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• Cover
• Table of Contents
• Executive Summary

Chapters

• Management Summary
• Research Design & Time Line
• Environment & Native American Culture
• GIS Design
• Archaeological Database
• Archaeological & Environmental Variables
• Model Development & Evaluation
• Model Results & Interpretation
• Project Applications
• Model Enhancements
• Model Implementation
• Landscape Suitability Models
• Summary & Recommendations

Appendices

• Archaeological Predictive Modeling: An Overview
• GIS Standards & Procedures
• Archaeology Field Survey Standards, Procedures & Rationale
• Archaeology Field Survey Results
• Geomorphology Survey Profiles, Sections, & Lists
• Building a Macrophysical Climate Model for the State of Minnesota
• Correspondence of Support for Mn/Model
• Glossary
• List of Figures
• List of Tables
• Acknowledgments

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

8.12 BORDER LAKES SUBSECTION OF NORTHERN SUPERIOR UPLANDS SECTION

 

8.12.1 Environmental Context
8.12.2 Site Probability Model
8.12.3 Survey Probability Model
8.12.4 Survey Implementation Model
8.12.5 Other Models

 

This regional model report is organized as described in Sections Sections 8.2.1 through 8.4.1. Refer to these sections of the report for explanations of the tables, variables, and statistics.

 

8.12.1 Environmental Context

Border Lakes is a subsection of the Northern Superior Uplands section of the Laurentian Mixed Forest Province (Figure 8.1) (Minnesota DNR 1998). It is characterized by a large number of lakes and rocky ridges on glacially-eroded bedrock and poor soils. Much of this subsection is occupied by the Boundary Waters Canoe Area (BWCA).

 

The northern boundary of the subsection is the US-Canada border. The western border follows the transition from lake-laid mineral sediment and ground moraines on the west to a bedrock-dominated landscape to the east. This transition zone follows the Vermilion River north from Lake Vermilion to where the river turns east towards Crane Lake. From there it angles to the northwest along the south shore of Kabetogama Lake to the west end of Rainy Lake. The southern boundary is the interface between the Rainy Lobe moraine material to the north and the Superior Lobe moraine material to the south. The west part of the southern boundary also coincides with the change from a landform characterized by exposed bedrock to one covered by thicker sediments (Dan Hanson, personal communication, 1997).

 

This subsection consists of scoured bedrock uplands or shallow soils, with large numbers of lakes (Figure 8.12.1.a) occupying east-west oriented valleys left by glacial activity. The quaternary geology map (Figure 8.12.2.a) shows a very homogeneous landform occupied by ground moraine. Topography is mostly rolling, with irregular slopes and craggy outcrops. The highest point in Minnesota (2,301 feet) is located in the Border Lakes subsection. Well-drained soil conditions (Figure 8.12.2.b) dominate the subsection. Lakes cover about 13 percent of the subsection's surface. An extensive drainage network of streams and rivers is also present (Minnesota DNR 1998).

 

Historic vegetation (Figure 8.12.1.b) consisted of three major forest types: jack pine, white-red pine, and mixed hardwood and pine. Jack pine barrens were common in areas not occupied by small lakes. White and red pines dominated in the central area from Lake Vermilion northeast, along the northern border, and in the northwest. In each of these areas, aspen-birch forest was also present. Conifer bogs were interspersed with upland forests throughout the central, southern, and eastern portions of the subsection.

 

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8.12.2 Site Probability Model

8.12.2.1 Description

Areas of high and medium site potential for archaeological sites in the Border Lakes subsection are concentrated around lakes and within river and stream corridors (Figure 8.12.3). Zones of high and medium site potential extend away from water features where there is level topography.

 

In northwestern portion of the subsection, large contiguous areas of high site potential are within and adjacent to Rainy and Kabetogama Lakes. The lakes were not mapped in the model because they are classified as artificial by National Wetlands Inventory. However, they are assigned the value zero for the vertical distance to water variables (Figure 8.12.6.a and 8.12.6.b). Consequently, much of this high probability area is actually under water. Still a considerable area of high site potential surrounds the lakes where elevations are low and the terrain relatively smooth. Areas of more rugged terrain are classified as low site potential.

 

Along the Canadian border in north central St. Louis County, zones of high site potential follow lakeshores and are interconnected around Namakan, Mukooda, Johnson and Franklin lakes, and Lac La Croix. Along the Canadian border, in the northwest corner of Cook County, areas of high site potential occur around lakes and in areas of lower elevation and level terrain extending away from lakes. When high site potential occurs farther from water, the vertical distance to water is small.

 

Areas of high site potential are less extensive and more scattered in the eastern portion of the subsection, where they are associated with the shores of a large number of lakes. There are also sparse, scattered occurrences of high site potential in a subtle dendritic pattern throughout the eastern portion of the subsection. However, they correspond only weakly with stream locations.

 

In addition to the lakes mentioned above, Namakan and Crane lakes are classed as artificial in the National Wetlands Inventory and therefore were not mapped on this model.

 

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8.12.2.2 Evaluation

The site probability model for the Border Lakes subsection performed very well. It is based on 16 variables (Table 8.12.1) representing topography, vegetation, soils and hydrology.

 

Table 8.12.1. Site Probability Model, Border Lakes Subsection.

8.12.2.3 Interpretation

The Border Lakes site probability model is made up of 16 variables. Half of the variables are hydrologic in nature while the remaining are linked to vegetation (Table 8.12.4).

 

Table 8.12.4.  Model Variable Statistics.

Border Lakes	Modeled Sites		Surveyed Areas		Modeled Sites		Sites in Low Prob.		Neg. Survey Points		Random Points
Model Variable	Coeff.	Sign.	Coeff.	Sign.	Mean	S.D.	Mean	S.D.	Mean	S.D.	Mean	S.D.
Prevailing orientation	-0.264	0	-0.19	0	88.41	61.21	110.61	52.71	100.93	59.64	102.85	60.27
Size of major watershed	0.246	0.124	-0.411	0	2114.07	531.32	1939.67	540.14	1404.29	259.59	2150.8	475.37
Size of minor watershed	-	-	0.426	0	12246.4	4543.32	10941.5	4194.32	14215.41	5238.58	9548.1	4399.1
Slope	0.079	0.006	0.04	0.581	2.79	2.85	3.45	4.33	3.49	3.07	3.26	3.3
Surface roughness	0.068	0	-0.434	0	225.78	36.6	231.82	48.88	197.74	25.02	245.22	40.35
Direction to nearest water or wetland (sine)	-0.018	0	-0.113	0	0.18	0.74	0.07	0.76	0	0.75	0.31	0.76
Distance to edge of nearest large lake	-0.433	0	0.23	0	21.45	35.82	41.87	39.53	71.91	33.94	36.07	24.43
Distance to edge of nearest large wetland	0.363	0	0.241	0	55.95	21.81	44.3	23.45	49.75	25.47	39.41	22.91
Distance to edge of nearest swamp	0.321	0	0.186	0	16.11	7.55	12.29	10.34	13.48	8.19	10.62	7.94
Distance to nearest lake inlet/outlet	-0.025	0.451	0.31	0	38.06	23.17	39.33	27.98	58.87	29.66	36.04	17.37
Distance to nearest lake, wetland, organic soil, or stream	-0.418	0	0.018	0.235	5.15	8.71	10.92	10.68	12.11	8.47	8.16	6.96
Vertical distance to permanent water	-0.031	0	-0.197	0	1.84	16.68	10.26	39.61	14.46	31.11	32.57	47.75
Vertical distance to water	-0.034	0	-	-	1.65	16.99	10.38	39.65	14.34	31.06	30.53	45.56
Distance to aspen-birch	0.177	0	0.098	0	35.41	26.06	30.99	24.27	34.49	22.65	29.86	24.76
Distance to brushlands	0.076	0	0.298	0	416.57	31.1	411.91	31.88	425.13	22.99	405.89	39.26
Distance to hardwoods	-0.086	0.927	0.144	0	150.3	70.11	161.75	69.6	198.77	48.81	149.16	70.55
Distance to river bottom forest	-	-	0.25	0	332.56	36.24	326.19	36.02	330.86	21.71	322.61	46.54
Distance to sugar maple	0.209	0	-	-	97.93	31.41	89.64	32.81	78.66	22.98	88.69	32.04
Distance to well-drained soils	-	-	-0.024	0.209	1.81	7.09	2.08	7.02	2.25	7.03	1.59	5.71
Frequency counts	960		2230		960		106		1140		2163

See Interpretation section for discussion of the statistics:
Coeff. = correlation coefficient
Sign. = significance of the Mann-Whitney U test
S.D. = standard deviation
Distances and areas are expressed in square roots of meters. Square the values in the table to get actual values.

 

 

8.12.2.3.1 Differences between Modeled Sites and Random Points for Individual Model Variables

Four of the 16 model variables, all hydrologic, have coefficients of 0.3 and higher. Sites in the Border Lakes subsection tend to be located nearer large lakes (Figure 8.12.10c) or lakes, wetlands, organic soils, or streams (Figure 8.12.11c) than would be expected by chance. They are further from swamps (Figure 8.12.11a), and large wetlands (Figure 8.12.10b). Difference between sites and random points for vertical distance to permanent water (Figure 8.12.6.a and Figure 8.12.11d) and vertical distance to water (Figure 8.12.6.b and Figure 8.12.12a) are large, even though their correlation coefficients are quite small. Clearly, known sites in this region are at elevations only slightly above the nearest water body. Sites also tend towards a less northerly aspect (Figure 8.12.9a) and are situated more to the north of water bodies (Figure 8.12.10b) and on less steep slopes (Figure 8.12.9d) and less rugged terrain (Figure 8.12.10a). They are further from aspen-birch (Figure 8.12.12b), brushlands (Figure 8.12.12c), and sugar maple (Figure 8.12.13b). The largest concentration of sugar maple is in the southcentral portion of the subsection, northeastern St. Louis County, where few surveys have been recorded (Figure 8.12.5a). Brushlands are outside the subsection , but near the same area.

 

The Mann-Whitney U test (Table 8.12.4) indicates that there is a significant difference, at the 0.05 level, between sites and random points on these 13 variables to consider them as being drawn from different populations. The three model variables that are not significant are size of major watershed (Figure 8.12.9b), distance to nearest lake inlet/outlet (Figure 8.12.11b), and distance to hardwoods (Figure 8.12.12d) .

 

8.12.2.3.2 Relationships between Variable Pairs

Table 8.12.5 is a correlation matrix of the variables used in the site model. Sixteen of the variable-pairs have values of 0.5 or greater. One pair of variables are has nearly identical distributions. Vertical distance to permanent water and vertical distance to water have a correlation coefficient of 0.99. Another pair, distance to nearest large lake and distance to nearest lake, wetland, organic soil or stream are very highly correlated (0.87). Because the members of each of the two pairs are quite similar, they exhibit similar relationships to the other variables.

 

Distance to nearest large lake and distance to nearest lake, wetland, organic soil or stream are negatively associated with size of major watershed and surface roughness but positively related to distance to nearest lake inlet/outlet. This can be interpreted to mean that water bodies, particularly large lakes, occur in large watersheds with associated with high degrees of surface roughness and a greater number of lake inlet/outlets than would be expected by chance. Likewise, areas of high surface roughness tend to occur near lake inlet/outlets and in large watersheds. As surface roughness increases, distance to hardwoods decreases (-0.68) but distance to sugar maple (Figure 8.12.5.a) increases. The negative relationship between distance to hardwoods and distance to sugar maples may be the result of low resolution mapping of these variables. Steep slopes, as expected, occur in areas high above water or permanent water.

 

Table 8.12.5. Variable Correlation Matrix.

Border Lakes	Prevailing orientation	Size of major watershed	Slope	Surface roughness	Direction to nearest water or wetland (sine)	Distance to edge of nearest large lake	Distance to edge of nearest large wetland	Distance to edge of nearest swamp
Prevailing orientation	1
Size of major watershed	0.05	1
Slope	-0.26	0.02	1
Surface roughness	-0.06	0.5	0.34	1
Direction to nearest water or wetland (sine)	0.25	0.28	-0.18	0.1	1
Distance to edge of nearest large lake	-0.05	-0.72	0	-0.56	-0.3	1
Distance to edge of nearest large wetland	0	-0.04	0.14	0	-0.09	0.05	1
Distance to edge of nearest swamp	0.02	-0.17	-0.01	-0.22	-0.06	0.13	0.28	1
Distance to nearest lake inlet/outlet	-0.03	-0.49	-0.07	-0.6	-0.16	0.53	0.16	0.27
Distance to nearest lake, wetland, organic soil, or stream	-0.01	-0.72	-0.02	-0.57	-0.29	0.87	0.1	0.25
Vertical distance to permanent water	-0.13	0.23	0.57	0.43	-0.15	-0.22	-0.01	-0.13
Vertical distance to water	-0.11	0.23	0.57	0.43	-0.16	-0.22	-0.01	-0.14
Distance to aspen-birch	-0.03	0.01	0.1	-0.21	0.02	0.03	0.36	0.19
Distance to brushlands	0.02	-0.26	0.04	0.19	-0.06	0.17	0.33	0.1
Distance to hardwoods	-0.05	-0.5	0.03	-0.68	-0.17	0.4	0.12	0.18
Distance to sugar maple	0.01	0.3	-0.04	0.56	0.11	-0.31	0.1	-0.12
	Distance to nearest lake inlet/outlet	Distance to nearest lake, wetland, organic soil, or stream	Vertical distance to permanent water	Vertical distance to water	Distance to aspen-birch	Distance to brushlands	Distance to hardwoods	Distance to sugar maple
Prevailing orientation
Size of major watershed
Slope
Surface roughness
Direction to nearest water or wetland (sine)
Distance to edge of nearest large lake
Distance to edge of nearest large wetland
Distance to edge of nearest swamp
Distance to nearest lake inlet/outlet	1
Distance to nearest lake, wetland, organic soil, or stream	0.54	1
Vertical distance to permanent water	-0.21	-0.23	1
Vertical distance to water	-0.21	-0.23	0.99	1
Distance to aspen-birch	0.22	0.03	0.01	0.02	1
Distance to brushlands	0.05	0.13	-0.05	-0.05	0.19	1
Distance to hardwoods	0.4	0.45	-0.17	-0.17	0.18	-0.23	1
Distance to sugar maple	-0.27	-0.33	0.1	0.09	-0.02	0.39	-0.53	1

Refer to "Relationships between variable pairs" for interpretation of the table values.

 

 

8.12.2.3.3 Sites in Low Probability Areas

Sites in the low site probability areas are distinct in many ways from the total population of modeled sites (Table 8.12.4). They are similar in distribution to all modeled sites only for five variables: size of major watershed, direction to nearest water or wetland, distance to nearest lake inlet/outlet, vertical distance to water, and vertical distance to permanent water . Their mean values for four other variables are approximately midway between those for random points and all modeled sites. The remaining eight variables place the sites in low site potential areas nearest random points.

 

In five of these cases, the mean random point values fall between those of all modeled sites and sites in low potential areas. These variables best discriminate between the two groups of sites. Sites in low potential areas have a more northern exposure, are on steeper slopes, and are further from the nearest large lake and nearest lake, wetland, organic soil, or stream than all modeled sites. Such locations indicate they are more likely to be temporary use sites than long-term habitations.

 

Of the 106 sites in low site potential areas, one is a quarry, one an earthwork, three are rock art, 44 are artifact scatters, and 57 are lithic scatters. If the model predicted all site types equally well, 11.04 percent of each site type should be found in the low probability areas. Lithic scatters are slightly underrepresented in low site potential areas, where 9.66 percent of all lithic scatters are found. All other site types are somewhat overrepresented, with low probability areas containing 13.66 percent of artifact scatters, 25 percent of quarry sites, 14.28 percent of mounds/earthworks, and 20 percent of rock art. More surveys away from water may help clarify the environmental components of these site types.

 

8.12.2.3.4 Relationships between Cultural Context, Descriptive, or Settlement Variables and Site Potential

Table 8.12.6 presents the results of analyzing the distribution of sites in the three site potential classes by three archaeological variables. Unlike most other subsections, sites with Archaic occupations occur somewhat more commonly in high site potential areas compared to sites lacking these components. Similarly, sites lacking pottery are also more likely to be in high site potential areas than those containing ceramics. Finally, single component sites are less likely to occur in high site potential areas vis a vis multi-component sites. However, the Chi-square tests indicate that none of the relationships is significant at the 0.05 level.

 

Table 8.12.6.  Summary of Bivariate Relationships between Three Dichotomous Archaeological Variables and Three Areas of Site Potential, Border Lakes (Site Probability Model).

	Low Potential		Medium Potential		High Potential		Total		Chi-square Test
Dichotomous Variable	N	%	N	%	N	%	N	%	value	df	p-value
All Modeled Sites	106	11.6	70	7.0	780	81.3	954	100.0	-	-	-
Archaic	2	16.7	0	0.0	10	83.3	12	100.0	1.78	2	0.41
Not Archaic	25	14.1	23	13.0	129	72.9	177	100.0
Aceramic	2	15.4	0	0.0	11	84.6	13	100.0	1.94	2	0.38
Not Aceramic	25	14.2	23	13.1	128	72.7	176	100.0
Single Component	24	16.3	19	12.9	104	70.7	147	100.0	2.94	2	0.23
Not Single Component	3	7.1	4	9.5	35	83.3	42	100.0

 

 

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8.12.3 Survey Probability Model

8.12.3.1 Description

Zones of high survey potential are concentrated in the northwestern corner of the Border Lakes subsection, within the present-day Voyageurs National Park, and around Rainy Lake. They are on smooth terrain, whether they are near or far from water bodies (Figure 8.12.7). The concentration of surveys in the northwestern part of the subsection is attributable to federal agency activities. There are over 1000 negative survey points within the vicinity of the Voyageurs National Park. The eastern tip of Kabetogama peninsula alone has as many as 150 surveyed points within 37 sq. km, almost 4 surveyed points per square km. That surpasses survey frequency in the rest of the subsection by many times. This biased survey pattern undoubtedly prevented consideration of all types of environments in the development of the model. In the remainder of the subsection, small areas of high survey potential are found adjacent to lakes and on smooth terrain.

 

8.12.3.2 Evaluation

The survey probability model is based on 17 variables (Table 8.12.7) representing topography, vegetation, soils and hydrology. It predicted surveyed places very well, suggesting strong bias in the location of surveys in the subsection.

 

Table 8.12.7. Survey Probability Model, Border Lakes Subsection.

Intercept	1.085
ln (nonsites/sites)	-0.030505438
Variable	Regression Coefficient	Probability
Direction to nearest water or wetland (sine)	-0.3540385	100.0
Distance to aspen-birch	-0.01133333	100.0
Distance to brushlands	0.04027300	100.0
Distance to edge of nearest large lake	-0.02052092	100.0
Distance to edge of nearest large wetland	0.01912088	100.0
Distance to edge of nearest swamp	0.07416973	100.0
Distance to hardwoods	-0.00510560	100.0
Distance to nearest lake inlet/outlet	0.01470742	100.0
Distance to nearest lake, wetland, organic soil or stream	-0.1260467	100.0
Distance to river bottom forest	-0.02172435	81.4
Distance to well-drained soils	-0.03571622	100.0
Prevailing orientation	-0.003849223	100.0
Size of major watershed	-0.001457547	100.0
Size of minor watershed	0.00004223934	100.0
Slope	0.1525837	100.0
Surface roughness	-0.03420938	100.0
Vertical distance to permanent water	-0.008117256	100.0

 

 

In this model, 82.73 percent of all surveyed places are in high/medium survey potential areas, which make up 18.89 percent of the landscape (Table 8.12.8). This produces a strong gain statistic of 0.77167 (Table 8.6.14).

 

Table 8.12.8. Evaluation of Survey Probability Model, Border Lakes Subsection.

	Region (30 meter cells)		All random points		All negative survey points		All sites		All sites and negative survey points
	#	%	#	%	#	%	#	%	#	%
Low	7842144	75.57	1635	75.59	166	14.56	211	19.36	377	16.91
Medium	490054	4.72	103	4.76	60	5.26	126	11.56	186	8.34
High	1470267	14.17	294	13.59	910	79.82	749	68.72	1659	74.39
Water	500694	4.82	120	5.55	0	0	3	0.28	3	0.13
Steep Slopes	74523	0.72	11	0.51	4	0.35	1	0.09	5	0.22
Mines	0	0	0	0	0	0	0	0	0	0
Total	10377682	100	2163	100	1140	100	1090	100	2230	100

 

 

This region exhibits a very strong degree of survey bias. The survey probability model was able to predict surveyed places with almost the same precision than the site probability model was able to predict sites. Except for the northwest portion of this subsection, sites have been found throughout the limited area surveyed. The large numbers of sites (1090) and negative survey points (1140) in this region are quite closely confined to the margins of lakes and streams. It is likely that most surveys were conducted by canoe, as that is the main form of transportation through much of the area. Although Border Lakes has the fourth highest survey frequency (0.24 surveys per square kilometer) and second highest site frequency (0.12 known sites per square kilometer) in the state, these figures are clearly deceptive.

 

The database included a total of 2230 surveyed places. Two preliminary survey probability models had 89 percent agreement. The Kappa statistic for these models was 0.72217 (Table 8.6.14). This is most stable survey probability model in the state, confirming that the survey bias mapped is authentic. The conditional Kappa statistics (Table 8.12.9) are lowest for the medium site potential zone, but this is less than 5 percent of the subsection area.

 

Table 8.12.9. Conditional Kappa Values for Survey Probability Model, Border Lakes Subsection.

Class	Ki	Proportion Correct
Low	0.8114	0.9529
Medium	0.4154	0.4739
High	0.8074	0.8363

 

 

This subsection is an excellent example of how survey bias can result in deceptively good statistical results for a site probability model. High gain statistics and high model stability alone do not make a good model. It is critical that these results be evaluated with respect to the distribution of all the relevant data. This is the intent of the survey probability model.

 

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8.12.3.3 Interpretation

The survey probability model for the Border Lakes subsection is composed of 17 variables (five topographic, seven hydrologic, three vegetative, and one soils). These include fourteen variables from the site probability model, as can be expected from models with such similar geographic patterns.

 

Four survey probability model variables have correlation coefficients of 0.3 and above (Table 8.12.4). These relationships indicate that surveys have been conducted largely in smaller major watersheds (Figure 8.12.9b) but larger minor watersheds (Figure 8.12.9c), in areas with less surface roughness (Figure 8.12.10a), and further from lake inlets/outlets (Figure 8.12.11b) than would be expected by chance. Other variables indicate that surveys have been farther from large lakes (Figure 8.12.10c), large wetlands (Figure 8.12.10d), river bottom forests (Figure 8.12.13a)and vertically closer to permanent water (Figure 8.12.11d) than random points.

 

The results of the Mann-Whitney U tests (Table 8.12.4) are significant at the 0.05 level for all variables except slope (Figure 8.12.9d), distance to nearest lake, wetland, organic, or stream (Figure 8.12.11c), and distance to well-drained soils (Figure 8.12.13c). Thus, surveys have been conducted in areas that are significantly different from random points on a large number of environmental variables. These differences are great enough that the surveyed and random points can be considered to have been drawn from separate populations.

 

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8.12.4 Survey Implementation Model

The survey implementation model for Border Lakes (Figure 8.12.8) reflects the strong degree of survey bias in the subsection in that 70.77 percent of the total area is classified as unknown (Table 8.12.10). The low and possibly low site probability areas occupy only 4.8 percent of the landscape. This clearly illustrates the degree of survey bias discussed above, sites are likely to be found in nearly all of the kinds of environments surveyed.

 

Table 8.12.10. Evaluation of Survey Implementation Model, Border Lakes Subsection.

	Region (30 meter cells)		Random Points		Negative Survey Points		Single Artifacts		Sites
	#	%	#	%	#	%	#	%	#	%
Unknown	7344499	70.77	1528	70.64	163	14.3	10	7.69	73	7.6
Possibly Low	185494	1.79	33	1.53	52	4.56	0	0	7	0.73
Low	312566	3.01	69	3.19	237	20.79	2	1.54	26	2.71
Suspected Medium	265691	2.56	58	2.68	1	0.09	13	10	34	3.54
Possibly Medium	63589	0.61	10	0.46	3	0.26	1	0.77	2	0.21
Medium	160661	1.55	30	1.39	134	11.75	4	3.08	34	3.54
Suspected High	231954	2.24	49	2.27	2	0.18	8	6.15	73	7.6
Possibly High	240971	2.32	60	2.77	5	0.44	13	10	103	10.73
High	997040	9.61	195	9.02	539	47.28	79	60.77	604	62.92
Water	500694	4.82	120	5.55	0	0	0	0	3	0.31
Steep Slopes	74523	0.72	11	0.51	4	0.35	0	0	1	0.1
Mines	0	0	0	0	0	0	0	0	0	0
Total	10377682	100	2163	100	1140	100	130	100	960	100

 

 

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8.12.5 Other Models

No Landscape Suitability Models have been developed for this subsection. The Rainy River model (Section 12.5) is just to the west of the Border Lakes subsection. The potential for buried sites in Border Lakes should be considered where lacustrine or organic soils are present and in the lower reaches of stream valleys.

8.13 Chippewa Plains
8.14 Coteau Moraines / Inner Coteau
8.15 Glacial Lake Superior Plain/Northshore Highlands/ Nashwauk Uplands
8.16 Hardwood Hills
8.17 Laurentian Highlands
8.18 Littlefork-Vermilion Uplands
8.19 Mille Lacs Uplands
8.20 Minnesota River Prairie
8.21 Oak Savanna
8.22 Pine Moraines & Outwash Plains
8.23 Red River Prairie
8.24 Rochester Plateau
8.25 St. Croix Moraines and Outwash Plains (Twin Cities Highlands)
8.26 St. Louis Moraines/ Tamarack Lowlands
8.27 Conclusion
        References

 

 

The Mn/Model Final Report (Phases 1-3) is available on CD-ROM. Copies may be requested by visiting the contact page.

 

Acknowledgements

MnModel was financed with Transportation Enhancement and State Planning and Research funds from the Federal Highway Administration and a Minnesota Department of Transportation match.

 

Copyright Notice

The MnModel process and the predictive models it produced are copyrighted by the Minnesota Department of Transportation (MnDOT), 2000. They may not be used without MnDOT's consent.