In cooperation with: 

The University of Missouri-Columbia, 

The Missouri Department of Conservation, and 

NatureServe 

Mapping Vegetation Communities in 

Ozark National Scenic Riverways: 

Final Technical Report to the National Park 

Service 

By Robert A. Chastain, Matthew A. Struckhoff, Keith W. Grabner, Esther D. Stroh, Hong 

He, David R. Larsen, Timothy A. Nigh and Jim Drake 

Open-File Report 2006-1354 

U.S. Department of the Interior 

U.S. Geological Survey 
U.S. Department of the Interior 

Dirk Kempthorne, Secretary 

U.S. Geological Survey 

Mark Myers, Director 

U.S. Geological Survey, Reston, Virginia 2006 

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Suggested citation: 

Chastain, R.A., Struckhoff, M.A., Grabner, K.W., Stroh, E.D., He, H., Larsen, D.R., Nigh, 

T.A., and Drake, J., 2006, Mapping vegetation communities in Ozark National Scenic 

Riverways–final technical report to the National Park Service: U.S. Geological Survey 

Open-File Report 2006-1354, 90 p., plus appendices.

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Although this report is in the public domain, permission must be secured from the 

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report. 
Mapping Vegetation Communities in Ozark National Scenic Riverways: 

Final Technical Report to the National Park Service 

Matthew Struckhoff, Keith Grabner, Esther Stroh 

U.S. Geological Survey-Columbia Environmental Research Center 

Robert Chastain, Hong He and David Larson 

University of Missouri-Forestry Department 

Timothy Nigh 

Missouri Department of Conservation 

Jim Drake 

NatureServe 

In conjunction with 

Mike Story and Karl Brown (National Park Service Vegetation Mapping Coordinators) 

National Park Service, Natural Resources Information Division, Inventory and 

Monitoring Program 

October 2006 

i
 

ii 
Executive Summary 

Abstract 

Vegetation communities were mapped at two levels in Ozark National Scenic Riverways 

(ONSR) using a hybrid combination of statistical methods and photointerpretation. The primary 

map includes 49 cover classes, including 24 classes that relate to vegetation associations 

currently described by the United States National Vegetation Classification Standard (USNVC; 

The Nature Conservancy, 1994a). The remaining types include cultural features, ruderal 

communities on abandoned agricultural lands, and non-vegetated classes. Overall map 

classification accuracy is 63 percent. The secondary mapping level aggregates communities with 

similar appearance and ecologically related associations into Community Types. The resultant 

33-class Community Type map has an overall classification accuracy of 77 percent and identifies 

groups of communities based on resource management goals within the park. Important 

additional products include 1) a general probability map for all vegetation associations, which 

can be used to assess final classification certainty, and 2) individual probability maps for each 

association, which can be used to identify areas that have a high likelihood of supporting a given 

type, beyond where that type was identified in the final map products. Other secondary map 

products include data layers derived from primary color-infrared imagery, secondary imagery 

data and digital elevation models. A field key and photo guide to associations and complete 

community descriptions were produced, along with a photo guide of fuel conditions. Wildland 

fuels data were used to generate a fuels map based upon Anderson’s fuels models (1982). 

Setting 

Ozark National Scenic Riverways encompasses approximately 33,257 ha (82,180 acres) 

along the Current and Jacks Fork Rivers in southeastern Missouri. It is located in the Current 

River Hills Subsection of the Ozark Highlands Section (Avers et al. 1994). The park purchase 

unit includes an additional 23,000 ha, most of which is private land and lands owned by other 

groups, including the State of Missouri. The mapping region is 141,854 ha (350,529 acres) and 

encompasses ONSR as well as areas immediately surrounding the park land (Figure ES-1). The 

landscape is characterized by gently rolling dissected plains underlain by resistant sandstones of 

the Roubidoux formation and deeply dissected drainages that cut into dolomites of the 

Ordovician Gasconade Formation. The deepest drainages cut into Cambrian Eminence-Potosi 

Formation; one area of the subsection is defined by domes of Precambrian igneous substrate, 

portions of which have been exposed by erosion (Nigh and Schroeder, 2002). 

The Ozarks are perhaps the oldest continuously exposed land mass in North America; the 

region has likely supported plant life for 100 million years, and was not glaciated during any of 

the last four major continental glaciation events. The continuous exposure and lack of glaciation 

has resulted in high biological diversity of plant communities and high levels of endemism (The 

Nature Conservancy, 2003). Presettlement vegetation was characterized by oak and pine 

woodlands and forests heavily influenced by aboriginal and natural fires (Guyette and Cutter, 

1991; Ladd, 1991) and interspersed with small and large patches of other natural communities 

including fens, wetlands, and glades (The Nature Conservancy, 2003). The landscape is now 

dominated by second-growth forest; most of the Ozarks were logged between 1880 and 1920 

(Cunningham and Hauser, 1992). The second growth forests contain less pine, while uniform, 

Executive Summary iii
younger forests have replaced the woodland/forest mosaic, and many glades have overgrown 

with eastern red cedar (Juniperus virginiana, Nigh and Schroeder, 2002). 

When the Ozark National Scenic Riverways was established in 1964, it was created from 

public and private lands with a variety of uses, including relatively intact areas with minimal 

evidence of human disturbance, parcels of land that were being used for grazing, logging, and 

row cropping (and various combinations of these activities), and areas that had been previously 

used in these manners but where activity had ceased prior to the establishment of the park. Many 

of these activities continue to a limited extent on privately- and publicly-owned parcels within 

the ONSR purchase unit. The result is a landscape of immense native biological diversity 

overlaid with a diversity of past and continuing human uses in various stages of regrowth, with 

some plant communities fairly intact and others significantly altered from what they had been 

prior to Euro-American settlement. 

Figure ES-1. Study area map. 

*Nigh and others, 2000. 

Approach 

To develop a USNVC association-level vegetation community map to the required 

accuracy standards, it was necessary to predict landscape scale vegetation community patterns 

based on the combined influence of environmental resource gradients, landform, and the 

Executive Summary iv
influence of past and present land use patterns and disturbance regimes. This challenge was met 

by acquiring numerous remote sensing, topographic and field-collected vegetation datasets from 

which we derived a large set of variables to train a statistical classification. Remote sensing data 

supplied variables that discriminated vegetation communities based on differences in their color, 

spectra, greenness, brightness, and texture. Topographic data facilitated differentiation of 

vegetation communities based on indirect gradients (e.g. landform position, slope, aspect), which 

can be related to variations in resource gradients (light, water, nutrients) and disturbance 

gradients (suitability for agriculture, fire susceptibility). The hybrid approach used in this study 

supplemented statistical classification based upon the above data with photointerpretation and 

digitization from current and historical aerial photographic sources. 

Data used to characterize community structure and composition and for use in 

multivariate data analysis were collected at 318 plots. These points, and an additional 751 

observation points were field-classified as a given vegetation association for use as training data 

for remote sensing. Data sufficient to characterize wildland fuel-loading for many USNVC 

associations were also collected at a subset of 777 observation points and plots. Accuracy 

assessment was based upon 2057 validation observations obtained from points withheld from the 

statistical classification, additional field observations, and from photointerpretation observations 

located within human-dominated land-cover categories. Details of the methods used for data 

processing, field data collection, statistical classification of vegetation communities, multivariate 

characterization of vegetation associations and accuracy assessment procedures are described in 

detail in the body of the report. 

Ecological Systems Mapping 

The National Vegetation Classification is a hierarchical system. The highest level of the 

USNVC, the “Class,” splits communities into broad physiognomic categories (forest, woodland, 

shrubland, herbaceous, etc.). Unfortunately, vegetation communities often are spatially related 

to one another, though they may have little in common structurally or compositionally. For 

example, within the mapping area, glades are herbaceous communities that are usually associated 

with physiognomically distinct woodlands. The spatial relationship of such communities 

confounds aggregation into broader groups within the USNVC hierarchy when mapping. 

Therefore, in addition to mapping such groups of communities to the association level, we 

adopted NatureServe’s (2005) ecological systems approach to generate a fewer-class, higheraccuracy Community Type map. Community Types, and the more broadly defined “Ecological 

Systems,” that we identified were based upon discussions with resource management staff both 

within the park and at other land management agencies in the area. They are more relevant to 

management goals and methods and improve mapping accuracy to a greater degree than would 

aggregation to the USNVC alliance level. A crosswalk of the Community Types and Ecological 

Systems identified for this project and their relationship to ecological systems developed by 

NatureServe is included in Appendix 5. A crosswalk comparing Ecological Systems identified 

in our study with management groups identified by two other widely used Missouri-based 

classification systems is included in Appendix 4. 

Executive Summary v
Significantly Altered Communities

The National Vegetation Classification Standard provides guidelines for classification of 

communities that can be considered neither natural nor semi-natural. However, early discussions 

with resource managers at ONSR indicated that adoption of this classification system would fail 

to account for many cultural features, timber management areas, and ruderal communities within 

the mapping area In order to map the full spectrum of communities extant in the park, our 

mapping approach included classification of those communities not treated by existing systems 

and their subtypes. We identified a suite of altered vegetation associations that could potentially 

be found within the study area based on our knowledge of the current conditions within the park 

and discussions with resource management staff regarding types that were critical to 

management activities. Descriptions of altered communities are attached as Appendix 16. 

Appendix 6 provides a crosswalk of our identified altered vegetation associations to Ecological 

Systems used in this study. 

Results 

Vegetation associations 

Twenty-four classes relating to USNVC vegetation associations were mapped using 

statistical classification techniques. Another 7 classes that represent cultural, significantly 

altered, and non-vegetated communities were also mapped statistically. Statistical classification 

was augmented by heads-up digitization for an additional 18 cultural and altered types, 

predominantly ruderal communities and timber management areas. All communities 

encountered during this project, as well as additional communities potentially extant within the 

study area are included in the list of vegetation associations (Appendix 2) and the field key to 

communities (Appendix 17). All of the USNVC associations either encountered during this 

study or possibly extant in the study area are described in Appendix 15. Significantly altered 

vegetation associations that require descriptions receive them in Appendix 16. 

Vegetation Maps 

The primary map product is a 49-class vegetation association map, which includes 24 

classes that relate directly to USNVC associations and which were classified using statistical 

methods. The map also includes two hand-digitize glade complex types, and 23 significantly 

altered communities or cultural classes (Figure ES-2 [detail]), Appendix 12 [full map]). Overall 

map classification accuracy is 63 percent. The secondary mapping level aggregates communities 

with similar appearance and ecologically related associations into Community Types, which also 

are based upon resource management issues within the park. These Community Types aided 

mapping to a much greater extent than USNVC alliances and are more relevant to resource 

management goals and methods within the park. The resultant 33-class Community Type map 

(Figure ES-3 [detail], Appendix 13 [full map]) has an overall classification accuracy of 77 

percent. 

Total area and percent coverage of USNVC communities and significantly altered land 

cover types are given in (Table ES-1). The two most common land cover types within both 

ONSR and the greater mapping area belong to the Black Oak - White Oak - (Scarlet Oak) Forest 

Alliance. The Black Oak-White Oak-Hickory/Dogwood Forest covers 19,843 ha (34 percent) of 

Executive Summary vi
the ONSR purchase unit and 43,388 ha (31 percent) of the mapping area. The Ozark Black OakScarlet Oak Forest accounts for 9,492 ha (16 percent) of the park and 27,555 ha (20 percent) of 

the mapping area. Together, these two vegetation associations provide more than 50 percent of 

cover at either scale. They have been mapped together in the 33-class Community Type map as 

the Mixed Oak-Hickory Forest. The third and fourth most abundant classes within ONSR are the 

White Oak/Dogwood Forest (5094 ha; 9 percent) and the White Oak - Mixed Oak Dry-Mesic 

Alkaline Forest (4869 ha; 8 percent). These communities have been aggregated in the 33-class 

map as the White Oak Forest Community Type, and collectively cover more than 16 percent of 

the park. The two next most abundant classes within the park include actively managed and 

abandoned agricultural fields in various successional stages. Together, these account for 9 

percent of the park area. Agricultural fields and pastures are the third most abundant class within 

the full mapping area. 

Wildland Fuels and Fuels maps 

Fuels data tables for each association (Appendix 18) summarize fuel loading and identify 

the most appropriate Anderson (1982) fuels model, National Fire Danger Rating System 

(NFDRS) model, and Comprehensive Fuels model (Scott and Burgan, 2005). Spreadsheet 

versions of these data allow one to join fuel models with vegetation associations and Community 

Types within a GIS platform, the first step toward integrating the vegetation map with a spatially 

explicit fire behavior simulation model. We generated digital and hard-copy maps of fuels 

classes for the 49-class USNVC vegetation association classification (Figure ES-4) based upon 

Anderson (1982), the most commonly applied fuels model. However, Anderson’s system was 

developed in western systems that are dominated by coniferous forests, and it may not provide 

the most appropriate model for fuels conditions in eastern deciduous forests. Therefore, 

classifications from the recently produced Comprehensive Fuels model (Scott and Burgan, 2005) 

have been provided in attribute tables for the USNVC vegetation association shapefiles. This 

fuels model identifies a greater number of classes, many of which were developed for deciduous 

forests and which may be more appropriate for the study area. However, the Comprehensive 

Fuels model has not yet been widely adopted in the United States. Classifications from the 

NFDRS, which can be used to assess fire danger across multiple scales, have also been provided 

in attribute tables for the USNVC vegetation association shapefiles. Fuels maps based upon 

either the Comprehensive Fuels model or the NFDRS can be generated in a GIS environment.

Executive Summary vii
Table ES-1. Area and percent coverage of USNVC associations and land cover types 

in the ONSR purchase unit (57,678 ha) and the larger mapping area (139,953 ha).

Park 

Area (ha)

Park % Full Map 

Area (ha)

Full 

Map %

USNVC Association/Landcover type (Code*) 49-Class 

Grid Code

19843.2 34.40 43388.34 31.00 Black Oak - White Oak - Hickory Forest (2076†) 5 

9491.6 16.46 27555.48 19.70 Ozark Black Oak, Scarlet Oak Forest (2399†) 10 

5094.2 8.83 9637.54 6.89 White Oak/Dogwood Forest (2066†) 2 

4868.8 8.44 9912.55 7.08 White Oak Dolomite Forest (2070†) 3 

2711.1 4.70 10175.82 7.27 Agricultural field/pasture (SA21, SA22, SA23) 37 

2509.2 4.35 3751.22 2.68 Wooded old field (SA10, SA11, SA13, SA14, SA15) 33 

2012.9 3.49 5119.91 3.66 Interior Highlands Shortleaf Pine-Oak Dry-mesic Forest 

(7489†) 

13 

1544.6 2.68 3177.67 2.27 Chinkapin Oak-Red Cedar Dry Alkaline Forest (2108†) 6 

956.5 1.66 4461.55 3.19 Shortleaf Pine-Black Oak Forest (2401†) 12 

878.7 1.52 986.11 0.70 River (non-vegetated, includes springs and tributaries) 28 

753.9 1.31 853.85 0.51 Ash-Oak-Sycamore Mesic Bottomland Forest (2410†) 22 

700.3 1.21 1984.19 1.42 White Oak-Red Oak-Sugar Maple Mesic Forest (2058†) 1 

674.4 1.17 1591.07 1.14 Chinkapin Oak-Ash/Little Bluestem Woodland (2143†) 7 

666.6 1.16 779.74 0.56 Box Elder Forest (5033†) 25 

659.2 1.14 743.33 0.53 Sycamore-Silver Maple Floodplain Forest (7334†) 26 

655.5 1.14 1030.88 0.74 Road (SA32) 34 

572.4 0.99 668.16 0.48 Sugar Maple-Oak-Hickory Mesic Bottomland Forest (2060†) 21 

570.3 0.99 995.86 0.71 Open old field (SA09, SA12, SA20, SA23, SA36) 43 

304.5 0.53 577.18 0.41 Igneous glade/woodland complex (includes 2075i†) 32, 45 

258.3 0.45 481.24 0.34 Utility corridor (SA33, SA34) 35 

252.3 0.44 350.3 0.25 Riverine Sand Flats (2049†, includes vegetated stream 

margins), Bare gravel bars† 

20, 27 

225.0 0.39 1797.74 1.28 Post-Black-(Blackjack) Oak/Little Bluestem Woodland 

(2149†) 

8, 29 

178.6 0.31 840.71 0.60 Regeneration Stand (SA02, SA05) 38 

173.2 0.30 951.63 0.68 Deciduous Shrubby Old Field (SA09†) 17 

140.6 0.24 271.26 0.19 Dolomite glade/woodland complex 44 

120.9 0.21 361.37 0.26 Midwest Post Oak-Blackjack Oak Forest (2075†) 4 

92.1 0.16 547.9 0.39 Cedar-Deciduous Wooded Old Field (SA13†) 18 

91.6 0.16 677.17 0.48 Shortleaf Pine-Oak Dry Woodland (2393†) 9 

88.5 0.15 737.35 0.53 Residential (SA16, SA17, SA18, SA19, SA35) 48 

88.5 0.15 105.4 0.08 Carolina Willow Shrubland (3899†) 24 

88.4 0.15 1466.15 1.05 Shelterwood cut (SA01, SA04) 39 

77.1 0.13 323.4 0.23 Cedar Old Field (SA15†) 19 

69.9 0.12 345.36 0.25 Deciduous Pole Stand (SA03, SA06, SA08) 40 

61.9 0.11 1180.01 0.84 Pine-Oak Regeneration Stand (SA05†) 14 

49.7 0.09 338.67 0.24 Other Clearing (SA31) 36 

47.5 0.08 583 0.42 Shortleaf Pine/Blueberry Forest (2400†) 11 

35.8 0.06 69.21 0.05 Igneous Glade (2243†) 30 

30.3 0.05 45.4 0.03 Witchhazel-Dogwood Gravel Wash (3898†) 23 

20.7 0.04 159.01 0.11 Pine Pole Stand (SA08†) 16 

9.3 0.02 61.27 0.04 Pine plantation/Timber management area (SA07†, SA07) 15, 41 

5.6 0.01 63.86 0.05 Surface water (non-vegetated pond and lakes) 42 

3.7 0.01 5.71 0.00 Blackjack Oak Scrub Woodland (2425†) 31 

0.6 0.00 233.06 0.17 Agricultural forested woodlot (SA37) 46 

0.0 N/A 63.34 0.05 Industrial/quarry (SA26, SA27) 49 

0.0 N/A 502.74 0.36 Urban (SA28, SA29, SA30) 47 

*For USNVC associations, the last for digits of the USNVC codes (CEGL00####) are displayed. Codes that begin 

with “SA” represent significantly altered vegetation communities not described by the USNVC. 

†Statistically classified types (all others were hand-delineated from aerial photographs). 

Executive Summary viii
Figure ES-2. Detail of 49-class USNVC vegetation association map*.

* Full 49-class USNVC vegetation association map is available in Appendix 12

Executive Summary ix
Figure ES-3. Detail of 33-class Community Type map with types aggregated to 

improve accuracy and aid in resource management planning*. 

*Full 33-class Community Type map is available in Appendix 13.

Executive Summary x
Figure ES-4. Detail of Anderson Fuels Model map. 

Executive Summary xi
Implications for Resource Management

Ecological Systems Groupings 

The Ecological Systems used in this project were designed to facilitate resource 

management planning within the mapping area. Given the lower overall accuracy of the 49-class 

USNVC association-level map and the complexity involved in interpreting it, we believe that the 

33-class Community Type map provides a more reliable and useful product. For many of the 

aggregated types, the 49-class map yields polygons that are too small to reasonably guide 

management. By contrast, within the 33-class map, the groupings of Community Types that are 

either similar to one another or spatially related provide a scale more appropriate to making 

management decisions. 

Utilizing Probability Maps 

We produced two types of probability maps in this project. This first type, or overall 

probability map, is a raster data file that is a by-product of the statistical mapping approach. The 

value of each grid cell indicates the probability that a given cell was individually classified as the 

same vegetation association as the polygon of which it is part in the final classification map. The 

49-class vegetation association map or the 33-class Community Type map can be displayed in a 

GIS platform over a probability map for all of the classification types. This allows resource 

managers to assess the certainty of map classifications. Figure ES-5 displays the 49-class map at 

50 percent transparency over the probability map displayed as a continuous variable for all 

classes, with brighter areas indicate higher classification certainty. Figure ES-5 illustrates the 

case which is often evident in the field: certainty of classification decreases near community 

boundaries as one type transitions into another. 

The second type of probability map is the individual probability map, which shows the 

probability that a cell was classified as a particular type regardless of the final classification of 

the polygon it occupies in the final classification map. High probabilities suggest that a given 

site possesses either the vegetative components (detectable in the photo data) or the ecological 

conditions (as evidenced in the photo data or the topographic variables used in the statistical 

classification) to support a given type. Individual probability maps may be used to identify 

appropriate sites when managing for target communities, beyond where they are identified in the 

final classification map. Figure ES-6 shows an example of how candidate sites for restoration 

management might be identified using individual probability maps. Polygons classified as the 

Chinkapin Oak-Ash/Little Bluestem Woodland (2143) are shown in red and the ecologically 

related Chinkapin Oak-Red Cedar Dry Alkaline Forest (2108) polygons are shown in green. The 

2143 type is important to resource managers for its high biological diversity. This type was once 

more common, but it is now infrequent due to invasion by red cedar, probably as a result of fire 

suppression. This change has likely caused many examples of the 2143 type to convert to the 

2108 type, while retaining certain component species. Areas identified as the 2108 type might 

be suitable for restoration to the 2143 type with mechanical removal of cedar and/or prescribed 

fire to control woody invasion. This map shows some areas within 2108 polygons that had high 

probability for 2143 types. In addition, there are some areas outside of either 2108 or 2142 

polygons with high probability of the 2143 type that can be further investigated in the field. 

Such maps can significantly aid resource management planning by suggesting candidate sites for 

management activity and can be created for those associations that were mapped statistically.

Executive Summary xii
Figure ES-5. 49-class map displayed over probability raster for all classes, with 

brighter areas indicating higher classification certainty.

Executive Summary xiii
Figure ES-6. Vegetation associations 2143 and 2108 displayed over probability raster 

for the 2143 type. 

Executive Summary xiv
Options for Management Decisions

The ecological systems approach used in this study increased both the accuracy and the 

utility of the final map products. This suggests that revisions could be made to the USNVC 

hierarchy to improve accuracy and management utility of vegetation maps. Other vegetation 

mapping efforts have experienced difficulties similar to those we encountered with respect to the 

broad conceptual jump between the USNVC formation and alliance levels, and the national 

program is being updated to address this issue (Federal Geographic Data Committee, 2006). 

However, the proposed changes may be unable to address the mapping problem encountered 

when physiognomically and floristically distinct communities occur together in ways that 

confound mapping but which are relevant to management. In our study, igneous glades and 

associated woodlands are an example of such communities, frequently occurring as complexes of 

physiognomically distinct units which are, individually, below the minimum mapping unit. In 

order to address the local spatial relationships that may exist between these types of 

communities, it may be necessary to maintain a separate parallel hierarchical system designed to 

address mapping goals and management interests. 

The USNVC currently does a poor job of addressing the numerous ruderal and timber 

management vegetation associations that occur in the ONSR mapping area. We identified 19 

significantly altered vegetation associations that are not treated under the current USNVC, but 

which have critical habitat implications for wildlife. It is our suspicion that many of the 

significantly altered vegetation associations we identified in our study are common in the Ozark 

Plateau as well as locations east of the Mississippi River. Revisions to the USNVC to account 

for significantly altered types would be a tremendous improvement to the classification system. 

Comparison of the results of pilot classifications of vegetation communities in the ONSR 

and the final classification of the entire ONSR mapping region indicate that a hybrid 

combination of statistical methods and photointerpretation is needed to obtain adequate overall 

and class-wise accuracy levels. We recommend that future vegetation mapping projects within 

this program incorporate a hybrid approach such as that used here, if it is within capabilities. 

References 

Anderson, H.E. 1982. Aids to determining fuel models for estimating fire behavior. USDA 

Forest Service General Technical Report INT-122. 22p. Intermountain Forest and Range 

Experiment Station. Ogden, Ut. 

Avers, P.E., Cleland, D.T., McNab, W.H., Jensen, M.E., Bailey, R.G., King, T., Goudey, C.B. 

and Russell, W.E. 1994. The national hierarchical framework of ecological units. 

ECOMAP, USDA Forest Service, Washington, D.C. 

Cunningham, B. and Hauser, C. 1992. The decline of the Missouri Ozark forest between 1880 

and 1920. In Journet, A.R. and Spratt, H.G., eds. Proc: Towards a Vision for Missouri’s 

Public Forests. March 27-28, 1992. Southeast Missouri State University, Cape Girardeau, 

MO. 14-19. 

Federal Geographic Data Committee. 1996. National Vegetation Classification Standard, 

Version 2-Working Draft. FGDC-STD-005 (Version 2). FGDC Secratariat, U.S. 

Geological Survey. Reston, VA. 62 p. 

Executive Summary xv
Guyette, R.P. and Cutter, B.E. 1991. Tree ring analysis of fire history in a post oak savanna in 

the Missouri Ozarks. Natural Areas Journal, 11: 93-99. 

Joy, S.M., Reich, R.M. and Reynolds, R.T. 2003. A non-parametric, supervised classification of 

vegetation types on the Kaibob National Forest using decision trees. International Journal 

of Remote Sensing, 24(9), 1835-1852. 

Ladd, D. 1991. Reexamination of the role of fire in Missouri oak woodlands. Charleston, IL: 

Proceedings of the Oak Woods Management Workshop: 67-80. 

NatureServe. 2005. International ecological classification standard: Terrestrial ecological 

classifications. NatureServe Central Databases. Arlington, VA. U.S.A. Data current as of 

30 September, 2005. (www.natureserve.org) 

Nigh, T.A. and Schroeder, W.A. 2002. Atlas of Missouri Ecoregions. Missouri Department of 

Conservation, Jefferson City, Missouri. 

Scott, J.H. and Burgan, R.E. 2005. Standard fire behavior fuel models: A comprehensive set for 

use with Rothermel’s surface fire spread model. USDA Forest Service. General 

Technical Report RMRS-GTR-153. Rocky Mountain Research Station. 72p. 

The Nature Conservancy. 1994a. Standardized national vegetation classification system. Report 

to U.S. Dept. of Interior. The Nature Conservancy, Arlington, VA and Environmental 

Systems Research Institute, Redlands, CA 

http://biology.usgs.gov/npsveg/classification/index.html. 

The Nature Conservancy Ozarks Ecoregional Assesment Team. 2003. Ozarks ecoregional and 

conservation assessment. Minneapolis, MN: The Nature Conservancy Midwestern 

Resource Office. 48 pp. + 5 appendices. 

Executive Summary xvi
Contacts 

U.S. Department of Interior 

United States Geological Survey - Biological Resources Division 

Website: http://www.usgs.gov

Mike Mulligan 

USGS Program Coordinator - USGS-NPS Vegetation Mapping Program 

U.S. Geological Survey 

Center for Biological Informatics 

P.O. Box 25046 

Building 810, Room 8000, MS-302 Denver Federal Center 

Denver, Colorado 80225-0046 

Phone: (303) 202-4240 

E-Mail: mike_mulligan@usgs.gov 

Website: http://biology.usgs.gov/cbi

Matthew Struckhoff, Keith Grabner, Esther Stroh 

Ecologists 

U.S. Geological Survey 

Columbia Environmental Research Center 

4200 New Haven Rd 

Columbia, MO 65201 

Phone: (573) 875-5399 

E-Mail: mstruckhoff@usgs.gov; kgrabner@usgs.gov; esther_stroh@usgs.gov

Website: http://www.cerc.usgs.gov/

Contacts xvii
University of Missouri-School of Natural Resources 

Department of Forestry 

Website: http://www.snr.missouri.edu/forestry

Robert Chastain 

Remote Sensing Specialist 

IMNW-LEW-PWE MS17 

Box 339500, Bldg. 2012 

Fort Lewis, WA 98433-9500. 

Phone: (253) 966-1664 

E-mail: robert.chastain2@us.army.mil

Hong He, David Larson 

Associate Professors 

203 ABNR 

University of Missouri 

Columbia, MO 65211 

Phone: (573) 882-7242 

E-mail: heh@missouri.edu; larsond@missouri.edu

Contacts xviii
Missouri Department of Conservation 

Website: http://www.mdc.mo.gov/

Timothy Nigh 

Resource Scientist 

Missouri Department of Conservation 

Resource Science Center 

1110 S. College Ave 65201 

Phone: (573) 882-9880 

E-mail: Timothy.Nigh@mdc.mo.gov

Contacts xix
NatureServe 

Website: http://www.natureserve.org

Jim Drake 

Program Coordinator 

1101 West River Parkway, Suite 200 

Minneapolis, Minnesota 55415 

Phone: (612) 331-0729 

E-mail: jim_drake@natureserve.org

Contacts xx
U.S. Department of the Interior 

National Park Service 

Website: http://www.nps.gov

Karl Brown 

NPS Program Coordinator - USGS-NPS Vegetation Mapping Program 

National Park Service, WASO 

1201 Oakridge Drive, Suite 200 

Fort Collins, CO 80525 

Phone: (970) 225-3591 

E-mail: Karl_Brown@nps.gov

Victoria Grant 

Natural Resource Manager 

Ozark National Scenic Riverways 

151 Highway 76 

Van Buren, MO 

Phone: (573) 323-4236 

E-Mail: Victoria_grant@nps.gov 

Website: http://www.nps.gov/ozar

Contacts xxi
Acknowledgements 

We would like to thank Victoria Grant of the National Park Service Ozark National 

Scenic Riverways for frequent feedback and helpful input throughout the entire project. Field 

assistance was provided by: Dan Tenaglia, Dan Twyman, Jessie Bebb, Todd Weakley, Raun 

Oberman, Benjamin King and Jean Perry. Wenchun Wu and Kevin Hosman scanned, 

orthorectified and mosaiced the original aerial photos, assisted by University of Missouri 

students Jessie Beck, Sarah Bellchamber, Joseph Berg, Yunjeong Jang, Annie Meyer and 

Geovanne Stighen. Philip A.Townsend and Jane R. Foster at the Appalachian Laboratory of the 

University of Maryland Center for Environmental Science assisted in applying the discriminant 

analysis method. 

Acknowledgements xxii
Contents 

Executive Summary ........................................................................................................iii

Contacts ....................................................................................................................... xvii

Acknowledgements ...................................................................................................... xxii

Contents...................................................................................................................... xxiii

List of Tables................................................................................................................xxv

List of Figures.............................................................................................................. xxvi

List of Appendices...................................................................................................... xxvii

Introduction ..................................................................................................................... 1

Background .................................................................................................................. 1

Setting.......................................................................................................................... 1

Objective ...................................................................................................................... 5

Classification Structure................................................................................................. 5

USNVC...................................................................................................................... 5

Additional Classification Elements ............................................................................ 6

Ecological Systems ................................................................................................ 6

Cultural and Altered Communities.......................................................................... 7

Missouri Classification Systems ................................................................................ 8

Methods .......................................................................................................................... 9

Approach...................................................................................................................... 9

Field Methods............................................................................................................... 9

Development of the Field Key ................................................................................... 9

Existing Community Characterization and Remote Sensing Training Data............. 10

New Community Characterization and Remote Sensing Training Data .................. 10

Vegetation Plot Data ............................................................................................ 11

Classification and Location Data .......................................................................... 12

Environmental Data.............................................................................................. 13

Vegetation Description Data................................................................................. 13

Wildland Fuels Data ............................................................................................. 13

Data Quality Control in Field Sampling................................................................. 14

Multivariate Analysis of Plot Data............................................................................... 14

Map Production .......................................................................................................... 16

Data......................................................................................................................... 16

Aerial Photos........................................................................................................ 16

2003 National Agriculture Imagery Program Photo Data...................................... 17

Landsat TM/ETM+................................................................................................ 18

Topographic Information ...................................................................................... 19

Field Training Data............................................................................................... 19

Statistical Classification Approach .......................................................................... 21

Pilot Area Test of Statistical Methods................................................................... 21

Discriminant Analysis Statistical Model ................................................................ 22

Decision Rule Classification for River Channel and Gravel Bars ......................... 22

Photointerpretation and Digitizing ........................................................................... 23

Validation ................................................................................................................... 23

Sample Location ..................................................................................................... 23

Contents xxiii
Classification and Location Data ............................................................................. 25

Environmental Data................................................................................................. 26

Vegetation Description Data.................................................................................... 26

Results .......................................................................................................................... 27

Field Sampling ........................................................................................................... 27

Sampled Communities ............................................................................................ 27

Natural Communities............................................................................................ 27

Altered Vegetation Associations........................................................................... 28

Multivariate Analysis of Plot Data............................................................................ 28

Local Expressions of USNVC Associations within the Mapping Area ..................... 31

Wildland Fuels......................................................................................................... 31

Community Mapping .................................................................................................. 33

Community Classification ........................................................................................ 34

Map Accuracy ......................................................................................................... 34

Probability Mapping................................................................................................. 41

Species Importance Values..................................................................................... 41

Discussion..................................................................................................................... 43

An Altered Landscape................................................................................................ 43

Undersampled Vegetation associations ..................................................................... 44

Field-based Sources of Map Discrepancies ............................................................... 44

Map Accuracies.......................................................................................................... 45

Probability Maps......................................................................................................... 47

Management Implications .......................................................................................... 52

Ecological Systems Groupings................................................................................ 52

Woodland/Forest Conundrum ................................................................................. 52

Wildland Fuels Map................................................................................................. 53

Options for Future Mapping Projects.......................................................................... 56

Field Sampling ........................................................................................................ 56

Remote Sensing...................................................................................................... 56

References.................................................................................................................... 58

Contents xxiv
List of Tables 

Table 1. USNVC classification structure......................................................................... 6

Table 2. Vegetative cover classes assigned to individual species within plots............. 12

Table 3. Mean fuel loading and standard errors (in parentheses) by USNVC 

association............................................................................................... 32

Table 4. Mean fuel loading and standard errors (in parentheses) for sampled altered 

vegetation associations............................................................................ 33

Table 5. Area and percent coverage of vegetation associations and land cover types in 

the ONSR purchase unit (57,678 ha) and the larger mapping area 

(139,953 ha). ........................................................................................... 35

Table 6. Error matrix of USNVC associations and other land cover categories. .......... 38

Table 7. Accuracy report for USNVC vegetation associations and other land cover 

categories. ............................................................................................... 42

Table 8. Accuracy report for aggregated OSNR Community Types............................. 42

Contents xxv
List of Figures 

Figure 1. Study area map............................................................................................... 3

Figure 2. Generalized depiction of the sedimentary geologic strata and associated 

potential vegetation in the Current River Hills subsection (from Nigh and 

others, 2000).............................................................................................. 4

Figure 3. Generalized depiction of Precambrian geology and associated potential 

vegetation communities in the Current River Hills subsection (from Nigh 

and others, 2000)....................................................................................... 4

Figure 4. Vegetation sampling plot design. .................................................................. 12

Figure 5. Correlogram of illumination/albedo surface indicating a high amount of spatial 

dependence at about 2 meters in both the x and y directions.................. 18

Figure 6. Distribution of sampling points and location of statistical classification pilot 

area.......................................................................................................... 20

Figure 7. Schematic representation of polygon centroid (the median X and Y 

coordinates, defined by the dark circles) calculation and the effects on 

sample point location. .............................................................................. 24

Figure 8. NMS ordination of USNVC association-level mean Importance Values for tree 

and shrub species for tree-dominated communities in ONSR. ................ 30

Figure 9. Detail of 49-class USNVC vegetation association map................................. 36

Figure 10. Detail of 33-class Community Type map with types aggregated to improve 

accuracy and aid in resource management planning............................... 37

Figure 11. Detail of 49-class map displayed over probability raster for all classes, with 

brighter areas indicating higher classification certainty............................ 49

Figure 12. Vegetation associations 2143 and 2108 displayed over probability raster for 

the 2143 type. .......................................................................................... 51

Figure 13. Detail of Anderson Fuels Model map. ......................................................... 55

Contents xxvi
List of Appendices 

Appendix 1. Schematic maps of quarter- and two-meter photomosaics. 

Appendix 2. Community list and hierarchical classification scheme for vegetation 

communities in the ONSR mapping region.

Appendix 3. Crosswalk comparing USNVC vegetation associations in Ozark National 

Scenic Riverways and surrounding area to communities of the Current 

River Hills Subsection included in the Atlas of Missouri Ecoregions (Nigh 

and Schroeder 2002) and to communities described in the Terrestrial 

Natural Communities of Missouri (Nelson 2005).

Appendix 4. Crosswalk of ecological systems used in the mapping area and 

management groups identified by the Missouri Ecological Classification 

System (MOECS; Nigh and others 2000) for the Current River Hills 

subsection.

Appendix 5. Crosswalk of ecological systems used in the mapping area with ecological 

systems developed by NatureServe.

Appendix 6. Crosswalk of ecological systems used to map altered communities within 

the mapping area and Anderson’s classification system for built-up and 

aquatic features (Anderson 1976) and the Federal Geographic Data 

Committee (FGDC) classification of cultivated lands.

Appendix 7. Automated classification data derivatives and transformation techniques.

Appendix 8. Results of importance value (IV) calculations for the major USNVC 

vegetation associations in the ONSR mapping area, with average cover 

and relative frequency presented by species and the number of field 

observations listed.

Appendix 9. List of Deliverables to ONSR personnel.

Appendix 10. Data Dictionary.

Appendix 11. Map codes and descriptions.

Appendix 12. 49-Class USNVC Vegetation Association Map (reduced copy). 

Appendix 13. 33-Class Community Type Map (reduced copy). 

Appendix 14. ROC Graphs of select groups of communities and the Mixed OakHickory/Dogwood Forest. 

Appendix 15. ONSR USNVC Natural Community Descriptions.

Appendix 16. ONSR Altered Community Descriptions.

Appendix 17. Field Key to ONSR Vegetation Communities.

Appendix 18. ONSR USNVC Community Fuel Loading Photo Key.

Appendix 19. Pilot Area Test of Two Statistical Classification Methods.

Appendix 20. Details of the Discriminant Analysis Statistical Model.

Contents xxvii
Contents xxviii
Introduction 

Background 

This study was conducted in cooperation with the U.S. Geological Survey (USGS) - 

National Park Service (NPS) Vegetation Mapping Program (USGS – NPS, 2005), a cooperative 

effort to classify, describe, and map vegetation communities in 280 national park units across the 

United States. The program uses the U.S. National Vegetation Classification Standard (USNVC) 

as the national standard to describe mapped vegetation communities. The USNVC is a 

hierarchical classification system with seven levels. The physiognomic levels (System, Class, 

Subclass, Group, and Formation) are a modification of the UNESCO world physiognomic 

classification of vegetation (The Nature Conservancy, 1999). The floristic levels (Alliance and 

Association) are described using procedures outlined in Grossman and others (1998) and are 

frequently refined and aggregated as work progresses in previously unmapped areas. The 

USNVC has been adopted to the formation level by the Federal Geographic Data Committee for 

use by all U.S. Federal agencies. While formal adoption by Federal agencies of the alliance- and 

association-level standard is still pending, it is the standard generally used in vegetation mapping 

projects of this sort. NatureServe maintains and updates all vegetation descriptions in 

cooperation with numerous conservation agencies and organizations in the United States 

(NatureServe, 2005). 

The project was developed through the collaboration of the U.S. Geological Survey, the 

University of Missouri-Columbia Department of Forestry (UMC) and the Missouri Department 

of Conservation (MDC). USGS personnel involved in this project had extensive experience in 

other projects related to Ozark flora. UMC faculty had similarly extensive experience in remote 

sensing and modeling of forests. Personnel at MDC had worked many years developing and 

refining an ecological classification system for Missouri. The location of all the collaborators in 

close proximity within one city (Columbia, Missouri) meant that field and remote sensing 

portions of the project would be intensely collaborative, with field personnel assisting in photo 

interpretation, and remote sensing personnel assisting with field work frequently throughout the 

project. All of the work was conducted in close collaboration with the National Park Service by 

providing once or twice yearly updates of progress, and requests for feedback on draft products. 

We also worked closely with NatureServe to refine USNVC associations and develop ecological 

systems groupings of similar vegetation communities. Our approach to constructing a vegetation 

map for the Ozark National Scenic Riverways was to link aerial photo interpretation, field data, 

other ancillary data and an ecological classification system model to develop a statistical 

vegetation classification approach of the natural and altered vegetation associations in the 

mapping area. 

Setting 

Ozark National Scenic Riverways encompasses approximately 33,257 ha (82,180 acres) 

along the Current and Jacks Fork Rivers in southeastern Missouri. It is located in the Current 

River Hills Subsection of the Ozark Highlands Section of the national hierarchical framework of 

ecological units (Avers and others, 1994). The park purchase unit includes an additional 23,000 

ha, most of which is private land and lands owned by other groups, including the State of 

Missouri. The mapping region (UTM Zone 15 North, NAD83; 611300, 4074000 by 691400, 

Introduction 1
4149400 or 36.79N, 91.753W by 37.484N, 90.841W) is 141,854 ha (350,529 acres) and 

encompasses ONSR as well as areas immediately surrounding the park land (Figure 1). 

The Missouri Ecological Classification System (MOECS, Nigh and others, 2000; Nigh 

and Schroeder, 2002) used environmental and floristic data from relatively intact and 

undisturbed communities in order to construct an ecological classification system for the Current 

River Hills Subsection. The MOECS classification subdivides the Current River Hills 

subsection into Landtype Associations (LTAs), landscape-scale divisions based upon 

topography, geologic parent materials, soil associations and potential vegetation alliances (Figure 

1). Within LTAs, Ecological Landtypes (ELTs) are site-scale units based upon topography, 

geologic parent materials, soil series and potential vegetation associations. Seventeen ELTs 

were described (based on field data) within nine LTAs in the Current River Hills Subsection 

(Nigh and others, 2002). Another fourteen ELTs relevant to our study area were identified but 

not sampled by the MOECS, and descriptions for these are based upon qualitative information 

for these types. 

The Current-Eleven Point Pine-Oak Woodland Dissected Plains LTA and the Current 

River Oak-Pine Woodland/Forest Hills LTAs are dominated by gently rolling dissected plains 

underlain by resistant sandstones of the Roubidoux formation and deeply dissected drainages that 

cut into dolomites in the upper portion of the Ordovician Gasconade Formation (Figure 2). The 

former LTA intersects minimally with the park, though it covers a larger portion of the mapping 

area outside park boundaries along the Jacks Fork River. The Current River Oak-Pine 

Woodland/Forest Hills LTA is represented primarily by a large patch near the downstream 

terminus of the park, though scattered portions can be found elsewhere in the park. Within the 

broader mapping area, this LTA is abundant along the Jacks Fork River, upper reaches of the 

Current River, and toward the southern terminus of the park. 

Portions of the park along the Jacks Fork River are dominated by the Jacks Fork River 

Oak-Pine Forest Breaks, while most of the remaining area in the park and greater mapping area 

lies in the Current River Oak Forest Breaks. These LTAs include the deepest drainages, which 

cut into the lower portions of the Gasconade formation and into the Cambrian Eminence-Potosi 

formation (Figure 2; Nigh and Schroeder, 2002). The Roubidoux and Upper Gasconade 

formations are less prominent, occasionally forming narrow, erosion resistant caps and 

secondary summits. The Eminence Igneous Glade/Oak Forest Knobs LTA forms the remaining 

portion of the park and mapping area, and is defined by domes of Precambrian igneous substrate, 

portions of which have been exposed by erosion (Figure 3; Nigh and Schroeder, 2002). 

Soils formed in Roubidoux residuum are low in soluble bases, and once supported vast 

tracts of forest dominated by shortleaf pine (Pinus echinata), particularly in the western, less 

dissected portions of the subsection. Root-restricting fragipans in the subsoil on ridges and 

benches tended to support open forests and woodlands dominated by Post oak (Quercus stellata) 

and pine, with other upland oaks and hickories. Anthropogenic and natural fires were a critical 

component in the evolution of both types of communities. Areas that are dominated by this 

stratum tend to be relatively flat, and have therefore been and continue to be subject to the most 

intensive agricultural activity, particularly grazing. 

Soils formed in the residuum of the upper portion of the Gasconade have moderate base 

saturation, though embedded chert nodules can weather to form more neutral soils. Furthermore, 

hillslope sediment from the Roubidoux formation can create an acidic mantle on top of soils 

derived from the upper Gasconade. For the most part, this geologic stratum supports forests 

dominated by generalist species of oak and hickory, though pockets of acidic soils can favor 

Introduction 2
open forest and woodlands dominated by plants that thrive in acidic soils, including pine. In 

some areas, bedrock is exposed or near the surface. Historically, these areas supported forest 

openings called glades, which are dominated by herbaceous plants and woody shrubs. Fire 

suppression has allowed invasion of glades by woody species, particularly eastern red cedar 

(Juniperus virginiana). 

The lower portion of the Gasconade and the Eminence-Potosi formations are higher in 

soluble bases and lack a significant chert component. As a general rule, these strata yield soils 

that support forests dominated by white oak (Quercus stellata). Where base saturation is highest 

(where bedrock is at or near the surface), vegetation is dominated by plants that thrive in basic 

soils. On protected slopes with northern or northeastern aspects, dominants other than white oak 

include ash (Fraxinus americana) and Chinkapin oak (Q. meuhlenbergii). On exposed slopes 

with more southerly aspects, these areas may support glades and open woodlands dominated by 

chinkapin oak and ash. 

Igneous bedrock weathers to acidic soils. Exposed igneous bedrock typically yields 

glades dominated by plants that can survive in acidic, seasonally xeric conditions. Usually these 

glades are interspersed with woodlands dominated by post oak, black oak (Quercus velutina) and 

blackjack oak (Q, marilandica), which give way to mixed oak and hickory forests as soil depth 

increases (Figure 3; Nigh and Schroeder, 2002; Nigh and others, 2000). 

Figure 1. Study area map.

*Nigh and others, 2000. 

Introduction 3
Figure 2. Generalized depiction of the sedimentary geologic strata and associated 

potential vegetation in the Current River Hills subsection (from Nigh and others, 2000).

Figure 3. Generalized depiction of Precambrian geology and associated potential 

vegetation communities in the Current River Hills subsection (from Nigh and others, 

2000).

Introduction 4
The Ozarks are perhaps the oldest continuously exposed land mass in North America; the 

region has likely supported plant life for 100 million years, and was not glaciated during any of 

the last four major continental glaciation events. The continuous exposure and lack of glaciation 

has resulted in extreme biological diversity of plant communities and high levels of endemism 

(The Nature Conservancy, 2003). Presettlement vegetation was characterized by oak and pine 

woodlands and forests heavily influenced by aboriginal and natural fires (Guyette and Cutter, 

1991; Ladd, 1991) and interspersed with small and large patches of other natural communities 

including fens, wetlands, and glades (The Nature Conservancy, 2003). The landscape is now 

dominated by second-growth forest; most of the Ozarks were logged between 1880 and 1920 

(Cunningham and Hauser, 1992). The second growth forests contain less pine; uniform, younger 

forests have replaced the woodland/forest mosaic, and many glades have overgrown with eastern 

red cedar (Juniperus virginiana, Nigh and Schroeder, 2002). 

When the Ozark National Scenic Riverways was established in 1964, it was created from 

public and private lands with a variety of uses, including relatively intact areas with minimal 

evidence of human disturbance, parcels of land that were being used for grazing, logging, and 

row cropping (and various combinations of these activities), and areas that had been previously 

used in these manners but where activity had ceased prior to the establishment of the park. Many 

of these activities continue to a limited extent on privately- and publicly-owned parcels within 

the ONSR purchase unit. The result is a landscape of immense native biological diversity 

overlaid with a diversity of past and continuing human uses in various stages of regrowth, with 

some plant communities fairly intact and others significantly altered from what they had been 

prior to Euro-American settlement. Many plant communities are altered to such an extent that 

they are considered “cultural” or “early successional” types. Since the USNVC has traditionally 

focused on accounting for “natural” or “semi-natural” types, no appropriate description of many 

of the most altered communities at the park currently exists in the National Vegetation 

Classification System. Nor are these types well represented by provisional types being 

considered for inclusion in the USNVC. 

Objective 

The main objective of this project was to create a vegetation community map at USNVC 

association-level using an extensive array of data from numerous sources, such as field-based 

and remotely sensed data. Classification approaches that are typically employed in remote 

sensing studies proved inadequate in pilot investigations, so a novel approach was developed to 

use the available data to its highest potential. Our approach involved mining the available data 

with the intent of capturing 1) species-level niche differentiation, 2) past land use history, 3) 

patterns of current land use, and 4) differential responses to disturbance, as all of these factors 

influence patterns of current vegetation communities seen on the landscape in the ONSR 

mapping region. 

Classification Structure 

USNVC 

The National Vegetation Classification is a hierarchical system based upon physiognomic 

characteristics in the top five levels and by floristic components in the lowest two levels (Table 

1). At the very upper end, the Class splits the USNVC into broad physiognomic categories 

(forest, woodland, shrubland, herbaceous, etc.). Subsequent divisions are based on refinements 

Introduction 5
to this division, down to the Formation level. The floristic levels of the USNVC are the Alliance 

and Association. Species composition, particularly abundant and diagnostic species in the 

uppermost strata, is the primary criterion used to define these two levels. Roughly defined, 

dominant plant species are those that are most abundant. Diagnostic species are strongly tied to 

a particular vegetation association, primarily due to environmental constraints. Each association 

if defined by its diagnostic or dominant species, which are frequently incorporated into the 

association name, as in the Pinus echinata / Vaccinium (arboreum, pallidum, stamineum) 

Forest). Alliances are groups of associations that share dominant and/or diagnostic species. 

Table 1. USNVC classification structure.

Level Primary Basis For Classification Example 

Class Growth form and structure of vegetation Forest 

Subclass Growth form characteristics (e.g., leaf phenology) Deciduous forest 

Group Leaf types, corresponding to climate Cold-deciduous forest 

Subgroup Relative human impact (natural/semi-natural or 

cultural) 

Natural/semi-natural 

Formation Additional physiognomic and environmental 

factors, including hydrology 

Temporarily flooded cold-deciduous 

woodland 

Alliance Dominant/diagnostic species of uppermost or 

dominant stratum 

Pinus echinata Forest Alliance 

Association Additional dominant/diagnostic species from any 

strata 

Pinus echinata / Vaccinium (arboreum, 

pallidum, stamineum) Forest 

Additional Classification Elements 

Ecological Systems 

During the scoping meetings and preliminary reconnaissance, we realized that in some 

instances, the USNVC would provide an inappropriate framework for mapping. Unfortunately, 

certain vegetation communities are frequently spatially related to one another, though they may 

have little in common structurally or compositionally. For example, within the mapping area, 

glades are herbaceous communities that are usually associated with physiognomically distinct 

woodlands. In the USNVC hierarchy, these two vegetation associations would be split out at 

Class, even though they have similar ecological processes and intergrade with one another 

frequently. Also, the two physiognomic classes often occur in complexes that hinder mapping 

each element individually because the units are often smaller than the minimum mapping unit of 

0.5 ha. Since these two communities are related in terms of ecological processes, vegetation, and 

other important attributes, and since the two associations cannot be reliably distinguished from 

one another using remote sensing, it is often appropriate to think of them as one map class. 

In order to address the above mapping issues, and to identify broader classes relevant to 

resource management within the park, we adopted NatureServe’s (2005) ecological systems 

approach to generate a fewer-class, higher-accuracy map. Ecological systems mapping 

approaches were developed to allow users to classify vegetation based on ecological processes 

and group associations with similar ecological processes but not necessarily similar 

physiognomy into units. These units, called systems, are well suited for combining related 

associations into higher level systems groupings. Ecological systems mapping allows us to 

accurately identify ecologically related, though physiognomically distinct communities in a 

manner that is useful to resource managers. We aggregated vegetation associations at two levels: 

The “Ecological System” is a broad scale division based upon physiognomy, hydrologic 

Introduction 6
attributes, dominant plant groups and, if an altered system, human activity. The “Community 

Type” level subdivides the Ecological System based primarily upon substrate and dominant plant 

species. Altered types are further subdivided by the nature of the human disturbance. Mapping 

in this project was performed as the USNVC association level and the Community Type level. 

Although ecological systems are technically not part of the USNVC, ecological systems 

are defined by their component USNVC association level units. Therefore, systems can be easily 

related back to the USNVC when needed. Using ecological systems improves both the utility 

and the accuracy of the end products. The Ecological Systems used in this study are based upon 

those developed by NatureServe, but they have been modified to address mapping issues and the 

concerns of resource managers at ONSR. Nevertheless, the Ecological Systems developed for 

ONSR relate to NatureServe’s ecological systems in much the same way that local community 

descriptions relate to their global USNVC counterparts. Local Ecological Systems may include 

only a subset of the communities identified in NatureServe’s systems, and information on the 

composition, abundance, distribution, and environmental parameters of local Ecological Systems 

are used to inform the description of the broader ecological systems. 

The specific ecological systems mapping approach incorporated in this project was 

preferable to grouping communities upward into the USNVC alliance level for three reasons. 

First, it addressed the problem of how to map physiognomically distinct communities that cooccur in the landscape. Second, it reduced the number of classes to a greater degree than 

alliance-level mapping would have done. Within the study area, mapping at the alliance level 

would have reduced the number of classes by only a few, while mapping at the Community Type 

level reduced the number of classes by 16, from 49 to 33. Finally, through discussions with 

resource management staff both within the park and at other land management agencies in the 

area, we were able to identify classification units that were relevant to management goals and 

methods. A crosswalk of the Community Types and Ecological Systems identified for this 

project and their relationship to ecological systems developed by NatureServe is included in 

Appendix 5. Conceptually, ecological systems are akin to an intermediate level between the 

alliance and formation levels of the USNVC system and function much like management groups 

identified in the MOECS and used by Missouri resource managers. A crosswalk comparing 

Ecological Systems identified in our study with management groups identified by MOECS is 

included in Appendix 4. 

Cultural and Altered Communities 

The National Vegetation Classification Standard (The Nature Conservancy, 1994a) 

established guidelines for classification of communities that can be considered neither natural 

nor semi-natural. This system combines elements of the land use and land cover classification 

system typically used to map urban and water features (Anderson and others, 1976), and a coarse 

system for classifying actively cultivated lands. However, early discussions with resource 

managers at ONSR indicated that adoption of these classification systems would fail to account 

for many vegetation associations that were known to occur within the mapping area. The park 

includes land that had once been privately owned and used for grazing, row crops, timber, and/or 

homesteads. Much of the land was abandoned in the 1930’s and most of the remaining uses of 

this type ceased when the park was created in 1964; these lands are now in various stages of 

succession and regrowth. Furthermore, there is a high concentration of public lands managed for 

timber outside of the park, but within the mapping area. As a result, there is currently an array of 

ruderal and timber management communities that are addressed neither by the USNVC, nor by 

Introduction 7
the broad categories of the land use/land cover and cultivated lands systems described above. 

Grossman and others (1998) provide a framework for identifying ruderal communities, but they 

stop short of suggesting ways to classify the various altered vegetation associations. In order to 

map the full spectrum of communities extant in the park and provide a better description of 

ruderal and timber management areas that might have critical habitat implications for wildlife, 

our mapping approach included classification of those communities not treated by existing 

systems and their subtypes. We developed a suite of altered vegetation associations that could 

potentially be found within the study area based on our knowledge of the current conditions 

within the park and discussions with resource management staff regarding types that were 

critical to management activities. This classification was updated as new information was 

gathered during field data collection. Descriptions of altered communities are attached as 

Appendix 16, which is retained as a separate electronic document (Appendix 16-ONSR Altered 

Community Descriptions). Appendix 6 relates altered vegetation associations to Ecological 

Systems used in this study. 

Missouri Classification Systems 

Two classification systems are widely accepted and used by natural resource managers in 

Missouri to describe local vegetation communities; the previously mentioned Missouri 

Ecological Classification System and The Terrestrial Natural Communities of Missouri (Nelson, 

1985; Nelson, 2005). The MOECS was not intended to give a map of current vegetation 

conditions, but resulted in a map and classification system akin to one of potential natural 

vegetation. The MOECS is used regularly by state and Federal agencies in Missouri for 

management planning purposes. Nelson’s (1985, 2005) work also focused on relatively intact 

native vegetation communities, and his original work (Nelson, 1985) formed the foundation for 

the USNVC global descriptions of many of the communities identified in our study. Near the 

completion of this project, a revised version of The Terrestrial Natural Communities of Missouri

was published (Nelson, 2005). We have included a crosswalk relating USNVC communities 

identified in this study to communities identified by MOECS and to Terrestrial Natural 

Communities identified by Nelson (2005) in Appendix 3. 

Introduction 8
Methods 

Approach 

To develop a USNVC association-level vegetation community map to the required 

accuracy standards, it was necessary to predict landscape scale vegetation community patterns 

based on the combined influence of landform-mediated resource gradients as well as the 

influence of past and present land use patterns and anthropogenic disturbance regimes. This 

challenge was met by acquiring numerous remote sensing, topographic and field-collected 

vegetation datasets from which we derived a large set of variables to train a statistical 

classification. We supplemented this method with photointerpretation and digitization from 

current and historical aerial photographic sources. We used a hybrid approach to produce the 

final vegetation community map; we combined a unique data mining statistical approach with a 

photointerpretative mapping approach using a spatial overlay. The logic behind the overlay 

order was based on the manner in which anthropogenic land cover modification is superimposed 

over relatively natural vegetation communities. 

Field Methods 

Development of the Field Key 

We developed a field key of vegetation associations prior to field sampling based on the 

extensive knowledge and experience of project personnel with vegetation communities in and 

around the park. USGS ecologists had led field data collection for the Missouri Ecological 

Classification System (MOECS) project; Missouri Department of Conservation personnel had 

developed the MOECS model and had been involved in development of Missouri community 

descriptions used in the USNVC. This collective experience and the reconnaissance activities 

performed prior to initial sampling provided sufficient information to determine the suite of 

communities that we would likely encounter in the mapping area. After field data collection 

began, modifications were made as needed to the field key in order to accommodate our 

expanding knowledge of the vegetation associations. If an unknown vegetation association was 

encountered (for example, a type that clearly failed to match the description for any of the 

communities likely to be in the mapping area), it was first compared to other existing USNVC 

vegetation associations that had not been included previously in our classification to determine if 

it matched any of those communities. If so, that association was incorporated into the field key. 

If not, the community was given a provisional name and plot data were collected as described in 

the following section. Furthermore, field notes were used to describe the new vegetation 

association and to determine its relationship, if any, to other known vegetation associations. 

Because evidence of human disturbance frequently is among the most obvious 

characteristics of a community, the key is first divided into two sections. The first section covers 

natural and semi-natural communities that are consistent with the USNVC. The second section 

covers culturally and significantly altered vegetation associations, which are not included in the 

USNVC. Some vegetation associations are represented in both sections of the key, because they 

exhibit characteristics that suggest that they could be classified as either natural or originating 

from human disturbance. 

Methods 9
Subsequent to the natural/altered division, the key follows the structure of the USNVC 

hierarchy through the initial levels: 

I. Physiognomic type (forests, woodlands, shrublands, herbaceous and sparse 

herbaceous), then 

A. Woody leaf phenology, if community is a forest, woodland or shrubland, or 

B. Habitat or substrate type, if community is herbaceous (or management 

activity, for cultural types). 

Beyond these divisions, communities are generally grouped together in a manner that 

parallels the USNVC hierarchy. Therefore, associations that are closely related in the USNVC 

hierarchy (for example, within the same alliance) tend to be closely grouped in the key. The 

field key is attached as Appendix 17, which is retained as a separate electronic document 

(Appendix 17-Field Key to ONSR Vegetation Communities). 

Existing Community Characterization and Remote Sensing Training Data 

Vegetation data had been collected from more than 500 sampling points in the study area 

for the MOECS project between 1996 and 2000. In the MOECS project, vegetation data were 

analyzed in relation to environmental conditions using multivariate analyses to classify relatively 

undisturbed ecological communities (Becker, 1999; Grabner, 2001). We used The Nature 

Conservancy’s guidelines for the USGS-NPS program for incorporating existing data into 

vegetation mapping efforts (The Nature Conservancy, 1996) to determine the utility of the 

MOECS data for our mapping effort. We determined that the data were sufficient for 

classification and mapping, except that they lacked a field-classification to a USNVC vegetation 

association and certain environmental measurements. Therefore, we collected classification and 

environmental data for each previously sampled point that we re-visited during this project and 

used the datasets for classification and mapping of USNVC communities. These data were 

augmented by new plot data collected during this project using methods described below. 

New Community Characterization and Remote Sensing Training Data 

Following USNVC guidelines for sampling large parks, we used a modification of the 

gradsect sampling technique (Austin and Heyligers, 1989), which deliberately samples across 

steep environmental gradients in order to capture the greatest diversity of sampling units. The 

mapping area had previously been classified according to Landtype Associations (LTAs) by the 

Missouri Ecological Classification System (Figure 1; Nigh and others, 2000; Nigh and 

Schroeder, 2002). We structured our sampling plan to capture important LTAs within the 

mapping area: the Current River Breaks, the Current River Hills, the Jacks Fork Breaks, and the 

Eminence (Igneous) Knobs. A fifth LTA, the Pine-Oak Plains, is minimally represented in the 

mapping area. Within each of these LTAs, we identified principal sampling areas based upon 

the following factors: 1) high ecological variability as suggested by the Ecological Landtype 

model developed for the region (Nigh and others, 2000); and 2) significant public land 

ownership. Where possible, sampling was directed to areas for which there were significant 

existing data in order to facilitate field classification of plots sampled during previous studies but 

for which no field classifications had been made. Also, these areas typically provided a high 

amount of public land ownership which allowed for more complete sampling across 

environmental gradients. Principal sampling area boundaries were drawn to incorporate the 

maximum ecological diversity. 

Methods 10
Initially, sampling within each principal sampling area was stratified by Ecological 

Landtype (ELT, Nigh and others, 2000; Nigh and Schroeder, 2002). We generated a random 

sampling point within each of ten randomly selected polygons of each ELT type. (If a polygon 

included a point that had been previously sampled during the MOECS project and for which 

there were sufficient data to characterize the vegetation association, that point was used rather 

than the randomly generated point.) During the second season, sampling was based upon a 

preliminary classification map generated using training data from the first year. We used this 

map to identify sites with provisionally high vegetation community diversity and to stratify 

second-season sampling. 

During both sampling years, we collected three types of data: 1) classification data used 

as training data for remote sensing, 2) plot data for characterizing community structure and 

composition and for use in multivariate data analysis, and 3) wildland fuels data for 

characterizing fuel loads within each vegetation association. We collected classification, 

environmental, vegetation description and wildland fuels data at every point visited. Plot data 

were collected at points only if additional data were needed in order to reach the ten sample 

minimum needed to characterize each vegetation association. Once we had plot data from ten 

examples of a given vegetation association, plot-level sampling was discontinued for that type, 

but wildland fuels and classification data were collected. 

Vegetation Plot Data 

Where plot data were collected, we followed guidelines established by the USGS-NPS 

Vegetation Mapping program (The Nature Conservancy, 1994b). Within forests and woodlands, 

we laid out a 20 x 20 m plot along the cardinal directions (Figure 4). Within this plot, we 

identified to species and measured the diameter at breast height (DBH) of every tree stem with a 

DBH greater than or equal to 10 cm. Within a 10 x 20 m subplot oriented along the cross-slope 

axis of the plot, we identified to species and measured to the nearest centimeter every woody 

stem taller than one meter and having a DBH less than 10 cm. For trees and shrubs, we assigned 

each stem to the appropriate vegetative stratum and assigned each species to a vegetative cover 

class within each stratum where it was recorded (Table 2). For herbaceous vegetation and for all 

woody stems providing foliar cover below one meter, we identified each plant within the 10 x 20 

m subplot to species and assigned each species to a cover class. Shrubby and herbaceous 

communities were sampled using a 10 x 20 m plot within which the same data were collected as 

in the 10 x 20 m subplot above. Data from previous projects that were used in this study had 

been collected using this plot design. 

Methods 11
10 m x 20 m (0.02 ha) subplot: 

Subcanopy and shrubs—all woody stems < 10 cm 

DBH identified to species, assigned to stratum and 

DBH measured. Species cover in each stratum 

estimated by cover class 

Groundflora—all woody species providing foliar 

cover below 1 m and all herbaceous plants identified 

to species and assigned to cover class

20 m x 20 m (0.04 

ha) canopy plot: 

All woody stems ≥ 10 

cm identified to 

species, assigned to 

stratum and DBH 

measured. 

Species cover in 

each stratum 

estimated by cover 

class

Figure 4. Vegetation sampling plot design.

Table 2. Vegetative cover classes assigned to individual species within plots. 

Code Range of Class* Class midpoint 

01 >0-< 1% 0.3% 

03 1-< 5% 3% 

10 5-<15% 10% 

20 15-<25% 20% 

30 25-<35% 30% 

40 35-<45% 40% 

50 45-<55% 50% 

60 55-<65% 60% 

70 65-<75% 70% 

80 75-<85% 80% 

90 85-<95% 90% 

98 95-<100% 97.5% 

*Adopted from The Nature Conservancy, 1994b 

Classification and Location Data 

We classified each point into a USNVC vegetation type using the field key, ranked its 

quality from 1 to 5 (the degree to which that community matched the description in the current 

USNVC), and estimated (by pacing to the closer of the community terminus or 100 m) its extent 

in meters in each cardinal direction from the sampling point. If the community designation at a 

given sampling point was assigned a low quality rank, a secondary designation of the vegetation 

association was assigned. Additionally, we collected the following spatial information as 

required by the Vegetation Mapping Program (The Nature Conservancy, 1994b.): Park Name 

(OZAR), Site, USGS quad name, USGS quad code, Easting, Northing and elevation (UTM 

NAD83, Zone 15N) derived using a hand-held Global Positioning System (GPS), GPS estimated 

Methods 12
positional error, and waypoint. We recorded any comments we had relating to the classification 

of the community and took at least one digital voucher photograph at each point. 

Location data were collected using a Thales MobileMapper GPS. This unit offers 3- to 5-

m real-time Wide Area Augmentation System (WAAS)-corrected accuracy, with sub-meter postprocessed accuracy. Final coordinates were calculated by averaging values recorded once every 

second for a minimum of 180 seconds per sample. Data were post-processed using Thales 

MobileMapper Office software and correction data downloaded from continually operating 

reference stations operated by the National Geodetic Survey (2005). Approximately 86 percent 

of the GPS data were corrected in this manner. An additional 12 percent of the GPS data were 

real-time WAAS-corrected. Two percent of data were uncorrected. 

While in the field, we also manually recorded coordinates into databases developed using 

HanDbase software for Palm handheld PDA’s. Unlike post-processed data from the GPS 

receiver, recorded values at each point represent the average value of a minimum of 180 

uncorrected field readings. Therefore, there were often slight discrepancies between fieldrecorded UTM coordinates and those derived from downloaded, post-processed data. Values 

derived using GIS software and post-processing correction data were assumed to be superior to 

field estimates. 

Environmental Data 

We also collected environmental data at each location. Data included slope, aspect, 

geological stratum, topographic position, field identification of the MOECS ELT, surficial 

geology and surface soil texture. The hydrologic regime of each point was assessed based on 

Cowardin and others (1979). 

Vegetation Description Data 

Vegetation description data follows Vegetation Mapping Program standards (The Nature 

Conservancy, 1994b.). We assigned each point to a physiognomic class, identified the dominant 

leaf type within the community, and identified the leaf phenology of both woody (evergreen or 

deciduous) and herbaceous (annual or perennial) vegetation. We identified between one and 

three dominant species within each vegetative stratum and assigned each to a cover class. 

Finally, we noted evidence of human disturbance, use of the area by animals, and diagnostic 

species outside of the plot (if a plot was used) or not listed among dominant plants. 

Wildland Fuels Data 

We estimated fuel loading and structure at the association level using standard fuel 

inventory techniques developed by Brown (1974) and Brown and others (1981). Along a 15.24 

m (50 ft) transect associated with each sampling point, we tallied woody debris in three diameter 

size categories: 1 hr (0-0.6 cm or 0-0.25 in), 10 hr (0.61-2.54 cm or 0.26-1 in), 100 hr (2.55 –

7.62 cm or 1.01-3 inch), and measured the diameter for 1,000 hr fuels (greater than 7.62 cm or 3 

in). 1 hr and 10 hr fuels were tallied in the first 1.82 m (6 ft) of the transect, 100 hr fuels were 

tallied in the first 3.65 m (12 ft) of the transect, and 1,000 hr fuels were sampled along the entire 

transect. Finally, we measured duff and litter depth and fuel height at 1.52 m (5 ft) intervals 

along the transect. Litter fuel loading estimates based on duff and litter depth followed Brown 

(1974). 

Wildland fuel loadings were tested to determine if there were differences among USNVC 

associations using Multi-Response Permutation Procedure (MRPP) in the multivariate statistical 

Methods 13
program PC-Ord4 (McCune and Grace 2002). MRPP was selected because it avoids the 

assumption of a normal distribution within the data for analysis. The results of MRPP are a 

measure of effect size and a p-value. Effect size described within MRPP is the chance-corrected 

within-group agreement (A) statistic. If A = 0, then the heterogeneity within groups equals 

expectation by chance (McCune and Grace, 2002). An A value near 0.1 is common when 

analyzing ecological data. Additionally, when results indicate a significant difference when the 

effect size is small (A=0.01) and the sample size is large (N=200), conclusions need to consider 

if results are ecologically significant or statistically significant. Significance of analysis was 

determined by evaluating the p-value (if p=0.05 then significant) and the A statistic (A=0.05 

random distribution), and if sample size is greater than 200, then results were evaluated both 

ecologically and statistically. Fine fuel loading analysis was conducted using litter, 1 hour, 10 

hour, and 100 hour fuel loadings. Total fuel loading was analyzed using MRPP using litter, 1 

hour, 10 hour, 100 hour, 1,000 hour solid, and 1,000 hour rotten fuels. 

Data Quality Control in Field Sampling 

One of the most detrimental potential problems encountered during field sampling was 

inconsistent use of the field key by different crews leading to inconsistent classification of 

communities. To mitigate the potential for sampling error of this type, each sampling season 

included two weeks of intensive botanical training and training in the use of the field key to 

vegetation associations. Other quality control measures included periodic, independent resampling of field-classified communities to ensure consistent classification. When discrepancies 

between the classifications applied by independent crews were discovered, the community was 

classified to the correct type by the USGS ecologist in charge of field sampling, and crews were 

re-instructed in use of the key and the critical criteria to consider in order to distinguish between 

the two confused types. 

Multivariate Analysis of Plot Data 

The classification process used a combination of qualitative and quantitative analyses to 

derive the final community list and plot assignments. Pre-existing datasets used in this project 

had been revisited and qualitatively classified by field crews. New plots had been qualitatively 

assigned to a USNVC association and plot data had been collected. Qualitative plot assignments 

from all datasets were then compared to the results of quantitative analyses to generate the final 

classification for each plot. 

The data for each plot were stored in three separate tables, one with groundcover data 

(forbs, graminoids, tree seedlings, and small shrubs), one with shrub and sapling data, and one 

with tree canopy and subcanopy data. The groundcover dataset used cover as the abundance 

measure. Shrub and tree data collected specifically for this project also included cover estimates, 

but pre-existing data from the MOECS project included only density and basal area. Because the 

groundcover and woody (tree and shrub) data were measured differently, we could not combine 

all three datasets unless we reduced the data to presence/absence. Many USNVC associations 

are differentiated based on relative abundances of certain species and we felt we would lose too 

much information if we used presence/absence data. Given that the MOECS data represented a 

large and valuable portion of the dataset and that much information would be lost if data were 

reduce to presence/absence resolution, we chose to combine the tree and shrub data and used 

importance value (IV) as the abundance measure for the quantitative analyses. We felt using the 

tree and shrub data would give a better classification for the mapping area because it is primarily 

Methods 14
forested. Additionally, it would better match what the mappers would see while working on map 

production. 

From the 318 points at which plot data were collected, we eliminated plots that were 

clearly disturbed or dominated by herbaceous vegetation. The former type was eliminated 

because the USNVC has traditionally focused on accounting for “natural” or “semi-natural” 

types. No appropriate description of many of the most impacted communities at the park exists 

in the National Vegetation Classification System. The latter type (herbaceous communities) was 

eliminated because there were too few plots to conduct a rigorous quantitative analysis. These 

were relatively easy to assign to USNVC associations simply by examining the individual plot 

data. 

The tree and shrub data from the remaining 314 plots were exported to PC-Ord (McCune 

and Mefford, 1999) for multivariate analyses. We maintained the difference between shrub and 

tree strata so a species could appear in a plot as both and the importance values would be treated 

separately during analysis. Analytical methods have different assumptions, strengths, and 

weaknesses so we employed more than one and compared results. For this project we used two 

ordination methods, Detrended Canonical Analysis (DCA) and Non-metric Multidimensional 

Scaling (NMS). 

Although the analytical techniques used are a common method to analyze ecological 

data, it is important to keep in mind that this was all done in the context of the USNVC 

(Grossman and others, 1998). In both the initial qualitative plot assignments and the quantitative 

analyses, we were continually comparing the results to existing USNVC associations to 

determine when groups of plots fit those USNVC associations. That is, during analysis we were 

trying to maintain a regional and national view instead of describing communities purely based 

on local variations. Maintaining a regional view while analyzing a local dataset sometimes 

affected how the plots were grouped. For example, there were Salix caroliniana-dominated plots 

and Platanus occidentalis-dominated plots that were similar throughout the analyses but they 

were separated into two associations based partly on the fact that we know there are Salix 

caroliniana stands and Platanus occidentalis stands in other parts of the central US that are not 

as closely related in species composition. 

We began with all plots provisionally tagged with a USNVC association name based on 

the opinion of the field crew and knowledgeable local ecologists. The process of giving final 

classification names to the plots was an iterative one in which the quantitative analyses were run, 

the results examined and interpreted, certain plots assigned to an USNVC association and 

removed, and then the process begun again. After each analysis iteration, summary statistics 

were calculated for each provisional USNVC association and the plots were compared for 

internal consistency and similarity to the range-wide USNVC description and for how they 

clustered on the ordinations. In general, when the plots provisionally tagged to a USNVC 

association were relatively similar to each other and to the general USNVC concept (as indicated 

by proximity on the ordination plots), they were classified as that USNVC association and 

removed from further analyses. Plots labeled as part of a given association that were separated 

from others on the ordination graph were examined for removal from that association and, if they 

were removed, were left in the dataset for further analyses. There were nine iterations of 

analyses before all plots were assigned to USNVC associations. 

Methods 15
Map Production 

Data 

The independent variables used as input for statistical classification include panchromatic 

and multispectral remote sensing image data that depict the magnitude of electromagnetic 

reflectance of the vegetation communities in different wavelengths as well as the textural 

patterns of their brightness, and topographic data that represent indirect gradients spatially 

segregating vegetation communities (Parker, 1982; Austin and Smith, 1989; Franklin, 1995). 

Specifically, the electromagnetic reflectance classification inputs include Landsat Thematic 

Mapper (TM) multispectral satellite image data obtained during both leaf-on (07/05/00) and leafoff (03/13/02) conditions; derivatives and indices of Landsat data that depict relative levels of 

greenness; reflectance and textural information derived from high resolution color-infrared (CIR) 

aerial photographs obtained in October, 2002 and summer 2003; and greenness indices derived 

from the aerial photographs. Topographic variables and indices were derived from digital 

elevation model (DEM) data, and serve as measurable surrogates to the direct gradients of 

exposure, moisture availability, temperature, and growing season length associated with 

landform position and morphology (Parker, 1982; Austin and Smith, 1989; McNab, 1989, 1993). 

These spatial classification inputs represent the spectral reflectivity of actual vegetation 

communities present on the landscape and characterize differences resulting from environmental 

niche specialization of component species in these communities, such as moisture and light 

environment differences. In this sense, these classification inputs represent both actual and 

potential vegetation community patterns on the landscape in the ONSR. 

Aerial Photos 

Color-infrared (CIR) 1:12,000-scale aerial photographs were acquired over the ONSR 

mapping region in October, 2002. The 612 photographs were scanned into digital form at a 

resolution of 0.25 m cells and orthorectified using the OrthoBASE functions contained in the 

Leica Photogrammetry Suite extension of the ERDAS Imagine® software, version 8.7. At least 

5 ground control points (GCPs) were obtained for each photo frame by locating ground features, 

such as road intersections and building corners that were discernable on both the October, 2002 

aerial photo images and corresponding National Agricultural Imagery Program (NAIP) 2m 

orthophotos that served as reference images. These GCPs were used to perform aerial 

triangulation between the camera and features on the images, and the results of the triangulation 

were then orthorectified using a 10 m resolution DEM and a nearest neighbor resampling method 

to create an output raster image with 0.25 meter square pixels. The positional accuracy of the 

orthorectified images was examined using the ‘swipe’ function in ERDAS Imagine to compare 

the locations of landmarks on these images with the NAIP orthophotos. A maximum tolerance 

of a 15 m spatial discrepancy between the two image sources was established. If a greater spatial 

discrepancy was found on any location on an image, it was re-rectified using additional GCPs. 

Photomosaics were produced from the individual orthorectified October, 2002 photo 

image frames using the mosaic tool in the ERDAS Imagine software. Hard cutlines were created 

to enforce transitions in overlapping areas between adjacent photo image frames, so that no 

photo image frame transitions abruptly into its neighboring photo image frame at the location of 

the cutline between them. Nine photomosaics were generated, with file sizes ranging from 5 to 

20 gb (Figure A1-1). 

Methods 16
Numerous data derivatives were obtained from the October, 2002 aerial photo image data 

to support the statistical classification of vegetation associations. These derivatives facilitate the 

discrimination of vegetation associations based on differences in their color, level of greenness 

and brightness, and texture. The large file sizes for the 0.25 m resolution photomosaic images 

precluded the extraction of derivative information, so a correlation analysis was performed on 

these image data to determine a level of spatial resolution that would reduce the size of these 

image files to a manageable level while maintaining a sufficient resolution to support the 

extraction of textural information. To accomplish this, illumination/albedo surfaces (the square 

root of the sum of the squares of the infrared, red, and green image bands from the October, 2002 

CIR image data) were generated for three test areas. Illumination/albedo was chosen as the 

image data derivation from which to assess image texture in forested areas, because this measure 

combines both illumination and overall reflectance variations in the three CIR bands, and 

accentuates the boundaries of individual tree canopies and the shadows on their peripheries 

(Warner and others, 1998). Correlograms were produced from the illumination/albedo surfaces 

to examine scales of spatial dependence in the three test areas, and a spatial resolution of 2 

meters was chosen as the best compromise between data size reduction and textural information 

retention (Figure 5). Four 2-meter resolution photomosaics were created for the ONSR mapping 

region, with files ranging in size from 534 to 850 mb (Figure A1-2). The October, 2002 photo 

image data was further degraded to 15- and 30-meter resolutions to assess the utility of 

derivatives obtained from these coarser resolutions. Table A7-1 in Appendix 7 lists the data 

derivatives obtained from the three spatial scales of the October, 2002 photo image data. 

In datasets that indicate masking of shadows, dark regions associated with vegetative 

crown shadowing were masked for the red, green, and near infrared spectral bands of the high 

resolution CIR aerial photo imagery to enhance the performance of the greenness indices 

calculated using these data. Identifying dark (shadowed) regions was undertaken by setting a 

brightness threshold that separated dark from bright areas apparent on an illumination/albedo 

surface, which was calculated as a combination of reflectance intensities in the three spectral 

bands of the CIR photo image data. The bright regions identified in the illumination/albedo 

surface served as a mask template that was then applied to the 2-meter resolution CIR aerial 

photo data, and the vegetation greenness indices (SQVI and NDVI) were subsequently calculated 

using these data. The 14-meter resolution masked CIR photo image data resulted from 

degrading the resolution of the 2-meter CIR aerial photo image data. 

2003 National Agriculture Imagery Program Photo Data 

Because the October, 2002 aerial photos were obtained during a period of phenological 

browning-down in the mapping area, additional aerial photographs were obtained for peak 

growing season conditions. A set of 2003 1-meter color-infrared National Agriculture Imagery 

Program (NAIP) photographs were acquired for this purpose. These data were degraded 2 meter 

similarly to the 2002 aerial photo data for data volume considerations. Greenness indices were 

derived and reflectance values in the infrared, red, and green wavelengths were obtained, as well 

as medians of these derivatives calculated within a 15x15 pixel moving window (Table A7-2). 

Methods 17
Figure 5. Correlogram of illumination/albedo surface indicating a high amount of spatial 

dependence at about 2 meters in both the x and y directions. 

Landsat TM/ETM+ 

The vegetation community mapping approach employed in our study employed both leafoff Enhanced Thematic Mapper (ETM+) and growing season multispectral Landsat Thematic 

Mapper (TM) image data from path 24 row 34. The winter (leaf-off) image was acquired on 

March 13, 2002, and was geographically referenced to UTM zone 15 NAD 83 coordinates using 

45 GCPs that were identifiable both on the Landsat and reference NAIP orthophoto images. The 

resulting average root mean square error (RMSE) was 0.2454 for the portion of the image that 

covered the mapping area, indicating that the spatial error between the map source and image 

data is expected to be less than one quarter of a 30x30 meter ETM+ pixel. The growing season 

TM image was obtained on July 5, 2000, and was geographically referenced to UTM zone 15 

NAD 83 coordinates using 40 GCPs that were identifiable on both the Landsat and reference 

NAIP orthophoto images with an average RMSE of 0.3135, indicating an expected spatial error 

of less than a third of a TM pixel. The TM and ETM+ image data were corrected to at-sensor 

reflectance following Markham and Barker (1986) and parameters published in the Landsat 7 

Science Data Users Handbook (Irish, 2000). 

An empirical topographic normalization technique was applied to the TM data to reduce 

the influence of differential solar illumination related to topography (Allen, 2000). An empirical 

model relating solar illumination angle to differential reflectance of forested pixels was 

developed band by band. The regression equation and parameter values for each band are given 

in Table A7-3 

The 6 Landsat visible and infrared bands typically contain substantial redundancy, which 

was reduced via principal components to three bands (PC1, PC2, and PC3), explaining more than 

95 percent of the variance in both of the original images. A tasseled cap transformation was 

Methods 18
applied using coefficients from Huang and others (2002) to derive the widely used Brightness 

(soils), Greenness (vegetation), and Wetness (plant canopy and soils) indices (Crist and Kauth, 

1986). In addition, the Normalized Difference Vegetation Index (NDVI), computed from the 

Landsat data as (NIR – Red) / (NIR + Red), and the Normalized Difference Moisture Index 

(NDMI, [MIR – red] / [MIR + red]) were derived from the original image data. These indices 

correlate to leaf area, biomass, percent green cover, productivity, and photosynthetic activity 

(Tucker and Compton, 1979; Sellers, 1985). The six original Landsat non-thermal spectral 

bands, the NDVI and NDMI vegetation indices described above, and other data transformations 

were combined into image data ‘stacks’ to be sampled for input into classification models (Table 

A7-4 in Appendix 7). 

Topographic Information 

Differences in color and other spectral reflectance characteristics are often not sufficient 

to distinguish similar plant communities from each other, so ancillary data are frequently used as 

input in remote sensing classifications to reduce this confusion. Specifically, topographic data 

can be used to distinguish among different suites of plant species based on niche specialization 

related to topographic gradients (Frank, 1988; Ohmann and Gregory, 2002). Topographic 

indices and other derivatives that have been developed as landscape-scale representations of 

gradients associated with landform shape and position were obtained from a 10 m DEM of the 

study area (Table A7-5 in Appendix 7). These indices represent measurements of indirect 

gradients hypothesized to control species distribution (Austin and Smith, 1989; Franklin, 1995). 

Topographic indices have been used to characterize the spatial distribution of species in 

predictive mapping (Parker, 1982; McNab, 1989, 1993; Iverson and others, 1997), but have 

infrequently been used with remote sensing data. From the DEM data, the indirect gradients of 

slope, Beers-transformed aspect (Beers and others, 1966), elevation, slope position, and slope 

curvature were derived to act as measurable surrogates to the direct gradients of exposure, 

moisture availability, temperature, and growing season length. The Arc/Info command SLOPE 

was used to calculate degrees of slope angle from DEM data. The Arc/Info command ASPECT 

was used to derive slope aspect in positive degrees from 0 to 360 based on the direction of 

maximum change in elevation from each cell. Beers-transformed aspect uses the equation 

cos(aspect - 45) + 1 to transform the circular distribution of slope aspect into a continuous linear 

distribution ranging from 0 to 2; 0 being a grid cell that faces southwest, a value of 1 indicating 

either northwest or southeast, and 2 being northeast. Relative slope position was calculated 

using an Arc macro language (AML) program in which the slope position is calculated relative 

to localized ridge tops and valley bottoms. The resulting grid cell values range from 0 to 100, 

where 0 is a valley bottom and 100 is a ridgetop. Slope curvature was calculated using the 

CURVATURE command available in the Arc/Info GRID module. The terrain relative moisture 

index (TRMI; included in deliverables) represents an additive combination of slope angle, slope 

position, slope aspect (Beers-transformed), and curvature (Parker 1982). These topographic 

gradients were used as independent variables similar to the remote sensing data to help 

differentiate USNVC vegetation associations based on the gradient affinities of their component 

species. 

Field Training Data 

A total of 3,237 field observations extracted data from the 92 discriminating variables 

listed in the tables of Appendix 7 (Figure 6). The 3,237 observation points were gleaned from 

Methods 19
1,069 field points, with the additional 2,168 observations located at a distance of 40 meters using 

field notations on the extent of the vegetation association in each cardinal direction from the 

primary sampling point. These derived “sample” points were weighted equally with the primary 

sampling location. Spatially continuous image files containing the discriminating variables 

obtained from the aerial photo images, Landsat images, and topographic data were sampled at 

the 3,237 sample points using the ‘Convert Pixels to ASCII’ ERDAS Imagine utility, so that 

3,237 values for each discriminating variable could be combined in an Excel tabular format. 

This table was then used as the input dataset for subsequent analysis in Statistical Analysis 

Software (SAS; SAS Institute Inc., 2004). 

Figure 6. Distribution of sampling points and location of statistical classification pilot 

area.

Methods 20
Statistical Classification Approach 

Pilot Area Test of Statistical Methods 

Statistical classification techniques entail a systematic search for pattern in data, and as 

such can produce predictions for unknown cases as well as yield information on the structure of 

the support data (Breiman and others, 1984). Whereas supervised classification methods using 

nonparametric, maximum likelihood or minimum distance decision rules to extrapolate 

continuous raster maps from training data have been widely used for vegetation mapping with 

remote sensing data, tree regression represents a novel approach, and one that is becoming more 

prominent in light of its ability to identify the inputs that produce the best separability in 

classification problems. Regression trees have been demonstrated to produce robust results for 

land cover classification in a number of environments and over a spectrum of scales (Friedl and 

Brodley, 1997; Hansen and others, 2000; Joy and others, 2003; de Colstoun and others, 2003). 

Regression tree-based models differ from linear and additive logistic models for classification 

problems in that they recursively split the data into homogeneous subsets. This layered approach 

represents a simpler method, with classes forming at each step by splitting the data at each node 

based on a function that maximizes the reduction of class impurity (Therneau and Atkinson, 

1997). Regression trees are also valuable data mining tools, in that the most relevant 

independent variables are chosen for the separate nodes (Venables and Ripley, 1994), and the 

structure of the resulting tree is heuristically valuable, providing insight into the predictive 

structure of the support data even in cases where it is not homogenous over the measurement 

space (Breiman and others, 1984). 

Before a methodology was developed to map vegetation communities for the entire 

ONSR mapping area, a pilot area was delineated to test candidate statistical methods to classify 

the numerous USNVC association types present. The goals of the pilot project were to explore 

within a spatial subset of the ONSR mapping area; 1) the potential for statistically classifying 

and subsequently mapping the USNVC vegetation association types to an accuracy level 

consistent with National Vegetation Mapping Program standards using remote sensing and 

topographic data inputs, and 2) the inherent differentiability of the various vegetation 

communities found in the ONSR region. The accuracy of maps produced through the use of a 

supervised maximum likelihood decision rule classification and a regression tree classification 

were compared so that both the relative utility of these approaches, as well as the inherent 

discernability of the different USNVC vegetation associations could be assessed. Details of the 

pilot area test of statistical methods are described in Appendix 19. 

Rather low accuracy results were obtained from the two classification approaches tested 

in this pilot study. The regression tree model for the association level vegetation communities 

was marginally more accurate overall (39.2% with a kappa value of 0.367). While the maximum 

likelihood decision rule classification result was only 5 percent less accurate overall at 34.5% 

(kappa = 0.314), certain vegetation associations were mapped more accurately using the former 

approach compared to the latter. The low accuracy results obtained in this pilot investigation 

point out some limitations associated with using statistical classifiers alone in complex land 

cover mapping problems. For example, numerous USNVC vegetation association types were not 

discernable by the regression tree model, and were consequently left out of the results. Some of 

the categories in a predetermined classification scheme will not be classified by regression tree 

model due to inseparability or rarity, especially when the tree model result is optimized through 

Methods 21
pruning. In addition, the maximum likelihood decision rule classification lacks an efficient and 

trustworthy method for variable selection, and thus often generates a non-parsimonious 

classification model. 

In this pilot investigation, regression trees indicated the importance of particular 

independent variables and where they are valuable for distinguishing both natural and human 

altered vegetation community classes. It was determined that the ultimate utility of regression 

tree classification for mapping USNVC vegetation associations may be realized by applying its 

information content towards a preliminary stratification of the study area so that separate 

classification models can be applied within each relatively more homogeneous region. The 

primary splits identified by the association level regression tree (Figure A19-2) separate the pilot 

study area into vegetation communities that occur on igneous knobs, in bottomland areas 

(including old fields), and those that occur on the remainder of the upland hills and breaks. The 

ONSR mapping area was split using Ecological Land Type (ELT) data to produce masks for the 

bottomland, igneous glade, and hills and breaks regions. Further, it was decided that due to the 

numerous categories in the USNVC association-level classification scheme and copious 

independent variables available for classification model input, discriminant analysis represented 

a more appropriate method to pursue, as this statistical approach has shown promise with large 

hyperspectral remote sensing input datasets (Clark and others, 2005; Karimi and others, 2005). 

Discriminant Analysis Statistical Model 

A discriminant analysis statistical model was developed to discern differences between 

the various USNVC vegetation association types. Discriminant analysis is a statistical approach 

in which the difference between two or more groups is discerned using several variables 

simultaneously (Klecka, 1980). This method can be used as a means to assign all of the cases in 

a dataset to the group to which they most closely resemble. In this application, discriminant 

analysis was used to classify all of the pixels in a study area as the land cover type to which they 

have the highest probability of membership using a linear combination of a set of discriminating 

variables derived from remote sensing and/or ancillary data. A collection of 92 discriminating 

variables obtained from remote sensing and topographic data sources served as potential training 

data for this statistical classification approach. Separate statistical classifications were performed 

for bottomland areas, hills and breaks, and igneous knobs as delineated using an ELT 

classification. The progression of this statistical classification approach entailed: 1) variable 

selection using stepwise discriminant analysis, 2) reduction of dimensionality using canonical 

discriminant analysis, 3) classification based on the probabilities generated using discriminant 

analysis with the canonical functions created in step (2), and 4) application of the canonical 

functions and discriminant analysis classification results to image datasets using ERDAS 

Imagine. This approach is described in more detail in Appendix 20. 

Decision Rule Classification for River Channel and Gravel Bars 

A decision rule classification separate from the discriminant analysis method described 

above and in Appendix 20 was applied to discriminate the river channel and non-vegetated 

(bright) gravel bar features in the ONSR mapping region. These features were classified 

separately so that a detailed characterization of the locations of the river channel and bare gravel 

bars could be obtained. Because the spatial expression of these ephemeral features changes with 

each high discharge event, this portion of the map should be regarded as somewhat of a 

‘snapshot’ of the location of the river channel and gravel bar complex in October, 2002 

Methods 22
(Jacobson and Gran, 1999). Furthermore, vegetative cover on gravel bars mapped in this manner 

is also ephemeral, such that these gravel bars are likely synonymous with Riverine Sand Flats as 

recognized by the USNVC. This classification was performed within an area limited to a spatial 

mask that contained river channel and bright gravel bar land cover features. Infrared, red, and 

green reflectance combined with the first and third principal components and vegetation index 

(IR/R) from the 2-meter October, 2002 photo images served as input data for this classification. 

The classification used the non-parametric parallelepiped decision rule with maximum likelihood 

used to solve for overlaps and unclassified pixels. 

Photointerpretation and Digitizing 

Portions of the ONSR mapping area that have been significantly altered, such as roads, 

fields, cutover areas, utility corridors, urban and residential areas, and quarries were 

photointerpreted and digitized from the 0.25-meter resolution October, 2002 aerial photo images 

using ArcMap. Other land cover features such as surface water and glade-woodland complex 

areas were also digitized from these images. The decision to digitize these features rather than 

attempting to discriminate them using the automated statistical classification approach was based 

on the low accuracy results obtained in the pilot area classifications. Some land cover features, 

such as the agricultural forested woodlots (SA37), were digitized and identified as woodlots 

rather than classified along with other forested areas because these areas are expected to function 

differently than more contiguous forested areas with respect to species composition and habitat 

characteristics (Boutin and Jobin, 1998; Andren, 1994; Nupp and Swihart, 2000; Reunanen and 

Grubb, 2005). Furthermore, these woodlots occur on private lands where sampling was not an 

option. Therefore, there is no way of characterizing the composition and structure of these types, 

nor any means of identifying subtypes or assessing the habitat function of them. 

In addition, historic agricultural fields and pastures were photointerpreted and digitized 

from an aerial photo mosaic obtained in the mid-1960s over the ONSR mapping region. This 

was done in an attempt to identify historic land-use patterns during this period, as more than two 

thirds of the fields apparent in this imagery that are located in what is now the ONSR park have 

been abandoned and are now in various successional stages of reforestation. The digitized 

human-dominated features, glade complexes, historic fields, and the classified river channel and 

bare gravel bars were superimposed over the discriminant analysis results for the hills and 

breaks, bottomland, and igneous glade regions. The logic of overlay order follows a manner in 

which anthropogenic land cover modification is superimposed over natural vegetation 

communities. 

Validation 

Sample Location 

A stratified sample of randomly located points intended to serve as locations for map 

validation observations was generated using an ArcGIS extension developed by Hawthorne tools 

(Beyer, 2004). This extension is designed to create a user-defined number of randomly located 

points in each polygon of a shapefile (grid files had been converted to shapefiles for this 

process). The sampling universe was limited to public lands to ensure that access would be 

possible for field personnel. Next, a GIS function (ArcGIS NEAR command) was used to 

exclude any randomly located field sampling points located within 30 meters from vegetation 

type polygon boundaries. This step was performed in an attempt to minimize both GPS spatial 

Methods 23
registration errors and to reduce sampling in transitional and/or ecotonal areas during field 

validation. In addition, polygon centroids were generated to supplement these random points so 

that smaller and irregularly-shaped polygons would also be included in the sampling universe. 

Sampling points within 30 m of a polygon edge were eliminated and replaced with a point 

located at the calculated polygon centroid. All such points were sampled to test map accuracy. 

Prior to accuracy assessment field sampling, we identified a number of anomalies in how 

the classification map interacted with field sampling, with potential consequences for accuracy 

assessment measures. First, some communities, while distinct and recognizable in the field, 

were frequently below the minimum mapping unit size of 0.5 ha. Mapping algorithms used to 

eliminate noise from the classification map frequently elided the small polygons representing 

these communities and classified them as the surrounding matrix community. This occurred in 

two common scenarios: 1) polygons with a small area, regardless of shape, and 2) linear features 

less than 30 m wide, even when the total area exceeded the minimum mapping unit. Second, 

some communities frequently occur as a complex of vegetation associations, all of which are 

below the minimum mapping unit. Algorithms used to simplify the classification map often 

forced these communities into a single type, rather than mapping each polygon within the 

complex. Finally, despite our criterion that eliminated sampling points within 30 m of the edge 

of mapped polygons, the points where classifications were made in the field frequently occurred 

on the transition between two or more types. This was likely due either to 1) spatial errors 

associated with the photo-rectification process, 2) the calculated location of the polygon centroid 

(Figure 7), or 3) field inaccuracies in GPS equipment leading to navigation error. 

Figure 7. Schematic representation of polygon centroid (the median X and Y 

coordinates, defined by the dark circles) calculation and the effects on sample point 

location. 

A C

We modified accuracy assessment sampling in order to address the above anomalies by 

collecting four different types of point features: Communities, Inclusions, Transitions and 

Complexes. Community features were defined as field sampling points in relatively large, 

homogenous areas. Inclusions were defined as communities below the minimum mapping unit 

that were distinct from the surrounding matrix community. Transitions were defined as the 

interface between two or more distinct communities. Narrow linear communities between two 

vegetation associations were mapped as transitions rather than as inclusions. Complexes were 

defined as aggregations of communities in which the individual polygons were below the 

minimum mapping unit. Inclusion of these multiple sample point types provided additional 

information to assess accuracy that simply moving sample points to homogenous areas would 

not have provided. For example, when sampling points fall on ecotones between two 

Methods 24
communities, identification of both communities present provides an accuracy assessment for 

more than one polygon. 

We established a goal of at least 30 validation points for every classified type. Given the 

linear nature of the mapping area and the implications that this had for sampling efficiency, it 

was necessary to identify appropriate sampling areas based upon community diversity, public 

land ownership, and accessibility. Therefore, we necessarily risked excluding mapped polygons 

of some of the rarer vegetation associations. However, within the pre-determined sampling 

areas, as the minimum goal was reached for the most abundant classes, we focused our attention 

on rarer types following guidelines established by the USGS-NPS Vegetation Mapping Program. 

Final sampling focused exclusively on the rarest communities. Field-visited points were 

augmented by previously visited points withheld from training data and photointerpretation 

observations located in human dominated land-cover categories. (Sampling distribution of 

vegetation associations for accuracy assessment is summarized in Table 6 and Table 7 of the 

“Map Accuracy” section that follows.) 

Classification and Location Data 

Field data collection procedures for accuracy assessment were similar to those used for 

community characterization and the development of training data, except that no plot data were 

collected. We classified each point into the most appropriate vegetation type and ranked its 

quality (the degree to which that community matched the description in the current USNVC). 

Because we wished to maintain the highest degree of field sampling independence, field crews 

were given only sample point locations, rather than polygon maps. We did not want mapped 

polygons to influence how field crews interpreted conditions on the ground. As a result, 

classification was based exclusively upon the vegetation visible from the sample point (typically 

between 0.5 and 1.0 hectares, or up to 100 m in every direction for forest and woodland types), 

rather than on a sample area that may have been suggested by mapped polygons. Transition 

areas were not included in the classification and ranking when sampling points fell in otherwise 

homogenous areas. If the community at a given sampling point was assigned a low quality rank, 

a secondary classification of the vegetation association was assigned. Additionally, we collected 

the following spatial information based upon USGS-NPS vegetation mapping guidelines (The 

Nature Conservancy, 1994b): Park Name, Site, USGS quad name, USGS quad code, Easting 

and Northing (UTM NAD83, Zone 15N), elevation (as derived with GPS), GPS estimated 

positional error, and waypoint. We recorded any comments we had relating to the classification 

of the community and took at least one digital voucher photograph at each point. Because we no 

longer needed training data for the development of photo signatures, the extent of the community 

in each cardinal direction was not estimated. 

For inclusions, transitions and complexes, we collected additional data in order to 

describe the spatial interaction of communities. For inclusions, we estimated the length, width 

and area, described the basic shape, and classified the matrix community. For transitions, we 

classified and recorded the azimuth to the community on either side of the sampling point. 

(Distance to each vegetation association was not estimated, because it was assumed to be zero 

where two or more communities meet.) For complexes, we identified the complex type and 

recorded all of the vegetation associations included in the complex around the sample point. 

Methods 25
Environmental Data 

Environmental data collected at each point were similar to that collected in previous 

years and included slope, aspect, geological stratum, topographic position, and surficial geology. 

Field classification of the ELT and description of the surface soil texture were not included. 

Vegetation Description Data 

To describe the vegetation at each point, we assigned it to a physiognomic class, 

identified the dominant leaf type within the community, and identified the leaf phenology of both 

woody and herbaceous vegetation. We made one modification that greatly reduced sampling 

time at each point and increased the number of sampling points visited during the season. Rather 

than describe the dominant species within each stratum, we described the dominant species only 

within the critical stratum used to identify the physiognomic class of the community: Canopy 

trees for forests and woodlands, shrub layers for shrub lands, and groundflora for herbaceous and 

sparse herbaceous types. Up to six dominant species were identified and assigned a cover class. 

We recorded evidence of human disturbance or use by animals, and identified any diagnostic 

species not included in the critical stratum (for example, in the shrub layers below a forest or 

woodland canopy). Finally, we recorded a justification for the classification applied to the 

community. 

Methods 26
Results 

Field Sampling 

Sampled Communities 

Natural Communities 

The final map includes 22 vegetation associations currently recognized by the USNVC 

and one provisional phase of a USNVC type. One USNVC vegetation association, the Post OakBlackjack Oak/Little Bluestem Woodland (2149∗

), is represented by two classes in the final map, 

because it was mapped separately on both dolomite and igneous substrates. This geological 

division resulted from the mapping process, rather than from any particular compositional or 

structural differences between the community depending on substrate. As a result, the final map 

includes 24 statistically-mapped classes that relate directly to USNVC associations. 

During the community characterization phase of this project, we encountered 26 

vegetation associations currently recognized by the USNVC. Two of these types were later 

subsumed into similar communities based upon preliminary multivariate analysis, qualitative 

field observations about the spatial patterns of and similarities between the communities, and 

discussions with park resource managers relating to the value of retaining these types as separate 

entities. A White Oak-Red Oak/Dogwood Forest (2067) was absorbed by the compositionally 

and structurally similar White-Oak/Dogwood Forest (2066). Combining these two types also is 

consistent with recommendations in the descriptions for these types in the current USNVC 

(NatureServe, 2005). A River Birch-Sycamore Forest (2086) was absorbed by the SycamoreSilver Maple Floodplain Forest (7334), both because it shares many compositional and structural 

features with the latter type, and because, in the few instances where it does occur, it tends to be 

well below the minimum mapping size of 0.5 ha. Two other types, the Water Lily Aquatic 

Wetland (2386) and the Ozark Fen (2404), had only one sample, leaving 22 USNVC 

communities with sufficient data to develop training sets for statistical classification. We also 

identified one new community, provisionally called Midwest Post Oak - Blackjack Oak Forest – 

Igneous Phase (2075i). Where encountered, this community consistently exhibited evidence of 

human disturbance. It has provisionally been included as a subtype of the existing Midwest Post 

Oak - Blackjack Oak Forest (2075). 

An additional 18 USNVC vegetation associations (for a total of 41 natural and seminatural types) have been included in the community descriptions for the project area. Five of 

these types were encountered in the study area only during the accuracy assessment phase of the 

project. These include Dry Dolomite Cliff (2291), Moist Dolomite Cliff (2292), Ozark Dolomite 

Glade (2398), Shortleaf Pine / Little Bluestem Woodland (2404) and Vegetated Spring Branch 

(not currently described by USNVC). Descriptions of these types are based on qualitative 

information collected at the sight and on Nelson (2005). Six of the additional communities had 

been previously encountered by researchers involved in this project in the vicinity of the 

mapping area: Prairie Fen (2416), Red Maple Forested Seep (2407), Dry Igneous Cliff (2286), 

Moist Igneous Cliff (2289), Dolomite Talus (2308) and Igneous Talus (5203). Four other 

 

∗

 Throughout this report, natural and semi-natural vegetation associations area are referred to by the last four digits 

the USNVC code in the format “CEGL00####”. Significantly altered communities are designated by a code in the 

format of “SA##”. Refer to Appendix 2 for a complete list of communities and corresponding codes. 

Results 27
types—Post Oak Flatwoods (2405), Overcup Oak Pond Forest (4642), Buttonbush Sinkhole 

Pond Marsh (4742) and Sinkhole Pond Marsh (2413)—were included because the Missouri 

Natural Heritage Database contains records of these communities near the mapping area. 

Finally, a Floodplain Canebrake type (3836) was included because it had been reported in the 

mapping area. However, this type appears intended to describe natural stands of cane growing 

without canopy cover. We encountered open-grown cane stands only along the margins of old 

fields, suggesting that within the mapping area this is not a natural vegetation association. 

All the USNVC communities encountered in this project, as well as communities not 

encountered in this project but reported from the study area, are included in the final list of 

communities (Appendix 2). Descriptions for these communities are attached as Appendix 15, 

which is retained as a separate electronic document (Appendix 15-ONSR USNVC Natural 

Community Descriptions). These communities are also included in the final key to vegetation 

associations (Appendix 17). 

Altered Vegetation Associations 

We encountered 25 cultural communities, altered vegetation associations and nonvegetated classes during this study. We had sufficient data from seven of the 25 encountered 

types to develop training sets for statistical classification. The remaining 18 encountered types 

were classified using heads-up photointerpretation techniques. An additional thirteen altered 

vegetation associations are known or believed to occur within the study area. These 

communities tend to occur within the study area on private lands, where no sampling was 

conducted. All altered vegetation associations are included in the list of vegetation associations 

(Appendix 2), the set of altered community descriptions (Appendix 16) and the field key to 

communities (Appendix 17). 

Multivariate Analysis of Plot Data 

Multivariate analysis examined the relationships between the natural vegetation 

associations for which plot data had been collected. These associations were divided into 16 

upland forests or woodlands, four floodplain forests, two floodplain shrublands, one herbaceous 

glade, and three herbaceous wetlands. One of the upland forest types (2075i) is thought to be a 

“phase” or variant of the Midwest Post Oak-Blackjack Oak Forest (2075). There are significant 

differences between the plots in these two groups, however, and it is likely that they would be 

split into two associations if more data were available. Descriptions of each USNVC vegetation 

association as it appears in the project area and its range-wide characteristics are in Appendix 15. 

The strongest gradient in the plot data appeared to be moisture. The initial analyses 

showed the floodplain shrublands and floodplain forests with the exception of the Box Elder 

Forest (5033) plots separated from the bulk of the upland forest and woodlands. The plots in 

these floodplain associations were dominated by species such as Platanus occidentalis, Acer 

negundo, Salix caroliniana, and Hamamelis vernalis with lesser amounts of Fraxinus 

pennsylvanica, Ulmus americana, and Lindera benzoin. Acer negundo is an aggressive species 

that can occur in a variety of settings after disturbance so the plots dominated by Acer negundo

and little else had a wider variety of associated species and environmental settings than is the 

case for most associations. The Acer negundo-dominated plots were kept in the dataset for 

further iterations of the analyses to see if some individual plots would fit better with other 

associations. Similarly, the Sugar Maple-Oak-Bitternut Hickory Mesic Bottomland Forest 

(2060) had been retained for further iterations, because its species composition included more 

Results 28
hardwoods that caused it to group with upland types. Final interpretation of the results suggested 

keeping this with the floodplain types. 

Further iterations essentially broke off groups of plots at one or both ends of the 

perceived moisture gradient. For example, after plots in the floodplain associations were 

removed, the Blackjack Oak Scrub Woodland (2425) was identified and removed. One break in 

this pattern was when forests and woodlands with thin soils over alkaline bedrock formed one 

end of the ordination plot. These forests and woodlands were dominated by Quercus 

muehlenbergii and/or Juniperus virginiana. Plots continued to be separated on the apparent 

moisture gradient after that until what remained was one large group of plots representing the 

widespread oak, oak-pine, and pine forests and woodlands of the Ozarks area. Patterns were 

apparent in the ordination graphs with plots in certain associations clustered in certain areas but 

there was a lot of overlap in species composition between these associations. When examining a 

subset of the data with a limited ecological gradient and floristic makeup, other factors such as 

past land use or differences between sampling protocols when collecting the data can have 

noticeable effects and may have explained some of the mixing. It is possible that having a 

dataset with the groundcover data combined with the shrub and tree data would help distinguish 

between some of these communities. 

The relationship between the associations as defined by the final plot assignments is 

shown in Figure 8. This figure was produced by combining all the individual plots in each 

association and generating an average importance value for each species across all plots in the 

association. These summary importance values were used in an NMS ordination where each 

“sample” was the association. Only associations dominated by trees were included to make the 

figure clearer. Broad groupings of associations can be seen clearly ranging from the floodplain 

forests on the lower left through the mesic upland forests, dry-mesic oak forests and alkaline 

forests and woodlands, up to the dry oak, oak-pine, and pine forests and woodlands at the top of 

the figure. The abundance of Pinus echinata did not appear to strongly separate associations any 

more than abundance of other species did. That is, even though Pinus echinata is readily 

distinguishable from the predominantly deciduous trees within ONSR in the field and in 

mapping exercises, it does not appear to signal a dramatic difference in ecological conditions as 

judged by ordinations of these plot data. The associations (2075i and 2425) on the upper part of 

the far right side of the ordination graph were characterized by an abundance of Quercus 

marilandica, Quercus stellata, Carya texana, and Ulmus alata. The 2425 community is 

restricted to dry sites, while the 2075i type had a broader ecological niche. Its species 

composition may be the result of fire suppression, possibly in conjunction with past grazing. 

Results 29
Figure 8. NMS ordination of USNVC association-level mean Importance Values for tree 

and shrub species for tree-dominated communities in ONSR.

When discrepancies were encountered between field classification and classification 

using multivariate techniques, data were revisited and compared with field knowledge of the plot 

in questions. Eighteen plots were reanalyzed in this manner, of which 10 were left unchanged. 

Four of the questionable plots included cover from evergreen species (Juniperus virginiana or 

Pinus echinata) at approximately 25 percent, the cusp between a mixed type and a deciduous 

community. These species can have relatively small crowns, even with large diameter trunks. 

Thus, field investigation of such types where evergreen cover is just below 25 percent may 

classify as a mixed type in ordination analysis, particularly when importance values are used. In 

short, ordination based on importance values weights evergreen species more than field 

investigations based on foliar cover. A similar phenomenon can occur where evergreen cover 

approaches 75 percent, the cusp between a mixed type (less than 75 percent relative cover by 

Results 30
evergreen species) and a true evergreen dominated community. Other common causes of 

discrepancies between field and ordination classifications arose from classifying the wrong 

community (3 plots). Typically, this was limited to floodplain areas where communities are 

often small and linear and displacement of a few meters due to navigational errors can place field 

crews into a different vegetation association. 

Local Expressions of USNVC Associations within the Mapping Area 

There were few occasions where sampled communities failed to match well the 

descriptions from the existing USNVC. Fewer than 180 of the more than 2000 sampled points 

were ranked as fair or poorer representatives of the vegetation association to which they were 

assigned in the field. Of these, only 26 (about 1%) were rated as being poorly representative of 

the type. Typically, such instances resulted from one of two scenarios: 1) Sampling points fell 

into areas that represented inclusions or transition zones between two or more vegetation types. 

In this scenario, sample points tended to exhibit characteristics of the composition and structure 

typical for both types, which made classification difficult. 2) Sampled areas exhibited significant 

evidence of human disturbance, though it was still recognizable as a particular USNVC 

vegetation association. Often in such instances, one vegetative stratum (usually the groundflora) 

would reflect the effects of human activity much more than other layers. 

Wildland Fuels 

Mean fuel loads for each fuel classes in every USNVC vegetation association are listed in 

Table 3 and for altered vegetation communities in Table 4. Some community types, such as 

lawn, roads, and other cultural features were not sampled for fuels or were not encountered 

during the study. Therefore, these tables collectively have fewer categories than the final 49-

class map. The results of our fine fuel loading MRPP analysis indicated that there was no 

statistical difference in fuel loading among USNVC associations, and the distribution of fine fuel 

loads were similar to a random distribution among associations (A=0.032, p=0.000). MRPP 

analysis indicated that there was no difference in total fuel loading among USNVC associations. 

Heterogeneity within total fuel loading data was similar to a chance distribution (A=0.011, 

p=0.003). Our results were determined as not significant because the A values were close to 0, 

meaning heterogeneity was similar to a chance distribution and the sample size for analysis was 

large (762 samples). A large sample size can result in a significant difference even when the 

effect size is small (McCune and Grace 2002). Thus determining significance of analysis 

consisted of evaluating both the A value and p-value, and we used a p-value >0.05 as a guide for 

significance. Nevertheless, the data collected and summarized for this project provide a broad 

picture of the range of fuel-loading conditions extant in the park. A wildland fuel loading photo 

guide for USNVC associations is attached as Appendix 18, which is retained as a separate 

electronic document (Appendix 18-ONSR USNVC Community Fuel Loading Photo Key). The 

wildland fuel photo guide contains mean, minimum, and maximum fuel loading for each 

USNVC association sampled, as well as photographs of representative examples of each 

vegetation association. 

Results 31
Table 3. Mean fuel loading and standard errors (in parentheses) by USNVC 

association.

USNVC Association Mean Fuel Loading Tons/Acre

Code Name N Duff Litter 1 Hour 10 Hour 100 

Hour 

1,000 

Solid 

1,000 

Rotten 

Total 

(Excl.duff)

2049 Riverine Sand Flats 

(Herbaceous Gravel Bar) 

1 0.00 

(0.00)

0.00 

(0.00) 

0.00 

(0.00) 

0.00 

(0.00) 

0.00 

(0.00) 

0.00 

(0.00) 

0.00 

(0.00) 

0.00 

(0.00) 

2058 White Oak-Red Oak-Sugar 

Maple Mesic Forest 

24 2.94 

(0.78)

2.72 

(0.37) 

0.25 

(0.003)

1.48 

(0.22) 

3.47 

(0.67) 

2.10 

(0.76) 

1.75 

(0.76) 

11.77 

(9.80) 

2060 Sugar Maple-Oak-Bitternut 

Hickory Mesic Bottomland 

Forest 

12 0.35 

(0.32)

1.68 

(0.34) 

0.22 

(0.004)

1.54 

(0.48) 

3.86 

(1.50) 

2.08 

(1.49) 

5.17 

(3.21) 

15.45 

(3.66) 

2066 White Oak/Dogwood DryMesic Forest 57 4.65 

(0.58)

3.02 

(0.18) 

0.29 

(2.40) 

1.59 

(0.14) 

2.87 

(0.40) 

6.38 

(2.02) 

2.14 

(0.72) 

16.44 

(2.18) 

2070 White Oak-Mixed Oak 

Dry-Mesic Alkaline Forest 

61 4.11 

(0.50)

3.12 

(0.17) 

0.32 

(0.002)

1.54 

(0.14) 

2.57 

(0.34) 

3.96 

(1.00) 

1.46 

(0.42) 

12.98 

(1.28) 

2075 Midwest Post OakBlackjack Oak Forest 4 8.39 

(2.26)

4.78 

(0.47) 

0.18 

(0.004)

1.13 

(0.73) 

1.56 

(1.56) 

2.80 

(2.80) 

0.00 

(0.00) 

10.46 

(3.20) 

2075i Midwest Post OakBlackjack Oak Forest 

(Igneous Phase) 

4 6.91 

(1.81)

3.00 

(0.41) 

0.21 

(0.10) 

1.99 

(1.12) 

2.63 

(2.00) 

0.00 

(0.00) 

0.00 

(0.00) 

7.84 

(3.35) 

2076 Black Oak-White OakHickory Forest 268 8.89 

(0.33)

3.60 

(0.008)

0.26 

(0.0006)

1.93 

(0.007)

3.24 

(0.19) 

4.68 

(0.89) 

2.38 

(0.42) 

16.09 

(1.01) 

2108 Chinquapin Oak-Red Cedar 

Dry Alkaline Forest 

29 5.39 

(0.88)

2.76 

(0.33) 

0.27 

(0.003)

1.10 

(0.18) 

2.72 

(0.43) 

0.49 

(0.22) 

1.12 

(0.54) 

8.47 

(0.88) 

2143 Chinquapin Oak-Ash/Little 

Bluestem Woodland 

20 4.61 

(0.97)

2.85 

(0.47) 

0.32 

(0.004)

1.27 

(0.16) 

1.79 

(0.53) 

1.67 

(0.96) 

1.06 

(0.61) 

8.97 

(1.42) 

2149 Post Oak-Blackjack 

Oak/Little Bluestem Wdlnd 

22 4.91 

(0.80)

2.61 

(0.29) 

0.21 

(0.003)

1.43 

(0.43) 

2.64 

(0.71) 

3.13 

(2.18) 

0.18 

(0.14) 

10.20 

(2.64) 

2243 Ozark Igneous Glade 2 3.61 

(3.61)

1.05 

(1.05) 

0.01 

(0.01) 

0.00 

(0.00) 

0.00 

(0.00) 

3.94 

(3.94) 

0.00 

(0.00) 

4.99 

(4.97) 

2393 Shortleaf Pine-Oak Dry 

Woodland 

7 8.74 

(2.87)

3.09 

(0.64) 

0.27 

(0.006)

1.88 

(0.62) 

2.66 

(1.05) 

1.03 

(1.03) 

3.28 

(3.28) 

12.21 

(3.89) 

2399 Ozark Black Oak-Scarlet 

Oak Forest 

73 10.64

(0.76)

3.79 

(0.19) 

0.24 

(0.001)

1.96 

(0.16) 

3.30 

(0.35) 

5.71 

(2.33) 

1.56 

(0.35) 

16.56 

(2.49) 

2400 Shortleaf Pine/Blueberry 

Forest 

5 15.56

(1.48)

2.82 

(0.15) 

0.23 

(0.004)

2.05 

(0.44) 

1.71 

(0.81) 

0.29 

(0.29) 

4.42 

(3.13) 

11.52 

(3.74) 

2401 Shortleaf Pine-Black Oak 

Forest 

36 11.69

(1.03)

4.04 

(0.26) 

0.30 

(0.002)

1.64 

(0.18) 

3.39 

(0.76) 

7.69 

(4.54) 

2.15 

(0.64) 

19.20 

(4.63) 

2404 Ozark Fen 1 0.43 

(0.00)

0.62 

(0.00) 

0.04 

(0.00) 

0.87 

(0.00) 

2.08 

(0.00) 

15.50 

(0.00) 

0.00 

(0.00) 

19.11 

(0.00) 

2410 Ash-Oak-Sycamore Mesic 

Bottomland Forest 

14 0.27 

(0.27)

0.82 

(0.17) 

0.25 

(0.004)

1.43 

(0.28) 

2.08 

(0.69) 

5.71 

(2.17) 

4.08 

(2.54) 

14.37 

(3.21) 

2425 Blackjack Oak Xeric Scrub 

Woodland 

3 11.76

(3.97)

5.14 

(2.23) 

0.13 

(0.005)

0.90 

(0.67) 

4.29 

(1.12) 

0.00 

(0.00) 

0.00 

(0.00) 

10.46 

(1.12) 

2899 Carolina Willow Shrubland 1 0.00 

(0.00)

0.00 

(0.00) 

0.00 

(0.00) 

0.00 

(0.00) 

0.00 

(0.00) 

0.00 

(0.00) 

0.00 

(0.00) 

0.00 

(0.00) 

5033 Box Elder Forest 15 0.03 

(0.03)

0.61 

(0.22) 

0.12 

(0.003)

0.87 

(0.16) 

1.94 

(0.59) 

1.83 

(0.79) 

0.57 

(0.33) 

4.07 

(1.31) 

7334 Sycamore-Silver Maple 

Floodplain Forest 

19 0.38 

(0.19)

1.32 

(0.34) 

0.22 

(0.003)

1.05 

(0.22) 

1.53 

(0.41) 

9.50 

(2.84) 

0.76 

(0.38) 

14.38 

(3.48) 

7489 Interior Highlands 

Shortleaf Pine-Oak DryMesic Forest 

51 10.40

(0.84)

3.66 

(0.20) 

0.27 

(0.002)

1.85 

(0.17) 

2.92 

(0.35) 

2.38 

(0.72) 

1.50 

(0.56) 

12.57 

(1.04) 

Results 32
Table 4. Mean fuel loading and standard errors (in parentheses) for sampled altered 

vegetation associations.

USNVC Association Mean Fuel Loading Tons/Acre

Code Name N Duff Litter 1 Hour 10 

Hour 

100 

Hour 

1,000 

Solid 

1,000 

Rotten 

Total 

 (Excl. 

duff) 

SA01 Oak-Hickory 

Shelterwood/Select 

Harvest 

8 6.69 

(2.07) 

3.24 

(0.31) 

0.26 

(0.005) 

1.00 

(0.48) 

4.24 

(1.65) 

8.15 

(3.91) 

0.98 

(0.62) 

17.88 

(3.77) 

SA02 Regeneration Stand 7 10.50 

(2.80) 

4.23 

(0.43) 

0.34 

(0.004) 

2.67 

(0.67) 

2.73 

(0.76) 

6.75 

(6.75) 

4.57 

(3.62) 

21.30 

(9.96) 

SA03 Oak-Hickory Pole 

Stand 

2 9.14 

(0.21) 

2.83 

(0.27) 

0.46 

(0.001) 

1.59 

(0.72) 

0.00 

(0.00) 

5.18 

(5.18) 

0.00 

(0.00) 

10.06 

(6.17) 

SA04 Pine-Oak 

Shelterwood/Select 

Harvest 

1 0.85 

(0.00) 

1.08 

(0.00) 

0.31 

(0.00) 

1.36 

(0.00) 

2.17 

(0.00) 

0.00 

(0.00) 

1.87 

(0.00) 

6.80 

(0.00) 

SA05 Pine-Oak 

Regeneration Stand 

1 5.53 

(0.00) 

4.42 

(0.00) 

0.45 

(0.00) 

8.71 

(0.00) 

6.24 

(0.00) 

0.00 

(0.00) 

0.00 

(0.00) 

19.82 

(0.00) 

SA07 Pine 

Plantation/Timber 

Management Forest 

2 3.61 

(0.64) 

2.40 

(0.62) 

0.26 

(0.06) 

0.22 

(0.22) 

1.05 

(1.05) 

0.00 

(0.00) 

0.00 

(0.00) 

3.93 

(1.51) 

SA08 Pine Pole Stand 1 7.65 

(0.00) 

2.71 

(0.00) 

0.57 

(0.00) 

3.48 

(0.00) 

0.00 

(0.00) 

0.00 

(0.00) 

0.00 

(0.00) 

6.76 

(0.00) 

SA09 Deciduous Shrubby 

Old Field 

3 3.26 

(3.26) 

1.83 

(1.03) 

0.08 

(0.08) 

0.14 

(0.14) 

2.07 

(2.07) 

0.00 

(0.00) 

0.00 

(0.00) 

4.14 

(2.57) 

SA10 Deciduous Forested 

Old Field 

20 3.42 

(1.26) 

2.39 

(0.28) 

0.25 

(0.003) 

1.53 

(0.24) 

3.54 

(0.88) 

1.93 

(0.90) 

1.93 

(0.94) 

11.58 

(1.73) 

SA11 Pine-Deciduous 

Wooded Old Field 

1 0.85 

(0.00) 

2.02 

(0.00) 

0.23 

(0.00) 

1.30 

(0.00) 

4.15 

(0.00) 

3.99 

(0.00) 

0.00 

(0.00) 

11.69 

(0.00) 

SA15 Cedar Old Field 2 2.76 

(2.33) 

1.24 

(0.46) 

0.08 

(0.06) 

0.69 

(0.69) 

0.00 

(0.00) 

0.00 

(0.00) 

0.00 

(0.00) 

2.01 

(0.29) 

To integrate fuel loading data with the vegetation map, we developed spreadsheet 

versions of fuels data tables for both the USNVC associations map and the Community Type 

map. The fuel data tables identify the Anderson (1982) fuel model, the National Fire Danger 

Rating System (NFDRS) model, and a new expanded set of fuel models known as the 

Comprehensive Fuels model (Scott and Burgan, 2005). Fuel models were selected that best 

described the fuel and fire behavior conditions associated with each association. Associating 

fuel models with vegetation associations and Community Types is the first step toward 

integrating the vegetation map with a spatially explicit fire behavior simulation model, such as 

Fire Area Simulator (FARSITE; Finney, 1998; Keane and others, 1999). 

Community Mapping 

Vegetation communities in Ozark National Scenic Riverways (ONSR) were mapped at 

two levels using a hybrid combination of statistical methods and photointerpretation. The 

primary mapping level is to the highly detailed association level of the USNVC. This map 

includes 49 cover classes. Twenty-four of these classes relate to USNVC vegetation 

associations. The remaining types include cultural features, non-vegetated features, and ruderal 

communities on abandoned agricultural lands. Overall map classification accuracy is 63 percent. 

The secondary mapping level combines communities with similar appearance and ecologically 

Results 33
related communities into Community Types, which are subunits of even more broadly defined 

Ecological Systems. Ecological Systems and Community Types identified for this project aided 

mapping to a much greater extent than alliance-level mapping and are more relevant to resource 

management within the park. This map includes 33 classes and overall classification accuracy is 

77 percent. The scale of both maps is 1:24000, and the grain of the raster-formatted vegetation 

map is 10 by 10 meter grid cells. Spatial accuracy is less than or equal to fifteen meters. 

Community Classification 

The USNVC association-level map for the ONSR mapping region produced through a 

combination of statistical classifications of vegetation communities and photointerpretation and 

digitization of land cover features contains 49 categories (Table 5; Figure 9 [map detail], 

Appendix 12 [full map]). As such, it has been produced to meet USGS-NPS vegetation mapping 

goals, at a much higher level of detail than the more typical six (Joy and others, 2003) or eleven 

categories (de Colstoun and others, 2003) of land cover maps produced using remote sensing 

inputs. The thematic classes of the map may be aggregated into more general scheme as needed 

to increase its overall or class-wise accuracy. For this project, we have aggregated thematic 

classes into 33 Community Types as part the ecological systems (Appendix 2) approach adopted 

to address management concerns and to increase map accuracy (Figure 10 [detail], Appendix 13

[full map]). 

Total area and percent coverage of USNVC communities and significantly altered land 

cover types are given in (Table 5). The two most common land cover types within both ONSR 

and the greater mapping area belong to the Black Oak - White Oak - (Scarlet Oak) Forest 

Alliance. The Black Oak-White Oak-Hickory/Dogwood Forest (2076) covers 19,843 ha (34 

percent) of the ONSR purchase unit and 43,388 ha (31 percent) of the mapping area. The Ozark 

Black Oak-Scarlet Oak Forest (2399) accounts for 9,492 ha (16 percent) of the park and 27,555 

ha (20 percent) of the mapping area. Together, these two vegetation associations provide more 

than 50 percent of cover at either scale. They have been mapped together in the 33-class 

Community Type map as the Mixed Oak-Hickory Forest. The third and fourth most abundant 

classes within ONSR are the White Oak/Dogwood Forest (2066; 5094 ha; 9 percent) and the 

White Oak - Mixed Oak Dry-Mesic Alkaline Forest (2070; 4869 ha; 8 percent). These 

communities have been aggregated in the 33-class map as the White Oak Forest Community 

Type, and collectively cover more than 16 percent of the park. The two next most abundant 

classes within the park include actively managed and abandoned agricultural fields in various 

successional stages. Together, these account for 9 percent of the park area. Agricultural fields 

and pastures (SA21, SA22, SA23) are the third most abundant class within the full mapping area. 

Map Accuracy 

The overall accuracy of the USNVC association level map (Figure 9 [detail], Appendix 

12 [full map], Table A11-1 in Appendix 11) for the ONSR mapping region is 62 percent, with a 

kappa statistic of 0.596. This estimate of overall accuracy is based on 2057 validation 

observations obtained from points withheld from the statistical classification, field observations 

obtained in the summer of 2005, and from photointerpretation observations located within 

human-dominated land-cover categories. The error matrix (Table 6) indicates areas where 

confusion occurred between mapping categories, and was used to calculate the user’s and 

producer’s accuracies for the various categories (Table 7). These tables include only 35 

categories, as some cultural map classes were aggregated for accuracy assessment purposes.

Results 34
Table 5. Area and percent coverage of vegetation associations and land cover types in 

the ONSR purchase unit (57,678 ha) and the larger mapping area (139,953 ha).

Park 

Area (ha)

Park % Full Map 

Area (ha)

Full 

Map %

USNVC Association/Landcover type (Code*) 49-Class 

Grid Code

19843.2 34.40 43388.34 31.00 Black Oak - White Oak - Hickory Forest (2076†) 5 

9491.6 16.46 27555.48 19.70 Ozark Black Oak, Scarlet Oak Forest (2399†) 10 

5094.2 8.83 9637.54 6.89 White Oak/Dogwood Forest (2066†) 2 

4868.8 8.44 9912.55 7.08 White Oak Dolomite Forest (2070†) 3 

2711.1 4.70 10175.82 7.27 Agricultural field/pasture (SA21, SA22, SA23) 37 

2509.2 4.35 3751.22 2.68 Wooded old field (SA10, SA11, SA13, SA14, SA15) 33 

2012.9 3.49 5119.91 3.66 Interior Highlands Shortleaf Pine-Oak Dry-mesic Forest 

(7489†) 

13 

1544.6 2.68 3177.67 2.27 Chinkapin Oak-Red Cedar Dry Alkaline Forest (2108†) 6 

956.5 1.66 4461.55 3.19 Shortleaf Pine-Black Oak Forest (2401†) 12 

878.7 1.52 986.11 0.70 River (non-vegetated, includes springs and tributaries) 28 

753.9 1.31 853.85 0.51 Ash-Oak-Sycamore Mesic Bottomland Forest (2410†) 22 

700.3 1.21 1984.19 1.42 White Oak-Red Oak-Sugar Maple Mesic Forest (2058†) 1 

674.4 1.17 1591.07 1.14 Chinkapin Oak-Ash/Little Bluestem Woodland (2143†) 7 

666.6 1.16 779.74 0.56 Box Elder Forest (5033†) 25 

659.2 1.14 743.33 0.53 Sycamore-Silver Maple Floodplain Forest (7334†) 26 

655.5 1.14 1030.88 0.74 Road (SA32) 34 

572.4 0.99 668.16 0.48 Sugar Maple-Oak-Hickory Mesic Bottomland Forest (2060†) 21 

570.3 0.99 995.86 0.71 Open old field (SA09, SA12, SA20, SA23, SA36) 43 

304.5 0.53 577.18 0.41 Igneous glade/woodland complex (includes 2075i†) 32, 45 

258.3 0.45 481.24 0.34 Utility corridor (SA33, SA34) 35 

252.3 0.44 350.3 0.25 Riverine Sand Flats (2049†, includes vegetated stream 

margins), Bare gravel bars† 

20, 27 

225.0 0.39 1797.74 1.28 Post-Black-(Blackjack) Oak/Little Bluestem Woodland 

(2149†) 

8, 29 

178.6 0.31 840.71 0.60 Regeneration Stand (SA02, SA05) 38 

173.2 0.30 951.63 0.68 Deciduous Shrubby Old Field (SA09†) 17 

140.6 0.24 271.26 0.19 Dolomite glade/woodland complex 44 

120.9 0.21 361.37 0.26 Midwest Post Oak-Blackjack Oak Forest (2075†) 4 

92.1 0.16 547.9 0.39 Cedar-Deciduous Wooded Old Field (SA13†) 18 

91.6 0.16 677.17 0.48 Shortleaf Pine-Oak Dry Woodland (2393†) 9 

88.5 0.15 737.35 0.53 Residential (SA16, SA17, SA18, SA19, SA35) 48 

88.5 0.15 105.4 0.08 Carolina Willow Shrubland (3899†) 24 

88.4 0.15 1466.15 1.05 Shelterwood cut (SA01, SA04) 39 

77.1 0.13 323.4 0.23 Cedar Old Field (SA15†) 19 

69.9 0.12 345.36 0.25 Deciduous Pole Stand (SA03, SA06, SA08) 40 

61.9 0.11 1180.01 0.84 Pine-Oak Regeneration Stand (SA05†) 14 

49.7 0.09 338.67 0.24 Other Clearing (SA31) 36 

47.5 0.08 583 0.42 Shortleaf Pine/Blueberry Forest (2400†) 11 

35.8 0.06 69.21 0.05 Igneous Glade (2243†) 30 

30.3 0.05 45.4 0.03 Witchhazel-Dogwood Gravel Wash (3898†) 23 

20.7 0.04 159.01 0.11 Pine Pole Stand (SA08†) 16 

9.3 0.02 61.27 0.04 Pine plantation/Timber management area (SA07†, SA07) 15, 41 

5.6 0.01 63.86 0.05 Surface water (non-vegetated pond and lakes) 42 

3.7 0.01 5.71 0.00 Blackjack Oak Scrub Woodland (2425†) 31 

0.6 0.00 233.06 0.17 Agricultural forested woodlot (SA37) 46 

0.0 N/A 63.34 0.05 Industrial/quarry (SA26, SA27) 49 

0.0 N/A 502.74 0.36 Urban (SA28, SA29, SA30) 47 

*Codes that begin with “SA” represent significantly altered vegetation communities not described by the USNVC. 

†Statistically classified types (all others were hand-delineated from aerial photographs). 

Results 35
Figure 9. Detail of 49-class USNVC vegetation association map*. 

* Full 49-class USNVC vegetation association map is available in Appendix 12

Results 36
Figure 10. Detail of 33-class Community Type map with types aggregated to improve 

accuracy and aid in resource management planning*. 

*Full 33-class Community Type map is available in Appendix 13. 

Results 37
Table 6. Error matrix of USNVC associations and other land cover categories.

Reference Data (ground truth) 

Map Classification 

(Bold boxes = Community Types; 

Agricultural Forested Woodlots and 

Surface Water not assessed.)

49-Class 

Grid codes 

2058 2066 2070 2076 2399 

Dolomite Glade 

Complex 

Igneous Glade 

Complex 

2108 2143 2149 2243 2425 2075 2393 2400 2401 7489 

White Oak-Red Oak-Sugar Maple Mesic Forest 

(2058) 

1 18 14 8 3 1 1 

White Oak/Dogwood Forest (2066) 2 3 57 37 27 2 5 

White Oak Dolomite Forest (2070) 3 4 17 32 28 2 4 4 2 2 1 

Black Oak-White Oak-Hickory Forest (2076) 5 15 3 242 18 1 5 3 2 

Ozark Black Oak, Scarlet Oak Forest (2399) 10 2 47 47 1 5 3 16 5 

Dolomite Glade Complex 44 10 5 1 5 2 

Chinquapin Oak-Red Cedar Dry Alkaline Forest 

(2108) 

6 5 1 69 9 1 8 6 5 

Chinquapin Oak-Ash/Little Bluestem Woodland 

(2143) 

7 1 4 2 18 1 1 1 

Post Oak-Blackjack Oak/Little Bluestem 

Woodland (2149) 

8,29 1 1 4 1 6 1 

Ozark Igneous Glade Complex (includes 2075i) 32,45 4 42 1 4 

Ozark Igneous Glade (2243) 30 1 3 3 3 3 1 1 

Blackjack Oak Xeric Scrub (2425) 31 2 1 2 1 

Midwest Post Oak-Blackjack Oak Forest (2075) 4 2 7 1 

Shortleaf Pine-Oak Dry Woodland (2393) 9 3 1 1 1 1 7 1 

Pine/Blueberry Forest (2400) 11 1 1 1 1 

Shortleaf Pine-Black Oak (2401) 12 5 1 2 3 1 42 20 

13 1 8 Interior Highlands Shortleaf Pine-Oak Dry-mesic 

Forest (7489) 1 5 1 4 35 

Bare Gravel Bar 27 

Riverine Sand Flats (2049) 20 

Witchhazel, Dogwood Gravel Wash (3898) 23 

Carolina Willow Shrubland (3899) 24 

Ash-Oak-Sycamore Mesic Bottomland Forest 

(2410) 

22 1 2 

Box Elder Forest (5033) 25 

Sycamore-Silver Maple Floodplain Forest (7334) 26 1 1 

Sugar Maple-Oak-Bitternut Hickory Mesic 

Bottomland Forest (2060) 

21 2 4 1 

Wooded Old Field 18,19,

33 2 1 2 1 

Open Old Field 17,43 2 1 

Shelterwood Harvest 39 1 

Regeneration/Pole Stand 14,16,

38,40 1 

Pine Plantation 15,41 1 

River 28 

Roads 34 

Utility Corridor 35 

Agricultural Field/Pasture/Other Clearing 36,37 

Residential/Urban/Industrial 47,48,

49 

Totals 28 110 95 372 73 12 89 37 22 45 4 9 32 28 2 76 76 

Results 38
Table 6. Error matrix of USNVC associations and other land cover categories (cont’d).

Reference Data (ground truth) 

Map Classification 

(Bold boxes = Community Types; 

Agricultural Forested Woodlots and 

Surface Water not assessed.)

49-Class Grid 

Codes 

Gravel Bar 

2049 3898 3899 2410 5033 7334 2060 

Wooded Old 

Field 

Open Old Field 

Shelterwood 

Regen. Stand 

Pine Plant. 

River 

Road 

Utility Corr. 

Field/Pasture 

Urban 

Total 

White Oak-Red Oak-Sugar Maple Mesic Forest 

(2058) 

1 6 2 7 2 1 1 64 

White Oak/Dogwood Forest (2066) 2 4 1 1 137

White Oak Dolomite Forest (2070) 3 1 8 1 106

Black Oak-White Oak-Hickory Forest (2076) 5 1 290

Ozark Black Oak, Scarlet Oak Forest (2399) 10 2 1 129

Dolomite Glade Complex 44 23 

Chinquapin Oak-Red Cedar Dry Alkaline Forest 

(2108) 

6 1 3 2 110

Chinquapin Oak-Ash/Little Bluestem Woodland 

(2143) 

7 2 1 2 1 34 

Post Oak-Blackjack Oak/Little Bluestem 

Woodland (2149) 

8,29 1 7 22 

Ozark Igneous Glade Complex (includes 2075i) 32,45 51 

Ozark Igneous Glade (2243) 30 15 

Blackjack Oak Xeric Scrub (2425) 31 6 

Midwest Post Oak-Blackjack Oak Forest (2075) 4 10 

Shortleaf Pine-Oak Dry Woodland (2393) 9 15 

Pine/Blueberry Forest (2400) 11 1 1 6 

Shortleaf Pine-Black Oak (2401) 12 1 75 

Interior Highlands Shortleaf Pine-Oak Dry-mesic 

Forest (7489) 

13 7 1 1 2 1 67 

Bare Gravel Bar 27 53 53 

Riverine Sand Flats (2049) 20 8 2 2 12 

Witchhazel, Dogwood Gravel Wash (3898) 23 2 5 4 11 

Carolina Willow Shrubland (3899) 24 2 28 2 1 33 

Ash-Oak-Sycamore Mesic Bottomland Forest 

(2410) 

22 1 2 23 10 25 6 16 2 2 2 92 

Box Elder Forest (5033) 25 4 25 8 4 2 43 

Sycamore-Silver Maple Floodplain Forest (7334) 26 3 3 4 55 3 1 71 

Sugar Maple-Oak-Bitternut Hickory Mesic 

Bottomland Forest (2060) 

21 1 2 8 17 6 41 

Wooded Old Field 18,19,

33 1 1 144 6 3 2 163

Open Old Field 17,43 1 19 12 1 3 39 

Shelterwood Harvest 39 3 4 

Regeneration/Pole Stand 14,16,

38,40 1 18 2 22 

Pine Plantation 15,41 1 2 

River 28 1 51 52 

Roads 34 72 2 74 

Utility Corridor 35 38 38 

Agricultural Field/Pasture/Other Clearing 36,37 2 68 4 74 

Residential/Urban/Industrial 47,48,

49 73 73 

Totals 53 10 10 41 35 42 105 35 221 24 18 25 1 52 80 43 68 84 2057

Results 39
The producer’s accuracy (total correctly classified observations divided by the total 

observations of one ground reference type classified into any category) indicates the probability 

that a reference location is correctly classified, and is a measure of omission error or how well a 

land cover type can be classified given the available training data (Jensen, 1996). User’s 

accuracy (total correctly classified observations divided by the total ground reference 

observations classified into a category that are correctly or incorrectly classified) is the 

probability that a location on a classification map actually represents that category on the ground, 

and is referred to as commission error (Lillesand and Kiefer, 1994). The USGS-NPS Vegetation 

Mapping program has a goal of 80 percent for both within class and overall accuracy. Where 

this goal can not be attained, the program allows for a number of approaches to increase 

accuracy levels, including aggregation of classes into broader categories, the approach used with 

the identification of local Ecological Systems and Community Types. 

Additional products of the statistical mapping approach used in this study are per-class 

receiver operating characteristic (ROC) curves and Kappa statistics. ROC curves have been used 

to assess the results of remote sensing classifications on a class-wise basis (Pontius and 

Schneider, 2001; Gardner and Urban, 2003). Kappa statistic is an indicator of the extent to 

which the percentage of correct values in an error matrix is due to true agreement with actual 

ground conditions as opposed to chance agreement (Lillesand and Kiefer, 1994). In other words, 

the kappa statistic is a measure of how well an overall classification represents a result that is 

better than a random result, and can be used to compare two or more similar classification error 

matrices (Jensen, 1996). 

Using the ecological systems approach developed early in the project, we also produced a 

33-class community-type map. Riverine shrublands and gravel bars were aggregated upward 

into an “Active Channel/Gravel Bar Complex” and bottomland forests were combined into a 

“Riverfront and Bottomland Forests” type. Dry-mesic, white oak dominated forests were 

aggregated into a “White Oak Forests” type, oak and hickory forests with greater than 25 percent 

pine cover were aggregated into a “Pine and Pine-Oak Forests” type, and oak and hickory 

dominated forests lacking significant pine were combined into a “Mixed Oak-Hickory Forests” 

type. Glades and associated woodlands on both igneous and dolomite substrates were combined 

into “Igneous Glade/Woodland Complex” and “Dolomite Glade/Woodland Complex” types, 

respectively. A crosswalk of USNVC types, OSNR Ecological Systems and NatureServe 

Ecological Systems is contained in Appendix 5. 

The above aggregations improved overall accuracy to 77.5 percent (kappa = 0.716) and 

provided a simpler, easier to understand map of communities within the mapping area (Figure 10

[detail], Appendix 13 [full map]). For example, when examined individually, the user’s 

accuracies for the Ozark Black Oak-Scarlet Oak Forest (2399) and the Black Oak-White OakHickory Forest were 36 and 83 percent. When combined into the Mixed Oak-Hickory Forest 

Community Type, user’s accuracy improved to 84 percent. Grouping the White Oak/Dogwood 

Forest (2066, user’s accuracy = 42 percent) and the White Oak-Mixed Oak Dry-Mesic Alkaline 

Forest (2070; user’s accuracy = 30 percent) into the White Oak Forest Community Type 

improved user’s accuracy to 59 percent (Table 8). Aggregation of the Shortleaf Pine/Blueberry 

Forest (2400), the Interior Highlands Shortleaf Pine-Oak Dry-mesic Forest (7489) and the 

Shortleaf Pine-Black Oak Forest (2401) into the Pine and Pine-Oak Forest Community Type 

improved user’s accuracy from a mean value of 42 percent to a collective value of 70 percent. 

Aggregation of communities into the Dolomite and Igneous Glade/Woodland Complexes 

improved user’s accuracy from mean values of 49 and 51 percent to 66 and 82 percent, 

Results 40
respectively. The “Active Channel/Gravel Bar Complex” had an aggregated user’s accuracy of 

95 percent, compared with a mean accuracy value of 65 percent for the three classes include in 

that Community Type. Inclusion of the separately-mapped, non-vegetated Bare Gravel Bars 

class increases accuracy to 97 percent. Finally, aggregation of riverfront and floodplain forests 

into the Riverfront and Bottomland Forests Community Type improved accuracy from a mean 

value of 53 percent for individual communities to 76 percent overall. 

Probability Mapping 

A method developed in the signal detection literature termed a receiver operating 

characteristic (ROC) curve has been increasingly used to assess the results of remote sensing 

classifications and other models which predict a homogeneous category in each grid cell (Pontius 

and Schneider, 2001; Gardner and Urban, 2003). ROC curves are generated by plotting the 

probability of detection against the probability of false positives (the probability of false alarm). 

Classification model validation data can be used to plot ROC curves for all of the categories of a 

land cover map, and the area under these curves (AUC) function as a comparative per-class 

measure of their discrimination strength or accuracy. AUC values were calculated from a family 

of ROC curves to obtain per-class measures of classification accuracy for all of the classified 

associations in the vegetation map (Figures A14-1 through A14-4 in Appendix 14). 

Communities 2075 and 2149 had high probabilities of detection only at high false positive 

values. Both of these vegetation associations are rare, with sample sizes of 5 and 4 respectively. 

Probability of detection may also be plotted against thresholds of the discriminant analysis 

probabilities mapped on a per-pixel basis to identify target class probability values (Figure A14-

5 in Appendix 14). For example, if you wished to identify only the pixels on the probability map 

with a probability of detection of 80 percent or greater, you can cross reference 0.8 on the y-axis 

with the threshold value of the mapped probability on the x-axis for every mapped class, and 

filter the individual association probability maps using the probability values obtained from the 

ROC curve plot for each association. 

Species Importance Values 

Although the identification of the spatial arrangement and prevalence of USNVC 

vegetation associations on the landscape are the most useful features of this vegetation map for 

management purposes, species importance values were also calculated for the primary USNVC 

vegetation associations found in the ONSR mapping area (Appendix 8). These importance 

values scores were calculated using observations obtained during validation data collected in the 

summer of 2005. The average cover of the individual species recorded was multiplied with the 

relative frequency of occurrence of these species at the validation locations where the vegetation 

map was ‘correct.’ Information on what species are present and their relative importance in each 

USNVC association type should serve as a useful guide to the vegetation composition found 

within the USNVC association map polygons. It can also serve as a baseline against which to 

compared future conditions and assess the impact of management activities. 

Results 41
Table 7. Accuracy report for USNVC vegetation associations and other land cover 

categories. 

USNVCAssociation/Land Cover Type 

(Forested Woodlots & Surface Water not assessed)

49-Class 

Grid Code

Producer’s 

Accuracy 

Producer’s 

Acc. N 

User’s 

Accuracy 

User’s 

Acc. N

White Oak-Red Oak-Sugar Maple Mesic Forest (2058) 1 0.64 28 0.28 64 

White Oak/Dogwood Forest (2066) 2 0.52 110 0.42 137 

White Oak Dolomite Forest (2070) 3 0.34 95 0.30 106 

Black Oak-White Oak-Hickory Forest (2076) 5 0.65 372 0.83 290 

Ozark Black Oak, Scarlet Oak Forest (2399) 10 0.64 73 0.36 129 

Dolomite Glade/Woodland Complex 44 0.83 12 0.43 23 

Chinquapin Oak-Red Cedar Dry Alkaline Forest (2108) 6 0.78 89 0.63 110 

Chinquapin Oak-Ash/Little Bluestem Woodland (2143) 7 0.49 37 0.53 34 

Post Oak-Blackjack Oak/Little Bluestem Woodland 

(2149) 

8,29 0.18 22 0.18 22 

Igneous Glade/Woodland Complex 32,45 0.93 45 0.82 51 

Ozark Igneous Glade (2243) 30 0.75 4 0.20 15 

Blackjack Oak Xeric Scrub (2425) 31 0.22 9 0.33 6 

Midwest Post Oak-Blackjack Oak Forest (2075) 4 0.22 32 0.70 10 

Shortleaf Pine-Oak Dry Woodland (2393) 9 0.25 28 0.47 15 

Pine/Blueberry Forest (2400) 11 0.50 2 0.17 6 

Shortleaf Pine-Black Oak (2401) 12 0.55 76 0.56 75 

Interior Highlands Shortleaf Pine-Oak Dry-mesic Forest 

(7489) 

13 0.46 76 0.52 67 

Bare Gravel Bar 27 1.00 53 1.00 53 

Riverine Sand Flats (2049; herbaceous gravel bars 

vegetated stream margins) 

20 0.80 10 0.67 12 

Witchhazel, Dogwood Gravel Wash (3898) 23 0.50 10 0.45 11 

Carolina Willow Shrubland (3899) 24 0.68 41 0.84 33 

Ash-Oak-Sycamore Mesic Bottomland Forest (2410) 22 0.66 35 0.25 92 

Box Elder Forest (5033) 25 0.60 42 0.58 43 

Sycamore-Silver Maple Floodplain Forest (7334) 26 0.52 105 0.77 71 

Sugar Maple-Oak-Bitternut Hickory Mesic Bottomland 

Forest (2060) 

21 0.49 35 0.41 41 

Wooded old field 18,19,33 0.65 221 0.88 163 

Open Old Field with Shrubby or Sparse Tree 17,43 0.50 24 0.31 39 

Shelterwood cut 39 0.17 18 0.75 4 

Regeneration/Pole stand 14,16,38,40 0.72 22 0.82 22 

Pine Plantation 15,41 0.00 1 0.00 2 

River (non-vegetated) 28 0.98 52 0.98 52 

Road 34 0.90 80 0.97 74 

Utility corridor 35 0.88 43 1.00 38 

Agricultural Field/Pasture/Other Clearing 36,37 1.00 68 0.92 74 

Residential/Urban/Industrial 47,48,49 0.87 84 1.00 73 

Table 8. Accuracy report for aggregated OSNR Community Types.

Community Type Producer’s 

Accuracy % 

Producer’s 

Validation N 

User’s 

Accuracy % 

User’s 

Validation N 

Dolomite Glade/Woodland Complex 78 160 66 189 

Igneous Glade/Woodland Complex 74 90 82 82 

Active Channel/Gravel Bar Complex 87 61 95 56 

Bottomland Forest 86 182 76 206 

White Oak Forest 70 205 59 243 

Mixed Oak-Hickory Forest 80 445 84 419 

Upland Pine and Pine-Oak Forest 68 154 70 148 

Results 42
Discussion 

An Altered Landscape 

The impacts of human activity are visible throughout the mapping area, even among 

those communities that represent “natural” types. Collectively, human-altered classes account 

for approximately 10 percent (7,000 ha; 17,000 ac) of the park purchase unit area. The most 

abundant altered types are actively managed fields and pastures (2,711 ha; 6,699 ac), and 

abandoned agricultural fields in various successional stages (2,509 ha; 6,200 ac). Roads occupy 

at least 600 ha (1,500 ac) within the park. 

During the early part of the 20th century, the forests were extensively harvested, with only 

the most difficult to reach communities left standing. Field evidence of this activity remains 

today in remnant stumps that are scattered across the landscape. For example, copious remnant 

pine stumps often are found in what is now a deciduous forest, suggesting that harvest practices, 

perhaps in combination with alterations of the fire regime, have altered the composition of many 

of the forests and woodlands to types that are dominated less by pine and more by oaks. Less 

obvious may be shifts in species composition due to selective harvesting. Such impacts are often 

only expressed as a subtle shift in species composition (often only in one vegetative strata) away 

from what one might expect on a given landform. 

At the same time, the effects of settlement activities are visible. In addition to those types 

that are obviously human-altered, such as old fields or non-vegetated areas, we frequently 

encountered types that exhibit only scant evidence of human alteration. These types may 

reasonably be classified as “natural,” though their expression tends to deviate from idealized 

concepts of the communities as understood from the USNVC descriptions. The deviation is such 

that these communities are distinguishable from truly “natural” types that simply represent the 

extremes of the natural range of expression for the USNVC type. Unlike timber management 

areas, which are distributed throughout the landscape, these communities tend to be limited to 

areas where topography favored subsistence farming, such as broad summits and floodplains 

along the main rivers, tributary creeks and intermittent upland waterways. 

Human disturbance did seem to have significant influence on the structure and 

composition of two vegetation associations that we encountered repeatedly in this study. Within 

floodplains, the Box Elder Forest (5033) was the second most abundant type, covering more than 

700 ha of the park and representing more that one quarter of the area mapped as bottomland 

forest. Nearly all examples exhibited vegetative and non-vegetative evidence of human 

disturbance (such as uniform stand age, an abundance of disturbance-favored species in one or 

more strata, fence lines, remnant roads and/or trash heaps). Therefore, as encountered in the 

study area, this vegetation association may not well represent that type described by the existing 

USNVC, even though its species composition and structure match the current USNVC 

description fairly well. 

Human activity is also the most likely origin of the type that appears to be derived from 

the Midwest Post Oak - Blackjack Oak Forest (2075). This type had provisionally been assigned 

the name Midwest Post Oak - Blackjack Oak Forest-Igneous Phase (2075i) in recognition of the 

fact that it was limited to igneous substrates. However, the type consistently included species 

that respond favorably to disturbance in the shrub and groundflora layers. This may have been 

the result of grazing. Furthermore, the canopy was typically comprised of scattered large oaks 

consistent with the community name, accompanied by a high abundance of black hickory of 

Discussion 43
uniform height and trunk diameter. This pattern suggests that black hickory has invaded areas 

that might otherwise better match the description for the Midwest Post Oak - Blackjack Oak 

Forest (2075). We suspect that grazing combined with fire suppression has allowed black 

hickory to become abundant in these forests. The two communities are retained as separate 

entities in this study. However, the altered type will probably be treated as a phase of the type 

that is currently described in the USNVC. 

Undersampled Vegetation Associations 

A few of the communities that were encountered in mapping area are exceptionally rare. 

This rarity makes these vegetation associations of particular interest to resource managers, but it 

also makes them difficult to map over a large area. The likelihood of encountering these types 

while collecting training data is low, a fact that hinders the development of dependable 

photosignatures for either automated, statistical classification or for manual photointerpretation. 

Two such types, the Water Lily Aquatic Wetland (2386) and the Ozark Fen (2404) were 

encountered only once during the early phases of sampling. The two examples of a Vegetated 

Spring Branch (no USNVC code) were not encountered until late in accuracy assessment 

sampling. These three types share the additional attribute that they tend to be small, and are 

often obscured by surrounding communities. Two other types, the Dry Dolomite Cliff (2291) 

and the Moist Dolomite Cliff (2292) are not particularly rare, but they occupy little horizontal 

space. This fact both reduces the likelihood that they would be encountered using the stratified 

approach initially employed in this study and means that there are little or no photographic data 

from which to develop a photosignature. Final mapping of these features was done in the field 

for a portion of the park (data file = river_cliffs), a step that may be required to accurately 

inventory all such rare and small vegetation associations. 

Field-based Sources of Map Discrepancies 

We sometimes encountered conflicts in how a community should be classified inherent to 

the community itself. For example, occasionally the canopy composition and structure of a 

forest would suggest classification as one association type, while the understory and/or 

groundflora would suggest a different type. In such cases, we favored the classification as 

indicated by what we termed the “critical” stratum, the uppermost stratum with at least 15 to 25 

percent cover, as this stratum is visible to remote sensors. However, if the evidence in inferior 

strata was strong enough to support it, we would provide a secondary classification and provide a 

justification for why the sample point was classified as one type and not the other. 

Secondly, we had to address a problem that faces all studies of this sort that must draw 

conceptual lines where they may not exist in nature. Frequently, we encountered communities 

that matched well the idealized type of two or more vegetation associations. For example, many 

sampled points could have reasonably been classified as either the Ozark Black Oak-Scarlet Oak 

Forest (2399) or the Black Oak-White Oak-Hickory Forest (2076). We approached this problem 

by ranking how well each sampled point represented an idealized version of the vegetation 

association and by providing secondary classifications where there was uncertainty about the 

proper classification to apply. (Usually, uncertainty was directly reflected in the amount of time 

a crew would spend trying to classify a given sample point.) During the data mining portion of 

the map classification, the ranking data provided the remote sensor with information that could 

be used to aid in the selection of training data. 

Discussion 44
Few classification errors were attributable to interpretation of the field key or to a poor 

understanding of the set of communities that could potentially be encountered in the study area. 

Quality control measures implemented during field sampling quickly indicated classification 

problems and ambiguities in the field key. For example, a random subset of communities was 

independently resampled to check for classification errors. Field crew classifications were 

compared to classifications applied by the USGS personnel in charge of field sampling (whose 

classifications were assumed to be correct). There was concurrence between independent 

samples in approximately 90 percent of communities sampled in this manner. Agreement 

between independent samples of points increased as sampling progressed and as updates were 

incorporated into the field key. 

Updates to the field key and classification included alterations to the key structure, 

clarification of ambiguous phrasing within the key, and the addition or elimination of vegetation 

associations as deemed necessary. We adopted a conservative approach when eliminating 

vegetation associations, doing so only after consultation with resource management staff 

regarding the implications of removal on both management of resources and map accuracy. For 

example, a White Oak-Red Oak/Dogwood Forest (2067) and the River Birch-Sycamore Forest 

(2086) initially included in the classification scheme were eliminated due to similarities in 

structure and species composition to other, more abundant types and because the management 

implications of eliminating them was minimal. Conversely, we liberally incorporated new types 

(on a provisional basis) to ensure that if repeatedly encountered they would be properly 

identified. One such example is the Midwest Post Oak-Blackjack Oak Forest-Igneous Phase 

(2075i), a type that was included in the classification on the day it was first encountered and was 

thereafter identified on numerous occasions. 

Map Accuracies 

The level of accuracy obtained for the 49 category USNVC association-level map was 

surprisingly high considering the limitations associated with representing vegetated landscapes 

using hard classification maps, as required by the USGS-NPS vegetation mapping program. 

Generating a classification scheme involves simplifying the complexity of the natural world so 

that every case can be placed in a single category. In vegetated landscapes, certain cases will 

inevitably not fit into a category as well as others, but instead suggest membership in more than 

one category or perhaps not fit distinctly into any of the categories of the classification scheme. 

This is a direct result of the fact that the ecological conditions that drive species distributions 

form complex mosaics of communities and continuous, gradual change from one vegetation 

association to another. Specifically, some vegetation communities encountered in observations 

in the ONSR mapping region are compositionally transitional, ecotonal, or are not well described 

by this categorization scheme. Historic and current land-use patterns as well as numerous 

natural disturbance regimes in the ONSR give rise to complexity that can be difficult to capture 

in a categorization scheme that is straightforward enough for use in a classification problem. 

The Importance Value calculations are a useful tool with which to identify potential sources of 

map error and to aid land managers in the interpretation of map products. 

For some of the aggregated types, mapping accuracy, though improved, is still below the 

80 percent goal desired by the USGS-NPS Vegetation Mapping Program. For example, the 

White Oak Forest Community Type has an accuracy of only 59 percent, probably due to 

confusion between its component associations (2066 and 2070) and the most abundant 

vegetation class, the Black Oak – White Oak – Hickory Forest (2076), which shares many 

Discussion 45
diagnostic species, particularly white oak. Confusion between these abundant types suppressed 

not only within class accuracy, but also overall accuracy. However, the Ecological Systems and 

Community Types used in this study were designed to address management issues as well as to 

address mapping difficulties arising from community similarities and spatial arrangement issues. 

Therefore, we believe that further aggregation to improve accuracy would diminish the utility of 

the final products for resource management planning. 

Many of the classification errors that were found during field accuracy assessment 

included cases that were misclassified into categories that are similar to that observed during 

field validation visits. For example, the validation data contains a total of 22 cases of confusion 

between White Oak-Red Oak-Sugar Maple Mesic Forest (2058) and white oak forest 

associations (2066 and 2070). This confusion is reasonable given the fact that the five species 

identified as being most important in the 22 cases that were misclassified as 2058, but observed 

to be 2066 or 2070 during field validation, are also identified as the most important species in the 

correctly classified 2058 validation observations. These misclassified cases can be said to be 

compositionally transitional communities that share similarities with both the mixed hardwood 

mesic forest and white oak forest associations. Confusion also existed between the Ozark Black 

Oak-Scarlet Oak Forest (2399) and Shortleaf Pine-Black Oak Forest (2401) associations. A total 

of 16 cases of confusion exist where a location was misclassified as 2399, but found to be 2401 

during field validation. In these confused cases, black oak was observed to be the most 

important species overall, and half of these cases contain less than 20 percent cover of shortleaf 

pine. Again, these errors are reasonable because the validation observations were obtained in 

what may be termed compositionally transitional communities. 

Confusion between upland woodland association types (2108, 2143, 2149 and 2393) and 

areas that have been shelterwood cut occurred due to similarities in their reflective signatures. 

The shelterwood management method opens up forest canopies in a manner that mimics the 

appearance of upland woodlands in some cases. For example, a user’s accuracy of only 15.8 

percent was obtained for the Post Oak-Blackjack Oak/Little Bluestem Woodland association 

(2149). Confusion with shelterwood cuts occurring outside the ONSR management area 

accounts for most of these misclassification errors. Also typical are the circumstances for 

Chinkapin Oak-Red Cedar Dry Alkaline Forest (2108), Chinkapin Oak-Ash/Little Bluestem 

Woodland (2143), and Shortleaf Pine-Oak Dry Woodland (2393), where user’s accuracies were 

lower outside the ONSR boundary compared to inside. This is related to the fact that timber 

management activities are more prevalent outside the ONSR boundaries. Unfortunately, 

secondary features of harvest activity such as roads do not dependably indicate harvest areas, as 

nearly all ridges (near harvested areas or not) within the mapping area have roads on them. 

Futhermore, these roads are inconsistently visible beneath the canopy. 

The results from the pilot study test of statistical classification techniques indicate that 

certain vegetation communities may be more accurately mapped through photointerpretation 

rather than through an automated classification approach. The search for repeated patterns that 

drives a statistical classifier tends to be undermined by the anomalous land use patterns that 

produce the altered (‘SA##’) vegetation and land cover types. For example, harvested and 

regenerating timber stands and the old field association types can occur in numerous landscape 

positions and have varying reflectance signatures, but we found that they can be readily 

photointerpreted and superimposed over a statistically classified map to improve mapping 

accuracy. A spatial overlay of this type would be logically consistent with the manner in which 

anthropogenic modifications are superimposed on a landscape matrix of relatively natural plant 

Discussion 46
communities. Furthermore, dolomite and igneous glades – which are sparsely treed savanna-like 

communities that contain bright rocky areas, green vegetation, and dark shadows – represent 

reflectively mixed targets that defy accurate statistical classification using remote sensing image 

data (Smith, 2001). It may likewise be more appropriate to photointerpret and superimpose these 

communities over the mapped results of an automated statistical classifier. 

Probability Maps 

Among the advantages of mapping USNVC vegetation associations using probabilities 

from discriminant analysis is that a probabilistic approach combines the ability to derive hard 

classifications (polygon maps) while retaining spatially continuous information on the 

probability of class membership for all of the categories in a classification scheme. This 

mapping approach permits analysis of the spatial structure of uncertainty and the potential for 

detecting transitional and ecotonal regions, thus characterizing a landscape mosaic of vegetation 

communities in a manner more consistent with actual ground conditions. In addition, a hard 

classification map typically contains no information about classification uncertainty, whereas it 

should be anticipated that uncertainty will rise as proximity to patch edges increases, due to 

decreasing homogeneity in the floristic and/or structural characteristics of vegetation 

communities near patch boundaries. In a pilot investigation of the spatial structure of uncertainty 

in the ONSR region vegetation map, statistically significant spatial structure was evident in the 

probabilities of class membership as a function of proximity to patch edge in the two most 

common vegetation association classes in the study area – the mixed oak-hickory/dogwood 

(2076) and black oak, scarlet oak forest (2399) classes. ANOVA results indicate that significant 

increases occur in average probability of class membership as distance increases from patch 

edges. The rise in classification uncertainty as patch boundaries are approached may be due to 

the prevalence of ecotonal communities in these regions. 

Probability maps for the various USNVC association categories can be utilized to assess 

the spatial structure of error and uncertainty in the ONSR vegetation map. While the overall 

accuracy of the final ONSR vegetation association map provides a map user with a general sense 

of map accuracy and reliability, it should be emphasized that per-class accuracy measures such 

as the user’s accuracy and ROC curves contain important measures pertaining to the reliability of 

individual classes. Specifically, ROC curves offer per-class comparative measures of 

discrimination strength, but they can also be used to determine which areas on the map contain 

more certainty compared to others. By plotting probability of detection against mapped 

discriminant analysis probability thresholds, ROC curve information can be used to reveal target 

levels of discernability, and actual patch boundaries may be located more accurately based on 

certainty benchmarks obtained from accuracy validation data and related back to class 

membership probabilities. 

As an example of how probability maps can be used by resource managers to assess the 

certainty of map classifications, either the 49-class vegetation association map or the 33-class 

Community Type map can be displayed in a GIS platform over a probability map for all of the 

classification types. This probability map is a raster data file that is a by-product of the statistical 

mapping approach. The value of each grid cell indicates the probability that a given cell was 

individually classified as the same vegetation association as the polygon of which it is part in the 

final classification map. For example, if a polygon is classified as a Chinkapin Oak-Ash 

Woodland (2143), the probability value for an individual grid cell within the polygon will be the 

probability that that cell was individually classified as the 2143 type (even if, individually, that 

Discussion 47
cell had a higher probability of being another type). Probability may be displayed as a 

continuous variable or it may be broken up into discreet classes. Figure 11 displays the 49-class 

map at 50 percent transparency over the probability map displayed as a continuous variable for 

all classes. Brighter areas indicate higher classification certainty. Figure 11 illustrates the case 

which was often evident in the field: certainty of classification (or representativeness of 

vegetation association) decreases near community boundaries as one type transitions into 

another. 

Discussion 48
Figure 11. Detail of 49-class map displayed over probability raster for all classes, with 

brighter areas indicating higher classification certainty.

Discussion 49
Probability maps for the individual classes may be used by resource managers to assess 

the classification certainty for specific communities of interest, and to identify locations within 

the mapping where a given type is likely to occur, beyond where it was identified in the final 

classification map. These can also be used to identify areas that may respond favorably to 

management when managing for a particular community. Unlike the probability map for all 

classes shown in Figure 11, individual probability maps show the probability that a cell was 

classified as a particular type, regardless of the final classification of the polygon of which it was 

a member in the final classification map. Therefore, even if a polygon was classified as one 

vegetation association, resource managers can assess the probability that the individual cells 

within that polygon were classified as another type. 

Figure 12 examines just such a relationship. Polygons classified as the Chinkapin OakAsh/Little Bluestem Woodland (2143) are shown in red and the ecologically related Chinkapin 

Oak-Red Cedar Dry Alkaline Forest (2108) polygons are shown in green. The 2143 type is 

important to resource managers for its high biological diversity. Once more common, it is now 

infrequent due to invasion by red cedar as a result of past fire suppression. This change has 

caused many examples of the 2143 type to convert to the 2108 type, though many of the 

component species are likely present. Areas identified as the 2108 type might be suitable for 

restoration to the 2143 type with mechanical removal of cedar and prescribed fire management to 

control future woody invasion. The probability raster allows one to assess the certainty of the 

polygons classified as 2143 (darker red indicates higher probability) and to find areas where the 

2143 type is more likely to be found, beyond where it was identified by final classification. Such 

maps can significantly aid resource management planning by suggesting candidate sites for 

management activity and can be created for those associations that were mapped statistically. 

Discussion 50
Figure 12. Vegetation associations 2143 and 2108 displayed over probability raster for 

the 2143 type. 

Discussion 51
Management Implications 

Ecological Systems Groupings 

The Ecological Systems used in this project were designed to facilitate resource 

management planning within the mapping area. Given the lower overall accuracy of the 49-class 

USNVC association-level map and the complexity involved in interpreting it, we believe that the 

33-class Community Type map provides a more reliable and useful product. For many of the 

aggregated types, the 49-class map yields polygons that are too small to reasonably guide 

management. By contrast, within the 33-class map, the groupings of vegetation associations that 

are either similar to one another or spatially related provide a scale more appropriate to making 

management decisions. Furthermore, though important for understanding ecological processes 

and assessing resource conditions, community boundaries may not relate to where management 

action can be applied practically. While aggregation of types does not necessarily address this 

disconnect between ecological realities and management practicality, it can help to identify 

broader areas over which to conduct management activities in order to maximize benefits. For 

example, when determining the location of fire lines for prescribed burns in igneous woodlands, 

polygons based on aggregated types will suggest a minimum area over which to apply treatment 

that would include nearby Midwest Post Oak-Blackjack Oak Forests (2075) that should respond 

favorably to fire. Indeed, applications such as this are the impetus behind the development of 

ecological systems nationwide (Comer and others, 2003). 

The fact the ecological systems approach used in this study increased both the accuracy 

and the utility of the final map products suggests that perhaps revisions should be made to the 

USNVC hierarchy. Other vegetation mapping efforts have experienced difficulties similar to 

those we encountered with respect to the broad conceptual jump between the USNVC formation 

and alliance levels, and the national program is being updated to address this issue (Federal 

Geographic Data Committee, 2006). However, the proposed changes may be unable to address 

the mapping problem encountered when physiognomically and floristically distinct communities 

often occur together in ways that confound mapping but which are relevant to management. In 

our study, igneous glades and associated woodlands are an example of such communities, 

frequently occurring as complexes of physiognomically distinct units which are, individually, 

below the minimum mapping unit. In order to address the local spatial relationships that may 

exist between these types of communities, it may be necessary to maintain a separate parallel 

hierarchical system designed to address mapping goals and management interests. 

Woodland/Forest Conundrum 

There are frequent discrepancies between conventions used by resource managers within 

the study area regarding usage of the terms “woodland” and “forest,” and these discrepancies are 

not necessarily resolved by USNVC classification. For example, many of the vegetation 

associations that are classified as “forests” based on canopy and subcanopy structure according 

to USNVC standards are considered “degraded” woodlands based upon standards commonly 

used in the Missouri Ozarks. Usually, fire suppression is cited as the reason for allowing 

invasion by less fire-tolerant species and canopy closure within the mapping area (Nigh and 

others, 2000, Ladd, 2005, Nelson, 2005). These interpretations are based upon an understanding 

of the historic disturbance regimes within which native flora evolved and the effect fire had on 

the distribution of plant communities. In this context, application of the “woodland” 

classification to types that are currently forested by USNVC standards represents an 

Discussion 52
identification of the “potential” vegetation community given a management regime that mimics 

the historic ecological processes, and is not a reflection of current conditions. As such, these 

interpretations offer a broader interpretation of woodland than does the USNVC. Conversely, 

many resource managers involved in timber management operations would classify as forests 

vegetation associations that would qualify as woodlands based upon USNVC standards. Their 

primary criterion may be the amount of merchantable timber available in a given area, a measure 

that frequently acts as a surrogate for tree density and height, but which may ignore components 

in lower vegetative strata. 

This study adheres to the USNVC standards for determining whether a type is forest or 

woodland. Forests were generally defined as having more than 80 percent cover in the canopy, 

with a complex understory of multiple layers. The forest designation can safely be applied to the 

more mesic vegetation associations, such as the White Oak Dolomite Forest (2070), the White 

Oak/Dogwood Forest (2066), mesic hardwood forests in both uplands and bottomlands (2058 

and 2060), and all of the floodplain forests (2410, 5033 and 7334). Woodlands were generally 

defined as having less than 80 percent cover in the canopy, or, if more than 80 percent cover, 

then having open understory and shrub layers and a well developed groundflora. Also included 

in woodlands were communities with stunted canopies which may or may not exceed 80 percent 

closure. The woodland designation can safely be applied to those types that are recognized as 

such by Nelson (2005) and Nigh and Schroeder (2002). In between these two ends of the 

spectrum, there are the Ozark Black Oak-Scarlet Oak Forest (2399), the Shortleaf Pine/Blueberry 

Forest (2400), the Midwest Post Oak-Blackjack Oak Forest (2075) and its human-modified, 

igneous expression (2075i). Based on current condition, it is appropriate to think of these as 

forests that, with management (particularly prescribed burning) can be transformed into 

woodlands more typical of the area prior to European settlement and the widespread suppression 

of wildfires. This appears to be the basis for much of the current resource management at Ozark 

National Scenic Riverways, particularly the application of prescribed burning. 

Wildland Fuels Map 

Fuels data were collected with the primary intent of describing the condition of wildland 

fire fuels within each vegetation association and generating a fuel-loading map based on broad, 

commonly applied fuels classes. We generated digital and hard-copy maps (Figure 13) of fuels 

classes based upon the United States Forest Service classification developed by Anderson and 

others (1976). At present, this is the most commonly applied fuels model. However, because 

Anderson’s system was developed in western systems dominated by coniferous forests, it may 

not provide the conceptual resolution appropriate for fuels conditions in eastern, deciduous 

forests. Therefore, polygons within the shapefile of USNVC types have been attributed with 

classifications from the recently produced Comprehensive Fuels model (Scott and Burgan, 

2005). This latter model identifies a greater number of classes, many of which were developed 

for deciduous forests and may be more appropriate for the study area. However, the 

Comprehensive Fuels model has not yet been widely adopted in the United States. Polygons 

within the shapefile of USNVC types have also been attributed with classifications from 

National Fire Danger Rating System (NFDRS) model, which can be used to assess fire danger 

across multiple scales. Fuels maps based upon either the Comprehensive Fuels model or the 

NFDRS can be generated in a GIS environment. 

The finding of no statistically significant differences in fuel loading between USNVC 

associations is not unexpected, given that fuels data collection was designed less to test for 

Discussion 53
differences between fuels loads than to provide a coarse description of fuels for each USNVC 

association. The primary factors restricting more intensive sampling were a limited budget and 

insufficient time. As a result, sampling intensity and distribution were not sufficient to overcome 

the inherent variability of fuel loading within each vegetation association. This was particularly 

true for rarer communities. 

Nevertheless, some general patterns emerge from the fuels data (Table 3 and Table 4). 

For example, USNVC associations that occur in the riparian zone generally have lower total fuel 

loading than other associations. There may be subtle differences in upland fuel loading, but our 

data was unable to capture those differences. Fuel loading in USNVC associations that are 

frequently managed with prescribed fire did not appear to be different from the other upland 

associations that were sampled. Sampling was not intense enough to properly assess these 

differences. Additionally we did not target communities that are currently managed with 

prescribed fire to compare against those communities that have not been treated with prescribed 

fire. 

Fuels data were used to develop data tables that are linked to the vegetation association 

map in a GIS platform, enabling the creation of a coarse fuel map for ONSR in which Anderson 

(1982) fuel models can be displayed for each USNVC association. This coarse fuel map can be 

used to evaluate the spatial continuity of fuel conditions throughout the mapping area. 

Additionally, the fuels map enables resource and fire managers to use spatial fire spread models, 

such as FARSITE (Finney, 1998; Keane and others, 1999), which will permit the evaluation of 

prescribed fire and wildfire scenarios within ONSR. This tool will enable managers to estimate 

the impacts of prescribed fire or wildfire decisions and to refine prescribed fire planning. 

Discussion 54
Figure 13. Detail of Anderson Fuels Model map.

Discussion 55
Options for Future Mapping Projects 

Field Sampling 

The sampling approach used in this study met programmatic requirements and facilitated 

development of the final classification map. Using a combination of gradsects sampling with 

stratification based upon the MOECS Ecological Landtype model ensured that we sampled 

across the broadest range of environmental conditions that we could expect to find in the park. 

Of necessity, sampling was clustered in study sites that had a high percentage of public land 

ownership and high ecological diversity, as well as being accessible. Nevertheless, study site 

selection was also guided by the objective of sampling from a broad range of Landtype 

Associations with good spatial distribution within the park. This was particularly true during the 

accuracy assessment phase, during which time particular emphasis was placed on sampling in 

areas for which data were lacking (Figure 6). 

The plot design utilized in this study provided adequate data for ordination analysis. A 

larger plot (such as the 20 x 50 m plot used in the NPS fire effects monitoring program) may 

have provided a more complete set of data for characterizing overstory trees, but would have 

added significantly to the time needed for sampling. Similarly, more intensive sampling at each 

point potentially could have improved fire fuels characterization. However, the greatest 

shortcoming of the fire fuels data from this project was the low sample size in many of the 

vegetation association types. Increasing sample intensity at each point would not have addressed 

this problem. 

Within the study area, collection of some environmental data provided little help in the 

field classification of vegetation associations, nor were such data valuable in the multivariate 

analysis of plot data. For example, surficial geology and soil texture and drainage were not 

incorporated into any of the analysis of data. Within the study area, these data are secondary to 

more readily assessable environmental conditions such as geologic parent material, landscape 

position and slope aspect as indicators to potential vegetation associations. Furthermore, 

classification of soil texture and drainage requires an additional suite of knowledge and training 

of field staff in order to provide precise and accurate data. Given the additional demands 

associated with collecting environmental data, future vegetation mapping projects may want to 

modify the program data collection standards to focus on those environmental parameters that 

are the best indicators of potential vegetation type in the mapping area and that can be accurately 

and precisely measured in a short amount of time with a minimal amount of training of field 

staff. 

Remote Sensing 

Comparison of the results of pilot classifications of vegetation communities in the ONSR 

and the final classification of the entire ONSR mapping region indicate that a hybrid 

combination of statistical methods and photointerpretation is needed to obtain adequate overall 

and class-wise accuracy levels. We recommend that future vegetation mapping projects within 

this program incorporate a hybrid approach such as that used here, if it is within capabilities. 

Splitting the study into three more homogenous regions to simplify the classification problem 

and applying a discriminant analysis approach based upon probabilities of USNVC association 

type membership proved to be a good alternative to the decision rule classification approach 

more typically applied with remote sensing data. While the decision rule classification approach 

Discussion 56
is robust, it lacks an objective input variable selection method. The mapping approach applied 

here also provided superior results compared to the regression tree approach applied during pilot 

investigations, as difficulties associated with missing categories in the classification output were 

avoided. Regression trees are able to objectively choose input variables, but they use only one 

variable at a time, and therefore do not take full advantage of the multidimensionality of the 

input data. Further, regression trees are often more effective in describing the input data, but can 

be notoriously unsuccessful in predicting novel cases. Finally, this mapping effort demonstrates 

the utility of the regression tree approach in providing a heuristic tool for landscape scale 

stratification when mapping complex systems. 

It may be practical to consider alternate data sources that are more cost-effective 

compared to obtaining new aerial photography data to map vegetation communities. The results 

of the correlogram analysis indicated that the highest spatial resolution needed for textural 

analysis of vegetation communities in the ONSR mapping region was two meters. For similar 

mapping applications, it may be more feasible to acquire commercial high resolution satellite 

CIR imagery such as Quickbird (launched 10/18/2001) rather than flying an aerial photography 

mission. At the time of publication of this document (2006), the cost for orthorectified, 2.4 m 

resolution, multispectral (blue, green, red, and infrared spectral bands) Quickbird imagery was 

$18 per square kilometer after an academic discount is applied. At this pricing rate, the cost for 

coverage for the ONSR mapping region (1418.5 square km) would total $25,533. This variety of 

high resolution commercial satellite imagery is produced in digital form, thereby further cutting 

the total cost of data acquisition by drastically reducing the extensive amount of time it takes to 

scan and orthorectify individual aerial photo frames. 

Imagery from the National Agricultural Imagery program may also be a useful, readily 

available data source. These data can be freely obtained from the United States Department of 

Agriculture Geospatial Data Gateway (http://datagateway.nrcs.usda.gov/). However, these data 

are collected during the height of the growth season, which means that they may not be as 

effective for remote sensing needs as data collected during periods of higher phenological 

separation between vegetation associations (for example, near fall senescence). NAIP data were 

not yet available at the initiation of this project and were therefore used only as a secondary data 

source. 

Discussion 57
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Appendix 1. Schematic maps of quarter- and two-meter photomosaics. 

Appendix 1. Schematic maps of quarter- and two-meter photomosaics. 

Figure A1-1. Mapping region schematic of the extents of the quarter-meter 

photomosaics shown with the footprints of the individual photo frames and the 

boundaries of the 1:24,000-scale USGS quadrangles. 

Figure A1-2. Mapping region schematic of the extents of the two-meter photomosaics 

shown with the footprints of the individual photo frames and the boundaries of the 

1:24,000-scale USGS quadrangles. 

A1-1 
Appendix 1. Schematic maps of quarter- and two-meter photomosaics. 

A1-2 
Appendix 2. Community list and hierarchical classification scheme for vegetation communities 

in the ONSR mapping region. 

Appendix 2. Community list and hierarchical classification scheme for vegetation 

communities in the ONSR mapping region. 

USNVC Vegetation Association USNVC 

Code 

ONSR Community 

Type 

ONSR 

Ecological 

System 

Forests 

Post Oak Flatwoods 2405 Post Oak Flatwoods 

Ozark Black Oak, Scarlet Oak Forest 2399 

Black Oak-White Oak-Hickory 

Forest 

2076 

Mixed Oak-Hickory 

Forests 

White Oak Dolomite Forest 2070 

White Oak/Dogwood Forest 2066 

White Oak Forests 

Upland Oak 

Forests 

White Oak-Red Oak-Sugar Maple 

Mesic Forest 

2058 Oak-Mixed Hardwood 

Mesic Upland Forests 

Mesic Upland 

Forests 

Pine/Blueberry Forest 2400 

Shortleaf Pine-Black Oak 2401 

Interior Highlands Shortleaf PineOak Dry-mesic Forest 7489 

Pine and Pine-Oak 

Forests 

Upland Pine and 

Pine-Oak Forests 

Overcup Oak Pond Forest 4642 Sinkhole Pond Forest 

Red Maple Forested Seep 2407 Forested Seep 

Wet Forests 

Sugar Maple-Oak-Bitternut Hickory 

Mesic Bottomland Forest 

2060 Mixed Hardwood 

Mesic Bottomland 

Forests 

Sycamore-Silver Maple Floodplain 

Forest 

7334 

Box Elder Forest 5033 

Ash-Oak-Sycamore Mesic 

Bottomland Forest 

2410 

Riverfront and 

Bottomland Forests 

Bottomland 

Forests 

A2-1 
Appendix 2. Community list and hierarchical classification scheme for vegetation communities 

in the ONSR mapping region. 

Woodlands 

Post Oak-Blackjack Oak/Little 

Bluestem Woodland 

2149 

Chinquapin Oak-Ash/Little Bluestem 

Woodland 

2143 

Chinquapin Oak-Red Cedar Dry 

Alkaline Forest 

2108 

Ozark Dolomite Glade 2398 

Dolomite Woodland 

Complexes 

Midwest Post Oak-Blackjack Oak 

Forest 

2075 

Midwest Post Oak-Blackjack Oak 

Forest (igneous phase) 

2075i 

Post Oak-Blackjack Oak/Little 

Bluestem Woodland 

2149 

Blackjack Oak Xeric Scrub 2425 

Ozark Igneous Glade 2243 

Igneous Woodland 

Complexes 

Upland Oak 

Woodlands 

Shortleaf Pine/Little Bluestem 

Woodland 

2402 

Shortleaf Pine-Oak Dry Woodland 2393 

Pine and Pine-Oak 

Woodlands 

Pine and PineOak Woodlands 

A2-2 
Appendix 2. Community list and hierarchical classification scheme for vegetation communities 

in the ONSR mapping region. 

Shrublands/Herbaceous 

Riverine Sand Flats (Herbaceous 

Gravel Bar) 

2049 

Witchhazel, Dogwood Gravel Wash 3898 

Carolina Willow Shrubland 3899 

Active 

Channel/Gravel Bar 

Complex 

Floodplain Canebrake 3836 Floodplain 

Communities 

Riverine 

Shrublands/ 

Herbaceous 

Communities 

Water Lily Aquatic Wetland 2386 Sloughs 

Vegetated Spring Branch N/A Springs 

Riverine 

Emergent 

Aquatic 

Communities 

Ozark Fen 2404 

Ozark Prairie Fen 2416 

Fens Fens 

Buttonbush Sinkhole Pond Swamp 4742 

Sinkhole Pond Marsh 2413 

Marshes Marshes 

Dry Igneous Cliff 2286 

Moist Igneous Cliff 2289 

Dry Dolomite Cliff 2291 

Moist Dolomite Cliff 2292 

Cliffs 

Dolomite Talus 2308 

Igneous Talus 5203 

Talus 

Cliffs and Talus 

A2-3 
Appendix 2. Community list and hierarchical classification scheme for vegetation communities 

in the ONSR mapping region. 

Altered Communities 

Oak-Hickory Regeneration Stand SA02 

Pine-Oak Regeneration Stand SA05 

Regeneration Stand 

Oak-Hickory Shelterwood/Select 

Harvest 

SA01 

Pine-Oak Shelterwood/Select 

Harvest 

SA04 

Shelterwood/ 

Select Harvest 

Oak-Hickory Pole Stand SA03 

Pine-Oak Pole Stand SA06 

Pine Pole Stand SA08 

Pole Stand 

Pine Plantation SA07 Pine Plantation 

Timber 

Management 

Area 

Pine-Deciduous Shrubby Old Field SA12 

Deciduous Shrubby Old Field SA09 

Herbaceous Old Field SA23 

Cedar-Deciduous Shrubby Old Field SA36 

Open Old Field with 

Shrubby or Sparse 

Trees 

Deciduous Forested Old Field SA10 

Pine-Deciduous Wooded Old Field SA11 

Cedar-Deciduous Wooded Old Field SA13 

Pine Old Field SA14 

Cedar Old Field SA15 

Wooded Old Field 

Old Fields 

Agricultural Forested Woodlot SA37 Agricultural Woodlot 

Hayfield/Grazing Land SA20 

Close Grown Monoculture SA21 

Row Crops SA22 

Agricultural 

Field/Pasture 

Agricultural 

Field/Pasture 

Deciduous Wooded Residence SA16 

Evergreen-Deciduous Wooded 

Residence 

SA17 

Evergreen Wooded Residence SA18 

Lawn SA19 

Residential (structures) SA35 

Residential Residential 

Industrial SA26 

Industrial/Commercial SA27 

Industrial/ 

Quarry 

Industrial/ 

Quarry 

Commercial SA28 

Commercial/Services SA29 

Mixed SA30 

Urban Urban 

Other Clearing SA31 Other Clearing Other Clearing 

Transportation Corridor (Road) SA32 Trans. Corridor Trans. Corridor 

Shrubby Utility Corridor SA33 

Herbaceous Utility Corridor SA34 

Utility Corridor Utility Corridor 

Hatchery SA24 

Lake/Pond SA25 

Surface Water Surface Water 

River (Non-vegetated portion) River River 

A2-4 
Appendix 3. USNVC—Atlas of Missouri Ecoregions—Terrestrial Natural Communities of 

Missouri Community Crosswalk 

Appendix 3. Crosswalk comparing USNVC vegetation associations in Ozark 

National Scenic Riverways and surrounding area to communities of the Current 

River Hills Subsection included in the Atlas of Missouri Ecoregions (Nigh and 

Schroeder 2002) and to communities described in the Terrestrial Natural 

Communities of Missouri (Nelson 2005). 

USNVC 

Vegetation 

Association 

Code Atlas of Missouri 

Ecoregions 

Terrestrial Natural 

Communities of Missouri 

Community Type 

and Subtype (italics) 

Forests 

Evergreen Forest 

Shortleaf 

Pine/Blueberry 

Forest 

2400 Shortleaf Pine/Bluestem 

Dry Chert or Sandstone 

Woodland 

Dry Chert or Sandstone Woodland 

Shortleaf pine, little bluestem 

Deciduous Forests 

Upland Deciduous Forest 

Midwest Post 

Oak-Blackjack 

Oak Forest 

2075 Post Oak-Blackjack 

Oak/Bluestem Dry Chert, 

Sandstone or Igneous 

Woodland 

Dry Igneous or Chert Woodland 

Post oak, blackjack oak, little 

bluestem 

Dry Sandstone Woodland 

Post oak, black oak, scarlet oak, 

little bluestem

Midwest Post 

Oak-Blackjack 

Oak Forest 

(igneous phase) 

2075i Post Oak-Blackjack 

Oak/Bluestem Dry Igneous 

Woodland 

Dry Igneous Woodland 

Post oak, blackjack oak, little 

bluestem

Dry-Mesic Igneous Forest 

Black oak, white oak, 

hickory/dogwood

Post Oak 

Flatwoods 

2405 Post Oak Flatwoods Upland Flatwoods 

Ozark Black OakScarlet Oak Forest 2399 Post Oak, Black Oak, 

Scarlet Oak Dry Chert or 

Sandstone Woodland 

Dry Chert or Sandstone Woodland 

Post oak, black oak, scarlet oak, 

little bluestem

Black Oak-White 

Oak-Hickory 

Forest 

2076 Mixed OakHickory/Dogwood DryMesic Chert (Sandstone, 

Igneous) Forest (or Upland 

Waterway Forest) 

Dry-Mesic Chert, Sandstone or 

Igneous Forest 

Black oak, white oak, 

hickory/dogwood 

Dry-mesic Bottomland Forest 

White 

Oak/Dogwood 

Dry-Mesic Forest 

2066 White Oak/Dogwood Drymesic Chert (Igneous) 

Forest 

White Oak/Dogwood DryMesic Chert (Igneous) 

Upland Waterway Forest 

Dry-Mesic Chert or Igneous Forest 

White oak, northern red 

oak/dogwood

Dry-mesic Bottomland Forest 

White Oak-Mixed 2070 White Oak-Mixed Dry-Mesic Limestone/Dolomite 

A3-1 
Appendix 3. USNVC—Atlas of Missouri Ecoregions—Terrestrial Natural Communities of 

Missouri Community Crosswalk 

Oak Dry-Mesic 

Alkaline Forest 

Oak/Redbud Dry-mesic 

Limestone/Dolomite Forest 

Forest or Woodland 

Dry-mesic Bottomland Forest 

White Oak-Red 

Oak-Sugar Maple 

Mesic Forest 

2058 Red Oak, White Oak, 

Sugar Maple Mesic 

Limestone/Dolomite Forest 

Mesic Limestone/Dolomite Forest 

White oak, northern red oak, sugar 

maple/spicebush 

Sugar maple, basswood/pawpaw

(less common) 

Mesic Bottomland Forest 

Bottomland Deciduous Forest 

Sycamore-Silver 

Maple Floodplain 

Forest 

7334 Sycamore, Cottonwood, 

Willow Riverfront Forest 

Riverfront Forest 

Sycamore, cottonwood, black 

willow

Box Elder Forest 5033 Green Ash, American Elm, 

Sugarberry Riverfront 

Forest 

Riverfront Forest 

Slippery elm, green ash, 

hackberry, oak

Ash-OakSycamore Mesic 

Bottomland Forest 

2410 Green Ash, American Elm, 

Sugarberry Riverfront 

Forest 

 Riverfront Forest 

Slippery elm, green ash, 

hackberry, oak

Sugar MapleBitternut Hickory 

Mesic Bottomland 

Forest 

2060 Red Oak, Sugar Maple, 

Bitternut Hickory Mesic 

Bottomland Forest 

Mesic Bottomland Forest 

Saturated Deciduous Forest 

Red Maple 

Forested Seep 

2407 Ozark Forested Fen Forested Fen 

Overcup Oak 

Pond Forest 

4642 N/A N/A 

Mixed Forest 

Interior Highlands 

Shortleaf PineOak Dry-mesic 

Forest 

7498 Shortleaf Pine, White Oak 

Dry-mesic Chert (Igneous, 

Sandstone) Forest 

Dry-Mesic Chert or Sandstone 

Forest 

Shortleaf pine, white oak/dogwood

Shortleaf PineBlack Oak Forest 2401 Shortleaf PineOak/Vaccinium Dry Chert 

(Sandstone, Igneous) 

Woodland 

Dry Chert, Sandstone or Igneous 

Woodland 

Shortleaf pine, oak, lowbush 

blueberry

A3-2 
Appendix 3. USNVC—Atlas of Missouri Ecoregions—Terrestrial Natural Communities of 

Missouri Community Crosswalk 

Woodlands 

Evergreen Woodlands 

Shortleaf 

Pine/Little 

Bluestem 

Woodland 

2402 Shortleaf Pine/Bluestem 

Dry Igneous Woodland 

Dry Igneous Woodland 

Shortleaf pine, little bluestem

Deciduous Woodlands 

Post OakBlackjack 

Oak/Little 

Bluestem 

Woodland 

2149 Post Oak-Blackjack 

Oak/Bluestem Dry Chert, 

(Sandstone, Igneous) 

Woodland 

Dry Chert, Sandstone or Igneous 

Woodland 

Post oak, blackjack oak, little 

bluestem 

Blackjack Oak 

Xeric Scrub 

2425 Igneous Glade Igneous Glade 

Chinquapin OakAsh/Little 

Bluestem 

Woodland 

2143 Chinquapin Oak-Ash (Red 

Cedar)/Little Bluestem Dry 

Limestone/Dolomite 

Woodland 

Dry Limestone/Dolomite 

Woodland 

Dry-Mesic Limestone/Dolomite 

Woodland 

Mixed Woodlands 

Shortleaf PineOak Dry 

Woodland 

2393 Shortleaf PineOak/Vaccinium Dry Chert 

(Sandstone, Igneous) 

Woodland 

Dry Chert Woodland 

 Shortleaf pine, post oak/lowbush 

blueberry

Chinquapin OakRed Cedar Dry 

Alkaline Forest 

2108 Chinquapin Oak-Ash (Red 

Cedar)/Little Bluestem Dry 

Limestone/Dolomite 

Woodland 

Dry Limestone/Dolomite 

Woodland 

Shrublands 

Evergreen Shrublands 

Floodplain 

Canebrake 

3836 N/A N/A 

Deciduous Shrublands 

Buttonbush 

Sinkhole Pond 

Swamp 

4742 Sinkhole Pond Shrub 

Swamp 

Pond Shrub Swamp 

Witch HazelDogwood Gravel 

Wash 

3898 Gravel Wash Gravel Wash 

Witch hazel, swamp dogwood, 

ninebark

Carolina Willow 

Shrubland 

3899 Gravel Bar Gravel Wash 

Willow/water willow

A3-3 
Appendix 3. USNVC—Atlas of Missouri Ecoregions—Terrestrial Natural Communities of 

Missouri Community Crosswalk 

Herbaceous 

Herbaceous Fens and Marshes 

Sinkhole Pond 

Marsh 

2413 Sinkhole Pond Marsh Pond Marsh 

Ozark Fen 2404 Ozark Marl Fen 

Ozark Deep Muck Fen 

Ozark Fen 

Marly 

Mucky

Ozark Prairie Fen 2416 Ozark Prairie Fen Prairie Fen 

Herbaceous Glades 

Ozark Dolomite 

Glade 

2398 Dolomite Glade Dolomite Glade 

Ozark Igneous 

Glade 

2243 Igneous Glade Igneous Glade 

Herbaceous Sloughs and Gravel Bars 

Riverine Sand 

Flats (Herbaceous 

gravel bar) 

2049 Riverine Sand Flats Gravel wash 

Willow/water willow 

Water Lily 

Aquatic Wetland 

2386 Midwest Water Lilly, 

American Lotus Deep 

Marsh 

Freshwater Marsh 

Water lily, American lotus deep 

marsh 

Vegetated Springs 

Vegetated Spring 

Branch 

N/A Groundwater Springs Limestone/Dolomite Spring 

Appendix 3 References 

Nelson, P.W. 1985. The Terrestrial Natural Communities of Missouri. Missouri Natural Areas 

Committee, Jefferson City. 197 pp. Revised edition, 1987. 

Nigh, T.A. and Schroeder, W.A. 2002. Atlas of Missouri Ecoregions. Missouri Department of 

Conservation, Jefferson City, Missouri. 

A3-4 
Appendix 4. ONSR Ecological Systems-MOECS Management Units Crosswalk 

Appendix 4. Crosswalk of ecological systems used in the mapping area and 

management groups identified by the Missouri Ecological Classification System 

(MOECS; Nigh and others 2000) for the Current River Hills subsection. 

USNVC 

Vegetation 

Association 

Code ONSR 

Community Type 

ONSR 

Ecol. 

System 

MOECS Management 

Groups 

Forests 

Post Oak Flatwoods 2405 Post Oak Flatwoods

Ozark Black Oak, 

Scarlet Oak Forest 

2399 

Black Oak-White 

Oak-Hickory Forest 

2076 

Mixed OakHickory Forests 

Mixed Oak (Scarlet, Black)-

Hickory/Dogwood/Desmodiu

m Dry-mesic Ultic (Chert, 

Igneous, Bottomland) Forest 

White Oak-Mixed 

Oak Dry-mesic 

Dolomite Forest 

2070 Mixed Oak-Hardwood/ 

Spicebush Mesic Dolomite 

Forest 

White Oak/ 

Dogwood DryMesic Forest 

2066 

White Oak Forests 

Upland 

Oak 

Forests 

Mixed Oak (White Oak, Red 

Oak)/Dogwood Dry-mesic 

(Chert, Igneous, Bottomland) 

Forest 

White Oak-Red 

Oak-Sugar Maple 

Mesic Forest 

2058 Oak-Mixed 

Hardwood Mesic 

Upland Forests 

Mesic 

Upland 

Forests 

Mixed Hardwood Mesic 

Bottomland Forest 

Pine/Blueberry 

Forest 

2400 

Shortleaf PineBlack Oak Forest 2401 

Pine-Oak/Vaccinium 

(Bluestem) Dry Ultic (Chert) 

Woodlands 

Interior Highlands 

Shortleaf Pine-Oak 

Dry-mesic Forest 

7489 

Pine and Pine-Oak 

Forests 

Upland 

Pine and 

PineOak 

Forests Mixed Oak (Scarlet, White)-

Pine/Vaccinium, Desmodium 

Dry Mesic Alfic (Chert) 

Forest 

Overcup Oak Pond 

Forest 

4642 Sinkhole Pond 

Forest 

Sinkholes 

Red Maple Forested 

Seep 

2407 Forested Seep 

Wet 

Forests 

Fens and Seeps 

Sugar Maple-OakBitternut Hickory 

Mesic Bottomland 

Forest 

2060 Mixed Hardwood 

Mesic Bottomland 

Forests 

Mixed Hardwood Mesic 

Bottomland Forest 

Sycamore-Silver 

Maple Floodplain 

Forest 

7334 

Box Elder Forest 5033 

Ash-Oak-Sycamore 

Mesic Bottomland 

Forest 

2410 

Riverfront and 

Bottomland Forests 

Bottomland 

Forests 

Riverfront Bottomland Forest 

A4-1 
Appendix 4. ONSR Ecological Systems-MOECS Management Units Crosswalk 

Woodlands 

Post Oak-Blackjack 

Oak/Little Bluestem 

Woodland 

2149 

Chinquapin OakAsh/Little Bluestem 

Woodland 

2143 

Chinquapin OakRed Cedar Dry 

Alkaline Forest 

2108 

Ozark Dolomite 

Glade 

2398 

Dolomite 

Woodland 

Complexes 

Dolomite Glade and 

Woodland Complexes 

Midwest Post OakBlackjack Oak 

Forest 

2075 

Midwest Post OakBlackjack Oak 

Forest (igneous 

phase) 

2075i 

Post Oak-Blackjack 

Oak/Little Bluestem 

Woodland 

2149 

Blackjack Oak 

Xeric Scrub 

2425 

Ozark Igneous 

Glade 

2243 

Igneous Woodland 

Complexes 

Upland 

Oak 

Woodla

nds 

Igneous Glade and Woodland 

Complexes 

Shortleaf Pine/Little 

Bluestem Woodland 

2402 

Shortleaf Pine-Oak 

Dry Woodland 

2393 

Pine and Pine-Oak 

Woodlands 

Pine and 

PineOak 

Woodla

nds 

Pine-Oak/Vaccinium 

(Bluestem) Dry Ultic (Chert) 

Woodlands 

A4-2 
Appendix 4. ONSR Ecological Systems-MOECS Management Units Crosswalk 

Shrublands/Herbaceous 

Riverine Sand Flats 

(Herbaceous gravel 

bar) 

2049 

Witchhazel, 

Dogwood Gravel 

Bar 

3898 

Carolina Willow 

Shrubland 

3899 

Active 

Channel/Gravel Bar 

Communities 

Gravel Bar and Gravel 

Washes 

Floodplain 

Canebrake 

3836 Floodplain 

Communities 

Riverine 

Shrubla

nds/Her

baceous 

Commu

nities 

N/A 

Water Lily Aquatic 

Wetland 

2386 Sloughs Gravel Bar and Gravel 

Washes 

Vegetated Spring 

Branch 

N/A Springs 

Riverine 

Emerge

nt 

Aquatic 

Commu

nities 

N/A 

Ozark Fen 2404 

Ozark Prairie Fen 2416 

Fens Fens Fens and Seeps 

Buttonbush 

Sinkhole Pond 

Swamp 

4742 

Sinkhole Pond 

Marsh 

2413 

Marshes Marshes Sinkholes 

Dry Igneous Cliff 2286 

Moist Igneous Cliff 2289 

Dry Dolomite Cliff 2291 

Moist Dolomite 

Cliff 

2292 

Cliffs Cliffs 

Dolomite Talus 2308 N/A 

Igneous Talus 5203 

Talus 

Cliffs 

and 

Talus 

N/A 

Appendix 4 References 

Nigh, T., Buck, C., Grabner, J., Kabrick, J. and Meinert, D. 2000. An ecological classification 

system for the Current River Hills. Working Draft. Missouri Department of Conservation, 

Jefferson City, MO. 

A4-3 
Appendix 4. ONSR Ecological Systems-MOECS Management Units Crosswalk 

A4-4 
Appendix 5. ONSR Ecological Systems-NatureServe Ecological Systems Crosswalk 

Appendix 5. Crosswalk of ecological systems used in the mapping area with 

ecological systems developed by NatureServe. 

USNVC Vegetation 

Association 

Code ONSR 

Community 

Type 

ONSR 

Ecological 

System 

NatureServe 

Ecological System 

(2005) 

Forests

Post Oak Flatwoods 2405 Post Oak 

Flatwoods 

Ozark Black Oak, 

Scarlet Oak Forest 

2399 

Ozark-Ouachita Dry 

Oak Woodland 

CES202.707 

Black Oak-White 

Oak-Hickory Forest 

2076 

Mixed OakHickory Forests 

White Oak Dolomite 

Forest 

2070 

White Oak/Dogwood 

Forest 

2066 

White Oak 

Forests 

Upland Oak 

Forests 

White Oak-Red OakSugar Maple Mesic 

Forest 

2058 Oak-Mixed 

Hardwood Mesic 

Upland Forests 

Mesic Upland 

Forests 

Ozark-Ouachita DryMesic Oak Forest 

CES202.708 

Pine/Blueberry Forest 2400 

Interior Highlands 

Shortleaf Pine-Oak 

Dry-mesic Forest 

7489 

Shortleaf Pine-Black 

Oak Forest 

2401 

Pine and PineOak Forests Upland Pine 

and Pine-Oak 

Forests 

Ozark-Ouachita 

Shortleaf Pine-Oak 

Forest and Woodland 

CES202.313 

Overcup Oak Pond 

Forest 

4642 Sinkhole Pond 

Forest 

Wet Forests Central Interior 

Highlands and 

Appalachian Sinkhole 

and Depression Pond 

CES202.018 

Red Maple Forested 

Seep 

2407 Forested Seep Interior Highlands 

Forested Acid Seep 

CES202.321 

Sugar Maple-OakBitternut Hickory 

Mesic Bottomland 

Forest 

2060 Mixed Hardwood 

Mesic 

Bottomland 

Forests 

Ozark-Ouachita 

Riparian CES202.703 

Sycamore-Silver 

Maple Floodplain 

Forest 

7334 

Box Elder Forest 5033 

South-Central Interior 

Large Floodplain 

CES202.705 

Ash-Oak-Sycamore 

Mesic Bottomland 

Forest 

2410 

Riverfront and 

Bottomland 

Forests 

Bottomland 

Forests 

North-Central Interior 

Floodplain 

CES202.694 

A5-1 
Appendix 5. ONSR Ecological Systems-NatureServe Ecological Systems Crosswalk 

Woodlands 

Post Oak-Blackjack 

Oak/Little Bluestem 

Woodland 

2149 Central Interior 

Highlands Dry Acidic 

Glade and Barrens 

CES202.692 

Chinquapin OakAsh/Little Bluestem 

Woodland 

2143 

Chinquapin Oak-Red 

Cedar Dry Alkaline 

Forest 

2108 

Ozark Dolomite Glade 2398 

Dolomite 

Woodland 

Complexes 

Central Interior 

Highlands Calcareous 

Glade and Barrens 

CES202.691 

Midwest Post OakBlackjack Oak Forest 2075 Ozark-Ouachita Dry 

Oak Woodland 

CES202.707 

Midwest Post OakBlackjack Oak Forest 

(igneous phase) 

2075i N/A 

Post Oak-Blackjack 

Oak/Little Bluestem 

Woodland 

2149 

Blackjack Oak Xeric 

Scrub 

2425 

Ozark Igneous Glade 2243 

Igneous 

Woodland 

Complexes 

Upland Oak 

Woodlands 

Central Interior 

Highlands Dry Acidic 

Glade and Barrens 

CES202.692 

Shortleaf Pine/Little 

Bluestem Woodland 

2402 

Shortleaf Pine-Oak 

Dry Woodland 

2393 

Pine and PineOak Woodlands Pine and PineOak 

Woodlands 

Ozark-Ouachita 

Shortleaf Pine-Oak 

Forest and Woodland 

CES202.313 

A5-2 
Appendix 5. ONSR Ecological Systems-NatureServe Ecological Systems Crosswalk 

Shrublands/Herbaceous 

Riverine Sand Flats 

(Herbaceous gravel 

bar) 

2049 

Witchhazel, Dogwood 

Gravel Wash 

3898 

Ozark-Ouachita 

Riparian CES202.703 

Carolina Willow 

Shrubland 

3899 

Active 

Channel/Gravel 

Bar Communities

Floodplain Canebrake 3836 Floodplain 

Communities 

Riverine 

Shrublands/He

rbaceous 

Communities 

Water Lily Aquatic 

Wetland 

2386 Sloughs 

South-Central Interior 

Large Floodplain 

CES202.705 

Vegetated Spring 

Branch 

N/A Springs 

Riverine 

Emergent 

Aquatic 

Communities 

N/A 

Ozark Fen 2404 

Ozark Prairie Fen 2416 

Fens Fens Ozark-Ouchita Fen 

CES202.052 

Buttonbush Sinkhole 

Pond Swamp 

4742 

Sinkhole Pond Marsh 2413 

Marshes Marshes Central Interior 

Highlands and 

Appalachian Sinkhole 

and Depression Pond 

CES202.018 

Dry Igneous Cliff 2286 

Moist Igneous Cliff 2289 

Central Interior Acidic 

Cliff and Talus 

CES202.689 

Dry Dolomite Cliff 2291 

Moist Dolomite Cliff 2292 

Cliffs 

Dolomite Talus 2308 

Central Interior 

Calcareous Cliff and 

Talus CES202.690 

Igneous Talus 5203 

Talus 

Cliffs and 

Talus 

Central Interior Acidic 

Cliff and Talus 

CES202.689 

Appendix 5 References 

NatureServe. 2005. International ecological classification standard: Terrestrial ecological 

classifications. NatureServe Central Databases. Arlington, VA. U.S.A. Data current as of 

30 September, 2005. (www.natureserve.org) 

A5-3 
Appendix 5. ONSR Ecological Systems-NatureServe Ecological Systems Crosswalk 

A5-4 
Appendix 6. Crosswalk of ecological systems used to map altered communities. 

Appendix 6. Crosswalk of ecological systems used to map altered communities 

within the mapping area and Anderson’s classification system for built-up and 

aquatic features (Anderson, 1976) and the Federal Geographic Data Committee 

(FGDC) classification of cultivated lands. 

USNVC Vegetation 

Association 

Code ONSR 

Community 

Type 

ONSR 

Ecological 

System 

Level 2 

Anderson or 

FGDC (italics) 

Level 1 

Anderson or 

FGDC (italics)

Vegetated Features 

Oak-Hickory 

Regeneration Stand 

SA02 

Pine-Oak Regeneration 

Stand 

SA05 

Regeneration 

Stand 

Oak-Hickory 

Shelterwood/Select 

Harvest 

SA01 

Pine-Oak 

Shelterwood/Select 

Harvest 

SA04 

Shelterwood/

Select 

Harvest 

Oak-Hickory Pole 

Stand 

SA03 

Pine-Oak Pole Stand SA06 

Pine Pole Stand SA08 

Pole Stand 

Pine Plantation SA07 Pine 

Plantation 

Timber 

Management 

Area 

Pine-Deciduous 

Shrubby Old Field 

SA12 

Deciduous Shrubby 

Old Field 

SA09 

Herbaceous Old Field SA23 

Cedar-Deciduous 

Shrubby Old Field 

SA36 

Open Old 

Field with 

Shrubby or 

Sparse Trees

Deciduous Forested 

Old Field 

SA10 

Pine-Deciduous 

Wooded Old Field 

SA11 

Cedar-Deciduous 

Wooded Old Field 

SA13 

Pine Old Field SA14 

Cedar Old Field SA15 

Wooded Old 

Field 

Old Fields 

Agricultural Forested 

Woodlot 

SA37 Agricultural 

Woodlot 

N/A N/A 

Hayfield/Grazing Land SA20 

lose Grown 

Monoculture 

SA21 

Close grown 

Row Crops SA22 

Agricultural 

Field/Pasture

Agricultural 

Field/Pasture 

Row Crop 

Herbaceous 

A6-1 
Appendix 6. Crosswalk of ecological systems used to map altered communities. 

Cultural and Built-up Features 

Deciduous Wooded 

Residence 

SA16 

Evergreen-Deciduous 

Wooded Residence 

SA17 

Evergreen Wooded 

Residence 

SA18 

Lawn SA19 

Residential 

(structures) 

SA35 

Residential Residential Residential 

Industrial SA26 Industrial 

Industrial/Commercial SA27 

Industrial/ 

Quarry 

Industrial/ 

Quarry Industrial and 

Commercial 

Complexes 

Commercial SA28 

Commercial/Services SA29 

Commercial 

and Services 

Mixed SA30 

Urban Urban 

Mixed Urban 

or Built-up 

Land 

Other Clearing SA31 Other Clearing Other 

Clearing 

Other Urban or 

Built-up Land 

Transportation 

Corridor (Road) 

SA32 Trans. 

Corridor 

Trans. 

Corridor 

Shrubby Utility 

Corridor 

SA33 

Herbaceous Utility 

Corridor 

SA34 

Utility Corridor Utility 

Corridor 

Transportation, 

Communicatio

n and Utilities 

Urban or Built-up 

Land 

Hatchery SA24 

Lake/Pond SA25 

Surface Water Surface Water Lakes Water (nonvegetated 

portion) 

Appendix 6 References 

Anderson, J.R., Hardy, E.E. and Roach, J.T. 1976. Land use and land cover classification System 

for use with remote sensing data. Geological Survey Professional Paper 964. A revision 

of the land use classification system as presented in US. Geological Circular 671. U. S. 

Government Printing Office, Washington, D.C. 

A6-2 
Appendix 7. Automated classification data derivatives and transformation techniques. 

Appendix 7. Automated classification data derivatives and transformation 

techniques. 

Table A7-1. Data derivatives obtained from the October 2002 aerial photo images.

ir2m Reflectance in the infrared wavelength from 2 meter resolution image data 

red2m Reflectance in the red wavelength (2 meter resolution) 

green2m Reflectance in the green wavelength (2 meter resolution) 

illalb2m Illumination/albedo surface; 2 meter resolution (Sqrt(IR2

 + Red2 + Green2))

div112m Diversity of brightness values in the illumination/albedo surface (11x11 window) 

sd112m Standard deviation of brightness values in the illumination/albedo surface (11x11) 

variance2m Variance of brightness values in the illumination/albedo surface (11x11) 

propbright15 Proportion of bright pixels in a 15x15 neighborhood (2 meter resolution) 

propbright25 Proportion of bright pixels in a 25x25 neighborhood (2 meter resolution) 

sqvi2m Square root vegetation index; 2 meter resolution (Sqrt(IR/Red)) 

ndvi2m Normalized difference vegetation index; 2 meter resolution (Red - IR/Red + IR) 

pc12m Principal component 1 (2 meter resolution) 

pc22m Principal component 2 (2 meter resolution) 

pc32m Principal component 3 (2 meter resolution) 

shadir2m Infrared reflectance with shadows masked out (2 meter) 

shadred2m Red reflectance with shadows masked out (2 meter) 

shadgreen2m Green reflectance with shadows masked out (2 meter) 

msqvi2m Square root vegetation index with shadows masked (2 meter resolution) 

mndvi2m Normalized difference vegetation index with shadows masked (2 meter) 

ir14m Reflectance in the infrared wavelength (14 meter resolution) 

red14m Reflectance in the red wavelength (14 meter resolution) 

green14m Reflectance in the green wavelength (14 meter resolution) 

illalb14m Illumination/albedo surface; 14 meter resolution (Sqrt(IR2

 + Red2 + Green2))

sqvi14m Square root vegetation index; 14 meter resolution (Sqrt(IR/Red)) 

ndvi14m Normalized difference vegetation index; 14 meter resolution (Red - IR/Red + IR) 

pc114m Principal component 1 (14 meter resolution) 

pc214m Principal component 2 (14 meter resolution) 

pc314m Principal component 3 (14 meter resolution) 

shadir14m Infrared reflectance with shadows masked out (14 meter) 

shadred14m Red reflectance with shadows masked out (14 meter) 

shadgreen14m Green reflectance with shadows masked out (14 meter) 

msqvi14m Square root vegetation index with shadows masked (14 meter) 

mndvi14m Normalized difference vegetation index with shadows masked (14 meter) 

ir30m Reflectance in the infared wavelength (30 meter resolution) 

red30m Reflectance in the red wavelength (30 meter resolution) 

green30m Reflectance in the green wavelength (30 meter resolution) 

illalb30m Illumination/albedo surface; 30 meter resolution (Sqrt(IR2

 + Red2 + Green2)) 

sqvi30m Square root vegetation index; 30 meter resolution (Sqrt(IR/Red)) 

ndvi30m Normalized difference vegetation index; 30 meter resolution (Red - IR/Red + IR) 

pc130m Principal component 1 (30 meter resolution) 

pc230m Principal component 2 (30 meter resolution) 

pc330m Principal component 3 (30 meter resolution) 

shadir30m Infrared reflectance with shadows masked out (30 meter) 

shadred30m Red reflectance with shadows masked out (30 meter) 

shadgreen30m Green reflectance with shadows masked out (30 meter) 

msqvi30m Square root vegetation index with shadows masked (30 meter) 

mndvi30m Normalized difference vegetation index with shadows masked (30 meter) 

A7-1 
Appendix 7. Automated classification data derivatives and transformation techniques. 

Table A7-2. Data derivatives obtained from the summer 2003 NAIP image data (2-

meter resolution). 

naipir NAIP infrared reflectance 

naipred NAIP red reflectance 

naipgreen NAIP green reflectance 

naipirmedian15 Median of infrared reflectance in a 15x15 neighborhood 

naipredmedian15 Median of red reflectance in a 15x15 neighborhood 

naipgreenmedian15 Median of green reflectance in a 15x15 neighborhood 

naipndvi Normalized difference vegetation index 

naipndvimedian15 Median of NDVI in a 15x15 neighborhood 

naipsqvi Square root vegetation index 

naipsqvimedian15 Median of square root vegetation index in a 15x15 neighborhood 

Normalization of TM data 

An empirical model relating solar illumination angle to differential reflectance of forested 

pixels was developed band by band for TM data. The regression equation and parameter values 

for each band are given in Table A7-3. An empirical model relating solar illumination angle to 

differential reflectance of forested pixels was developed band by band (cos(i), computed pixel by 

pixel from a 30 m digital elevation model and solar azimuth and elevation values for the Landsat 

overflight) such that illumination corrected reflectance (Ri) is calculated as: 

^

oi )cos( +−•−= RBMiRR

where M and B are regression parameters, Ro is the original reflectance, and ^R is the mean 

reflectance (Meyer and others, 1993). Regression parameters for each study area are reported in 

Table A7-3. 

Table A7-3. Model parameters applied to cos-i correction of Landsat data for the winter 

ETM+ (n=1,123) and summer TM (n=1,125) images. 

Parameter Enhanced Thematic Mapper (ETM) Bands 

ETM+1 ETM+2 ETM+3 ETM+4 ETM+5 ETM+6 

Winter ETM Image (03/13/02) 

Mean 66.47 49.79 56.65 67.82 94.79 60.59 

R2 0.7517 0.8543 0.8648 0.9262 0.8696 0.8362 

Intercept -147.72 -9.733 62.612 70.64 97.486 97.222 

Slope 4.72 3.54 1.836 1.415 0.729 1.145 

p >.0001 >.0001 >.0001 >.0001 >.0001 >.0001 

Summer TM Image (07/05/00) 

Mean 38.25 27.59 16.71 157.86 79.98 37.43 

R2 0.3467 0.4645 0.5691 0.8486 0.7339 0.654 

Intercept 146.89 148.83 139.72 112.23 110.21 130.54 

Slope 1.003 1.321 2.726 0.463 0.938 1.462 

p >.0001 >.0001 >.0001 >.0001 >.0001 >.0001 

Normalization equation takes the form of Y = old TM radiance – cosi · slope – intercept + mean where Y is the new 

TM/ETM radiance. 

A7-2 
Appendix 7. Automated classification data derivatives and transformation techniques. 

Table A7-4. Data derivatives obtained from summer and winter Landsat TM/ETM 

image data (30-meter resolution). 

tmsum_b1 Band 1 (blue: 0.45-0.52 μm) from July 5, 2000 TM image 

tmsum_b2 Band 2 (green: 0.52-0.60 μm) from July 5, 2000 TM image 

tmsum_b3 Band 3 (red: 0.63-0.69 μm) from July 5, 2000 TM image 

tmsum_b4 Band 4 (infrared: 0.76-0.9 μm) from July 5, 2000 TM image 

tmsum_b5 Band 5 (mid-IR: 1.55-1.75 μm) from July 5, 2000 TM image 

tmsum_b6 Band 7 (mid-IR: 2.08-2.35 μm) from July 5, 2000 TM image 

tmsum_pc1 Principal component 1 from July 5, 2000 TM image 

tmsum_pc2 Principal component 2 from July 5, 2000 TM image

tmsum_pc3 Principal component 3 from July 5, 2000 TM image

tmsum_tc1 Tasseled cap brightness from July 5, 2000 TM image 

tmsum_tc2 Tasseled cap greenness from July 5, 2000 TM image

tmsum_tc3 Tasseled cap wetness from July 5, 2000 TM image

tmsum_ndvi NDVI (IR - Red / IR + Red) obtained from July 5, 2000 TM image 

tmsum_ndmi NDMI (IR - MIR / IR + MIR) obtained from July 5, 2000 TM image 

tmwin_b1 Band 1 (blue: 0.45-0.52 μm) from March 13, 2002 ETM image

tmwin_b2 Band 2 (green: 0.52-0.60 μm) from March 13, 2002 ETM image

tmwin_b3 Band 3 (red: 0.63-0.69 μm) from March 13, 2002 ETM image

tmwin_b4 Band 4 (infrared: 0.76-0.9 μm) from March 13, 2002 ETM image

tmwin_b5 Band 5 (mid-IR: 1.55-1.75 μm) from March 13, 2002 ETM image

tmwin_b6 Band 7 (mid-IR: 2.08-2.35 μm) from March 13, 2002 ETM image

tmwin_pc1 Principal component 1 from March 13, 2002 ETM image

tmwin_pc2 Principal component 2 from March 13, 2002 ETM image

tmwin_pc3 Principal component 3 from March 13, 2002 ETM image

tmwin_tc1 Tasseled cap brightness from March 13, 2002 ETM image

tmwin_tc2 Tasseled cap greenness from March 13, 2002 ETM image

tmwin_tc3 Tasseled cap wetness from March 13, 2002 ETM image

tmwin_ndvi NDVI (IR - Red / IR + Red) obtained from March 13, 2002 ETM image 

tmwin_ndmi NDMI (IR - MIR / IR + MIR) obtained from March 13, 2002 ETM image

A7-3 
Appendix 7. Automated classification data derivatives and transformation techniques. 

Table A7-5. Topographic data derivatives obtained from 10 meter resolution digital 

elevation data. 

slope Slope angle in degrees – values range from 0-90 

beer100 Beers-transformed slope aspect; index ranges from 0 to 200, with 0 being a grid 

cell that faces southwest, 100 indicating either northwest or southeast and 200 

being northeast; the formula is (cos(aspect - 45) + 1)*100 

trnbeer Slope multiplied by Beers transformed aspect 

rsp Relative slope position; ranges from 0 to 100, where 0 is a valley bottom and 100 

is a ridgetop. 

tci Topographic convergence index (TCI) is a measure of potential wetness; the 

formula is lnα/tanβ where lnα is the log of the upslope contributing area of a grid 

cell and tanβ is the tangent of the slope of a grid cell 

trmi Parker’s terrain relative moisture index; computed as slope + aspect + curvature 

+ slope position; ranges in value from 0 to 60 

rise Elevation rise in meters from Current or Jacks Fork Rivers; river channels have 

an elevation value of 0 

Appendix 7 References 

Meyer, P., Itten, K.I., Kellenberger, T., Sandmeier, S. and Sandmeier, R. 1993. Radiometric 

corrections of topographically induced effects on Landsat data in an alpine environment. 

ISPRS Journal of Photogrammetry and Remote Sensing, 48(4): 17-28. 

A7-4 
Appendix 8. Results of importance value (IV) calculations for the major USNVC vegetation 

associations in the ONSR mapping area 

Appendix 8. Results of importance value (IV) calculations for the major USNVC 

vegetation associations in the ONSR mapping area, with average cover and 

relative frequency presented by species and the number of field observations 

listed. 

N.B. values are based upon vegetative cover estimates for dominant species collected when 

describing vegetation, rather than upon plot data 

Species Abbreviation Key: 

acerneg = Acer negundo 

acerrub = Acer rubra 

acersac = Acer saccharum 

acersacn = Acer saccharinum 

betunig = Betula nigra 

caryalb = Carya alba 

carycor = Carya cordiformis 

carygla = Carya glabra 

caryova = Carya ovata 

carytex = Carya texana 

celtocc = Celtis occidentalis 

fraxame = Fraxinus americana 

fraxpen = Fraxinus pennsylvanicus 

gledtri = Gleditsia triacanthos 

juglnig = Juglans nigra 

junivir = Juniperus virginiana 

nysssyl = Nyssa sylvatica 

pinuech = Pinus Echinata 

platocc = platanus occidentalis 

popudel = populus deltoides 

queralb = Quercus alba 

quercoc = Quercus coccinea 

quermac = Quercus macrocarpa 

quermar = Quercus marilandica 

quermue = Quercus muehlenbergii 

querrub = Quercus rubra 

quershu = Quercus shumardii 

querstel = Quercus stellata 

quervel = Quercus velutina 

salicar = Salix caroliniana 

sassalb = Sassafras albidum 

tiliame = Tilia americana 

ulmuala = Ulmus alata 

ulmuame = Ulmus americana 

umlurub = Ulmus rubra 

A8-1 
Appendix 8. Results of importance value (IV) calculations for the major USNVC vegetation 

associations in the ONSR mapping area 

USNVC = 2058, N = 14 

spp cover relfreq IV

queralb 28.40 0.43 0.1217

acersac 24.23 0.50 0.1211

carycor 21.97 0.50 0.1099

querrub 16.73 0.57 0.0956

quermue 12.17 0.43 0.0521

juglnig 14.25 0.29 0.0407

fraxame 14.33 0.21 0.0307

carygla 20.00 0.14 0.0286

platocc 8.52 0.21 0.0183

ulmurub 20.00 0.07 0.0143

caryalb 19.60 0.07 0.0140

tiliame 6.50 0.14 0.0093

ulmuame 10.00 0.07 0.0071

celtocc 8.50 0.07 0.0061

pinuech 0.85 0.07 0.0006

USNVC = 2066, N = 32 

spp cover relfreq IV

queralb 52.47 1.00 0.5247

querrub 19.16 0.81 0.1557

carygla 10.66 0.41 0.0433

quercoc 11.44 0.28 0.0322

quervel 8.80 0.31 0.0275

caryalb 12.60 0.16 0.0197

juglnig 7.12 0.25 0.0178

acersac 5.76 0.25 0.0144

carycor 7.88 0.13 0.0098

ulmurub 10.00 0.06 0.0063

pinuech 9.25 0.06 0.0058

nysssyl 5.33 0.09 0.0050

fraxame 3.00 0.16 0.0047

quermue 3.00 0.03 0.0009

fraxpen 1.89 0.03 0.0006

USNVC = 2070, N = 18 

spp cover relfreq IV

queralb 34.43 0.89 0.3061

quermue 17.12 0.78 0.1332

querrub 17.92 0.72 0.1294

carycor 10.54 0.44 0.0468

fraxame 8.44 0.50 0.0422

platocc 13.86 0.28 0.0385

A8-2 
Appendix 8. Results of importance value (IV) calculations for the major USNVC vegetation 

associations in the ONSR mapping area 

acersac 11.26 0.28 0.0313

carygla 20.00 0.11 0.0222

juglnig 5.09 0.33 0.0170

pinuech 6.95 0.22 0.0154

caryalb 20.00 0.06 0.0111

junivir 10.00 0.11 0.0111

nysssyl 6.50 0.11 0.0072

gledtri 4.10 0.17 0.0068

ulmurub 10.00 0.06 0.0056

quercoc 8.50 0.06 0.0047

fraxpen 1.89 0.06 0.0011

USNVC = 2075, N = 5 

spp cover relfreq IV

quervel 26.70 0.80 0.2136

querste 21.20 0.80 0.1696

carytex 26.25 0.40 0.1050

querrub 22.70 0.40 0.0908

queralb 8.80 0.60 0.0528

carygla 6.30 0.20 0.0126

celtten 3.80 0.20 0.0076

quercoc 3.80 0.20 0.0076

pinuech 3.00 0.20 0.0060

juglnig 1.89 0.20 0.0038

USNVC = 2076, N = 70 

spp cover relfreq IV

queralb 25.98 0.94 0.2450

quercoc 25.27 0.80 0.2021

quervel 19.89 0.83 0.1648

carygla 16.79 0.37 0.0624

caryalb 16.68 0.23 0.0381

pinuech 7.22 0.34 0.0247

querste 10.03 0.10 0.0100

querrub 7.28 0.11 0.0083

carytex 10.75 0.06 0.0061

juglnig 4.72 0.07 0.0034

caryova 20.00 0.01 0.0029

nysssyl 6.50 0.03 0.0019

caryalb 10.00 0.01 0.0014

ulmuala 10.00 0.01 0.0014

celtten 3.00 0.01 0.0004

USNVC = 2399, N = 38 

spp cover relfreq IV

A8-3 
Appendix 8. Results of importance value (IV) calculations for the major USNVC vegetation 

associations in the ONSR mapping area 

quervel 30.58 0.97 0.2977

quercoc 29.49 0.89 0.2639

queralb 10.54 0.84 0.0888

pinuech 9.26 0.53 0.0488

querste 12.13 0.29 0.0351

caryalb 11.58 0.11 0.0122

carygla 5.20 0.05 0.0027

carytex 2.21 0.08 0.0017

quermac 3.00 0.03 0.0008

celtten 2.55 0.03 0.0007

USNVC = 2108, N = 30 

spp cover relfreq IV

junivir 31.63 1.00 0.3163

quermue 19.31 0.93 0.1803

querrub 14.78 0.73 0.1084

queralb 14.18 0.33 0.0473

acersac 7.09 0.30 0.0213

faguame 10.55 0.20 0.0211

querste 9.02 0.17 0.0150

pinuech 6.22 0.13 0.0083

juglnig 4.49 0.17 0.0075

platocc 3.17 0.10 0.0032

carygla 8.50 0.03 0.0028

quervel 8.50 0.03 0.0028

carytex 6.30 0.03 0.0021

caryova 3.00 0.03 0.0010

sassalb 3.00 0.03 0.0010

USNVC = 2143, N = 12 

spp cover relfreq IV

quermue 21.63 0.92 0.1983

querrub 14.90 0.75 0.1118

querste 13.26 0.42 0.0553

queralb 16.67 0.25 0.0417

junivir 7.12 0.42 0.0297

fraxame 6.75 0.33 0.0225

juglnig 11.40 0.08 0.0095

caryalb 5.05 0.17 0.0084

acersac 4.65 0.17 0.0078

carycor 6.30 0.08 0.0053

caryova 6.30 0.08 0.0053

ulmurub 2.07 0.17 0.0035

USNVC=2149, N = 4 

A8-4 
Appendix 8. Results of importance value (IV) calculations for the major USNVC vegetation 

associations in the ONSR mapping area 

spp cover relfreq IV

querste 14.08 1.00 0.1408

quervel 20.00 0.67 0.1333

carytex 23.55 0.50 0.1178

queralb 10.00 0.75 0.0750

carygla 20.00 0.25 0.0500

pinuech 10.00 0.50 0.0500

querrub 6.30 0.25 0.0158

quercoc 3.00 0.25 0.0075

quermar 1.89 0.25 0.0047

USNVC = 2398, N = 8 

spp cover relfreq IV

junivir 25.00 0.50 0.1250

andrscop 40.00 0.25 0.1000

querste 15.75 0.50 0.0788

quermue 4.75 0.50 0.0238

querrub 6.50 0.25 0.0163

boutcurt 10.00 0.13 0.0125

commrich 10.00 0.13 0.0125

liatpych 10.00 0.13 0.0125

quershu 10.00 0.13 0.0125

rudbmiss 10.00 0.13 0.0125

USNVC = 2393, N = 7 

spp cover relfreq IV

pinuech 20.00 1.00 0.2000

querste 17.14 1.00 0.1714

quervel 18.00 0.71 0.1286

quercoc 16.67 0.43 0.0714

queralb 8.17 0.86 0.0700

quermar 8.67 0.43 0.0371

quermue 3.00 0.29 0.0086

USNVC = 2401, N = 38 

spp cover relfreq IV

pinuech 28.19 1.00 0.2819

quercoc 22.41 0.92 0.2064

quervel 18.94 0.89 0.1695

queralb 10.47 0.79 0.0826

querste 15.59 0.39 0.0615

carygla 5.20 0.05 0.0027

caryalb 10.00 0.03 0.0026

carytex 3.00 0.03 0.0008

quermar 3.00 0.03 0.0008

A8-5 
Appendix 8. Results of importance value (IV) calculations for the major USNVC vegetation 

associations in the ONSR mapping area 

USNVC = 7489, N = 24 

spp cover relfreq IV

pinuech 27.53 1.00 0.2753

queralb 22.81 1.00 0.2281

quercoc 17.16 0.83 0.1430

quervel 11.60 0.63 0.0725

carygla 11.40 0.33 0.0380

querrub 13.15 0.17 0.0219

caryalb 15.17 0.13 0.0190

querste 5.53 0.08 0.0046

juglnig 8.50 0.04 0.0035

nysssyl 2.94 0.04 0.0012

USNVC = 3899, N = 12 

spp cover relfreq IV

platocc 21.93 1.00 0.2193

salicar 17.51 1.00 0.1751

betunig 2.07 0.17 0.0035

diosvir 3.00 0.08 0.0025

ulmurub 1.80 0.08 0.0015

acersacn 1.14 0.08 0.0010

juglnig 1.14 0.08 0.0010

amorfru 1.00 0.08 0.0008

USNVC = 2410, N = 11 

spp cover relfreq IV

platocc 26.67 1.00 0.2667

juglnig 17.85 0.82 0.1460

fraxpen 23.17 0.27 0.0632

carycor 12.88 0.45 0.0585

acersacn 29.75 0.18 0.0541

acerneg 11.38 0.36 0.0414

quermac 19.00 0.18 0.0345

fraxame 12.00 0.27 0.0327

querrub 10.63 0.27 0.0290

ulmurub 6.80 0.27 0.0185

queralb 18.90 0.09 0.0172

ulmuame 10.00 0.09 0.0091

quershu 8.50 0.09 0.0077

acersac 3.00 0.09 0.0027

USNVC = 5033, N = 8 

spp cover relfreq IV

acerneg 43.53 1.00 0.4353

A8-6 
Appendix 8. Results of importance value (IV) calculations for the major USNVC vegetation 

associations in the ONSR mapping area 

platocc 20.64 0.50 0.1032

acersacn 26.67 0.38 0.1000

fraxame 30.00 0.13 0.0375

fraxpen 11.50 0.25 0.0288

ulmurub 11.28 0.25 0.0282

celtocc 20.00 0.13 0.0250

juglnig 10.00 0.25 0.0250

quermue 17.00 0.13 0.0213

gledtri 6.28 0.25 0.0157

betunig 10.00 0.13 0.0125

quermac 10.00 0.13 0.0125

USNVC = 7334, N = 31 

spp cover relfreq IV 

platocc 43.15 1.00 0.4315

acersacn 22.18 0.42 0.0930

acerneg 16.56 0.35 0.0588

ulmurub 14.68 0.32 0.0474

juglnig 13.25 0.19 0.0256

popudel 13.31 0.16 0.0215

betunig 11.86 0.16 0.0191

ulmuame 16.67 0.10 0.0161

liritul 34.00 0.03 0.0110

gledtri 10.00 0.06 0.0065

salicar 20.00 0.03 0.0065

celtocc 9.25 0.06 0.0060

fraxame 10.00 0.03 0.0032

fraxpen 10.00 0.03 0.0032

acersac 8.50 0.03 0.0027

queralb 8.50 0.03 0.0027

carycor 3.00 0.03 0.0010

quermac 3.00 0.03 0.0010

USNVC = 2060, N = 10 

spp cover relfreq IV

queralb 26.84 0.80 0.2147

acersac 22.95 0.80 0.1836

querrub 21.68 0.80 0.1734

carycor 21.18 0.50 0.1059

juglnig 16.00 0.50 0.0800

quermue 13.33 0.30 0.0400

platocc 18.30 0.20 0.0366

nysssyl 9.25 0.20 0.0185

tiliame 5.95 0.20 0.0119

faguame 10.00 0.10 0.0100

A8-7 
Appendix 8. Results of importance value (IV) calculations for the major USNVC vegetation 

associations in the ONSR mapping area 

quershu 10.00 0.10 0.0100

ulmurub 10.00 0.10 0.0100

caryova 3.00 0.10 0.0030

A8-8 
Appendix 9: List of Deliverables to ONSR personnel. 

Appendix 9. List of Deliverables to ONSR personnel. 

Ozar_map (polygon coverage) 

Ozar_classmap (shapefile) 

Ozar49class (10m GRID) 

Ozar33class (10m GRID) 

Ozar_probmap (10m GRID) 

All of the individual probability maps from the various vegetation associations 

River-cliff (shapefile containing 108 cliff features digitized on the Current River between Pulltite 

and Van Buren) 

Vegetation field plot locations used for training statistical classification (point file) 

Vegetation map validation observation locations (point file) 

Missouri Natural Heritage Database points clipped to ONSR mapping region 

Digitized old fields from 1960s aerial photo mosaic 

Aerial photo mosaic of northern section (Current River) of mapping area (2m resolution) 

Photo mosaic of middle section (Current River) of mapping area (2m resolution) 

Photo mosaic of southern section (Current River) of mapping area (2m resolution) 

Photo mosaic of western section (Jack’s Fork) of mapping area (2m resolution) 

9 sections of 0.25 meter resolution photo mosaics 

NAIP aerial photo mosaic of mapping area (2m resolution) 

DEM elevation 

DEM-derived elevation rise from river channel 

DEM derived slope in degrees 

DEM-derived slope aspect in degrees 

DEM-derived Beers transformed slope aspect (ranges from 0-2) 

DEM-derived slope and aspect multiplied 

DEM-derived relative slope position (ranges from 0-100) 

DEM-derived Iverson’s index (60% TCI + 40% Beers) 

DEM-derived TRMI (slope + aspect + curvature + slope position) 

DEM-derived TCI (lna/tanB) 

Landsat 6 band image stack 

Landsat first 3 principal components 

Landsat tasseled cap brightness, greenness, and wetness 

Landsat normalized difference vegetation index 

Landsat normalized difference moisture index 

Digitized features for entire ONSR mapping area 

LTA 

ELT 

Underlying geology 

Surface Geology 

A9-1 
Appendix 9: List of Deliverables to ONSR personnel. 

A9-2 
Appendix 10: Data Dictionary 

Appendix 10. Data Dictionary. 

Table A10-1. Delineation Codes for Photointerpreted Digitized Areas (INFO field in 

coverages north_delin, mid_delin, south_delin, jack_delin = type). 

type Potential Communities Description 

1 SA32 Road/Transportation Corridor 

2 SA33, SA34 Utility Corridor 

3 SA31 Other Clearings 

4 SA20, SA21, SA22 Agricultural Field/Pasture 

5 SA02, SA05 Regeneration Stand 

6 SA01, SA04 Shelterwood/Select Harvest 

7 SA03, SA06, SA08 Pole Stand 

8 SA07 Pine Plantation 

9 River Current, Jack’s Fork, and Small Feeder 

Streams and Springs 

10 SA24, SA25 Surface Water 

11 SA09, SA12, SA23, SA36 Open Old Field (shrubby or sparse trees)

12 2398, 2149, 2143, 2108 Dolomite Glade/Woodland Complex 

13 2243, 2149, 2425, 2075, 2075i Igneous Glade/Woodland Complex 

14 SA10, SA11, SA13, SA14, SA15 Wooded Old Field 

15 SA37 Agricultural Forested Woodlot 

16 SA28, SA29, SA30 Urban 

17 SA16, SA17, SA18, SA19, SA35 Residential 

18 SA26, SA27 Industrial/Quarry 

Table A10-2. Codes for Digitized old fields (‘INFO field in oldfield coverage = type). 

type Potential Communities Description 

1 SA23 row crop or fallow field (bright) 

2 SA20 pasture 

3 SA09, SA12, SA36 pasture with trees 

4 Selectively Cut (SA01, SA03, SA04, SA06), 

Regeneration Stand (SA02, SA05), or 

Old Field Successional Sere (SA10, SA11, 

SA13, SA14, SA15) 

clearing with many trees 

A10-1 
Appendix 10: Data Dictionary 

A10-2 
Appendix 11. Map codes and descriptions. 

Appendix 11. Map codes and descriptions. 

Table A11-1. Grid codes for 49 class “Vegetation Association” level map of Ozark 

National Scenic Riverways (OZAR49class; Overall accuracy = 62% (kappa = .596); INFO 

field in Ozar_map coverage = grid-code).

Statistically Classified Types 

grid-code Included types Description 

1 2058 White Oak-Red Oak-Sugar Maple Mesic Forest 

2 2066 White Oak/Dogwood Forest (includes types initially identified 

as White Oak-Red Oak Dry Mesic Acid Forest (2067)). 

3 2070 White Oak Dolomite Forest 

4 2075 Midwest Post Oak-Blackjack Oak Forest 

5 2076 Black Oak-White Oak-Hickory Forest 

6 2108 Chinkapin Oak-Red Cedar Dry Alkaline Forest 

7 2143 Chinkapin Oak-Ash/Little Bluestem Woodland 

8 2149 Post Oak-Blackjack Oak/Little Bluestem Woodland 

9 2393 Shortleaf Pine-Oak Dry Woodland 

10 2399 Ozark Black Oak, Scarlet Oak Forest 

11 2400 Shortleaf Pine/Blueberry Forest 

12 2401 Shortleaf Pine-Black Oak Forest 

13 7489 Interior Highlands Shortleaf Pine-Oak Dry-Mesic Forest 

14 SA05 Pine-Oak Regeneration Stand 

15 SA07 Pine Plantation/Timber Management Forest 

16 SA08 Pine Pole Stand 

17 SA09 Deciduous Shrubby Old Field 

18 SA13 Cedar-Deciduous Wooded Old Field 

19 SA15 Cedar Old Field 

20 2049 Riverine Sand Flats (Herbaceous gravel bar) 

21 2060 Sugar Maple-Oak-Bitternut Hickory Mesic Bottomland Forest 

22 2410 Ash-Oak-Sycamore Mesic Bottomland Forest 

23 3898 Witchhazel, Dogwood Gravel Bar 

24 3899 Carolina Willow Shrubland 

25 5033 Box Elder Forest 

26 7334 Sycamore-Silver Maple Floodplain Forest (includes types 

originally identified as River Birch-Sycamore Forest (2086)). 

27 Bare gravel bar Bare Gravel and/or Sand Bars 

29 2149 Post Oak-Blackjack Oak/Little Bluestem Woodland - 

Igneous Phase 

30 2243 Igneous Glade 

31 2425 Blackjack Oak Xeric Scrub 

32 2075i Midwest Post Oak-Blackjack Oak Forest-Igneous Phase 

A11-1 
Appendix 11. Map codes and descriptions. 

Table A11-1. Grid codes for 49 class “Vegetation Association” level map of Ozark 

National Scenic Riverways (OZAR49class; Overall accuracy = 62% (kappa = .596); INFO 

field in Ozar_map coverage = grid-code).

Photointerpreted Types 

grid-code Included types Description 

28 River Current, Jack’s Fork, and Small Feeder Streams 

and Springs 

33* SA10, SA11, SA13, SA14, 

SA15 

Wooded Old Field 

34 SA32 Road 

35* SA33, SA34 Utility Corridor 

36 SA31 Other Clearing 

37* SA20, SA21, SA22 Agricultural Field/Pasture 

38* SA02, SA05 Regeneration Stand 

39* SA01, SA04 Shelterwood Cut 

40* SA03, SA06, SA08 Pole Stand 

41 SA07 Pine Plantation 

42* SA24, SA25 Surface Water (lakes, ponds, etc) 

43* SA09, SA12, SA23, SA36 Open Old Field (shrubby or sparse trees) 

44 2398, 2149, 2143, 2108 Dolomite Glade/Woodland Complex 

45 2243, 2149, 2425, 2075, 2075i Igneous Glade/Woodland Complex 

46 SA37 Agricultural Forested Woodlot 

47* SA28, SA29, SA30 Urban 

48* SA16, SA17, SA18, SA19, 

SA35 

Residential 

49* SA26, SA27 Industrial/Quarry 

*Aggregated to Community Type hierarchical level. 

A11-2 
Appendix 11. Map codes and descriptions. 

Table A11-2. Grid codes for 33 class, Community Type level map of Ozark National 

Scenic Riverways (OZAR33class; Overall Accuracy = 77.5% (kappa = .761), INFO field 

in Ozar_map coverage = gridc77). 

Gridc77 Community Type Included Vegetation Associations 

1 White Oak - Red Oak - Sugar Maple 

Mesic Forest†

2058 

3* White Oak Forests 2066, 2070 

9 Shortleaf Pine-Oak Dry Woodland† 2393 

10* Mixed Oak-Hickory Forests 2076, 2399 

13* Pine and Pine-Oak Forests 2400, 2401, 7489 

14 Pine-Oak Regeneration Stand† SA05 

15 Pine Plantation† SA07 

16 Pine Pole Stand† SA08 

17 Deciduous Shrubby Old Field† SA09 

18 Cedar-Deciduous Wooded Old Field† SA13 

19 Cedar Old Field† SA15 

21 Sugar Maple - Oak - Bitternut Hickory 

Mesic Bottomland Forest†

2060 

24* Active Channel/Gravel Bar Complex 2049, 3898, 3899 

26* Riverfront and Bottomland Forests 2410, 5033, 7334 

27 Bare gravel and/or Sand Bars Bare Gravel bar 

28 Current, Jack’s Fork, and Small Feeder 

Streams and Springs 

River 

44* Dolomite Glade/Woodland Complex 2108, 2143, 2149, 2398 

45* Igneous Glade/Woodland Complex 

(includes statistically classified 2243) 

2075, 2075i, 2149, 2243, 2425 

33 Wooded Old Field SA10, SA11, SA13, SA14, SA15 

34 Transportation Corridor† SA32 

35 Utility Corridor SA33, SA34 

36 Other Clearing† SA31 

37 Agricultural Field/Pasture SA20, SA21, SA22 

38 Regeneration Stand SA02, SA05 

39 Shelterwood/Select Harvest SA01, SA04 

40 Pole Stand SA03, SA06, SA08 

41 Pine Plantation/Timber Management 

Area†

SA07 

42 Surface Water SA24, SA25 

43 Open Old Field with Shrubby or Sparse 

Trees 

SA09, SA12, SA23, SA36 

46 Agricultural Forested Woodlot† SA37 

47 Urban SA28, SA29, SA30 

48 Residential SA16, SA17, SA18, SA19, SA35 

49 Industrial/Quarry SA26, SA27 

*Aggregated to Community Type hierarchical level from 49 class map 

†Vegetation Association name for non-aggregated types 

A11-3 
Appendix 11. Map codes and descriptions. 

A11-4 

Codes for river_cliffs shapefile: 

Description – This file contains 108 cliff features immediately adjacent or very proximate to the 

Current River channel between Pulltite landing and the highway 60 bridge in Van Buren. 

Locations of these cliffs were obtained via GPS by boat. 

Ht_short: Text field indicating the shortest estimated height of cliff feature 

Ht_tall: Text field indicating the tallest estimated height of cliff feature 

Notes: Text field containing comments on the morphology of cliff feature 
Appendix 12. 49-Class USNVC Vegetation Association Map 

Appendix 12. 49-Class USNVC Vegetation Association Map (reduced copy*). * Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A12-1 
Appendix 12. 49-Class USNVC Vegetation Association Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A12-2 
Appendix 12. 49-Class USNVC Vegetation Association Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A12-3 
Appendix 12. 49-Class USNVC Vegetation Association Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A12-4 
Appendix 12. 49-Class USNVC Vegetation Association Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A12-5 
Appendix 12. 49-Class USNVC Vegetation Association Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A12-6 
Appendix 12. 49-Class USNVC Vegetation Association Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A12-7 
Appendix 12. 49-Class USNVC Vegetation Association Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A12-8 
Appendix 12. 49-Class USNVC Vegetation Association Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A12-9 
Appendix 12. 49-Class USNVC Vegetation Association Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A12-10 
Appendix 12. 49-Class USNVC Vegetation Association Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A12-11 
Appendix 12. 49-Class USNVC Vegetation Association Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A12-12 
Appendix 12. 49-Class USNVC Vegetation Association Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A12-13 
Appendix 12. 49-Class USNVC Vegetation Association Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A12-14 
Appendix 12. 49-Class USNVC Vegetation Association Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A12-15 
Appendix 12. 49-Class USNVC Vegetation Association Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A12-16 
Appendix 13. 33-Class Community Type Map 

Appendix 13. 33-Class Community Type Map (reduced copy*). * Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A13-1 
Appendix 13. 33-Class Community Type Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A13-2 
Appendix 13. 33-Class Community Type Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A13-3 
Appendix 13. 33-Class Community Type Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A13-4 
Appendix 13. 33-Class Community Type Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A13-5 
Appendix 13. 33-Class Community Type Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A13-6 
Appendix 13. 33-Class Community Type Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A13-7 
Appendix 13. 33-Class Community Type Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A13-8 
Appendix 13. 33-Class Community Type Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A13-9 
Appendix 13. 33-Class Community Type Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A13-10 
Appendix 13. 33-Class Community Type Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A13-11 
Appendix 13. 33-Class Community Type Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A13-12 
Appendix 13. 33-Class Community Type Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A13-13 
Appendix 13. 33-Class Community Type Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A13-14 
Appendix 13. 33-Class Community Type Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A13-15 
Appendix 13. 33-Class Community Type Map 

* Other than Map Index, map scale as displayed is 1:100,000. Original maps are 1:24,000. 

A13-16 
Appendix 14. ROC Graphs of select groups of communities and of the Mixed OakHickory/Dogwood Forest (2076) 

Appendix 14. ROC Graphs of select groups of communities and the Mixed OakHickory/Dogwood Forest. 

Figure A14-1. ROC graph of mesic and upland oak and hickory forests 

Figure A14-2. ROC graph of pine and pine-oak forests and woodlands 

A14-1 
Appendix 14. ROC Graphs of select groups of communities and of the Mixed OakHickory/Dogwood Forest (2076) 

Figure A14-3. ROC graph of dolomite woodlands and glades

Figure A14-4. ROC curve graphs of bottomland forests. 

A14-2 
Appendix 14. ROC Graphs of select groups of communities and of the Mixed OakHickory/Dogwood Forest (2076) 

Figure A14-5. ROC curve graph for the Mixed Oak-Hickory/Dogwood Forest (2076) 

with the probability of detection plotted against the thresholds of the mapped 

probabilities obtained from discriminant analysis. 

A14-3 
Appendix 14. ROC Graphs of select groups of communities and of the Mixed OakHickory/Dogwood Forest (2076) 

A14-4 
Appendices 15-18 

Appendices 15-18. 

The following appendices are contained in separate documents: 

Appendix 15. ONSR USNVC Natural Community Descriptions.

Appendix 16. ONSR Altered Community Descriptions. 

Appendix 17. Field Key to ONSR Vegetation Communities.

Appendix 18. ONSR USNVC Community Fuel Loading Photo Key. 

A15-18 
Appendices 15-18 

A15-18 
Appendix 19. Pilot Area Test of Two Statistical Classification Methods 

Appendix 19. Pilot Area Test of Two Statistical Classification Methods. 

Training data used to support a classification using both nonparametric and maximum 

likelihood decision rules were obtained using ERDAS Imagine version 8.7 to create feature 

space objects that function as nonparametric signatures. These signatures were produced by 

drawing polygonal training areas around field survey plot locations, so that the spatial data in the 

immediate vicinity could be associated with those locations. A subset of less than half of the 

available survey points was used to train the classification, and the remaining data were set aside 

to validate the resulting map. The signatures were evaluated to determine which of the inputs 

should be included to optimize the classification by examining the transformed divergence, a 

measure of statistical distance between signatures. A nonparametric parallelepiped decision rule 

was used to assign individual training data cases to vegetation association types based on 

patterns in the independent variables. Cases that either fell into more than one class (overlap), or 

were not within any of the parallelepiped class boundaries (unclassified) were placed in one of 

the classes based on the result of a maximum likelihood decision rule. The choice to classify all 

of the training cases was motivated by the desire to emulate as closely as possible the greedy 

algorithm employed by the regression tree classification also performed for this pilot study, thus 

enhancing the comparability of the results of the two classification approaches. The map of the 

pilot area that resulted from this classification is shown in map A of Figure A19-1. 

The Recursive Partitioning and Regression Trees (RPART) package in the R statistical 

software version 1.8.1 was used to develop a regression tree based on the training data described 

above. The class method was used to create a regression tree model to classify plant community 

associations, using the Gini index of impurity as a splitting rule to maximize impurity reduction 

during data splitting at tree nodes with prior probabilities p proportional to observed class 

frequencies. A generalized Gini index of impurity is defined as the expected cost of 

misclassification. No pruning was applied to the greedy outcome of the regression tree to 

maximize the number of vegetation associations classified by the tree model. A set of if-then 

statements based on the parameters in the regression tree was coded and run in the ArcGIS GRID 

module to produce the pilot area association level map shown in map B of Figure A19-1. 

Rather low accuracy results were obtained from the two classification approaches tested 

in this pilot study. The regression tree model for the association level vegetation communities 

was marginally more accurate overall (39.2% with a kappa value of 0.367). While the maximum 

likelihood decision rule classification result was only 5 percent less accurate overall at 34.5% 

(kappa = 0.314), certain vegetation associations were mapped more accurately using former 

approach compared to the latter (e.g. 2058, 2070, 2149, SA02 and SA04). The low accuracy 

results obtained in this pilot investigation point out some limitations associated with using 

statistical classifiers alone in complex land cover mapping problems. For example, numerous 

USNVC vegetation association types were not discernable by the regression tree model, and 

were consequently left out of the results. Some of the categories in a predetermined 

classification scheme will not be classified by regression tree model due to inseparability or 

rarity, especially when the tree model result is optimized through pruning. In addition, the 

maximum likelihood decision rule classification lacks an efficient and trustworthy method for 

variable selection, and thus often generates a non-parsimonious classification model. 

" A19-1
Appendix 19. Pilot Area Test of Two Statistical Classification Methods 

a)

Classified Communities (see 

Appendix 2 for community names)* 

b)

Figure A19-1. Comparison of maps produced using decision rule classification (a, 28 

classes), and regression tree classification (b, 22 classes).

*Legend applies to both maps 

In this pilot investigation, regression trees indicated the importance of particular 

independent variables and where they are valuable for distinguishing both natural and human 

altered vegetation community classes. It was determined that the ultimate utility of regression 

tree classification for mapping USNVC vegetation associations may be realized by applying its 

information content towards a preliminary stratification of the study area so that separate 

classification models can be applied within each relatively more homogeneous region. The 

primary splits identified by the association level regression tree (Figure A19-2) separate the pilot 

study area into vegetation communities that occur on igneous knobs, in bottomland areas 

(including old fields), and those that occur on the remainder of the upland hills and breaks. The 

ONSR mapping area was split using Ecological Land Type (ELT) data to produce masks for the 

bottomland, igneous glade, and hills and breaks regions. Further, it was decided that due to the 

" A19-2
Appendix 19. Pilot Area Test of Two Statistical Classification Methods 

numerous categories in the USNVC association-level classification scheme and copious 

independent variables available for classification model input, discriminant analysis represented 

a more appropriate method to pursue, as this statistical approach has shown promise with large 

hyperspectral remote sensing input datasets (Clark and others, 2005; Karimi and others, 2005). 

Figure A19-2. Regression tree used to produce maps in Figure A19-1, with structural 

features that suggested performing three separate classifications on each of the groups 

identified.

Appendix 19 References 

Clark, M.L., Roberts, D.A. and Clark, D.B. 2005. Hyperspectral discrimination of tropical tree 

species at leaf to crown scales. Remote Sensing of Environment, 96(3-4): 375-398. 

Karimi, Y., Prasher, S.O., McNairn, H., Bonnell, R.B., Dutilleul, P. and Goel, R.K.. 2005. 

Classification accuracy of discriminant analysis, artificial neural networks, and decision 

trees for weed and nitrogen stress detection. Transactions of the ASAE, 48(3): 1261-

1268. 

" A19-3
Appendix 19. Pilot Area Test of Two Statistical Classification Methods 

" A19-4
Appendix 20. Details of the Discriminant Analysis Statistical Model 

Appendix 20. Details of the Discriminant Analysis Statistical Model. 

Stepwise discriminant analysis was first applied to determine which of the 92 discriminating 

variables from the remote sensing and topographic data layers were most valuable in predicting 

land cover type membership for all of the pixels in the study area. The STEPDISC procedure in 

SAS software was applied using the stepwise selection method on the complete set of 92 variables 

in the training dataset. A total of 58 out of 92 variables (63 percent) were chosen for the breaks and 

hills section of the study area, 24 out of 92 variables (26.1 percent) were chosen for the igneous 

areas, and 38 out of 92 variables (41.3 percent) were chosen for the bottomlands portion of the 

overall study area. 

The SAS software canonical discriminant analysis (CANDISC) procedure was then applied 

using variables chosen by the stepwise analysis. A canonical discriminant function is a linear 

combination of the discriminating variables, and takes the mathematical form: 

fkm = u0 + u1X1km + u2X2km + ... + upXpkm

where fkm = value (score) on the canonical discriminant function for case m in group k, Xikm

= value on discriminating variable Xi for case m in group k, and ui = coefficients which produce the 

desired characteristics in the function. The maximum number of unique canonical functions that 

can be derived is equal to the number of categories in the classification minus one, or the number of 

discriminating variables, whichever is fewer. Thus, canonical functions are useful as a method to 

reduce the dimensionality of classification problems. For example, the 38 discriminating variables 

that went into the bottomlands classification were reduced to 6 uncorrelated canonical variables 

though this method, because there is a total of 7 USNVC association types that serve as target 

categories for classification in the bottomland areas. 

3) The discriminant analysis (DISCRIM) procedure in SAS software was applied to 

classify the bottomlands, igneous areas, and hills and breaks separately using the canonical 

discriminant functions that were created in the CANDISC procedure as calibration data. This 

classification method utilizes a linear combination of the canonical functions to predict the category 

(USNVC association) to which each training data case most likely belongs. Posterior probabilities 

of category membership were then computed from the results of this classification approach 

(Klecka, 1980). 

4) The ERDAS Imagine Model Maker was used to apply the results from the discriminant 

analysis to image datasets. The first step involved mathematically combining the various images 

representing the input discriminating variables to produce images containing continuous 

representations of the canonical discriminant functions used in the classification model. This was 

accomplished by copying the raw canonical coefficients from SAS software output into a ‘function 

definition’ in ERDAS Imagine Model Maker (Figure A20-1). Next, the discriminant function 

coefficients from SAS software output were similarly incorporated into a second ERDAS model 

wherein of all of the canonical discriminant functions were combined to produce images containing 

classification scores for all of the USNVC association categories in the bottomland portion of the 

ONSR mapping region (Figure A20-2). Using these classification scores, posterior probabilities of 

group membership for all of the USNVC association types (ERDAS model not shown) were 

computed. A final ERDAS model was used to combine all of the USNVC association categories 

into a single thematic image using a conditional statement in the function definition dialog to assign 

to each cell the map category with the highest probability for that cell location (Figure A20-3). A 

second image was also created in this model to store the value of the highest probability for each 

cell. 

" A20-1
Appendix 20. Details of the Discriminant Analysis Statistical Model 

Figure A20-1. Graphic environment of the ERDAS Imagine Model Maker showing the 

creation of the first canonical discriminant function for the bottomland portion of the ONSR 

mapping region with the mathematical combination of the input images in the function 

definition dialog shown in the lower left.

" A20-2
Appendix 20. Details of the Discriminant Analysis Statistical Model 

Figure A20-2. Graphic environment of the ERDAS Imagine Model Maker showing the 

combination of all of the canonical discriminant functions to produce images containing 

classification scores for all of the USNVC association categories in the bottomland portion 

of the ONSR mapping region.

" A20-3
Appendix 20. Details of the Discriminant Analysis Statistical Model 

Figure A20-3. Graphic environment of the ERDAS Imagine Model Maker showing 1) the 

generation of the USNVC association thematic image using a conditional statement in the 

function definition dialog to assign the category with the highest probability to each cell 

(right), and 2) a second image that stores the value of the highest probability for each cell 

(left).

" A20-4