pin corce blanche, une espce clef dans les rgionsalpines de louest Canadien, est essentielle pour les activits de conservation qui ont pour but damliorer les effets nocifsprovoqus par la rouille vsiculeuse du pin blanc, le dendroctone du pin ponderosa, et la comptition interspcifique. Nousavons compar lanalyse base sur un arbre de dcision avec lanalyse base sur la r ression logistique de faon dfinir lacapacit de chaque mthode schmatiser la prsence ou labsence du pin corce blanche rsolution spatiale moyenne,et avec des variables topographiques dans la rgion du Parc National des Lacs-Waterton. Les deux mthodes se sont avresefficaces, permettant de crer des cartes de distribution dune qualit thmatique similaire (avec une prcision globale de91 % et un coefficient kappa de 0,76). Par contre, le modle bas sur la rgression logistique tait int essante de par sacapacit gnrer des surfaces de probabilits une echelle de mesure de type rapport, tandis que lanalyse base sur unarbre de dcision tait plus simple, plus rapide, et permettait de crer un modle de classification plus quilibr au niveau delexactitude de la catgorisation de chaque classe. Finalement les utilisateurs qui ont le choix entre ces deux techniquesdoivent choisir la plus simple et la plus flexible, tout en exerant de bonnes mthodes de schmatisation.



Whitebark pine (Pinus albicaulis) is a five-needle pinespecies that occurs in high-elevation environments acrosswestern Canada and the United States (Critchfield and Little,1966). Widely regarded as a keystone species (Mattson et al.,2001), whitebark pine (WBP) plays a central role in wildlifehabitat and watershed protection in the timberline forests that itinhabits (Farnes, 1990; Tomback et al., 2001; Copeland et al.,2007). The seeds of WBP are large and nutritious (Lanner andGilbert, 1994) and represent an important food source for avariety of species, including the grizzly bear (Mattson and

Merrill, 2002), red squirrel (Mattson et al., 2001), and Clarksnutcracker (Hutchins and Lanner, 1982). So valuable is WBPas a food resource that when whitebark seeds are abundant,grizzly bears eat virtually nothing else (Craighead et al., 1982),and the presence of a healthy cone crop is thought be a keyfactor governing the interaction between grizzly bears andhumans (Mattson et al., 1992; Mattson and Reinhart, 1997).Unfortunately, however, WBP is declining throughout much ofits range, being adversely impacted by a variety of factors,including exotic white pine blister rust (Zeglen, 2002;McKinney and Tomback, 2007), epidemics of mountain pinebeetle (Perkins and Roberts, 2003; Waring and Six, 2005),

356 2008 CASI

Can. J. Remote Sensing, Vol. 34, No. 4, pp. 356366, 2008

Received 31 December 2007. Accepted 18 June 2008. Published on the Canadian Journal of Remote Sensing Web site at on 12 September 2008.

G.J. McDermid1 and I.U. Smith. Foothills Facility for Remote Sensing and GIScience, Department of Geography, University of Calgary,Calgary, AB T2N 1N4, Canada.

1Corresponding author (e-mail:

climate change (Koteen, 1999), and competition fromshade-tolerant tree species (Keane and Arno, 1993) bolsteredby more than a century of fire suppression (Hallett and Walker,2000). Concerns regarding the future of WBP, includingconsideration for formal listing as a threatened species, haveled to a variety of conservation initiatives, including fieldsurveys (Campbell and Antos, 2000), genetic inventories(Beardmore et al., 2006; Bower and Aitken, 2006), and an arrayof proactive intervention programs (Schoettle and Sniezko,2007).

Although WBP is known to exist throughout much of theCanadian Cordillera south of the Peace River, detailedinformation regarding the occurrence of the species at the patchor stand level is seriously lacking (Wilson and Stuart-Smith,2002). Previous work in the Kootenay, Yoho, and Lake Louiseregions of Alberta and British Columbia (Wilson and Marcoux,2005) has investigated the use of logistic regression models forexplaining patterns of WBP occurrence using topographicvariables derived from a digital elevation model (DEM).However, the effort was only able to account for 9% of theobserved variance, revealing, perhaps, the limitation ofmoderate-scale topographic datasets for supporting this type ofdemanding stand-level modeling work. Other studies havedemonstrated the potential of spectral variables from remotesensing for capturing patterns related to the distribution ofspecific vegetation species. For example, Kokaly et al. (2003)used hyperspectral Airborne Visible/Infrared ImagingSpectrometer (AVIRIS) data to compile a spectral library for anumber of vegetation cover types in Yellowstone National Park,including WBP, and used a spectral feature-fitting algorithmand expert system rules to generate vegetation maps withaccuracy levels of 74% overall. Goodwin et al. (2005) usedanalysis of variance to document statistical differences betweenspecies in an Australian eucalypt forest using 80 cm CompactAirborne Spectrographic Imager-2 (CASI-2) imagery andshowed that broad species groups could be separated withsupervised maximum likelihood strategies. Hamada et al.(2007) employed hierarchical clustering procedures to classifytamarisk communities in riparian habitats in southernCalifornia using high spatial and spectral resolution airbornedata. However, each of these studies relied on highly detailedairborne imagery and employed methods that would be difficultto duplicate over large areas.

Other researchers have shown that detailed vegetationpatterns can be modeled effectively withmoderate-spatial-resolution satellite imagery using datasetscomprised of combined spectral and topographic explanatoryvariables. For example, Wulder et al. (2006) usedmultitemporal Landsat Enhanced Thematic Mapper Plus(ETM+) imagery and a variety of topographic descriptors tomodel red-attack mountain pine beetle damage with anaccuracy of 86%. Lawrence and Wright (2001) achievedsimilar results mapping vegetation communities in theYellowstone ecosystem with combined TMDEM explanatoryvariables. The potential of combined datasets for modelingpatterns of WPB occurrence was demonstrated by

Landenburger et al. (2006), who achieved overall accuraciesapproaching 90% in the Yellowstone region. However, thereremains a paucity of examples of this type of work in theremote sensing literature, particularly in the Canadian context,and we require additional research efforts designed toilluminate specific strategies for mapping WBP, and othervegetation communities of special concern, across the complexhabitats in which they occur. Of particular interest is the role ofspecific modeling techniques designed to handle thepresenceabsence information that typically comprises theresponse variables available for this type of work. Twotechniques, binary logistic regression and classification treeanalysis, show particular promise. Logistic regression is a formof generalized linear modeling designed to handle the variancepatterns normally generated by binary response variables(Crawley, 2002). The procedure is attractive in that it generatesa continuous probability surface that represents a flexible,ratio-level information product that can be interpreted in avariety of manners. However, the model construction andcriticism process is time-consuming and requires specializedstatistical prowess. Classification tree analysis (CTA) is analternative statistical procedure designed to identify rule-baseddecision trees that predict the most probable classificationcategories using a series of recursive binary divisions (Breimanet al., 1984; Venables and Ripley, 1997). Although thelow-level, categorical output is not as desirable as theprobabilistic surface provided by logistic regression analysis,CTA is attractive in its ability to handle large numbers ofdiverse explanatory variables quickly and efficiently, andprevious studies (e.g., Vayssiers et al., 2000; Miller andFranklin, 2002) have demonstrated its broad effectivenessacross a variety of experimental conditions.

The objectives of the research reported in this paper weretwo-fold. The first was to compare the abilities of logisticregression and CTA to model the distribution of WBP withspectral and topographic variables across a diverse environmentin the northern portion of the species range. We posed thefollowing questions: Can mediu