Environmental Gradients in Shenandoah National Park

Ecological Land Units of Shenandoah National Park

John Young

Table of Contents:

Introduction

Background

Step 1: Assemble data

Step 2. Derive moisture index map

Step 3. Derive landform (terrain shape) index map

Step 4. Derive and reclassify slope and aspect maps

Step 5. Reclassify and combine maps to derive landform classes

Step 6. Reclassify geology map into rock unit types

Step 7. Reclassify elevation map

Step 8. Combine landforms, elevation, and geology into TNC ecological
land units (ELU) map

ELU combination key

Reclassification tables

Literature cited

Introduction:

This page describes an initial effort to quantify the environmental gradients present in Shenandoah National Park as a precursor to vegetation sampling. The environmental gradients identified will assist researchers from the USGS-BRD and the Va. Natural Heritage program in locating field plots for assessment of vegetation communities and in extrapolating vegetation patterns out to un-sampled areas. Methods used to quantify environmental gradients closely follow those used by other researchers, and make use of geographic information systems and digital elevation models. The overall goal in this effort is to describe gradients that are important for structuring vegetation communities, and to use this information to create sampling schemes that capture the range of environments observed.

Background:

A number of researchers have examined vegetation data in relation to environmental gradients derived from digital elevation data. Table 1 lists several recent publications that have examined vegetation in relation to environmental gradients. Important gradients that recur in these studies are: 1) slope direction (eg. Aspect), 2) slope position, 3) slope shape, 4) moisture, 5) light, and (less commonly) 6) rock type, and 7) elevation. We derived environmental gradients following the examples set in these studies to capture gradients important for vegetation growth in the mountains of western Virginia. These are gradients of soil moisture, light, slope orientation (aspect), slope shape, elevation, exposure, and rock type.

Newell and Peet, 1998

From: Vegetation of Linville Gorge Wilderness, North Carolina, C. L. Newell and R. K. Peet, Castanea 63(3): 275-322, September 1998.

A species composition and vegetation-environment relationships study

Variables considered:

Beer’s transformed aspect
Distance to nearest stream
Distance to nearest ridge
Terrain shape index (after McNab)
Topographic complexity
Potential solar radiation (from Solarflux)
Topographic Moisture Index (after Parker)

Anderson and Merrill, 1998

From: Connecticut River Watershed: Natural Communities and Neotropical Migrant Birds, M.G. Anderson and M. D. Merrill, Final Report, The Nature Conservancy, Eastern Regional Office, Boston, MA, October 15, 1998.

An ecological communities assessment project. Ecological Land Units were derived to assist in a regional planning and assessment project

Variables considered:

Slope (degrees)
Moisture Index (after Moore, I.D.)
Landscape position
Lithology
Elevation

Franklin, et. al. 2000

From: Terrain variables used for predictive mapping of vegetation communities in Southern California, J. Franklin, P. McCullough, and C. Gray, in Terrain Analysis: Principles and Applications, J. P. Wilson and J. C. Gallant, eds., John Wiley and Sons, New York, 2000, pp. 331-353.

A predictive vegetation modeling study

Variables considered:

Slope
Aspect
Potential Solar Radiation (from Solarflux)
Upslope catchment area
Topographic wetness
Surface curvature
Distance to stream
Distance to Ridge

For a first cut analysis, I followed methods in Anderson and Merrill (1998) for combining gradient layers into an ecological land units map. This method builds an ecological units map from derivations of digital elevation models, classified into discrete ranges, and combined. Models of aspect, moisture, slope, and slope shape are assembled to produce maps of landform units. These landforms are combined with elevation and geologic types to produce a final ecological units or ELU map.

Individual steps in the process and maps resulting from intermediate and final stages are described below. These methods closely parallel those in Anderson and Merrill (1998) as defined for ArcInfo by Frank Biasi of The Nature Conservancy (see the collection of processing macros at gis.tnc.org). However, certain analytical methods and reclassification tables were modified to fit the data layers available and environment of Shenandoah National Park. Examples are displayed for the Old Rag quad in the central district of Shenandoah National Park. Browse-able maps will be available on an interactive mapping website soon.

Step 1. Assemble data:

The following data layers were assembled for this analysis:

10-meter USGS digital elevation model: This is a compilation of newly available oversampled 1:24,000 elevation models available for all areas of the park except the northern most quad (Front Royal). Data postings are at 10m intervals and improve on the 30m data that have been available in the past. However, these data have artifacts from the source data (contours) that may affect resulting models such as artificial terracing and pits. All topographic gradients were derived using this layer. This layer covers the entire park and surrounding 1:24,000 topo quads except for the far northern section (Front Royal quad).
geologic units map: This map was incorporated as provided by Dan Hurlbert, GIS Specialist for Shenandoah National Park. Broad scale (e.g. 1:62,500) polygons representing rock formations mapped by Gathright (1976) were reclassified into a map of rock unit types (explained in more detail below). This map covers the entire park except for small portions of the southern disctrict on the east and west sides.
hydrology layers: I incorporated 1:24,000 hydrologic maps as supplied by Dan Hurlbert including streams, ponds, and wetlands. I converted the hydrologic data into a gridded representation that matches the 10m grid cells of the DEM layer.

Step 2. Derive moisture index map:

I derived a moisture index from the DEM using methods proposed by Ian Moore (1990) and adopted by Anderson and Merrill (1998) as well as others. This index is computed as the log of the ratio between the amount of water entering each cell and the slope of the cell. The flow accumulation function in ArcInfo is used to compute a relative amount of water entering each cell from it’s upstream neighbors (values are number of upstream cells flowing into each cell). Slope is computed for each cell in the DEM as percent slope. The formula for computation of the moisture index (as given by Anderson and Merrill (1998)) is then:

moisture_index = ln((flowaccumulation + 1) / (slope + 1))

In order to generalize the map slightly to remove spurious features, I filtered the resulting moisture index map using the “majority filter” routine in ArcInfo that replaces cell values with the value occurring at the majority of the 4 orthagonal neighbors of a 3x3 scanning window.

Other moisture index maps can be substituted here if desired such as the Topographic Relative Moisture Index proposed by Parker (1982).

dry = brown, moist = blue

Step 3. Derive landform (terrain shape) index map:

I derived a landform index using a routine provided by Zimmerman (2000) that computes a terrain shape or landform index in a slightly different manner than that of Anderson and Merrill (1998). Both techniques compute terrain shape in a manner similar to that proposed by McNab (1989) whereby elevations at each pixel are compared to the mean of elevations in neighboring pixels. Elevations greater than their neighbors are local highs representing convex shapes, while elevations lower than their neighbors are local lows reflecting concave shapes.

Both methods compute a weighted mean of terrain shape from assessments at different spatial scales (e.g. different size scanning windows). However, the Zimmerman routine maintains the terrain shape value for the most influential scale rather than averaging over all scales. The basic calculation implemented in ArcInfo is as follows:

Terrainshape ^X = dem - focalmean(dem, circle, radius (X))

Or in other words, terrain shape at scale X is equal to the elevation value at each cell on the DEM minus the mean elevation of pixels in a surrounding circular window of size X.

concave (ravine) = purple, convex (ridge) = orange

Step 4. Derive and reclassify slope and aspect maps:

I derived aspect (e.g. slope direction) in ArcInfo using the standard routine that classifies slope direction into degrees using compass directions (0-360 degrees). I transformed aspect using Beer’s transformation such that slopes facing 50^o (described as optimal for southern Appalachian vegetation response by Newell and Peet 1998) are given a value of 2. Other slopes that are with 90^o of this optimal NE direction are given a value of 1 and SW facing slopes are given a value of 0.

The formula for computation with ArcInfo is given as:

BeersAsp = cos(50^o – aspect) + 1

Aspects were further simplified for computation of the ELU units as either NE or SW. These cutoffs correspond to the perpendiculars to Beer’s aspect (eg. 320 and 140^o).

Slope (not pictured) was calculated in degrees using standard functions in ArcInfo. Slope is calculated as the maximum angular rate of change (in elevation) of a plane fit to a 3x3 window surrounding each pixel on the DEM.

orange = SW facing slope, blue = NE facing slope

Step 5. Reclassify and combine maps to derive landform classes:

I derived landform classes by re-classifying and combining the above maps, closely following the techniques of Anderson and Merrill (1998) as defined by Biasi. In Biasi’s routine, each map is reclassified into discrete classes (see remap files below) and combined in a specific order to derive the following landform classes:

10 = Steep slope N/NE

11 = Steep slope S/SW

12 = Slope crest

13 = Upper slope

14 = Flat summit/ridge

20 = Sideslope N/NE

21 = Cove/ravine N/NE

22 = Sideslope S/SW

23 = Cove/ravine S/SW

30 = Dry flat

31 = Moist flat

33 = Slope bottom

40 = Stream

42 = Lake/river

First slope and landform maps are reclassified and combined, then moist and dry flat areas are coded and combined into the landform map (including wetland areas). Next, aspect maps are reclassified and incorporated into the landform map. Finally, streams and lakes are incorporated.

Step 6. Reclassify geology map into rock unit types:

As stated above in step 1, the geology map was developed from 1:62,500 maps of geological formations. Formations were recoded into 3 rock unit types: 100 = basaltic, 200 = siliclastic, and 300 = granitic. Polygons were converted to a grid representation to match the topographic variables. Here is how all of the formation types were reclassified:

Catoctin fm. = basaltic = 100

Erwin fm. = siliclastic = 200

Hampton fm. = silliclastic = 200

Old Rag fm. = granitic = 300

Pedlar fm. = granitic = 300

Swift Run fm. = siliclastic = 200

Weverton fm. = siliclastic = 200

Note: unlike the DEM and the layers derived from it, the geology map only covers areas inside the park boundary.

Step 7. Reclassify elevation map:

Elevations from the DEM (originally in meters) were reclassified to correspond to 3 broad elevation ranges: 0- 1500 ft., 1500 – 3000 feet, and greater than 3000 feet. Gary Fleming of Va. Natural Heritage suggested these classes as having the greatest influence on vegetation distribution.

Reclassifying the DEM into elevations was accomplished using a simple recode operation in ArcInfo. Numeric codes were assigned to correspond to the above classes as follows:

0 –1500’ = 1000

1500 – 3000’ = 2000

> 3000’ = 3000

Step 8. Combine landforms, elevation, and geology into TNC ecological land units (ELU) map:

In the final step, maps from steps 5, 6, and 7 are combined to produce the final ecological land units map. Since the elevation map in step 7 is coded into the thousands, the geology map in step 6 is coded into the hundreds, and the landform map in step 5 is coded into the tens, a simple addition of the three maps results in the final landform class combinations.

See below for a table that lists the separate classes from the input maps to get an idea of the output ELU types. Obviously, the more input types, the greater the number of output ELU’s. This final step will take some tweaking to produce the desired number of ecological land types for sampling.

ELU combination key:

Reclassification tables:

Literature Cited:

Anderson, M.G. and M. D. Merrill. 1998. Connecticut River Watershed: Natural communities and neotropical migrant birds, final report. The Nature Conservancy. Boston, MA.

Biasi, F. 2001. ELU AML pack: annotated AML code and documentation for generating ecological land units (ELUs) from a DEM and geology grid using ArcInfo. The Nature Conservancy (website) ftp://gis.tnc.org/pub/software/Analysis_tools/eluamls.zip

Franklin, J., P. McCullough, and C. Gray. 2000. ‘Terrain variables used for predictive mapping of vegetation communities in southern California’. In Terrain Analysis: Principles and applications. Wilson, J.P. and J.C. Gallant, eds. Wiley and Sons, New York, 331-353.

McNab, W. H. 1989. Terrain Shape Index: Quantifying effect of minor landforms on tree height. Forest Science. 35(1): 91-104.

Newell, C.L. and R.K. Peet. 1998. Vegetation of Linville Gorge Wilderness, North Carolina. Castanea 63(3): 275-322.

Parker, A. J. 1982. The topographic relative moisture index: An approach to soil moisture assessment in mountain terrain. Physical Geograpgy. 3(2): 160-168.

Zimmerman, N.E. 2000. Tools for analyzing, summarizing, and mapping of biophysical variables. (website) http://www.wsl.ch/staff/niklaus.zimmermann/progs.html

U.S. Department of the Interior || U.S. Geological Survey
11649 Leetown Road, Kearneysville, WV 25430, USA
URL: http://www.lsc.usgs.gov
Maintainer: lsc_webmaster@usgs.gov
Last Modified: March 3, 2006, jay
Privacy Policy and Disclaimers || FOIA || Accessibility