Development and Evaluation of a Riparian Buffer 

Mapping Tool 

By Lesley E. Milheim and Peter R. Claggett 

Open-File Report Series 2008–1266 

U.S. Department of the Interior 

U.S. Geological Survey 
ii

U.S. Department of the Interior 

DIRK KEMPTHORNE, Secretary 

U.S. Geological Survey 

Mark D. Myers, Director 

U.S. Geological Survey, Reston, Virginia: 2008 

For product and ordering information: 

World Wide Web: http://www.usgs.gov/pubprod 

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its natural and living resources, natural hazards, and the environment: 

World Wide Web: http://www.usgs.gov 

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Suggested citation: 

Milheim, L.E., and Claggett, P.R., 2008, Development and evaluation of a riparian buffer mapping tool: U.S. 

Geological Survey Open-File Report 2008-1266, 15 p., available only online at 

http://pubs.usgs.gov/of/2008/1266/. 

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endorsement by the U.S. Government. 

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

copyright owners to reproduce any copyrighted material contained within this report. 
iii

Contents 

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

RBMapper Overview .......................................................................................................................................................... 1

RBMapper Availability ....................................................................................................................................................... 2

Evaluation of the RBMapper Software ........................................................................................................................... 2

Strengths .......................................................................................................................................................................... 3

Weaknesses .................................................................................................................................................................... 3

Conclusions ......................................................................................................................................................................... 3

Acknowledgments .............................................................................................................................................................. 3

References Cited ................................................................................................................................................................ 4

Appendix 1—RBMapper Evaluation and Reviewer Comments ................................................................................. 5

Figures 

1. RBMapper interface displaying the tutorial image with the windows for setting preferences open. 

Tutorial image courtesy of GeoEye. ........................................................................................................................ 6

2. RBMapper interface displaying the results of the water detection function using default settings. 

Image courtesy of GeoEye. ...................................................................................................................................... 7

3. RBMapper interface displaying the results of the buffering function. Image courtesy of GeoEye. ........... 8

4. RBMapper interface displaying the results of the detect vegetation and detect forest functions. Image 

courtesy of GeoEye. ................................................................................................................................................... 9

5. RBMapper interface displaying water detection of nonwater features. Image courtesy of GeoEye. ..... 11

6. Example of mixed water identification. Image courtesy of GeoEye. .............................................................. 14
Development and Evaluation of a Riparian Buffer 

Mapping Tool 

By Lesley E. Milheim and Peter R. Claggett 

Introduction 

Land use and land cover within riparian areas greatly affect the conditions of adjacent water 

features. In particular, riparian forests provide many environmental benefits, including nutrient 

uptake, bank stabilization, steam shading, sediment trapping, aquatic and terrestrial habitat, and 

stream organic matter. In contrast, residential and commercial development and associated 

transportation infrastructure increase pollutant and nutrient loading and change the hydrologic 

characteristics of the landscape, thereby affecting both water quality and habitat. Restoring riparian 

areas is a popular and cost effective restoration technique to improve and protect water quality. 

Recognizing this, the Chesapeake Executive Council committed to restoring 10,000 miles of 

riparian forest buffers throughout the Chesapeake Bay watershed by the year 2010 (Chesapeake 

Executive Council, 2003). In 2006, the Chesapeake Executive Council further committed to “using 

the best available...tools to identify areas where retention and expansion of forests is most needed 

to protect water quality” (Chesapeake Executive Council, 2006). 

The Chesapeake Bay watershed encompasses 64,000 square miles, including portions of six 

States and Washington, D.C. Therefore, the interpretation of remotely sensed imagery provides the 

only effective technique for comprehensively evaluating riparian forest protection and restoration 

opportunities throughout the watershed. Although 30-meter-resolution land use and land cover data 

have proved useful on a regional scale, they have not been equally successful at providing the detail 

required for local-scale assessment of riparian area characteristics. Use of high-resolution imagery 

(HRI) provides sufficient detail for local-scale assessments, although at greater cost owing to the 

cost of the imagery and the skill and time required to process the data. 

To facilitate the use of HRI for monitoring the extent of riparian forest buffers, the U.S. 

Forest Service (USFS) and the U.S. Geological Survey (USGS) Eastern Geographic Science Center 

funded the development of a prototype semiautomated image classification tool, RBMapper, that is 

designed for use by technicians with limited image processing training. 

The remainder of this document provides an overview of the RBMapper tool, instructions 

on how to obtain the RBMapper tool and tutorial datasets, and a summary evaluation of the tool. 

Appendix 1 provides additional information from the reviewers. 

RBMapper Overview 

RBMapper was developed in 2006 by GDA Corporation in conjunction with the USGS and 

the USFS. RBMapper is a prototype image processing software tool that classifies land cover 

within riparian areas from HRI and ancillary hydrography and elevation datasets. While other 

software packages are available to perform image processing, they are complex and require highly 

1
skilled users to effectively process the HRI. RBMapper was developed to semiautomate the water 

delineation, buffering, and classification processes for users with limited training. It provides the 

following functions: 

• Atmospheric adjustment and pan-sharpening of the image, 

• Specialized algorithms for water detection, 

• Edit functions for user correction of the detected water features, 

• Buffer delineation based on topographic or Euclidean distance, 

• Discrimination of general land cover classes (water, bare ground, herbaceous vegetation, forest) 

within the buffers, and 

• Calculation of associated land cover statistics (for example, total areas of forest, ground 

vegetation, and bare ground). 

An initial version of RBMapper was delivered to the USFS and the USGS in November 

2006. The first round of tests identified numerous issues and problems, many of which were 

addressed. A stable version of RBMapper that was suitable for evaluation by a larger audience was 

released on July 6, 2007. This prototype version is the one referred to in this report. 

RBMapper Availability 

RBMapper Version 1.2.8 can be downloaded from the GDA Corporation Web site at 

http://gdacorp.com/projects/RBMapper.html. Instructions for downloading, installing, and 

registering the software and for obtaining tutorial datasets are also available from the Web site. The 

Web site allows access to digital copies of the user’s manual, tutorial, and release notes. To use the 

RBMapper software, one is required to register the software and obtain a software key code from 

GDA Corporation. To use the software tutorial, one must download a copyrighted IKONOS 

image, courtesy of GeoEye, and ancillary datasets. 

The tutorial datasets include an IKONOS image in GeoTIFF format of a location in eastern 

Kent County, Maryland, and ancillary data for the entire Chesapeake Bay watershed. The ancillary 

datasets include a 30-meter digital elevation model, a linear shapefile for streams, and a polygon 

shapefile for water bodies. The hydrography shapefiles were generated from 1:24,000 scale 

National Hydrological Dataset (NHD) and Digital Line Graph (DLG) files. The tutorial datasets are 

projected to UTM Zone 18, WGS 1984 datum. 

Evaluation of the RBMapper Software 

Following release of the prototype RBMapper in July 2007, the tool was reviewed by 

representatives of the Federal Government, State governments, and nongovernmental organizations 

to evaluate its potential for use by these organizations. The focus of the evaluation was on 

RBMapper’s functionality, accuracy, and usability, especially for users with limited knowledge of 

and skill in processing remote-sensing data. Each of the reviewers completed the tutorial and then 

experimented with the tool’s capabilities as those functions might be used in his or her official 

duties. Details on the evaluation and the reviewers’ comments are provided in Appendix 1. 

2
Strengths 

RBMapper provides fast reproducible estimates of forest canopy cover within user defined 

buffers of water features. RBMapper enables the correction of water features, which is a necessity 

when using 1:24,000 scale hydrography datasets with HRI. RBMapper is easily learned, has an 

intuitive user interface, and allows persons with limited remote-sensing experience to process 

images covering large areas relatively quickly. The accuracy and quality of the output are highly 

dependent on the amount of water feature correction required, how much of that correction is 

performed, and the skill with which RBMapper functions are used. The reviewers agreed that 

RBMapper provides satisfactory results given a minimum level of training. 

Weaknesses 

RBMapper’s primary weaknesses are instability, uneven automated detection of water 

features, variability of results based on user knowledge and experience, and dependence on 

IKONOS imagery. Such issues as software instability and data dependence are common in the 

prototype phase of software development, and although most of the instability issues were resolved 

prior to release of the prototype release in July 2007, some issues remained unresolved. 

There are obvious tradeoffs between software usability and functionality. In developing the 

RBMapper, the challenge was to maximize functionality while not compromising usability by 

persons with limited training. Given the variability in image quality, even for images from the same 

platform, it was necessary to add functionality at the cost of requiring increased user knowledge 

and experience. RBMapper was designed to work only with IKONOS imagery to minimize the 

problem of image quality on the software development process for this proof-of-concept tool. 

Although the exclusive use of IKONOS imagery provides the desired level of spectral and spatial 

resolution, the cost of the imagery may preclude its use by local governments and watershed 

organizations, especially because aerial imagery is often available to them at little or no cost. 

Conclusions 

The RBMapper is new software that provides a semiautomated approach to mapping 

riparian forest buffers from HRI and requires minimal technical experience on the part of users. 

While it is only prototype software, it can be used in its current state by Federal, State, and local 

Chesapeake Bay Program Partners to map riparian buffers. The USGS and the USFS are exploring 

further refinements to the tool to increase its functionality, accuracy, and usability by local 

governments and nongovernmental organizations that lack remote-sensing expertise and software. 

Planned refinements to the RBMapper include (1) use with color-infrared aerial photography; (2) 

wall-to-wall mapping of tree canopy, herbaceous vegetation, and bare ground; and (3) clarification 

of the user interface. 

Acknowledgments 

We gratefully acknowledge the participation of our independent reviewers, who are as 

follows: Harry Berquist, Virginia Institute of Marine Science; Seth Coffman, Potomac 

Conservancy; Fred Irani, Maryland Department of Natural Resources; Leroy Ledon, University of 

Puerto Rico; Jarlath O’Neil-Dunne, University of Vermont; Stephanie Orndorff, The Nature 

Conservancy; James Pugh, Virginia Department of Forestry; Chris Robinson, NOAA C-CAP; 

Vanessa Torres, University of Puerto Rico. 

3
References Cited 

Chesapeake Executive Council, 2003. Expanded riparian forest buffer goals: Chesapeake Executive 

Council Directive No. 03-01, 2 p. 

 

Chesapeake Executive Council, 2006, Protecting the forests of the Chesapeake watershed: 

Chesapeake Executive Council Directive No. 06-1, 2 p. 

4
Appendix 1—RBMapper Evaluation and Reviewer Comments 

The original agreement between GDA Corporation and the USGS Eastern Geographic 

Science Center described the development of prototype software to test the automation of mapping 

riparian buffers. As a prototype, it was understood that the software would likely not be suitable for 

use in a production environment without further refinements and error checking. The original goal 

was to simplify and streamline riparian buffer mapping by developing new algorithms and tools, 

which, combined with IDRISI (Clark Labs, Clark University) raster geographic information system 

(GIS) software to provide visualization and editing capabilities, could do the following 

automatically: (1) perform at-sensor correction of the imagery; (2) subset and reproject any 

ancillary data to the extent, resolution, and coordinate system of the image; (3) pan-sharpen the 

imagery; (4) use spectral, textural, and contextual information in mapping the riparian buffers; and 

(5) map riparian buffers using terrain to estimate the distance from the water. During development 

of the prototype, however, GDA recommended developing a custom visualization interface and 

abandoning the proprietary platform (IDRISI) for visualization. This recommendation was accepted 

by the USGS and the USFS and resulted in a more cohesive product. This change raised reviewer 

expectations that the RBMapper would function with an intuitive user interface and minimum 

errors rather than as a prototype of what could be achieved through additional refinements. 

RBMapper was evaluated by nine individuals with a wide range of skills, knowledge, and 

experience in mapping riparian buffers. Two reviewers were summer interns with classroom 

experience using GIS but no remote sensing experience. They learned about RBMapper and 

ERDAS Imagine (an image processing software package produced by Leica Geosystems LLC) 

using tutorials and then compared their ability to process the same images using both tools. 

The remaining seven reviewers are highly trained and skilled professionals, each with 5 to 

25 years of experience with GIS and remote sensing. These individuals represent Federal and State 

agencies, nongovernmental organizations, and one university. These reviewers all used the tutorial 

and the tutorial data to evaluate RBMapper. One reviewer also performed a comparison between 

RBMapper with ERDAS Imagine techniques used by his agency. Many of the other reviewers 

discussed their expectations of RBMapper given their experience with other image processing 

tools. 

Results 

While individual opinions differed on various implementation details, particularly regarding 

the user interface, all the reviewers agreed that RBMapper provides reasonable results without great 

difficulty and that it is easily used by those without significant training in remote sensing and image 

processing. 

RBMapper was developed for use by those with limited skill and experience in image 

processing; nonetheless, training is required to effectively use RBMapper. The summer interns 

struggled with RBMapper and seemed to have more trouble with RBMapper shutting down than 

the other reviewers. However, they found it easier to use than the more sophisticated image 

processing tools. Those who professionally research land cover using image processing techniques 

acknowledged that the tool has benefit, especially for those with limited remote sensing and GIS 

experience; given their level of expertise and professional goals, however, these researchers felt 

that the more sophisticated image processing software packages were more suitable for their 

purposes. 

5
Usability 

Generally, RBMapper works as described in the user’s manual and in the tutorial. Many 

reviewers commented on the intuitive interface. Figure 1 shows the interface with the windows for 

setting preferences open. 

Figure 1. RBMapper interface displaying the tutorial image with the windows for setting 

preferences open. Tutorial image courtesy of GeoEye. 

RBMapper assumes the use of default pathnames for the tutorial data, although the user can 

specify new pathnames and data. RBMapper controls are accessed through the use of buttons 

located in the upper left portion of the window. The controls are invoked using a series of function 

buttons for processing the image and for editing the ancillary data. The functions produce and 

operate on the color-coded data layers located immediately to the right of the control buttons. The 

function buttons allow the user to detect cloud cover (not implemented at this time), detect water, 

finalize the water mask, detect the maximum water level, generate the buffer area, detect 

vegetation, detect forest, filter the land cover classes, and generate land cover output statistics. 

These functions are intended to be used in sequence, although the user can go back and re-execute a 

step to refine the process, but this will cause the previous results to be lost. 

6
RBMapper automates the image processing functions to detect water, to buffer that water, 

and to determine land cover within those buffers (figs. 2–4). Most of the experienced reviewers 

wanted more information on the parameters and the algorithms used in water detection and land 

cover classification so that they could better understand and control the process. 

Figure 2. RBMapper interface displaying the results of the water detection function using default 

settings. Image courtesy of GeoEye.

7
Figure 3. RBMapper interface displaying the results of the buffering function. Image courtesy of 

GeoEye. 

 

8
Figure 4. RBMapper interface displaying the results of the detect vegetation and detect forest 

functions. Image courtesy of GeoEye. 

 

Operational Issues 

The most common concerns raised about the RBMapper relate to its stability, the inability 

to save user inputs for future processing, restrictive imagery requirements, use of 1:24,000 scale 

hydrography data, the question of performance over a variety of physiographic provinces, and the 

level of expertise and effort required to develop a high-resolution water feature dataset. These 

issues are explained in greater detail below. 

Software Instability. RBMapper occasionally and unexpectedly shuts down during 

processing. There is no consistent pattern that leads to an unexpected shutdown. Instability causes 

lost time in that data are not retained and must be regenerated after RBMapper shuts down 

unexpectedly. The user can save the project frequently, but this does not alleviate the time lost in 

restarting the tool. The stability issue is complicated by the fact that it may be related to the training 

of and shared expectations between the developers and the professional-level users. The less 

knowledgeable users who do not share this background may unknowingly make mistakes that 

cause errors within RBMapper. 

Inability to Save Preferences. RBMapper assumes a default location for the image and 

ancillary data files. The user may specify an alternative pathname and filename by selecting File > 

9
Preferences and navigating to the appropriate pathname for each of the three ancillary datasets. 

However, these alternative locations are not saved and restored when the RBMapper project is 

saved and re-opened, so the user must respecify the preferences for subsequent work. Similarly, 

the RBMapper functions assume default parameters that the user may change, but these changes are 

not saved for repeated use of the function. 

Restrictive Imagery Requirements. The exclusive use of IKONOS imagery can also be 

considered a weakness. Although this imagery does provide the desired level of spectral and spatial 

resolution, the imagery expense is difficult for the intended user community to justify because 

many local government users have existing aerial imagery or other spectral data available to them 

at less or no cost. 

Scale of Data Can Be Problematic. High-resolution hydrography data of less than 1:5,000 

scale are needed to align well with water features identifiable using such high-resolution imagery as 

IKONOS. The hybrid 1:24,000 scale National Hydrography Dataset – Digital Line Graphs ancillary 

dataset1

 used with the RBMapper requires significant editing to align it with water features 

identified from IKONOS imagery. The amount of editing is particularly significant in urbanizing 

areas where water features may be greatly altered from their original mapped state. The hybrid 

hydrography dataset is also limiting because it includes a water identification field (for example, 

freshwater, or saltwater) that is not available in the standard NHD dataset. The use of nonstandard 

fields to describe water features requires the user either to use the tutorial hydrography dataset with 

the user-supplied imagery or to modify a local hydrography dataset to include the necessary data 

fields for use with RBMapper. 

The Chesapeake Bay watershed is very large, encompassing parts of four major 

physiographic regions: coastal plain, piedmont, ridge and valley, and Appalachian plateau. Land 

cover and water feature patterns and densities vary across these regions, which may impact 

RBMapper performance. The RBMapper has yet to be systematically tested in different ecoregions. 

Each ecoregion may need different default parameter settings to optimize land cover classification. 

Higher-Than-Expected Level of Expertise Required for Some Functions. The higherthan-expected level of expertise and effort required to operate RBMapper may be seen in (1) the 

extensive corrections to the water detection results that may be needed to create the water mask, (2) 

the access to and knowledge of a GIS to create additional fields required in the hydrography 

dataset, and (3) the knowledge of parameter effects on the RBMapper functions. Additional 

documentation on parameters and processing algorithms would alleviate the latter comment. 

The uneven detection of water features and inaccuracies within the ancillary data require the 

user to correct the water mask before generating the buffers and classifying the land cover within 

those buffers. RBMapper creates a water mask using a water detection algorithm and the stream 

and water body datasets. The mask then serves as input to the buffering function. However, water is 

not always detected (fig. 2) or is detected where there is no water (fig. 5). Alternatively, the stream 

and water bodies are misplaced because of ancillary data accuracy issues or land cover change 

between the source date of the ancillary data and the source date of the image. These problems can 

require significant correction to the water mask. Such correction may be a multistep process. For 

example, RBMapper may classify a lake as five water classes: ancillary lake/pond class, lake/pond 

class, ancillary river/stream (feature boundary) class, river/stream class, and water class. The 

simplest way to correct this feature is to delete pixels belonging to each of the classes in separate 

 

1 At the time the RBMapper was developed, the 1:24,000 scale NHD was not yet complete for the entire 

Chesapeake Bay watershed. 

10
steps, then to use the “grow” function to identify the actual feature from the imagery reflectance 

values. Similar problems occur where streams cross roads. 

Figure 5. RBMapper interface displaying water detection of nonwater features. Image courtesy of 

GeoEye. 

 

The correction of the water mask may be substantial, depending on the terrain and the 

amount of development since the collection of the ancillary data. Seasoned professionals may not 

notice the effort required to correct the water mask, but it may cause difficulty for nonprofessional 

users. Making similar corrections to the water mask during subsequent analyses of the same area 

for different time periods may affect temporal trend statistics because spectral interpretation may 

delineate larger or smaller water areas or configurations depending on in situ conditions for those 

time periods. 

The use of nonstandard hydrologic data requires a higher level of training and access to 

software tools to modify the data than is implied by the manuals. Other aspects of the software, 

such as parameters for water detection, the “grow” function, and land cover class differentiation 

require a greater understanding of remote sensing than is intended for this application. Software 

characteristics such as these increase the level of training and experience needed to achieve highquality results using RBMapper. 

11
Accuracy 

The accuracy and quality of the RBMapper output are highly dependent on the amount of 

correction required to complete the water mask, how much of that correction is performed, and the 

skill with which the RBMapper functions are used. However, the reviewers agreed that, given the 

level of training required, RBMapper generates adequate results. This section summarizes the 

comments on the accuracy of processing the tutorial image, differences across physiographic 

provinces, and the effects of hydrography datasets of different resolutions. 

Several reviewers commented on the presumed accuracy of the output because of the 

similarity in techniques between RBMapper and in-house image-processing procedures, but only 

one reviewer actually performed a direct comparison. This reviewer compared the results of 

riparian buffer mapping using RBMapper with an in-house technique using ERDAS 8.7 (Leica 

Geosystems LLC) and eCognition 5.0 (Definiens). The technique using eCognition 5.0 takes about 

1 hour, but requires substantial training. The technique using RBMapper also took about 1 hour, but 

the RBMapper produced more forest speckling (even though filtering was used), and the output 

shows more confusion with agricultural areas. 

A visual inspection of land cover classified using default settings compared with the source 

image reveals that what is classified as vegetation is typically vegetation; the forest class, however, 

may represent either forest or vegetation and the bare ground class may represent bare ground, 

impervious surfaces, or vegetation. In an urbanized setting, this confusion in classification and 

hence in the land cover statistics could be misleading, even though the goal is to identify tree 

crowns near streams and not bare ground or impervious surfaces. These classifications may be 

altered by manipulating the classification parameters, but this manipulation requires knowledge of 

those parameters and their effects. 

One reviewer processed images from three physiographic regions: one image each from 

Kent and Talbot Counties, Maryland, in the coastal plain; one image from Dauphin County, 

Pennsylvania, in the Appalachian uplands; and one image from Frederick County, Maryland, in the 

piedmont. In general, the results were similar. However, the image from Pennsylvania required 

more significant correction of the hydrography data than the other images. This difference may be 

because of the greater amount of urbanization shown in that image and the presence of more 

braided and meandering stream beds. An interesting result of using a hydrography dataset for other 

than its original purpose was the classification of islands in the Susquehanna River as lakes. The 

islands appear as polygons in the river, but there is no provision for identifying a land structure in 

this dataset and RBMapper interprets unspecified polygons as fresh water bodies. 

An early concern in evaluating RBMapper was the resolution of the tutorial’s elevation and 

hydrography data. The hydrologic data are mapped at 1:24,000 scale and the elevation is 30-meter 

resolution whereas the IKONOS imagery contains 4-meter resolution multispectral data sharpened 

to 1-meter resolution using the panchromatic band. Several reviewers commented that the use of 

higher resolution ancillary data would improve the results of RBMapper. 

However, when RBMapper was evaluated using synthetic stream data generated using 

elevations of varying resolution. It was found that the use of higher resolution stream data did not 

significantly change the land cover statistics calculated by RBMapper, although, the number of 

gaps did increase with the higher resolution stream data. The expected differences may not have 

manifested because of the homogeneity of the test landscape or because the same water body 

dataset was used for all tests. Therefore, the results of this experiment are inconclusive. 

12
Additional Comments 

A few reviewers provided additional comments or summary statements, such as the 

following: 

1. There is greater data analysis capability with other software packages, but they also require 

more knowledge of remote sensing and greater effort. RBMapper provides a straightforward 

approach to extracting forest vegetation within user-specified buffers. I prefer eCognition 

(Definiens), but see applicability of RBMapper for resource managers or planners with little or 

no remote-sensing knowledge. 

2. An intern liked RBMapper’s more user-friendly program for one of limited experience. 

3. RBMapper must be used with process standards, such as whether or not to include bridges 

when correcting the water mask and how small a stream or pond to include. These items have 

the potential to significantly affect the buffer statistics and require a consistency in the buffer 

definition and classification. 

4. The homogeneous conditions in the tutorial imagery represent a best case scenario for mapping 

land cover. Pixel based classifiers work less well under more heterogeneous conditions. 

5. The tutorial datasets were conveniently all of the same projection. While RBMapper will 

project all datasets to the image’s projection on-the-fly, it is a time-consuming process. 

6. The user must also be aware that intermediate files are kept in the output folder within the 

IKONOS imagery folder. If the user wishes to restart the riparian buffer mapping process from 

the beginning, he or she should delete the output folder and its contents before restarting. 

Otherwise, those intermediate files are re-used. 

7. The distinction between the data layers is not clear. For instance, what distinguishes the 

lake/pond layer from the ancillary lake/pond layer? The speckling of these layers with the 

RBMapper-detected water layer makes the water mask correction more difficult than it needs to 

be (fig. 6). This point was emphasized by one reviewer who created an empty water body 

dataset and removed the boundaries of large water features (for example, a broad river) that 

extend outside the image footprint from the stream dataset, leaving the smaller streams 

delineated by a single line, and then used these alternative datasets to process the image. This 

technique greatly reduced the amount of correction because RBMapper (1) grew the water 

features with good boundaries that needed no correction, although not all water bodies were 

detected, as mentioned earlier; and (2) did not identify water bodies with as many mixed 

classes. 

13
Figure 6.  Example of mixed water identification. Image courtesy of GeoEye.

Conclusions 

The reviewers may have had unrealistic expectations of the software because six of them 

were highly trained technicians who were accustomed to using very flexible image processing 

software (for example, ERDAS Imagine, ENVI, Definiens) with minimal errors and detailed help 

and tutorial files. Two of the reviewers with minimal training were students and appeared to 

struggle most with the tool, in part because they had little understanding of the purpose of mapping 

riparian buffers. One of the remote sensing technicians found the tool potentially useful, but 

expressed the need to use it with aerial photography that is freely available. The use of IKONOS 

imagery limited the evaluation of almost all reviewers to the tutorial dataset. 

Remote-sensing technicians tended to prefer their current image classification software and 

were disinclined to use less flexible tools like the RBMapper. Therefore, those with limited 

experience and knowledge in remote sensing may be more appropriate users of RBMapper. 

However, the need to build nonstandard hydrologic datasets, to correct the water mask, and to 

“tinker” with the details is nontrivial and may deter use of the tool by less trained users. Input from 

such potential users (for example, resource managers or planners) on RBMapper’s ability to 

provide a needed service is vital. 

14
15

Assuming continued RBMapper development, the following issues identified in this 

evaluation and in the initial testing should be addressed: (1) The interface and its multiple 

classification layers, including the use of the nonstandard hydrologic data, need to be clarified and 

documented, and perhaps redesigned; (2) The processing of alternative high-resolution imagery and 

datasets needs to be evaluated; (3) The processing standards for use with RBMapper, such as a 

minimum mapping unit, need to be identified; (4) An accuracy assessment needs to be performed; 

and (5) RBMapper should be tested over all major physiographic provinces in the Chesapeake Bay 

watershed to identify any necessary changes in default parameter settings.