Mapping Bare Ground Using Digital Orthophoto Quadrangles (DOQs)

Robert A. Davidson

US Department of Agriculture\
Forest Service\
Uinta National Forest\
(435) 654-7233

radavidson@fs.fed.us

Vicky Johnson

US Department of Agriculture\
Forest Service\
Remote Sensing Application Center

\(801\) 975-3759

vjohnson@fs.fed.us

Henry Lachowski

US Department of Agriculture\
Forest Service\
Remote Sensing Application Center\
(801) 975-3753

hlachowski@fs.fed.us

**ABSTRACT**

# Bare areas such as exposed soil, rock outcrops, and unpaved roads are potential sources of sediment within a watershed. These areas tend to be highly reflective and are commonly seen as bright values on digital orthophoto quadrangle (DOQs). This paper examines this characteristic of DOQs to generate estimates of bare areas for use in mapping and for use in predicting soil erosion in a watershed. This procedure assumes that bright values on the DOQ represent bare areas on the ground. {#bare-areas-such-as-exposed-soil-rock-outcrops-and-unpaved-roads-are-potential-sources-of-sediment-within-a-watershed.-these-areas-tend-to-be-highly-reflective-and-are-commonly-seen-as-bright-values-on-digital-orthophoto-quadrangle-doqs.-this-paper-examines-this-characteristic-of-doqs-to-generate-estimates-of-bare-areas-for-use-in-mapping-and-for-use-in-predicting-soil-erosion-in-a-watershed.-this-procedure-assumes-that-bright-values-on-the-doq-represent-bare-areas-on-the-ground. .Abstract .unnumbered}

# INTRODUCTION

Natural and human-induced soil erosion and resulting sedimentation sites
are critical environmental hazards with serious consequences to
ecological stewardship and watershed management. Without comprehensive
surface-erosion survey data collected on a contiguous scale across the
landscape, watershed management must rely upon extrapolation of
qualitative, incomplete data that can lead to miscalculations and costly
errors. Successful watershed management and restoration planning
require:

-   Current identification of erodible features collected on a
    continuous scale across the entire watershed.

-   Sedimentation sites.

-   Accurate acreage data for each erosion feature and sedimentation
    site.

-   Efficient application of resource information to determine actual
    stream sedimentation rates.

# OBJECTIVE

The objective of this cooperative project between the Remote Sensing
Application Center and the Uinta National Forest was to develop remote
sensing and GIS procedures for assessing bare ground areas associated
with soil erosion in the watershed.

# METHODS

Recently, a watershed restoration report was developed for the
Strawberry Valley watershed (Figure 1), located in Wasatch County, Utah
within the Uinta National Forest^1^. Past management practices,
including those that predate Forest Service stewardship, have left the
watershed in less than desirable condition. Of particular concern are
soil erosion problems throughout the watershed. Areas experiencing high
erosion rates needed to be identified, prioritized and mapped in the
restoration plan.

![](media/image1.jpeg){width="3.33125in" height="2.6958333333333333in"}

Figure 1. Strawberry Valley watershed showing the 7.5 minute quadrangle
for Co-Op Creek.

![](media/image2.jpeg){width="3.3333333333333335in" height="2.5in"}

Figure 2. Photo taken looking north across Strawberry reservoir. Note
bare ground areas having southern exposures on the far side of the lake.

## Study Area

The Strawberry Valley watershed (HUC-5 level watershed) is approximately
136,072 acres in size and includes the Strawberry Reservoir (17,600
surface acres at full capacity). The watershed is located in the eastern
portion of the Uinta National Forest, on the Heber District, and varies
from moderately steep mountain ridges to valleys and meadows. Elevations
range from 7,000 to 10,000 feet.

## Remote Sensing Approach

Bare areas such as exposed soil, rock outcrops, and unpaved roads are
potential problem areas for soil erosion and resulting sediment sources
within a watershed (Figure 2). As shown in Figure 3, these bare-area
features tend to be highly reflective on DOQs and commonly show up on
aerial photography as bright areas. A DOQ is a digital image that
combines the characteristics of a photograph with the geometric
qualities of a map. A simple method was developed to pull out these
bright features on the DOQ to create a GIS layer of bare areas.

![](media/image3.jpeg){width="3.332638888888889in"
height="2.4368055555555554in"}

Figure 3. Bare-area features shown as bright areas.

Using ERDAS Imagine^©^ software, the first step is to create a thematic
version of the DOQ that can be edited once a threshold value for the
bright features has been determined. The dialog box to accomplish this
task is accessed through the main toolbar by clicking on
*Interpreter/Topographic Analysis/Level Slice*. Enter only the input and
output file names in the dialog box. This process rescales values on the
output thematic DOQ to a range of 0 (dark) to 255 (bright). It is
important to note that the DOQ output thematic image is slightly darker
than the original DOQ.

The second step is to open both the original and thematic DOQs in the
same Imagine Viewer. The original DOQ is opened first as the bottom
layer in the viewer so as to assess feature brightness. Next, the
thematic DOQ is opened in the same viewer with the transparency set to a
value of 0. This allows viewing the original DOQ while actually making
changes to the thematic DOQ. An inquire cursor is then used to explore
the brightness values on the original DOQ and determine the
corresponding value from the thematic DOQ (Figure 4). This process is
made as a first cut at a threshold value representing the dividing line
between areas not considered bare (below the threshold value) and those
areas that are considered bare (at and above the threshold value).

![](media/image4.jpeg){width="3.327777777777778in"
height="2.283333333333333in"}

Figure 4. The red crosshair represents the location of the inquire
cursor being used to explore brightness values on the original DOQ.
Information about the thematic DOQ at that location appears in the box
inset. The circle number represents the rescaled brightness value.

![](media/image5.jpeg){width="3.3291666666666666in"
height="2.5909722222222222in"}

Figure 5. Thematic DOQ showing colorized bright areas.

Finally, a range of colors, beginning with red at the maximum value of
255 and ending with yellow at the threshold value, is assigned to the
thematic DOQ. The transparency setting for the color-ramped values is
then adjusted to 1 to make them appear in the viewer (Figure 5).

## Field Validation

There were initial concerns about using the thematic DOQ: Were the
colorized "bare areas" real or simply highly reflected vegetation or
senesced grasses or forbs? As shown in Figure 6, field validation
verified the location, shape, and extent of an identified bare area. The
photo point is indicated by an *X* on the thematic DOQ. This validation
quickly confirmed that the thematic DOQ could be trusted to identify the
bare areas. As shown in the thematic DOQ, red corresponds to bare,
exposed surfaces with sparse vegetation (Figure 6).

![](media/image6.jpeg){width="3.3319444444444444in"
height="2.5548611111111112in"}

Figure 6. A bare area identified on the DOQ (left) as seen on aerial
photography (top right) and from the ground (bottom right).

Based on field validation, the red areas were typically exposed soil or
rock outcrops. The gold and yellow areas commonly represented bare
ground with increasing amounts of vegetation but were still considered
erodible. Visual confirmation helped refine the process to make a second
cut for threshold values.

A four-tiered color ramp resulted after extensive field validation of
the threshold values:

-   Red -- bare exposed ground with none to very sparse vegetation and
    litter cover.

-   Orange -- bare exposed ground with sparse vegetation and litter
    cover.

-   Gold -- bare ground with vegetation and litter cover.

-   Yellow -- some bare ground with considerable vegetation and litter
    cover.

With the thematic DOQs, the identified bare areas were sampled during
the 2002 field season (Figure 7). At each sample location, vegetation
density, canopy cover, ground cover, and soil texture were measured
using the nested frequency method^2^. The frequency method is an
objective and repeatable means for data collection and involves sampling
various-sized plots nested within a half-square-meter sampling frame.
Samples are taken at randomly selected points. Radiating out from each
sampling point, are three 100-foot transect lines. Four samples are
collected along each of the three transects every five feet, for a total
of 240 samples at each sampling location.

Figure 8 shows photos taken at representative sampling locations for the
four-tiered thematic color scheme. Each of the images show surveyed--GPS
transects as performed in the field validation.

![](media/image7.jpeg){width="3.3319444444444444in"
height="2.467361111111111in"}

Figure 7. Nested frequency sampling collects data every five feet along
100-foot transects using a half-square-meter sampling frame.

![](media/image8.jpeg){width="3.3208333333333333in"
height="2.4881944444444444in"}

Figure 8. These images (upper corners) depict the color correlation to
bare ground seen in corresponding photograph. The blue lines mark the
100-foot survey transects. The black arrow indicates the location and
direction of photo points.

# DISCUSSION

Sampling correlations were examined across the entire Strawberry Valley
watershed. Correlation is good for data collected within a single DOQ.
However, correlation is poor for data collected between different DOQs
due to the variability of brightness levels. It should be noted that
each individual DOQ has a unique aerial flight date and time, all of
which affect the exposure and brightness of bare areas. Therefore, each
DOQ has its own unique threshold value. Results are therefore consistent
within a DOQ, but not between DOQs.

The correlation data for percent cover (vegetation plus litter) versus
thematic color are shown in Figure 9 for the Co-Op Creek DOQ. Overlap
occurs between the orange and red, and between the gold and orange, but
no overlap exists between the yellow and gold, the yellow and red, or
the gold and red.

![](media/image9.jpeg){width="3.332638888888889in"
height="3.328472222222222in"}

Figure 9. Correlation of ground cover and thematic color for the Co-Op
Creek DOQ.

![](media/image10.jpeg){width="3.325in" height="2.363888888888889in"}

Figure 10. DOQ (1m resolution) on the left. Landsat-7 image (30m
resolution) on the right shows vegetation and soil differences using
4,5,3 R,G,B band combination.

The amount of bare ground is directly proportional to the amount of
ground cover (i.e., 80 percent ground cover, 20 percent bare ground).
Although there is good correlation for the amount of ground cover, the
amount of bare soil cannot be automatically assumed.

Problems exist where exposed soil (highly erodible) is mixed with rock
outcrops (not a significant source of erosion material). As the photo in
Figure 8 (orange) demonstrates, bare ground can exist as a mixture of
both exposed, bare soil and rock outcrops. As developed, the method is
not capable of distinguishing between exposed soil and rock outcrops.

Landsat 7 satellite imagery could help distinguish between rock outcrops
and bare, exposed soil during moist conditions. Selected infrared
Landsat band combinations can help distinguish differences between soils
(bands 7,5, and 3), or differences between vegetation and bare soils
(bands 4,5, and 3) as shown in Figure 10.

Another limitation is the fact that the DOQs in this study were based on
photographs acquired during the fall when seasonal vegetation is often
senesced, resulting in a greater occurrence of exposed soil than during
the earlier portion of the growing season. The biggest problem presented
by senesced vegetation in this study area is Wasatch bluebell
(*Mertensia brevistyla*). The bluebell is very prolific in the northern
and upper reaches of the Strawberry watershed. In early summer, foliage
is lush and dense, but then it dies back and senesces in late July to
early August. The senesced vegetation litter decomposes rapidly on
exposed hillsides, leaving bare, exposed soil in late summer and early
fall which persists until the following spring. The upper-left photo in
Figure 8 (yellow) shows a bluebell hillside in early September.

The senesced vegetation problem can be rectified by multidate sampling,
spring/summer satellite imagery will likely separate true bare soil from
bluebell vegetation sites.

# SUMMARY

The ability to screen a watershed for bare ground using DOQs provides a
quick and reliable method to map bare ground and to stratify potential
problem areas on a large scale. This remote sensing method offers a
plausible solution for mapping teams to map bare ground when budget and
time constraints are limiting the amount of on-the-ground reconnaissance
work necessary to accurately delineate bare ground problem areas on a
large scale. Problems occur when separating differences between rock
outcrops and exposed soil areas. Potential problems occur where late
season senesced vegetation (e.g., Wasatch bluebell) decomposes leaving
bare, exposed soil that persists until the following growing season.

# REFERENCES

1.  Davidson, R.A. (editor), Gecy, R.A., and Project Core Team.
    Strawberry Watershed Restoration Report. US Department of
    Agriculture, Forest Service, Uinta National Forest, Heber Ranger
    District. April 2004 (entire document).

2.  Range Analysis Handbook, FSH 2209.21. Region 4 Amendment
    2209.21.44-44.4. United States Department of Agriculture, Forest
    Service. 1986.