a gis-based methodology for landcover reconstruction

Prairie Perspectives 283

A GIS-based methodology for landcoverreconstruction utilizing Dominion Land SurveyTownship diagrams

Mr. Brent N. Joss, University of ReginaDr. Dion J. Wiseman, Brandon University

Abstract:Dominion Land Survey (DLS) township diagrams and surveyornotebooks represent the most comprehensive documentation of pre-European settlement landcover for much of western Canada. The intentof this research is to develop a GIS-based methodology for utilizing thesedata in conjunction with contemporary physiographic and edaphic datato objectively reconstruct pre-European settlement landcover. Therelationships between known pre-settlement landcover from DLStownship diagrams and contemporary landform and soil characteristicsare established and logistic regression analysis utilized to predict landcovertype. Two alternative approaches to applying the resulting regressionmodel were evaluated resulting in overall classification accuracies of 60and 73 percent.

Introduction

One must understand the past in order to appreciate the presentand predict the future. This adage is certainly germane to anunderstanding of the complex relationships between landcover andlanduse change. An understanding of the ways in which landusepractices have evolved and influenced landcover over time is aprime concern of the resource management community.Characteristics of landcover have important impacts on climate,soils, hydrology, and the diversity and abundance of bioticorganisms (Hastings and Turner, 1965). Therefore, the ability to

Prairie Perspectives284

reconstruct past and predict future landcover change is essentialfor managing the natural environment.

The Dominion Land Survey:The first survey of Canada’s prairies under the Dominion Lands

Survey commenced in 1869 and within 30 years most of the arableland had been parceled out into farm-sized quarter sections(MacGregor, 1981). Although the physical subdivision of the landwas of primary concern, surveyors had a number of other tasks toperform. The most notable of these tasks was the collection of datain the form of surveyor’s field notebooks and the closely relatedtownship diagrams (Figure 1). These were probably the finest andmost comprehensible data collected by the Department of theInterior during its administration of the Dominion Lands Survey(Tyman, 1995).

Surveyors’ notebooks and original township diagrams providea valuable source of information for landcover reconstruction.Considering the amount of qualitative and quantitative data theyprovide, the resolution at which they were collected, and theimmense area they represent, the records of the Dominion LandSurvey must be considered the most comprehensive set of dataavailable regarding pre-settlement landcover conditions in westernCanada.

Methods of Landcover Reconstruction Using DLS Data:Landcover reconstruction is a very intricate part of trying to

understand how landuse and management affect the environment.Therefore, the scientific methods underlying landcoverreconstruction must be objective and replicable. Research onlandcover reconstruction using DLS township diagrams andsurveyor notebooks has historically been subjective and very muchinfluenced by the Gestalt method. This method is based mainly onvisual interpretation with little or no objective consideration ofassociated factors such as physiography, soils, or vegetation;producing regions based on subjective judgment rather than explicitrules (Bailey, 1996). This methodology relies heavily on theresearchers artistic ability and an intimate knowledge of the area in


question and, provided this is the case, may produce valuable results(Sobkowitch, 1998; Hamilton and Nicholson, 1999; Hanuta, 2000).

Bailey (1996) suggests that the result of such methods, whereno rules exist for recognizing regions, are essentially toponymicregions, classified by the places themselves rather than by objectivecriteria that characterize individual regions. Further, he argues thatregions established without acknowledging the criteria examinedin their classification are difficult to convey effectively to others

Figure 1: Example of first edition DLS township diagram.


and essentially impossible to evaluate or duplicate. Methods thatconsider explicit criteria for distinguishing between regions ofdiffering landcover are required to produce results that are bothcomprehensible and easily repeated. That is, objectivity must beexercised if a state of scientific predictability is to be attained byresearch in landcover reconstruction (Bailey, 1996).

Toward this purpose, more objective methods of landcoverreconstruction using DLS data have evolved. Tracie (1992) andArchibold and Wilson (1980) employ original DLS townshipdiagrams and notes to aid in reconstructing pre-settlement landcover.Although at differing resolutions, both methodologies used thenorth-south transects on township diagrams depicting actual pre-settlement landcover to calculate the proportion of grassland,woodland, scrub, wetland, and so on. From this, the landcovercomposition of each transect segment was determined and theadjacent sections classified accordingly. While the spatial detailattainable on the resulting landcover reconstructions was relativelylow, and the final product arguably less aesthetically pleasing thanmore artistic subjective renditions, these methods are replicableand objective, and results from different geographic areas may becompared statistically.

Objective

Contemporary researchers have access to technologies notavailable to earlier investigators. One of the most valuable tools attheir disposal is geographic information systems (GIS). Not onlydoes GIS facilitate the integration and interpolation of compileddata, but also promotes objectivity. The successful application ofGIS for landcover reconstruction and landuse change analysisrepresents an alternative approach that supports the developmentof an objective and replicable research methodology.

The objective of this research is to develop a methodology forreconstructing pre-settlement landcover utilizing GIS technology.The study incorporates ancillary data to guide the landcoverreconstruction. The parameters that are considered are those relatedto characteristics of the environment that have remained relativelyunchanged since European settlement; specifically, landform and


soil characteristics. The relationships between landcover type andlandform and soil characteristics are established through the use oflogistic regression analysis to identify areas that possess acombination of edaphic and physiographic characteristics favorableto the occurrence of a specific landcover type. Once the relationshipbetween independent variables (i.e. physiographic and edaphicparameters) and the dependent variable (i.e. landcover) isestablished in a training area, it is hypothesized that the type anddistribution of pre-settlement landcover can be predicted in adjacenttest areas where similar relationships between landform, soils, andlandcover type exist.

Study Area

The study area consists of two adjacent municipalities locatedsouth of Riding Mountain National Park in southwestern Manitoba(Figure 2). These are the Rural Municipality of Clanwilliam (Twp.17 and 18, Rng. 17 and 18, W 1) and the Rural Municipality ofHarrison (Twp. 19 and 20, Rng. 16, 17, and 18, W1).

The area is characterized by elements of the Mid-BorealUplands, Boreal Transition, Aspen Parkland, and Lake ManitobaPlain ecoregions and includes highland plateaus, rolling forestedhills and meadows, wetlands, lakes, and streams (CanadianBiosphere Reserve Association, 1998).

According to Ahrens (1994), the climate is humid continentalwith cool summers and cold winters. Mean daily temperaturesrange from approximately 15.5°C in July and August to –20.6°Cin January. Mean annual precipitation is approximately 476mm,the majority of which falls in July and August.

Surficial geology is dominated by extensive end moraine andground moraine deposits with intermittent lacustrine andglaciofluvial deposits (Klassen, 1979). Three dominant soilassociations occur throughout the study area. These are the Onanole/Rackham Association of glaciolacustrine origin, the Granville/Waitville Associations developed from deep, moderate to stronglycalcareous moraine deposits, and the Bog/Half Bog soils occurringin depressions and low lying areas (Erhlich, 1958).


Methodology

The methodology used is comprised of four principle stages.First, data are collected and compiled to create the themes for bothdependent and independent variables. Secondly, the relationshipsexisting between landcover and edaphic and physiographicparameters are calculated through logistic regression. Next, theserelationships are utilized to create a series of predictive grids and ,finally, these grids are subsequently incorporated into two differentlandcover reconstruction approaches.

Figure 2: Location of study area (modified from McGinn, 2000).


In order to evaluate the resulting regression model and overalllandcover reconstruction methodology, the regression model wasfirst developed utilizing landcover, landform, and soil dataassembled for the R.M. of Clanwilliam. The resulting regressionmodel was then evaluated using landform and soils data assembledfor the adjacent R.M. of Harrison. The overall classificationaccuracy was then evaluated by comparing the predicted landcoveragainst actual landcover derived from DLS township diagrams andsurveyor notebooks.

GIS Database Development:The data used to identify actual pre-settlement landcover along

survey line transects was extracted from DLS township diagrams.A scanner was used to digitally acquire the diagrams and preparethem for entry into the GIS. The images were then rectified andactual pre-settlement landcover depicted adjacent to survey linetransects was on-screen digitized to produce a vector polygon themein the GIS. The resulting polygons were classified using nominalvalues of “forest,” “wetland,” and “prairie/grassland” to describethe type of landcover occurring at specific locations along sectionlines.

Data from reconnaissance soil surveys were combined withexisting soil coverages acquired from Agriculture Canada to producethe soil theme of the study area. The attributes that have a perceivedeffect upon the distribution of landcover were appended to theexisting soil coverage. The attributes selected were: 1) averagesolum depth or the average depth of the upper horizons (usuallyhorizons A and B) of a soil above the parent material in which theprocesses of soil formation are active; 2) estimated permeability orhydraulic conductivity (cm/hr), which is the effective flow velocityor discharge velocity in soil; 3) depth of engineering division 1(cm) or the depth of the first major soil division or horizon; 4)average soil pH of engineering division 1, and; 5) percent organicmatter (Eilers and Lelyk, 1990). These five edaphic attributes werethen individually converted into their own independent grid themesin ArcView.

Physiographic variables were derived by digitizing contour linesfrom 1:50,000 topographic maps of the study area. A TIN, or


triangulated irregular network was generated from the resultingcontour theme, which in turn was converted to a grid coveragewith a 4 m grid cell resolution. The grid was then used to createthemes depicting slope, aspect and a measure of local variability oftopography determined by calculating the standard deviation fromthe mean elevation of the study area.

Logistic Regression Analysis:In logistic regression, the magnitude of occurrence of the

phenomenon being modeled by the dependent variable is unknown.Instead, the dependant variable is nominal and dichotomous; thatis, the form of the dependant variable is, in this case, the presenceor absence of forest, wetland, or prairie/grassland. The independentvariables are interval or ratio level data describing the characteristicsof, in this case, the eight independent variables previously described.Logistic regression is then used to predict the probability with whicha phenomenon will exist at un-sampled locations; in this case, theareas of unknown landcover between section lines.

The eight grid coverages representing the independent variableswere combined into a single multivariate data structure referred toas a stack using ArcInfo GRID module MAKESTACK command.This stack and grids representing each of the dependant variables,that is, the presence or absence of forest, wetland and prairie/grassland, were then entered into the SAMPLE function of ArcInfo’sGRID module. The SAMPLE function was used in order to generatea random sample of points each possessing a particular value forthe eight independent variables and one independent variable. Thiswas performed three separate times, once for each of the threedifferent dependent variables. The resulting ASCII files createdusing the SAMPLE function were then entered into theREGRESSION command using the LOGISTIC option.

The results of the REGRESSION command are a regressionconstant and eight coefficients, one for each independent variableincluded into the sample file. These coefficients estimate the effectsof the independent variable on the dependent variable across thelevels of the other independent variables (Jaccard et al., 1990). Thus,these coefficients represent the relationships that existed between


landcover and physiographic and edaphic variables (independentvariables) in the R.M. of Clanwilliam.

Predictive Grids:The coefficients and regression constant produced for each

dependent variable through logistic regression were then enteredinto a predictive equation to create three probability grids, one foreach of the three landcover types. The calculated cell values foreach grid theme indicated the probability of occurrence of forest,wetland, or prairie/grassland having existed there prior to Europeansettlement.

Alternative Methods For Landcover Reconstruction:Two alternative approaches to applying the resulting regression

model were evaluated. Each approach differed only in terms of theestimated probability at which a particular grid cell was assignedto a specific landcover class. The first approach used a 70 percentrule in which the estimated probability of a grid cell belonging toany landcover class had to be equal to or greater than 70 percent tobe classified, or it would be left unclassified. In the second approach,called the original proportions approach, the proportion of eachlandcover type indicated along survey transects was determined.The regression equation was then applied such that differentprobability levels were selected as “cut-offs” for each landcovertype. By selecting such “cut-offs” a landcover compositioncomparable to estimated proportions of original landcover wouldbe reconstructed.

Results

Once landcover was reconstructed for the R.M. of Clanwilliamthe model was evaluated by applying it to the R.M. Harrison. Thereconstructed landcover maps produced by both the 70 percent ruleand original proportions approaches (Figures 3 through 6) wereevaluated for their accuracy by comparing predicted to actuallandcover for the R.M. of Harrison derived from DLS townshipdiagrams and surveyor notebooks. In addition, the distribution ofpredicted landcover for the R.M. of Clanwilliam was evaluated by


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comparing it with the calculated proportions of original landcoverfrom the DLS township diagrams.

Application of the regression model using the 70 percent ruleresulted in an overall classification accuracy of 56% for the R.M.of Harrison (Figure 5). In general, the performance of the 70 percentrule is felt to be less than satisfactory since it failed to predictlandcover for nearly half of the total area of Harrison andClanwilliam municipalities (Figures 3 and 5). As well, theproportion that each landcover type encompassed differs greatlyfrom the original landcover composition. For example, the predictedlandcover in Clanwilliam municipality consists of approximately53% forest, 19% wetland, and 28% prairie/grassland while originallandcover depicted along transect lines consisted of 82% forest,17% wetland, and 1% prairie/grassland.

Further examination revealed that despite an incompletereconstruction of the landcover, the 70 percent rule did reconstructlandcover in a realistic manner. That is, forest is predominantlyfound in areas higher in elevation, wetland coincides well with thedistribution of wetland areas, and prairie is interspersed throughout.

Evaluation of the original proportions approach resulted in anoverall classification accuracy of approximately 68% for the R.M.of Harrison (Figure 6). Examination of the predicted landcover forClanwilliam using the original proportions rule suggests that a morecomprehensive reconstruction has been achieved (Figure 4). Thepredicted landcover clearly shows a realistic distribution oflandcover classes similar to that depicted on the original townshipdiagrams. That is, forest dominates, particularly in areas of higherelevation, wetland is distributed in close proximity to lakes andstreams, and prairie is sparsely distributed throughout the area.

Conclusion

The results of this study suggest that the application of GIStechnology and logistic regression analysis for reconstructing pre-settlement landcover from DLS township diagrams is promising.The overall accuracies of the two approaches used were 56% and68%. The original proportions method, in which the originalproportions of each landcover type were used to select “cutoffs”


for assigning grid cells to landcover classes, seemed to performbetter than the 70% rule.

Despite the accuracy levels produced, weaknesses in themethodology were evident. In particular, the selection of predictorsand the quality of soil data used seem to significantly affect theoverall performance of the model. For original landcover to becorrectly predicted each landcover type needed to have its‘determining’ parameters included in the analysis. Therefore, it issuggested that more background research be conducted to determineprecisely which physiographic and edaphic features are mostimportant in determining or controlling landcover in a particulararea.

In addition, it was found that the resolution and quality of soildata were less than adequate. Data collected at coarse resolutionssystematically affects the accuracy and resolution of results.Therefore, it is recommended that soil data at a suitable scale orresolution be used to ensure the most accurate results.

Further, at this point the proposed methodology may be moreappropriate for local or landscape scale reconstructions as opposedto regional reconstructions. The current lack of digital high-resolution soils and physiographic databases necessitates asignificant investment of labour for assembling and compiling thesedata. However, these preliminary results certainly warrant furtherinvestigation and as the variety and quality of these databasesimproves the utility of such methods for pre-settlement landcoverreconstruction will become increasingly apparent.

References:

AHRENS, C.D. 1994 Meteorology Today: An Introduction to Weather,Climate, and the Environment (New York: West PublishingCompany)

ARCHIBOLD, O.W. and WILSON, M.R. 1980 ‘The natural vegetationof Saskatchewan prior to agricultural settlement’ Canadian Journalof Botany 58, 2031-2042

BAILEY, R.G. 1996 Ecosystem Geography New York: SpringerCANADIAN BIOSPHERE RESERVE ASSOCIATION 2000 Riding

Mountain Biosphere Reserve <http://www.cbra-acrb.ca/english/biosphere_reserves/riding_mountain/default.asp>


EILERS, R.G. and LELYK, G.W. 1990 Soils of the South Riding MountainPlanning District Report D35

EHRLICH, W.A., PRATT, L.E., POYSER, E.A., and LeCLAIRE, F.P.1958 Report of Reconnaissance Soil Survey of West-Lake Map SheetArea Manitoba Department of Agriculture

HAMILTON, S. and NICHOLSON, B.A. 1999 ‘Ecological islands andVickers Focus adaptive strategies in the pre-contact plains ofsouthwestern Manitoba’ Plains Anthropologist 44-167, 5-25

HASTINGS, J.R. and Turner, R.M. 1965 The Changing Mile: AnEcological Study of Vegetation Change with Time in the Lower Mileof an Arid and Semiarid Region Tucson: University of Arizona Press

JACCARD, J., Turrissi, R. and WAN, C.K. 1990 Interaction Effects inMultiple Regression Sage University

KLASSEN, R.W. 1979 Pleistocene Geology and Geomorphology of theRiding Mountain and Duck mountain Area, Manitoba-SaskatchewanGeological Survey of Canada, Bulletin 396

MacGREGOR, J. G. 1981 Vision of Ordered Land: The Story of theDominion Land Survey Saskatoon: Western Producer Prairie Books

McGINN, R.A. 2000 ‘Ice-shoved hills and related glaciotectonic featuresin the Glacial Lake Proven Basin, Riding Mountain Uplands,Manitoba’ Prairie Perspectives, Geographical Essays edited by J.I.Romanowski 3:84-96

PARKS CANADA 1998a Importance of Riding Mountain NationalPark – Management Plan <http://www.parkscanada.pch.gc.ca/parks/manitoba/riding_mountain/english/Mpchp2e.html>

PARKS CANADA 1998b The Riding Mountain Situation <http://www.parkscanada.gc.ca/parks/manitoba/riding_mountain/English/ECPcp2E.htm>

SOBKOWITCH, D. 1998 Pre-settlement Landcover of the R.M. ofClanwilliam Unpublished Map

TRACIE, C.J. 1992 ‘Pre-settlement Vegetation in a Mixed PrairieWoodland Area of Northern Alberta: A Reconstruction fromSurveyors’ Notes’ The Canadian Geographer 36, 3, 260-266

TYMAN, J.L. 1995 By Section, Township, Range: Studies in PrairieSettlement Brandon University: Leech Printing Ltd.

a gis-based methodology for landcover reconstruction

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