This file was created by scanning the printed publication.
Errors identified by the software have been corrected; however, some errors may remain.
1
2
3
4
3
Abstract-Ecological classifications of potential and existing vegetation patterns are of particular importance to assessments of ecosystem health for at least two reasons: 1) they provide a useful framework for summarizing resource information from disparate sources, and 2) they can be mapped across large areas by using techniques that integrate remote sensing, geographic information systems, and ecological modeling. In this paper, we describe methods for mapping potential and existing vegetation in rangeland environments based on previous research conducted within a mixed grass prairie ecosystem of western North Dakota, U.S.A. We also illustrate how the intersection ofthese thematic layers can be used to derive indices of rangeland health and resource condition. Two examples are provided-total biomass production and year-long cattle forage production.
Most assessments of rangeland health within the United
States rely on measures of current landscape conditions
(e.g., vegetation and soil properties) collected from field plots in a random, grid-based sampling design (Burkart et al.
1994; National Research. Council 1986; USDA 1993;
Woudenberg and Farrenkopf 1995) . Interpretation of such data is complicated, however, because: 1) the attributes collected for field plots often vary between inventory programs; 2) methods for extrapolating plot data across large areas are seldom developed; and 3) "natural" reference conditions cannot be established readily from plot data.
Ecological classifications of potential and existingvegetation environments provide a useful framework for summarizing resource information from disparate sources (Jensen
Ipaper presented at the North American Science Symposium: Toward a
Unified Framework for Inventorying and Monitoring Forest Ecosystem
Resources, Guadalajara, Mexico, November 1-6,1998.
2Mark E. Jensen is Project Leader, Ecological Applications Service Team,
USDA, Forest Service, Missoula, MT 59807. Phone: (406) 329-3039. Fax: (406)
329-3347. e-mail: mjensenlr1@fs.fed.us
3Roland L. Redmond is Wildlife Spatial Analysis Lab, Montana Cooperative Wildlife Research Unit, The University of Montana, Missoula, MT 59812.
3Melissa M. Hart is Wildlife Spatial Analysis Lab, Montana Cooperative
Wildlife Research Unit, The University of Montana, Missoula, MT 59812.
4Iris A. Goodman is Research Environmental Scientist, US. Environmental Protection Agency National Exposure Research Laboratory, Environmental Sciences Division, Las Vegas, NV 89119.
Srrerrence M. Sobecki is Project Leader, Monitoring, USDA, Natural
Resources Conservation Service, Washington, DC 20250. et al. 1991; Host et al. 1996; Scott and Jennings 1998), and they can be mapped across large areas by means of remote sensing, geographic information systems, and ecological modeling technologies (Franklin 1995). Such maps facilitate rapid and cost-effective· assessments of ecosystem health because they provide a basis for describing current conditions, "natural" baseline reference conditions, and potential resource values.
The primary objective of this paper is to describe how two fundamental ecological map themes, potential and existing vegetation, can be intersected to create novel information and indices useful for monitoring rangeland resource conditions and health. We present derived maps of rangeland health indices that demonstrate how a spatially explicit use of ecological classifications can be more informative than variables derived from traditional, site-based techniques.
This paper synthesizes previous research pn biophysical modeling of potential vegetation patterns (Jensen et al.
1998a), use of satellite imagery and potential vegetation to map existing vegetation patterns (Winne et al. 1998), and the assessment of rangeland health based on ecological classifications (Jensen et al. 1998b).
This study was conducted within the Little Missouri
National Grasslands (LMNG) of western North Dakota
(Fig. 1), an area of approximately 809,380 ha that is managed primarily by the USDA, Forest Service, Custer National Forest, for cattle grazing, oil and gas leasing, wildlife habitat, and recreation. Terrain is diverse, and vegetation patterns are characteristic of the mixed grass prairie region of the Northern Great Plains (Bailey 1995). Dominant plant species include western wheatgrass (Agropyron
smithii), green needlegrass (Stipa viridula), needle and thread grass (Stipa comata), blue grama (Bouteloua graci-
lis) and threadleaf sedge (Carex filifolia). Various broadleaf and coniferous trees and shrubs are found on steep north slopes, in narrow drainages and draws, and in wide valleys along streams and rivers. The initial list of potential vegetation types considered for biophysical modeling in this study included six grassland, six shrubland, and six woodland habitat types (Table 1). Further details can be found in Jensen et al. (1998a).
USDA Forest Service Proceedings RMRS-P-12. 1999 381

382
Subsections:
II
331Fd
• 331Fe
• 331Fg
• Field Sites
r.
II
Dickinson
Figure 1.- Generalized display of the field plots used in describing known habitat type locations within the Little Missouri National Grasslands, stratified by geoclimatic subsections.
Potential vegetation was mapped according to habitat type, where a given combination of diagnostic plant species
(e.g.,Agropyron smithii / Stipa viridula) represents a unique environment for management (see Daubenmire 1968; Pfister et al. 1977). Existing vegetation was m~pped by land cover type (e.g., low cover grasslands); additional analyses were conducted at the more detailed level of dominance types, which are defined by the dominant plant species within specified layers of vegetation at a site (e.g., Idaho fescue/ bluebunch wheatgrass).
A total of 2,617 field plots were collected by Custer National Forest personnel between 1987-1996 for a variety of objectives, including resource inventories, development of habitat type and seral plant community classifications, and ground-truth sampling for mapping land cover. Although sampling designs differed, all plots were collected according to standardized procedures outlined in the Ecological Inventory and Analysis Guide of the USDA, Forest Service, Northern Region (USDA-FS 1988). Field plots were 0.18 ha in size and were located without preconceived bias (Mueller-Dombois and Ellenberg 1974) on representative habitat types and dominance types across the landscape. Data collected at each plot included: general environmental characteristics
(e.g., slope, aspect, elevation, geology, landform, landform position), production by lifeform, canopy cover by lifeform, ground cover, canopy cover and plant height by species, and
Table 1.-Listing ofthe 18 primary habitat types (potential vegetation environments) identified within the Little
Missouri National Grasslands. This list reflects a synthesis of habitat types as described by Hirsh and Baker (1984), Girard et al. (1989), Hansen et al. (1984), and Jensen et al. (1992). Habitat types denoted with an (x) are those identified in the final discriminant analysis of potential vegetation environments stratified by lifeform (grasslands, shrublands, woodlands) and subsection.
Missouri
Breaks
Little Missouri
Badlands Habitat Type
Grassland Habitat Types
Agropyron smithiCStipa comata
Agropyron smithiCStipa viridula
Agropyron smithiCStipa viridula-Bouteloua gracilis
Andropogon scoparius-Carex fififoilia
Calamovilfa longifolia-Carex
Stipa comata-Carex filifolia
Shrubland Habitat Types
Artemisia cana-Agropyron smithii
Artemisia tridentata wyomingensis-Agropyron smithii
Juniperus horizontalis-Andopogon scoparius
Rhus aromatica-Agropyron spicatum
Rhus aromatica-Muhlenbergia cuspidata
Sarcobatus verrniculatus-Agropyron smithii
Woodland Habitat Types
Fraxinus pennyslvanica-Prunus virginiana
Fraxinus pennsylvanica-Symphoricarpos occidentalis
Fraxinus pennsylvanicalUlmus americana-Prunus virginiana
Juniperus scopulorum-Oryzopsis micrantha
Populus deltoides-Juniperus scopulorum (CT)
Quercus macrocarpa-Prunus viriginiana
Missouri
Plateau
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
USDA Forest Service Proceedings RMRS-P-12. 1999

geographic location (latitude, longitude). For 1296 plots, habitat types were determined based on floristic and abiotic characteristics following an hierarchical dichotomous key developed for the study area by Jensen et a1. (1992); existing vegetation was assessed for all 2617 plots and assigned to one of 94 land cover types (Redmond et a1. 1997).
A map of potential vegetation (habitat type) was developed by: 1) delineating appropriate biophysical strata for modeling; 2) developing rule sets through multivariate statistical analysis to predict potential vegetation for all appropriate strata; and 3) extrapolating these rule sets to create a continuous map of habitat types at 30 m resolution (Jensen et a1.. 1998a).
First, each field plot was assigned nine climatic (Waltman
et al. 1997), six topographic, and seven spectral variables
(Table 2) using ARCIINFO Geographic Information System
(GIS) software; these were then used as predictor variables for modeling potential vegetation. To facilitate development of predictive rule sets, plot data then were stratified by geoclimatic subsection (Fig. 1; Nesser et a1. 1997), and by lifeform of existing vegetation (i.e., grasslands, shrublands, and woodlands) as mapped previously for the study area
(Redmond et a1. 1997). Nine groups resulted; Table 1 outlines the habitat types modeled within each one.
For each group, stepwise multivariate analysis of variance test (MANOVA) was used to determine which variables (Table 2) best predicted the habitat types associated with each field plot. Variables that significantly (P<O.Ol) reduced Wilks' Lambda in the above stepwise MANOV A analysis were then used to compute Fisher canonical discriminant functions for predicting habitat types in each stratum. Again for each group, classification accuracy was assessed using ajackknife procedure (Norusis 1985). Using the Fisher functions, predicted habitat types were mapped at 30 m resolution.
Existing vegetation was mapped in two general stages
(Winne et a1. 1998): polygons first were delineated through an image segmentation process, then a land cover label was assigned to each polygon through supervised classification
(Table 3). The entire process relied on several sources of digital data, including potential vegetation, ground-truth data, and two Landsat Thematic Mapper (TM) images.
Further details about the land cover mapping process can be found in Winne et a1. (1998).
Because maps of existing vegetation derived from satellite imagery do not always meet the needs of research and management, we explored refinement of the land cover classification to better match the needs of potential users.
Working with the field plots that were used to drive the classification, we stratified four grassland ~over types by
Table 2.-List of raster based biophysical environment predictor variables used in potential vegetation modeling.
Variable symbol Variable name
Climatic variables
CST_AM OX
CST_BI05
CST_BI08
CST_FFPX
CST_GOD
CST_MAAT
CST_MSDX
CST_PET
CST_SRPG
Topographic variables
TSI
Ele
Flat
High
Annual moisture surplus/deficient (mm)
Biological window (days) at 5 °C
Biological window (days) at 8 °C
Frost-free period (days)
Growing degree days at 10°C
Mean annual air temperature (0C)
Mean summer moisture deficient (mm)
Potential evapo-transpiration (mm)
Soil ratings for plant growth
Low
Sip
Terrain shape index
Elevation (m)
No solar aspect class (slopes < 5%)
High solar aspect class
(aspect 135°- 315° and slopes> 5%)
Low solar aspect class
(aspect 316° - 134° and slopes> 5%)
Slope (%)
Satellite imagery variables
MNDVI Modified normalized vegetation index
TM1 Landsat thematic mapper band 1
(blue, 0.45 - 0.52 Ilm)
TM2 Landsat thematic mapper band 2
TM3
(green, 0.52 - 0.60 Ilm)
Landsat thematic mapper band 3
TM4
TM5
TM6
TM7
(red, 0.63 - 0.69 Ilm)
Landsat thematic mapper band 4
(NIR, 0.76 - 0.90 Ilm)
Landsat thematic mapper band 5
(MIR1, 1.55 - 1.74 Ilm)
Landsat thematic mapper band 6
(thermal, 10.4 - 12.5 Ilm)
Landsat thematic mapper band 7
(MIR2, 2.08 - 2.35 Ilm)
Table 3.-Procedure for classifying existing vegetation and land cover
(Winne et al. 1998).
Delineate polygons
• Unsupervised classification of Landsat TM data, seeding the first loop with potential vegetation, to create unsupervised classification image.
• Principal components analysis of Landsat TM data, and subsequent textural filter: Create first principal component image and apply a filter to create structure image.
• Aggregate pixels into polygons of specified minimum mapping unit: Using the structure image as a mask, run sequential MERGE programs to create merged image from unsupervised classification image.
Label polygons
• Convert merged image to ARC/INFO GRID format: Identify individual polygons in gridfile; for each, add attributes to use in supervised classification (mean values for all TM bands, elevation, aspect, and slope).
• Supervised classification of polygons in gridfile: Assign cover type labels to unsampled polygons based on attribute similarity with polygons sampled by field plots (n = 2617).
• Stratify existing vegetation by potential vegetation to create an additional set of labels.
USDA Forest Service Proceedings RMRS-P-12. 1999 383

potential vegetation, then compared the types before and after stratification (Winne et al. 1998).
To construct ratings of resource condition and ecological integrity, 1296 field plots were summarized according to site, soil, and vegetation attributes for selected ecological classification groupings. Resource value ratings, such as forage production, hiding cover, structural cover ,and fire behavior, then were calculated for each plot from vegetation measurements (Jensen et al. 1998a). Summary tables were constructed from the plot data for each ofthree classification schemes-potential vegetation, existing vegetation, and existing vegetation stratified by potential vegetation (hereinafter termed "refined existing vegetation"). For each ofthe potential vegetation types, summaries of potential natural communities (or late seral vegetation expressions) were constructed. In a similar manner, summary tables were constructed for each existing vegetation type. Association of the known habitat type membership for each field plot with its predicted cover type facilitated development of a more refined classification of existing vegetation. This process increased the number of existing vegetation classes from 26 to approximately 150, and greatly improved the ability to display differences in resource condition and ecological integrity across the study area (Winne et al. 1998). Landtype association polygons (1:100,000 scale) were used as a base for generalizing both potential and refined existing vegetation patterns, as well as their associated ecological classification information (Jensen et al. 1998c).
Figure 2a illustrates the map of predicted habitat types at
30 m resolution for a small portion (13,287 ha) of the study area ~ the Bullion Butte 7.5' quadrangle. Seventeen habitat types were mapped in this area (including water and nonvegetated lands). Grassland habitat types were well distributed and formed the landscape matrix, occupying 65% of the quadrangle. Shrubland habitat types were predicted to occur on about 14% of the quad, and often were associated with more dissected topography. Woodland habitat types covered about 12% of the area, and were found primarily in draws and riparian areas.
For the entire study area, accuracy of the habitat map was relatively high (Jensen et al. 1998a) as compared to other studies using similar discriminant analysis procedures (Franklin et al. 1989, Franklin and Wilson 1991, Jensen et al.
1990, Lowell 1991). Predictions of habitat types based on biophysical environment variables (Table 2) were consistently more accurate within woodlands (70-100%) as opposed to shrublands (62-100%) and grasslands (54-77%) across all three subsections of the LMNG. Accuracy values also varied by subsection; across all three lifeforms, accuracy was highest in the Missouri Breaks, followed by the Missouri
Plateau and the Little Missouri Badlands subsections.
Although a 30 m resolution habitat type map (Fig. 2a) can provide a valuable base map for land use planning (J ensen
1991, Jensen et al. 1998c), management does not usually occur at such a fine resolution. Thus, it is often necessary or desirable to generalize this fine-scale information to make it more effective for land use planning. In Figure 3, we illustrate how this can be done by associating the 30 m habitat type data for the entire LMNG with broad-scale ecological units (landtype associations).
Within the study area, 382,121 patches were mapped; each was assigned one of32 land cover types (Table 4). Mean thematic accuracy of the cover type map was assessed at
74.4%, as compared to about 60% for an earlier map that did not incorporate potential vegetation (Winne et al. 1998).
Nearly half (48%) of the study area was mapped as grasslands; shrublands and woodlands covered 19% and 11% of the area, respectively. Also, a noteworthy proportion (19%) of the area was mapped as agricultural lands. Figure 2b illustrates land cover within the Bullion Butte quadrangle, where 28 of the 32 cover types were mapped. Within Bullion
Butte, distributions were generally similar to those for potential vegetation, but patches were smaller and landscape patterns more fragmented. As with potential vegetation, grasslands dominated the Bullion Butte landscape, occupying 60% of the area, followed by shrublands at 22%, woodlands at 8%, and agricultural lands at 4%.
Looking at numbers of patches, nearly all (96%) were mapped as natural vegetation cover (i.e., not urban, agriculture, water or cloud cover types); in terms of area, however, natural vegetation comprised only 80% of the study area.
Mean patch size for all types was 4.07 ha, but areas of urban , agriculture, water, and clouds tended to occur in substantially larger patches than the natural vegetation cover types
(x = 19.3 ha vs 3.4 ha, respectively). This points to the importance of maintaining relatively fine-scale data when natural land cover patterns are of interest: our ability to draw such distinctions between natural vegetation cover and anthropogenic/unvegetated types suggests that in the move from pixels to polygons, we have not lost the ability to describe patterns of ecological importance.
Although capturing pattern is critical, adequately detailed classifica tion ofvegeta tion types is equally important.
When plots assigned to four grassland cover types were stratified by potential vegetation, the resulting 13 plot groupings appeared to have more narrowly defined environmental ranges than the original four (Jensen et al. 1998b).
In general, for the 13 types, between-class variability in composition of dominance types was reduced as compared to the between-class variability in composition for the original four grassland cover types. The refinements obtained through stratification offered potential improvements to interpretations of current vegetative conditions; for example, differences in graminoid production were apparent between the
13 grassland types, but not the original four (Winne et al.
1998b). Thus, we suggest that existing and potential vegetation should be considered in concert when describing a landscape. To date, however, our stratification has been plot-based. Although taking the next step and spatially stratifying existing vegetation by potential vegetation is a simple GIS operation, interpreting the stratified map is not
384 USDA Forest Service Proceedings RMRS-P-12. 1999

---~-
Grasslands
(.J
I-
Z w l-
0
Cl..
LU
- l
-<
Z
0
~
I-'
LU
• AGSMISTCO
.AGSMISTVI
• AGSMISTVIIBOGR
• SleO/CAFI
ShrubJands
• ARCA/AGSM
• ARTSIAGSM
• JUHO/ANSC
• RHARJAGSP
• RHARIMUCU
Woodlands
• fRPElPRVI
• FRPElSYOC
.,USc/ORMI
• PODElJUSC
Other
• BADLANDS
• WATER
Z
0
~ l-
LU t..:I
L.U
t..:I
Z t=
~
II
DRY AGRIC .8ROADLEAF
_WET AGRIC
• PONDEROSA PINE
GRASS (NON-NATIVE) • ROCKY MTN JUNIPER
Ov
LOW GRASS
• BROADLEAF/CONIFER
II
LOW COV GRASS • WATER
• 8ROADLEAF RIPARIAN
• BROAD/CON RIPARIAN • MODIHIGH GRASS
• MESIC SHRUB • MIXED RIPARIAN
• SILVER SAGE • GRAM/FORB RIPARIAN
• CREEPING JUNIPER • SHRU8 RIPARIAN
.WY BIG SAGE
• SHRU81HE:R8 RIPARIAN o
MESIC SHRUBIGRASS • BADlANDS
III
XERIC SHRUB/GRASS
• SHRUB BADLANDS
• TREEISHRUB • GRASS BADLANDS
Figure 2.For the Bullion Butte 7.5' quadrangle, illustration of a) potential vegetation (habitat type); b) existing vegetation (cover type); c) existing vegetation for the Agropyron smithiilStipa viridu/a habitat type (AGSM/STVI), which occupies 56% of the quadrangle. necessarily straightforward. Nonetheless, some interesting patterns emerge. For example, theAgropyron smithii / Stipa
viridula habitat type, which occupies 56% of Bullion Butte quadrangle (Fig. 2c), has largely been converted to agriculturallands and non-native grasses. If conservation of this grassland habitat type were a priority, a map of existing vegetation within the type (as shown) could be used to identify potential locations for reserves and to otherwise direct management decisions.
In a more detailed study, Jensen et al. (1998c) derived three indices of ecological health and integrity, along with three resource value ratings, which suggested some departure from reference conditions in the LMNG, particularly for shrublands (ecological integrity) and woodlands (resource value). Rather than repeat these here, we will illustrate the process for two types of index ratings, ecological integrity and resource value, and present one example for each. We reiterate that, in practice, multiple measures are critical for accurate and reliable interpretations.
Ecological Integrity Ratings-Indicators of rangeland ecological integrity or sustainability are commonly based on those features (or measurable surrogates) that directly relate to the long-term soil productivity and plant demographics at a site (National Research Council 1994). For this example, we chose total biomass production as a measure of ecological integrity and a surrogate for long-term soil productivity (Fig. 4).
Interpretation of ecological integrity ratings is influenced by the level of generalization used to display such relations.
For example, summarized at a broad level (i.e., across all lifeforms), and based on total biomass production alone, the ecological integrity of the study area does not appear to be at risk: current conditions are similar to reference conditions.
This interpretation is not surprising because the grassland settings ofthe LMNG are likely to drive overall impressions; they represen t a majori ty ofthe area, and grassland soils are highlyresilienttoherbivory(Jensenetal.1992). Shrublands and woodlands, however, which are relatively limited in spatial extent, are selectively utilized by livestock such that their ecological integrity may not be adequately represented in a generalization of lifeforms by landform setting.
USDA Forest Service Proceedings RMRS-P-12. 1999 385

General Planning Strata
Group 1
Group 2
Group 3
Group 4
Group 5
•
Group 6
Group 7
Group 8
•
Group 9
7Q
KILOMETERS
Figure 3.Generalized potential vegetation settings of the Little
Missouri National Grasslands appropriate to regional and subregional scale ecological assessments and land use planning activities.
Viewed by lifeform (Fig. 4), shrubland settings display the largest departure from reference conditions, followed by grasslands and woodlands. Woodlands, with their high proportion of woody biomass, might be expected to align most closely with reference conditions. The fact that shrubland settings consistently show the lowest ecological integrity relative to undisturbed reference conditions suggests that current (or past) grazing systems might have had an impact on the long-term sustainability or resilience of these systems. This interpretation, however, needs to be tested by additional field sampling; we offer it only as an example of how one might interpret this rating of ecological integrity.
Class ratings like the one presented in Figure 4 also can be used as efficient starting points for stratified, random sampling designs to support subsequent field assessments of ecological indicators (e.g., soil compaction, erosion, plant vigor). For example, because changes in total biomass production are probably correlated with other ecological indicators, the four classes displayed for total biomass production in shrublands could be used as an initial stratification for more detailed ecological condition assessments of shrub land health. Random selection of landtype association polygons by these strata in subsequent field sampling could reduce the number of samples required, because between-polygon variability with respect to ecological indicators may be reduced. Stratified, random sampling designs have proven effective in other ecological assessment efforts (Austin and
Heylingers 1989, 1991; Bourgeron et al. 1994, Engelking et al. 1994), and should be considered for future assessments of rangeland health.
Resource Value Ratings-Ratings of resource value are commonly used in rangeland health assessments (National Research Council 1994, RISC
1~83) to describe the value of current vegetative conditions for various resource uses. Calculation of these ratings is made by contrasting the current vegetative properties of a potential vegetation environment to those for a desired plant community for that environment. In this study, we used the potential natural community (or late seral dominance type) as the desired plant community for a habitat type in calculating resource value; in most cases, these dominance types possess the highest value for a variety of resource uses (Jensen et al.
1992).
We selected year-long cattle forage production as an example of a resource value condition rating for the study area (Fig. 5). This was calculated for each landtype association polygon based on a proportioning of individual plant species production values by a forage preference value rule set. When summarized across alllifeforms, year-long cattle forage production is highly similar to reference conditions
(Fig. 5). When individuallifeforms are examined, however, woodlands show rather large departures from reference conditions, and grasslands closely follow reference conditions. We assume that two reasons are responsible for the fairly high similarity to reference conditions for most grassland environments in the LMNG: 1) as mentioned before, grassland soils in the study area are highly resilient to herbivory; and 2) numerous crested wheatgrass (Agropyron cristatum) seedings have been established across the study area, and these have high value for year-long cattle forage production. Because grasslands are widely distributed in the LMNG, they dominate the overall picture in terms of year-long cattle forage production, such that without stratification, the low val ues in woodland settings would not have been apparent. Furthermore, if interpretations were based solely on total biomass production, woodlands might appear to be in the best ecological health of the three lifeform settings. Simply considering another rating can dramatically alter interpretations; again, we emphasize the need to consider multiple measures of ecological integrity and resource value.
Accurate maps of both potential and existing vegetation are required if the ecological classification approach described herein is to be used effectively in future studies. With this in mind, we offer three recommendations to readers contemplating the use of these methods. First, the techniques used to produce each map--discriminant analysis for potential vegetation, and image segmentation coupled with supervised classification for existing vegetation-are datahungry, and require large numbers offield plots if they are to produce satisfactory results. Second, consideration of scale is a critical aspect of the mapping process. To successfully map vegetation patterns of interest, the characteristic range of landscape patch sizes to be mapped must match
386 USDA Forest Service Proceedings RMRS-P-12. 1999

Table 4.-Total number of patches, mean patch size (ha), and total area (ha) mapped for land cover types in the study area, shown as edge matched from two classified Landsat TM scenes (Winne et al. 1998).
Cover type
1100 Urban or developed lands
2010 Agriculture-dry
2020 Agriculture-wet
3111 Non-native grass
3130 Very low cover grasslands
3140 Low cover grasslands
3150 Low/moderate cover grasslands
3160 Moderatelhigh cover grasslands
3210 Mixed mesic shrubs
3309 Silver sage
3313 Creeping juniper
3352 Wyoming big sagebrush steppe
3510 Mesic shrub-grassland complex
3520 Xeric shrub-grassland complex
3530 Tree/shrub complex
4140 Mixed species broadleaf forest
4205 Limber pine
4206 Ponderosa pine
4214 Rocky mountain juniper
4300 Mixed broadleaf/conifer forest
5000 Water
6120 Broadleaf riparian
6130 Mixed broadleaf/conifer riparian
6140 Mixed forest/non-forest riparian
6210 Graminoid/forb dominated riparian
6310 Shrub dominated riparian
6400 Mixed shrub/herbaceous riparian
7600 Badlands
7601 Shrub badlands
7602 Grass badlands
9800 Clouds
9900 Cloud shadow
Total
# Patches
10
7,474
6,912
12,021
22,530
49,305
49,075
24,085
11,670
3,319
9,151
4,499
34,880
4,824
7,949
15,336
109
532
8,099
3,084
1,643
11,986
1,358
533
11,121
9,580
12,522
19,697
34,642
4,061
47
67
Patch size Total area
2.22
2.06
1.86
2.83
1.99
2.09
4.77
1.39
1.90
1.96
2.12
13.07
3.99
1.89
1.99
2.56
62.93
20.05
20.05
4.73
2.11
7.13
3.87
2.68
2.49
2.34
2.15
1.57
2.31
1.60
10.44
11.76
4.07
629
149,843
138,557
2,561
1,061
28,428
22,423
26,906
30,876
79,872
6,491
491
788
1,554,068
56,891
47,498
351,515
189,824
64,629
29,007
7,370
18,886
8,381
98,835
9,579
16,620
73,223
151
1,010
15,902
6,539
21,480
47,803 reasonably with the grain size of the predictor variables used in modeling. When possible, the variables considered for inclusion in a predictive vegetation model should be tested at multiple spatial scales to determine the most appropriate grain size. For example, climate interpolation models (Thornton et a1. 1997, Waltman et a1. 1997) can be run at 30 m, 90 m, and 1 km resolution to assess the spatial scale at which different climatic attributes influence the pattern of interest. Also with regard to scale, limiting the extent of the area dealt with at one time may be of use; by stratifying a study area into more homogeneous environments, performance can be greatly improved. Third, to facilitate direct testing of assumed relations between biophysical variables and vegetation patterns, gradient-oriented field sampling (Austin et a1. 1994, Austin and
Heylingers 1991, Bourgeron et a1. 1994) should be incorporated in plot selection.
Finally, land managers and conservationists are often faced with difficult decisions to resolve issues of conflicting land use. This is especially true in rangeland settings where the pattern ofland ownership is complex, and where there may be strong economic pressures to fully utilize resources on both private and public lands. The ecological classification approach described herein presents several advantages over grid-based site level methods traditionally used to monitor land use and condition on public lands:
• Results are spatially explicit and can be continuously mapped in a standardized manner over mixed ownerships and large geographic areas.
• Existing field data, often collected for different purposes and by different agencies, can be standardized and utilized, thereby saving time and money associated with collecting additional field data.
• Multiple indices can be easily derived and mapped, which, in turn, can facilitate more thorough and reliable evaluations of ecosystem conditions, compared to ones based on just one or two indicators oflimited reliability.
For these reasons, we believe this approach deserves wider application for assessing ecosystem health and integrity in other landscapes.
USDA Forest Service Proceedings RMRS-P-12. 1999 387

Primary funding for this research was provided by the
USDA Forest Service, Washington Office, Ecosystem Management Staff; USDA Forest Service, Northern Region; U.S.
Environmental Protection Agency, National Exposure Research Laboratory; and the USDA Natural Resource Conservation Service, Washington Office, Strategic Planning
Staff. A number of Forest Service personnel deserve special mention for coordinating and carrying out various project responsibilities. These include Jeff DiBenedetto, Cheri
Bashor, and John Lane at the Custer NF; Ann Rys-Sikora and Pat Hettick at the Lolo National Forest, as well as
Martin Prather, Ken Brewer, John Caratti, Judy Tripp, and
Greg Enstrom at the Northern Regional Office. Sue Kvas,
Luke Lunde, Harlan Olson, Mark Schroeder, Susanne
Nuemiller, Gary Treana, Susan Muske, Kurt Hansen, and
Jonathan Wheatley served on one or more of the seasonal field crews that were coordinated and led by Reggie Clark,
Brian Kempenich, and Jeff Tomac. Finally, we thank Chip
Fisher, Gary Gooch, Poody McLaughlin, Jim Schumacher,
Brian Steele, and Wendy Williams, our colleagues at the
Wildlife Spatial Analysis Lab, for their help with data inputs, analyses, as well as final figure preparations.
Notice-This is a preliminary draft. It has not been formally released by the U.S. Environmental Protection
Agency (EPA), and should not at this time be construed to represent agency policy. This manuscript is being circulated for comments on its technical merit and potential for policy implications. Do not cite or quote.
Austin, M.P.; Heylingers, P.C. 1991. New approach to vegetation survey design: gradsect sampling. In: Margules, C.R.; Austin,
M.P., eds. Nature conservation: cost effective biological surveys and data analysis. Melbourne, Australia: CSIRO: 31-36.
All ~
II >
900/0
II
70- 90
0/0
50-70 0
/ 0 o
<
500/0 r;
Figure 4.Total biomass production class ratings for the Little Missouri National Grasslands, expressed as the percent difference between current and reference conditions.
388 USDA Forest Service Proceedings RMRS-P-12. 1999

Austin, M.P.; Smith, T.M. 1989. A new model for the continuum concept. Vegetatio. 83: 35-47.
Austin, M.P.; Nicholis, A.O.; Doherty, M.D.; Meyers, J.A. 1994.
Determining species response functions to an environmental gradient by means of a beta-function. Journal of Vegetation
Science. 5: 215-228.
Bailey, RG.; Jensen, M.E.; Cleland, D.T.; Bourgeron, P.S. 1994.
Design and use of ecological mapping units. In: Jensen, M.E.;
Bourgeron, P.S., eds. Ecosystem management: principles and applications: eastside forest ecosystem health assessment. Gen.
Tech. Rep. PNW-318. US. Department of Agriculture, Forest
Service, Pacific Northwest Research Station, Portland, OR
2: 101-112.
Bailey, Robert G. 1995. Description of the ecoregions of the United
States 2d ed. rev. and expanded (1st ed. 1980). Misc. Publ. No.
1391 (rev.). US. Department of Agriculture, Forest Service,
Washington, DC. 108p. with separate map at 1:7,500,000.
Burkart, M.R; Oberle, S.L.; Hewitt, M.J.; Pickus, J. 1994. A framework for regional agroecosystems characterization using the
National Resources Inventory. Journal of Environmental Quality.23(5):866-874.
Cleland, D.T.; Avers, P.E.; McNab, W.H. [and others]. 1997. National hierarchical framework of ecological units. In: Boyce, Mark
S.; Haney, Alan, eds. Ecosystem management: applications for
All r.
I sustainable forest and wildlife resources. Yale University Press,
New Haven and London: 181-200.
Daubenmire, R 1968. Plant communities, a textbook of plant synecology. Harper and Row Publishers, New York. 300 p.
DiBenedetto, JeffP. 1998. Hierarchical relations of ecological classification and mapping systems within mixed grass prairie.
Montana State University, Bozeman, MT. M.S. thesis.
Engelking, L.D.; Humphries, H.C.; Reid, M.S.; DeVelice, RL.;
Muldavin, E.H.; Bourgeron, P.S. 1994. Regional conservation strategies: assessing the value of conservation areas at regional scales. In: Jensen, M.E.; Bourgeron, P.S., eds. Volume II: Ecosystem management: principles and applications. Gen. Tech. Rep.
PNW-GTR-318. US. Department of Agriculture, Forest Service,
Pacific Northwest Research Station, Portland, OR: 208-224.
Franklin, Janet. 1995. Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients. Progress in Physical Geography. 19(4): 474-499.
Franklin, S.E.; Peddle, D.R; Moulton, J.E. 1989. Spectral! geomorphometric discrimination and mapping ofterrain: a study in Gros Morne National Park. Canadian Journal of Remote
Sensing. 15: 28-42.
Franklin, S.E.; Wilson, B.A. 1991. Vegetation mapping and change detection using SPOT MLA and LANDSAT imagery in Kluane
National Park. Canadian Journal of Remote Sensing. 17: 2-17.
r.
I
II >
900/0
70 - 90
0/0
50-70
%
D <
Figure 5.- Year-long cattle forage production for the Little Missouri National Grasslands, expressed as the percent difference between current and reference conditions.
389 USDA Forest Service Proceedings RMRS-P-12. 1999

Gauch, H.G. 1982. Multivariate analysis in community ecology.
Cambridge University Press, New York.
Girard, M.M.; Geotz, H.; Bjugstad, AJ. 1989. Native woodland habitat types of southwestern North Dakota. Res. Pap. 281. US.
Department of Agriculture, Forest Service, Rocky Mountain
Forest and Range Exp. Sta., Fort Collins, CO. 36 p.
Hansen, P.L.; Hoffman, G.R; Bjugstad, AJ. 1984. The vegetation of Theodore Roosevelt National Park, North Dakota: a habitat type classification. U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Exp. Sta., Ft. Collins,
CO. 35 p.
Hirsh, K.J.; Baker, W.T. 1984. Classification of grasslands and shrublands in southwestern North Dakota. In: Proceedings,
North Dakota Academy of Science; April 1984. Grand Forks,
North Dakota. The Academy. Vol. 38. 82 p.
Host, G.E.; Polzer, P.L.; Mladenoff, D.J.; White, M.A..; Crow, T.R
1996. A quantitative approach to developing regional ecosystem classifications. Ecological Applications 6: 608-618.
Hutchinson, C.F. 1982. Techniques for combining LANDSAT and ancillary data for digital classification improvement. Photogrammetric Engineering and Remote Sensing. 48: 123-130.
Jensen, M.E.; Simonson, G.H.; Dosskey, M. 1990. Correlation between soils and sagebrush-dominated plant communities of northeastern Nevada. Soil Science Society of America Journal.
54: 902-910.
Jensen, Mark E.; McNicoll, Cecilia H.; Prather, Martin. 1991.
Application of ecological classification to environmental effects analysis. J. of Env. Qual. 20: 24-30.
Jensen, M.E.; DiBenedetto, J.D.; Heisner, F. 1992. An ecological classification for the Little Missouri National Grasslands. Missoula, MT: U.S. Department of Agriculture, Northern Region
Jensen,M.E.;Hann, W.;Keane,RE. [andothersl.1994.ECODATAa multiresource database and analysis system for ecosystem description and evaluation. In: Jensen, M.E.; Bourgeron, P.S., eds. Volume II: Ecosystem management: principles and applications. Gen. Tech. Rep. PNW-GTR-318. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station,
Portland, OR: 192-205.
Jensen, MarkE.; Bourgeron, Patrick; Everett, Richard; Goodman,
Iris. 1996. Ecosystem management: a landscape ecology perspective. Journal of the American Water Resources Association.
32: 203-216.
Jensen, Mark; Goodman, Iris; Brewer, Ken. [and others]. 1997.
Biophysical environments of the basin. In: Quigley, Thomas M.;
Arbelbide, Sylvia J., tech. eds. An assessment of ecosystem components in the Interior Columbia Basin and portions of the
Klamath and Great Basins: Volume 1. Gen. Tech. Rep. PNW-
GTR-405. Portland, OR: US. Department of Agriculture, Forest
Service, Pacific Northwest Research Station. 335 p.
Jensen, Mark E.; BiBenedetto, Jeff P.; Bourgeron, Patrick S.;
Brewer, C. Kenneth. 1998a. [In reviewl. Biophysical modeling of rangeland potential vegetation at a landscape scale. Journal of
Ecosystem Health.
Jensen, Mark E.; Redmond, Roland L.; Goodman, Iris A; Sobecki,
Terrence; Bourgeron, Patrick S. 1998b. [In review l. Use of ecological classifications in the assessment of rangeland health. Journal of Ecosystem Health.
Jensen, M.E.; Crespi, M.; Lessard, G. 1998c. A national framework for integrated ecological assessments. In: Cordel, H. Ken;
Bergstrom, John C., eds. Integrating social sciences with ecosystem management: human dimensions in assessment policy and management. Sagamore Press, Champaign "Urbana," IL.
Keane, RE.; Jensen, M.E.; Hann, W.J. 1990. Ecodata and Ecopac: analytical tools for integrated resource management. The Compiler. 8(3): 24-37.
Kuchler, AW. 1964. Potential natural vegetation of the conterminous United States. American Geographical Society, Special
Publication No. 36. 116 p.
Lowell, K.E. 1991. Utilizing discriminant function analysis with a geographical information system to model ecological succession spatially. International Journal of Geographic Information Systems. 5: 175-191.
Meidinger, D.; Pojar, J. 1991. Ecosystems of British Columbia. Spec.
Rep. Series 6. Victoria, BC: Research Branch, Ministry of Forests.
Mueller-Dombois, D.; Ellenberg, H. 1974. Aims and methods of vegetation ecology. John Wiley and Sons, New York. 547 p.
National Research Council. 1986. Soil conservation: assessing the
National Resources Inventory. Committee on Conservation Needs and Opportunities, Board on Agriculture, National Research
Council. National Academy Press; Washington, DC.
National Research Council. 1994. Rangeland health: new methods to classify, inventory, and monitor rangelands. National Academy Press, Washington, DC. 182 p.
Nesser, John A; Ford, Gary L.; Maynard, C. Lee; Page-Dumroese,
Deborah, S. 1997. Ecological units of the Northern Region: subsections. Gen. Tech. Rep. INT-GTR-369. US. Department of
Agriculture, Forest Service, Intermountain Research Station,
Ogden, UT. 88 p.
Norusis, M.J. 1985. Statistical package for the social sciences.
SPSS, Inc., Chicago, IL.
Pfister, RD.; Kovalchick, B.L.; Waide, J.B.; Presby, RC. 1977.
Forest habitat types of Montana. Gen. Tech. Rep. INT-34. U.S.
Department of Agriculture, Forest Service, Intermountain Forest and Range Exp. Sta., Ogden, UT. 174 p.
Redmond, RL.; Winne, J.C.; Tady, T.P.; Thornton, M.; Troutwine,
J.; Ma, Z. 1997. Existing vegetation and land cover of the Little
Missouri, Grand River, and Sheyenne National Grasslands.
Final Report, Contract #53-034304-000012, submitted to US.
Department of Agriculture, Forest Service, Northern Regional
Office. Montana Cooperative Wildlife Research Unit, The University of Montana, Missoula. 174 p.
RISC. 1983. Guidelines and terminology of range inventories and monitoring. Report of Range Inventory Standardization Committee. Denver, CO: Soc. Range Manage. 28 p.
Scott, J.M.; Jennings, M.D. 1998. Large-area mapping ofbiodiversity. Annals of the Missouri Botanical Garden. 85: 34-47.
Soil Survey Staff. 1975. Soil Taxonomy. US. Department of Agriculture, Hand. 436, US. Gov. Print. Office, Washington, DC.
Thornton, P.E.; Running, S.W.; White, MichaelA. 1997. Generating surfaces of daily meteorological variables over large regions of complex terrain. Journal of Hydrology. 190: 214-251.
U.S. Department of Agriculture, Forest Service. 1988. Ecosystem classification handbook, chapter 4 - ecodata sampling methods.
Northern Region, Missoula, MT.
US. Department of Agriculture, Forest Service. 1991. Ecological classification. In: Ecological classification and inventory handbook, FSH 2090.11. Washington, DC.
U.S. Department of Agriculture. 1993. Soil survey manual. USDA
Handbook No. 18. U.S. Government Printing Office, Washington, DC.
US. Department of Agriculture, Soil Conservation Service. 1976.
National range handbook. Washington, DC.
US. Department of Interior, Bureau of Land Management. 1984.
National range handbook. Washington, DC.
Waltman, W.J.; Ciolkosz, E.J.; Mausbach, M.D. [and othersl. 1997.
Soil climate regimes of Pennsylvania. The Pennsylvania State
Univ. Agric. Exp. Sta. Bull. No. 873. University Park, PA.
Whitman, W.C. 1978. Analysis of grassland vegetation on selected key areas in southwestern North Dakota. A Report on a Project of the North Dakota Regional Environmental Assessment Program Contract No. 7-01-2. Department of Botany, North Dakota
State University, Fargo, ND. 199 p. .
Winne, J. Chris; Redmond, Roland L.; Jensen, Mark E.; Hart,
Melissa M.; DiBenedetto, JeffP.; Goodman, Iris A. [In reviewl.
Integrating satellite imagery and potential vegetation to delineate pattern and map existing vegetation in a mixed grass prairie ecosystem. Journal of Ecosystem Health.
Woudenberg, S.W.; Farrenkopf, T.O. 1995. The Westwide forest inventory data base: user's manual. Gen. Tech. Rep. INT-GTR-
317. US. Department of Agriculture, Forest Service, Intermountain Research Station, Ogden, UT. 67 p.
Zonneveld, I.S. 1989. The land unit-a fundamental concept in landscape ecology, and its applications. In: Landscape Ecology.
The Hague, Netherlands: SPB Academic Publishing. 3(2): 67-89.
390
USDA Forest Service Proceedings RMRS-P-12. 1999