Landscape Ecology 15: 741–754, 2000. © 2000 Kluwer Academic Publishers. Printed in the Netherlands.
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Testing methods to produce landscape-scale presettlement vegetation maps from the U.S. public land survey records Kristen L. Manies∗ and David J. Mladenoff Department of Forest Ecology and Management, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI 53706, USA ∗ (Corresponding author: Current address: U.S. Geological Survey, 345 Middlefield Rd., Menlo Park, CA 94025, USA, E-mail:
[email protected]) Received 19 July 1999; Revised 3 March 2000; Accepted 13 April 2000
Key words: forest landscape, hemlock-hardwoods, interpolation, kriging, Sylvania Wilderness Area, Michigan, U.S. Public Land Survey
Abstract The U.S. Public Land Survey (PLS) notebooks are one of the best records of the pre-European settlement landscape and are widely used to recreate presettlement vegetation maps. The purpose of this study was to evaluate the relative ability of several interpolation techniques to map this vegetation, as sampled by the PLS surveyors, at the landscape level. Field data from Sylvania Wilderness Area, MI (U.S.A.), sampled at the same scale as the PLS data, were used for this test. Sylvania is comprised of a forested landscape similar to that present during presettlement times. Data were analyzed using two Arc/Info interpolation processes and indicator kriging. The resulting maps were compared to a ‘correct’ map of Sylvania, which was classified from aerial photographs. We found that while the interpolation methods used accurately estimated the relative forest composition of the landscape and the order of dominance of different vegetation types, they were unable to accurately estimate the actual area occupied by each vegetation type. Nor were any of the methods we tested able to recreate the landscape patterns found in the natural landscape. The most likely cause for these inabilities is the scale at which the field data (and hence the PLS data) were recorded. Therefore, these interpolation methods should not be used with the PLS data to recreate pre-European settlement vegetation at small scales (e.g., less than several townships or areas < 104 ha). Recommendations are given for ways to increase the accuracy of these vegetation maps.
Introduction Understanding human effects on the environment has become a recent priority in ecological studies (Foster et al. 1996). Much of this interest comes from recognizing the impacts of society upon ecological landscapes and how the frequency and intensity of these impacts have increased in the last two hundred years. One consequence is a decrease in the forested area in the United States with a corresponding increase in fragmentation of the remaining wooded areas (Whitney 1994). To better understand human influences upon the landscape and the resulting changes, researchers often wish to reconstruct the original forest vegetation, frequently using the period before European settlement as their baseline.
Many of these reconstructions use data from the U.S. General Land Office Public Land Survey (PLS; Fassett 1944; Finley 1951; Stroessner and Habeck 1966; Kapp 1978; Mladenoff and Howell 1980; Grimm 1984; Iverson 1988; White and Mladenoff 1994; Whitney 1994; Delcourt and Delcourt 1996, Radeloff et al., 1999). This survey, which took place from 1785 until 1910, was used to divide unsettled land from Ohio to the west coast into parcels that could be sold (Stewart 1935). Surveyors first partitioned land into six-mile square townships. These townships were then subdivided into thirty-six 1 mi2 (2.5 km2 ) sections (Figure 1). Surveyors marked the legal boundaries of the townships and sections by placing a post or a stone at the intersection of section lines (section corners) and the midpoints between section corners
742 (quarter corners). They also marked the location where section lines crossed navigable rivers, bayous, or lakes (meander corners). At each of these corners the surveyor blazed two to four trees, known as bearing or witness trees. The species, diameter, compass bearing, and distance to the corner of each tree were recorded in the surveyors’ notebooks (Stewart 1935). The dominant forest and understory species observed by the surveyor during his travels were sometimes also noted, though this information was recorded sporadically. Witness tree data have become one of the most important sources for reconstructing pre-European settlement vegetation. These data, however, only exist for the section, quarter, and meander corners. Therefore, interpolation is required to estimate the vegetation between data points as well as to create boundaries between different vegetation types. Studies such as White and Mladenoff (1994) and Batek (1999) have used geographical information systems (GIS) for this interpolation. This study examines how well-dispersed point data, such as the PLS data, can be used to recreate the vegetation of a landscape. To test this question a present day landscape that has remained relatively undisturbed was resurveyed at the same scale as the PLS data using similar methods as the surveyors. These data were then interpolated using GIS algorithms and computer-based geostatistical models. The resulting maps were compared to a map of the ‘true’ vegetation, created from classified aerial photographs. The results of this study can aid in understanding how well these interpolation methods reconstruct the pre-European settlement forested landscapes using the PLS survey data.
Methods Study area The area chosen to be resurveyed was the Sylvania Wilderness Area in Ottawa National Forest, Michigan, U.S.A. Sylvania is located in the Upper Peninsula of Michigan, along the Michigan-Wisconsin border (46◦130 N 89◦ 170 W; Figure 2). As Sylvania is one of the least impacted areas within the northern Great Lakes region it best represents the vegetation types and patterns found on mesic soils in this area before European settlement (Pastor and Broschart 1990; Mladenoff and Pastor 1993; C. Rasmusson, pers. commun.). Sylvania comprises 8107 ha (20018 ac), or
86% of a survey township. Some portions of the township are excluded as they lie below the MichiganWisconsin border while other areas occur outside the wilderness boundaries. The vegetation is dominated by old-growth eastern hemlock (Tsuga canadensis) and hardwood (e.g., sugar maple [Acer saccharum] and yellow birch [Betula alleghaniensis]) forested uplands with smaller patches of lowland conifers (e.g., northern white cedar [Thuja occidentalis] and spruce [Picea spp.]) and wetlands. The climate is characterized by short, mild summers and long, cold winters. Seasonal mean temperatures are approximately −9 ◦ C in winter (December–February) and 17 ◦ C in summer (June–August). Annual precipitation is ∼86 cm (Jordan 1973). Field sampling All quarter, section, and meander corners in Sylvania were resurveyed using a hand held compass and a Sonin Combo Pro distance measuring device (Sonin 1995). In some cases, where weather or topography made the Sonin impractical, pacing was used instead. Measurement errors of both the pacing and Sonin methods were calculated using closed traverses and determined to be less than 100 ft (30.5 m) per halfmile (0.8 km). While these methods did not insure that the precise locations of the original survey corners were always located, the purpose of this project was to replicate data at the same scale as the original survey, not to return to the precise location of each original corner. The location of each point was recorded using a global positioning system (GPS). Along with GPS locations, we also recorded the species, diameter, distance, and bearing to the corner of five trees in each of the four quadrants (NW, NE, SW, and SE) surrounding each corner. Like the PLS surveyors, selective criteria were used to determine which five trees would be recorded. These criteria include species, size, and location (Bourdo 1956; Manies 1997). All trees selected also had to be healthy and without any advanced signs of disease or mortality (Stewart 1935). The first tree recorded was always the closest tree to the corner > 1 inch (2.5 cm) in diameter. We recorded the closest tree in each quadrant to estimate what the surveyors would have recorded if they had not exhibited any biases when selecting witness trees. The next four trees recorded were the closest trees to the corner that were > 4 inches (10.2 cm) and ≤ 22 inches (55.9 cm) in diameter. These size limits
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Figure 1. Township boundaries of Wisconsin. The township referred to as Township 42 North Range 4 East has been expanded to show the thirty-six square mile sections that comprise a township. Examples of section, quarter, and meander corners are also shown.
were chosen so that the field data would more closely mimic the data the original surveyors recorded. The lower limit was selected for two reasons. First, the 1851 surveyor instructions recommend that trees less than 5 inches in diameter (12.7 cm) should not be used (Bourdo 1956). Also, an examination of over thirty-three thousand PLS witness trees for northern Wisconsin found only 1% had diameters less than 4 inches (10.2 cm). Though the surveyor instructions never stated a maximum witness tree size, larger trees have a diminished length of survival and were more likely to be cut for lumber, thereby being less likely to be chosen as witness trees by the surveyors (Hushen et al. 1966). The 22 inch (55.9 cm) limit was chosen because most trees within this forest type approach the lower limits of their life expectancy at this size (C. Lorimer, pers. commun.) and only 2% of the PLS witness trees examined had diameters ≥ 22 inches (55.9 cm). Exceptions to both diameter limits were made when trees could not be located within a quad-
rant that fit the above criteria (e.g., trees in an open swamp). Data processing We recorded more field data than the original surveyors (a maximum of twenty trees per point versus four trees) to allow us to filter the field data and approximate PLS data with different levels of bias. Two subsets were created from the field data. Both data sets contained two trees per corner, the number of trees usually recorded by the surveyors. The first data set used the closest trees to the corners. These data, referred to as the ‘objective’ data set, represent the PLS data if it existed without any bias. The first tree in this data set was chosen by selecting the closest tree to the corner in any of the four quadrants. The second tree was chosen by selecting the closest tree in the opposite quadrant. The next data set consisted of the two ‘best’ trees. These trees were chosen in the field keeping the qualitative criteria the original surveyors
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Figure 2. The location of Sylvania Wilderness Area within the northern Great Lakes region.
used in mind. These criteria included preferences for close trees of longer-lived species, medium diameters (usually between 8 to 16 inches [20.3 to 40.6 cm]), which were easily visible, and not located along quadrant lines (Bourdo 1956; Hushen et al. 1966; Grimm 1984; Manies 1997). These data will be referred to as the ‘biased’ data set. Maps created with these two data sets were compared to a map of Sylvania created from 1:24000 color infrared stereo photography. These photographs were taken before leaf-on in May 1980 (Pastor and Broschart 1990). This map was reclassified from a 10 m grid to a 50 m grid using the Arc/Info RESAMPLE command (ESRI 1994a). The air-photo map represents the canopy composition of Sylvania, whereas the field data, like the PLS data, represents the entire forest composition. In order to make the field and the air-photo data comparable only canopy trees from the field data were used in analysis. Canopy trees were defined as those trees > 25 cm (9.8 inches) in diameter (Runkle 1982). Modifying the field data assumes that the techniques tested in this study work the same when interpolating the entire forest composition as when interpolating only the canopy composition. This assumption is valid if the map of the canopy data is similar to a map that uses trees from all canopy levels. Therefore, we compared a map of the canopy field data to a map of the entire field data to test the extent of any differences that might occur between the two.
Only upland species were interpolated and mapped because the lowland patches were too small to be accurately recreated at the PLS data scale. We also did not recreate the boundaries of lakes and ponds, as these are relatively stable over time. Therefore, no analysis was done for these data classes. Such a process is justified since GIS coverages of these data are now available for much of the United States (e.g., Wisconsin Wetlands Inventory [Wisconsin Department of Natural Resources 1992], National Wetlands Inventory [Tiner et al. 1987]). Lowlands and water bodies were clipped from the air-photo map using a GIS and overlaid onto the upland results so that they would be represented in the interpolated maps. For both data sets, individual species at each point were depicted as presence/absence values and as percents (species X comprises either 0, 50, or 100% of the trees recorded at that point). Maps were made by creating interpolation maps for each individual species that comprised greater than 2% of the data set (Figure 3a). These individual maps were then combined, following predetermined decision rules (Table 1), to create the forest-type map. Data at each individual point can also be represented as species groups (Conifer, Hardwood, Hemlock, or Mixed Hemlock-Hardwood). Therefore, species at each point were also combined to form these groups using the same decision rules as above. Maps for data in this format were then created by interpolating these species groups (Figure 3b). Data analysis The GPS coordinates of each data point, along with the corresponding species data, were imported into the Arc/Info GIS. Once the field data were in a GIS format they were processed in one of two ways (Table 2). The first method began by creating a triangulated irregular network (TIN; ESRI 1994b). The surface of this TIN was transformed into a grid using quintic interpolation. The second process converted the data points into thiessen polygons (ESRI 1994c), which in turn were converted into a grid. The third interpolation method, indicator kriging (Isaaks and Srivastava 1989), used S+SpatialStats software (MathSoft Inc. 1996). Indicator kriging has an advantage over Arc/Info’s interpolation techniques because it was designed to use presence/absence data, whereas the Arc/Info techniques were designed to be used with continuous data. Indicator kriging is a geostatistical technique that is based on spatial autocorrelation. Species representing the biased data set were
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Table 1. Decision rules used to group species. These rules were used when combining individual interpolation maps into forest type maps and for classifying individual species at a point into species groups. Conifers consist of cedar, fir, white pine, red pine, and spruce. Hardwoods are sugar maple, red maple, yellow birch, aspen, ash, paper birch, and linden. First vegetation type at a point
Second vegetation type(s) at a point
Classified as forest type
Conifer Conifer Conifer Hardwood Hemlock Hemlock Hemlock Hemlock
Conifer(s) Hardwood(s) only Hardwood(s) and conifer(s) Hardwood(s) Conifer only Hardwood(s) Hardwood(s) and conifer Hemlock
Conifer Hardwood Mixed conifer-hardwood Hardwood Hemlock Mixed hemlock-hardwood Mixed hemlock-hardwood Hemlock
Figure 3. Methods by which final forest type maps were created. Example A uses data represented by individual species in either presence/absence or percent format. Individual species maps are interpolated and then combined to create the forest type map. Example B classifies the individual species into species groups. These groups are then interpolated to create the forest type map.
746 Table 2. Matrix detailing the possible combinations of interpolation methods and data formats. Cells marked with an X signify analyses performed using this combination on either the biased data set, the objective data set, or both. Interpolation method
Representation of the data Individual species – Individual species – presence/absence percent
Arc/Info – quintic Arc/Info – thiessen S+ indicator kriging
X X X
examined using semi-variograms, which describe the spatial correlation between observations. A spherical model (Isaaks and Srivastava 1989) was used to model the variograms of those species for which autocorrelation was found. Ordinary kriging was then used to predict the occurrence of these species within a grid and these prediction values were imported into Arc/Info. Species for which spatial autocorrelation was not predicted were modeled using inverse distance weighting (IDW Arc/Info command; ESRI 1994a). Spatial clustering distances were used to define the radius within which a species probability was calculated (hemlock = 105 m, white pine = 55 m; Frelich et al. 1993). When the three interpolation methods were completed, each grid was reclassified so that cells with values less than 0.30 (or 30% probability) for any species or species group were changed to zero. This cutoff was used to avoid using data from cells with extremely low probabilities (Batek 1999). During preliminary analysis other cutoffs were tried but did not cause a significant change in results. Several areas within the interpolation map were without vegetation data. These regions were caused by points without canopy trees, areas where all species simultaneously reached a zero probability, or areas where the boundaries of the field data were smaller than the boundaries of the air-photo map. The airphoto map was masked to contain the same open patches of no data as the interpolated map to which it was being compared. These masked areas were not included in any accuracy analysis. The interpolated and air-photo maps were then compared on an area, cell by cell, and patch size basis. Error matrices for each vegetation type were calculated as were overall accuracy, producer’s accuracy (error of omission), and user’s accuracy (error of commission; Lillesand and Kiefer 1979).
X
Species groups
X
Results Data summary A total of 132 points were recorded in the field at the same scale as the original surveyor corners (Figure 4). Fifteen species were recorded (Table 3) with diameters ranging from 1.5 inches (3.8 cm) to 49.4 inches (125.5 cm). The objective data set had the same species as the full database, minus black ash (Fraxinus nigra). Diameters of trees within the objective data set ranged from 1.5 inches (3.81 cm) to 33.6 inches (85.3 cm). The biased data set had twelve species, with ash, aspen (Populus spp.), and paper birch (Betula papyrifera) dropping out. Diameters ranged from 2.9 inches (7.3 cm) to 26.4 inches (67.1 cm). Only species that comprised over 2% of the data set were used for interpolation. For the biased data set these species were cedar, hemlock, sugar maple, white pine (Pinus strobus), and yellow birch. Six species were above 2% for the objective data set: cedar, fir (Abies balsamea), hemlock, sugar maple, red maple (A. rubrum), and yellow birch. Over 78% of the forest area was classified the same between the map made with canopy trees and the map using both canopy and sub-canopy trees. Map interpolation Over 67% of the area within maps created with the biased and objective data were classified as the same forest type. The accuracy analysis and patch size distributions for the two data sets were also very similar. Therefore, while findings discussed here apply to both the objective and biased data sets (Manies 1997), only values for the biased data set are presented. Both the quintic and thiessen Arc/Info interpolation methods, using either of the three data representations (Table 2), replicated the order of dominance
747 Table 3. Frequency of species for the full field data set using all trees recorded at a point. Tree species
Common Name
Percent
Tsuga canadensis Acer saccharum Abies balsamea Betula alleghaniensis Thuja occidentalis Acer rubrum Pinus strobus Picea glauca, P. mariana Tilia americana Pinus resinosa Ostraya virginiana Betula papyrifera Larix laricina Populus grandidentata, P. tremuloides Fraxinus nigra Not applicable
hemlock sugar maple fir yellow birch white cedar red maple white pine spruce linden red pine ironwood paper birch tamarack aspen ash illegible
41.1 22.9 10.1 6.5 5.6 4.8 2.1 1.7 1.1 1.0 0.9 0.7 0.6 0.6 0.1 0.1
Table 4. Difference in total area (%) of the interpolated maps in reference to the air-photo map. Data shown are for the biased data set. Forest type
Hemlock Hardwood Mixed hemlock-hardwood Conifer
Interpolation method & data representation Quintic interpolation Presence/absence Percent Species groups
Thiessen interpolation Presence/absence
−27.0 23.4 29.6 323.4
−13.8 31.4 −23.9 649.0
of each forest type in the air-photo map. Total areas, calculated for each vegetation type, were less accurately represented (Figure 5). The differences in area between the air-photo and interpolated maps ranged from 13–39% for Hemlock, Hardwood, and Mixed Hemlock-Hardwood forest types and up to 650% for Conifer (Table 4). Interpolation maps consistently underestimated Hemlock while Hardwood and Conifer were always overestimated. Results were also relatively inaccurate when comparing the interpolated (Figure 6a) and air-photo maps (Figure 6b) on a cell by cell basis. The overall accuracy was very similar for all four maps created, between 36–40%. Producer’s and user’s accuracy were also fairly consistent between the methods and data formats (Table 5). The Hemlock forest type was classified most accurately, followed by Mixed Hemlock-Hardwood, Hardwood, and finally
−13.3 39.5 −13.8 386.3
−30.1 19.6 31.1 476.8
Conifer. Differences were also evident in the patch size distributions (Figure 7). All air-photo maps had a much higher frequency of smaller patch sizes than did the interpolated maps. The other interpolation method used was indicator kriging. Spatial autocorrelation, on which indicator kriging is based, was not found for two species (hemlock and white pine; Figure 8). Variograms for cedar, sugar maple, and yellow birch do show spatial autocorrelation (Figure 9). Large areas of the kriged map did not have predicted vegetation (i.e., were classified as no data; Figure 10). The amount of area without data may explain why the kriging overall accuracy (20.5%) is less than those of the two Arc/Info interpolation methods. The producer’s and user’s accuracy are similar to those calculated for the Arc/Info interpolation methods, ranging from ∼1% to over 60% (Table 6).
748 Table 5. Producer’s and user’s accuracy values (%) for biased data. Forest type
Producer’s Accuracy Hemlock Hardwood Mixed Hemlock-Hardwood Conifer User’s Accuracy Hemlock Hardwood Mixed Hemlock-Hardwood Conifer
Interpolation method & data representation Quintic interpolation Presence/absence Percent Species groups
Thiessen interpolation Presence/absence
43.65 21.92 37.79 4.12
50.25 27.13 27.41 3.57
41.59 22.66 38.18 5.67
49.91 27.01 22.90 10.00
60.17 18.01 29.43 1.01
58.27 19.80 32.00 0.75
59.89 18.95 29.31 1.01
57.91 20.56 30.17 1.28
Figure 5. An example of the total area (ha) calculated for each forest type of an Arc/Info interpolated map and the corresponding air-photo map. Table 6. Producer’s and user’s accuracy (%) for the indicator kriging interpolation.
Figure 4. The township in which Sylvania Wilderness Area is situated. The PLS section, quarter, and meander corners as well as the GPS locations of the field data are marked. Areas of the map without these points lay outside of Sylvania’s boundaries.
The distribution of patch sizes for the kriged map (Figure 11) is also similar to those from the Arc/Info interpolation methods. Again, the interpolated map has lower frequencies of smaller patches and higher frequencies of larger patches than the air-photo map.
Forest type
Producer’s accuracy
Users’ accuracy
Hemlock Hardwood Hemlock-Hardwood Conifer
49.19 26.33 28.22 3.26
60.32 18.02 29.41 0.95
Discussion Canopy versus all trees Only slight differences were found between the field data map created using all trees and the field data map from only canopy trees. These differences occurred for
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Figure 9. Variograms showing spatial autocorrelation for A) cedar, B) sugar maple, and C) yellow birch.
Figure 7. An example of the patch size distributions calculated for an Arc/Info interpolated map (A) and the corresponding air-photo map (B).
one of two reasons. First, areas that were classified as either Hemlock or Hardwood in the canopy map changed to Mixed Hemlock-Hardwood for the map with all trees. These classification changes occurred because the second tree at a corner, which had previously been classified as a non-canopy tree and not considered during classification, now was included in this process. Some areas along edges of forest types also changed classification due to the slightly different probabilities calculated using all trees at a corner. Because the majority of the area for these two maps was classified as the same forest types, it is valid to assume that the results using only canopy trees would also apply to data with all trees. Comparison of interpolated and air-photo maps
Figure 8. Variograms showing no spatial autocorrelation for A) hemlock and B) white pine.
All three interpolation methods accurately estimated the relative forest composition of the landscape and the order of dominance of different vegetation types. Estimates of the actual area occupied by each vegetation type were not as reliable. The amount of error in these values suggests that such area measurements,
751 calculated from small scale interpolated maps (e.g., one township), may not be accurate enough to calculate changes between pre-European settlement and present day vegetation. The ability of the interpolation methods to recreate the vegetation of the air-photo map on a cell by cell basis was also quite low. For all methods tested, both the producer’s and user’s accuracy rarely were above 50% and were often much less. Predictive power appears to be related to the order of vegetative dominance on the landscape. In other words, Hemlock, which dominates the landscape, was classified most accurately, followed by Mixed Hemlock-Hardwood and Hardwood. Conifer areas, which occupied the least amount of area within Sylvania, were poorly predicted. The low accuracy values for Conifers are likely due to the small size of these vegetation patches, which are difficult to recreate. Small differences in how Conifers were classified between the air-photo and the interpolated maps may also be affecting the results. One reason for the low accuracies of these forest types is clear when an interpolated map and the comparable air-photo map are visually compared. The interpolated map is composed of several larger, simple polygons while the air-photo map has more numerous and complex polygons. The interpolation methods are unable to recreate the intricate patterns found in the natural landscape (Mladenoff et al. 1993). One reason the interpolation methods are ineffective is that the scale at which the field data (and PLS data) were taken was not fine enough to capture this natural complexity. Corners, which are located up to one-half mile apart and arranged only on the exterior of sections, do not represent a sufficiently fine-grained sampling scheme. These issues of scale would also affect reconstruction of the forest vegetation at a township scale using the PLS data. Therefore, reconstructions using the PLS data to map vegetation at a small scale (i.e., less than several townships) may not be valid for determining landscape patterns of the pre-European settlement vegetation or to calculate indices such as diversity, complexity, and patch density. An additional problem related to scale was encountered when interpolating with indicator kriging. The scale of the field (and PLS) data was too coarse to detect spatial autocorrelation for all species. Because kriging relies on spatial autocorrelation, alternative methods were required to create vegetation maps for some species. In light of this result, indicator kriging may not be a viable interpolation method unless one of two conditions is met. First, species within the study
area must have spatial autocorrelation at scales large enough that they can be observed in the PLS data. Or, additional sources of data (e.g., field studies that study patch formation) are needed to provide values for species with small scale spatial autocorrelation. Misclassification of forest types may also explain some of the differences between the interpolated and air-photo maps. Final forest types were based on which species were present at a point (see Table 1). Because only two trees were used it is likely that at times species were not listed that would change the forest type classification. For example, some points classified as a hardwood or hemlock forest type could in reality be mixed hemlock-hardwood or mixed conifer-hardwood. Because surveyors usually recorded only two species at each point, this issue is an intrinsic problem of the PLS records. Recommendations There may be ways in which the accuracy of these interpolation methods can be increased. The precision of these techniques, however, is untested. Disadvantages also exist for each technique. One option is to use other natural boundaries that might display the same patterns as the vegetation (e.g., soils or in some cases topography) to help create the boundaries between different vegetation types. These variables have been used in some PLS vegetation mapping projects (Comer and Raab 1996), though not in a GIS format. Using ecological factors to determine vegetation boundaries means, however, that the vegetation map is no longer independent from these variables, thereby excluding researchers from studying the relationships between the pre-European settlement vegetation and these environmental variables. Another possible technique would be to use additional information from the PLS notebooks, such as the section line descriptions or the plat maps. Studies described by Galatowitsch (1990) used these sources to recreate forested riparian areas of Colorado and Oregon. Using these sources is time intensive, not as feasible for large-scale projects, and not without its own problems. First, surveyors did not always note when they entered and left different ecosystems, especially if moving from one forested region into another (e.g., from Hemlock to Hardwood). Also, these descriptions and maps are only valid for the portions of the township the surveyors traveled, most of which was only along the section lines. Lastly, such narrative
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Figure 11. Patch size distributions calculated for the interpolated map created with indicator kriging (A) and the corresponding air-photo map (B).
descriptions are more subjective, increasing variation among surveyors. The last option is to broaden the landscape extent and use the PLS data at a much larger scale, such as to the county, state, or regional level (e.g., Finley 1951; Whitney 1994). At such scales the fine-scale complexity of the natural vegetation boundaries diminishes, creating simpler polygon structures that the interpolation techniques are better able to recreate. This last option may be one of the best alternatives when using computer based interpolation methods with the PLS data. For such a method to work the minimum mapping unit required to obtain accurate results needs to be determined. Delcourt and Delcourt (1996) reported that a 259 ha grid cell (1 mi2 ) was adequate to resolve nine different vegetation types within the mixed conifer-northern hardwoods region of the Upper Peninsula of Michigan. This value, how-
ever, could change depending on the soils, topography, and disturbance regimes of the study area. For example, locations with high variation in topography (e.g., drumlins) or small-scale disturbance regimes (e.g., blowdowns) may require smaller cell sizes to capture the landscape grain. Clay plains and areas with stand replacing fire regimes (e.g., barrens) may not need cells of such a fine scale. It is noteworthy that differences between the maps that were to represent the different biases of the PLS data, the biased and objective data sets, were minimal. This fact may mean that although biases exist within the PLS data, they do not significantly affect the results when mapping vegetation at a landscape scale. The bias that is most likely to affect mapping studies is species bias. When species bias is present the surveyor shows preference for certain species as witness trees over others. Which species the surveyor can chose from, however, is ultimately constrained by environmental factors. Therefore, the vegetation classes formed from these witness trees represent the same forest types, even if the trees with which the forest type is determined have been chosen in a non-random fashion. Analysis of the individual witness trees that comprise each forest type, however, would continue to be affected by surveyor bias (Manies 1997). Though this study learned much about our abilities to map the PLS data, other methods need to be studied to determine more accurate means for reconstructing the vegetation on a landscape level. The scale at which the vegetation is best recreated is another factor that requires further study. However, we determined that the methods examined here are useful for obtaining very general representations of the preEuropean settlement vegetation when using the PLS data.
Acknowledgements This research was supported in part by the Wisconsin Department of Natural Resources with funds from the Federal Aid in Wildlife Restoration Act under Pitman-Robertson Project W-160-P. Funding was also provided by the Lois Almon Small Grants Program, which is sponsored by the Wisconsin Academy of Sciences, Arts, and Letters, the Wisconsin chapter of The Nature Conservancy, the Wisconsin Department of Natural Resources Bureau of Endangered Resources, and the Citizens Natural Resources Association. We would also like to thank Craig Lorimer, Steve Ventura,
754 and two anonymous reviewers for their comments and Christian Schumacher for his assistance in the field. This work is part of a thesis by the first author.
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