Prairie Monitoring Protocol Development: 

North Coast and Cascades Network 

Open-File Report 2008-1168

Prepared in cooperation with the 

North Coast and Cascades Network, National Park Service

U.S. Department of the Interior

U.S. Geological Survey

Prairie Monitoring Protocol Development: 

North Coast and Cascades Network 

By Allen McCoy and Craig Dalby, Pacific West Region GIS Group, Seattle, Washington 

Prepared in cooperation with the 

North Coast and Cascades Network, National Park Service

Open-File Report 2008-1168

U.S. Department of the Interior

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

KEN SALAZAR, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2009

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Suggested citation:

McCoy, A., and Dalby, C., 2009, Prairie monitoring protocol development: North Coast and Cascades 

Network: U.S. Geological Survey Open-File Report 2008-1168, 10 p.

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iii

Contents 

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

Monitoring Protocol Development..................................................................................................................................2

Technical Information......................................................................................................................................................10

Conclusions .......................................................................................................................................................................10

Figures

Figure 1. American Camp land cover classified into seven land-cover types, 1997..........................................4

Figure 2. American Camp land cover classified into seven land-cover types, 2007..........................................4

Figure 3. American Camp land-cover change, 1997–2007......................................................................................5

Figure 4. American Camp orthoimage, 1997..............................................................................................................6

Figure 5. English Camp land cover classified into seven land-cover types, 1997 ..............................................8

Figure 6. English Camp land cover classified into seven land-cover types, 2007 ..............................................9

Figure 7. English Camp land-cover change, 1997–2007 ..........................................................................................9

Tables

Table 1. Acreages of the seven land-cover categories for American Camp from 1997 and 2007 

orthoimagery ...................................................................................................................................................................3

Table 2. Land-cover change for American Camp, 1997–2007 ................................................................................3

Table 3. Acreages of seven land-cover categories for English Camp from 1997 and 2007 orthoimagery....7

Table 4. Land-cover change for English Camp, 1997–2007.....................................................................................7
iv

Conversion Factors 

SI to Inch/Pound 

Multiply By To obtain

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft) 

Inch/Pound to SI 

Multiply By To obtain

acres 0.4047 hectares
1

Prairie Monitoring Protocol Development: North Coast 

and Cascades Network

By Allen McCoy1and Craig Dalby

Introduction

1

The purpose of the project was to conduct research that will guide development of a standard 

approach to monitoring several components of prairies within the North Coast and Cascades Network 

(NCCN) parks. Prairies are an important element of the natural environment at many parks, including 

San Juan Island National Historical Park (NHP) and Ebey’s Landing National Historical Reserve 

(NHR). Forests have been encroaching on these prairies for many years, and so monitoring of the 

prairies is an important resource issue.

This project specifically focused on San Juan Island NHP. Prairies at Ebey’s Landing NHR will 

be monitored in the future, but that park was not mapped as part of this prototype project. 

In the interest of efficiency, the Network decided to investigate two main issues before 

launching a full protocol development effort: (1) the imagery requirements for monitoring prairie 

components, and (2) the effectiveness of software to assist in extracting features from the imagery. 

Several components of prairie monitoring were initially identified as being easily tracked using 

aerial imagery. These components included prairie/forest edge, broad prairie composition (for example, 

shrubs, scattered trees), and internal exclusions (for example, shrubs, bare ground). In addition, we 

believed that it might be possible to distinguish different grasses in the prairies if the imagery were of 

high enough resolution. 

Although the areas in question at San Juan Island NHP are small enough that mapping on the 

ground with GPS (Global Positioning System) would be feasible, other applications could benefit from 

aerial image acquisition on a regular, recurring basis and thereby make the investment in aerial imagery 

worthwhile. The additional expense of orthorectifying the imagery also was determined to be costeffective.

 

1

Pacific West Region GIS Group, Seattle, Washington 
2

Monitoring Protocol Development

Specifications for imagery adequate for mapping of prairie edges, composition, and exclusions 

called for 15-cm (approximately 0.5 ft) resolution with a horizontal accuracy of approximately 1 m, and 

natural color or color infrared (CIR) images. Natural color imagery rather than CIR was selected 

because it will be useful for a wider variety of applications at the park. 

After testing various rules of inclusion/exclusion for manually digitizing features in early 2006 

using 1997 images of San Juan Island NHP, we proceeded in fall 2006 with testing an automated 

method for extracting landscape classes from the imagery using an ArcGIS®

extension called Feature 

Analyst®

 (Visual Learning Systems, Inc.). Feature Analyst®

The transition from mostly manual digitizing to a semi-automated digitizing process also 

allowed us to eliminate one of our initial land-cover classes: mixed forest and prairie. Because the 

feature extraction software was able to digitize so much detail far faster than we could have manually 

digitized it, we were able to capture virtually every forest grove and even a majority of individual trees. 

 extracted about 80–90 percent of the 

features desired when used with properly defined training sets. This level of success enables us to 

suggest using the extension, or a similar one, in a semi-automated digitizing process for monitoring 

prairies.

New aerial orthoimagery was originally planned to be acquired in early summer 2006, but 

contracting difficulties precluded this from happening. As a result, the aerial imagery acquisition 

occurred in June 2007. The contractor provided an initial delivery in July 2007 and a supplemental 

delivery in September 2007. These data were reviewed for accuracy and found acceptable.

Several tests were run with Feature Analyst®

After experiments with defining multiple cover types in a single run of Feature Analyst

 on the new 2007 imagery. After multiple 

adjustments to the training sets, a very satisfactory data layer for the forest cover was achieved, which 

required only a few hours of clean up. The principal factor in obtaining acceptable results from the 

program was digitizing training polygons throughout the entire image. Variations in color and texture 

from place to place, even for a single land-cover type, made it absolutely essential to include samples 

from one end of the park to the other. ®

One disappointing result became apparent in the process of digitizing the land cover at American 

Camp. We were unable to discern with much accuracy all shrubs from grasses in the prairie zone when 

digitizing land cover. In many cases, it was quite clear that an area was covered with shrubs, we 

presume mostly 

 proved 

disappointing, the program was used to delineate only forest areas in a single run. The land-cover 

categories, water, roads, bare earth, and buildings were small enough in size to manually digitize, and 

the prairie category was simply defined as all leftover polygons. 

Rosa nutkana, but in many other instances it was just not possible to make a reliable 

identification. We therefore decided not to make the distinction and to leave all shrubs in the prairie 

category. Subsequent work will examine the effectiveness of using CIR to delineate shrubs in the prairie 

areas. Lidar data were not available to help distinguish between taller and shorter vegetation. 
3

Most of the other land-cover classes were fairly homogeneous. However, several classes of land 

cover were grouped into the bare earth category. Areas of driftwood, beaches, sand dunes, and rock 

were put into this single class.

Acreages of the seven land-cover categories for American Camp, compiled from the 1997 and 

2007 orthoimagery, are shown in table 1. Table 2 shows the acreages that remained as prairie or forest 

land-cover types, as well as the acreage that changed from one type to the other from 1997 to 2007. 

Table 1. Acreages of the seven land-cover categories for American 

Camp from 1997 and 2007 orthoimagery. 

American Camp 

land-cover category

1997 

acreage

2007 

acreage

Bare Earth 85.1 85.4

Buildings 0.1 0.1

Forest 361.6 435.8

Managed grassland 5.3 5.2

Prairie 771.3 695.7

Roads 16.7 16.7

Water 9.0 10.1

Table 2. Land-cover change for American Camp, 1997–2007. 

Units, in acres Prairie in 2007 Forest in 2007

Prairie in 1997 675.8 77.4

Forest in 1997 4.1 357.3
4

Figures 1 and 2 show the results of the land-cover classification at American Camp for 1997 and 

2007, respectively. 

Figure 1. American Camp land cover classified into seven land-cover types, 1997.

Figure 2. American Camp land cover classified into seven land-cover types, 2007.
5

The most obvious change in land cover of American Camp since 1997 has been the reduction of 

prairie by more than 75 acres, coupled with the growth of forest by nearly the exact same amount. This 

is not a coincidence, as even a cursory examination of the spatial data shows (fig. 3). In 2007, the forest 

at American Camp, especially in the northwest corner of the park, has encroached substantially on what 

10 years ago was prairie. Figure 3 depicts the forest and prairie land-cover classes, and highlights in 

bright green the substantial area that changed from prairie to forest. 

 Figure 3. American Camp land-cover change, 1997–2007.
6

An initial delivery of land-cover data for American Camp was made to the North Coast and 

Cascades Network in November 2007. However, a problem became apparent after the data were 

delivered: several areas that were forest zones in 1997 apparently had been converted to prairie in 2007. 

A subsequent investigation determined that in most instances the cause for this change was 

misinterpretation of the 1997 imagery. This, in turn, was largely due to the timing of the 1997 imagery, 

which was flown in September; some shrubs appeared to be trees as a result of the longer shadows that 

were cast at that time of year. A sample shrub area from the 1997 imagery (fig. 4), which was originally 

misclassified as forest, is enclosed with a yellow polygon, and three trees among the shrubs are noted 

with yellow points. Note the shadow along the northern edge of the shrubs, which makes them look 

much like trees. The 2007 imagery was flown in June making it easier to distinguish the short 

vegetation from the tree cover. These errors were corrected by manually re-digitizing the polygons. 

Figure 4. American Camp orthoimage, 1997. Dark green area surrounded by the yellow polygon

was originally misclassified as forest as a result of the longer shadows cast at the time of year 

the imagery 

was flown.
7

To complete the project, we turned our attention to English Camp where our experience changed 

our view somewhat as to the most effective method of using the Feature Analyst®software. At 

American Camp, all forested areas appeared fairly homogeneous in the orthoimagery. However, this 

was not the case at English Camp. Because of the low altitude at which the imagery was flown to obtain 

high resolution, there was pronounced “tree lean,” which can arise from the apparent flattening of 

vertical objects in a radial pattern away from the center of each individual image. Where adjacent 

images were merged to form a mosaic, there was a noticeable seam in the final orthoimage. Such 

noticeable seams did not occur in the orthoimages for American Camp even though the amount of 

forested area was smaller. Furthermore, English Camp has a larger mix of broadleaf and needleleaf 

forest as compared to American Camp, where almost all trees are conifers. Therefore, processing with 

Feature Analyst®

Overall, English Camp showed only a small trend toward conversion of prairie to forest with an 

increase of 1.8 acres of forest and a decrease of 2.0 acres of prairie. However, the summary statistics are 

somewhat misleading, in part because of management interventions in the process. Several trees are 

known to have been removed from the south side of Young Hill after the 1997 imagery was taken, and, 

as a result, these previous forest areas were recorded as prairie in the 2007 imagery. Table 4 shows that 

there was actually a sizeable change from prairie to forest (15.2 acres) and forest to prairie (13.9 acres) 

at English Camp, which is not obvious in the summary statistics. 

 produced much better results by stratifying the forest class “training”-areas into 

several subgroups, each of which was more homogeneous than the forest as a whole, and then 

combining the results into a single category. The final acreages for all seven categories are shown in 

table 3.

Table 3. Acreages of seven land-cover categories for English Camp 

from 1997 and 2007 orthoimagery. 

English Camp 

Land-Cover Category

1997 Acreage 2007 Acreage

Bare Earth 1.2 1.0

Buildings 0.2 0.2

Forest 428.6 430.4

Prairie 65.9 63.9

Managed Grassland 7.3 8.3

Roads 3.6 3.5

Water 0.4 0 

 Table 4. Land-cover change for English Camp, 1997–2007.

(Units in Acres) Prairie in 2007 Forest in 2007

Prairie in 1997 46.0 15.2

Forest in 1997 13.9 414.0
8

A geographic breakdown of the forest-prairie conversion polygons revealed that in the Young 

Hill area (the southeastern part of the camp), 6.5 acres converted from prairie to forest while 12.7 acres 

converted from forest to prairie. Elsewhere in English Camp, outside the Young Hill area, 8.7 acres 

converted from prairie to forest, while only 1.2 acres were recorded as converting from forest to prairie. 

This last figure may be due to several factors, including management activity, windthrow, and simple 

inherent inaccuracies in digitizing the forest edge, sometimes due to the previously mentioned “tree 

lean” issue.

The apparent loss of water from 1997 to 2007 was entirely due to different tidal phases when the 

imagery was taken. The tide was higher in the 1997 imagery than in the 2007 imagery. 

Figures 5 and 6 show the results of the land-cover classification at English Camp for 1997 and 

2007, respectively. Figure 7 shows the prairie and forest land-cover types for English Camp, and the 

areas that changed between the two classes. 

In November 2007, we attempted to determine whether imagery other than natural color images 

would allow us to separate out shrubs from grasses in the prairies. We obtained color infrared imagery 

of the park from the Washington Department of Natural Resources. Our inspection and testing of the 

images led us to conclude that compiling data on the locations of shrubs within prairies at San Juan 

Island NHP, in particular American Camp, would not be any easier using color infrared imagery than 

with natural color imagery. There are few clues other than tone for distinguishing shrubs and grasses, 

and there is such fine gradation of reds to pinks in the various vegetated areas within the prairies that 

choosing a “break point” between the two classes would be largely arbitrary. 

 Figure 5. English Camp land cover classified into seven land-cover types, 1997.
9

 

 Figure 6. English Camp land cover classified into seven land-cover types, 2007.

 Figure 7. English Camp land-cover change, 1997–2007.
10

Technical Information

The processing in this project was done using ArcGIS (ArcInfo) 9.2 from Environmental 

Systems Research Institute, Inc. of Redland, California, and Feature Analyst®

Four images were used in this project, all of them in SID format, each one compressed from 

multiple, original TIFF images. The 1997 images (1-ft resolution) were taken in September, and the 

2007 images (15-cm resolution) were taken in June. All imagery was taken in 24-bit natural color. 

 (ArcGIS extension) 4.1 

from Visual Learning Systems, Inc of Missoula, Montana. These tools were largely chosen for cost 

considerations. Other software for analysis or feature extraction may be suitable as well.

Most features were compiled with Feature Analyst®

Processing time for Feature Analyst

. Training polygons were selected for each 

image processed. It was essential to obtain training polygons over the entire image area for each feature 

type being compiled. However, where there were substantial differences in appearance between areas in 

the same category, the software performed much better by treating these different areas as belonging to 

separate categories and then merging them in ArcGIS afterward. During the manual clean up phase of 

digitizing, we found that the TIFF files provided better performance for panning and zooming than the 

SID files. ®

Conclusions

 generally ranged from 1 to 4 hours. However, the English 

Camp 2007 image took an unusually long time to process: 36 hours. Visual Learning Systems, Inc. ran 

the image through the same process for us to try to determine the cause, but found it ran in only 4 hours. 

We have not yet discovered the cause for this anomaly.

Prairie edges and many other land-cover categories can be efficiently compiled using automated 

feature extraction software, such as Feature Analyst®

Lidar-derived surface data may be of use in future studies. These data would help, for example, 

in distinguishing between trees and shrubs based on canopy height. However, unless costs for compiling 

Lidar data are significantly reduced, it will remain prohibitively expensive to obtain updated surface 

information for change detection analyses. 

. However, we also determined that compiling 

detailed information, such as grass types or even shrub/grass delineations, from imagery is not practical. 

The image resolution required for those applications would be so high that the cost for obtaining the 

imagery probably would exceed the cost of mapping the same information on the ground with a high 

precision GPS unit. 

To reduce future costs, we suggest subsequent prairie monitoring be tested using lower 

resolution imagery, perhaps 1-m or 0.5-m images from the National Agriculture Imagery Program 

(NAIP). This may provide two other benefits: (1) processing time might be significantly reduced with 

the smaller file sizes; and (2) the problems related to “tree lean” may be minimized, because this 

imagery is flown at a much higher altitude.
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For more information concerning the research in this report, contact the 

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McCoy and Dalby— Prairie Monitoring Protocol Development: North Coast and Cascades Network—Open-File Report 2008-1168