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The accuracy of deflection-lines derived from digital elevation models Christie, David Alexander 1994-02-26

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THE ACCURACY OF DEFLECTION-LINESDERIVED FROM DIGITAL ELEVATION MODELSbyDAVID ALEXANDER CHRISTIEB.S.F., The University ofBritish Columbia, 1990A THESIS SUBMITTED iN PARTIAL FULFILMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE FACULTY OF GRADUATE STUDIESDepartment ofForestryWe accept this thesis as conformingto the required standardTIlE UNIVERSITY OF BRITISH COLUMBIAJune 1994© David Alexander Christie, 1994In presenting this thesis in partial fulfilment ofthe requirements for anadvanceddegree at the University of British Columbia, Iagree that the Library shallmake itfreely available for reference and study. I furtheragree that permission for extensivecopying of this thesis for scholarly purposesmay be granted bythe head of mydepartment or by his or her representatives.It is understood that copyingorpublication of this thesis for financial gain shallnot be allowed without my writtenpermission.(Signature)___________________________Department of -The University of British ColumbiaVancouver, CanadaDate______________DE-6 (2/88)11AbstractLong-reaching skyline cable yarding systems have seen increased use within theBritishColumbia coastal forest industry. Deflection-line analysis,which estimates themaximum yarding distance and locates the harvest boundary, is the key componentinplanning for skyline systems. Traditional deflection-line analysis involves fieldsurveyswhich may be very difficult to perform in the terrain associated with skylines. As analternative, deflection-lines may be derived from Digital Elevation Models (DEMs).Concern regarding the elevational accuracy ofthe topographic forest planningmapsused to create the DEMs has limited their use for deflection-line analysis. Betterunderstanding ofthe magnitude and nature ofelevational errors and their effect upondeflection-line analysis are needed before DEM-derived deflection-lines may be usedwith confidence.This study was performed in cooperation with Canadian Forest Products Limited(Canfor) in Woss, British Columbia (B.C.). Deflection-line analyses were performed forDEM-derived deflection-lines to test for error in yarding distance estimates. Errors inyarding distance estimates for DEM-derived deflection-lines were caused by interactionsbetween some or all ofthe following: the terrain shape (concavity/convexity), largeelevational errors and their location on the deflection-line, and the deflection-line length.While a majority ofyarding distance estimates from DEM-derived deflection-lines werenot in error (70%), the erroneous estimates may result in costly planning errors.111Restricting the use of DEM-derived deflection-lines to the efficientpre-planning offieldsurveys could help avoid these mistakes.A blunder was detected in one ofthe study cutblock maps. Distortions werediscoveredin the maps for two other study cutbiocks where photogrammetrically derived andground surveyed maps had been joined through rubber sheeting. While random errorwas detected in the analyses, systematic error appeared to contribute more to both thegeneral level ofelevational error and to the presence of large elevational errors.Different types ofsystematic error were detected, with at least some types evident in allofthe deflection-line comparisons. Smoothing error was observed where terrainvariation had been reduced or eliminated, and positional errors were the most commonand influential systematic errors detected. The positional error ofmap features, andpositional error introduced using traditional surveying methods, may also affectoperational field surveying ofdeflection-lines, logging roads, and harvest boundaries.The presence ofpositional error and its subsequent effects upon harvest planning iseither not known or is ignored altogether.Detecting the presence of systematic error in topographic forest planning maps is thefirst step towards using DEMs confidently for deflection-line analysis. Further studiesinvolving the effects ofpositional error on DEM elevational error will allow the DEMsto be predicted and subsequently accounted for. Advances in map creation, computers,and Geographic Information Systems will allow for the acquisition and manipulation ofmore accurate digital elevation data now and in the future.AbstractTable ofContentsiv113.2.53.3 DEM3.3.13.3.23.3.33.3.43.3.53.3.63.3.74 Study Site5 Methods5.1 Cutbiock and Deflection-line5.2 Field Surveys5.3 Digital Elevation Models5.4 Estimating Yarding Distance5.4.1 Statistical Analysisx1466669121213151719202022252527293035viviiTable of Contents ivList of TablesList ofFiguresAcknowledgements1 Introduction2 Objectives3 Background and Literature Review3.1 Operational Harvest Planning3.1.1 Terminology3.1.2 Skyline Yarding Systems .3.1.3 Deflection and Deflection-line3.2 Digital Elevation Models3.2.1 Terminology3.2.2 Elevational Data Sources3.2.3 The Regular Rectangular Grid3.2.4 The Triangulated IrregularNetworkAnalysisUsing DEMs in Operational Harvest PlanningAccuracyErrors: Blunders, Random, and SystematicSmoothing ErrorPositional AccuracyAge ofMap DataSurveying QualityData RelevanceDEM Accuracy Standards and TestsSelection464646484950V5.5 DEM Elevational Error 525.5.1 Statistical Analysis 546 Results 586.1 Estimating Yarding Distance 586.2 DEM Elevational Error 607 Discussion 637.1 Estimating Yarding Distance 637.1.1 Deflection-line Length and Concavity 637.1.2 Large Elevational Errors 667.2 Elevational Error and Error Patterns 727.2.1 Blunders 737.2.2 Distortions Caused by Rubber Sheeting 757.2.3 Random Error 797.2.4 Systematic Error 837.2.5 Smoothing Error 837.2.6 Effect of Site 867.2.7 Positional Error 887.2.8 AgeofMapData 918 Conclusions 959 Recommendations 100Literature Cited 105Appendix A Sources ofError Affecting DEM Accuracy 110Appendix B Yarding distance estimates 115Appendix C Summaries ofDEM Elevational Error 117Appendix D Glossary of Terms 121viList ofTablesTable I Summary ofcutbiock characteristics 44Table 2 Distribution of deflection-lines and settings within cutbiocks 45Table B Yarding distance (m) estimated for thirty-one deflection-line pairs . . 116Table Cl Summary ofDEM elevational error (m) by cutbiock 118Table C2 Summary ofDEM elevational error (m) by setting 119Table C3 Summary ofDEM elevational error (m) by deflection-linecomparison 120viiFigure 1Figure 2Figure 3Figure 4Figure 5Figure 6Figure 7Figure 8Figure 9Figure 10Figure 11Figure 12Figure 13Figure 14Figure 15Figure 1678101116• . . 2436• 37• 38• 39• 40• 41• 42• 43656518List ofFiguresCutbiock design showing settings, landings and yarding roadsDeflection ofcarriage below chordSkyline yarding system with carriage and payload of logsClearance ofpayload load-path and selection ofboundary location.TIN DEM integrates important spot elevationsRegularly spaced grid points do not properly represent importantfeatures, such as peaks and passes.Deflection-lines overlayed onto a TIN DEMMap ofcutblock locations within Canfor operating areaMap of AR1 cutblock and deflection-line locationsMap ofAR6 cutbiock and deflection-line locationsMap ofAT cutbiock and deflection-line locationsMap of CU cutbiock and deflection-line locationsMap ofGC cutbiock and deflection-line locationsMap ofNW cutbiock and deflection-line locationsMap ofTR cutbiock and deflection-line locationsA positive elevational error in concave terrain resulted in theincorrect placement ofthe boundary for the NW-2 DEM-deriveddeflection-lineFigure 17 The concave shape ofboth deflection-lines for CU-21 providedadequate clearance for the entire length ofthe deflection-linesFigure 18 A large elevational error at the end ofthe GC-5 DEM-deriveddeflection-line resulted in an incorrect boundary location 67vii’Figure 19 Example of a large negative elevationalerror in the centre ofthedeflection-line, causing an overestimate ofyardingdistance 68Figure 20 The deflection-line analysis piotsfor AT-202 show that thelanding was located properly on both plots despitethe 33 metreunderestimation ofyarding distance from theDEM-deriveddeflection-line (upper plot)71Figure 21 Comparison plot for AR1-il showsdivergence of deflection-lines,apparently due to a map blunder74Figure 22 Comparison plots for AR1-12 showsdivergence ofdeflection-lines, apparently due to a map blunder74Figure 23 The well matched deflection-line pairfor CU-20 is an example ofthe deflection-lines contained entirely within thephotogrammetrically derived portion ofthe map78Figure 24 Comparison plot for CU-23shows a large discrepancy inelevational error between the photogrametrically derived portionofthe map (left side ofplot) and the ground surveyed portion(right side)78Figure 25 Deflection-line comparison plot for GC-5 indicates minor, randomvariation in elevations80Figure 26 Elevational error histogram for GC-5 describes a normal curve81Figure 27 Elevational error histogram for AT-200 is right skewed, indicatingthe presence of systematic error 81Figure 28 Comparison plot for AT-200 shows a well matched deflection-linepair, with the DEM-derived deflection-line tending to be higher inelevation 82Figure 29 Differing tree heights, due in part to variable sight conditions,have resulted in an even forest canopy which masked the terrainvariation from the photogrammetrist 84Figure 30 Past wind history created an even forest canopy which preventedthe photogrammetrist from detecting terrain variation 85Figure 31 Comparison plot for AT-202 indicates presence ofpositional error. . . 90ixFigure 32 Comparison plot for AT-203, which shares a common landingwith AT-202 (Figure 31), also indicates presence ofpositionalerror 90Figure 33 A PIP near deflection-line AR6-1 helped to reduce the effects ofpositional error when the deflection-line location was transferredto the map 92Figure 34 Deflection-line AR6-3 was not located close to a PIP andtherefore experienced positional error when transferred to the map. . . 93xAcknowledgementsI wish to express my appreciation to both graduate supervisors I have had duringthetime ofmy studies: first to Joe McNeel for initiating the study and for his enthusiasmand sense ofhumour; and second to Glen Young for taking on this role in the middle ofthe project and for his valuable insights. I would also like to thank the members ofmycommittee: Val LeMay for statistical guidance and general advice on all thingsforestry; John Nelson for sharing his practical field experience and down to earthadvice; and Brian Klinkenberg for his GIS expertise and innovative recommendations.I would like to thank the Englewood Logging Division ofCanadian Forest Products fortheir financial and technical support. In particular I want to express my gratitude toPhil Winkle for guiding me through some ofthe more difficult portions ofmy thesiswith advice, encouragement, and humour. Also with Canadian Forest Products I wouldlike to acknowledge John Argast, Tom Normand, Paul Nuttal, and Warren Jukes forassistance with field surveys and technical advice on planning for skyline systems.The Science Council ofBritish Columbia provided a GREAT award which made thisproject possible. Digital Resource Systems provided TerraSoft and specialized software,and Dan Lemkow took valuable time to advise on the project. Craig Spears of Softreeprovided the Roadeng software and critical advice. Andy and Gordon Cooper ofECCCO Management performed the GPS and laser surveys and gave freely oftheirknowledge.I am especially grateful for the support I have received from Astrid van Woudenbergthroughout my entire studies. Her encouragement and dedication were instrumental tothe completion ofthis study. I especially thank Astrid for her infectious sense ofhumour which has picked me up on so many occasions.Finally, I would like to thank my parents, Doug and Jean Christie, who have once againsupported me through my education with love, encouragement, and more support than ason could dare to ask for.I shall be telling this with a sighSomewhere ages and ages hence:Two roads diverged in a wood, and I -I took the one less travelled by,And that has made all the difference.Robert Frost - The RoadNot TakenOfwhat avail are forty freedoms without a blank spot on the map?Aldo Leopold - A Sand County Almanac11 IntroductionChanging social, economic and terrain conditions have led to increased utilization oflong-reaching skyline cable yarding systems within the British Columbia coastal forestindustry. These systems present new challenges for operational harvest planners.Deflection-line analysis, used to check for clearance between suspended logs and theground, is the foundation ofthe planning process and especially critical for skylineplanning (Conway 1982). Deflection-line surveys become more difficult when dealingwith the long yarding distances and rough terrain associated with skyline systems. Boththe effectiveness and the productivity ofthe planning process may suffer. Also,adequate clearance is generally more critical for skyline systems than conventionalsystems such as highlead and grapple yarders.The prospect of using Digital Elevation Models (DEMs), pseudo-three dimensionalrepresentations ofthe terrain, for deflection-line analysis had been suggested as early as1974 (Burke 1974). Young and Lemkow (1976) developed a Digital Terrain Simulator(a DEM) that could be used for many aspects offorest operations planning, includingdeflection-line analysis. Limitations with speed and storage capabilities ofthe desktopcomputers used by remote forest operations had prevented the use of such models atthat time. Too often these limitations resulted in a compromise ofeither the accuracyofthe data and/or the integrity ofthe models used to manipulate the data.2These conditions persisted until the rapid improvements in the abilities ofpersonalcomputers in the late 1980s. Coincident with these improvements came the increasingsophistication of Geographic Information Systems (GIS), computerized mapping andanalysis systems. Integrated DEMs and GIS would allow deflection-line analysis toincorporate relevant information from the GIS database. This combination could helpmeet the increasing challenge for better planning for forest operations.While personal computer limitations have been reduced, concern regarding theelevational accuracy ofthe source data, primarily large scale topographic forest planningmaps, continues. Quantification ofelevational accuracy is necessaiy before a DEM maybe used confidently for deflection-line analysis. Ifthe accuracy is within acceptablelevels, then DEMs may be used. Ifthe level ofDEM accuracy is unacceptable then abasis is needed on which to set standards for the creation ofsuitable elevational data. Ifa technique can be developed to rectify or at least quantify and classify this elevationalerror, more planning would be possible using DEMs.This study was initiated by Canadian Forest Products Limited (Canfor) in Woss, BritishColumbia (B.C.). Canfor wished to assess the feasibility ofusing DEMs, developedfrom their 1:5000 scale topographic planning maps, as tools for deflection-line analysis.Field surveyed deflection-lines were compared with deflection-lines derived from thesame locations on DEMs. Deflection-line analyses were performedfor each pair to seeifthe yarding distance estimate for the DEM-derived deflection-line was influencedbyelevational error. The elevational error between paired pointson the field surveyed andDEM-derived deflection-line were quantified and analyzed. The nature and potentialcauses ofthe elevational error were investigated, analyzed, and discussed.Recommendations have been made regarding the best approach to using existingtopographic maps, as well as the acquisition ofnew elevation data, for deflection-lineanalysis.342 ObjectivesThis study investigated the elevational accuracy of deflection-lines derived from DigitalElevational Models (DEMs). The DEMs were developed from large scale topographicforest planning maps. Specific objectives were:1) to assess the accuracy ofyarding distances estimated from DEM-deriveddeflection-lines;2) to quantify the level ofelevational error ofDEM-derived deflection-lines;3) to investigate the nature and potential causes ofthe elevational error oftheDEM-derived deflection-lines;4) to develop recommendations for analyzing elevational error in DEM-deriveddeflection-lines, which are applicable to other maps, DEMs, and geographiclocations;5) to develop recommendations for the best use ofDEM-derived deflection-lines,including recommendations for minimizing or predicting the systematic errorwhich affects the elevational error ofthe DEM-derived deflection-lines; and6) to develop recommendations for acquiring and using new elevational data forDEM-derived deflection-line analysis.563 Background and Literature Review3.1 Operational Harvest Planning3.1.1 TerminologyA cutbiock is the smallest individual unit in operational harvest planning. A cutblockisaccessed by a road or roads, which allow access for yarding equipment, logging trucks,and logging crews. A landing is a widening in the road, where the road is more or lesslevel and the skyline yarding machine has thebest access to the logs in the cutblock.Yarding is the process oftransporting logs from where they are felled within thecutblock, to the landing, where they are loaded onto logging trucks. Thepath the logsfollow while being yarded is called a yarding road.The area yarded from oneindividual landing is called a setting, and several yarding roads will usuallybe neededto reach all ofthe logs within that setting. One cutblockmay contain several settings,as multiple landings are often needed for yarding theentire cutbiock (Figure 1).3.1.2 Skyline Yarding SystemsOperational harvest planning has become more demandingfor coastal forest operationsin British Columbia. Two major interrelated forces havedriven this change. The firstis the shift ofharvesting into steeper, more ruggedterrain. The second stems fromenvironmental concerns regarding the high densitiesofroads and ground disturbance7Figure 1 Cutbiock design showing settings, landings and yarding roadsassociated with conventional cable yarding systems (Sauder et a!. 1987). New, morerestrictive plans require the use of alternative harvesting systems that reduce these andother detrimental impacts ofconventional yarding. For these reasons, a number ofBritish Columbia forest companies have acquired long-reaching, skyline cable yardingsystems. Skyline systems can help alleviate these problems whilemaintaining anacceptable level ofproductivity (Sears 1991, McNeel etal. 1991, Chittick 1991).A skyline is a wire rope suspended between two or more points(Conway 1982). Theskyline yarding system uses a carriage which movesalong a skyline. This provides fullclearance for logs when they are yarded to the landing (Figure2). A skyline systemcan avoid dragging logs on the ground during yarding whichother conventional cableYarding roadsCutbiock-boundaryF.-Setting boundary8Figure 2 Deflection ofcarriage below chord.systems (highlead and grapple yarder) cannot. Fully suspended logs minimize soil andsite disturbance and concurrently reduce damage to logs and equipment. Thesedisturbances have been linked to soil erosion and landslides in sensitive areas (Sauder eta!. 1987). The skyline system also offers considerable flexibility for meeting otherharvesting objectives ofprotecting environmentally sensitiveareas. For example, manycutbiocks contain creeks with critical riparian wildlife habitat. Withthe full suspensioncapability ofthe skyline system, suspended logs maybe lifted over standing timber leftto protect this habitat.The long reach capabilities ofthe skyline systemreduce the need for roads, particularlyin sensitive mid-slope areas with unstable soils (Sauderet a!. 1987, Hemphill 1991).9Road failures and subsequent landslides from mid-slope roads have been a major sourceofcontroversy between the forest industry and the environmental movement.Public concern regarding the aesthetic aspects of forest harvesting has prompted theMinistry ofForests to increase the emphasis on Visual Quality Objectives (VQO), ameasure ofthe alteration ofthe landscape due to human activity (Winkle 1992, Preus1992). Mid-slope roads are particularly undesirable since they create a stark contrast tothe natural surroundings. Slides caused by the construction ofthese roads exacerbatethe problem.3.1.3 Deflection and Deflection-line AnalysisThe one condition that is an absolute necessity for skyline systems in any situation isdeflection (Conway 1982). Deflection is the vertical distance, or sag, between thecarriage and an imaginary chord connecting the top ofthe skyline supports at either endofthe skyline (Figure 3). The greater the deflection the heavier the payload oflogs thatthe skyline system can support. This usually equates to higher production and loweryarding costs. As well, when dragging logs on the ground is avoided, site damage andequipment damage is minimized.Skyline systems are most effective for yarding over long distances and in ruggedterrain. For these same situations, it may be very difficult to obtain adequate deflectionnecessary for the desired payload of logs. The most important aspect ofplanning for10skyline systems is performing deflection-line analyses. Deflection-line analysis uses aprofile ofthe ground (a deflection-line) to determine ifthere is sufficient clearance ofpayload at the required deflection (Figure 4). This process is also an integralcomponent in determining the maximum possible yarding distance which in turn dictatesthe harvest boundary location. Insufficient clearance at a critical point on thedeflection-line will prevent achievement ofthe required deflection, and yarding will notbe possible beyond that point.Deflection-lines are obtained through surveys run in the proposed locationofthe skylineyarding roads. Stations, key points where elevations are determined, are situatedatsignificant changes in the slope (10% or more) along the deflection-line survey.TheFigure 3 Skyline yarding system with carriage and payload of logs.11.4for logsClearanceMaximumyarding distanceFigure 4 Clearance ofpayload load-path and selection ofboundary location.elevations ofthese stations are the most critical for clearance.Deflection-line analysis may be performed by manual or automated methods. While themanual approach is more common, it is also very simplistic and restricted. Deflection-line analysis works best as an interactive process where several different combinationsofroad, landing, and boundary locations may be investigated to find the best overallsolution. When using manual, field-based methods, the number ofdifferentcombinations which may be analyzed is limited, especially for skyline cutblocks, andoverall planning consequently suffers.Harvestboundary12Automated methods, which require computers, may be used to perform very complexand comprehensive analyses. These automated methods may be combined withdeflection-lines derived from a Digital Elevation Model (DEM). This extremely rapidapproach to deflection-line analysis allows for a complexity ofplanning not possibleusing manual methods. While automated deflection-line analysis should never replacefield surveys, it should be used to pre-plan those surveys, ensuring that the most criticalareas are checked in the field. This is becoming essential as pressure for better forestpractices increases.3.2 Digital Elevation Models3.2.1 TerminologyDigital Elevation Models (DEMs) are pseudo-three dimensional computer models whichBurrough (1986) defined as any “digital representation ofthe continuous variation ofreliefover space. Webb (1990) described them as a “representation ofa terrain surfaceconsisting ofX, Y, Z coordinates stored in digitalform.HThe term Digital TerrainModel (DTM) is often used synonymously with DEM. Burrough (1986) distinguishedthe two by specifying that DEMs contain only elevational data and that the word terrainspecifies additional information (slope, aspect) about the landscape. The term DEM hasbeen used here since this study dealt solely with elevational data.13There are two distinct types ofDEMs: the Triangulated Irregular Network (TIN) and theRegular Rectangular Grid (Grid). It is common practice to refer to DEMs and Grids asone and the same. From this, a comparison between DEMs and TINs is often madeincorrectly when the latter is actually a specific example ofthe former.3.2.2 Elevational Data SourcesPhotogrammetrically-measured contour maps are the most common form ofmodemtopographic maps (Petrie 1991). An overlapping pair of aerial photographs, called astereoscopic pair, is used to determine the elevation ofground features. This is possiblebecause ofstereoscopic vision, which allows an observer to gain the impression ofdepth when viewing an object from two different viewpoints (Wolf 1980). This is thesame principle by which human eyes perceive the depth ofan object. The apparentdisplacement ofthe object is called parallax, and the parallax difference between thebottom of an object and the top may be used to calculate the height ofthat object(Avery and Berlin 1985).Photo interpretation is the Metection, identification, description, and assessment ofsignificance of objects and patterns imaged on a photograph” (Wolf 1980). A photo-interpreter may use parallax differences to calculate the height oftrees on a stereoscopicpair of aerial photographs (Loetsch and Hailer 1964). The same principles are utilizedin a more complex process to determine ground elevations at different points on thephotographs. When the area ofconcern is heavily forested it may be extremely difficult14to see the ground (Loving 1980). Openings where the ground isvisible are used tomeasure both the ground elevation and the height ofthe adjacent trees. These treeheights are then used to estimate ground elevations where the ground is not visible.Aerial photography can be a relatively inexpensive method to obtain relevant data fortopographic mapping over a large area. The area to be mapped is flown in a series ofroughly parallel flight lines while vertical photographs are taken ofthe surface.Photographs are taken so that they overlap approximately 60 percent in the direction ofthe flight lines and 20 to 30 percent between flight lines (Avery and Berlin 1985). Thisprovides necessary stereoscopic coverage for the entire mapping area.A photo-interpreter may produce either analog or digital topographic maps from aerialphotographs. Analog maps are made directly from the aerial photographs withoutdigital information being stored, but maps must subsequently be converted to digitalform ifthey are to be used for automated deflection-line analysis. This process istedious, time consuming, expensive, and may introduce significant error into the digitalmap.This undesirable conversion process may be avoided altogether using recent techniqueswhich allow digital elevation data to be digitized directly from aerial photographs. Thisapproach has gained strong support with the advent ofGeographic Information Systemsand DEMs that are supported on powerful new personal computers.153.2.3 The Regular Rectangular GridThe regular rectangular grid is the most common and most readily available form ofDEM (Burrough 1986). It consists ofa regular rectangular grid containing Cartesiancoordinates in three-dimensions (Peucker et at. 1976). While there are many sources ofdata from which grids may be derived, digitized topographic contour maps aretraditionally the most commonly used (Maedel and Gaudreau 1989). The BritishColumbia government has recently created the Terrain Resource Information Mapping(TRIM) digital terrain maps. While these maps were created digitally and with amodern coordinate referencing system, they were also created at a scale of 1:20000 andwith a spot height densities appropriate for 20-metre contour intervals. The large scale(1:5000) topographic contour maps used in B.C. coastal forest planning typically havecontour intervals of 5 to 10 metres. It is unlikely that the TRIM maps could meet theaccuracy ofthese topographic planning maps.Grid DEMs are created by mathematically overlaying a grid ofpoints onto the digitalcontour map and interpolating the grid point elevations from the neighbouring contours(Maedel and Gaudreau 1989). The many different algorithms used for interpolatingthese points tend to ‘smooth out’ important terrain features such as peaks, ridgelines,gulleys, rockbluffs, and saddles. Ifthese terrain features are not properly represented,error in yarding distance estimation may occur during deflection-line analysis. Figure 5shows a scenario where a peak and adjacent ridge, indicated by spot elevations, werenot properly represented by regular grid points. Grid elevations are interpolated from16neighbouring contour and spot elevations and a weighting algorithm may be used togive more emphasis to the closest elevations. Unless a grid point falls directly on thespot elevation, the feature represented by the spot elevation will not be properlyrepresented by the interpolated grid elevations.The predominance ofregular rectangular grid DEMs is largely due to the ease withwhich they can be handled by the computer (Burrough 1986). Creation, storage andmanipulation ofelevational data is easily accomplished when it is in the regular gridformat. A variety ofuseful information may be derived from a grid DEM such ascontours, slope and aspect, hill shading, and automatic basin delineation. Grids are alsoeasy to conceptualize.17Problems associated with using regular grid DEMs in irregular terrain are welldocumented (Burrough 1986; Peucker et at. 1978). Foremost is their inadequacy indescribing irregular features within a regular framework. For example, a grid resolutionthat accurately displays the roughest terrain on a map results in great data redundancy inthe areas of lesser variation. This high density of data creates problems with storageand with the speed ofgeneration, handling and manipulation ofthe DEM. Conversely,ifresolution is decreased to reduce redundancy then accurate terrain representation iscompromised. While multiple-pass models exist which can increase grid density forareas ofhigher variation, a regular sampling pattern is still utilized and redundancy stilloccurs.3.2.4 The Triangulated Irregular NetworkThe Triangulated IrregularNetwork (TIN) model is based upon the philosophy that it isbetter to represent an irregular surface within an irregular data structure. In theirpioneering work, Peucker et a!. (1978) determined that it was necessary to have anelevation model where the locations ofdata points were dictated by the reliefofthesurface being modelled. This model would also have to be computatively efficient.TINs represent surfaces through locating data points at key topological features such aspeaks, pits, passes, ridges and channels (Peucker et a!. 1976). This results in irregularlyspaced points which are connected by lines to form a continuous sheet oftriangles.TIN models work best in areas with sharp breaks in slope where the edges ofthe18triangle may be aligned with those same breaks (Goodehild and Kemp 1990). Like thegrid model, the TiN model may also be used to derive other useful information.While TIN models may be created from contour data, proper selection ofdata pointsshould begin at the aerial photograph interpretation stage. Rather than deriving contoursfrom the photographs the interpreter should choose points that accurately represent all ofthe critical terrain features. Points are placed precisely on peaks, pits, and passes, andtriangle sides aligned very closely to ridge lines, break lines, and channels. The TINmodel thus allows data points to be concentrated in areas ofcomplex reliefwhile fewerpoints are collected from areas of smooth relief (Burrough 1986; Goodchild nd). Thisallows for both efficient and accurate modelling ofareas with variable terrain. Figure 6Regularly spaced grid points do not properly represent importantfeatures, such as peaks and passes.19shows a TIN representing the same data set fromFigure 5. The critical pointsrepresented by the spot elevations are incorporateddirectly into the TiN without anysmoothing oftheir elevations.3.2.5 Using DEMs in Operational Harvest PlanningThe potential ofusing DEMs for forest harvest planning had beensuggested as early as1974 (Burke 1974). Young and Lemkow (1976) developed a DigitalTerrain Simulator(a DTM) that could be used for many aspects offorest operations planning, includingdeflection-line analysis. Limitations with speed and storagecapacities ofdesktopcomputers ofthat time prevented the efficient use of such models.These limitationsoften resulted in a compromise ofthe accuracy ofthe data andlorthe integrity ofthemodels used to manipulate the data.These conditions persisted until rapid improvements in the abilities ofpersonalcomputers (PCs) occurred in the late 1980s. PC-based GeographicalInformationSystems (GIS) have allowed harvest planners to create and use their own complexDEMs. These GIS’s have employed mostly grid DEMs because oftheir ease ofhandling when using computers (Burrough 1986; Macdel and Gaudreau 1989). Thesemodels are poor representations ofthe ground surface and they have not achieved thefull potential ofDEMs in forest harvest planning.20The TiN model provides a method by which surfaces may be represented withaccuracies which should be acceptable to the demands ofharvest planning. Asdescribed above, in order to utilize TIN’s to their fullest potential, they should becreated from data collected specifically for triangular representation. Creating a TINfrom contour maps or grid will add additional error. Petrie (1990) stated that theaccuracy of photogrammetrically derived contours is typically only one third that ofspotheights measured directly from the same aerial photographs.Obviously remapping will not be immediately practical for many forest operations. Inthe interim, existing analog contour maps may be used so long as proper considerationis given to their limitations with respect to accuracy. Thus, when the opportunity arisesfor remapping, the operator will have an increased awareness ofthe accuracy issuespertaining to the use ofDEMs in forest operations planning.3.3 DEM Accuracy3.3.1 Errors: Blunders, Random, and SystematicThe United States Geological Survey (USGS) classifies DEM errors into threecategories: blunders, random errors, and systematic errors (Caruso 1987). Blunders aregross errors that are usually easy to detect and therefore edit. It should be noted thatblunders which go undetected could have severe effects upon DEM accuracy and theaccuracy ofresulting analysis.21Random error is lack ofprecision caused by measurement error. This could occurwhenthe photo-interpreter makes an erroneous measurement ofground elevation for one pointon the aerial photograph. This error would be a ‘blip’ on the map, not consistentwiththe surrounding errors. Ifthese errors are truly random, then they will tend to cancelout with increased sample size. For example, a surveyor reading a compass makes arandom measurement error ofone degree to the west. Since this error occurred bychance the more measurements the surveyor takes, the more chance the original errorwill be cancelled out by a random measurement error ofone degree to the east.Systematic errors may be due to bias in measurements. For example, a photo-interpreter may consistently underestimate the height oftrees on the aerial photographswhen trying to estimate the ground elevation. This would lead to a consistentoverestimation ofground elevations, spread evenly across the resulting map, whichwould be fairly easy to correct (Shearer 1991).If more than one factor is causing bias, then there may be an interaction effect on theerror. Tree height is somewhat dependent upon topography, with taller trees in valleybottoms and shorter trees on ridge tops. Ifthe photo-interpreter assumes all the trees tobe the same height, then ground elevations may be overestimated in the valley bottomand underestimated on the ridge. This would result in systematic error spread unevenlyacross the map which takes more skill to detect and correct.22The presence ofsystematic error confounds attempts to measure random error (Li1991). This study was focused on the causes and nature ofthe systematic error. Therewere two major sources ofsystematic error: smoothing error and positional error.Appendix A contains a summary list ofpotential sources oferror which may also affectthis study but which were beyond its scope.3.3.2 Smoothing ErrorSmoothing error occurs when natural terrain variation has been lost or ‘smoothed out’due to measurement error, interpolation patterns, or data transformations. Where thevariation is not represented properly by contours, grid points, or triangle edges, then thecontours, grid points, or triangle edges are positionally incorrect. Increased samplingcan reduce smoothing error by increasing the representation ofterrain variability. Theuse of intelligent interpolation routines which incorporate local terrain characteristics topredict terrain variability is an alternative to increased sampling. While smoothing erroris a result ofpositional error, the effects of smoothing error will be consideredindependent since they have particular importance to this study.The causes ofsmoothing error may be considered in two steps. The first step includesthe processes involved in the creation ofthe analog source maps. K.C. Soel, Limited,created a significant portion ofthe 1:5000 scale topographic maps on Vancouver Islandincluding those for Canfor’s Englewood Division (Soel 1992). Soel claimed that thecreation ofaccurate contour maps from aerial photographs is a combination ofthe23practical field experience ofthe interpreters, combined with their ability to see theground through the forest canopy. Combs (1980) stated that success in photo-interpretationllargely depends on the training and experience ofthe interpreter,characteristics ofobjects to be studied, and the quality ofthe photographs being used.”Using contours to represent terrain reliefwill contribute to smoothing error. Bychoosing set contour intervals, detail located between those intervals is systematicallyexcluded. When contours are created from aerial photographs, a fixed elevation ischosen and that elevation is traced from the photographs to create the contour (LaPrade1980). When tracing an elevation through rough terrain, it is very difficult to accuratelycapture all ofthe detail. Sharp terrain breaks could be somewhat rounded. Transfer ofinformation from one media to another and drafting can also have an effect which willcontribute to smoothing error as well.The second step includes the conversion ofcontour maps to digital form and thesubsequent extraction ofthe deflection-lines. When cartographic lines, such ascontours, are represented “as sets ofdigitized points joined by straight line segments”(Veregin 1989), generalization error occurs. The degree of generalization error willnormally increase as line complexity rises (Burrough 1986). For example, when handdigitizing contours, there is a tendency to literally cut corners on the contours as terrainvariability increases. This generalization error in the contours will equate to smoothingerror in DEM-derived deflection-lines.24Additional elevation points could help alleviate theeffects of smoothing error. Spotelevations could either be added for hill tops,ridges, and river or creek courses, or theycould be extrapolated from adjacent contours.The TIN creation process connects elevation points into acontinuous sheet oftriangles.There should be no loss of accuracy in this step because allofthe original informationis retained. This also holds true for the deflection-lineextraction procedure. Elevationsfor individual deflection-line points will be interpolatedfrom their location on a trianglewhich should not add any new error (Figure 7).253.3.3 Positional AccuracyFor this study, positional accuracy refers to the horizontal orplanimetric accuracy ofany feature represented on a map, analog or digital.Veregin (1989) defined positionalaccuracy as the accuracy of feature locations after transformationshave been applied.Ifthe process of measuring a feature for graphical and/ordigital representation can beconsidered a transformation, then this definition supports the terminologyused for thestudy.Positional error in a feature’s map location could exacerbate theelevational error ofthatfeature (Veregin 1989). When the feature is not properly located on the DEM,theextracted elevations will most likely be in error. Positional errormay be introduced inmost stages ofmap making and DEM creation and it is heavilydependent upon the ageofthe data, the quality ofsurveying used to collect the data, and upon therelevance ofthe data to the intended use (Burrough 1986).3.3.4 Age ofMap DataTie-points are features which are easily identified both in the field and on the map.Surveys are conducted from tie-points to deflection-lines, effectively ‘tying’ themtogether. This helps locate the deflection-line when it is transferred to the maps. Theaccuracy oftie-points was a very important aspect ofthis study.26The age ofthe data used to represent a dynamicfeature on a map influences itsaccuracy as a tie-point. Water bodies such as lakes and creeks are mostcommonlyused. Over time the shore lines and banks may change.The older the map data, themore likely that this has occurred.Photo-identification-points (PIPs) are features which are easily identifiable in thefieldfor which the exact coordinates have been determined at the time of map making.Unfortunately, many ofthese features are subject to change with age. Forexample,lone trees in openings are often used for PIPs. These trees may die and fallover,rendering them useless as a PIP.Logging roads are quite often used as tie-points for deflection-lines. Logging roadsurveys are affected by the quality ofthe surveying methods used and the type ofequipment used as well. These are often performed a few kilometres at a time,over thespan of many years. Error in bearing or in length can be compounded over the years aseach new survey inherits any uncorrected error from previous surveys. This isdiscussed further in the next section.Age ofthe data can affect the orientation of a feature when transferred to a map.Magnetic north shifts over time, and ifthe compass declination is not adjustedaccordingly there is the potential to introduce error in orientation.27Another very significant consequence ofdata agecomes from a change in majorreference systems. The North American Datum (NAD)was adopted as the standardcoordinate referencing system by Canada, the United States,and Mexico in 1913 (Pinch1990). The origin for the NAD was a marked pointknown as Meades Ranch, locatedin Kansas. In 1927 recomputations were begun to eliminateunacceptable errors in theNAD coordinates. This became known as the NorthAmerican Datum of 1927(NAD27).As the NAD27 network was extended and densified over the next fiftyyears, there wasan accumulation ofsystematic errors (Pinch 1990). Thissystematic error, combinedwith the inherent limitations ofthe system led to the development ofthe NorthAmerican Datum of 1983 (NAD83). The NAD83 uses the centre of mass oftheEarthas its origin, which makes it useful for global satellite positioning. Errors in theNAD83 coordinates are much smaller, less systematic, and insignificant compared toNAD27 coordinates. Converting coordinate data from the NAD27 to the NAD83involves transformations which add positional, and therefore, elevational error.3.3.5 Surveying QualityField surveys are prone to error, the level ofwhich depends upon the precision andcondition ofthe equipment used. Traditional surveying methods for harvest planningutilize a nylon chain, hand-held compass, and a clinometer. The rigorous conditions ofthe harvest surveys can affect the equipment precision. The nylon chain can stretch28after being repeatedly wetted. The compass and clinometercan lose precision whendropped or struck against hard objects. Ifthese instruments are not checkedforaccuracy, they can introduce positional error to the survey data. Positionalerror fromlogging road surveys can accumulate as new surveys are performed based upontheerroneous locations ofexisting logging roads.Human error in surveying may result in the displacement ofthe feature beingsurveyed.If a surveyor has a tendency to read bearings incorrectly in the same direction, then biaswill be introduced. The survey will diverge from the intended bearing, which is thebearing with which the feature will be represented on the map.Elevations will beextracted from the intended location on the DEM, and will appear to be in error whencompared to the field surveyed elevations. This will be more evident in more variableterrain.Elevational data is obtained through some type ofsurvey, usually aerial photographicsurveys. The quality ofthe equipment used, the atmospheric conditions, and thecondition ofthe film can all influence the positional accuracy ofmap features.Presence ofhaze when the aerial photographs were taken will make it more difficult forthe photo-interpreter to see the ground, compared to aerial photographs taken when theair was clear. This could result in incorrect estimation ofthe ground elevation whichcould, in turn, result in positionally incorrect contours. While these errors are likely tooccur, they will have much less influence on elevational accuracy than field surveyingerror.293.3.6 Data RelevanceAnother factor which can lead to positional erroris the relevance ofthe data to thepurpose for which they are applied. Roads andcreeks were often drafted on maps morefor representational purposes than for accuracy. Theroads may be broken to fit sidehillcreek crossings, with little concern for the accuracy ofthe road inbetween thecrossings. Using these features as tie-points will likely add positionalerror, whichcould be significant when very high accuracy isrequired.Sidehill creeks, often used as tie-points, may not bevisible on aerial photographs. Thelocation ofthe creek is indicated by the presence of a smallvalley running down thesidehill, evident in the elevations ofthe forest canopy. The exactlocation ofthe creekchannel will be estimated by the photo-interpreter andthen represented by a sharp lineon the map. When road plots are transferred to the planning mapsthe creeks are oftenused to locate the roads. Any positional error in the location ofthe creekwill beinherited by the road.The description accompanying a PIP can often be very ambiguous. For example,a PIPmay be described as being in the Northwest corner ofcleared area.” The cornerofthiscleared area may have appeared to be a sharp line to the photogrammetrist. On theground the boundary ofthe cleared area could be transitionalover many metres makingthe exact corner very difficult to locate. To make matters worse, thecleared area maysince have been extended from the corner. The PIP will then be located somewhere30along the edge ofthe cleared area, perhapsimpossible to locate. When accuracies ofless than five metres are desired these types oftie-points are notvery useful.3.3.7 DEM Accuracy Standards and TestsThompson (1956) recognized early that a map’s accuracy was not independentofthepurpose for which it was to be used. Thompson(1960) also discussed many cases inwhich topographic maps were being used in which an accuracy ispresumed that wasnever intended.’ This is still relevant today when most ofthe existingtopographicmaps were created before the advent ofsmall, powerful, andextremely precise GISprograms. Converting these maps to DEMs for use in more demandingoperations maynot be feasible. Ultimately, new elevational data should be collectedspecifically forDEMs at an accuracy level suitable to the new use. Sincethis will not always bepossible, old topographic maps may be used, provided their limitationsare taken intoconsideration. Essential knowledge ofthese limitations may be gained through testingmaps with methods appropriate to their new intended use.There are several accuracy standards in existence for both topographic maps and DEMs.In the United States, the definitive standard is the National Map Accuracy Standard(NMAS) which is applied to the USGS topographic map series (Veregin 1989). Testsare based upon “a comparison of at least 20 well-defined map points relative to asurvey ofhigher accuracy.” For vertical accuracy, NMAS states that not more than 1031percent ofthe elevations tested should be in error of more than one-halfthe contourinterval (Thompson 1988).In British Columbia, the Surveys and Resource Mapping Branch ofthe Ministry ofEnvironment, Lands, and Parks uses accuracy standards set out under the NorthAmerican Treaty Organization’s (NATO) specifications for 1:5000 and 1:2500 scaletopographic maps and DEMs (M0ELP 1992). For 1:5000 scale “ninety percent ofalldiscrete spot elevations and DEM points shall be accurate to within 1.25 metre”. Whilethis value is statistically derived, the error is assessed using the same pass/fail methodas the NMAS.Both the NMAS and the NATO tests are intended for compliance to an accuracystandard and provide virtually no information as to the magnitude ofindividual errors(Veregin 1989). There can be a significant difference in accuracy between one mapwith ninety percent ofsampled elevations just under one-halfcontour interval in errorand another map with ninety percent ofthe points free from error altogether. Similarly,a rejected sample elevation could be in error ofjust over one-halfofthe contourinterval and be considered the same as one that is, for example, in error by three timesthe contour interval.The American Society ofCivil Engineers has developed the Engineering Map AccuracyStandard (EMAS) for large scale maps (Veregin 1989). The EMAS is designed to be32application specific and checks error data against maximum acceptable limits as set bythe map user. EMAS tests both the sample mean errors and the standard deviation.An alternative statistic that has been used in the United States is called the Root MeanSquare Error (RMSE) (Gustafson and Loon 1981):RMSE = ±(e12/n) RMSE Root Mean Squared Errore = elevational error ofthe ith test pointn = number oftest pointsThe RMSE is preferred to the 90% criteria “since it is completely unambiguous andnot easily subject to misuse.” The RMSE is equivalent to the standard deviation whenmean error is equal to zero. RMSE is often equated with standard deviation for allcases without making proper distinctions.Another option is Koppe’s formula which recognizes the relationship between verticalerror and terrain slope (Gustafson and Loon 1981). A contour that is positionallyincorrect by 5 metres will cause less vertical error on a 5 percent slope than on an 80percent slope. The Koppe formula has incorporated an increasing tolerance for verticalerror as terrain slope increases.Gustafson and Loon (1981) noted that most map accuracy standards assume thatblunders and systematic errors have been eliminated from map error and that the33residual random error is normally distributed. In reality,systematic error will always bepresent to some degree, especially in areas that are difficult tomap.Thompson (1960) singled out terrain covered with tall,dense, coniferous forests as areaswhich caused difficulties in mapping. Theproblem ofaccuracy testing in heavilyforested areas is dealt with in much thesame way: it is avoided. For example, a studyon DEM accuracy testing in Great Britain stated that“not every [test] point wasmeasured because a certain number fell in awoodland area or on some other unsuitablefeatures” (Li 1991).Existing accuracy standards often state thatpoints tested must be clear and well defined.For example, the NATO standards used in B.C.specif’ that stated elevational accuracies“relate to ground not sufficiently obscured by vegetationor other features to causesignificant error” (M0ELP 1992).Finally, it is possible to have a deflection-line derivedfrom a DEM that has anacceptable mean error but that has oneor more single large errors which willsignificantly affect the analysis. Forexample, the photo-interpreter may failto detect aridge due to the nature ofthe forest cover.The ridge would not be plotted on thetopographic map and subsequentlynot represented by the DEM. Deflection-lineanalysis from field surveys will identifythe ridge as the yarding boundary.Analysisperformed using the DEM could resultin a longer yarding distance estimatedue to the34absence ofthe ridge. This could lead to significant equipment, log and site damage, aswell as lower productivity.For the purposes of deflection-line analysis, traditional statistics may not be sufficient toadequately assess the elevational accuracy ofDEM-derived deflection-lines. The meanerror indicates a trend in the elevational error, but it does not represent any individualfeature on the deflection-line which may cause problems. Large individual errors maybe masked by the mean error through the cancelation ofpositive and negative errors.354 Study SiteField surveys were performed on northern Vancouver Island, British Columbia, incooperation with the Englewood Logging Division ofCanadian Forest Products (Canfor)Ltd. The Englewood Logging Division, located in Woss, operates under Tree FarmLicence (TFL) 37 and Forest Licence (FL) A19233 in the Nimpkish Valley region(Figure 8). The dominant landforms in the region are ofglacial origin with someintermixed volcanic influence (intrusions). The Nimpkish region is within the CoastalWestern Hemlock biogeoclimatic zone.Seven proposed skyline cutblocks (individual harvest units) were selected for study,three in the TFL and four in the FL (Figure 8). These cutbiocks were chosen primarilydue to their scheduling for harvest and secondarily for representation ofthe forest andterrain types within which skyline systems operate. A total ofthirty-one deflection-lineswere located at potential landings within the seven cutbiocks (Figures 9-15). Generalcharacteristics ofeach cutbiock are shown in Table 1. Table 2 shows the distribution ofindividual deflection-lines and settings within each cutblock. All cutblocks were locatedin old growth forest types.36Figure 8 Map ofcutbiock locations within Canfor operating area.Legend/•1-4IFrjI‘%4-I/•1SI¶—10 kmS/VncouvenIslondCutbiock ® RGLokesRiuersTFL Boundary — — —FL BoundôryRoecjs37Figure 9 Map of AR1 cutbiock and deflection-linelocations.-, —7 / (/ I - ( — ----——-. —S,/_/131 !/ Th/ FI(// II —ii1II(/1/t:1 — I2L4\\\ -7 ))II / —‘—-— J 11 ——-ThII\ I (\\\___\‘b‘_i1’21 235\ \\N\ \___LegendDeflection-line30.5 m contourIs II7.7 m contourRoodCreek38Figure 10 Map of AR6 cutbiock and deflection-line locations.Legendoriect1on—I meC COntOM7.7 m COflteu*Rad = = =Creek — -I100 metresFigure 11 Map of AT cutbiock and deflection-linelocations.Legend39100 metresIDePIection- me30.5 m contotr7.7 rn contourReedCreek40Figure 12 Map of CU cutblock and deflection-line locations.100 metresLegendDerection-I me30.5 m contour7.7 rn cont.ourRoodCreekGround survey[ZZ E..,41Figure 13 Map of GC cutbiock and deflection-line locations.100 METRESIr1LegendOePectiori— me30.5 m cortour7.7 m contourRoodCreek —Ground surveyr42Figure 14 Map ofNW cutbiock and deflection-line locations.IrLegend100 metresDerIection-ire30.5 m contour7.7 m contourRoeciCreek —River43Figure 15 Map of TR cutbiock and deflection-line locations.100 metresILegend4Oerlection-I me30.5 m contour7.7 m contourRoedCreek —44Table 1 Summary ofcutbiock characteristics.Cutbiock Slope1)Elevations Brokenness2)Forest Type3)(%) (m)AR1 30 90 - 200 M CWHb1, CWHb2(0-146) H - creek canyon Hw, Cw,BaAR6 50 180-500 M CWHb1,2(2-120) H - upper slopes Hw, Ba, (Cy ,Ss)AT 46 400 - 800 S - lower flat area CWHb1,2(0-104) L - slope and ridge Hw, Ba, Cw, CyCU 47 600 - 850 M CWHb2, MHa(11-72) Cw, Hw, Ba, (Cw)GC 46 200 - 550 M CWHa1(0-131) Df, Hw, (Cw)NW 44 150 - 550 M - upper slope CWHb1(0-96) L - valley bottom Hw, Ba, (Cw)TR 55 350 - 700 H CWHb2(2-150) Hw, Ba, Cw, Cy1) average slopes found from deflection-line surveys (range in brackets)2) S - SmoothL - LowM - ModerateH-High- terrain is basically flat, no impediment to traverse.- gently undulating, rock outcrops, no impediment to traverse.- significant rock bluffs, creek canyons, difficult to traverse.- cliffs, sheer sided creek canyons, difficult to impossible totraverse.3) Biogeoclimatic Units (Nuszdorfer et al. 1985)MHa - Mountain Hemlock, Maritime ForestedCWHa - Coastal Western Hemlock, Maritime, dryCWHb1 - Coastal Western Hemlock, Windward Submontane Maritime, wetCWHb2 - Coastal Western Hemlock, Windward Montane Maritime, wetDominant Tree Species (Watts 1983)Ba - Abies amabilis (Dougl.) Forbes (amabilis fir, Pacific silver fir, balsam)Cw - Thujaplicata Donn (western red cedar)Cy - Chamaecyparis noolkatensis (D. Don) Spach (yellow cedar, cypress)Fd - Pseudotsuga menziesii (Mirb.) Franco var. menziesii (Douglas-fir)Hw - Tsuga heterophylla (Raf.) Sarg. (western hemlock - may be mountain hemlockin upper elevations - not specified)Ss - Picea sitchensis(Bong.) Carr. (sitka spruce - adjacent to main creek, AR691)45Distribution of deflection-lines and settings within cutblocks.Cutblock1)Setting Deflection -linesAR1 AR1-Si AR1-ilAR1-S2 AR1-12AR6 AR6-S1 AR6-1AR6-S2 AR6-2, AR6-3AR6-S3 AR6-4AR6-S4 AR6-5AR6-S5 AR6-la, AR6-2aAT AT-Si AT-200, AT-201AT-S2 AT-202, AT-203CU CU-Si CU-20, CU-21CU-S2 CU-22, CU-23GC GC-L1 GC-i, GC-2, GC-3GC-S2 GC-4, GC-5GC-S3 GC-7, GC-8GC-S4 GC-iONW NW-Si NW-i, NW-2TR TR-S1 TR-i, TR-4TR-S2 TR-2, TR-3Cutblock names have been shortened for clarity and for consistency with settingand deflection-line names. The full names are listed below:AR1 AR16OAR6 AR691AT AT295BCU CU7GC GC6NW NW74TR TR38Table 2465 Methods5.1 Cutblock and Deflection-line SelectionThirty-one deflection-lines were located in the seven proposed skyline cutbiocks by theEnglewood engineering staff. The first step involved visual inspection ofthetopographic maps to identify potential landings for the skyline machine. Landings werelocated on established, traversed or proposed road locations depending on the area andthe state ofits development. Deflection-lines started from these landings and weresurveyed on constant bearings. The bearings were chosen in areas which,through thevisual inspection, were potential problem areas for clearance. All landingslocated onsurveyed roadway were surveyed to the nearest established roadway.5.2 Field SurveysField surveys were conducted using Canfor’s standard method oftight-chaining witha50-metre nylon chain, hand-held compass and clinometer. Survey stations werelocatedat changes in terrain slope often percent or more, and at significantfeatures such ascreeks and traversed and established roads. Foresights and backsights were takenwiththe compass to identify and eliminate bearing errors due to human error ormagneticanomalies. The nylon chain had tags set at increments 1.0 metres, withtags at 0.1metre for the first metre. Length measurements were takenin such a way as to ensurethat they were accurate to within the 0.1 metre markings. The compass hada minimum47increment of 2 degrees. The clinometer had increments of 1 percent for slopes of 0 to70 percent and 2 for slopes of70 to 150 percent.Tie-point surveys were conducted to locate the deflection-lines. Initial tie-points wereestablished or surveyed roads and junctions, creeks and junctions, as well as PIPs whichwere located from the original air photo mapping. Potential tie-points were firstselected on maps and then attempts were made to locate them in the field. It becameevident that, for the most part, the map representation ofthese tie-points contained ahigh degree of positional error and most ofthem were found to be unreliable.Furthermore, some tie-points were extremely hard to find in the steep and heavilyforested terrain ofthe study cutblocks.In an attempt to improve tie-point accuracy, a Trimble 4000SE Land Surveyor (TrimbleNavigation Ltd. 1992) Global Positioning System (GPS) in conjunction with a Criterion(Laser Technologies Inc. 1992) hand held laser surveyor were tested in theGCcutbiock. The GPS was used to locate four well distributed tie-points and then the laserwas used to traverse the deflection-lines and tie them to the tie-points.All ofthe cutblock maps were in NAD27 coordinates and any recent additions tothedata, from sources such as GPS, were in NAD83. To bypass the problems associatedwith transferring between these two datums, GPS survey datawere integrated by fittingthem to the map features, independent ofthe reference system. This avoided thenecessary complex transformations.48Deflection-line and tie-point survey notes were compiled using ROADENG (SoftreeTechnical Systems 1992) forest engineering software. The deflection-line and tie-pointlocations were plotted from ROADENG using a HP DraftMaster 1-drum plotter(Hewlett-Packard Company 1987). The plots were used to transfer deflection-linelocations to the maps.5.3 Digital Elevation ModelsCanfor provided mylar map copies for each ofthe seven cutblocks. These maps wereof 1:5000 scale with a 7.62-metre (25-foot) contour interval. The original mylar basemaps were compiled in 1973 at a scale of 1:4800 (1 inch = 400 feet) and later photo-reduced to 1:5000 scale. The base maps were primarily photogrammetrically-measured.Although small portions ofthe GC and CU cutblock maps were field surveyed, theanalysis focused on the photogrammetric portion ofthe maps.Contours and relevant features were hand digitized into TerraSoft version 10.03 (DigitalResource Systems Ltd. 1992) map files using an Altek Model AC3O (GentianElectronics Ltd. 1987) digitizing tablet. Contours were stream digitized, with pointsrecorded every 0.3 millimetres (1.5 metres ground scale) along the contours. Sincesmoothing error had already existed in the maps, additional effort was made to keep thecross hairs ofthe digitizer puck in the centre ofthe contour at all times. While this wasoften difficult, especially in the most variable terrain, the addition ofmore smoothingerror was likely kept to a minimum. Contour points were thinned using a weeding49corridor of 1.0 metres to reduce data redundancy (Digital Resource Systems Ltd. 1992).Deflection-line locations were digitized from the mylar maps into the correspondingmap files.Triangulated Irregular Networks (T]Ns) were created for each map. The TIN modelwas chosen over the grid model due to its superior representation ofterrain features.Specialized software extracted deflection-line data by draping the location ofthedeflection-lines onto the DEM. The software allowed the horizontal spacing oftheelevation points to be set at one metre to conform with the precision ofthe fieldsurveys. An ASCII file was produced in the form ofcumulative horizontal and verticalvalues referenced to the origin ofthe deflection-line.5.4 Estimating Yarding DistanceDeflection-line analysis was performed on the deflection-line pairs to estimate theyarding distance. These distances were compared to see ifthe DEM-derived deflectionline caused any erroneous estimations. The Terrain module ofROADENG (SoftreeTechnical Systems 1992) was used for the deflection-line analysis. Cableconfigurations, machine specifications, type ofanalysis, calculation parameters, andother options were selected to conform to Canfor’s procedures.Analyses for deflection-line pairs were performed keeping most, but not all, optionsconstant. Within given ranges, different payload weights and equivalent mid-span50deflections were allowed for analysis ofeach deflection-line pair. The DEM-deriveddeflection-line was analyzed independent ofthe analysis ofthe field surveyeddeflection-line and vice versa. This was done to mimic an operational deflection-lineanalysis in which a forest engineer would be using one type of deflection-line, or theother, but not both.For example, a midsiope bench, evident on a field surveyed deflection-line, may proveto be an impassable boundary for yarding when performing deflection-line analysis.Since reducing payload or mid-span deflection cannot overcome this obstacle, theseoptions would not be pursued. Ifthe bench were less evident on the correspondingDEM-derived deflection-line, reducing payload and mid-span deflection may provideclearance over the bench, causing an erroneous yarding distance estimate. In anoperational use ofDEM-derived deflection-lines, the forest engineer would not have thefield surveyed deflection-line with which to compare, and would therefore make theerroneous estimation.5.4.1 Statistical AnalysisDeflection-line analyses were performed using the DEM-derived and field surveyeddeflection-line pairs to estimate yarding distances. These distances were then comparedto see ifthe DEM-derived deflection-lines give significantly different results. Thehypothesis for this experiment was:51H1o: There is no significant difference between yarding distance estimatedusing paired DEM-derived and field surveyed deflection-lines.H1a: There is a significant difference between yarding distance estimated usingpaired DEM-derived and field surveyed deflection-lines.Failure to reject the null hypothesis indicates that the elevational error ofDEM-deriveddeflection-lines does not affect yarding distance estimates. Rejection ofthe nullhypothesis shows that the elevational error ofDEM-derived deflection-lines does affectyarding distance estimates.The errors in yarding distance estimates were tested using Systat 5.2 (Systat 1992). Theerrors were checked for normalcy using the Lilliefors Test, a modification oftheKolmogorov-Smirnov test used for non-standardized data (Systat 1992). A significancelevel of oc=O. 1 was used. This large alpha value (0.1) was used to reduce the chance ofa Type 2 error, accepting the null hypothesis when it is false (Walpole 1982). Inpractical terms, the test was designed to reduce the chance ofindicating that the yardingdistance estimates from the DEM-derived deflection-lines were the same as those fromthe field surveyed deflection-lines, when they were not. Since the error displayed anon-normal distribution, a sign-test was used to test the mean.525.5 DEM Elevational ErrorElevations were sampled using deflection-lines, which are ground profiles. Gossard(1976) lists profiles as an objective means for analyzing elevational errors, while Kellieand Bryan (1981) found no statistical differences between elevational error estimatesbased upon profile data and on randomly located point data. Kellie and Bryan locatedthe profiles randomly, unlike the methodology in this study, in an attempt to estimatethe general elevational accuracy ofthe DEM. The systematic selection ofdeflection-line locations was considered to be within the bounds ofthe study, since the studyfocussed on accuracy issues related to DEM-derived deflection-lines.The ASCII files were loaded into a spreadsheet for manipulation and comparison withfield surveyed deflection-line data. Macros were created to eliminate extraneous dataand to interpolate and extract points that aligned with the survey stations. This allowedthe field surveyed and DEM-derived deflection-line elevations to be compared atcommon horizontal points. These deflection-line pairs were plotted together as a visualcheck for blunders in the elevations ofthe DEM-derived deflection-line. Whendeflection-lines indicating blunders were detected, the DEMs and topographic mapswere checked to determine the source ofthe blunder. Ifthe blunder could not becorrected, analyses were performed with and without the affected deflection-lines andthe results compared.53The field surveyed and DEM-derived deflection-line elevations were given the sameelevation, or calibrated, at one station. This station was chosen as the one where thephoto-interpreter could obtain the best measurement ofthe ground elevation. Thiswould be the station on the DEM-derived deflection-line with the most accurateelevation. This was usually a creek crossing, if it was sufficiently visible on the aerialphotographs. Ifthere was no major creek, or ifthe creek was hard to see on the aerialphotographs, a ridge-top or an open rock-bluffwas used.Deflection-line pairs were then compared at individual stations to obtain elevationaldifferences. The field surveyed elevations were the controls, and any differencesbetween them and the DEM elevations were considered to be errors. The error waspositive when the DEM elevation was higher than the field surveyed elevation.Conversely, the error was negative when the DEM elevation was lower than the fieldsurveyed elevation.It had been assumed that the elevations ofthe field surveyed deflection-lines weremeasured without error. Although this was not possible, most accuracy tests specifythat DEM elevations should be tested against a survey ofhigher accuracy. Since thefield surveyed elevations should be, on average, more accurate than those ofthe DEMderived deflection-lines, this criteria of accuracy testing had been met. However, whenconsidering the results, the possible confounding effects of field surveying error shouldbe considered.545.5.1 Statistical AnalysisElevational errors were grouped by individual deflection-line, setting, cutbiock, and byentire study area. Characteristics common to these groupings might haveinfluenced thenature and/or magnitude ofthe elevational errors. For example, some ofthe cutbiockshad distinctly different forest types when compared to other cutbiocks. Thesedifferentforest types may have had differing effects upon the photo-interpreters ability toaccurately measure ground elevations. These differences may have led to varying levelsoferror, or different patterns ofsystematic error, depending upon the forest type.Error was grouped by settings to test the quality ofthe tie-points used to locate thelandings within each setting. The landing within each setting served as a common tie-point for all the deflection-lines contained within that setting. Some settings containedonly one deflection-line, a result ofthe limited resources available for manuallysurveying skyline deflection-lines. Error grouped by deflection-lines was used to assessthe elevational accuracy ofthe deflection-lines themselves and the comparison plotswere used to detect and illustrate the different error patterns which may exist in DEMsand DEM-derived deflection-lines.The hypothesis for this section ofthe study was:H2o: There is no significant difference between the elevation ofpaired pointson the DEM-derived and field surveyed deflection-lines.55H2a: There is a significant difference between the elevation ofpaired points onthe DEM-derived and field surveyed deflection-lines.The statistical testing determined ifthe mean error was significantly different from zero.This was intended to show ifthe elevations ofthe DEM-derived deflection-linesadequately represented the elevations ofthe field surveyed deflection-lines. Whileforest planners may be willing to accept mean errors which are not equal to zero, it wasnot possible to pick one mean error level acceptable for every deflection-line.Therefore, error testing was restricted to detecting mean errors which were significantlydifferent from zero, allowing forest planners to judge how much error may acceptable.Existing accuracy standards had various inadequacies which made them unacceptablefor testing the elevational accuracy ofDEM-derived deflection-lines. Statistically basedstandards assume that the error is normally distributed; an assumption not made in thisstudy. Other standards stated that ground points tested for elevational error must beclear and well defined, a situation not often found in the heavily forested areas plannedfor harvesting. Finally, some standards required only ninety percent ofthe errors tomeet an acceptable level. The largest errors, which were the most critical for estimatingyarding distance, were therefore ignored. This was not acceptable for assessing theelevational accuracy ofDEM-derived deflection-lines.Data from the spreadsheet comparisons was imported into Systat 5.2 (Systat 1992) inorder to perform the statistical analyses. Histograms were created for visual inspection56ofthe distribution ofthe errors. The data were then checked for normality using theLilliefors Test, a modification ofthe Kolmogorov-Smirnov test used for nonstandardized data (Systat 1992). A significance level ofx=0.1 was used for all tests(results atcc=0.05 are included). The large alpha value (0.1) was used to reduce thechance of a Type 2 error, accepting the null hypothesis when it is false (Walpole 1982).In practical terms, the test was designed to reduce the chance of indicating that theDEM-derived elevations were the same as the field-surveyed elevations when they werenot.For samples with normal distributions, a paired t-test was used. For samples with non-normal distributions, a sign-test was used. The sign test is less efficient than the t-testin that it does not utilize as much information (Walpole 1982). In this study, ifthenumber of negative errors was approximately equal to the number ofpositive errors, thesign test indicated that the mean error was not significantly different from zero. Resultsofthis test could have been misleading since it did not consider the size ofeachindividual error. A deflection-line could have had an equal number ofpositive andnegative elevational errors, but the positive errors were greater in absolute magnitudethan the negative errors. This would have been indicative ofsome type ofsystematicerror which could have had a very significant effect upon the yarding distanceestimates. Also, the severity and type of error in the yarding distance estimates mayhave varied, depending upon whether individual elevational errors were positive ornegative. Neither the sign test nor the paired t-test provided this information. To avoidthese scenarios, results ofhypothesis tests were evaluated in conjunction with57histograms, comparison plots, and basicdescriptive statistics (mean error, meanabsoluteerror, maximum negative and positive errors,the range ofthe error, and thestandarddeviation). As well, on the ground experiencegained from field surveying eachindividual deflection-line added valuable insight tothe evaluation.Mean and mean absolute errors were usedas a guide to the relative levels ofsystematicand random error in the elevations oftheDEM-derived deflection-lines. Ifboth themean error was low (less than two metres)and mean absolute error was low (less thanfour metres) then there was a low level ofboth types oferror. Ifthe mean errorwaslow and the mean absolute error was highthen the error was mostly random. Ifthemean error was high but very similar to the meanabsolute error (within two metres)then the error was mostly systematic. Ifthe mean error was high and the mean absoluteerror more than two metres higher thenthere was both random and systematic errorpresent. The size ofthe mean andmean absolute errors used to detect the presence ofthe different error patterns were determined by comparingdeflection-lines whichdisplayed obvious error patterns with deflection-lineswhich displayed no obvious errorpatterns.586 ResultsVisual inspection of both deflection-line comparison plots for the AR1 cutbiock, AR1-11 and AR1-12, showed an apparent blunder. Both deflection-line pairs divergedsteadily indicating a possible error in the contour interval ofthe source map. Since thecause ofthis blunder could not be determined conclusively (and corrected), analyseswere conducted with and without the cutbiock for comparison.Portions of both the GC and the CU cutbiock maps had been created from field surveyswhile the rest were photogrammetrically derived. The GC deflection-lines were alllocated within the photogrammetric portions ofthe maps so they were not affected. ForCU approximately one halfofdeflection-lines CU-22 and CU-23 were located withinthe field surveyed map. It was possible that significant positional error could haveoccurred at the boundary between the two different mapping techniques. This errorcould not have been accounted for and therefore would have had an unknown influenceupon the elevational error. Due to this uncertainty, analyses were also conducted withand without these two deflection-lines.6.1 Estimating Yarding DistanceError in yarding distance estimates was deemed positive when the estimate from theDEM-clerived deflection-line was longer (an overestimation) and negative when theestimate was shorter (an underestimation). Twenty-two ofthirty-one (71%) deflection-59lines produced the same yarding distance estimates. For all thirty-onedeflection-linesthe mean error in yarding distance estimates was -4.1 m and the range oferror was -54.1 m to 127.7 m. Two errors were positive and seven were negative. Themean errorwas not significantly different from zero using a sign test(cc= 0.1). The inherentinefficiency ofthe sign test should be taken into account when considering these results.Ofthe four deflection-lines which were eliminated, only AR1-12 displayed an error inyarding distance estimate and this error was the largest from all thirty-one deflection-lines, at 127.7 m. When CU-22, CU-23, AR1-1 1, and AR1-12 were eliminated, themean error in yarding distance estimates was significantly different from zero atcc= 0.1but not significantly different from zero atcc= 0.05 (p0.07). Once again the sign testwas used, and the results analyzed accordingly. The mean error was -9.4 m, and rangedfrom -54.1 m to 7.8 m. Nineteen oftwenty-seven deflection-line pairs (70%) producedthe same yarding distance estimates. More detailed information is presented inAppendix B.Longer deflection-lines had more tendency towards erroneous yarding distance estimatesthan did shorter deflection-lines. The four longest deflection-lines had erroneousyarding distance estimates, and six ofthe nine deflection-lines with different estimationswere from the eight longest deflection-lines. Considering the error in yarding distanceestimates as a percentage ofthe deflection-line length, the average error percentage was2.4% for all deflection-lines and 8.2 % for the nine incorrect yarding distance estimates.606.2 DEM Elevational ErrorDescriptive and inferential statistics were calculated for the elevational differences atindividual stations for all pairs ofthe thirty-one deflection-lines. The mean error for alldata (n=675) was 1.4 m, which was significantly different from zero forcx=0.1. Themean absolute error was 5,8 m. The error ranged from -22.9 m to 26.8 m and thestandard deviation was 7.5 m. Elimination ofAR1-1 1 and AR1-12 made virtually nodifference to the mean error, range, or standard deviation. The same was true whenCU-22 and CU-23 were eliminated. Additional results for AR1 and CU, by cutblock,setting, and deflection-line are tabulated in Appendix C.All ofthe following results are for the twenty-seven deflection-lines remaining after theelimination of CU-22, CU-23, AR1-11, and AR1-12. The mean error for all data(n=594) was 1.6 m, which was significantly different from zero forx0.1. The meanabsolute error was 5.3 m, the error range was 49.2 m (-22.4 m to 26.8 m), and thestandard deviation was 6.9 m. Data were also analyzed by cutblock, setting, and byindividual deflection-line toidentifS’patterns and attempt to isolate the error sources.Detailed results, including the statistical tests used for individual analysis, are tabulatedin Appendix C.The mean error by cutblock ranged from -1.0 to 5.8 m. Three cutblocks had positivemean errors and three had negative mean errors. The mean absolute error ranged from614.4 to 7.0 m. The smallest range oferror was 29.2 m and the largest was 42.7 m. Thestandard deviations ranged from 5.7 m to 8.8 m.The mean error was significantly different from zero for AT, GC, NW, and TR(ccO.1).AT had the highest overall error with the largest mean error, mean absolute error,positive error, range, and standard deviation. CU had the lowest overall error with thesmallest mean error, mean absolute error, range and standard deviation.The mean error by setting ranged from -2.2 to 8.4 m and thirteen ofthe eighteensettings had positive mean errors (72%). The mean absolute error ranged from 3.5 to12.0 m. The smallest range oferror was 15.4 m and the largest was 42.7 m. Thestandard deviation ranged between 4.5 m and 14.2 m.The mean error was significantly different from zero for five ofeleven settings. One ofthese was GC-S1, which had the lowest overall error, and which had the smallest meanabsolute error, range, and standard deviation. Another setting with a mean errorsignificantly different from zero was AT-S2 which had the highest mean error, meanabsolute error, maximum positive error, range, and standard deviation.For the error analyzed by deflection-line, the mean ranged from -4.1 to 7.4 m. Elevenoftwenty seven (41%) deflection-lines had mean errors that were significantly differentfrom zero (x0.1). Nineteen deflection-lines (70 %) had positive mean errors. Themean absolute error ranged from 2.7 to 9.5 m. The smallest range was 9.2 and thelargest was 42.3. The smallest standard deviation was 2.8 and the largest was 10.4.GC-2 had the overall lowest error with the smallest mean absolute error, range, andstandard deviation. TR-4 had the overall highest level oferror with the largest meanabsolute error, range, and standard deviation.62637 Discussion7.1 Estimating Yarding DistanceStatistical analysis had shown that the error in yarding distances, estimated from DEMderived deflection-lines, was not significantly different from zero(cx=0.1) when thethirty-one deflection-lines were tested and was significantly different from zero(x=0.1)when CU-22, CU-23, AR1-1 1, and AR1-12 were eliminated. Conclusions were notmade based upon these results since a sign test was used for the analysis. It was morevaluable to investigate the deflection-lines which produced erroneous yarding distanceestimates and to discuss the cause ofthose errors.7.1.1 Deflection-line Length and ConcavityOfthe thirty-one deflection-lines analyzed, nine (29%) displayed error in yardingdistance estimates, and most ofthese nine were ofthe longest deflection-lines. Thisshows that error in yarding distance estimates was more likely to occur as deflectionline length increases. There were several possible reasons for this, one being that thelonger the deflection-line the more chance that elevational error sufficient to influenceyarding distance estimates would occur. A more likely cause was an interactionbetween deflection-line length, the elevational error, and the concavity ofthe deflectionline.64For the skyline systemconsidered in this study,adequate skyline deflectionwasobtainable only on primarilyconcave terrain surfaces.Problems with concavitygenerally occur at thebeginning and/or end ofthedeflection-line whereit rolls overonto a ridge or similar feature.These were generally poor areasfor clearance due tothe proximity to eitherthe landing or the boundary.Relatively small errorsin elevationon the DEM-derived deflection-linecan cause problems withobtaining adequatedeflection and subsequentlycause problems with yardingdistance estimates.Evidence ofthis can be seen in thedeflection analysis plots forthe five longestdeflection-lines withincorrect yarding distance estimates;NW-i, AT-203, NW-2, AT-202, and AT-200. Adequateclearance was not obtainable forthe entire length ofthesefive deflection-lines. For example,deflection-line NW-2 had problemswith deflectionin the convex terrain around300 metres (Figure 16). When thisoccurred and adequatedeflection could not be obtainedfor the entire deflection-line, asmall elevational erroron the DEM-derived deflection-linecaused an incorrect yarding distanceestimate.By comparison, CU-21 providedadequate clearance for theentire length ofboth thefield surveyed and DEM-deriveddeflection-lines. It was thereforeeasier to obtain thesame yarding distance estimatesfor the deflection-line pair. This hadto do with theposition ofthe deflection-line relative tothe terrain (Figure 17). CU-21 wassymmetrically located withrespect to the valley so that it couldbest take advantage ofthe terrain concavity. NW-2on the other hand started at the bottom ofa valley,continuing up the side of a ridge andinto an area ofconvex terrain.—200—250— 3DDFigure 1665Figure 17 The concave shape ofboth deflection-lines for CU-21 providedadequate clearance for the entire length ofthe deflection-lines.—1500LaU’o a 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 in 0 WI 0 IL, 0 WI 0 WI 0 It) 0 WI 0 It) 0 WIIn — — CN CN fl flq WI WI <0 10 N N CO 0) 0) oi1111111 11111111111111111111111111111111111111111111 11111111111111111111111111111A positive elevational error in concave terrain resulted in the incorrectplacement ofthe boundary for the NW-2 DEM-derived deflection-line.rIIJLJIJ950900t600—550500- In .- C.I CN Ffl rfl ‘ ‘ In Wi <0 (0 N N 0) 0) (7,r i....i....i....i....i....i....i....i ....i....i66Ofthe four deflection-lines which were eliminated, only AR1-12 displayed anerroneousyarding distance estimate, an overestimate of 127.7 metres. This was the longesterrorfor all the deflection-lines and AR1-12 was the shortest deflection-line with an incorrectyarding distance estimate. AR1-12 was primarily convex, and the elevational errorswere ofa size which may have not influenced yarding distance estimated on concaveterrain. The other deflection-line eliminated from cutbiock AR1, AR1-11, hadelevational errors of similar magnitude, but it was on concave terrain and did notexperience error in yarding distance estimates. The two deflection-lines eliminated formCU, CU-22 and CU-23, were much longer than AR1-12 but did not experience error inyarding distance estimates. These two deflection-lines were similar to CU-21 (Figure17) in that they were symmetrically located in concave terrain. These results emphasizethe important relationship between elevational errors, and their location with regard tothe terrain, and their effect upon yarding distance estimates.7.1.2 Large Elevational ErrorsWhile the mean error gives a good indication ofthe general trend in the elevationalerror ofa DEM-derived deflection-line, large individual errors are much more relevantwhen analyzing potential problems with yarding distance estimates. A large error mayindicate a significant local feature such as a ridge that was not represented properly onthe DEM. This appears to have been the problem for the three remaining deflectionlines which had incorrect yarding distance estimates. In Figure 18 it can be seen that a67missing rock bluff on the DEM-derived deflection-line had affected the yarding distanceestimate, resulting in a difference of-35.4 metres.A small mean error and standard deviation could hide the presence of a few largeindividual errors. Only one large error is needed to cause an erroneous yarding distanceestimate. Ifa large negative error occurs at the end ofa deflection-line, anunderestimation may result such as occurred for GC-5. Conversely, a large positiveerror at the end ofthe deflection-line could lead to an overestimation.A large negative error towards the centre ofthe deflection-line could cause anoverestimation ofyarding distance (Figure 19). Conversely, a large positive errorFigure 18 A large elevational error at the end ofthe GC-5 DEM-deriveddeflection-line resulted in an incorrect boundary location.—3O0250200—l0C<‘I5001,C‘ DCwC C C C C C C C C C C C C C CC C Lfl c ‘r c tt C Lfl C C Lfl C W CLfl — - CN C Ui In <o o r— r—11111 I 11111111111111 I 11111111 I I III I liii,, III •I I 1111111 I 11111168Figure 19 Example ofa large negative elevational error in the centre ofthedeflection-line, causing an overestimate ofyarding distance.towards the centre ofthe deflection-line could cause an underestimation ofmaximumyarding distance. These errors were much less likely to occur simply because there wasusually more clearance at the centre of a deflection-line than at the end.While the above scenarios were possible, they do not appear to have had a largeinfluence on the study. The large elevational errors were usually indicative ofalocalized feature which caused other adjacent large errors. In the case of GC-5, itappears that the deflection-line ended on the edge ofone feature that was notrepresented by the DEM. Ifthe deflection-line had incorporated more ofthis feature,DEM69more elevations would have been affected . This would be reflected in either the meanor the standard deviation or both.Seven ofthe nine errors in yarding distance were underestimates (Table B1).Underestimating the maximum yarding distance will always result in boundary locationsplaced too close to the landing (unless corrected by field surveys). This equates toreduced access to timber, which may be compensated for by increased road densitywhen it is not necessary. When additional roads are not possible, the timber will be leftunharvested when in fact it is accessible.The effects ofunderestimation ofmaximum yarding distance will also depend somewhatupon the individual deflection-line. Reduced payload clearance may result ifthe properlocation for the boundary should have been a raised feature such as the top of a rockbluff. An example ofthis, once again, is Figure 18. The rock-bluffafforded betterclearance than the location chosen using the erroneous DEM-derived deflection-line.AR6-5 displayed the other overestimated yarding distance, which was caused by a largepositive error at the end ofthe deflection-line. This will always result in lowerclearance or lower payloads. Inadequate clearance may lead to the carriage beingdragged on the ground which can cause significant and expensive damage. Logs maybe damaged leading to reduced value and lower volume recovery. Damage may alsooccur to the site when the carriage and or logs are dragged across the surfaceexposing70or displacing the soil. Turn times will likely increase as will the per cubic metre costofyarding.When the boundary occurs at a prominent feature then error in yarding distanceestimates from DEM-derived deflection-lines may be negated when the boundary isfinally located in the field. For example, the yarding distance estimate for the AT-202DEM-derived deflection-line was found to be 33 metres shorter than that for the fieldsurveyed deflection-line. Careful visual inspection ofthe deflection-line analysis plots(Figure 20) indicated that the landing was placed in the same location on the ridge forboth analyses.The difference between the yarding distances appears to be due to a positional error inthe start location for the DEM-derived deflection-line (left side ofplot). The significantyarding boundary placement occurred at the landing since the ridge was part ofCanfor’sForest Licence boundary. As well, the other yarding boundary occurred at the bottomofthe ridge, since the flat on the left portion ofthe deflection-line had already beenharvested. Since the ridge was such a prominent feature, the field surveys placed thelanding and boundary in the correct location.While the error in maximum yarding distance estimates considered as a percentage ofdeflection-line length was low, the economic impact ofthese errors couldbe substantial.Building more roads than are necessary, especially in steep and sensitive terrain, isextremely costly, both financially and environmentally. With increasing emphasis on71400350300250200450o a o a o a a a o a a 0 0 0 0 0 0 00 0 In 0 In 0 In 0 Lfl 0 In 0 tO 0 In 0 In 0 InIn r — CN CN tfl fl In In <0(0 N N <0 <0 01400350300250200o a 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 In 0 In 0 In 0 In 0 In 0 In 0 In 0 In 0 InIn — — CN t’i fl In In <0 (0 N N <0 <0 fl ClI 1111111 111111 1111,11111111111111111111111111111111111111111120 The deflection-line analysis plots for AT-202 show that the landingwas located properly on both plots despite the 33 metreunderestimation ofyarding distance from the DEM-derived deflection-line (upper plot).72better forest practices, and the consequent penalties, environmentally damaging practiceswill also become heavy financial burdens. Ifroad densities are not increased thentimber will be left standing when it could have been harvested. Damage to site, logs,and equipment as well as the consequent reductions in yarding productivity could beextremely costly. These substantial impacts ofeither underestimated or overestimatedyarding distances may be tempered ifDEM-derived deflection-lines are used to pre-planfield surveys only. The most critical deflection-lines can be targeted for field surveyingwith crews given the freedom to adjust plans where the field surveys dictate.7.2 Elevational Error and Error PatternsComparison plots used to illustrate the presence of one type of error may also displayother types oferror. It is likely that most types of error discussed in this section, withthe exception ofblunders and rubber sheeting, may be found in all ofthe deflection-line comparisons. For some comparisons, different types oferror have interactedmaking it difficult to identify any particular error as the most influential. For thecomparison plots used as examples in this section, the error type being discussed wasnot the only error type present, just the most prominent.The mean error of all deflection-line data was positive. The majority of data groups(cutbiock, setting, and deflection-line) also had positive mean errors. This suggests atrend towards overestimation ofground elevation by the DEMs which indicates thepresence of systematic error. Processes used to create the DEMs and extractthe73deflection-lines likely cause small, random errors. Systematic error ofthe sizedetermined was likely present in the analog maps having been introduced during thephoto-interpretation process. The error was likely exacerbated by positional errorpresent in the source maps and introduced during various stages ofthe study. It is thevarious potential sources ofthis systematic error on which the following discussion willconcentrate.7.2.1 BlundersVisual analysis ofdeflection-line comparison plots turned up an apparent global error,or blunder, in the AR1 cutblock map. Figure 21 and Figure 22 show divergencebetween the DEM and field surveyed deflection-lines.The elevational error for cutbiock AR1 was not significantly different from zero. Thesame was true for AR1-11. For AR1-12 the elevational error was significantly differentfrom zero. This was also the same deflection-line that gave the greatest error in yardingdistance estimate. This error, 127.7 m, was larger than the range oferror from the othereight erroneous estimates.There were two 1:5000 scale mylar maps available for cutblock AR1. Only one had agrid reference system and it was chosen for that reason. After the problems weredetected, the two mylar maps were compared and it was found that there were somesignificant differences in contour locations between the two maps. The two maps were74Deflection—line ARt—1110075S500 50 100 150 200250 300Horizontal distance (m)I_—Survey-—flEMIFigure 21 Comparison plot for AR1-1 1 shows divergence ofdeflection-lines,apparently due to a map blunder.Deflection—line AR1 —1210075S50—25—500 50 100 150 200 250 300Horizontal distance (m)—s— Survey—IE—OEMFigure 22 Comparison plots for AR1-12 shows divergence ofdeflection-lines,apparently due to a map blunder.75compared with the field surveyed deflection-lines.It was found that the unused mapwas more representative ofthe field surveyeddeflection-lines than the map thatwasused. The reason for the differences betweenthe two maps was not determined.The error in yarding distance estimatefor AR1-12 was caused by a poorly representedridge on the DEM-derived deflection-line.More field surveys are needed to see iftheerror, evident in this major feature, wasglobally distributed throughout the map.Itshould be noted, however, that without testing theDEM-derived deflection-line thisblunder may not have been detected. Thisdemonstrates the extreme value offieldchecking the accuracy oftopographic planningmaps.7.2.2 Distortions Caused by Rubber SheetingIt was expected that the GPS and laser survey of GCwould reduce the general level ofelevational error found in this cutblock. Surprisingly,the level of error increased forthe cutblock as a whole, for three ofthefour settings, and for four ofthe eightdeflection-lines. This unexpected result may havebeen due to the presence oftwotypes ofmapping in the cutblock (Figure 13). Theright portion ofthe map was fieldsurveyed and the left portion created fromaerial photographs.The only setting which experienced a decrease in error, GC-S3,was comprised entirelyoftwo ofthe deflection-lines which had also hadreduced error. These deflection-lines76were located entirely in the photogrammetrically-measured portions ofthe maps. Themain tie-points used were all in this portion ofthe map as well.Two ofthe three settings which showed increased error, GC-S1 and GC-S2, werelocated by the laser survey after it crossed the boundary between the two differentmapping types. The other setting with increased error, GC-S4, was also entirely in thephotogrammetric portion ofthe map but was very close to the mapping type boundary.Rubber sheeting is the process ofrectifying one map to a more accurate map depictingthe same geographic location. Common tie-points are identified on both maps, and amathematical correction is applied to the entire map, based upon the corrections thatwere used to align the tie-points (Burrough 1986). It was suspected that when the twomap formats in GC were merged, there was significant difference between the two anda certain amount ofvery crude rubber sheeting was performed.The GPS-laser survey located all deflection-lines as one complete survey and crossedthe map type boundary several times. The results indicated that something was wrong.Since three ofthe four deflection-lines that have increased error were located by atraverse that crossed the map type boundary it was believed that the distortion at theboundary caused the increase in error. The GPS-laser survey was very tightlycontrolled and was ofa precision that would not allow this degree oferror. Theaccuracy ofthe GPS-laser survey, and the confidence in it, was what allowed thisdistortion to be detected. When the deflection-lines were originally placed on the map77they were located individually, using creek crossings as tie-points. Distortionsdue tothe mapping type boundary were not relevant since the deflection-lines were nottiedtogether by a survey which crossed the map type boundary.The CU cutbiock also contained portions ofthe map which were field surveyed.Whenthe problems with GC were discovered the results from CU were re-analyzed.Deflection-lines CU-20 and CU-21 were located entirely within the photogrammetricallyderived portion ofthe map (Figure 12). The comparison plots for CU-20 shows anexample ofhow well matched these deflection-line pairs were (Figure 23).Deflection-lines CU-22 and CU-23 started from a landing that was located in the fieldsurveyed portion ofthe map. The boundary between the mapping types was just to thenorth side ofthe main creek. The comparison plots for CU-23 showed a largediscrepancy in elevational error between the left side ofthe creek, which was fieldmapped, and the right side, which was photogrammetrically mapped (Figure 24). Thiswas likely caused by an error in reconciling the two different mapping types. Thisdiscrepancy may have been due to positional error instead, since shifting the DEMderived deflection-line to the right would have made for a better match. However, thiswas probably not the case since the two profiles were tied together at the creek crossingwhich was a very good tie-point. Even ifthe DEM-derived deflection-line was shifted,it still would not match well on both sides ofthe creek for the same alignment.78Deflection==line CU—2 0250o200100 200 300 400 500 000 700 000Horizontal distance (m)—8—Survey—IE—DElIFigure 23 The well matched deflection-line pair for CU-20 is an example ofthedeflection-lines contained entirely within the photogrammetricallyderived portion ofthe map.Def1ection1ine CU-23350150) 100 200 300 400 500 600 700 800Horizontal distance (m)—8—Survey DEMFigure 24 Comparison plot for CU-23 shows a large discrepancy in elevationalerror between the photogrametrically derived portion ofthe map (leftside ofplot) and the ground surveyed portion (right side).79The mean elevational error was not significantly different from zero foreither CU-22 orCU-23(cx=0.1). These results were obtained using a sign test which is notan efficienttest. Figure 23 shows quite obvious errors in the elevations ofthe DEM-deriveddeflection-line. These errors were fairly evenly distributed between positive andnegative errors. The same was the case for CU-22. Since the sign test only checkspositive differences against negative differences, and not the magnitude ofthosedifferences, the test had probably failed to detect important errors in both CU-22 andCU-23.Even ifthese two DEM-derived deflection-lines did have mean errors which weresignificantly different from zero, they did not cause any errors when estimating yardingdistance. This was due to the very favourable terrain location ofthese deflection-lines.As was mentioned for CU-21 previously, the deflection-lines were symmetricallylocated in the valley to take best advantage ofthe terrain concavity. Ifthe deflection-lines were not symmetrically located, or the rubber sheeting error occurred in lessfavourable terrain, then errors in yarding distance estimates may have occurred. Onceagain, this demonstrates the importance offield testing topographic planning maps.7.2.3 Random ErrorMany ofthe DEM-derived deflection-lines displayed patterns indicative ofrandomerror. GC-5 was a good example where the elevational error appears to be mostlyrandom in nature. Looking at the comparison plot (Figure 25) it can be seen that the80Def1ection1ine GC=-52502250 100 200 300 400 500 600Horizontal distance (m)——Survey —-- DEMFigure 25 Deflection-line comparison plot for GC-5 indicates minor, randomvariation in elevations.two deflection-lines have the same basic shape with only minor, random variations.The histogram ofthe error (Figure 26) describes a normal curve. Statistical testsshowed the elevational error to be normally distributed and the mean error was notsignificantly different from zero (x0.1).Other deflection-lines had normally distributed error and mean errors which weresignificantly different from zero. The histogram for AT-200 is a narrow, normaldistribution that is skewed slightly to the right (Figure 27), indicating the presence ofsystematic error. The comparison plot shows that the deflection-lines were well81025-020 -0.15 -0.05-Figure 27 Elevational error histogram for AT-200 ispresence of systematic error.Ce0.—20.00 —12.00 —4.00 4.00Error (m)12.00 20.00Figure 26 Elevational error histogram for GC-5 describes a normal curve.0.400.30cema.0.20-C:C;0.10 - - -—20.00 —12.00 —4.00 4.00 12.00 20.00Error (m)right skewed, indicating the82matched although the DEM-derived deflection-line had a tendency to be higher (Figure28). For some reason, the photo-interpreter overestimated the ground elevation for thisarea. This may have been caused by site characteristics which produced taller treesthan the photo-interpreter believed. Systematic error ofthis sort was evident in most ofthe deflection-line comparisons to some extent. Potential sources ofthis systematicerror are dealt with in the following section.Deflectiom=line AT=200400350-:___________I0 100 200 300 400 500 600 700 800 900Horizontal distance (m)—8--Survey-41E--DEMFigure 28 Comparison plot for AT-200 shows a wellmatched deflection-line pair,with the DEM-derived deflection-line tending to be higherin elevation.837.2.4 Systematic ErrorThere was strong evidence that systematic errorhad influenced the overall elevationalerror ofthe DEM-derived deflection-lines. Theproportion ofelevation error due tosystematic error probably exceeds the proportiondue to random error. Reduction ofsystematic error in DEM-derived deflection-lineswould probably result in yardingdistance estimates which are the sameas those from the field surveyed deflection-line.The systematic error identified in thisstudy was primarily the result ofpositionalerrorand smoothing error. These errors were introducedthrough processes used to create theoriginal source material, processeswhich were controlled by this study.As well, bothpositional and smoothing error were likely introducedduring the study, although to alesser extent.7.2.5 Smoothing ErrorSmoothing error occurs when natural variationin the terrain, evident in the fieldsurveyed deflection-lines, has been‘smoothed out’ in the DEM-deriveddeflection-lines.Figure 29 is a good example ofsmoothing error. The left portionofthe comparisonplot shows how both the minorconcave and convex portions ofthefield surveyeddeflection-line have been lost in theDEM-derived deflection-line. Thisportion oftheGC cutbiock had been influenced by past fire activitywhich had created differing standcomposition.84Differing tree heights, due in part to variable sight conditions, haveresulted in an even forest canopy which masked the terrain variationfrom the photogrammetrist.Much ofthe variability in the ground surface between about 175 metres and the end ofthe deflection-line had been eliminated by the DEM. The concave portion ofthe fieldsurveyed deflection-line was a superior growing site compared to the convex portion.Old growth Douglas-fir trees, which survived the fire, predominated in this area.The convex portion ofthe field surveyed deflection-line was rocky and dry. The firecaused more damage here resulting in a younger forest ofshorter western hemlocktrees. It was quite likely that the top ofthe forest as a whole was very consistent. TheDef1ection1ine GC-=1S00.1.50 100 150 200 250Horizontal distance (m)300 350Figure 29E8Survey— DEM85photogrammetrist may have seen a consistent, rising slope and then plotted the contoursaccordingly.The comparison plot for TR-4 (Figure 30) shows smoothing occurring consistentlyalong the entire DEM-derived deflection-line. The TR-38 cutblock had fairly consistentforest cover with no recent evidence offire. It was a wetter site with much evidence ofreplacement by windfall. Wind may have had a greater influence in this area, shearingoffthe tops oftrees and creating a consistent canopy surface. Once again theDeflection— line TR—425o-25‘— —75—1oo -—125—150— —175.-2O0I I I0 100 200 300 400 500 600 700Horizontal distance (m)—s---Survey—*--DEMFigure 30 Past wind history created an even forestcanopy which prevented thephotogrammetrist from detecting terrain variation.86photogrammetrist may have believed that the ground surface was less variable than itactually was.While it appears that shifting the DEM-derived deflection-line to the left mighteliminate most ofthe differences, the variation shown in the field surveyed deflection-line would still not have been represented in the DEM-derived deflection-line. The twodeflection-lines were tied together at the left side which was on an open ridge. For thisreason it was believed that the two lines were fairly well matched. The discrepancyevident at the creek crossing, the only portion ofthe comparison plot where the fieldsurveyed elevations were higher, was probably due to some site characteristics whichhad affected the estimation ofthe ground elevations. This is dealt with in more detailin the next section.7.2.6 Effect of SiteThe nature ofthe cutblocks, that they were planned for forest harvesting, ensures theywere heavily forested. It appears that the photo-interpreters who created the mapsgenerally underestimated tree heights, thereby overestimating the ground elevation. Thismay have been confounded by the topographic location ofthe cutbiocks. Cutbiockswere primarily located in valley bottoms and on lower slopes. Soil and moistureconditions on these sites tend to produce taller trees. For the GC cutblock, the trees inthe valley and lower slope areas were 50 to 60 metres tall. Progressing upslope alongeach deflection-line, the tree heights gradually decreased. For the deflection-lines which87ended on the ridge top, GC-7, GC-8, and GC-10, trees were approximately 30 to 35metres tall.While this trend was evident for other cutbiocks, it was most pronounced in GC. GCwas the only cutbiock which was predominantly forested with Douglas-fir and westernhemlock. The other cutbiocks were dominated by western hemlock, amabilis fir, andwestern red cedar. The presence ofDouglas-fir in GC may indicate better growingconditions than in the other cutbiocks. Within GC, there was more of a pronounceddifference in growth between the better valley bottom sites and the poorer ridge topsites.For the AT cutblock the deflection-lines indicated a different pattern oftree heights.There was no major valley in this cutblock. The deflection-lines started on a plateau,extended along this plateau and then up the adjacent ridge top. The tree heights werelow, 30 to 35 metres on the plateau and on the ridge top while they were around 40 to45 metres on the lower and mid-slopes. The trees were shorter on the plateau becauseit was a very moist site with limited tree growth. Vegetation was probably sparse andthe ground elevation therefore easy to estimate. These conditions would combine togive the error pattern for AT-200 which was discussed previously(p. 80).For deflection-lines which did extend to ridge tops the ground elevation was wellestimated. The trees tended to thin out in these locations allowing for less obstructedviews ofthe ground. Adding to this, on the aerial photographs, these locations were88closer to the camera lens and therefore at a larger scale than the valley bottoms. Moredetail was evident and the ground easier to detect at the larger scale.All deflection-lines were located on lower slopes and usually in valley bottoms. Onlyeleven ofthe twenty-seven deflection-lines extended to ridge tops. Therefore, themajority ofdeflection-lines did not benefit from the better ground elevation estimationthat occurred on the ridge tops. Even for the eleven deflection-lines that extended tothe ridge tops, most ofthe elevation points were not located on the ridge top. For alldeflection-lines either most, or all, elevation points were erroneously estimated due toinaccurate tree height estimations.7.2.7 Positional ErrorPositional error was evident in the deflection-line comparison plots and throughout theerror analysis. The main cause ofthe positional error appeared to come from poortiepoints. When deflection-line were transferred to the maps they may not have beenproperly located. Ifthis occurred, then subsequent elevations extracted from the DEMwould most likely be in error. This positional error was likely exacerbated by thetraditional surveying methods used in the study. This influence could have beenminimized by using high accuracy GPS and laser surveying equipment for all fieldsurveying.89The effect ofpositional error upon elevational error may be significant. Thecomparison plots for AT-202 and AT-203 (Figure 31 and Figure 32) displayed the mostobvious cases of positional error. Both piots appeared to be fairly well matched inshape but not in elevation. Both deflection-lines had some ofthe highest error levels ofall the deflection-line comparisons. Amongst the cutbiocks, AT had the highest level ofelevational error, and amongst the settings, AT-S2 had the highest level of elevationalerror. The large errors in AT were likely due to a lack ofeffective tie-point features inthe cutblock or in the adjacent area. The deflection-lines were tied to the intersection ofa creek with an adjacent lake. This tie-point was approximately 250 m from the closestdeflection-line and 1200 m from the furthest deflection-line. Considering the lowaccuracy ofthe surveying methods used in the study, which mimicked operationalmethods, these distances were too far for reliable tie-points.In fact, it appeared that tie-points features were only useful when they directlyintersected the deflection-line, such as a creek, or were very close to it (50 m). Incomparison to AT, the deflection-lines ofNW did not exhibit much positional error.This was most likely due to the presence ofa large river which both deflection-linescrossed. The river was about 50 metres wide and very obvious on the aerialphotographs. This would have enabled the photo-interpreter to make precisemeasurements ofthe river banks, channels, sandbars, and other easily identifiablefeatures.90Deflection—line AT—2 02400350‘30OI0 100 200 300 400 500 600 700 800 900Horizontal distance (m)—a—Survey—*—OEMFigure 31 Comparison plot for AT-202 indicates presence ofpositional error.Deflection—line AT—2 034003501300 —250200150100500a—50 I I I0 100 200 300 400 500 600 700 800 900Horizontal distance (m)—a—Survey —*—DEMFigure 32 Comparison plot for AT-203, which shares a common landing withAT-202 (Figure 31), also indicates presence ofpositionalerror.91CU had the lowest level oferror and did not appear to suffer from positional error. Thecreek in CU was much narrower than the creek in GC yet CU had even lower errorthan GC. The main creek in CU was sufficiently open to be seen effectively on aerialphotographs. This creek was also in a sheer rock canyon which prevented the creekfrom shifting over time. In other cutblocks such as GC, it appeared that the creek hadshifted significantly and this caused difficulties with the placement of some deflection-lines.7.2.8 Age ofMap DataThe age ofmap data was a extremely important when considering the reliability oftie-points. The maps used in this study contained PIPs and it was only possible to find oneofthese during the study. Other PIPs marked on the maps had disappeared in the fieldthrough age related processes. One particular PIP in cutblock AR6, a large tree on apoint in the main creek, was easily identifiable on the aerial photographs. Whenchecked in the field it was found that the creek had since shifted course and the entirepoint of land, tree and all, had washed away.The one PIP that was found in AR6, along the main creek, was approximately 25metres from AR6-1. The comparison plot for AR6-1 shows that the deflection-line waslocated well when transferred to the map (Figure 33). AR6-1 also had some ofthelowest levels ofelevational error displayed by all ofthe deflection-lines.92Figure 33 A PIP near deflection-lineAR6-l helped to reduce the effectsofpositional error when the deflection-linelocation was transferred tothemap.AR6-3 was approximately300 metres away from AR6-1 andthe comparison plotsuggests that it was not locatedproperly when transferred tothe map (Figure 34). Thispositional error was caused bya lack ofproximity to a reliabletie-point. The maincreek, which AR6-3 crossed,was not a good tie-point due to itslow visibility on theaerial photographs.While the issue ofage has come lastin this discussion, it is by nomeans the leastimportant. In fact, many ofthe issuesofthis study were age relatedto some degree.Deflection=line AR61275250225S200175150. 125‘ 100C);7550250—25200 300 400Horizontal distance (m)—8--Survey — DEM93Deflection=line AR6=33002752501225Iii0 100 200 300 400 500 600 700 800Horizontal distance (m)—8---Survey —— DEMFigure 34 Deflection-line AR6-3 was not located close to a PIPand thereforeexperienced positional error when transferred to the map.The issue ofPIPs and the shifting of creeks over time were both mentioned. Theshiftfrom NAD27 to NAD83 coordinates, while avoided in this study,did preclude certainoptions for data transfer especially with the GPS and laser survey.The age ofmap data is also relevant when considering the effects oftechnologicalobsolescence. Surveying methods and equipment used twentyyears ago may have beenadequate for analog maps and the analysis that was possible at thattime. Thosemethods are certainly not adequate for the level of analysisnow possible with theadvent ofmicro-computers and Geographic InformationSystems.94For example, Canfor acquired new digital orthopohoto maps for their entire operatingarea in 1993. Their entire network ofestablished road was correctly located at thattime, eliminating past surveying and transfer errors. The maps were also in NAD83coordinates facilitating the addition ofnew data from such sources as GPS. Any furtheradditions ofroad locations surveyed with a nylon chain and hand-held compass willcompromise these new levels ofaccuracy.While it is possible to use existing topographic planning maps for deflection-lineanalysis they must be used with an understanding oftheir limitations. These limitationscannot be realized without proper field checking. It may be necessary to field checkdynamic features, such as creeks, on a periodic basis. Eventually it will be necessaryfor maps to be updated to detect all changes at one time and to keep pace with currenttechnologies. This should be done with careful planning notjust for present but forfuture needs, whether those needs are known or just anticipated.958 ConclusionsThe majority ofyarding distances estimated using DEM-derived deflection-lines werenot in error. The magnitude ofthe errors was relatively low, when considered as apercentage ofdeflection-line length. However, many potential economic andenvironmental impacts associated with these errors make it prohibitive to completelyreplace field surveyed deflection-line analysis with DEM-derived deflection-lineanalysis.Field surveying will negate some ofthe impacts oferror in yarding distance estimates.Prominent features which physically define a boundary will likely be chosenby fieldcrews, regardless ofthe estimated yarding distances. Boundaries definedby suchfeatures may be identified during pre-planning with DEM-derived deflection-linesanddesignated for special attention by field crews.Errors in yarding distance estimates for DEM-derived deflection-lines were causedbyinteractions between a few or all ofthe following: the deflection-linelength, the terrainshape (concavity/convexity), elevational errors and their locationon the deflection-line.When assessing the suitability ofDEM-derived deflection-linesfor estimating yardingdistance, these interactions were more critical than themean elevational error. Themean elevational error indicated a trend in the data and did not indicateany individualfeature which was not properly represented.96The longest DEM-derived deflection-lines had more tendency to display error in yardingdistance estimates than did the shortest DEM-derived deflection-lines. These errorsoccurred when the shape ofthe deflection-line changed from concave to convex.Elevational errors, particularly large ones, had a greater influence on yarding distanceestimates in convex terrain, where clearance was often reduced. Shorter deflection-linesusually did not reach convex portions ofthe terrain. Symmetrically located deflection-lines on concave terrain displayed no error in yarding distance estimates.The majority oferrors in yarding distance were underestimates. Underestimates mayreduce access to timber, necessitating an increase in road densities. Building moreroads than necessary, especially in steep and sensitive terrain, is extremely costly, bothfinancially and environmentally. With increasing emphasis on better forest practicesand potential penalties, environmentally damaging practices could also become heavyfinancial burdens.Overestimation ofyarding distance may lead to inadequateclearance which may in turncause site, equipment and log damage. Damage may be minimizedby methods such asreducing either yarding speeds or payloads. Productivitywill subsequently suffer whenyarding techniques are adjusted to minimize the damage.Ifplanning crews are allowedflexibility during field surveying, both underestimatesand overestimates may becorrected in the field.97Existing elevational accuracy standards did not adequately address the importance oflarge elevational errors and were therefore not suitable for testing DEM-deriveddeflection-lines. These accuracy standards also assumed that the elevational error wasnormally distributed, an assumption not made in this study.Several data groups did not have normal distributions, apparently affected by systematicerror, which had confounded the elevational error and skewed the distributions.Whilerandom error was detected in the analyses, systematic error appeared to contribute moreto both the general level ofelevational error and to the presence of large errors.Inefficient statistical tests were therefore required making it difficult to drawconclusions with reasonable confidence from the results ofhypotheses tests.Instead,deflection-lines were assessed by considering the resultsofhypotheses tests inconjunction with descriptive statistics, plots ofDEM-derived and fieldsurveyeddeflection-line comparisons, histograms, and insight gained during the fieldsurvey ofeach deflection-line.A blunder was detected in one ofthe study cutbiockmaps. This map was being usedfor operational planning and had likely never beenchecked for elevational accuracy. Aswell, distortion was found in the mapsfor two other study cutblocks wherephotogrammetrically derived and ground surveyed mapshad been joined through rubbersheeting. High accuracy GPS and laser surveying detectedthe distortion, which hadalso not been detected due to a previous lack ofaccuracy testing.98For most comparisons the mean elevational error was positive, suggesting a trendtowards overestimation ofthe DEM elevations. This may have been due, in part, to thelocation ofdeflection-lines relative to the overall terrain. Most deflection-lines werelocated in valley bottoms and lower to mid slopes, where taller than expected trees mayhave lead the photo-interpreter to overestimate ground elevations. Since overestimatingthe ground elevation may not be a problem when dealing with the more open forestconditions ofthe upper slopes and ridge tops, the level of elevational error detected inthis study may not be applicable to all areas of a DEM.The presence ofsystematic error was the biggest impediment to using DEM-deriveddeflection-lines confidently for estimating yarding distance. Different types ofsystematic error were detected, with at least some types evident in all ofthe deflection-line comparisons. Smoothing error was observed where terrain variation had beenreduced through various steps in the creation ofthe original maps or through the datatransformations and manipulations which occurred during this study. Smoothing errormay have been caused primarily by the photo-interpreter’s inability to detect variation inground elevations when the ground was not visible through the forest canopy. Past fireand wind disturbances may have combined with site characteristics to create smooth andlevel canopies. The photo-interpreter would have had no way ofdetecting the actualvariation ofthe ground and would have plotted the contours accordingly.Positional errors were the most common and influential systematic errors detected.Inadequate tie-points prevented features from being properly located when transferred to99maps. Positional error was strongly influenced by the age ofthe map data.Particularly, dynamic features used as tie-points varied over time including creeks whichhad shifted their course. The relevance of a tie-point feature for the purpose ofaccurately locating deflection-lines was important when considering positional error.Many commonly used tie-point features, such as logging roads, were not plotted onmaps with precision equivalent to that required for deflection-line analysis.Positional error introduced through traditional surveying methods exacerbated both thelevel ofelevational error and the errors from yarding distance estimates. Positionalerror increased the magnitude ofelevational error when deflection-lines were transferredto the wrong map location and the elevations were subsequently extracted from thewrong location on the DEM. This produced deflection-lines which were misalignedwith the field surveyed deflection-lines but which had the same general shape. Severalofthe erroneous yarding distance estimates placed the yarding boundary in the properlocation, negating the effects ofthis error.The positional error ofmap features, and that introduced using traditional surveymethods, may also affect operational field surveying of deflection-lines, logging roads,and harvest boundaries. The presence ofpositional error and its subsequent effectsupon harvest planning is either not known or ignored altogether.1009 RecommendationsThe substantial impacts oferroneous yarding distancesestimated from DEM-deriveddeflection-lines may be temperedifDEM-derived deflection-linesare used only toefficiently pre-plan and guidefield surveys. Automateddeflection-line analysisusingDEM-derived deflection-lines maybe used to perform rapiditerative planning oflanding, road, and boundarycombinations which is not possibleusing traditional fieldsurveyed deflection-lines. Forestengineers could then choosethe most promisingcombinations for confirmationthrough field surveys.Ifthe forest engineerdecides to field check onlycritical DEM-derived deflection-linesthen standards to guidethe field checks are necessary.Differing terrain, stand, and siteconditions may cause different levelsofelevational error. Theseelevational errors willalso have varying impacts on theestimation ofyardingdistance depending upon theirlocation on the DEM-derived deflection-line.The best recommendation forusing DEMs for deflection-line analysisis to obtain newelevational data specific forthat use. New aerial photographs shouldbe obtained forthe mapping area at a scaleappropriate for the intendeduse. The photo-interpretershould have relevant field experiencein order to better understandand interpret theforest and terrain images. Photo-interpretationshould select elevation points, ridgelines, valleys, and other features compatiblewith a TIN model. Data from the photo-101interpretation should be created digitally. Hard copy is ofsecondary importance andmay be produced later.Since creating new maps is an expensive process that some organizations cannot afford,other approaches may prove satisfactory for the interim. Canfor has consideredconverting their existing 1:5000 scale maps to digital form and then reconciling thesemaps to their new 1:20 000 scale digital base maps using sophisticated rubber sheetingtechniques. These base maps consist of recently aquired digital orthophotographs andnew B.C. government Terrain Resource Information Mapping (TRIM) digital maps.Large scale forest planning maps should be accuracy tested prior to their use for DEMderived deflection-line analysis. Testing should consider both the positional andelevational accuracy offeatures, with particular attention given to detecting the presenceofblunders, distortions, and systematic errors. The nature ofthese errors and how theyaffect deflection-line analysis should be investigated. The continuous samplingprovided by deflection-line surveys facilitates the detection ofpatterns which indicateblunders, distortions, and systematic errors. High precision GPS and laser surveys maybe particularly useful for detecting the presence ofblunders and distortions.If deflection-lines are surveyed using high precision equipment, then the introduction ofadditional error will be minimized. Traditional tight-chain, hand-held compass, andclinometer surveys are not adequate for future or even present demands for accuracy.GPS has been suggested for performing entire deflection-line surveys but at present this102is not a feasible option. The type ofGPS receiverwhich could provide the precisionnecessary for deflection-line analysis is not designedfor rapid, mobile surveys. Heavyforest cover and steep terrain may block satellite signals,necessitating time consumingreacquisition ofthe signals. The high precision GPS shouldinstead be used to create afew high accuracy tie-points, and then a laser surveyorused to perform the deflection-line and tie-point surveys. This survey data is collecteddigitally and may be importedinto a GIS for locating the DEM-derived deflection-lines.With new maps created usingthe NAD’83 coordinate reference system, GPS surveyedtie-points may be referenceddirectly to GIS map coordinates.It may be possible to predict the effect of differentsystematic errors on the elevationalerror ofthe DEM-derived deflection-lines. Ifsystematicerrors could be predicted thenthey could be incorporated into an automated deflection-lineanalysis program.Performing the deflection-line analysiswithin the GIS graphics environment wouldallow forest cover and site information to be extracted from the GIS databaseandterrain information extracted from the DEM. The deflection-line analysisprogramcould then indicate when payload clearance is less than the levelofelevational errorpredicted by the regression model.Further research should be performed to determine the relative effects ofdifferentpositional errors on the elevational error ofthe DEM-derived deflection-lines. Therelationship between these errors and the terrain conditions should also be investigated.103The different positional errors may then be ranked and priorized for elimination,minimization, or prediction.The automated detection ofblunders and large elevational errors may be facilitatedthrough the use of a local filter with the DEM. This filter would traverse the DEM,checking for small groups ofelevations that are significantly different from theirneighbours. The filter could be passed over the DEM several times using varyingtolerance levels to detect different magnitudes ofelevational error. This processbecomes more practical as computing power and speed improve.‘Intelligent’ deflection-line analysis programs could be developed which would reducethe influence ofsmoothing error on the elevational error ofDEM-derived deflection-lines. For example, a deflection-line derived from a TIN DEM is composed of a seriesofstraight segments which represent the individual triangular facets. For eachtriangular facet intersected by a deflection-line segment, the program could investigatethe neighbouring triangular facets to see ifthey form a concave, convex, or flat surface.The deflection-line segment which intersects this facet could then be adjustedaccordingly to give a more realistic representation ofthe local terrain.An automated and iterative routine could be developed to locate the least number ofdeflection-lines needed to analyze a proposed cutblock. 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Skyline Logging, presentation, 49th Annual Truck Loggers AssociationConvention and Equipment Show, January 21, 1992, Vancouver, B.C.Sauder, E.A., R.K. Krag, G.V. Weliburn, 1987. Logging and Mass Wasting in thePacific Northwest with Application to the Queen Charlotte Islands, B.C.: ALiterature review, Special Report Number SR-45, Forest Engineering ResearchInstitute of Canada (FERIC), Vancouver, B.C.,pp.7-21.Sears, J., 1991. Balancing the Shift to Skyline Yarding, In Canadian Forest Industries,April 1991, T. Tolton Ed.,pp.14-20.108Shearer, J.W., 1991. G. Petrie, T.J.M. Kennie, The Accuracy OfDigital TerrainModels, Terrain Modelling in Surveying and Civil Engineering, 1991, McGraw-Hill, Inc.,pp.315-336.Soel, K., 1992. Personal communication. K.C. Soel Limited. Victoria, BritishColumbia.Softree Technical Systems, 1992. Roadeng: Road Engineering Software, Version 1.62,Softree Technical Systems, West Vancouver, B.C.,pp.135.Star, J.L., J.E. Estes, 1990. Geographic Information Systems - An Introduction, PrenticeHall Inc., Englewood Cliffs, New Jersey, pp. 85-91.Thompson, M.M., 1988. Maps for America: third edition, U. S. Government PrintingOffice, Washington, D.C., 1988,p.104.Thompson, M.M., H. Gruner, 1980. Chapter 1, Foundations ofPhotogrammetry, InManual ofPhotogrammetry: fourth edition, American Society ofPhotogrammetry, Falls Church, Virginia,pp. 1-38.Thompson, M.M., 1960. A Current View of The National Map Accuracy Standards,Surveying and Mapping, December, 1960,pp.449 - 457.Thompson, M.M., 1956. How Accurate is that Map?, Surveying and Mapping, April -June, 1956, Vol. XVI, No. 2,pp.164 - 173.Trimble Navigation Ltd., 1992. 4000SE Land Surveyor Operation Manual, TrimbleNavigation Limited., Sunnyvale, CA., 232pp.Veregin, H., 1989. A Taxonomy ofError in Spatial Databases, Technical Paper 89-12,National Centre for Geographic Information and Analysis, GeographyDepartment, University ofCalifornia, Santa Barbara, California,p.26.Walpole, R.E., 1982. Introduction to Statistics: third edition, MacMillan Publishing Co.,Inc.,p.296.Warner, W.S., W.W. Carson, 1991. Errors Associated with a Standard DigitizingTablet, In ITC Journal, 1991-2, Ensohede, The Netherlands,pp.82-85.Watts, S.B., Ed., 1983. Forestry Handbook for British Columbia: fourth edition, TheForestry Undergraduate Society, Faculty ofForestry, University ofBritishColumbia, Vancouver, B.C., D.W. Friesen and Sons Ltd., Cloverdale, B.C.,pp.135-144.109Webb, H., 1990. G. Petrie, T.J.M. Kennie,.The Accuracy OfDigital TerrainModels, Terrain Modelling in Surveying and Civil Engineering, 1991, McGrawHill, Inc.,p.73.Wilkinson, L., M. Hill, S. Miceli, G. Birkenbeuel, E. yang, 1992. Systat for Windows:Statistics, Version 5 Edition. Evanston, Ii., Systat, Inc., 1992, 750 pp., p. 495.Winkle, P.G., 1992. Personal communication, Timber Appraiser, Canadian ForestProducts Ltd., Englewood Logging Division, Woss, B.C.Wolf, P.R., 1980. Chapter 19: Definitions ofTerms and Symbols Used inPhotogrammetry, In Manual ofPhotogrammetry: fourth edition, AmericanSociety ofPhotogrammetry, Falls Church, Virginia, pp. 995-1045.Appendix A Sources ofError Affecting DEM Accuracy110111Sources ofError Affecting DEM Accuracy“How the error is distributed across the area of any one DEM is currentlyunknown, andfactors that may affect the distribution oferror are largelyunresearched.H(Fisher1991).DEM accuracy is a function ofthe accuracies ofthe equipment, the operators andmethods used to acquire, process and manipulate the source data. While it is possibleto measure the error associated with each step the errors may not be additive (Bolstadand Smith 1992). It is preferential, and more practical, to measure the accuracy oftheresultant DEM. The following is a partial list offactors which may affect the accuracyofa DEM:The Nature ofthe Earth’s Surface (Thompson and Gruner 1980):1) the reliefofthe terrain,a) shadows,b) parallax - trying to fit a 3D surface onto a 2D map,2) atmospheric refraction,3) curvature ofthe Earth,4) forest cover (Soel 1993, Young 1978).Light conditions will also effect the photo-interpreter’s ability to see the ground.Shadows cast by local terrain features may obscure portions ofthe aerial photographs(Loving 1980). The terrain will also limit the light reaching the valley bottoms evenwhen not in shadow. Shadows cast by individual trees can hide the ground in smallopenings. Time of day, time ofyear, weather, latitude, and slope aspect may all havesignificant effects upon the photo-interpreter’s ability to see the ground. Theseconditions will be more influential in valley bottoms further affecting the estimation ofground elevation.Obtaining and Processing the Basic Data (Thompson and Gruner 1980).Aerial photography:1) exact position and altitude ofphoto,2) optical axis oflens should be known when photograph taken,3) azimuth orientation ofcamera,4) forward movement ofaircraft during exposure period,5) lens distortion and optic quality,6) metrical characteristics ofcamera,7) orientation ofemulsion-bearing surface,8) uniformity and resolution of emulsion,9) dimensional stability offilm base,11210) atmosphericconditions.Aircraftcan drift laterally,run intoheadwinds,be pushedby tailwinds,gain andlose altitude,vibrate, etc.Compatibilityand scale(Petrie1992):1) thescale andresolutionofthe aerialphotography,2) theflying heightat whichthe photographswere taken,3)the base:heightratio (geometry)oftheoverlappingphotographs.Processingthe Data(Thompsonand Gruner1980):1) developingthe negativefilms orplates,2) makingpositive printsfrom thenegatives,3) operatingthe photogrammetricplottinginstruments,a) theequipment,b) theoperator -experienceObviousSourcesofError(Burrough1986):1) ageof data,a) datacollecteddiscontinuouslyover time,b) naturalchanges(watercoursesshift),c)differentdata standards/ purposes,d)data medium(paperwarping, shrinkage).1) arealcoverage,2) mapscale,3)data relevance,4)data format,5) accessibility,6)costs.RandomError andMeasurementError(Burrough1986):1)positionalaccuracy,a) ageofmap(watercoursesshift),b)cartographicrepresentation(1 mm mapline width= 5 mon the ground),2) accuracyofcontent(qualitativevs. quantitative),3)the sourcesofvariationin data,a) measurementerrors,b) fielddata,c) laboratoryerrors,d) spatialvariationand mapquality.113Errors Associated with Digitizing (Warner andCarson 1991; Burrough 1986).The digitizing tablet:1) warm-up period (electrostatic),2) anomalies associated withwires in tablet,3) cursor orientation,4) resolution (digitizing is sampling!).The digitized material and software:1) temperature and humidity,2) handling, storage, and folding,3) registration process,4) age.The digitizer operator,1) training and skill,2) experience,3) visual acuity,4) personal daily condition (ie. fatigued).Processing Digital Data (Burrough 1986):1) numerical errors in the computer,a) the limitations of computer representations ofnumbers,2) faults arising through topological analyses,a) misuse of logic,b) problems associated with map overlay,3) classification and generalization problems,a) methodology,b) class interval definition,c) interpolation.DEM Creation (Burrough 1986):1) type ofDEM (TIN or Grid),2) method ofconversion - mathematical,3) method ofconversion - TIN from spot elevation, grid from contours,4) precision ofcomputer.114Check Point Surveying QualityHolmes (1989) gives the following list of common causes oferror intight-chaining:(i) the care and attention given to the project by thepeople doing thechaining...(ii) the topography and ground conditions.(iii) the instruments used to maintain the correct direction andto read theslope angle.(iv) accuracy ofthe chain used. Check the nylon or steel chain occasionallyagainst a known precise tape.Rotation error or error in compass bearing could have been due to a number of factors,Holmes gives the following list for common causes oferror in compasswork:(i) Magnetic articles near the compass that attract the compassneedle.(ii) Careless set up -- compass not directly over the T.P. [tie-point], or thesighted T.P. not vertical.(iii) Forgetting to let the needle down on the pivot.(iv) Reading the wrong end ofthe needle.(v) Sighting across the needle rather than along it when reading the bearing,thus introducing parallax.(vi) Reading south for north or vice versa when bearings are near due east orwest, or reading east for west when or vice versa when bearings are neardue north or south.(vii) Reading the wrong side ofthe 10th degree — i.e.,510instead of49°.Some causes will result in random error which will tend to cancel out. Errors, such asthose due to the needle being attracted to a magnetic article, will most likely result inbias. This bias will introduce positional inaccuracies when the feature is transferred tothe map. Many ofthe above causes could be reduced or eliminated through propertraining, experience, and attention to detail.Field traverses are normally carried out with a range of accuracy which varies between1/100 to 1/1000 (Holmes 1989). Considering the topography and ground conditions ofa skyline deflection-line assuming 1/100 would be the cautious approach.Appendix B Yarding distance estimates115116Table B Yarding distance (m) estimated forthirty-one deflection-line pairs.Deflection-line Length of Yarding Distance YardingDistance ErrorDeflection-line (Survey) (DEM)AR1-11 260.5 125.4125.4 0.0AR1-12 310.2 182.5 310.2 127.7AR6-1 625.3 614.9 614.9 0.0AR6-2 712.7 712.7 712.7 0.0AR6-3 821.0 736.7685.6 -51.1AR6-4 682.8 662.5 662.5 0.0AR6-5 543.1 535.3 543.1 7.8AR6-la 472.2 472.2 472.2 0.0AR6-2a 428.9 428.9 428.9 0.0AT-200 888.8 684.0 657.4 -26.6AT-201 904.0 701.5 701.5 0.0AT-202 907.2 877.2 844.2 -33.0AT-203 942.5 916.6 896.1 -20.5CU-20 806.9 806.9806.9 0.0CU-21 817.7 817.7 817.7 0.0CU-22 779.1 779.1 779.1 0.0CU-23 793.9 793.9 793.9 0.0GC-1 323.8 323.8 323.8 0.0GC-2 352.8 352.8 352.8 0.0GC-3 406.9 406.9 406.9 0.0GC-4 328.3 328.3 328.3 0.0GC-5 514.6 514.6 479.2 -35.4GC-7 548.7 548.7 548.7 0.0GC-8 687.9 687.9 687.9 0.0GC-10 623.0 623.0 623.0 0.0NW-i 1057.4 828.3 787.1 -41.2NW-2 931.7 724.4 670.3 -54.1TR-1 841.5 841.5 841.5 0.0TR-2 427.1 427.1 427.1 0.0TR-3 501.9 501.9 501.9 0.0TR-4 735.1 735.1 735.1 0.0Appendix C Summaries ofDEM Elevational Error117118Table Cl Summary ofDEM elevational error (m) by cutbiock.Cutbiock n Mean Mean Max(-) Max(+) Range sd Test(Abs)AR1 28 -3.6 10.9 -24.9 13.8 38.7 12.4 t-testAR6 136 -0.1 4.9 -22.4 16.0 38.4 6.5 signAT 95**5.8 7.0 -15.9 26.8 42.7 7.5 signCU - all 113 -0.4 6.3 -17.1 23.8 40.9 8.2 signCU-20/21 60 -1.0 4.4 -17.1 12.1 29.2 5.7 t-testGC 166**1.2 4.9 -16.0 15.1 31.1 6.1 t-testNW 51**0.8 4.9 -18.4 11.4 29.8 6.5 signTR 86**2.5 5.8 -19.2 23.1 42.3 7.2 t-test*significant for a0.10**significant for czO.05n = sample size = number ofelevation points sd = standard deviation119Table C2 Summary ofDEM elevational error (m) by setting.n Mean Mean Max(-) Max(+) Range sd TestSetting (Abs)AR1-Si 17 -0.5 12.0 -24.9 13.8 38.7 142 signAR1-S2 ii**8.4 9.3 -16.3 3.2 19.5 6.9 t-testAR6-S1 20 1.4 3.5 -6.1 9.3 15.4 4.5 t-testAR6-S2 46*-2.2 6.1 -22.4 13.8 36.2 7.8 t-testAR6-S3 21**4.2 4.8 -5.2 16.0 21.2 46 t-testAR6-S4 17 0.8 4.5 -12.1 8.6 20.7 5.5 t-testAR6-S5 32 -1.3 4.2 -15.7 8.7 24.5 5.6 signAT-Si 44**3.9 4.8 -4.0 21.3 25.3 5.8 signAT-S2 51**7.4 8.9 -15.9 26.8 42.7 8.4 t-testCU-Si 60 -1.0 4.5 -17.1 12.1 29.2 5.7 t-testCU-S2 53 2.0 8.3 -15.0 23.8 38.8 101 signGC-Si 60**1.5 4.1 -12.9 8.8 21.6 4.8 t-testGC-S2 38 0.6 4.3 -10.9 12.0 22.9 5.4 t-testGC-S3 42 -1.1 5.6 -16.0 14.6 30.5 ‘U t-testGC-S4 26**5.2 6.4 -6.9 15.2 22.1 6.1 t-testNW-Si 51**0.8 4.9 -18.4 11.4 29.8 65 signTR-Si 56**3.3 6.7 -19.2 23.1 42.3 80 t-testTR-S2 30 1.0 4.1 -12.8 10.2 22.9 2 t-test*significant for cz=0.10**significant for czO.05n = sample size = number ofelevation points sd = standard deviation120Table C3 Summary ofDEM elevational error (m) by deflection-linecomparison.Deflection- n Mean Mean Max(-) Max(+) Range sd Testline (Abs)AR1-li 17 -0.5 12.0 -24.9 13.8 38.7 14.2 signAR1-12 11**8.4 9.3 -16.3 3.2 19.5 6.9 t-testAR6-1 20 1.4 3.5 -6.1 9.3 15.4 4.5 t-testAR6-2 22*-1.9 3.8 -7.9 6.6 14.5 4.2 t-testAR6-3 24 -2.5 8.3 -22.4 13.8 36.2 10.2 t-testAR6-4 21**4.2 4.8 -5.2 16.0 21.2 46 t-testAR6-5 17 0.8 4.5 -12.1 8.6 20.7 5.5 t-testAR6-la 17 -0.5 3.3 -8.9 8.7 17.6 4.6 t-testAR6-2a 15 -2.2 5.3 -15.7 7.9 23.6 6.7 t-testAT-200 20**3.8 4.3 -2.0 13.7 15.7 4.7 t-testAT-201 24*4.0 5.2 -4.0 21.3 25.3 6.7 signAT-202 26**7.4 8.6 -11.0 26.8 37.8 8.5 t-testAT-203 25**7.4 9.3 -15.9 20.1 36.0 8.6 t-testCU-20 33 0.9 4.2 -6.0 12.1 18.1 5.2 t-testCU-21 27**-3.3 4.6 -17.1 5.9 23.0 5.6 t-testCU-22 26 3.5 8.6 -14.9 23.8 38.7 10.8 signCU-23 27 0.5 8.1 -15.0 15.9 30.9 9.3 signGC-1 19 0.7 4.2 -6.4 8.2 14.6 4.9 t-testGC-2 21 1.9 2.7 -3.0 6.2 9.2 2.8 t-testGC-3 20 1.7 5.5 -12.8 8.8 21.6 6.3 signGC-4 12 -0.4 4.3 -9.0 9.3 18.3 5.7 t-testGC-5 26 1.0 4.3 -10.9 12.0 22.9 5.3 t-testGC-7 20 2.3 4.2 -3.6 14.6 18.2 5.3 signGC-8 22**-4.1 6.8 -16.0 9.7 25.7 7.4 t-testGC-10 26**5.2 6.4 -6.9 15.2 22.1 61 t-testNW-i 28 1.3 3.9 -11.9 11.4 23.3 5.1 t-testNW-2 23 0.2 6.0 -18.4 11.3 29.7 7.9 t-testTR-1 29**2.3 4.1 -5.8 10.5 16.3 4.8 t-testTR-2 13 1.1 4.3 -9.8 10.2 20.0 57 t-testTR-3 17 0.9 3.9 -12.8 7.4 20.2 5.0 t-testTR-4 27**44 9.5 -19.2 23.1 42.3 10.4 sign*significant for a0.10**significant for ccO.05n = sample size = number of elevation points sd = standard deviationAppendix B Glossary ofTerms121122Analog maps - Map information stored on hard copy map such as mylar or paper.Asopposed to digital maps.Cutblock - the individual unit area ofoperational harvest planning. Acutblock may befurther subdivided into individual settings (Conway 1982).Deflection - the vertical distance, or sag, between a chord connecting the top oftheskyline supports at either end ofthe skyline.Deflection-line - profiles acquired when straight line surveys are run on the grounddirectly beneath the proposed location for the skyline cable.Deflection-line Analysis - the process used to determine the feasibility ofyarding in aparticular location by checking for adequate clearance between suspended logsand the ground. This, in turn, is used to locate the harvest boundary.Deflection-linepairs - the matched pair of deflection-lines used in this study. Onedeflection-line was field surveyed in a proposed harvest area. A DEM wascreated for the harvest area and then the second deflection-line was derived,using the DEM, in the same spatial location as the field surveyed deflection-line.DigitalElevation Models (DEM) - pseudo three-dimensional computer models. Any“digital representation ofthe continuous variation ofreliefover space” (Burrough1986). A “representation of a terrain surface consisting ofX, Y, Z coordinatesstored in digital form” (Webb 1990).Digitalmaps - Map information stored in digital format on a computer. GIS maps aredigital maps.Digital Terrain Models (DTM) - a digital elevation model which also contains terraininformation such as slope or aspect.Geographic Information System (GIS) - a GIS is a “set oftools for collecting, storing,retrieving at will, transforming, and displaying spatial data from the real worldfor a particular set of purposes” (Burrough 1989). This set oftools may beincorporated into a system of computer software, hardware, and peripherals.Landing - a widening ofthe road which provides room for the yarding machine, logs,log loader and log trucks. It is a transition location for logs after they areyarded and before they are loaded onto trucks.Operational Harvest Planning - the planning for individual cutbiock level harvestoperations.123Parallax - “The apparent displacement ofthe position of a body,with respect to areference point or system, caused by a shift in the point ofobservation” (Wolf1980).Photogrammetry - “The art, science, and technology of obtaining reliable informationabout physical objects and the environment, through processes ofrecording,measuring, and interpreting images [photographs] and patterns ofelectromagneticradiant energy and other phenomena” (Wolf 1980).Photogrammetrically-ineasuredcontourmapsPhoto interpretation - “The detection, identification, description, and assessment ofsignificance of objects and patterns imaged on a photograph” (Wolf 1980).Positionalaccuracy - “the accuracy offeature locations after transformations havebeen applied” (Veregin 1989).Positional error - error resulting from a feature with low positional accuracy.RegularRectangular Grid (Grid) - the most common and most readily available formofDEM (Burrough 1986). They consist of a regular rectangular grid containingcartesian coordinates in three-dimensions (Peucker et al. 1976).RubberSheeting - the process ofrectifying one map to a more accurate map depictingthe same geographic location. Common tie-points are identified on both maps,and a mathematical correction is applied to the entire map, based upon thecorrections that were used to align the tie-points (Burrough 1986).Setting - smallest individual unit in operational harvesting process (Conway 1982). Thesetting is the area logged from one landing.Skyline YardingSystem - a type of cable yarding system which utilizes a skyline cableto provide improved lift when yarding logs.Smoothing error - when natural terrain variation has been lost or ‘smoothed out’ due tomeasurement error, interpolation patterns or data transformations.TriangulatedIrregularNetwork (TIN) - a type ofDEM which represents surfacesthrough locating data points at key topological features such as peaks, pits,passes, ridges and channels (Peucker et at. 1976). This results in irregularlyspaced points connected by lines to form a continuous sheet oftriangles.TriangulatedIrregularNetwork - A type ofdigital terrain model where the terrain isrepresented by irregularly spaced elevation points connected together to form acontinuous sheet oftriangular facets.124USGS - United States Geological SurveyYarding - the act oftransporting logs from where they were felled to the landing wherethey will be loaded onto trucks.Yarding machine - the machine used to move the cables which transport logs to thelanding. The yarding machine utilizes a tower or crane like structure to lift thecables offofthe ground.Yarding road- the path the logs follow while being yarded from within the setting tothe landing. The yarding road is the area under where the skyline cables are setand the area adjacent that can be reached from the skyline carriage.

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