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BLM/OC/ST-10/003+1737
Multiple Indicator Monitoring (MIM)

of Stream Channels and Streamside Vegetation

Timothy A. Burton, Fisheries Biologist/Hydrologist

Retired USDI Bureau of Land Management/USDA Forest Service

Steven J. Smith, Team Leader and Rangeland/Riparian Specialist 

National Riparian Service Team, USDI Bureau of Land Management

Ervin R. Cowley, Range Management/Riparian Specialist

Retired USDI Bureau of Land Management

Technical Reference 1737-23

2011
Suggested citations:

Burton, T.A., S.J. Smith, and E.R. Cowley. 2011. Riparian area management: Multiple 

indicator monitoring (MIM) of stream channels and streamside vegetation. Technical 

Reference 1737-23. BLM/OC/ST-10/003+1737. U.S. Department of the Interior, 

Bureau of Land Management, National Operations Center, Denver, CO. 155 pp.

U.S. Department of the Interior. 2011. Riparian area management: Multiple indicator 

monitoring (MIM) of stream channels and streamside vegetation. Technical Reference 

1737-23. BLM/OC/ST-10/003+1737. Bureau of Land Management, National Operations 

Center, Denver, CO. 155 pp.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

PREFACE

iii

PREFACE

Interest in riparian area management has increased tremendously over the past 25 years. 

This interest has created a growing need to effectively monitor the attributes and processes 

that occur in these valuable systems. Monitoring the most sensitive attributes is critical to 

understanding how management influences streams and riparian areas. Monitoring within 

stream channels and at their margins is particularly useful to the management of streamdependent resources, including water quality and quantity, aquatic biota, and near-stream 

terrestrial biota (Winward 2000).

Due to the widespread use of stubble height for monitoring and managing riparian 

areas by the Federal land management agencies, in 2003, the U.S. Department of the 

Interior (USDI) Bureau of Land Management and U.S. Department of Agriculture (USDA) 

Forest Service commissioned the University of Idaho to evaluate how the agencies were 

applying stubble height. As a result, the university established a study team consisting of 

researchers, university professors, livestock producers, and agency technical specialists. 

Based on their findings, the team recommended that monitoring not be limited to just 

short-term, livestock grazing use indicators, but that it also focus on indicators of longterm resource conditions to determine if objectives are being met. In addition, the team 

recommended that data from multiple indicators (short- and long-term) needed to be 

statistically reliable to provide a sound basis for management decisions (University of Idaho 

Stubble Height Review Team 2004). 

The multiple indicator monitoring (MIM) protocol was developed in response to the team’s 

recommendations. It is based on the following objectives: 1) address multiple short- and 

long-term indicators, 2) measure the most important indicators relevant to detecting 

change, 3) use existing procedures to the extent possible, 4) improve efficiency through the 

use of electronic data collection, 5) yield statistically acceptable results within realistic time 

constraints, and 6) provide useful data to inform management decisions. 

The development of the MIM protocol is intended to foster increased and focused efforts to 

collect fundamental and much needed riparian monitoring data. Developing, testing, and 

peer review of this protocol have resulted in additions, deletions, and refinements to the 

procedures described in Burton et al. (2008) and previous versions, and revisions have been 

made to improve applicability, statistical reliability, and efficiency.

MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

ACKNOWLEDGMENTS

v

ACKNOWLEDGMENTS

The “multiple indicator monitoring” concept was originally the vision of members of the 

Interagency Stubble Height Review Team composed of: 

Dr. Larry Bryant, Rangeland Ecologist, USDA Forest Service

Dr. Wayne Burkhardt, Range Consultant and Affiliate Professor, University of Idaho

Tim Burton, Fisheries Biologist, USDI Bureau of Land Management

Dr. Warren Clary, Range Scientist (Retired), USDA Forest Service

Rick Henderson, Fisheries Biologist, USDA Forest Service

David Nelson, Rancher, Idaho Cattle Association

Warren Ririe, Rangeland Management Specialist, USDA Forest Service, Boise and 

Sawtooth National Forests

Dr. Ken Sanders, Professor of Rangeland Ecology and Management, University of Idaho

Ron Wiley, Team Leader, National Riparian Service Team (Retired), USDI Bureau of 

Land Management

The development of this protocol would not have been possible without the review and 

comments made by a distinguished panel of experts, including: 

Dr. John Van Sickle, Environmental Statistician, Western Ecology Division,

U.S. Environmental Protection Agency

Rick Hopson, Hydrologist, USDA Forest Service

Dr. David M. Merritt, Riparian Ecologist, USDA Forest Service

Wayne Padgett, Ecologist, USDA Forest Service

Mary E. Manning, Ecologist, USDA Forest Service

Dave Weixelman, Ecologist, USDA Forest Service

Dr. Marc Coles-Ritchie, Riparian Ecologist, formerly with USDA Forest Service

Jim Fogg, Hydrologist (Retired), National Training Center, USDI Bureau of 

Land Management

Don Prichard, Fisheries Biologist (Retired), National Operations Center, USDI Bureau of 

Land Management

Field Monitoring Team, Rawlins Field Office, USDI Bureau of Land Management 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

vi ACKNOWLEDGMENTS

Rick Henderson, Fisheries Biologist, Medicine Bow-Routt National Forest,

USDA Forest Service

Dr. Mary H. O’Brien, Utah Forest Project Manager, Grand Canyon Trust

Wayne Elmore, Team Leader, National Riparian Service Team (Retired), USDI Bureau of 

Land Management

Dr. Warren P. Clary, Range Scientist (retired), USDA Forest Service

Janice Staats, Hydrologist, National Riparian Service Team, USDA Forest Service

Dr. Philip R. Kaufmann, Research Physical Scientist, Western Ecology Division, 

U.S. Environmental Protection Agency

Dr. Sherman Swanson, State Extension Range Specialist, University of Nevada-Reno

Sandra Wyman, Rangeland Management Specialist, National Riparian Service Team, 

USDI Bureau of Land Management

Susan Holtzman, Team Coordinator, National Riparian Service Team, USDA Forest Service

Dr. Mark Gonzalez, Riparian Ecologist (Soils)/Geomorphologist, National Riparian 

Service Team, USDI Bureau of Land Management

There were many anonymous contributors as well.

We acknowledge the many comments and suggestions received during the past 6 years 

of testing from numerous individuals at training sessions held throughout the West. 

This input shaped the protocol and enforced its underlying philosophy of efficiency, 

effectiveness, and applicability to land management. 

We also respectfully acknowledge the invaluable pioneering riparian work of Dr. Alma 

Winward, Ecologist (Retired), USDA Forest Service. 

In addition, the authors would like to thank Linda Hill and Janine Koselak at the BLM 

National Operations Center for editing, layout, design, and production of the final 

document.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

CONTENTS

vii

CONTENTS

I. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

II. SELECT THE DESIGNATED MONITORING AREA (DMA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

A. Determine Monitoring Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

B. Stratify the Stream Reach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

C. Establish the DMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

D. Set Up the DMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

 1. Select a Random Starting Point . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

 2. Mark the DMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

E. Follow Monitoring Guidelines and General Procedures . . . . . . . . . . . . . . . . . . . . . . . . . 10

 1. Determine Which Indicators to Monitor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

 2. Determine When to Monitor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

 3. Determine How Often to Monitor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

III. LOCATE THE GREENLINE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

A. The Significance of Monitoring at the Greenline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

B. Using Rules to Establish the Greenline Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

 1. Defining the Greenline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

 2. Placing the Monitoring Frame on the Greenline . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

 3. Greenline Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

IV. MEASURE INDICATORS USING SYSTEMATIC PROCEDURES AND METRICS . . . . . . . . . . . . . 21

A. Systematic Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21

B. Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

 1. Stubble Height . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

 2. Streambank Alteration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

 3. Woody Species Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

 4. Greenline Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

 5. Woody Species Height Class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

 6. Streambank Stability and Cover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

 7. Woody Species Age Class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

 8. Greenline-to-Greenline Width (GGW) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

 9. Substrate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

 10. Residual Pool Depth and Pool Frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

viiiCONTENTSV. USE THE DATA ENTRY AND ANALYSIS MODULES AND THE MIM DATABASE . . . . . . . . . . . . 69

A. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

B. Data Entry Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

C. Data Analysis Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

D. PFC Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

E. MIM Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

VI. ANALYZE, INTERPRET, AND EVALUATE MONITORING DATA . . . . . . . . . . . . . . . . . . . . . . . . 75

A. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

 1. Precision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

 2. Ability to Detect Change at a DMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

B. Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

C. Interpretation and Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

 1. Elk Creek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

 2. Hardtrigger Creek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

D. Trend Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

E. Adaptive Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

APPENDIX A: GREENLINE EXAMPLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

APPENDIX B: RECORDING FIELD DATA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

APPENDIX C: EQUIPMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

APPENDIX D: STREAMBANK ALTERATION EXAMPLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

APPENDIX E: STREAMBANK STABILITY AND COVER EXAMPLES . . . . . . . . . . . . . . . . . . . . . . 111

APPENDIX F: TESTING PRECISION AND ACCURACY AND DETECTING CHANGE . . . . . . . . . . . 119

APPENDIX G: GREENLINE-TO-GREENLINE WIDTH (GGW) EXAMPLES . . . . . . . . . . . . . . . . . . 133

APPENDIX H: PLANT LIST INFORMATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

GLOSSARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

LITERATURE CITED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

LIST OF FIGURES AND TABLES

ix

LIST OF FIGURES AND TABLES

Figure 1. Reach stratification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Figure 2. Multiple indicator monitoring frame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Figure 3. Center bar placement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Figure 4. Frame placement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Figure 5. Rotating frame 75 degrees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Figure 6. Frame placement at the base of woody plants . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Figure 7. Frame placement between woody plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Figure 8. Frame placement with no canopy cover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Figure 9. The first sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Figure 10. The DMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Figure 11. Location for determining stubble height . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Figure 12. Measuring stubble height . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Figure 13. Key species not in corner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Figure 14. Monitoring frame and streambank alteration . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Figure 15. Woody species use plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Figure 16. Estimating vegetation percent cover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Figure 17. Woody species age class plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Figure 18. Worksheets in the Data Entry Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

Figure 19. A “Header” worksheet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

Figure 20. The worksheets in the Data Analysis Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Figure 21. An example of a “Data Summary” worksheet . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

Figure 22. An example table in the “Graphs” worksheet . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Figure 23. MIM database switchboard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

x LIST OF FIGURES AND TABLESTable 1. Woody Species Browse Height by Animal Class . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Table 2. Woody Species Use Classes and Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Table 3. Greenline Ecological Status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Table 4. Site Wetland Rating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Table 5. Modified Greenline Stability Rating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Table 6. Vegetation Biomass Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Table 7. Plant Diversity Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Table 8. Woody Species Height Class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Table 9. Shade Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Table 10. Woody Species Age Class for Single-Stemmed Species . . . . . . . . . . . . . . . . . . . . 53

Table 11. Multistemmed (Clumpy) Woody Species Age Class . . . . . . . . . . . . . . . . . . . . . . . 53

Table 12. DMA and Exclosure Metrics for Elk Creek in 2005 and 2008 . . . . . . . . . . . . . . . . . 77

Table 13. Test Site Data for Hardtrigger Creek between 2005 and 2009 . . . . . . . . . . . . . . . 78
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

INTRODUCTION

1

I. INTRODUCTION

Multiple Indicator Monitoring (MIM) of Stream Channels and Streamside Vegetation 

was developed to provide information necessary for managers, landowners, and 

others to adaptively manage riparian resources. Through 6 years of testing, continuous 

improvements have been made to this protocol to minimize subjectivity while maintaining 

a reasonable level of precision and accuracy. The MIM protocol is designed to be objective, 

efficient, and effective for monitoring streambanks, stream channels, and streamside 

riparian vegetation. Indicators and procedures in this protocol were selected and developed 

primarily to monitor impacts of livestock and other large herbivores on wadable streams 

(usually less than 10 m wide). The MIM protocol integrates annual grazing use and 

long-term trend indicators allowing for evaluation of livestock grazing management. 

Because the MIM protocol includes procedures for documenting stream condition and 

trend, users will also find that the long-term indicators described in this protocol are useful 

for monitoring changes that occur on the streambank and in the channel as a result of 

management activities other than grazing. The MIM protocol was developed and tested 

on relatively low-gradient (less than 4 percent), perennial snowmelt-dominated and 

spring-fed streams in the Western United States and is most applicable to those systems. 

Streamside riparian and wetland vegetation is a critical component within those systems 

for stabilizing physical stream processes and functions that influence streambank stability 

and channel geometry. 

Previous monitoring approaches have been relatively inefficient partly because they 

addressed only one or two indicators at a time. Greenline vegetation would be gathered 

using the Winward method, for example, and then in separate sampling, stubble height 

would be obtained using the methods in USDI, BLM (1996b). Sometimes data were 

acquired using different stream reaches at varying times of the year, making it difficult to 

develop relationships between grazing influences and the observed stream conditions. 

The MIM protocol combines observations of up to 10 indicators along the same stream 

reach into one protocol, using mostly simple adaptations of existing procedures. Travel to 

field sites represents a considerable time commitment, so the collection of more than one 

indicator at one location with one protocol likely saves time and is more efficient. 

Elzinga et al. (1998) defined monitoring as “the collection and analysis of repeated 

observations or measurements to evaluate changes in condition and progress toward 

meeting a management objective.” In contrast, inventory is “the systematic acquisition and 

analysis of information needed to stratify, describe, characterize, or quantify resources for 

land-use planning and management of the public lands” (USDI, BLM 1996a). Information 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

2 INTRODUCTIONderived from inventory, such as characterization and stratification, is an important part of 

establishing a monitoring program. Because the location of monitoring sites is a critical 

component of obtaining useful monitoring data, the MIM protocol addresses stratifying 

riparian vegetation complexes and stream segments and locating designated monitoring 

areas (DMAs). The DMA is the location on the stream where all monitoring procedures 

described in this protocol occur. 

This protocol includes procedures for monitoring 10 indicators. Three indicators provide 

data from which short-term livestock (or other herbivore) use information can be derived: 

1. Stubble height (adapted from USDI, BLM 1996b) and Challis Resource Area (1999) 

2. Streambank alteration (Cowley 2004)

3. Woody species use (adapted from USDI, BLM 1996b) 

Short-term indicators provide information necessary to help determine whether the 

current season’s livestock grazing is meeting grazing use criteria. They can be used as 

early warning indicators that current grazing impacts may prevent the achievement 

of management objectives and can also be used to help explain changes in riparian 

vegetation and channel conditions over time.

Seven indicators provide data from which long-term resource condition 

information can be derived:

1. Greenline composition (adapted from Winward 2000 and USDI, BLM 1996a) 

2. Woody species height class (Kershner et al. 2004)

3. Streambank stability and cover (adapted from Kershner et al. 2004)

4. Woody species age class (adapted from Winward 2000) 

5. Greenline-to-greenline width (Burton et al. 2008)

6. Substrate (Bunte and Abt 2001) 

7. Residual pool depth and pool frequency (Lisle 1987)

Long-term indicators provide data to assess the current condition and trend of 

streambanks, channels, and streamside vegetation. They help determine if local livestock 

grazing management strategies and other land management actions are making progress 

toward achieving the long-term goals and objectives for streamside riparian vegetation 

and aquatic resources. 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

INTRODUCTION

3

In addition to providing procedures for monitoring the 10 indicators described above, the 

MIM protocol suggests establishing permanent photo points. Photo points provide visual 

records of long-term streambank and riparian vegetation condition and trend. 

To facilitate data recording and analysis, two Microsoft Excel applications were developed 

as part of the MIM protocol. The Data Entry Module is for entering data into handheld 

computers using Windows Mobile in the field. These devices save about 1 hour per DMA 

compared with recording on paper. However, the data may be recorded on the MIM Field 

Data Sheet (see appendix B) if a handheld computer is not available.

The Data Analysis Module provides calculations of various metrics (see chapter V) and 

permits analysis of the data. These metrics are used as indicators of streamside vegetation 

use and vegetation and stream channel conditions. The Data Analysis Module requires a 

computer with full versions of Windows and Excel. The Data Analysis Module provides a 

format to export the summary data to a local MIM database, which is a Microsoft Access 

database used to store metric summary data for future reference, evaluate conditions and 

trends through time, and develop reports. This database is designed for individual units 

and does not represent a national agencywide database. A national geodatabase will be 

developed by the BLM to accommodate MIM data. Data analysis within the MIM protocol 

addresses precision, the ability to detect change, and minimizing observer variability and 

subjectivity through an emphasis on strict compliance with rule sets and required training. 

Interpretation and evaluation are also discussed relative to assessing trends and providing 

useful information to support adaptive management.

This document is organized according to the order in which this protocol is conducted: 

1) select the designated monitoring area; 2) locate the greenline; 3) measure indicators 

using systematic procedures and metrics; 4) use the Data Entry and Data Analysis Modules 

and the MIM database; and 5) analyze, interpret, and evaluate monitoring data. 

MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATIONSELECT THE DESIGNATED MONITORING AREA (DMA)

5

II. SELECT THE DESIGNATED 

MONITORING AREA (DMA)

A. Determine Monitoring Objectives

Monitoring objectives should be determined quantitatively for each DMA. These objectives 

often drive the kind of DMA selected for monitoring (representative, critical, or reference 

DMA as described later in this chapter). Broad-scale objectives developed in land use plans 

should be carefully evaluated to ensure the riparian complex associated with the DMA 

has the potential to meet them. If a reference in the same riparian complex is available, 

objectives may be quantified by measuring the indicators within the reference DMA. When 

the potential of the riparian complex is not known, interim objectives may be developed 

and refined as more data become available.

B. Stratify the Stream Reach

The stratification process divides stream reaches into segments with similar vegetation and 

physical characteristics, thereby increasing efficiency by focusing on important parts of the 

stream. Streams are first stratified according to riparian complexes and land uses. Riparian 

complexes are defined by overall geomorphology, substrate characteristics, stream 

gradient, and vegetation patterns along the stream. They develop and function in response 

to the interacting features of valley bottom gradient, geology and soil characteristics, 

valley bottom width, elevation, and climate (Winward 2000). 

Streams are usually stratified by using the following: valley bottom type (Rosgen 1996), 

dominant soil family (USDA, Forest Service 1992), stream profile or gradient, vegetation 

patterns along the stream (as viewed from aerial or satellite photography), stream channel 

type (Rosgen 1996), and land uses, including pastures within an allotment. An example 

is shown in figure 1. Within the grazed unit, there are two complexes or strata. Complex B 

is wider and dominated by shrubs. Complex C is narrower and dominated by herbaceous 

vegetation types adjacent to the stream. Note that the B and C complexes form repeating 

patterns in the upstream direction. The repeating sequences of the B and C complexes 

reflect the influence of side valley fans that locally affect stream gradient, soil type, and, 

therefore, valley width and dominant streamside vegetation types.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

6 SELECT THE DESIGNATED MONITORING AREA (DMA)

The stratification of the stream should be conducted by an experienced interdisciplinary 

team and documented thoroughly. The interdisciplinary team must provide clear and 

comprehensive rationale for the stream stratification and identification and the selection 

and delineation of the complexes. This rationale should include a short discussion of the 

DMA selection process and the intent of collecting data at that site. 

Once the stream has been stratified within the grazed unit, one or more complexes are 

selected for monitoring. The complex selected depends upon the monitoring objectives. 

Generally, the complexes that are the most sensitive to management influences should 

be used for monitoring. Once the most sensitive complexes are located, the location of the 

DMA is randomly selected as described in this chapter, which results in a stratified random 

sampling design. 

Figure 1. Reach stratification on Telephone Draw near Montpelier, Idaho. Note repeating complexes 

within the grazed unit (pasture). Complex “A” is likely the same riparian/stream type as “B” but 

because it is located outside of the grazed unit, it is a different land use and is thus given a different 

name or identifier.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATIONSELECT THE DESIGNATED MONITORING AREA (DMA)

7

C. Establish the DMA

A DMA is a permanently marked segment of a stream at least 110 m long that has been 

selected for monitoring. Longer segments may be needed for monitoring larger streams 

(having a greenline-to-greenline width or GGW of more than 5.5 m). For such streams, 

the DMA should be at least two meander lengths or approximately 20 times the GGW. For 

example, a DMA on a stream segment with an average GGW of 8.3 m would be 8.3 m x 20, 

or 166 m in length. 

The DMA concept was initially established for grazing management applications, but DMAs 

may also be used to monitor recreation impacts, the effects of roads, and other activities. 

It is important that DMAs are established by an interdisciplinary team of highly 

experienced personnel with knowledge of the management area.

There are three types of DMAs:

•	 Representative DMA: A representative DMA is a monitoring site in a riparian complex 

that is representative of a larger area. This is the most common type of DMA used by land 

managers. Representative DMAs should be located within a single riparian complex. 

When more than one riparian complex occurs in a management unit, the DMA should 

be placed in the riparian complex that is the most sensitive to management influences. 

The premise is that if the DMA is placed in the most sensitive complex and that complex 

is being monitored and managed to achieve desired conditions, then the other less 

sensitive complexes will also be managed appropriately. 

The criteria for selecting representative DMAs are that: 

– The riparian complex for the DMA is selected by an experienced interdisciplinary team.

– The DMA is located in a complex that represents and is accessible to the management 

activities of interest.

– The DMA is randomly located in the riparian complex that is the most sensitive to 

the management activities of interest. When the most sensitive riparian complex 

is spatially discontinuous within a management unit (i.e., multiple subsections or 

reaches of the same complex are interrupted by other complexes), the subreach 

selected for the DMA location is randomly chosen.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

8 SELECT THE DESIGNATED MONITORING AREA (DMA)

– Within the most sensitive complex, the DMA is located on a site that is sensitive to 

disturbance and is not located on reaches impervious to disturbance (for monitoring 

streambank stability and streambank alteration). Such reaches may be appropriate 

for monitoring woody species age class and woody species use.

– The DMA will respond to the management influence of interest and resource 

objectives can be achieved at the DMA; i.e., the site has the potential to respond to 

and demonstrate measureable trends in condition resulting from changes in grazing 

management or other management activities influencing stream channels and 

riparian vegetation (also applicable to a reference DMA).

– The gradient of the stream reach at the DMA is generally less than 4 percent. The 

gradient may exceed 4 percent if the reach has a distinctly developed floodplain 

and the riparian vegetation heavily influences channel stability (also applicable to a 

reference DMA).

– The DMA is located outside of a livestock concentration area. DMAs should not be 

located at water gaps or locations intended for livestock concentration or in areas 

where riparian vegetation and streambank impacts are the result of site-specific 

conditions (such as along fences where livestock grazing use is not representative of 

the riparian area). These local areas of concentration may be monitored to address 

highly localized issues if necessary (in which case, they would be described as critical 

DMAs as defined in the next section).

– The DMA is free from the influence of compounding activities. DMAs should not be 

located in areas compounded by activities that make it difficult to establish causeand-effect relationships. For example, an area used heavily by both recreationists and 

livestock would not make a good DMA to determine the effects of livestock grazing on 

stream conditions.

•	 Critical DMA: A reach that is not representative of a larger area but is important 

enough that specific information is needed at that particular site is a critical DMA. 

Critical DMAs are monitored for highly localized purposes and to address site-specific 

questions. For example, small critical spawning reaches may be monitored when there 

is concentrated livestock use. Extrapolating data from a critical DMA to a larger area may 

not be appropriate within the complex containing the critical area. A critical DMA does 

not have to meet the criteria for a representative DMA.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATIONSELECT THE DESIGNATED MONITORING AREA (DMA)

9

•	 Reference DMA: A reach chosen to obtain reference data useful for identifying 

potential condition and for establishing initial desired condition objectives for a similar 

riparian complex is a reference DMA. A common example is a grazing exclosure where 

livestock access to the stream is restricted. Ungrazed references used for reference DMAs 

need to be carefully analyzed to ensure their usefulness as a comparison. Reference 

DMAs meet many of the same criteria as representative DMAs.

When the monitoring objective is to assess management effects over time, both a 

representative DMA and reference DMA might be used. For example, referring back to 

figure 1, complex A could be chosen as the reference DMA since it is outside of the grazed 

unit and complex B as the representative DMA since it is common within the pasture and 

comparable in channel and vegetation characteristics to the reference complex. If the 

monitoring objective is to assess management effects on cutthroat trout habitat, a critical 

DMA might be established in complex C where spawning or some other critical life history 

requirement is concentrated.

The number of DMAs per grazing unit depends on the resource values in the area. Usually, 

one measured DMA per pasture is adequate. Supplemental DMAs, with photographs only, 

may be included in addition to the measured DMA to validate that the representative DMA 

actually is characteristic of the conditions throughout the complex. 

D. Set Up the DMA

1.Select a Random Starting Point: Randomly locate the starting point (lower end) 

of the DMA within the selected complex. A good way to randomly select the starting 

point is to measure the total length of the stream in the selected complex, subtract 

110 m from the total length of the stream, then select a random number between 

1 and the difference. For example, if the stream within the complex is 465 m, a random 

number between 1 m and 355 m (465 m – 110 m = 355 m) would result in a random 

starting point. When the location has been determined, check the site against the DMA 

criteria. If the DMA does not meet the criteria, another random point is selected and the 

process is repeated.

2.Mark the DMA: Once the DMA is selected, a permanently marked DMA sample reach 

is established. The following procedure should be followed when establishing the reach:

a. Permanently Mark the Lower and Upper Ends of the Sample Reach with 

Markers: Place the lower marker (starting point) on the left-hand side (looking 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

10SELECT THE DESIGNATED MONITORING AREA (DMA)

upstream) and the upper marker 110 m upstream (farther if a longer reach is used) 

on the right-hand side. The marker should be located at least 2 m away from the top 

of the bank to reduce the risk of losing the marker. Reach markers should be made of 

securely capped or bent-over rebar or similar material. Straight, jagged, rebar stakes 

present a serious hazard to animals. 

b. Place a Permanent Marker as a Reference Point to Help Relocate the DMA:

Reference markers are needed as a way to relocate the DMA since rebar is often difficult 

to find. Reference markers should be located well away (at least 33 m) from the DMA 

sample reach. Reference markers can be steel posts, a marked post in a fence line, a 

marked tree or unique rock, or other natural feature. Be aware that single steel posts 

tend to attract livestock and can create concentrated impacts where they are placed.

c. Document the Location of the Markers: Provide a global positioning system 

(GPS) location in degree decimal latitude and longitude for both the reach 

markers and the reference marker. Universal Transverse Mercator (UTM) coordinates 

are optional. Also record the datum and the zone. Sketch the monitoring setup to 

ensure the same starting point on the stream is used during future visits. Use the 

Header spreadsheet in Data Analysis Module (as discussed in chapter V) to document 

the location from a sketch and/or satellite photograph.

d.Take Photographs: After the DMA markers are placed, photographs should be taken 

before data are collected because the monitoring process may result in some visible 

disturbance to the site. At a minimum, take photographs (showing the marker) at the 

following locations:

•	 From thelower marker lookingupstream

•	 Across thestream from thelower marker

•	 Downstream from theupstream marker

•	 Across thestream from theupstream marker

Take additional photographs as needed and describe the location of each photo in relation 

to the downstream marker. 

E. Follow Monitoring Guidelines 

and General Procedures

1. Determine Which Indicators to Monitor: Use only the indicators and 

corresponding procedures appropriate for the site that will help answer monitoring 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATIONSELECT THE DESIGNATED MONITORING AREA (DMA)

11

questions. For example, if there are no monitoring questions related to the condition of 

the streambed, do not collect substrate data. If the site does not have the potential for 

woody species with appropriate management, do not include the woody species age 

class and woody species use data as part of the monitoring for the site. However, if the 

site objectives include woody species, but no woody species are present, woody species 

should be included to document their establishment as the site recovers.

2. Determine When to Monitor: The procedures in the MIM protocol are designed to 

be completed at low waterflows. High waterflows obscure greenlines and streambanks. 

Attempts to collect data during these periods will greatly limit the usefulness of the 

data. These monitoring procedures should not be used immediately following a flood 

or high-flow event resulting in sediment deposition and scour. Sediment deposition 

and scour make it difficult, if not impossible, to determine the effects of current season 

livestock use, and some vegetation may be temporarily buried. Because estimates 

of trend are made at each individual DMA, the source of variability associated with 

temporal variation may be introduced if resampling is not done at the same time of 

year. The best time to sample vegetation is when the plants are flowering. However, 

this occurs at different times during the growing season. To accurately evaluate trend on 

greenlines, it is important to obtain repeat samples during the same stage of seasonal 

progression or the same time period that baseline data was collected. For example, if 

seasonal runoff occurred early during the year that baseline data was gathered, and 

sampling was done about a month after the high flows, every effort should be made 

to collect repeat data under the same conditions that were present when baseline 

data was collected, even if it occurs later or earlier in the season. Thus, if monitoring 

was previously conducted prior to or well after high seasonal streamflows, then repeat 

sampling should be conducted at that same time of year to determine trend. Monitoring 

later in the season or when vegetation has been grazed may make plant identification 

more difficult, but because this is when the data helps answer typical issues with 

livestock grazing, the procedures described in this protocol should be done at the time 

the information will be most useful for evaluation and interpretation. Woody species 

utilization data cannot be gathered until the annual growth of new leaders on woody 

species begins along the greenline.

It is also important for users to carefully consider the objectives and purpose for 

gathering the monitoring data in determining the most appropriate time to monitor. 

For instance, since streambank alteration can influence streambank stability, long-term 

streambank stability conditions would be most appropriately and accurately monitored 

prior to livestock use and after the stream has recovered from the previous year’s 

disturbances. In addition, monitoring greenline vegetation on a DMA that has received 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

12SELECT THE DESIGNATED MONITORING AREA (DMA)

significant alteration will cause the greenline to be located farther up the bank and 

likely in another plant community than if done prior to grazing or after recovery the 

following year. Users need to understand these relationships and clearly determine why 

they are collecting data and how they are to be used.

Short-term indicator data may be collected in a different season than the trend 

data; however, short-term data should be collected when it is appropriate, typically 

immediately following livestock use. Data may be collected prior to livestock grazing so 

that other uses, e.g., wildlife, wild horse, or recreation impacts, may be estimated. If the 

management prescription requires a certain amount of residual vegetation remaining 

to protect streambanks during high winter or spring flows, monitoring should be done 

after the growing season has ended and livestock have been removed from the area.

3. Determine How Often to Monitor: Long-term (trend) monitoring data should 

be gathered at 3- to 5-year intervals. The first repeat monitoring following adjustments 

in management should be done 2 or 3 years after establishment. Riparian areas are 

resilient and vegetation usually responds quickly following management adjustments. 

Consequently, management can be evaluated early, making it possible to establish 

a trend. More frequent trend monitoring establishes a more definitive trend curve. 

However, following the initial analysis and interpretation, the long-term monitoring 

cycle may be extended to every 3 to 5 years. In some cases, the period may be extended 

because of slower recovery rates. Ten years should be the longest interval used on any site.

Short-term monitoring data may be gathered annually. Answers to specific questions, 

e.g., livestock versus elk streambank alteration, may require monitoring some indicators 

two or more times during the year.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

LOCATE THE GREENLINE

13

III. LOCATE THE GREENLINE

Locating the greenline is key to using the MIM protocol. Most of the monitoring procedures 

in this protocol require the identification of the greenline as a reference point for collecting 

sampling data.

A. The Significance of Monitoring at the Greenline

The “greenline” as defined by Winward (2000) is the “first perennial vegetation that forms a 

lineal grouping of community types on or near the water’s edge.” Given the annual scour of 

the stream, this line often forms at or just below the bankfull level of the stream channel. 

The greenline often coincides with the presence of water in the plant rooting zone, which 

allows for the growth of robust, hydrophytic plant species with deep roots that resist the 

erosive forces of the stream (Winward 2000). Plant species distribution in arid and semiarid 

ecosystems is largely controlled by the availability of water from ground water or instream 

sources (Jewett et al. 2004). As stated by Cagney (1993):

Typically, a soil moisture gradient is exhibited when moving away from the channel in 

a riparian area. In a trend transect placed in a typical western floodplain, a different 

soil moisture could conceivably be encountered at each plot. Attempting to average the 

vegetation found in these divergent plots into a single set of data can be problematic. 

The greenline is a point of reference that minimizes problems associated with changing 

moisture gradient.

The greenline represents a particularly critical location for monitoring. Minimizing the 

problems associated with the moisture gradient allows more efficient monitoring and 

more effective results and best reflects influences of grazing and other disturbances. 

Because changes occur here more rapidly, the land manager can make an early evaluation 

of effects (Winward 2000). Livestock and other ungulates are attracted to streamside areas, 

which can affect the condition of streamside vegetation, streambanks, and the streambed 

(Wyman et al. 2006; Clary and Kruse 2004; Platts 1991). Not only is the riparian ecosystem 

affected, but the channel and stream habitat are also strongly influenced by actions at this 

location. Changes to riparian vegetation at the greenline may also result in: 1) accelerated 

streambank erosion, 2) increased width/depth ratios, 3) altered channel patterns, 

4) increased sediment supply, 5) decreased sediment transport capability, and 6) damaged 

fisheries habitat (Rosgen 1996).
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

14LOCATE THE GREENLINEThe formation of the greenline is strongly influenced by the flood regimes of the stream 

and sometimes occurs at the bankfull level. The shape of the channel cross section 

reflecting the bankfull level is related to the annual flood level. As stated by Rosgen (1996):

The term ‘bankfull’ was originally used to describe the incipient elevation on the bank 

where flooding begins. In many stream systems, the bankfull stage is associated with 

the flow that just fills the channel to the top of its banks and at a point where the water 

begins to overflow onto a floodplain. 

The energy of the stream inside the active channel tends to peak at bankfull discharge, causing 

the formation of the trapezoidal- or rectangular-shaped channel. Vigorously growing 

vegetation at the channel margin is constantly attempting to expand in distribution, even 

inward of the channel, but the flood zone or zone of semiconstant watering inhibits or 

limits such encroachment. This process contributes to formation of the greenline. The flood 

that occurs, on the average, about once every 1.5 years has been equated with the bankfull 

channel (Dunne and Leopold 1978; Rosgen 1996). It is the frequency of this flood regime that 

allows for doing the work of shaping the channel and controlling the advance of streamside 

vegetation. Stream channels that are associated with highly variable streamflows within 

season and from year to year, such as arid channels in the desert Southwest, and that often 

discharge high volumes of sediment, may form complex greenlines. These greenlines vary 

in elevation along the margins of the channel and are difficult to sample.

The greenline was selected as a monitoring location because unpublished data (Henderson 

2003) suggest that observers more consistently identified the greenline than the bankfull 

position on the streambank, thus improving the precision. These data also correspond to 

the authors’ experiences.

B. Using Rules to Establish the Greenline Location

1. Defining the Greenline: The greenline is a linear grouping of live perennial vascular 

plants, embedded rock, or anchored wood above the waterline on or near the water’s 

edge (adapted from Winward 2000). It often forms a relatively continuous line of 

perennial vegetation adjacent to the stream (Cagney 1993). However, the greenline 

can also be patches of vegetation on sandbars and other areas where new vegetation 

is growing. Individual linear groupings are considered part of the greenline when they 

meet the rules described in this chapter. The greenline can also be composed partially 

or entirely of embedded rock and/or anchored wood. The greenline is commonly located 

at the edge of the floodplain or at the lip of the first bench above the water line. For 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

LOCATE THE GREENLINE

15

entrenched streams, the greenline may be located above the floodplain on a terrace 

(Winward 2000). In these cases, the greenline may include, or be limited to, nonhydric 

species, i.e., upland species. See appendix A for greenline examples.

2. Placing the Monitoring Frame on the Greenline: A frame is placed on the 

greenline to designate 

the observation point. The 

monitoring frame consists 

of two side-by-side, 20- 

by 50-cm Daubenmire 

quadrats (figure 2), which 

are commonly applied to 

vegetation sampling. The 

center bar, or longwise 

orientation of the frame 

is placed on the greenline 

(figure 3). Elzinga et al. 

(1998) stated: “It is best if 

the quadrat length (i.e., the 

length of the long side of 

the quadrat) is longer than 

the mean distance between 

clumps.” Because streamside 

vegetation is usually high 

in spatial density, a quadrat 

plot 50 cm in length is 

adequate to avoid empty 

spaces between clumps 

and small enough to be 

reasonably efficient. Details 

for constructing the frame 

are found in appendix C.

The greenline plot is located by moving the monitoring frame in a perpendicular 

direction from the streamflow at the water’s edge up the streambank to the location 

closest to the channel that meets the greenline rules described in the following section 

(within 6 m of the water’s edge). The center bar of the monitoring frame is placed along 

the edge of the perennial vegetation, embedded rock, and/or anchored wood or at the 

base of the overstory shrubs or trees.

Figure 2. Multiple indicator monitoring frame. Based on field 

experience, this is the preferred frame configuration. It is light, 

easy to carry, and easy to manipulate in shrub type vegetation. 

Dashed lines are apparent but not part of the actual frame. 

Observers must be careful to extend these lines within the 

confines of the frame.

Figure 3. Center bar placement. The monitoring frame is placed 

with the center bar on the greenline.

50 cm

1 2 3 4 5

20 cm 20 cm

12.5 cm
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

16LOCATE THE GREENLINE

To meet the greenline rules, 

the monitoring frame may 

be rotated up to 75° from 

parallel to the streamflow 

until the cover rules are 

met (figures 4 and 5).

3. Greenline Rules:

a. Perennial Herbaceous 

Vegetation, Shrub/Tree 

Seedlings, Embedded 

Rock, Anchored 

Wood: The greenline 

can be comprised of 

any combination of 

perennial herbaceous 

vegetation, shrub/tree 

seedlings, embedded 

rock, or anchored wood 

provided that there are no 

patches of bare ground 

(rocks smaller than 

15 cm are considered 

bare ground), litter, 

or nonvascular plants 

greater than 10 by 

10 cm within the plot:

1) Perennial Herbaceous Vegetation, Shrub/Tree Seedlings: There must be at least 

25 percent foliar cover of live perennial herbaceous vegetation and/or shrub/tree 

seedlings rooted in the plot. Foliar cover (live plant parts, stems, and leaves over 

the ground) is the shadow cast if the sun was directly overhead. Small openings in 

the canopy or overlap within the plant are excluded. Shrub and tree seedlings are 

defined as woody plants less than 0.5 m tall.

2) Embedded Rock: The greenline may include rock that is at least 15 cm in diameter 

and at least partially embedded in the streambank with no evidence of erosion 

behind it, talus slopes, and bedrock. Rock must be above the scour line and 

not in the active channel (see appendix A, figures A20 and A21).

Figure 4. The frame is placed along the line of vegetation closest to 

the stream. The center of the frame is placed along the edge of the 

perennial vegetation, rock, or wood. If there is a bare patch within 

the frame, as shown in this diagram, the frame is rotated as shown 

in figure 5.

Figure 5. Either end of the frame may be rotated away from the 

stream until the cover requirements are met. The rotation should not 

exceed 75° from parallel with the streamflow.

75° from parallel
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

LOCATE THE GREENLINE

17

3) Anchored Wood: The greenline may include logs or root wads that are anchored into 

the streambank and large enough such that high flows are not likely to move them. 

There should be no evidence of erosion behind them. Wood must be above the 

scour line and not in the active channel (see appendix A, figures A21 and A22).

b. Overstory (Young and/or Mature Shrubs or Trees): If young or mature woody plants 

are located closer to the water’s edge than qualifying perennial herbaceous vegetation, 

rock, or wood (as described 

above), the greenline is 

located at the base of 

young or mature shrubs or 

trees as shown in figure 6. 

Young and mature shrubs 

and trees are defined as 

woody plants at least 

0.5 m tall. Foliar cover 

of young and mature 

shrubs and trees is not 

considered for identifying 

the greenline. When there 

is no understory beneath 

the shrub/tree canopy, the 

greenline is located at the 

rooted base of the shrubs 

or trees or beneath the 

shrub/tree canopy along a 

simulated line connecting 

the rooted base of adjacent 

shrubs and/or trees roughly 

parallel to the stream (see 

figures 7 and 8 and appendix 

A, figures A10 and A11).

c. Exposed Roots: Exposed 

live shrub or tree roots 

above the scour line of 

the stream are part of the 

greenline (see appendix A, 

figure A16).

Figure 6. The monitoring frame is placed at the base of 

a mature woody plant.

Figure 7. When there is woody overstory and no understory, and if 

the shrub or tree canopy is directly overhead, the frame is placed on a 

simulated line connecting the rooted base of the shrubs or trees (on 

the stream side of the shrubs or trees).

Figure 8. When there is no canopy cover above the line joining the bases 

of woody species, the frame should be moved away from the stream 

until the greenline is reached or the distance from the stream is 6 m.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

18LOCATE THE GREENLINE

d. Flooded Greenline: Avoid sampling when the greenline is flooded during high 

streamflow (see appendix A, figure A12). 

e. Perennial Vegetation Growing in Water: Some perennial vegetation, e.g., spike 

rush, sedges, rushes, or willows, may grow in the margins of the stream and in slow 

backwaters or even inside the wetted channel at seasonal low flow. When this occurs, 

the greenline will be at the edge of the water (see appendix A, figures A13 and A14).

f. Plants Occupying the Entire Wetted Channel: For dewatered channels and 

channels with very low flows, if the vegetation occupies the entire width of the 

channel, the greenline is at the deepest part of the channel (see figure A15).

g. Floating Plant Species: Some species that commonly float on or are submerged in 

the water have minimal root systems and are not part of the greenline. These species 

may include, but are not limited to, whorlgrass or brookgrass (Catabrosa aquatica), 

white water crowfoot (Ranunculus aquatilis), watercress (Rorippa nasturtiumaquaticum), American speedwell (Veronica americana), water knotweed (Polygonum 

amphibium), and other species that have similar rooting characteristics (see appendix 

A, figures A17, A18, and A19).

NOTE: When whorlgrass or brookgrass (Catabrosa aquatica) is rooted above the water 

level on the streambank above seasonal low waterflow, it is considered part of the 

greenline and should be recorded in the greenline vegetation composition.

h. Detached Blocks of Vegetation: Blocks of vegetation detached from the 

streambanks (slump blocks) are not the greenline. When deep-rooted hydric 

vegetation covers the block from the water’s edge to the terrace wall, creating a new 

floodplain (false bank), the greenline is the edge of the vegetation along the stream 

(see appendix A, figures A23, A24, and A25). To be detached or separated from the 

bank, a block or section of streambank must have slipped down or broken away from 

the bank wall so that there is less than 25 percent foliar cover of perennial vegetation 

in the area between the block and the bank wall.

i. Islands: Islands are defined as areas surrounded by water at summer low flow or 

bounded by a channel that is scoured frequently enough to keep perennial vegetation 

from growing. The greenline follows the outside channel on each side of the 

island and does not cross onto an island (see appendix A, figures A26, A27, and A28).
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

LOCATE THE GREENLINE

19

j. No Greenline Present: When the greenline is not present within 6 m from the 

water’s edge, the greenline is considered absent at that plot (NG is recorded in the 

vegetation composition column in the Data Entry Module or on the MIM Field Data 

Sheet shown in appendix B). The monitoring frame is then placed on the edge of the 

first bench above the waterline, and the other indicators are read (e.g., streambank 

alteration, streambank stability, etc.). If there is no bench present, the frame is 

placed at the position on the streambank 6 m from the water’s edge, and the other 

appropriate indicators are read (see appendix A, figure A29).

In instances where the waterlines are less than 6 m apart due to the presence of a 

sharp meander bend with a peninsula between them, and the greenline rules cannot 

be met between the waterlines, the frame is placed at the top of the peninsula.

MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

MEASURE INDICATORS USING SYSTEMATIC PROCEDURES AND METRICS

21

IV. MEASURE INDICATORS USING 

SYSTEMATIC PROCEDURES AND METRICS

This chapter provides definition and context for the monitoring indicators and their 

associated metrics and describes how to measure each. As used in this document, a metric 

is defined as a system of measurement that facilitates the quantification of a monitoring 

characteristic. For example, greenline composition is a monitoring indicator that is 

measured in the field. Greenline vegetation ecological status is a metric that quantitatively 

describes the seral status of that vegetation. 

A. Systematic Procedures

Step 1. Develop a Species List: Prior to data collection, do a reconnaissance of the 

greenline vegetation within the DMA and record species and species codes that may be 

found in the Data Entry Module. If the code is not in the Data Entry Module, use the official 

plant codes in the PLANTS Database (USDA, Natural Resources Conservation Service 2010). 

Collect plants for identification if not known in the field.

Step 2. Determine the Appropriate Sampling Interval: Random systematic sampling 

along the greenline and within the channel allows for even spacing and precise estimation 

of vegetation and site characteristics (Elzinga et al. 1998).

The sampling interval is the distance between plots and should be large enough to achieve 

the desired precision. An interval of 2.75 m provides 40 plot locations on each side of 

the stream (at least 80 total plots in the DMA) in a 110-m DMA. This frequency provides 

a sample size large enough to estimate the mean within 10 percent of the true mean as 

described in table F6 (appendix F) for most of the streams evaluated in the MIM tests. 

When the site is very complex or highly variable, the interval may be shortened to increase 

the number of samples. For example, the interval may be shortened to 2 m providing 

55 sample locations on each side of the stream. When the DMA reach is longer than 110 m, 

divide the total DMA length by 40 to obtain the sampling interval.

Step 3. Measure the Sampling Interval: The sampling intervals may be measured or 

paced. A typical 2-m rod is useful for measuring the interval. When pacing the sampling 

interval, the user determines the length of his/her step by marking a distance, usually 

30 m, and counting the number of steps over the selected distance. This step length and 

number of steps are then used to pace the sampling interval.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

22MEASURE INDICATORS USING SYSTEMATIC PROCEDURES AND METRICSStep 4. Establish the Location of the First Sample: Pick a random number between 

1 and 10. Beginning at the lower reach marker on the left-hand side (looking upstream), 

take that number of steps within the stream channel. Turn and face the left-hand 

bank and proceed 

perpendicularly to 

the stream until 

the greenline is 

encountered. Place 

the monitoring frame 

with the center bar 

oriented along the 

greenline (figure 9).

Monitoring of indicators should be completed in the following order to minimize 

movement of the frame: greenline composition, woody species height class, 

streambank alteration, streambank stability and cover, stubble height, GGW, 

woody species age class, woody species use, and substrate. All data except the 

residual pool depth and pool frequency are recorded at this location, and subsequent plots 

are spaced at equal intervals from this first sample.

Step 5. Sample the Entire DMA: Monitor along the greenline at the appropriate sample 

interval to the end of the DMA. The minimum number of samples on each side of the 

stream is 40. After sampling the last plot on the left-hand side of the stream, measure 

or pace the distance from the last sample location to the end marker, cross the stream, 

and from the marker, continue measuring or pacing until the plot interval is reached. For 

example, if the sample interval is 2.75 m and it is 1 m from the last sample location to the 

stake, cross the stream beginning at the marker, measure 1.75 m and place the monitoring 

frame on the greenline. 

Repeat the procedure 

down the right-hand 

side (looking upstream) 

using the same interval 

(2.75 m) to the end 

marker. The entire 

length of DMA on both 

sides of the stream is 

sampled (figure 10).

Steamow

Figure 9. The first sample. The monitoring frame is placed at the end of the 

toe with the center of the frame along the greenline. 

Figure 10. The DMA. A random systematic sampling design is used for 

monitoring. The first plot location is randomly selected and the remaining 

samples are regularly spaced along the reach. Black squares delineate 

DMA markers.

Streamow

Greenline
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

MEASURE INDICATORS USING SYSTEMATIC PROCEDURES AND METRICS

23

Step 6. Ensure the Appropriateness of the Sample Size: After completing both 

sides of the stream, the sample size estimator at the top of the “DMA” worksheet in the 

Data Entry Module may indicate that more samples are needed to achieve the desired 

level of precision. Calculations for the sample size estimator are described on the “Stats” 

worksheet in the Data Entry Module. They require the calculation of a standard deviation 

and thus apply only to quantitative indicators that produce a statistical mean (streambank 

alteration, stubble height, greenline-to-greenline width, woody species age class, and 

woody species use). The default confidence level is set at 95 percent, and the level of 

precision varies as shown on the “Header” worksheet in the Data Entry Module. These levels 

can be adjusted by the user to derive the desired confidence and precision; however, the 

default precision levels should be adjusted only after careful consideration because the 

precision levels provided in the module are based on confidence interval widths from field 

tests (see appendix F for additional details).

To determine the number of additional samples needed, subtract the number of samples 

taken from the number of samples indicated on the sample size estimator for each 

indicator not meeting the desired sample size. Divide the DMA reach length by the number 

of samples for the indicator needing the most additional samples. For example, if the 

sample size estimator indicates an additional 10 samples are needed for an indicator, and 

the DMA length is 110 m, divide 110 by 10, which equals 11 m. Beginning at the lower 

reach marker (left-hand side looking upstream), measure or pace 11 m upstream, place 

the monitoring frame on the greenline on the left-hand bank. Sample all indicators not 

meeting sample size criteria. The next sample site will be 11 m upstream on the greenline 

on the right-hand bank. Continue sampling alternating streambanks to the upstream 

marker. Sample all the necessary indicators along the entire reach, even if the sample size 

criteria are met before reaching the end of the DMA.

B. Indicators

1. Stubble Height:

a. Purpose: Stubble height is a measure of the residual height of key herbaceous 

vegetation species remaining after grazing. The amount of foliar cover remaining is 

important for keeping plants healthy, maintaining or promoting strong root systems, 

protecting streambanks from erosion, slowing water during high streamflows, and 

building floodplains (Clary and Webster 1989). The measurement may be used in at 

least two ways: first, to determine when livestock should be moved from the riparian 

area, often called trigger monitoring, and second, at the end of the grazing season 

and growing season to help determine cause-and-effect relationships between 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

24MEASURE INDICATORS USING SYSTEMATIC PROCEDURES AND METRICS

livestock grazing and stream-riparian conditions and whether livestock grazing 

management changes may be needed the following year.

b. Background: The stubble height procedure is described in Utilization Studies and 

Residual Measurements (USDI, BLM 1996b). The procedures in this protocol were 

modified for use with a quadrat in the systematic random sampling design for 

locating quadrats. Because many of the important riparian graminoid species are 

rhizomatous, they grow in dense matlike patches of vegetation making it difficult to 

identify individual plants. Therefore, a 3-inch (8-cm) circle of vegetation is used rather 

than an individual plant.

Within the randomly located quadrat, the number of plants measured is limited to 

key species, which are plants that are important in the plant community, are relatively 

palatable to livestock use, and serve as indicators of change.

c. Assumptions and Limitations: Stubble height has been widely used to measure 

livestock vegetation use in riparian areas (Clary and Leininger 2000). It allows a 

large number of samples to be collected in a short period of time. It can be used as a 

trigger for moving livestock to another grazing unit or as an indicator of the amount 

of use after grazing (University of Idaho Stubble Height Review Team 2004). Stubble 

height is not a substitute for vegetation condition or trend; however, it does provide 

information that may be used to determine the degree to which livestock grazing is 

influencing the achievement of objectives. 

Stubble height is not an appropriate measure on streams that are dominated by 

woody species, boulders, or bedrock and should not be used where herbaceous 

species are infrequently scattered along the DMA.

d. Relationship to Other Indicators: Stubble height data can be enhanced when 

analyzed with percent of livestock use (not in the MIM protocol), woody species use, 

and streambank alteration to estimate levels of grazing intensity during the current 

grazing season. Coupled with other short-term and long-term monitoring indicators, 

stubble height may be used to develop relationships between condition and trend 

and livestock grazing management. Stubble height alone does not provide adequate 

information to develop a relationship between livestock grazing and vegetation 

conditions on the streambank. Commonly, streambank disturbance, measured by the 

streambank alteration procedure, is the most important factor relating to streambank 

stability conditions. 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

MEASURE INDICATORS USING SYSTEMATIC PROCEDURES AND METRICS

25

e. Procedure:

Step 1. Determine Key Species: An interdisciplinary team should select the key 

species, as defined earlier, prior to monitoring. Deeper rooted plants, such as hydric 

species, are preferred because of their contributions to stability. If palatable hydric 

graminoids are severely lacking or absent, palatable mesic graminoids are chosen. 

Measure the stubble height of each key species occurring within the monitoring 

frame. More than one key species may be used if necessary.

Step 2. Select Plants:

After placing the 

frame, select the key 

species that occur 

nearest the handle of 

the frame (figure 11). 

Most riparian key 

species grow 

tightly together, 

forming dense mats with little distinct separation of 

individual plants. As a result, the sampling method 

uses a 3-inch-diameter circle of the vegetation for a 

single species (figure 12).

When the key species does not occur in a mat near 

the handle of the frame but as an individual plant 

or several individual plants less than 3 inches in 

diameter, select the key species plant within the plot 

that is nearest the handle (see figure 13). Measure 

the average height of all the leaves of the plant(s).

When a key 

species does not 

occur within the 

quadrat, leave 

the cell blank

(on the MIM Field 

Data Sheet or in 

the Data Entry 

Module).

Figure 11. Residual vegetation height is measured within a 

3-inch-diameter circle at the back right-hand corner of the quadrat 

nearest the frame handle.

Figure 12. Stubble height is 

measured by forming your hand 

into an approximate 3-inchdiameter circle, grasping the 

vegetation, and determining the 

average leaf length.

Greenline

Figure 13. When key species plants are not in the corner by the frame 

handle, select the key species plants nearest the handle. Identify the 

plant and measure the average leaf height of all key species plants 

rooted within the circle.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

26MEASURE INDICATORS USING SYSTEMATIC PROCEDURES AND METRICS

Step 3. Measure Key Species: Using the frame handle (or a ruler) with 1-inch (or 

2-cm) increments, measure the average leaf length of the vegetation within the 

circle (figure 11 and 13) and round it to the nearest inch (or 2 cm). Grazed and 

ungrazed leaves are measured from the ground surface to the top of the remaining 

leaves. All leaves within the circle should be lifted to determine their length. 

Account for very short leaves as well as tall leaves. Do not measure seed stalks 

(culms). Determining the “average” residual vegetation height will take some 

practice. Be sure to include all of the key species’ leaves within the sample. The 

easiest method of doing this is to grasp the sample, stand the leaves upright, and 

then measure the average height (figure 12).

f. Timing:

1) Pregrazing Monitoring: Stubble height monitoring may be done throughout the 

season depending on the questions that need to be answered. If there is a concern 

regarding the amount of utilization by elk, domestic or wild horses, or other large 

herbivores, monitoring may be done prior to livestock entering the grazing unit.

2) Monitoring During Grazing: Determining when livestock should be moved to meet 

a grazing prescription, as described previously, is done while livestock are still in 

the area and when the vegetation is close to reaching the prescribed height.

3) Postgrazing Monitoring: The most common time to measure stubble height 

is at the end of the grazing period and the growing season (called postgrazing 

monitoring) to provide some of the information necessary to develop relationships 

between the condition and trend and livestock grazing. 

g. Metrics: Stubble height data are summarized using the following metrics: 

•	 Average (Mean) Stubble Height of All Key Species: This metric is the average of 

all stubble height measurements made at the DMA (see Data Analysis Module – 

“Summary” worksheet).

•	 Median (Midpoint) Stubble Height of All Key Species: The median stubble height 

value taken from all measurements at the DMA (see Data Analysis Module – 

“Summary” worksheet). 

•	 Average (Mean) Stubble Height of the Most Dominant Key Species: This 

metric represents the average of all stubble height measurements for the key 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

MEASURE INDICATORS USING SYSTEMATIC PROCEDURES AND METRICS

27

species most frequently encountered at the DMA (see Data Analysis Module – 

“Graphs” worksheet).

•	 Average Stubble Height for Each Key Species Measured: This metric represents the 

average for each key species. It is provided because the most palatable species may 

not be the most frequently encountered at the DMA (see Data Analysis Module – 

“Graphs” worksheet).

Stubble heights are scaled measurements and sometimes fit a normal probability 

distribution. When data are skewed, as is often the case for combined stubble height 

for all species, a log transformation may be used to normalize the distribution. 

Stubble heights for individual species, particularly those that are palatable, 

commonly fit normal probability distributions.

2. Streambank Alteration:

a. Purpose: The physical alteration of streambanks by animals can degrade the 

integrity of stream systems. Platts (1991) observed that alteration may negatively 

affect water quality and aquatic habitat. Trampling of streambanks by livestock may 

result in an increase in stream width, making the stream channel wider and shallower 

(Clary et al. 1996). As a result, water temperatures may increase from increased 

exposure to solar radiation; sediment is deposited within the channel rather than on 

the streambanks; streambank erosion increases; and the water storage capacity of the 

streambanks decreases, forcing streamside plants to shift from willows and sedges 

to drier site species with low root density (Bengeyfield 2006). All of these changes 

combine to result in the loss of habitat for aquatic species (Platts 1991). 

Similar to stubble height, streambank alteration is an annual or short-term indicator 

of the effect of grazing impacts on long-term streambank stability. As such, it can be 

used as a tool to assess grazing intensity and to determine when such intensity may 

be excessive. It can also be used to help determine cause-and-effect relationships 

between livestock grazing and stream-riparian conditions and whether livestock 

grazing management changes may be needed the following year. 

The importance of streambank alteration as a short-term indicator has only recently 

emerged in the literature (Heitke et al. 2008). In measurements of forage utilization, 

stubble height, and streambank alteration at 14 stream reaches in southwestern 

Montana, Bengeyfield (2006) found: “. . . the only streams that showed significant 

improvement were those where the streambank alteration levels were met. Neither a 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

28MEASURE INDICATORS USING SYSTEMATIC PROCEDURES AND METRICS

forage utilization of 45 percent nor a stubble height at 4 inches initiated the upward 

trend in stream channel shape that is necessary to achieve riparian function.” 

b. Background: Stream channels are naturally dynamic with varying rates of annual 

disturbance, but streams are constantly striving to achieve stability and to maintain 

channel capacity and competence (Leopold et al. 1992). As a result, streams have the 

ability to repair a certain degree of streambank disturbance each year. Several factors, 

including stream gradient or slope, streambed material composition, streambank 

soil composition, vegetation cover and type, channel geometry, flow rate and timing, 

and frost action determine the amount of alteration that streambanks can repair 

each year. As stated by Clary and Kruse (2004): “. . . concentrated impacts under 

rotation systems can cause sufficient woody plant or streambank damage in a single 

season or year that recovery might take several years. Therefore, the best approach is 

to limit grazing stress to the site’s capability for annual recovery.” This capability for 

annual recovery would be evaluated by measuring both streambank alteration and 

streambank stability at the DMA as described under “Relationship to Other Indicators.”

Heitke et al. (2008) evaluated several methods of monitoring streambank alteration 

along the greenline using data collected in Montana in 2003 and 2004. The greenline 

(GL) method used a line intercept procedure that records presence or absence of 

disturbance. A sample line was 92 cm long, centered on the greenline. The line was 

placed perpendicular to the stream channel at the point of the toe of the observer. 

Each line was recorded as altered or not altered. A line was considered altered if 

current year’s disturbance occurred along any part of the line. A sample was recorded 

at each step along the greenline. The amount of alteration was calculated by dividing 

the number of lines with alteration by the total number of samples. Results were 

expressed as a percent.

The greenline precise (GLP) method was exactly the same as the GL method, except 

the length of each disturbance along the line was measured. For example, at the first 

sample location, 20 cm of disturbance was measured and recorded along the 92-cm 

line. This procedure was repeated on both sides of the stream making an observation 

and measurement at each step. The total length of disturbance measured was divided 

by 92 times the number of samples taken. The product was expressed as a percent. 

Heitke et al. (2008) also discussed the bankfull (BF) method. This method was a 

precursor to the MIM protocol and was later modified to use the greenline rather than 

the bankfull line because observers more often agree on the location of the greenline 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

MEASURE INDICATORS USING SYSTEMATIC PROCEDURES AND METRICS

29

than the bankfull line (Henderson 2003). The monitoring frame was further modified 

to its present configuration to facilitate measuring other indicators. The frame was 

shortened to 50 cm, and to prevent double counting of average sized hoofprints, the 

number of lines was reduced to 5, spaced at approximately the average diameter 

of the hoofprint. The width of the frame was increased to allow using 20- by 50-cm 

Daubenmire plots on each side of the center bar. 

However, the BF method was not evaluated in Heitke et al. (2008). The BF method 

also uses line intercept. It differs from the previous two methods by using a 30.5- by 

61-cm monitoring frame. The frame was similar to the one described in figure 2. The 

width evaluated was about 15 cm either side of the center bar. The center bar 

was placed at the bankfull line. Ten lines were projected perpendicular to the 

center bar and alteration was recorded when any of the lines intersected current 

year’s disturbance.

Heitke et al. (2008) assessed variability among observers for the different protocols 

described above. They used the standard deviation between observations made by 

the same or different observers. The GLP had the lowest overall observer variability 

as measured by the standard deviation (4.7, with coefficient of variation or CV = 56), 

followed by GL (6.3, CV = 20), and BF (8.1, CV = 35). The authors conducted 

35 tests for observer variability on the MIM streambank alteration procedure, which 

is refined from the BF procedure analyzed by Heitke et al. (2008). The standard 

deviation between observers for the MIM tests was 4.3 and the CV was 22.7. The 

CV is a dimensionless index of variability between and among observers’ repeated 

observations and is represented by the standard deviation divided by the mean.

The procedure described here estimates the amount of streambank alteration along 

the greenline as a result of large herbivores (e.g., cattle, horses, sheep, bison, elk, and 

moose) walking along or crossing the greenline during the current grazing season. 

The part of the streambank that is measured using this procedure is a plot 50 cm 

long and 42 cm wide (two Daubenmire plot widths plus the 2-cm-wide center bar), 

centered on the greenline. This part of the streambank focuses the observation where 

stability is most affected by the erosive effects of water (see appendix D). 

c. Assumptions and Limitations: There are five cross-plot lines on the sampling 

frame used to detect and record occurrences of alteration. These lines are 

perpendicular to the center rib of the frame and extend away from it on each side. 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

30MEASURE INDICATORS USING SYSTEMATIC PROCEDURES AND METRICS

As the center rib is placed on the greenline, intercept lines extend 20 cm into both 

the vegetated and nonvegetated side of the greenline. If more than one alteration 

intercepts this line on either or both sides of the center rib, a value of 1 is recorded in 

the Data Entry Module or on the MIM Field Data Sheet. 

The number of alteration intercepts or “hits” is limited to five per sample because: 

1) alteration occurs primarily on the nonvegetated side of the greenline, and doublecounting the vegetated side would underestimate the frequency of disturbances 

along the greenline; and 2) the spacing between intercept lines approximates the 

diameter of a hoofprint, which minimizes double-counting of single hoof impressions.

Trampling impacts must be the obvious (i.e., easily seen, clear to the eye, not to be 

doubted, or plain) result of current season use. “Obvious” streambank alterations are 

defined as those that are readily observed from no closer than approximately 2 feet 

from the streambank. In general, these are impacts that are evident without kneeling 

close to or lying on the ground. 

The streambank alteration procedure described here is an intercept procedure 

recording presence/absence of current year’s disturbance along the greenline. It is not 

a measure of the percent of the area of streambank altered, but rather an estimate of 

the percent of the length of bank altered along the greenline based on the presence 

or absence of a hoofprint(s) intercepting one (or more) of the five lines within a 

plot. This procedure samples only that part of the streambank associated with the 

greenline, often at the top of the streambank, and only within a 42- by 50-cm plot. 

The streambank may be wider or narrower than the width of the plot.

The monitoring frame is 42 by 50 cm (or 2100 cm2

) and the average cattle hoofprint 

is 12 cm by 17 cm or 208 cm2

. Therefore, one hoofprint in the frame represents 

approximately 10 percent of the area within the frame that is altered. The width of 

an average hoofprint oriented along the greenline is 12 cm, so its length along the 

greenline is 12/50 cm or about 24 percent. Because the MIM protocol uses a line 

intercept approach with the intercept lines spaced slightly wider than the average 

hoofprint, that same hoofprint would intercept one of the five lines and be recorded 

as 20 percent alteration for that plot. Thus alteration using the MIM protocol more 

closely approximates length of greenline altered, not the area of the plot altered. The 

advantages of this method over others is that it is more efficient and precise and has 

been widely tested.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

MEASURE INDICATORS USING SYSTEMATIC PROCEDURES AND METRICS

31

The authors measured the dimensions of actual cattle hoofprints and shears where 

they were clearly identifiable. Using simulations that randomly selected these 

measured prints and randomly placed them on the plot at varying numbers and 

locations, a relationship was established between true streambank alteration and 

MIM estimates. The MIM protocol closely estimates the percent of greenline length 

altered (percent greenline length altered = .914 times MIM alteration + 5 percent, 

R2

 = .85). There was a weaker relationship to the area of plot altered (percent plot 

area altered = .32 times MIM alteration + 3 percent, R2

 = .55). Thus, if the MIM 

alteration is observed at 20 percent, the simulations predict the percent of greenline 

length altered to be 23 percent, and the area of plot altered to be 9 percent. The plot 

area in these simulations included both the vegetated and nonvegetated sides of 

the greenline. The majority of streambank alterations are typically observed on the 

nonvegetated side of the plot. If only the nonvegetated side of the plot is used to 

estimate plot area from these simulations, the relationship between MIM alteration 

and plot area altered is closer (20 percent MIM alteration with 18 percent plot 

area altered). Note, however, that as percent alteration increases, the ratio of MIM 

alteration to plot area altered also increases (e.g., 60 percent MIM alteration equates 

to 44 percent plot area altered using the nonvegetated plot). Note also that the ratio 

of MIM alteration to length of greenline altered does not change dramatically (e.g., 

60 percent MIM alteration equates to 60 percent length of greenline altered). Thus, 

the MIM protocol tends to overestimate plot area altered but more closely estimates 

length of greenline altered.

d. Relationship to Other Indicators: Because streams have the ability to repair 

a certain amount of streambank alteration after disturbance, it is important that 

the intensity of disturbance, or streambank alteration, be less than the amount of 

streambank stability repair. The amount of repair can be estimated by measuring the 

recovery after disturbance. To estimate the amount of repair after disturbance, both 

streambank alteration and streambank stability would be measured immediately 

after grazing and then again just before the next grazing period. This would allow 

an estimate of the change in streambank stability during the “off” season, reflecting 

natural processes of streambank recovery, along with natural sources of streambank 

alteration (e.g., wild ungulates and stream flooding). 

The most effective method of determining the potential streambank stability repair 

rate is to compare measurements of streambank stability along the stream reach of 

interest with a comparable stream reach within a reference area. Changes in stability 

caused by flooding, ice scour, and other natural effects can then be factored into the 

relationship between streambank alteration and streambank stability. 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

32MEASURE INDICATORS USING SYSTEMATIC PROCEDURES AND METRICS

e. Procedure: 

Step 1. Locate the 

Intercept Lines:

This procedure 

uses the entire 

42- by 50-cm 

monitoring 

frame. Five lines 

are projected 

across the frame 

perpendicularly 

to the center bar 

of the frame (figure 14).

Step 2. Count the Lines that Intercept Alteration: Look down at the entire frame and 

determine the number of lines within the plot that intersect streambank alteration 

(see appendix D). The streambank is considered altered when:

– Streambanks are covered with vegetation and have hoofprints that depress the 

soil and expose bare soil at least 13 mm deep (measured from the top of the 

displaced soil to the bottom of the hoof impression).

– Streambanks exhibit broken vegetation cover resulting from animals walking 

along the streambank that have a hoofprint at least 13 mm deep.

– Streambanks have compacted soil caused by animals repeatedly walking over 

the same area even though the animals’ hooves sink into and/or displace the soil 

less than 13 mm.

Step 3. Record the Number of Lines that Intersect Streambank Alteration: Record 

only one occurrence of alteration, trampling, or shearing per line. Record only the 

current year’s streambank disturbance—disturbance features that are 

obvious (old features tend to be nondistinct). Follow these guidelines when 

determining which number to record:

– When there is a vertical or near-vertical wall, pace in the stream or along the 

greenline on top of the terrace, and place the center of the frame along the 

greenline. Record only direct alteration occurring on the vertical wall (hoof 

Figure 14. A monitoring frame with five lines projected on the plot. There 

are four hoofprints on this plot. Lines 2 and 4 each intersect one hoofprint. 

Line 3 intersects two hoofprints. Three lines intersect hoofprints, so the 

number of alterations on this plot is recorded as a 3.

Greenline

1 2 3 4 5
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33

shear), on the streambank, and/or at the base of the vertical wall as viewed 

down the slope within the plot (see appendix D, figure D5).

– Hoofprints or trampling on streambanks with fully developed, deep-rooted 

hydric vegetation (e.g., Carex spp., Juncus spp., and Salix spp.) is not recorded 

as alteration unless plant roots or bare soil is exposed. Hoof shearing along the 

streambank is considered alteration.

– Compacted livestock trails on or crossing the greenline, that are the obvious 

result of the current season’s use, are counted as trampling (see appendix D, 

figure D3).

– When there is no greenline identified within 6 m from the water’s edge and “NG” 

(no greenline) is recorded, the frame is placed on the first slope break (bench) 

above the waterline for measuring the streambank alteration (and other 

appropriate indicators). If no bench is present, place the frame at the position on 

the streambank 6 m from the water’s edge.

This process is repeated at the predetermined interval on each side of the stream.

f. Timing: Streambank alteration is measured annually after grazing. It is most effective 

if measured as soon as possible after livestock have been moved from the area so 

that alteration by livestock can easily be distinguished from natural disturbances by 

wild ungulates. It may be used during the grazing season to trigger a need to move 

livestock out of the pasture. A target or criterion for streambank alteration could be 

established as a trigger to move or as an end-of-season indicator to evaluate the 

effectiveness of the management prescription. As indicated, streambank alteration 

may also be measured, just prior to livestock entry into the pasture, to isolate the 

added effect of wild ungulates. Streambank alteration may also be measured in rest 

years, when livestock are not scheduled to be in the pasture, to evaluate other sources 

of natural disturbance or to assess alterations resulting from livestock trespass.

g. Metrics: The following metric is used to summarize streambank alteration data:

•	 Percent Alteration: Since streambank alteration is estimated from 400 individual 

observations along the greenline (if 80 plots are used), streambank alteration 

is a metric that represents the percent of hits (or lines intercepting hoofprints or 

shears) lineally along the greenline. 
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34MEASURE INDICATORS USING SYSTEMATIC PROCEDURES AND METRICS

The metric is calculated as the average number of hits (intercepts of alteration) per 

plot, expressed in a percent. This is the same as tallying the total number of hits for 

all plots, dividing by the total number of plots to obtain an average number of hits, 

and then dividing by 5 to obtain percent alteration. Thus if a survey resulted in 100 

hits from 80 plots, 100 divided by 80 and then divided by 5 equals 0.25 (25 percent). 

In a typical survey, most plots contain 0, 1, or 2 hits. For that reason, streambank 

alteration data tend to be distributed with a distinct skew to the left and therefore 

do not fit a normal probability distribution (see Data Analysis Module – “Data 

Summary” worksheet).

3. Woody Species Use:

a. Purpose: Woody species use is a short-term indicator of grazing utilization on 

woody plants, shrubs, and trees along streambanks. Woody vegetation (shrubs and 

trees) is an important component of many stream-associated riparian areas. Many 

healthy woody species provide strong, deep root systems that stabilize streambanks, 

filter sediment, shade streams, and provide habitat diversity. Most riparian woody 

plants (shrubs and trees) require freshly deposited or disturbed soil to germinate 

and establish. The most frequent deposition or disturbance is along the streambank. 

This area, within 3 feet of the greenline, has the highest occurrence of woody species 

establishment along a stream (Winward 2000). Cattle commonly graze on palatable 

woody plants occurring on gravel and sandbars and deposits along the floodplain 

(Kauffman et al. 1983).

Woody-species use may serve as a trigger for moving livestock at a predetermined 

level of use (e.g., light, moderate, etc.). It may be used to determine the level of 

browsing during the grazing period. Woody species use may help establish the 

relationship between the level of grazing use by large herbivores (e.g., cattle, sheep, 

horses, elk, moose, and deer) and the long-term condition of woody plants along the 

greenline. This indicator may also be used to help distinguish between livestock and 

wildlife browsing. 

b. Background: The procedure described here was adapted from the landscape 

appearance method described in Utilization Studies and Residual Measurements

(USDI, BLM 1996b), which is an ocular estimate of key woody species (e.g., willow, 

alder, birch, dogwood, aspen, and cottonwood) use. It is based on the amount 

(percentage) of the current year’s leaders on the woody species rooted within a plot 

2 m wide (centered on the greenline) and the length of the sample interval. Estimates 
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35

are based on a range or class of use of the available current year’s leaders on a 

single plant.

The method was adapted for use along streambanks to evaluate livestock and other 

large herbivore use on those shrubs, that most directly impact the streambanks. Only 

shrubs with more than 50 percent of the current year’s leaders within reach of grazing 

animals are evaluated.

Other methods were considered, including the twig length measurement and 

Cole browse methods. These methods were developed for upland shrubs, such as 

bitterbrush and mahogany, which have limited water availability. None of these 

methods have been extensively tested on riparian shrubs.

The twig length measurement method was found to be time consuming and to have 

high observer variability (Hall and Max 1999). The variability was a result of the 

uncertainty about knowing what and how to measure. This was further complicated 

by twig growth continuing after grazing on some riparian shrubs and the apparent 

stimulation of the growth of lateral twigs following grazing. Another compounding 

factor was the inability to differentiate use by bud-eating birds such as grosbeaks 

from use by large herbivores (Hall and Max 1999).

The Cole browse method uses incidence of leader (twig) use, i.e., the percent of 

individual twigs used on an available shrubs. This method appeared to have some 

of the same problems as the twig length method, particularly the stimulation of the 

lateral twig growth, continued growth after grazing, and inability to differentiate 

between different animals using the terminal bud (USDI, BLM 1996b).

c. Assumptions and Limitations: Where they have the potential to occur, woody 

riparian plants are important for the stability of streambanks; they also provide shade 

and habitat diversity. Hall and Max (1999) suggest that it is difficult to measure 

livestock use on riparian woody plants with any reliable degree of accuracy and 

precision. Since these plants are important, it is assumed that having an estimate 

of the use is important for determining the success of a grazing management 

prescription. Detailed rules for describing browsing on woody vegetation help 

with consistency.

Many stream reaches have low numbers of riparian woody species due to years of 

heavy use, mechanical and chemical removal, and stream channel alteration activities 
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36MEASURE INDICATORS USING SYSTEMATIC PROCEDURES AND METRICS

such as straightening. Low numbers of woody plants result in relatively small sample 

sizes that produce lower precision and accuracy. Because samples are taken randomly 

along the streambanks, these low numbers of plants make it difficult to get an 

adequate sample without sampling the same plants twice. 

The average percent use should not be used as a grazing use standard, but rather 

it should be an indication of the browsing impacts within a use class range. For 

example, if the woody species use is 38 percent, which is in the upper part of the light 

category, the amount of use should be described as light to moderate. This provides 

managers with information necessary to determine if the management prescription 

is likely making progress toward the objectives or if adjustments to the management 

prescription should be considered.

d. Relationship to Other Indicators: Woody species use along with woody species age 

class and greenline composition can be used to help determine the health of the woody 

plants within 1 m of the greenline. The health of these plants is an important factor 

contributing to the stability of the streambanks, aquatic habitat, and water quality.

e. Procedure:

Step 1. Establish the 

Plot Size: Woody 

species use is 

observed within 

a plot 2 m wide 

(1 m on each side 

of the greenline) 

by the length 

of the interval 

between quadrats 

(figure 15). It is 

helpful to use 

the measuring 

rod or handle 

of the frame to 

determine if a 

plant is rooted 

within the plot.

Figure 15. Select the first woody plant (A) within the plot and 

determine the utilization on that plant. This is repeated for each key 

woody species (B and C) within the plot. A 2-m measuring rod centered 

on the greenline is often used to locate plants within the plot. The 

monitoring frame has a 1-m-long handle, which may also be used to 

determine if individual woody plants are rooted within the plot.

The length of the woody species use plot is the 

distance between quadrats.

Quadrat

Quadrat

Greenline

The width of the woody species use plot 

is 2 meters centered on the greenline.

A B C
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37

Step 2. Determine the 

Available Current 

Year’s Growth:

Available woody 

species are plants 

having more than 

one-half (50 percent) 

of the current year’s 

leaders within reach 

of the grazing animal. 

When the first plant has more than 50 percent of the current year’s leaders above 

the reach of the grazing animal, the shrub is considered unavailable for grazing and 

the plant is not considered for woody species use. For example, table 1 shows that 

cattle typically graze to a height of 1.5 m above the ground. The observer(s) would 

only consider key woody plants that have most of their current year’s leaders below 

1.5 meters. Woody plants with over 50 percent of the current year’s leaders above 

5 feet are considered unavailable for cattle.

Step 3. Evaluate the Closest Plant: Observer(s) evaluate the first available key woody 

plants rooted within the plot for grazing use (see figure 15). If the first plant of a 

species is not available, go to the next closest plant within the plot. Common key 

woody species include most species of willow, alder, birch, dogwood, cottonwood, 

and aspen. If no key woody plants are encountered within the plot, leave the cell in 

the MIM Field Data Sheet blank.

Step 4. Determine the Woody Species Use Class: Plants are classified into a “use class” 

(see table 2). These use class descriptions are the standards by which use is judged. 

This process is repeated for each key woody species within the plot. Review grazing 

class descriptions periodically while reading the plots to maintain precision and 

accuracy. Record by species the midpoint (see table 2) for the appropriate use class.

f. Timing:

1) Isolating Livestock Use: Woody species use may be measured just before and just 

after livestock enter the grazing unit. This helps to determine how much use was from 

livestock and if there was grazing use on woody species by other large herbivores.

Table 1. Woody Species Browse Height by Animal Class 

(USDI, BLM 1992)

Class of Animal Height Grazed

Feet Meters

Cattle 5.0 1.5

Sheep, antelope, bighorn sheep 3.5 1.1

Horses, elk, and moose 7.0 2.1

Deer 4.5 1.4
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2) Trigger Monitoring: Measuring may be done during the grazing season to 

determine if grazing use on woody species has reached a predetermined amount, 

which would trigger moving livestock from the grazing area. Such measuring may 

also provide an early warning of impending damage to the plants.

3) End of Season: Use may be measured after the livestock grazing season to 

determine the amount of use on woody species. 

g. Metrics: The following metrics are used to summarize woody species use data:

•	 Average Use by Woody Species: This metric is calculated as the arithmetic average 

of woody species use values recorded for each plot, based upon the use category or 

class for each species. As stated previously, this metric value should be reported in 

terms of the use class; i.e., if the average of all plots for Salix boothii is 38 percent, 

the use class is “light to moderate” (see Data Analysis Module – “Graphs” worksheet).

•	 Average Use for All Woody Species: This metric is calculated using the same 

equation as for average use by individual woody species, but it is the weighted 

Table 2. Woody Species Use Classes and Descriptions 

(adapted from the landscape appearance method, USDI, BLM 1996b)

Class Midpoint Description

Unavailable Blank Shrubs and trees that have most (over 50%) of their actively growing 

stems over 1.5 m (5 feet) tall for cattle grazing. This should be adjusted if 

the questions to be answered involve other herbivores (see table 1).

Slight

(0%-20%)

10 Browse plants appear to have little or no use. Available leaders may 

show some use, but 20% or less of the current year’s leaders have use.

Light

(21%-40%)

30 There is obvious evidence of use of the current year’s leaders. The 

available leaders appear cropped or browsed in patches and 60%–79% 

of the available current year’s leaders of browse plants remain intact.

Moderate

(41%-60%)

50 Browse plants appear rather uniformly used and 40%–59% of the 

available current year’s leaders remain intact.

Heavy

(61%-80%)

70 The use of the browse gives the general appearance of complete search 

by grazing animals. Most available leaders are used and some terminal 

buds remain on browse plants. Between 20% and 39% of the available 

current year’s leaders remain intact. 

Severe

(81%-100%)

90 The use of the browse gives the appearance of complete search by 

grazing animals. There is grazing use on second and third years’ leader 

growth. Plants show a clublike appearance, indicating that most active 

leaders have been removed. Only between 0% and 19% of the current 

year’s leaders remain intact.
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39

average for all woody species at the site (see Data Analysis Module – “Data 

Summary” worksheet).

4. Greenline Composition:

a. Purpose: Riparian vegetation is critically important for the stability of streambanks, 

streambank morphology (width, depth, and shape), water quality, and aquatic 

habitat quality (Hansen et al. 1988). Livestock grazing, as well as other anthropogenic 

disturbances, may impact vegetation through reduced vigor, soil compaction, 

changing species, and physical disturbance of the streambanks (Platts 1991; Wyman 

et al. 2006). Sampling along the greenline is designed to account for the continuous 

line of vegetation occurring along most streambanks (Winward 2000). Since streams 

are dynamic, measuring vegetation along the greenline, which can move in response 

to annual streamflow levels, is particularly effective for understanding the overall 

condition and health of the stream reach. Determining the species of plants along the 

streambanks provides an indication of the condition, based on the health and amount 

of deep, strong-rooted vegetation, and the trend toward or away from the objectives 

established for the stream reach.

The greenline can also be composed partially or entirely of embedded rock and/or 

anchored wood, which influences stream function and habitat quality. Because of 

this, both embedded rock and anchored wood are also recorded.

b. Background: The concept of greenline composition was developed to provide a way 

to observe and measure the vegetation that is most critical to maintaining stream 

channel stability (Winward 2000). Winward describes a method using a continuous 

measurement and stratifies vegetation by riparian community type. Previously, a 

dominant and subdominant procedure was used for recording the relative proportion 

of plants within the plot (limiting subdominant plants to those with a minimum 

of 25 percent cover in the plot). The procedure was changed to record the relative 

vegetation composition for each herbaceous species having 10 percent or more foliar 

cover to improve efficiency. Use of the markings on the monitoring frame makes it 

possible to rapidly estimate the foliar cover of the species. Data recorded by percent 

cover rather than dominance provides a more accurate species composition.

c. Assumptions and Limitations: The greenline follows the streambank as erosion and 

deposition occur along a stream. Therefore, the composition of vegetation in this zone 

directly affects the condition of streambanks and overall stream condition. The major 

plant species along the greenline are helpful for analyzing the effects of livestock 

grazing along a stream.
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The procedure described here is not intended to identify all plant species along the 

greenline. It is intended to show those plants that are in a large enough proportion 

to directly affect streambanks. This procedure may be modified to identify all species. 

However, such a procedure would be much more time consuming and require a high 

degree of plant identification skills.

This procedure is not intended to provide data concerning vegetation on the floodplain 

or other associated riparian areas. When there are management questions concerning 

these areas, use other procedures such as vegetation cross-section composition 

(Winward 2000), riparian cross-section vegetation (PIBO-EM 2008), and ecological 

type identification and ecological status determination (Weixelman et al. 1997).

d. Relationship to Other Indicators: Greenline composition is closely related to 

streambank stability, woody species age class, and greenline-to-greenline width. 

Streambanks dominated by deep-rooted riparian vegetation result in stable streambanks, 

narrow channel widths, shading, habitat diversity, and terrestrial insect production.

e. Procedure:

Step 1. Develop a Species List: Prior to collecting vegetation data on the greenline, it 

is critical for observers to identify the plant species located on the site. Complete 

a reconnaissance of the site to identify and make a list of all vascular plant species 

that may occur along the greenline before sampling the plants.

Step 2. Record Herbaceous Vascular Plants (Perennial): Viewing from directly above 

the plot at 90 degrees to the ground surface, record by species the relative amount 

of foliar cover for 

herbaceous plants 

rooted in the plot 

having 10 percent 

or more foliar cover 

by composition. The 

monitoring frame is 

marked to provide 

references for 10-, 

25-, and 50-percent 

areal extent (see 

figure 16).

25% 50%

10%

Figure 16. The frame is marked with modified Daubenmire 

quadrat markings. These markings provide a visual estimate of the 

proportions of the quadrat. 
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For example, if a plot contains 25 percent foliar cover of Nebraska sedge and 

25 percent cover of Kentucky bluegrass with 50 percent bare ground, the observer 

would record compositions of 50 percent Nebraska sedge and 50 percent Kentucky 

bluegrass. Embedded rock and anchored wood compositions would also reflect 

their relative contributions to cover. For example, if a plot contained 25 percent 

foliar cover of Nebraska sedge, 25 percent cover of Kentucky bluegrass, 25 percent 

bare ground, and 25 percent embedded rock, the relative compositions would 

be 33 percent Nebraska sedge, 33 percent Kentucky bluegrass, and 33 percent 

embedded rock. Nothing is recorded for bare ground, litter, or nonvascular plants.

The total for all understory combinations (herbaceous plants, and/or embedded 

rock and/or wood, and/or woody plant seedlings) must not exceed 100 percent. 

Step 3. Record Woody Species Understory: Seedling woody plants, as defined in 

tables 10 and 11 in the “Woody Species Age Class” section, are not considered 

overstory and are recorded as percent foliar cover by composition along with the 

understory herbaceous vegetation.

Step 4. Record Woody Species Overstory: Overstory includes all young and/or mature 

woody plant species, as defined in tables 10 and 11 in the “Woody Species Age 

Class” section, either rooted in or overhanging the plot. Woody plants overhanging 

the plot must be rooted on the side of the stream being sampled. Do not record 

plants that are rooted on the opposite bank.

Foliar cover is not used for woody species overstory composition. If any part of a 

woody plant occurs in the overstory directly above the plot, it is counted as part of 

the composition. The observer does not attempt to estimate its relative cover but 

records 100 percent if there is one species in the overstory, 50 percent for each if 

there are two species in the overstory, 33 percent for each if there are three species 

in the overstory, and so forth. 

Step 5. Record Embedded Rock and Anchored Wood: Rock that is at least 15 cm in 

diameter and at least partially embedded in the streambank with no evidence 

of erosion behind it, talus slopes, and bedrock and/or logs or root wads that are 

anchored into the streambank and large enough such that high flows are not likely 

to move them are considered. Record the percentage of the total of understory 

vegetation, rock, and/or wood.
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Step 6. Record Grouped Plants: To the extent possible, all plants with 10 percent or 

more foliar cover should be identified by species. When individual plant species are 

less than 10 percent, but together comprise at least 10 percent of the foliar cover, 

they may be combined into groups, such as mesic forbs (MFE for early seral and 

MFL for late seral) or mesic graminoids (MG), dry shrubs (DS) or dry grass (DG), 

sedge (CAREXRH for rhizomatous and CAREXTF for tufted), and rush (JUNCUS). 

Step 7. Record Important Plants with Less Than 10 Percent Foliar Cover: Do not 

record any plant species with less than 10 percent foliar cover in the data entry 

form. Any important plants, such as noxious weeds or rare plants, may be recorded 

in the comments sheet by plot number.

Step 8. Record No Greenline Cover: When no greenline cover exists (i.e., vegetation, 

embedded rock, or wood) within 6 m of the water’s edge, record “NG.” The 

Data Analysis Module uses values of bare ground, early ecological status, and a 

greenline streambank stability rating of “0.”

f. Timing: Samples should be collected when plants are identifiable. Timing may vary 

according to climate and intensity of grazing use. The greenline should not be flooded 

at the time of sampling.

g. Metrics: The following metrics are used to summarize greenline composition data:

•	 GreenlineComposition bySpecies(Percent):This metric represents the relative 

proportions of individual species to the whole vegetative composition. It is 

calculated by summing the percent composition for each species over all plots 

divided by the number of plots in which the particular species was encountered 

times 100. The composition is presented by vegetation type (forb, graminoid, 

shrub, tree, other). This metric is presented in the “Graphs” worksheet of the Data 

Analysis Module (see Data Analysis Module – “Graphs” worksheet).

•	 GreenlineEcologicalStatus Rating: This is a measure of the average ecological 

status rating of plants as defined by Winward (2000). Plants are weighted according 

to their percent composition. This metric is calculated using plant successional 

status ratings and Winward’s riparian capability groups (table 3). It is further 

adjusted where a woody overstory component should be present but currently is 

not present (see Data Analysis Module – “Data Summary” worksheet).
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•	 SiteWetland Rating: This metric 

represents the average wetland rating 

of plants as computed using the site 

wetland rating (Coles-Ritchie 2005) 

(table 4). Wetland indicator status 

(USDI Fish and Wildlife Service 1993) 

values for individual species may vary 

by region. The Data Analysis Module 

accounts for that variation (see Data 

Analysis Module – “Data Summary” 

worksheet).

•	 Hydric Plants Percent: This is a 

measure of the proportion of the 

composition consisting of hydric 

plants. It is calculated by summing the 

total percent composition of plants 

rated as “hydric” divided by the total 

percent composition of all plants. 

“Hydric” is defined as those plants 

classified in the wetland indicator 

status (USDI Fish and Wildlife Service 

1993) as facultative wetland to 

obligate (see Data Analysis Module – 

“Data Summary” worksheet). 

•	 ModifiedWinward GreenlineStability

Rating: This metric represents the 

average stability rating of plants as 

defined in the plant list in the Data Analysis Module. Winward (2000) greenline 

stability ratings (table 5) are derived from categories as explained previously (see 

Data Analysis Module – “Data Summary” worksheet).

•	 PercentWoodyComposition: This is a measure of the percentage of plots 

containing woody plants. It is calculated by summing the number of plots with 

woody plants divided by all plots in the survey (see Data Analysis Module – “Data 

Summary” worksheet).

Table 3. Greenline Ecological Status 

(Winward 2000)

Value Rating

0-15 Very Early

16-40 Early

41-60 Mid

61-85 Late

86+ Potential Natural 

Community (PNC)

Table 4. Site Wetland Rating (Coles-Ritchie 2005)

Value Rating Value Rating

0 UPL 58 FAC+

8 UPL+ 67 FACW17 FACU- 75 FACW

25 FACU 83 FACW+

33 FACU+ 92 OBL42 FAC- 100 OBL

50 FAC

Table 5. Modified Greenline Stability Rating 

(Winward 2000)

Value Rating

<4 Low

5-6 Moderate

>6 High
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•	 Hydric Herbaceous Percent: This metric represents the percentage of plots 

containing hydric (facultative wetland to obligate) herbaceous plants. It is 

calculated by summing the number of plots in which a hydric herbaceous species 

was encountered and dividing by all plots in the survey (see Data Analysis 

Module – “Data Summary” worksheet).

•	 Vegetation BiomassIndex: This 

metric represents an index that is 

proportional to the estimated total 

biomass of vegetation along the 

greenline (table 6). It is calculated 

by averaging the heights of woody 

plants, weighted by species 

composition, adding this to the 

average stubble height of herbaceous 

plants, also weighted by species composition, and multiplying the sum by the 

average percent cover along the streambanks (Saunders and Fausch 2007) (see 

Data Analysis Module – “Data Summary” worksheet).

•	 PercentForbs: This is a measure of the percent of all plants in the sample identified 

as “forbs” in the plants list in Data Analysis Module. This metric is calculated by 

dividing the total composition of forbs by the total composition of all plants in the 

survey (see Data Analysis Module – “Data Summary” worksheet).

•	 Plant DiversityIndex: The plant 

diversity index is calculated by 

multiplying the number of plant 

species by average species 

composition on the plots and dividing 

by the standard deviation of plant 

species composition (table 7). This 

metric is calculated using the macro 

in the “Graphs” spreadsheet of the 

Data Analysis Module

5. Woody Species Height Class:

a. Purpose: This indicator estimates the heights of woody plants adjacent to the 

stream. Because heights are estimated by observation, height classes were developed 

Table 7. Plant Diversity Index

Index Relative Value

<1 Very Low 

1 – 2 Low

3 – 4 Moderate

5 – 6 High

>6 Very High

Table 6. Vegetation Biomass Index

Index Relative Value

<10 Very Low

10 - 20 Low

20 - 30 Moderate

30 - 40 High

>40 Very High
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to facilitate a reasonable level of observer agreement. Heights are indicators of stream 

shading and woody biomass production. Woody species height is useful in monitoring 

trends in woody plant structure adjacent to the stream. 

b. Background: The temperature of a stream is an important factor that determines 

the types, abundance, and distribution of aquatic organisms that live in a stream 

(Gordon et al. 2004). Water temperature in streams (particularly small streams less 

than 10 m wide) is directly affected by the amount of shading along the stream 

(Allan and Castillo 2007). Woody species adjacent to the stream are most effective 

for providing shade and thermal insulation to the water (Gordon et al. 2004). 

Temperature is a common water quality issue for cold water biota in many states.

Woody species along the edge of the stream provide a large amount of biomass. 

Woody species along with herbaceous vegetation influence terrestrial insect 

production. Recent research has demonstrated that terrestrial insect production 

associated with streamside vegetation is a major component of the diets for salmonid 

fishes and can be influenced by livestock grazing effects on that vegetation (Saunders 

and Fausch 2007). 

The procedure for measuring woody species height is described in PIBO-EM (2008) 

and is based upon the protocol documented in Bonham (1989). It is an easy and 

efficient method of describing the structure of woody vegetation along the edge of 

the stream channel. 

c. Assumptions and Limitations: Many woody species encountered along the 

streambanks are shrubs and small trees that are less than 8 m tall. These plants may 

include species such as willow, alder, birch, snowberry, and rose. This procedure 

allows for describing the overstory layers of woody vegetation along the streambanks 

by identifying the height class by species. 

The tallest height class used in this procedure is all woody vegetation greater than 

8 m. Thus, trees more than 8 m tall, such as aspen, cottonwood, conifers, and alder, 

are estimated by a broad range greater than 8 m. If there are layers of vegetation over 

8 m tall, their relative position in the layer will not be recognized. 

When actual heights of woody species would answer specific questions, the 

procedure must be modified to measure the actual heights of the woody plants. 

This modification increases the amount of time needed to collect the data. The Data 

Analysis Module would also need to be modified to recognize the measured height, 
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46MEASURE INDICATORS USING SYSTEMATIC PROCEDURES AND METRICS

rather than the height class. The Data Analysis Module estimates average woody 

species height by applying the average height class to a regression equation that 

predicts actual height in meters. 

d. Relationship to Other Indicators: Woody species height class provides additional 

information describing the condition of greenline vegetation. It provides information 

concerning the growth of woody species over time. 

Woody species height class provides useful input to vegetation height in the optional 

shading variable of the stream segment temperature (SSTEMP) model widely used to 

predict stream temperature (Bartholow 2002). Shading is one factor that contributes 

to stream temperature regulation. GGW may also correlate to the stream width.

e. Procedure: Record the height class of 

each woody species plant with cover 

over the plot using the ranges from 

PIBO-EM (2008) as shown in table 8. 

Record the tallest height class (inside or 

outside the plot) of an individual with 

at least some cover over the plot. For 

example, if a willow has one branch 

hanging over the plot at 1 m above the 

ground, yet when looking at the entire 

plant, it is 3 m high at its tallest point, 

record class 4 (2.01 to 4 m). When multiple layers of woody plants occur over the plot, 

record the height class for each woody species listed in the greenline composition.

f. Timing: Woody species height should be measured at the same time as the greenline 

composition.

g. Metrics: The following metrics are used to summarize woody species height class data:

•	 Percentile Height: Percentiles from the frequency distribution are summarized 

showing the height of woody plants at various frequencies of occurrence. For 

example, the 50 percentile is that height for which 50 percent of the plants are 

shorter (see Data Analysis Module – “Graphs” worksheet).

•	 ShadeIndex: This metric represents the average height of all woody plants divided 

by the average GGW. Shade increases with increasing plant height and decreasing 

Table 8. Woody Species Height Class

(PIBO-EM 2008)

Height Class Height Range

1 0.0 – 0.5 m

2 > 0.5 – 1.0 m

3 > 1.0 – 2.0 m

4 > 2.0 – 4.0 m

5 > 4.0 – 8.0 m

6 > 8.0 m
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GGW (Bartholow 2002) (table 9) 

(see Data Analysis Module – “Data 

Summary” worksheet).

•	 AverageWoody Plant Height: The 

average woody plant height class 

is applied to a regression equation 

to compute predicted woody plant 

height. In the “Data Summary” 

worksheet in the Data Analysis Module, the average for all woody species heights 

is presented. In the “Graphs” worksheet, the average height is displayed for each 

woody species. The regression equation was developed using the log of species 

height versus age class and has an R squared of 0.99. The equation is:

H =10^(-0.7083129+0.297*Average Height Class)

(See Data Analysis Module – “Data Summary” worksheet.)

 6. Streambank Stability and Cover:

a. Purpose: Streambanks are the steep-sloped sides of the stream channel and are 

most susceptible to erosion during high flow events. Stability along the edge of the 

first bench or bankfull elevation down to the stream’s scour line is the area within the 

channel that is most vulnerable to erosion by water because streamflow up to the 

bankfull level occurs almost every year (Leopold 1994). Bankfull discharge performs 

most of the geomorphic work in most river systems (Wolman and Miller 1960). 

Streambank stability is strongly influenced by streamside vegetation (Bauer and 

Burton 1993). The loss or modification of deep-rooted bank vegetation is problematic 

for stability.

Streambanks can become unstable or unable to resist the erosive effects of high 

streamflows as a result of improper livestock grazing. Bare streambanks, either in 

erosional or depositional positions of the stream, are considered unstable due to 

their vulnerability to erosion. The effect of excessive grazing is to alter the streamside 

vegetation composition resulting in a dominance of plants that are more vulnerable 

to erosion (Platts 1991; Bauer and Burton 1993). Mass wasting may also result from 

breakoffs, hoof slide, and hoof shear related to the physical disturbances of trampling 

(Bauer and Burton 1993; Powell et al. 2000). Unstable streambanks can lead to 

accelerated bank erosion and subsequent channel widening, increased sediment 

supply, decreased sediment transport capability, and damaged fisheries habitat. 

Table 9. Shade Index

Value Shading

<0.5 Very Low

0.5 – <1.0 Low

1.0 – <2.0 Moderate

2.0 – <4.0 High

≥4.0 Very High
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At each plot location, features that describe streambank stability are recorded. 

Those features are used in the Data Analysis Module to compute percent streambank 

stability and cover. Plots are a subsample of the length of the streambank; therefore, 

streambank stability using this procedure estimates the proportion of streambank 

that is stable and that is covered. 

b. Background: This procedure is based upon an earlier version described by Bauer 

and Burton (1993) and later by Overton et al. (1997). Modifications were later made 

by the PACFISH/INFISH Biological Opinion Effectiveness Monitoring (PIBO-EM) Team 

to minimize subjectivity (Kershner et al. 2004). The current version further reduces 

subjectivity by allowing observers to record features that define the condition rather 

than to record the stability class. Rules are used to increase measurement precision. 

c. Assumptions and Limitations: Streambank stability can be used to monitor 

livestock grazing and, potentially, other disturbance effects only if the procedures 

are adhered to strictly and the definitions are understood and followed. Streambank 

stability must be assessed by well-trained observers. 

Because of how the observations are made, streambank stability can only be assessed 

when the stream is flowing below the scour line, usually well after the seasonal peak 

flow event. Streambank stability monitoring is voided by assessments made when 

the banks are flooded.

Archer et al. (2004) found that only 18 samples were needed to detect a change of 

5 percent (Type I error of 0.1), and from tests on 12 different streams, the authors 

determined an average of 54 samples were needed to detect a change of 10 percent. 

However, site variability may have significant influence on the sample size needed. Tests 

conducted by the authors indicated sample size estimates as low as 5 and as high as 102 

to estimate streambank stability within 10 percent of the mean. Use of an electronic 

sample size estimator built into the Data Entry Module will help ensure an adequate 

number of samples was collected before leaving the field site.

In tests of repeatability for streambank stability, using an earlier version of the 

method, Archer et al. (2004) found the maximum deviation between crews was only 

18 percent corresponding to a coefficient of variation of 4.6 percent. On average, 

crews agreed 82.7 percent of the time. This compares with the author’s testing from 

43 replicates, which resulted in an average difference between observers of 

8.2 percent (stability) and 8.5 percent (cover), with coefficients of variation of 

9 percent (stability) and 8 percent (cover). More details can be found in appendix F.
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49

d. Relationship to Other Indicators: Streambank alteration and GGW are affected by 

streambank stability. In addition, the Winward greenline stability rating, an estimator 

of the vegetative contribution to bank stability, is related.

e. Procedure:

Step 1. Determine Streambank Location: Streambanks are defined as that part of the 

channel between the scour line and the edge of the first relatively flat bench above 

the scour line. The figures in appendix E provide examples of streambank locations.

Step 2. Observe Factors Influencing Stability on the Streambanks Associated with 

the Frame: The plot (area observed for streambank stability) extends parallel to 

the stream a distance of one frame length (50 cm) and perpendicular to the stream 

between the scour line and the lip of the first bench. Typical scour line indicators 

are the elevation of the ceiling of undercut banks at or slightly above the summer 

low-flow elevation or, on depositional banks, the lower limit of sod-forming or 

perennial vegetation. The lip of the first bench is at the point on the bench where 

the slope changes from the relatively flat top to the slope toward the stream. 

Step 3. Answer the Following Questions: Each of the following questions is answered 

with a letter abbreviation (such as “D” for depositional). One set of questions is 

answered for the streambank associated with each plot location and the answers 

are entered into columns F, G, and H of the “DMA” worksheet in the Data Entry 

Module or on the MIM Field Data Sheet (appendix B, part 2).

1)What kind of streambank is it? The choices are “Depositional” or “Erosional”:

•	 Depositional (D): This applies to all streambanks associated with sand, 

silt, clay, or gravel deposited by the stream. These are recognizable as “bars” 

in the channel margins adjacent to the greenline and at or above the scour 

line. Stream bars are typically lenticular-shaped mounds of deposition on the 

bed of the stream channel adjacent to or on the streambank. Depositional 

streambanks are usually at a low angle from the water surface and are not 

associated with a bench.

•	 Erosional (E): This applies to all banks that are not “Depositional.” Erosional 

streambanks are normally at a steep angle to the water surface and are 

usually associated with a bench and/or terrace. Such banks typically occur 

on the outside of meander bends and on both sides of the stream in straight 
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reaches. When there is sufficient stream energy, they may also occur on the 

inside bank of a meander bend.

2) Is the streambank covered? The choices are “Covered” or “Uncovered”:

•	 Covered (C): This applies to banks with at least 50 percent foliar cover of 

perennial vegetation (including roots); at least 50 percent cover of rocks 15 cm 

or larger; at least 50 percent cover of anchored large woody debris (LWD) with 

a diameter of 10 cm or greater; or a combination of the vegetation, rock, and/

or LWD covering at least 50 percent of the bank area (50 cm wide from the 

scour line to the first bench).

•	 Uncovered (U): This applies to all banks that are not “Covered.” 

3) Is the streambank stable? This applies to erosional banks only. For depositional 

banks, leave this cell blank. The Data Entry and Analysis Modules allow only one 

code in each cell, so the observer records “Fracture,” “Slump,” “Slough,” “Eroding,” 

or “Absent” for the single most prominent feature:

•	 Fracture (F): This applies to the top of the bank where a visible crack is 

observed. The fracture has not separated into two separate components or 

blocks of a bank. Cracks indicate a high risk of breakdown. The fracture 

feature must be at least one-fourth of a frame length.

•	 Slump (S): This applies to a streambank that has obviously slipped down 

resulting in a separate block of soil/sod separated from the bank. The slump 

feature must be obvious and at least one-fourth of a frame length.

•	 Slough or “Sluff” (SL): This applies to banks where soil or sod material has 

been shed or cast off and has fallen from and accumulated near the base of 

the bank. “Slough” typically occurs on banks that are steep and bare. The 

slough must be obvious and at least one-fourth of a frame length.

Slumps and sloughs may be created by excessive animal trampling (see 

appendix E, figures E8 and E9). 

•	 Eroding (E): This applies to banks that are bare and steep (within 10 degrees 

of vertical), usually located on the outside curves of meander bends in the 

stream. Undercut banks are scoured or eroded below the elevation of the 

base of sod or the roots of vegetation, and because such erosion occurs mostly 
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51

below the scour line, it is not considered “eroding” bank. Such undercut banks 

are stable as long as there is no slough, slump, fracture, and/or erosion above 

the scour line or ceiling of the undercut bank. The erosion feature must be 

obvious and at least one-fourth of a frame length. 

•	 Absent (A): This applies when none of the above listed characteristics are present.

f. Timing: Streambank stability is measured in conjunction with streambank 

alteration to quantify the levels of streambank alteration that do not constrain 

streambank stability condition or recovery. In establishing allowable levels of 

streambank alteration, streambank stability should be measured in conjunction 

with streambank alteration annually, both before and after grazing. If a reference 

is available, measurements both before and after grazing would be made in the 

ungrazed reference area to isolate natural effects. In the absence of the need to 

quantify streambank alteration criteria, streambank stability should be measured 

approximately once every 3 years to evaluate changes over time. An ungrazed 

reference should be used to assess trends related to livestock grazing.

g.Metrics: The following metrics are used to summarize streambank stability 

and cover data:

•	 StreambankStability: The number of plots classified as “stable” are divided by 

the total number of plots and expressed as a percent (see Data Analysis Module – 

“Data Summary” worksheet).

•	 StreambankCover: The number of plots recorded as “covered” are divided by the 

total number of plots and expressed as a percent (see Data Analysis Module – 

“Data Summary” worksheet).

7. Woody Species Age Class:

a. Purpose: Woody species age class data help determine if woody plants are 

establishing along the streambank. Winward (2000) found that use of the greenline 

edge as the center of the measurement helps to ensure that sampling is done in a 

setting where regeneration of woody riparian species is most likely to occur. 

b. Background: Winward (2000) concluded that understanding the age class, structure, 

and density of woody species along the stream margins provides information 

necessary to evaluate the results of management. Woody species regeneration, as 

described by Winward (2000), consists of a 6-foot wide belt adjacent to and on each 
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52MEASURE INDICATORS USING SYSTEMATIC PROCEDURES AND METRICS

side of the greenline and on both sides of the stream. All woody species (excluding 

rhizomatous woody species) were counted and placed in an age class defined in the 

procedure described in Winward (2000). The procedure for the MIM protocol was 

modified by using a 0.42 by 2 m plot (two lengths of the 42- by 50-cm monitoring 

frame placed perpendicular to and with 1 m on each side of the greenline) instead 

of a continuous belt, which increases precision and allows evaluating the data using 

statistical methods. Single- and multiple-stem species are grouped by age class and 

the number of plants is recorded for each class. 

c. Assumptions and Limitations: Stream disturbance or sediment deposition is often 

required for germination and establishment of many woody species along streams 

(Winward 2000). The most frequent sediment deposition is along the margins of 

streams resulting from relatively frequent small floods, those with return frequencies 

of every 2 or 3 years. This deposition creates a relatively frequent return of conditions 

conducive for woody species to germinate and establish. 

The procedure described here is designed to provide decisionmakers with 

information concerning the recruitment of woody species along streams. For systems 

with the potential to produce woody vegetation, this procedure helps provide 

an understanding of whether the woody species are increasing, decreasing, or 

maintaining numbers and age classes. 

d. Relationship to Other Indicators: Woody species age class is only a part of the 

data needed to understand condition and trend. It should be used in conjunction with 

greenline composition, streambank stability and cover, and GGW. Woody species use 

provides information to assess whether browsing is a factor contributing to a change 

in the population and health of the woody vegetation along the greenline.

e. Procedure:

Step 1. Determine Plot 

Placement: The woody 

species plot is 0.42 m 

wide by 2 m long 

(1 m on each side of the 

greenline), with the frame 

placed perpendicular to 

the greenline as shown 

in figure 17. Place the 

Figure 17. The monitoring frame placement. The monitoring frame is 

placed perpendicular to the greenline.

Streamow
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53

frame end-to-end on each side of the greenline so that the entire 2 m 

are sampled.

Step 2. Distinguish Individual Plants: It is sometimes difficult to distinguish 

individual plants from one another when shrubs have multiple stems close 

together. In such cases, consider all stems within 0.3 m of each other at ground 

level as the same plant and record the age class of the entire shrub to which that 

stem is connected, even if part of the shrub is outside of the plot. The presence of 

even one stem within the frame requires the observer to determine if that stem is 

connected to others outside of the frame.

Step 3. Determine Age 

Class: Place the end 

of the monitoring 

frame on and 

perpendicular to 

the greenline, and 

determine the age 

class (see tables 10 

and 11) of each 

woody plant by 

species rooted within 

the monitoring frame. 

Record the number of 

all woody plants by 

species according to 

their age class. Do not 

count woody species 

overstory not rooted 

within the frame.

Step 4. Record Woody Root Sprouting and Rhizomatous Species: It is difficult to age 

class rhizomatous and root sprouting species such as coyote willow (S. exigua), 

wild rose (Rosa spp.), snowberry (Symphoricarpos spp.), cottonwood root sprouting 

(Populus spp.) and golden currant (Ribes aureum), etc.; therefore, if root sprouting 

and rhizomatous species occur in the plot, record a 1 in the rhizomatous column.

Step 5. Record Low-Growing Shrubs: Some low-growing shrubs are considered 

mature when they are less than 0.5 m tall. etc. When a question arises, use the 

Table 10. Woody Species Age Class for Single-Stemmed Species 

[e.g., cottonwood or aspen (Populus spp.)]

Age Class Single Stem Species

Seedling Stem is <1 m tall or <2.5 cm in diameter at 

50% of height from ground level.

Young Stem is >1 m tall and 2.5 cm to 7.6 cm in 

diameter at 50% of height from ground level.

Mature Stem is > 1 m tall and >7.6 cm in diameter at 

50% of height from ground level.

Table 11. Multistemmed (Clumpy) Woody Species Age Class 

(e.g., most willows, alder, and birch)

Age class Stems and Height

Seedling 1 stem <0.5 cm in diameter at the base 

and <0.5 m tall.

Young 2 to 10 stems less than 1 m tall or 1 stem 

>0.5 cm in diameter at the base and 

less than 1 m tall 

Mature >10 stems over 1 m tall
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plant growth form descriptions in the literature to determine the appropriate 

age class.

f. Timing: Sampling should be conducted when woody plants can be identified and at 

the same time as greenline composition and woody species height class is done. 

g.Metrics: The following metrics are used to summarize woody species age class data:

•	 PercentSeedlings(BySpecies): This metric represents the proportion of woody 

plants encountered on all plots that are classified as “seedlings.” It is calculated by 

summing all seedlings encountered and then dividing the sum by the total number 

of woody plants encountered in the survey (see Data Analysis Module – “Data 

Summary” worksheet).

•	 PercentYoung (BySpecies): This metric is the same as the percent seedling metric 

except that it is calculated only for those plants classified as “young” (see Data 

Analysis Module – “Data Summary” worksheet).

•	 PercentMature(BySpecies): This metric is the same as the percent seedling except 

that it is calculated only for those plants classified as “mature” (see Data Analysis 

Module – “Data Summary” worksheet).

•	 Percent RhizomatousWoody: This is a measure of the proportion of woody 

plants classified as “rhizomatous.” It is obtained from the total of all plots in which 

rhizomatous woody plants were encountered divided by the total number of plots 

in which any woody plant was encountered (see Data Analysis Module – “Data 

Summary” worksheet).

8. Greenline-to-Greenline Width (GGW):

a. Purpose: Greenline-to-greenline width (GGW) is the nonvegetated distance 

between the greenlines on each side of the stream. It provides an indication of the 

width of the channel, reflecting disturbance of the streambanks and vegetation. 

As stream channel margins are disturbed by trampling or excessive vegetation 

consumption, streams may erode the streambanks, causing a lateral erosion of the 

streambank and streamside vegetation. This results in a shifting out, or widening 

of the distance between greenlines within the nonvegetated channel. The GGW 

measurement may also reflect the bankfull width. As stated by Winward (2000):
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Most often the greenline is located at or near the bankfull stage. As flows 

recede and the vegetation continues to develop summer growth, it may 

be located part way out on a gravel or sandbar. At times when banks are 

freshly eroding or, especially when a stream has become entrenched, the 

greenline may be located several feet above bankfull stage.

The loss of vegetative integrity and breakdown of streambanks by livestock trampling 

may lead to bank erosion and subsequent channel widening (Rosgen 1996). Because 

vegetation is frequently related to bank stability, the nonvegetated width between 

greenlines is an excellent way to monitor this effect on the channel. As channels 

widen, water depth decreases with potential negative effects on aquatic habitat and 

water temperature.

b. Background: Many stream channels become overwidened as a result of vegetative 

changes and physical disturbance to streambanks from improper livestock grazing 

(i.e., streambank trampling and shearing) or other physical disturbances to the 

streambanks. Improper livestock grazing can alter stream habitats by channel 

widening and/or incision (Clary et al. 1996; Clary 1999; Clary and Kinney 2002; 

Kauffman and Krueger 1984). Under improper grazing, protective vegetation is 

weakened or removed, and trampling may induce a sloping streambank profile (Clary 

and Kinney 2002). Subsequent erosion of weakened streambanks during floods 

results in a wider, shallower stream channel profile. These changes to stream 

channels can be detrimental to biota (Bohn 1986). Observations at research sites 

indicated stream width reductions in overgrazed streams with improved grazing 

management of riparian zones. The average amount of narrowing was inversely 

associated with the level of grazing intensity. Between 1990 and 1994, width changes 

(measured as a proportion of the original measurement) were: a 41 percent reduction 

in areas with no grazing, a 34 percent reduction in areas with light grazing, and an 

18 percent reduction in areas with medium grazing. Stream depth, on the other hand, 

was variable through time and appeared to change primarily in response to climatic 

events. After a flood event in 1996, channel depth at the ungrazed site increased to 

2.33 times the original depth. This vertical scour likely resulted from the longer term 

effect of channel narrowing (Clary 1999).

Commonly, the width of stream channels is determined by measuring channel width 

at the bankfull level. Detailed measurements of width and depth are accomplished 
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by surveying channel cross-section profiles. This method, at a large number of 

positions along the stream, is impractical because it requires identification of bankfull 

indicators, which in disturbed streams are often missing. Testing by the authors 

indicates that too few width measurements do not adequately estimate mean 

greenline-to-greenline width dimension due to site variability (see details of testing 

in appendix F).

As summarized by Bauer and Ralph (2001), the major concern with use of width 

measurements at bankfull level is the reliability of the method. Bankfull width is 

determined by using field characteristics such as sediment surfaces and profile breaks 

to identify the elevation of the active floodplain surface. These definitions are vague, 

and the actual selection of bankfull level is, at best, subjective.

Other field methods have measured the “wetted width” of the stream. Although 

this level in the channel is easily identifiable, unfortunately, wetted width varies 

dramatically by streamflow. Because it is normally measured during low or 

intermediate streamflows, it provides little information about the overall channel 

characteristics of the measured stream.

To achieve an adequate sample for estimating the mean GGW, take measurements 

at each plot location. The results are a mean width of the nonvegetated stream 

channel. As streambanks recover, the stream channel typically narrows and the 

average GGW is reduced.

c. Assumptions and Limitations: GGW is the average nonvegetated distance 

between the greenline on each side of the stream. It provides an indicator of the 

stream channel width. As disturbed and usually overwidened streams recover, the 

channel will narrow. Hence, narrowing GGWs are indicators of stream recovery. 

Objectives specific to GGW should be developed from reference sites when such 

information is available.

Results of the authors’ testing (54 tests) indicated reasonably good repeatability. The 

coefficient of variation averaged 8 percent, which according to the literature, indicates 

good agreement among observers (see appendix F). The average difference between 

observers was less than 0.5 m. The number of samples needed to predict the mean, 

within 10 percent, at 90 percent confidence, averaged 67. As with other indicators, 

the adequate sample sizes varied among test streams, from a low of 21 to a high of 109.
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The measurement of GGW assumes the use of some kind of measuring instrument 

(measuring rod, tape, laser rangefinder) with an instrument accuracy of less than 

0.5 m (0.1 m preferred).

d. Relationship to Other Indicators: The GGW reflects the vegetative composition 

and stability of the streambanks. As vegetation shifts from deep-rooted hydric types 

to more shallow-rooted mesic and xeric types, streambanks become vulnerable to 

erosion and lateral migration, allowing the GGW to expand. Likewise, as streams 

recover from past disturbance, a greater abundance of deep-rooted riparian-wetland 

vegetation may become established on the streambanks, resisting stream erosion 

and building more stable streambanks. As banks become stabilized, vegetation may 

encroach on the channel, particularly on bars in the margins of the stream, allowing 

the GGW to narrow. Data from measuring GGW should be used with greenline 

composition and streambank stability and cover to evaluate these effects. 

One way to evaluate the precision in locating the greenline is to consider the 

repeatability of GGW measurements. The average difference among observers is 

0.43 m. Since observers must use the same procedures to locate the greenline, 

presumably differences among observers are influenced, and probably largely 

determined by, the bias in its location. Thus it is encouraging that observers are 

reasonably in agreement with GGWs.

e. Procedure:

Step 1. Measure the Distance between the Greenlines on Each Side of the Stream 

and Perpendicular to the Waterflow at Each Plot Location: Using a laser 

rangefinder is the most expedient way of measuring the distance but may be 

difficult in woody vegetation. A rangefinder reduces the time required to do the 

measurements by about two-thirds. Other less expensive options include using 

measuring rods and tape measures.

Measure from the greenline associated with the center bar on the monitoring 

frame (near the toe of the boot) (see appendix A, figure A1), to the greenline on 

the opposite side of the stream. The measurement is usually taken from only one 

side of the stream. If there is an inadequate number of samples, measurements 

may be taken from the opposite side of the steam. Measure the width to the 

nearest 0.1 meter. 
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Step 2. Subtract Vegetated Island Measurements: When a vegetated island (with 

at least 25 percent foliar cover) is encountered along the line, determine the total 

distance between the greenlines and deduct the length of the vegetated portion of 

the island to determine the nonvegetated GGW (see appendix G, figures G1, G3, and G5).

Nonvegetated islands are included in the GGW measurement even if they consist of 

embedded rock or anchored wood (see appendix G, figure G5, line A).

NOTE: Do not measure GGW when no greenline (NG) is recorded. Leave the 

MIM Field Data Sheet (or electronic data entry) blank.

f. Timing: Measurements should be made during the low streamflow period after 

annual vegetative encroachment has ended. This will usually occur after streamflow 

has drawn down sufficiently to allow drying of streambanks. Streams that are 

confined by the actively flowing channel at low flow, such as those controlled by base 

or spring flows, will have very little vegetative encroachment. Streams that have 

considerable historic disturbance or flashy flows or are steep in the declining limb of 

the hydrograph may experience a great deal of vegetative encroachment soon after 

high water. This indicator helps document stream channel recovery over time. Since 

the recovery process may be relatively slow, it is recommended that the procedure be 

repeated every 3 to 5 years. The procedure is relatively easy and only requires about 

1 hour per DMA. 

g. Metrics: The following metric is used to summarize GGW data:

•	 GGW:This metric is the average of GGW measurements at a site in meters. The 

metric is based on measured data that usually fits a normal probability distribution 

(see Data Analysis Module – “Data Summary” worksheet).

9. Substrate:

a. Purpose: Bed material sampling is used to detect impacts of channel disturbance 

and the effects of management prescriptions and mitigations on the substrate over 

time. Channel instability often leads to channel widening, where the energy balance 

between erosion and deposition shifts toward deposition and therefore fining of the 

substrate (Powell et al. 2000). Such increases in fines may degrade aquatic habitat 

by restricting the living spaces of substrate-dwelling organisms and by limiting the 

oxygen transfer to incubating eggs (Powell et al. 2000). 
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b. Background: There is a sizeable amount of literature that supports the contention 

that excess substrate fines are adverse to salmonids (Bauer and Ralph 2001; McHugh 

and Budy 2005; Platts 1991). Bunte and Abt (2001) provide an excellent literature 

summary with guidelines for substrate assessment. They also found that pebble 

counts are an efficient and effective method for measuring surface substrate size 

distribution and percent fines. In applications to a national forest, Potyondy and 

Hardy (1994) found pebble counts to be reasonably simple, rapid, and useful in 

determining the effects of land management activities and land disturbances on 

instream fine sediment. They concluded that pebble counts can only detect change 

when relatively high magnitude impacts (low precision) are evaluated. They used 

just 100 particles to estimate substrate-size distribution. Testing by the authors of 

the MIM protocol suggested higher precision can usually be obtained with more than 

100 particles. To characterize the substrate, pebble counts sample a preset number of 

particles (at least 200) along transects across the channel. The cross-channel transects 

are evenly spaced along the length of the DMA.

c. Assumptions and Limitations: This procedure applies primarily to wadable, graveland cobble-bed streams. Such streams have mean particle sizes ranging from 2 to 

256 mm in diameter (Bunte and Abt 2001). Because of the wide range of bed material 

sizes, and because of the complex interactions of particles during erosion, transport, and 

deposition, the substrate may become spatially heterogeneous and difficult to sample.

To adequately sample gravel- and cobble-bed streams, reaches of at least 5-7 channel 

widths in length should be included (Bunte and Abt 2001). Sampling the entire 

length of the DMA (20 channel widths) is recommended to ensure spatial variability 

is accounted for in the sample scheme. If not, variability through time may reflect 

spatial heterogeneity more than actual adjustments in substrate size distributions. 

Because the purpose is to sample particle-size distribution related to channel 

instability, the surface of the streambed is the focus of this procedure. Sampling the 

subsurface strata (e.g., particles at depth) is more intensive and beyond the purpose 

and scope of this monitoring procedure.

It may be challenging to collect surface particles in some situations. In addition, 

care must be taken to avoid sampling algae, which may appear in the form of fine 

particulates covering the surfaces of rocks in the substrate. Algae are not part of the 

substrate and should not be counted as part of the fines. Individual fine particles 

located between larger particles may be difficult to pick from the substrate, others 
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may be heavily embedded into the substrate and hard to dislodge, still others may 

be too heavy to pick up. As noted by Bunte and Abt (2001), using different methods 

to sample substrate at the same location may yield different results. Thus trend over 

time should be based upon the same technique applied to each sampling event.

The guidelines on bed material sampling provided by Bunte and Abt (2001) include 

an excellent summary of the literature and the principal base reference for this protocol.

With respect to repeatability, the statement by Bunte and Abt (2001) is especially 

appropriate for substrate sampling:

Operator training is extremely important. When selecting particles from a 

predefined streambed location, or even when measuring particle sizes in 

a preselected sample of rocks, there is less variability between the results 

of experienced operators than between those obtained by novices. Field 

personnel need to be trained to perform procedures accurately, to avoid 

bias, and to use equipment that reduces operator induced error.

Observers should take caution when sampling cross sections associated with fast 

water (>1.5 m/sec). Fine particles can become easily washed away when collecting 

from the substrate, causing inaccuracies. In a DMA containing a cascade, for example, 

if the plot cross-section occurs at the cascade, the observer should be careful to avoid 

losing particles in fast water. Likewise, sampling in locally deep water is problematic 

for the observer because the substrate is beyond arm’s length. However, as suggested 

by Bunte and Abt (2001), sampling in deep water can be mitigated by visually 

estimating particle size classes or by collecting substrate with a long-handled scoop 

or “reach extender.” Diving with a wet or dry suit may also be used in situations with 

abundant deep water.

As summarized by Bunte and Abt (2001), sources of error in pebble counts may result 

from observer variability. The most common error is to favor larger sized particles 

when sampling substrates with fines lodged between larger particles. Rather than 

collect the fines, the observer selects the larger particle, often out of convenience. 

The systematic procedure described here helps to reduce this error by requiring the 

observer to collect the sample directly beneath the tape or rod at the given point of 

measurement. Still, the use of the index finger to touch the substrate and then select 

what is touched as the sample may miss small interspaces and the associated fines 

between particles using either procedure. For this reason, percent fines and the lower 

percentile particles (D16 and D30) may be underestimated. 
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One way to reduce bias associated with bed sampling, particularly in fast mountain 

streams with a lot of cobble-sized particles, is to use the sampling frame designed by 

Bunte and Abt (2001). This frame is placed on the bed of the stream at the designated 

points along the tape or rod within the cross section. It contains a grid intersection 

that defines exactly where the particle is located. The observer then attempts to 

extract that particle, or if very small and subject to being washed away, records its 

size class visually. In this way, if the point of measurement occurs exactly on the 

interspace between two cobbles, the material within that interspace is collected for 

the sample. The frame is metal so as to easily submerge and rest on the substrate 

and it is 60-cm square. Elastic bands are stretched across the frame in two directions 

to form the grid intersection. Multiple elastic bands can be used to make multiple 

grid intersections.

Despite known bias towards larger particles and away from fines, note that there was 

better observer agreement (repeatability) at the authors’ test sites on percent fines 

(coefficient of variation or CV = 6 percent) than on median particle size (CV = 29 percent). 

This agreement is likely because median particle sizes are calculated from size classes 

(slots in the template) that represent broader ranges as particles increase in size, 

and fines are measured in small slots that are closer to the actual size of the particle. 

Additionally, most of the test sites were located on low-gradient streams where the 

frequency of encountering larger particles that mask fines in their interspaces is low. 

Typically sand and gravel with dispersed cobbles dominated these sites and allowed 

for less bias in collecting the smaller particles from the substrate. 

With respect to accuracy, as summarized in Bunte and Abt (2001), a 100-count 

particle-size sample is usually too low to compare particle-size distributions over 

time. The authors’ testing indicated that adequate sample sizes range between 

74 and 384 (with an average of 229) to estimate the mean within 10 percent at 

90 percent confidence (see appendix F). The range of sample sizes varied according 

to heterogeneity of the substrates at the test sites. Small sample sizes of 74 were 

indicated at a site with relatively homogeneous substrate, and large sample sizes 

were indicated at a heterogeneous or highly variable site.

d. Relationship to Other Indicators: Substrate size distributions and fine sediment 

abundance are related to channel stability indicated by streambank stability 

and cover, greenline composition, GGW, and streambank alteration. Streamside 

vegetation consumption and bank trampling by livestock can lead to streambank 

destabilization, which may lead to increased bank erosion, subsequent channel 

widening, decreased water velocity, and increased deposition.
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e. Procedure: Beginning with the second plot in the survey, samples are collected at 

every other plot location (or 20 total transects), evenly spaced along the entire length 

of the DMA. Transects are located at even numbered plots, from plot 2 to plot 40, in 

the upstream direction of the survey only. Collect and measure the diameter of 10 

pebbles at each transect. Samples should be collected within the active channel only. 

Never sample a particle above the scour line. Depositional features (e.g., point bars) 

that are not covered by vegetation and located below the scour line are considered 

streambed material and should be included in the sample.

Step 1. Determine the Interval Length to Obtain 10 Particles in the Cross Section:

Use a measuring rod or tape stretched across the stream at the plot location. Divide 

the width of the active channel by 10 (the active channel is located between the 

scour lines of the stream). Alternatively, if a measuring rod or tape is not used, 

count the number of heel-to-toe steps across the active channel width, divide by 10, 

and collect samples at each division. For very small streams, collect five samples on 

each of two crossings (i.e., cross once, move upstream 0.5 m, then cross again).

Step 2. DeterminetheFirstSampleLocation and Begin Sampling Particles: Start 

the cross channel transect at one-half the interval length, and then collect all 

subsequent particles at the full interval length. For example, if the width of the 

active channel at the sample location is 5 m, the sample interval is 0.5 m and the 

first sample is collected at 0.25 m from the scour line. All subsequent samples are 

collected at 0.5-m intervals, and the last sample, or particle number 10, should be 

approximately 0.25 m from the scour line on the opposite side of the channel. 

The observer locates the sample interval, places the index finger at that point, and 

without looking at the streambed, reaches into the stream and obtains the first 

particle in the substrate that touches the index finger. Sample the entire streambed 

width at each transect. If pacing, measure to the starting point (i.e., 0.25 m as 

above) with the rod, collect the first sample there, and then pace at approximately 

0.5-m intervals from that location to the other sample points across the channel. 

Step 3. Measure the Diameter of Samples Collected: Place the particle in the 

smallest slot in the template through which it will pass, or if a template is not used, 

measure the middle width (intermediate or “B” axis) of the particle in millimeters. 

Visualize the B axis as the smallest width of a square hole that the particle could 

pass through. A template is an excellent tool for measuring particle sizes and is 

highly recommended to reduce subjectivity in selecting and measuring the B axis. 
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If a small particle falls into the fines category, is touched in between larger particles, 

and the observer is unable to collect it, the particle size can be estimated (i.e., <6 mm).

The Stream Systems Technology Center (1996) provides a complete description 

of the template. The instrument can be purchased from commercial sources for 

approximately $50.00 each.

Sampling with templates (gravelometers) provides for higher accuracy of 

measurements than with rulers because of reduced bias or observer error (Bunte 

and Abt 2001). In addition, measuring particle sizes with a template makes 

sampling quick and more efficient. Openings in the template match the Wentworth 

scale and can be used to estimate the particle-size class or phi based on Krumbein 

and Sloss (1963).

Spacing between particles must be greater than the largest particle within the 

cross section to avoid double sampling of the same particle, which may bias the 

sample or cause serial correlation towards larger particles. If it is not possible to 

obtain 10 particles in one pass, move upstream 0.5 m (or at least a distance to 

avoid the same boulders), and use the sampling interval estimated as the channel 

width divided by the remaining number of particles. More than two passes may be 

required for some small streams.

f. Timing: Substrate is easier to sample when less water is in the stream. The summer 

low-water season is preferred over periods of bankfull or near-bankfull flows. 

To evaluate trends over time, sampling every 3 to 5 years should be applied as a 

minimum. Sampling should also be done after large flow events, when substrates 

undergo the greatest changes.

g.Metrics: The following metrics are used to summarize substrate data:

•	 Percentiles: The computation of the cumulative-frequency distribution is of use with 

particle-size analyses (Bunte and Abt 2001). The result is a plot of the percentage 

cumulative-frequency distribution, showing the percent fines by size class. 

Percentile values can then be used to describe size classes for which x percent is 

finer. The Data Analysis Module uses the D16 (16 percent), D50 (50 percent), and D84

(84 percent) percentiles. The percentile is calculated as follows (Bunte and Abt 2001):

DX = (A2 - A1) ∙ (BX – B1/B2 – B1)+A1
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Where: DX is the diameter of the particle at the desired cumulative-frequency level x,

B1

 and B2 are the cumulative percent frequency just below and above the 

desired cumulative percent frequency x, and

A1 and A2 are the particle sizes that correspond to the cumulative 

frequencies B1

 and B2.

(See Data Analysis Module – “Substr” worksheet.)

•	 PercentFines: In addition to the particle-size percentiles, the Data Analysis Module 

calculates the percent of particles finer than 6 mm. It computes the percentage of 

particles that passed or fit through the smaller slots (the 2-, 2.8-, 4-, and 5.6-mm 

slots) in the template as a proportion of the total sample (see Data Analysis 

Module – “Data Summary” worksheet). 

•	 D16, D50, and D84 ParticleSizes:The particle size that is representative of all 

particles for which 84, 50, or 16 percent of the particles are smaller is presented 

in diameter (mm) and the value on the phi scale (Krumbein and Sloss 1963). The 

phi scale is a log transformation of the particle sizes and is calculated by taking the 

negative log (base 2) of the median particle diameter. This metric is particularly 

useful in assessing particle-size statistics since the Wentworth scale is nonlinear 

(see Data Analysis Module – “Data Summary” worksheet). 

10. Residual Pool Depth and Pool Frequency:

a. Purpose: Two procedures, water width and maximum (thalweg) depth, have been 

removed from the protocol described in Burton et al. (2008) as testing results and 

reviews by experts found such measures, as designed, are of questionable value for 

monitoring trend through time. Both water width and thalweg depth are streamflow 

dependent; therefore, changes may largely reflect stage differences rather than 

management effects. Channel width should be monitored by measuring cross 

sections at the bankfull elevation. This measurement may require a survey of the 

hydraulic geometry of the channel, particularly where bankfull width indicators are 

lacking. Field techniques for surveying the bankfull channel cross section and width 

are contained in Harrelson et al. (1994). Because these techniques require surveying 

equipment and a substantial amount of field time, they are beyond the scope of the 

MIM protocol.
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A more appropriate approach to monitoring the maximum (thalweg) depth or 

structure of the channel is to measure the “thalweg profile” to estimate residual 

pool depth as described by Lisle (1987), with field techniques also summarized in 

Harrelson et al. (1994). This technique measures water depth along the deepest part 

of the channel and calculates the difference in depth between riffle crests (or pool 

tails) and pool bottoms (or maximum depth of pools). That difference is the residual 

pool depth. Residual pool depths can be measured independent of stream discharge, 

which is important in detecting trends.

b. Background: Because pools are vital to the rearing and production of fishes, pool 

depth has been an important component of stream habitat measurements. For 

example, Mossop and Bradford (2006) found a positive correlation between mean 

maximum residual pool depth and the density of Chinook salmon in 16 tributary 

reaches to the Yukon River in Canada. As described previously, livestock grazing 

can result in the breakdown of streambanks and the loss of stabilizing vegetation. 

These impacts can lead to secondary effects within the channel itself. In a review of 

the literature, Powell et al. (2000) concluded that channel characteristics, including 

channel width and depth, as well as bed material were often reported to be affected 

by livestock grazing in riparian areas.

c. Assumptions and Limitations: Field testing found poor repeatability among 

observers when pool structure within the channel is complex. Such conditions appear 

to be common in cobble- and boulder-dominated substrates. In those instances, 

there is scouring that often results in the development of small pocket pools that can 

be missed by some observers. The procedure discussed here appears to work well in 

gravel, gravel/cobble, sand, silt, and clay bottom streams. Also, very small streams, 

or those that are intermittent in streamflow, may not develop good pool structure. 

Observers are cautioned about the use of this indicator in such conditions. 

d. Relationship to Other Indicators: Residual pool depth is related to streambank 

stability and cover as well as GGW and particle-size distribution. As summarized 

by Powell et al. (2000), as the channel margins become less stable, greenline-togreenline or channel width will usually increase. Such an increase will usually be 

associated with a decrease in channel depth. This reduced channel depth is often 

caused by a decrease in the ability of the stream to scour the bed and may also be 

associated with a higher sediment load in the channel.
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e. Procedure: Pools are defined as any depression in the bed of the stream resulting 

in relatively low water velocity and often as relatively flat water surfaces when 

measured at low streamflows (usually mid- to late summer). Such streamflows 

should be well below the bankfull stage.

Distances are measured using a rod, tape measure, or laser rangefinder and depths 

are measured using a measuring rod.

Step 1. Identify the Riffle Crest: Starting at the downstream marker of the DMA, 

proceed upstream and identify the first riffle crest (pool tail). The riffle crest is best 

identified when viewed from downstream and is the upstream end of shallow, 

rippling water where it exits or spills from a pool. To be classified as a pool it must 

be at least as wide as one-half the wetted width of the stream (small pocket pools 

are not counted). The distance from the lower marker in the DMA to the first riffle 

crest is not measured. An effective technique is to have one individual wade in 

advance of the observer to sense the maximum or thalweg depths of the channel.

Step 2. Determine the Thalweg Depth of the Riffle Crest: The observer measures and 

records the thalweg depth. The depth measurement is made in the thalweg or 

deepest part of the channel in the stream cross section.

Step 3.Measurethe Distancefromthe RiffleCrest to Pool Bottomand the Pool

BottomDepth: Proceed upstream into the pool bottom (the deepest point in the 

pool) and record the distance from the riffle crest to the pool bottom. The pool 

should occupy at least one-half of the stream width. The depth of the pool at the 

deepest point is also measured and recorded. Continue measuring and recording 

both the distance between riffle crests and pool bottoms and the depth of each 

at the thalweg until reaching the top of the DMA. When a riffle crest is within the 

DMA and the pool bottom is beyond the upstream DMA marker, measure and 

record the riffle crest depth, the distance to the pool bottom and the pool bottom 

depth of the pool upstream of the upper marker.

f. Timing: The residual pool depth and pool frequency indicators help document 

stream channel recovery over time. Because the recovery process may be relatively 

slow, it is recommended that the procedure be repeated every 3 to 5 years. The 

procedure is relatively easy and requires about one-half hour per DMA.
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g.Metrics: The following metrics are used to summarize residual pool depth and 

frequency data:

•	 PoolFrequency:This is a count of all pools encountered divided by the thalweg 

length of the DMA. This metric is the relative frequency or number of pools per mile 

(see Data Analysis Module – “Data Summary” worksheet).

•	 Mean Residual Pool Depth: Residual depth is calculated as the average of all 

differences between riffle crest depth and pool maximum depth in the survey (see 

Data Analysis Module – “Data Summary” worksheet).

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V. USE THE DATA ENTRY AND ANALYSIS 

MODULES AND THE MIM DATABASE

A. Introduction

A Data Entry Module has been developed for use with personal digital assistants (PDAs) or 

digital field data recorders using a Microsoft Office Excel spreadsheet format. The file can be 

downloaded into Excel on a personal computer (PC) and then converted to Pocket Excel or 

Excel Mobile with the PDA through synchronization or by using a data card, thumb drive, or 

Bluetooth. Instructions are included in the module. Calculations and analyses are limited in this 

module to avoid delays caused by the much-reduced processing speed of handheld computers.

The Data Analysis Module is designed to calculate metrics and summarize raw data for 

interpretation. Data are entered directly or collected from the Data Entry Module using a 

routine that automatically opens the file, copies the entered data, and pastes it into the Data 

Analysis Module. The Data Analysis Module will also format the data for export to a Microsoft 

Access database. Alternatively, it is possible to record data on paper forms and then enter the 

data directly to the Data Analysis Module rather than the Data Entry Module, which is used for 

electronic field entry only. Detailed instructions are contained within the Data Analysis Module.

B. Data Entry Module

This module is used exclusively for entering data electronically from the field. Any hand-held 

computer that supports Microsoft Excel (e.g., Excel Mobile) can be used to enter the data. The 

module must first be uploaded from a PC. Many hand-held computers come with a universal 

serial bus (USB) or serial port that can be connected by cable to the PC. They also come with 

Microsoft ActiveSync software that allows synchronization for copying files between devices. 

If the field unit has Bluetooth capability, it can communicate with Bluetooth-enabled devices 

without cables. Files can usually be transferred between units by enabling Bluetooth on 

both units and making the devices discoverable to each other. Another method of file transfer 

is to use CompactFlash (CF) and/or Secure Digital Input/Output (SDIO) cards if the devices have 

these slots. Check the computer’s user guide for methods of connectivity and file transfer.

The Data Entry Module contains nine worksheets, which are usually accessible on the 

hand-held computer from a drop-down menu. On the PC they are displayed at the bottom 

of the screen as shown in figure 18.

Figure 18. The worksheets in the Data Entry Module.
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70USE THE DATA ENTRY AND ANALYSIS MODULES AND THE MIM DATABASEThe “Instructions” tab leads to information describing the contents and use of each 

worksheet. Data are entered into the “Header,” “DMA,” “Substr,” “Thal,” and “Comments” 

worksheets. An example of a “Header” worksheet is shown in figure 19.

Figure 19. A “Header” worksheet from the Data Entry Module.
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71

An important component of the “Header” worksheet is the DMA selection rationale. This is 

where the interdisciplinary team documents their DMA selection process. The type of DMA 

is indicated and a narrative describes the location of the DMA.

The “Codes” worksheet contains code and class definitions for various indicators in the 

system. The “Plant Lists” worksheet contains a description of the common and scientific 

names and codes for common riparian plants. The “Stats” worksheet contains a description 

of the statistics used to estimate sample size for some of the indicators.

C. Data Analysis Module 

The Data Analysis Module calculates 32 basic metrics in the “Data Summary” worksheet, 

plus 8 additional metrics for individual plant species and woody plant heights in the 

“Graphs” worksheet. The basic metrics are described in previous sections for each indicator. 

The Data Analysis Module is organized much like the Data Entry Module and contains 

worksheets as shown in figure 20. 

Figure 20. The worksheets in the Data Analysis Module.

The main difference between this module and the Data Entry Module is the addition of 

the “Data Summary,” “Export,” “Calcs,” and “Graphs” worksheets. The 32 basic metrics are 

displayed in the “Data Summary” worksheet (figure 21). This worksheet also contains a 

proper functioning condition (PFC) validation worksheet that displays many of the metrics 

relative to PFC checklist items (Prichard et al. 1998). The “Graphs” worksheet (figure 22) 

contains more detailed metric summaries for plant species, including a graph of relative 

species composition. The “Export” worksheet contains two rows of data displaying metric 

summary data used in the MIM Access database and in the appropriate table structure 

for ease of transfer to the database. The “Calcs” worksheet describes how each metric is 

mathematically derived.

Several of these metrics contain condition descriptors such as “good,” “low,” or “medium.” 

Tables for these are contained in the “Calcs” worksheet.
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72USE THE DATA ENTRY AND ANALYSIS MODULES AND THE MIM DATABASE

D. PFC Validation

The MIM protocol can be used to validate proper functioning condition (PFC) assessments 

for lotic riparian-wetland areas (Prichard et al. 1998). The PFC method assesses a much 

broader reach of stream; however, it is a qualitative method for assessing the condition 

of riparian-wetland areas and is not designed to be a long-term monitoring tool. The 

PFC assessment uses hydrology, vegetation, and erosion/deposition (soils) attributes and 

processes to qualitatively assess the condition of riparian-wetland areas. Many of these 

same attributes can be quantitatively measured using the MIM protocol. Procedures for the 

PFC assessment are found in Prichard et al. (1998).

Use the Data Analysis Module to address or validate PFC checklist items and final ratings. 

The “Data Summary” worksheet in that module presents quantitative values for several 

of the checklist items in the PFC assessment. The PFC assessment user guide states that 

Figure 21. An example of a “Data Summary” worksheet.
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73

“there will be times when items from the checklist need to be quantified” and that 

“these quantitative techniques are encouraged in conjunction with the PFC assessment 

for individual calibration, where answers are uncertain, or where experience is limited” 

(Prichard et al. 1998). The PFC validation table in the Data Analysis Module provides a list 

of indicators and their quantitative values for the applicable checklist item(s), along with a 

note describing the indicator’s relevance to the item. 

E. MIM Database

A geodatabase has been developed for the storage of local MIM data. This database is 

designed for individual units and does not represent a national agencywide database. A 

national geodatabase will be developed by the BLM to accommodate MIM data. The local 

database includes the metrics derived from the “Summary” worksheet of the Data Analysis 

Module and the header information, including geographic coordinates for the monitoring 

site. This database can be used to summarize the monitoring data over geographic regions 

through time. It can be used to store data over time and to facilitate condition and trend 

Figure 22. An example table in the “Graphs” worksheet. Frequency-distribution graphs are also displayed here.
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74USE THE DATA ENTRY AND ANALYSIS MODULES AND THE MIM DATABASE analyses and prepare reports. Figure 23 contains a screenshot that generally describes the 

database and its components.

The switchboard on the top of the database provides an easy way to access the two MIM 

data tables. Once open, data from the “Export” worksheet in the Data Analysis Module can 

then be added to the tables. The instructions that accompany the database explain how 

to transfer summary data from the Data Analysis Module to the database. Two reports are 

available: one that summarizes DMA descriptions, including rationale for selecting the DMA 

for each site in the database, and another that presents all metric data for every DMA in 

the system. Access allows users to easily query and sort data and to display it in geographic 

information systems. To obtain a copy of this Access database, contact the riparian resource 

specialist at BLM’s National Operations Center.

Figure 23. MIM database switchboard showing the components of the system, including the 

DMA and metrics tables.
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VI. ANALYZE, INTERPRET, AND 

EVALUATE MONITORING DATA

A. Introduction

Effective monitoring programs address management objectives. These objectives are 

driven by key questions related to determining whether or not progress is being made 

towards meeting the specific, predetermined management objectives. The best way to 

evaluate whether progress is being made towards achieving the management objectives is 

to have a monitoring program that detects change through time. To do that, a reasonable 

level of accuracy and precision in the measurement procedures must be applied.

Field time per site varies from 2 to 4 hours depending upon the indicators chosen for 

monitoring. If the Data Entry Module is used in the field, data transfer to the Data Analysis 

Module with subsequent calculation of metrics can be accomplished in less than 1 hour. If 

data collected in the field is recorded on paper, approximately 2 hours are required to input 

the data and to verify copy accuracy (for a total of 4 to 6 hours per site).

This protocol was tested in the field using a variety of approaches to evaluate its precision 

and accuracy. Test results are presented in appendix F.

1. Precision: Precision denotes agreement between repeat observations taken at the 

same time (within the low-flow season) and at the same place to estimate observer 

variation. There may be broad-scale yearly variations, such as those associated with 

climatic variances (Larsen et al. 2004), but within-season variations are expected to 

be minor in the same year because the indicators are not influenced by streamflow at 

low flow or the late summer to fall season when the protocol should be applied. If an 

unusual streamflow event should occur within the low-flow season, the data should be 

interpreted according to such influences. In this case, a reference reach (reaches) would 

be useful for calibrating the influence of the unusual event. Potential errors associated 

with relocating the DMA reach (Larsen et al. 2004) would be minimized as long as the 

reach is properly monumented. 

Observations may be repeated by the same or different individuals. Differences 

between samples arise from the bias of individual observers. If bias in sampling occurs, 

results may be inaccurate (Elzinga et al. 1998). Precision is important for interpreting 

compliance and trend. If, for example, the stubble height grazing use criterion is 4 inches 
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76ANALYZE, INTERPRET, AND EVALUATE MONITORING DATA

and the precision of the measurement is 0.96 inches, an observation of 3.6 inches would 

not imply that the criterion has been exceeded. With respect to trend, if the objective for 

streambank stability is to achieve 80 percent stability and the precision is 8 percent, an 

85 percent observation does not mean that the objective has been met. A discussion of 

test results on MIM indicators, including estimates of precision, is contained in appendix F.

Another form of precision is the sample size. Larger sample sizes come closer to the true 

mean value for the indicator. A good statistic for this form of precision is the confidence 

interval, calculated from the sample mean and standard deviation. The sample size 

needed to achieve desired levels of precision can be predicted by using the standard 

normal distribution and from field data where the mean and standard deviation are 

known. The equation is described in appendix F.

Electronic data entry may be used to assess sample size levels while collecting field data. 

The MIM protocol uses a Microsoft Excel workbook, the Data Entry Module, designed 

for use with PDAs, which allows computation of the sample sizes needed to predict the 

mean for various levels of confidence and precision (appendix F).

2. Ability to Detect Change at a DMA: The ability to detect change is critical to 

effective monitoring results. In stream and riparian systems, change is a relatively 

frequent natural process. The ability to isolate changes due to management requires 

that the method be reasonably precise. One statistic that has commonly been used to 

evaluate precision is the coefficient of variation (CV), but as Kaufmann et al. (1999) 

point out, CV is strongly criticized as a means of estimating precision. In testing precision 

of the MIM protocol, CV was used to assess differences between repeat samples 

(repeatability). To incorporate site variability into the assessment of precision, replicate 

samples were combined to calculate the mean of all observations, and the confidence 

interval of those combined samples was used to describe range of variability around 

samples drawn from tests of the same DMA. More details, including results of testing, 

are presented in appendix F. 

B. Training 

As with any monitoring protocol, training is required to become proficient in its use. 

Heitke et al. (2008) concluded that training reduced estimates of observer variability in 

streambank alteration. Archer et al. (2004) found that with training observer variability 

could be reduced to less than 20 percent of the total variability for most instream attributes 

evaluated. Testing of the MIM protocol also indicated lower observer variability for most 

indicators after training (see appendix F). 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATIONANALYZE, INTERPRET, AND EVALUATE MONITORING DATA

77

C. Interpretation and Evaluation 

There is exhaustive literature describing the effects of livestock grazing on streams and 

riparian vegetation. A few excellent summaries are contained in Platts (1991), Powell et al. 

(2000), and Wyman et al. (2006). Recognition of cause-and-effect relationships between 

both the short- and long-term indicators is important. Streambank alteration, for example, 

influences streambank stability and potentially substrate size distribution. The pathways of 

effect do not stop there. Loss of streambank stability may result in channel destabilization, 

channel widening, decreased water velocity, increased water temperature, increased 

substrate deposition, increased sediment supply, decreased substrate space, and decreased 

pool depth. 

The MIM protocol has been tested at a number of sites to evaluate livestock grazing effects. 

For the most part, these tests have been conducted where an ungrazed reference site was 

located in proximity to the grazed site. So far, because of the newness of this protocol, 

limited data are available to assess trends, and only short-term trends can be assessed. 

However, the data that are available have been very instructive. The following examples 

are provided to give some indication of the usefulness of these kinds of data.

1. Elk Creek: A comparison of metrics for samples taken at the DMA and within an 

exclosure in 2005 and again at the DMA in 2008 is summarized in table 12. Comparing 

metrics both within and outside the grazing exclosure helped to determine which factors 

were most influenced by livestock use in the area. The best short-term indicator was 

streambank alteration. Woody use and stubble height showed little difference between 

grazed and ungrazed estimates. As expected, streambank stability differences reflect 

the influence of streambank alteration on the long-term indicators. Also as expected, 

Table 12. DMA and Exclosure Metrics for Elk Creek in 2005 and 2008

Year Stubble Height 

(in)

Streambank 

Alteration (%)

Streambank 

Stability (%)

Site Wetland 

Rating

% Hydric 

Vegetation

DMA Exclosure DMA Exclosure DMA Exclosure DMA Exclosure DMA Exclosure

2005 5.1 6.6 40 1 37 85 79 73 72 89

2008 11.2 1 35 61 47

Year GreenlineGreenline-to 

Width

Woody Species 

Use (%)

% Woody 

Composition

% Seedlings/

Young

Winward 

Greenline 

Stability Rating

DMA Exclosure DMA Exclosure DMA Exclosure DMA Exclosure DMA Exclosure

2005 4.7 4.72 59 58.1 5 25 8 97 5.28 6.57

2008 6.27 5 9 0 4.8
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

78ANALYZE, INTERPRET, AND EVALUATE MONITORING DATA

the vegetation indicators do not differ greatly between exclosure and DMA; however, 

the abundance of woody plants, including the regeneration of saplings and young, was 

much greater in the exclosure. Because the unit was rested in 2008, stubble heights 

were much greater and streambank alteration and woody browse were much lower. 

Yet even though it was rested in 2008, long-term indicators for the unit showed mostly 

declines as compared with 2005.

2. Hardtrigger Creek: Monitoring at the Hardtrigger Creek test site annually from 2005 

to 2009 indicated changes due to grazing events in recent years (table 13). In 2005 and 

2006, light grazing use was related to good streambank stability, high wetland rating, 

and riparian vegetation dominated by hydric species. In 2007, a drought year, cattle use 

concentrated along the stream and resulted in much more streambank trampling and 

streambank alteration. Consequently, streambank stability declined, as did the wetland 

vegetation rating and percent hydric vegetation. Although there was some regrowth 

of residual vegetation, as reflected in the stubble height at the end of 2007, bank 

conditions did not recover. Streambank alteration and stability were again measured 

prior to livestock use early in 2008. There had been a partial recovery in streambank 

stability of 16 percent during that interim period of rest. However, livestock use in 2008 

once again altered the streambanks, resulting in a loss of bank stability. Prior to grazing 

in 2009, streambank stability had recovered substantially from 30 percent in 2008 to 

72 percent. These observations suggest the need to assess streambank alteration and 

streambank stability together and to account for annual streambank stability recovery 

to assess the effects of streambank alteration.

Table 13. Test Site Data for Hardtrigger Creek between 2005 and 2009 

(blank cells denotes no data collected)

Year Stubble 

Height

Streambank 

Alteration

Streambank 

Stability

Site

Wetland

Rating

Hydric

Vegetation

2005 8” 13% 81% 80 73%

2006 6” 7% 88% 75 59%

2007 Spring 4” 43% 30% 66 41%

2007 Fall 9” 49% 30%

2008 Before 14% 46%

2008 After 5” 55% 30% 61 48%

2009 Before 15% 72%
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATIONANALYZE, INTERPRET, AND EVALUATE MONITORING DATA

79

D. Trend Assessment

When assessing trends, there are two basic questions that need to be answered: 1) can 

we conclude that significant change has occurred, and 2) can we conclude that there is a 

difference between population A and population B based on samples drawn from those 

two populations? Population A represents data collected at a specific time and population B 

represents data collected at some later time. Answering these questions requires statistical 

testing. Most of the commonly used tests (e.g., the t statistic) are designed for scalar data 

that fit normal or bell-shaped distributions. For some of the metrics, graphs are provided 

in the Data Analysis Module to facilitate a first-level assessment of the shape of the 

distribution. Not all metrics fit a normal probability distribution. Streambank alteration, for 

example, is heavily skewed to the left (most of the time the majority of samples are 0 or 1, 

with few higher values). Also some metrics are categorical or ranked data. An example is 

streambank stability, which is associated with classes. For nonnormal and categorical data, 

nonparametric statistics are required. 

The underlying assumptions associated with parametric statistics are that: 1) the data are 

normally distributed, 2) the data are taken from a random sample of the population, 

3) the observations are spatially and temporally independent, and 4) the errors in the data 

are randomly distributed. There are statistical tests for all of these assumptions; however, 

the MIM protocol is designed to obtain random independent samples from a population 

representing the riparian complex. An excellent summary of statistical tests, including 

a thorough discussion of power (probability of detecting a change) and the level of 

significance (probability that a difference is not real but simply due to chance) is contained 

in MacDonald et al. (1991).

E. Adaptive Management

Williams et al. (2007) defined adaptive management as “a systematic approach for 

improving resource management by learning from management outcomes.” The 

Department of the Interior (Secretary’s Order No. 3270 - superseded 02/01/08 by 

522 DM 1) and the U.S. Forest Service (Forest Service Handbook 2209.13, chapter 90, 

section 92.23b) have adopted policies emphasizing adaptive management in their 

program and procedural guidance. To be consistent with the agency policies would require 

developing specific riparian and stream management objectives, a grazing management 

plan designed to meet those objectives, and short-term and long-term monitoring 

criteria to evaluate success. Annual monitoring of livestock use helps determine if grazing 

management is being implemented as planned. Long-term (trend) monitoring is used to 

determine if resource management objectives are being achieved. 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

80ANALYZE, INTERPRET, AND EVALUATE MONITORING DATAShort-term monitoring of livestock use is generally used in two ways: 1) as trigger 

indicators; and 2) as endpoint indicators. The short-term indicators (e.g., stubble height, 

woody species use, streambank alteration) are monitored to help determine when to 

move the animals to another grazing area. Endpoint indicators of livestock use, also the 

short-term indicators, are monitored after the end of the growing and grazing season to 

determine if the use or disturbance was within prescribed levels or to provide a warning 

that the amount of grazing use or streambank disturbance may prevent the achievement 

of long-term management objectives. Endpoint monitoring data provide information 

necessary to evaluate the effect of grazing on long-term trends in stream and streamside 

riparian habitat conditions.

Annual indicators of use alone do not provide adequate information from which to 

make good decisions (University of Idaho Stubble Height Study Team 2004). Information 

about the short-term indicators of livestock use, combined with long-term indicators of 

condition, is key to identifying cause-and-effect relationships between livestock grazing 

and stream-riparian function. This information is important for making good management 

decisions. Because of site and management complexity, it may not be possible to know in 

advance which indicator(s) best detect management influences on stream and streamside 

riparian vegetation condition. For that reason, using multiple indicators is suggested as a 

more complete and useful approach. Once relationships have been established, it may be 

possible to select the specific indicator(s) that is (are) most effective for detecting change 

at the site or addressing specific management issues or objectives. 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX A: GREENLINE EXAMPLES

81

APPENDIX A: GREENLINE EXAMPLES *

Figure A1. The greenline is the first relatively continuous line of perennial vegetation above the water.

Figure A2. Greenlines follow the relatively continuous line of live perennial vegetation 

with at least 25 percent foliar cover. 

* The dashed line shown in some figures represents the greenline.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

82APPENDIX A: GREENLINE EXAMPLES

Figure A3. The greenline cannot exhibit patches of bare ground, litter, or nonvascular plants exceeding 

10 by 10 cm or 10 percent of the quadrat. Excessive trampling may cause the greenline to move away 

from the stream.

Figure A4. Often the greenline is on the edge of the terrace above the water level.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX A: GREENLINE EXAMPLES

83

Figure A5. The first bench (floodplain) is the first relatively flat area adjacent to the stream with an 

abrupt edge going into the water. It is the active floodplain that is frequently flooded. The second 

bench (terrace) is elevationally higher than the floodplain. The greenline often follows the edge of the 

floodplain or the terrace edge closest to the stream. 

Figure A6. The greenline on the left side of the photo is in two segments, with the lower segment near 

the water’s edge and the upper segment along the edge of the terrace with upland vegetation. The 

greenline on the right side of the stream is continuous along the perennial vegetation located on a bar.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

84APPENDIX A: GREENLINE EXAMPLES

Figure A7. Nonvascular plants such as moss and lichens are not considered part of the greenline.

Figure A8. The greenline must have at least 25 percent cover and there are no patches of bare ground 

larger than 10 percent of the plot. Area “A” is at least 10 percent; therefore, the monitoring frame is 

not on the greenline.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX A: GREENLINE EXAMPLES

85

Figure A9. Frequently the greenline is near the bankfull stage. The greenline is located upslope of 

vegetation closer to the waterline because that vegetation has less than 25 percent foliar cover. 

Figure A10. When mature trees and shrubs are present and there is no understory beneath the canopy, 

the greenline is located by drawing a line connecting the base of the plants on the stream side.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

86APPENDIX A: GREENLINE EXAMPLES

Figure A11. When under a tree or shrub canopy (within the drip line), the greenline is on a line between 

two woody species, tree or shrub.

Figure A12. High waterflow obscures the vegetation, floodplain, and channel characteristics needed to 

obtain an accurate sample. Sampling should be avoided when the greenline is flooded. 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX A: GREENLINE EXAMPLES

87

Figure A13. The greenline is located along the edge of the water line when vegetation is growing in 

shallow water. The inset picture shows spikerush (Eleocharis sp.) growing in shallow water along the 

margin of the stream. Photo - PIBO, U.S. Forest Service.

Figure A14. The greenline follows the sedge (Carex sp.) or the edge of the water if the sedge is in the 

water. Floating plants such as speedwell (Veronica sp.) and watercress (Rorippa nasturtium-aquaticum) 

are not considered part of the greenline. Photo - PIBO, U.S. Forest Service.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

88APPENDIX A: GREENLINE EXAMPLES

Figure A15. Coyote willow (Salix exigua) spreads by rhizomes. At times, it will sprout within the stream 

channel on streams that have very low or no flow during part of the growing season. When this occurs, 

the greenline is along the edge of the water . When plants occupy the entire channel, the greenline is 

down the thalweg of the channel. Measure streambank alteration at the edge of the first bench or, when 

a bench is not evident, at the waterline. Photo - PIBO, U.S. Forest Service. 

Figure A16. Exposed live shrub or tree roots are part of the greenline. 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX A: GREENLINE EXAMPLES

89

Figure A17. Watercress (Rorippa nasturtium-aquaticum) is not considered part of the greenline. It should 

be noted in the remarks section of the data worksheet in the Data Entry Module.

Figure A18. The greenline follows the sedge (Carex sp.) on each side of the stream. Water speedwell 

(Veronica anagallis-aquatica) growing in the stream is not part of the greenline. Photo - PIBO, U.S. 

Forest Service.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

90APPENDIX A: GREENLINE EXAMPLES

Figure A19. Brookgrass (Catabrosa aquatica) is a short-lived perennial grass that 

occasionally grows on the streambank. It grows mostly in the margin of a stream. It is not 

considered part of the greenline unless rooted above the scour line on the streambank.

Figure A20. Rock “A” is embedded and above the scour line. Active erosion exists on the streambank side 

of rock “B,” and it is below the scour line. 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX A: GREENLINE EXAMPLES

91

Figure A21. This complex greenline includes vegetation, embedded rock, and anchored wood. Segment 

“A” is embedded rock and segment “B” is anchored wood. Both rock and wood are above the scour line.

Figure A22. When a logjam that crosses the stream is encountered, the greenline continues over the 

logjam and is recorded as anchored wood. Photo - PIBO, U.S. Forest Service.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

92APPENDIX A: GREENLINE EXAMPLES

Figure A23. Slump blocks along the far streambank are not attached by vegetation to the terrace wall 

and therefore are not part of the greenline. Photo - PIBO, U.S. Forest Service.

Figure A24. The slump block is detached from the bank wall and there is an obvious fracture between 

the slump block and the streambank wall. The greenline is located on the streambank as shown.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX A: GREENLINE EXAMPLES

93

Figure A25. The greenline follows the continuous line of vegetation. Note the blocks that have fallen into 

the stream channel and the blocks that are broken from the bank but have not fallen into the stream. The 

greenline is located behind the broken bank.

Figure A26. The greenline follows the outer streambank greenline. It does not cross the small channel 

running along the left of the island. Photo - PIBO, U.S. Forest Service.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

94APPENDIX A: GREENLINE EXAMPLES

Figure A27. The vegetation patches (“A”) are considered islands because the scoured channels (“B”) do 

not have perennial vegetation growing across them.

Figure A28. Vegetation is not well established between the slump block and the vertical bank, creating a 

scour line between the vegetation block and the streambank. This block is considered an island.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX A: GREENLINE EXAMPLES

95

Figure A29. When no greenline is present within 6 meters of the water’s edge, record “NG.” 

MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX B: RECORDING FIELD DATA

97

APPENDIX B: RECORDING FIELD DATA

Multiple Indicator Monitoring Field Data Sheet—Part 1, Site Information

Allotment Forest/District Ranger District/Field Office Observers Date

DMA ID Stream Name DMA Name and/or Description

Woody Species

1. Are woody plants supposed to be present at this 

site? (Y/N)

2. Are there any hydric woody plants present? (Y/N)

3. Are all age classes of hydric woody plants present? 

(Y/N)

Comments:

DMA Site Selection Criteria

1. Was the riparian complex selected by an 

interdisciplinary team? (Y, N, or N/A)

2. Is the DMA in a riparian complex that represents 

management activity and is accessible to the activity? 

(Y, N, or N/A)

3. Is the DMA randomly located in the riparian complex 

most sensitive to management? (Y, N, or N/A)

4. Is the DMA sensitive to disturbance (not armored)? 

(Y, N, or N/A)

5. Will the DMA site respond to management? 

(Y, N, or N/A)

6. If the stream is over 4 percent gradient, does it have a 

well-developed floodplain? (Y, N, or N/A)

7. Is the DMA located outside of a livestock 

concentration area? (Y, N, or N/A)

8. Is the DMA free from the influence of compounding 

activities? (Y, N, or N/A)

Is it a critical DMA? (Y or N) Is it a reference DMA? (Y or N)

Narrative:

Downstream Marker Upstream Marker Reference Marker

Latitude Longitude Latitude Longitude Latitude Longitude

Zone UTM UTM UTM

Step

Interval

Step Length 

(m)

Sample

Interval

Stream 

Gradient

Substrate Stubble Height Recorded in 

(I) inches or (C) centimeters

Plant Region Subwatershed 

(6th Field HUC)

Photo

Log

File name

Lower Across Lower Upstream Upper Across Upper Downstream
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

98APPENDIX B: RECORDING FIELD DATA

Plot No.

Greenline 

Composition

Woody Species Height Class (1 to 6 or blank)

Streambank Stubble 

Height

GGW (0.1 m)

Woody Species Age Class Woody 

Species Use

Species/Rock/ Wood

Percent

Alteration (0 to 5)

Stability

Species

Height (cm/in) 

Species

Seedling (no.)

Young (no.)

Mature (no.)

Rhizome (1/blank)

Species

Use (midpoint)

Erosional/Depositional (E/D)

Covered/Uncovered (C/U)

Stability (F, S, SL, E, A)*

Multiple Indicator Monitoring Field Data Sheet—Part 2 Page ____ of _____

DMA: Allotment: Pasture: Stream: Date:

*F-fracture, S-slump, SL-slough, E-eroding, or A-absent

Note: Usually eight Field Data Sheets, Part 2, are needed for each monitoring site.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX B: RECORDING FIELD DATA

99

Multiple Indicator Monitoring Field Data Sheet—Part 3, Substrate

DMA: Allotment: Pasture:

Stream: Date: Used Gravelometer (Y or N)?

Plot 

No.

Pebble (mm) Notes

1 2 3 4 5 6 7 8 9 10

2

4

6

8

10

12

14

16

18

20

22

24

26

28

30

32

34

36

38

40

42

44

46

48

50

52

54

56

58

60
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

100APPENDIX B: RECORDING FIELD DATAMultiple Indicator Monitoring Field Data Sheet—Part 4, Residual Pool Depth and Pool Frequency

DMA: Allotment: Pasture:

Stream: Date:

Distance between 

riffle crest and 

pool bottom

Depth of riffle 

crest or pool 

bottom

Riffle crest (R) or 

pool bottom (P)

Distance between 

riffle crest and 

pool bottom

Depth of riffle 

crest or pool 

bottom

Riffle crest (R) or 

pool bottom (P)

R R

P P

R R

P P

R R

P P

R R

P P

R R

P P

R R

P P

R R

P P

R R

P P

R R

P P

R R

P P

R R

P P

R R

P P

R R

P P

R R

P P

R R

P P

R R

P P
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX B: RECORDING FIELD DATA

101

Multiple Indicator Monitoring Field Data Sheet—Part 5, Comments

DMA: Allotment:

Pasture: Stream: Date:

Plot No. Narrative

MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX C: EQUIPMENT

103

APPENDIX C: EQUIPMENT

The following equipment is needed to complete the MIM protocol:

1. Waders or wading shoes are useful. It is easier to monitor most streams by pacing in the 

stream rather than on the streambank.

2. Laser rangefinder, measuring rod, or tape measure (metric preferred). 

3. Handheld computer (PDA) with the Microsoft Excel spreadsheet from the Data Entry 

Module uploaded. Extra batteries or extended life batteries are required.

4. MIM Field Data Sheets (if not using field computer or data recorder).

5. Global positioning system (GPS) receiver with extra batteries.

6. Appropriate plant identification keys for riparian plants.

7. MIM monitoring frame is shown in figure C1. Construct the monitoring frame prior 

to going to the field. It can be constructed from the materials in table C1 and the 

instructions that follow.

50 cm

20 cm

2 cm

20 cm

10 cm 12.5 cm

C

E

D

B

B

A

B

B

A

Figure C1. Monitoring frame.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

104APPENDIX C: EQUIPMENTMonitoring frames may be constructed of various materials including metal (usually 

aluminum) or 1/2-inch PVC schedule 40 plastic pipe. Metal frequency plot frames (typically 

40 by 40 cm) may be used by extending the tines to 50 cm in length and marking the four 

incremental segments with lines or alternating colors. 

Schedule 40 PVC is rigid and does not warp as much as lighter pipe. This material is 

inexpensive, light, and easy to use to make the frames. Carefully measure each of 

the products before they are glued together as fittings (tees) are not uniform among 

manufacturers. To construct a monitoring frame using 1/2-inch PVC pipe: 

a.Cut pipe to the appropriate lengths (see table C1).

b. Apply PVC cement to one end of pipe part B and the tee (part A) and slide them 

together. Repeat the procedure on the opposite end of the tee. Repeat the process on 

the second tee (part A). Remember PVC cement cures rapidly (within a few seconds). 

There are no second chances.

c. Apply cement to the short pipe (part E) and the tee of one of the previously constructed 

parts (see b). Slide them together.

d. Apply cement to the tee (part C) and the end of part E. Slide the two parts together, 

making sure the tee is perpendicular to part A so that the handle can be used properly.

Table C1. Monitoring Frame Material List

Item Part 

Label

Number Length*

Inches Centimeters

1⁄2-inch tee (3 slip joints) A 2 -- --

PVC pipe (Schedule 40) B 4 7.75 19.7

1⁄2-inch tee (2 slip joints and 1 treaded female joint) C 1 -- --

PVC pipe (Schedule 40) D 1 16 .9 43

PVC pipe (Schedule 40) E 1 1.25 3.2

PVC pipe (handle) F 1 39 100

1⁄2-inch threaded coupler (male) G 1 -- --

PVC cement -- -- -- --

Colored electrical tape -- -- -- --

Teflon tape -- -- -- --

*Cut the pipe into the lengths listed as the tees will add the required length.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX C: EQUIPMENT

105

e.The center pipe (part D) may or may not be glued into place between the previously 

constructed parts. If the center pipe is glued, make sure the two ends are level. Not 

gluing the center pipe allows the frame to be taken apart and transported. On the other 

hand, it may come apart occasionally when being used.

f. Construct the handle (figure C2) by cementing the male 

threaded connector (part G) to one end of the pipe (part F).

 Put Teflon tape on the threads prior to screwing the parts 

together, which makes it much easier to remove the 

handle when needed.

g.Screw the handle into the frame and mark the handle 

in 1-in (or 2-cm) increments beginning at ground level. 

Proceed up the handle for 1 m. Cut off excess material. 

h.The markings on the frame provide references for 

observers to project lines and estimate the amount of 

vegetation in the quadrat. Electrical tape wrapped 

around the pipe is a good material for marking the 

alternating colors. Tape does not come off the pipe as 

easily as paint does.

1 m

F

G

Figure C2. Monitoring 

frame handle.

MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX D: STREAMBANK ALTERATION EXAMPLES

107

APPENDIX D: STREAMBANK 

ALTERATION EXAMPLES *

Figure D1. Disturbance is considered trampling when a track caused by a large herbivore exposes at least 

13 mm (1/2 inch) of bare soil.

Figure D2. The monitoring frame is centered on the greenline and the number of lines (0 to 5) that 

intersect streambank alteration (trampling or shearing) is counted and recorded. Lines 1, 2, 3, 4, and 5 

intersect streambank alteration. A “5” is recorded. 

* The dashed line shown in some figures represents the greenline.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

108APPENDIX D: STREAMBANK ALTERATION EXAMPLES

Figure D3. When livestock trails occur within the DMA, they are considered for streambank alteration. 

The example above shows the frame is moved perpendicular to the stream until the rules for greenline 

have been met. Part of the frame (below the terrace) is over the trail that has been recently used by 

livestock so a “5” is recorded.

Figure D4. This example is heavily trampled and all five lines intersect streambank alteration. 

A “5” is recorded.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX D: STREAMBANK ALTERATION EXAMPLES

109

Figure D5. Trampling on the terrace is not recorded as streambank alteration. Alteration is only recorded 

if it occurs on the steep face of the bank. The lines are projected for the greenline down the bank, within 

the monitoring frame. In the example above, line 1, nearest the handle, does not intersect alteration. 

Lines 2 through 5 intersect shears so a “4” is recorded. 

Figure D6. Lines 1, 2, and 4 intersect streambank alteration . A “3” is recorded.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

110APPENDIX D: STREAMBANK ALTERATION EXAMPLES

Figure D7. Livestock have caused alteration at lines 1 and 2. A “2” is recorded.

Figure D8. No streambank alteration is obvious. A zero is recorded.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX E: STREAMBANK STABILIT Y AND COVER EXAMPLES

111

APPENDIX E: STREAMBANK STABILITY 

AND COVER EXAMPLES

Figure E1. The first bench is the first relatively flat area above the scour line or edge of the water. An 

abrupt steep face from the edge of the bench to the scour line is characteristic of a “cut bank.” Slough from 

the bank wall has direct access to the stream. Vegetation in the channels forms islands.

Figure E2. A new floodplain has developed creating the first bench at a lower elevation. Slough from the 

second bench does not go directly into the stream, as it is filtered by the first bench or new floodplain.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

112APPENDIX E: STREAMBANK STABILIT Y AND COVER EXAMPLES Figure E3. Erosional features help determine the stability of a streambank. Slump blocks that 

are detached from the streambank and now isolated to the channel are not considered part of the 

streambank. Slumps must be obviously sliding down but still attached as part of the streambank. 

Fractures must be obvious at the top of the streambank or on the bench. 

Figure E4. This large fracture has at least 50 percent vegetation cover and is thus 

recorded as erosional (E), covered (C), and fracture (F). 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX E: STREAMBANK STABILIT Y AND COVER EXAMPLES

113

Figure E5. The stream in this photo is flowing at the scour line. The streambank is erosional (E) and 

slump (S) is recorded.

Figure E6. The dashed line represents the scour line. “A” is a point bar; therefore, it is depositional (D) and 

covered (C). “B” shows the length of streambank evaluated. The streambank evaluated is erosional (E), 

uncovered (U), and eroding (E) or slough (SL). Photo - PIBO, U.S. Forest Service.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

114APPENDIX E: STREAMBANK STABILIT Y AND COVER EXAMPLES

Figure E7. The left streambank is a point bar that is depositional (D) and covered (C). Slump blocks on 

the right bank are mostly covered with vegetation and are still attached to the streambank above the 

scour line and therefore are recorded as erosional (E), covered (C), and slump (S). The dashed line is the 

greenline. Photo - PIBO, U.S. Forest Service.

Figure E8. Slump blocks and slumping banks covered with vegetation are recorded as erosional (E), 

covered (C), and slump (S). The dashed line is the greenline.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX E: STREAMBANK STABILIT Y AND COVER EXAMPLES

115

Figure E9. Four different conditions are shown at this location. The left side is an outside bend that is 

erosional (E), with no vegetation, rock, or wood cover (U), and slough (SL) directly entering the stream. 

The upper middle section is an erosional (E) streambank, uncovered (U) with slump (S). The upper right 

section is a streambank that has slumped in the past and is reattached, so it is recorded as erosional (E), 

covered (C), erosional activity absent (A). The lower right streambank is a point bar that is depositional 

(D) and covered (C). 

Figure E10. The dashed line represents the scour line. The streambank on the left side of the stream is an 

erosional bank (E) that is uncovered (U) and eroding (E), and the other side is depositional (D) and covered (C).
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

116APPENDIX E: STREAMBANK STABILIT Y AND COVER EXAMPLES Figure E11. The streambank has an obvious scour line. The bank evaluated is above the scour line to the 

first bench and is recorded as erosional (E), uncovered (U), and eroding (E) or slough (SL).

Figure E12. The streambank is erosional (E) and not covered with vegetation, rock, or wood. It has a 

bank angle of more than 10 degrees (22 percent) from vertical with no bench to capture the sediment, 

and thus the sediment enters directly into the stream as slough; therefore, it is recorded as uncovered (U) 

and slough (SL). 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX E: STREAMBANK STABILIT Y AND COVER EXAMPLES

117

Figure E13. Trampling by large herbivores has caused obvious “slumping.” The slump is greater than 

one-fourth of the plot length and is recorded (S).

Figure E14. There is a hoofprint in the bank, but no slump or slough is associated with the hoofprint; 

therefore, there is no indicator of instability so covered (C) and absent (A) are recorded. 

MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX F: TESTING PRECISION AND ACCURAC Y AND DETECTING CHANGE

119

APPENDIX F: TESTING PRECISION AND 

ACCURACY AND DETECTING CHANGE

To be useful and effective, results of monitoring should reflect a reasonable degree of 

precision and accuracy and be responsive to factors that affect the ability to detect change 

for each monitoring indicator. Precision is the closeness of repeated measurements of the 

same indicator, and accuracy is the closeness of a measurement to its true value (Elzinga 

et al. 1998). The ability to detect change depends to a large degree on the accuracy and 

precision of the measurement protocol. Studies of stream-habitat-related monitoring 

variables have shown that indicators that rely upon visual estimates, while attractive 

because of their rapidity, have low precision (Kaufmann et al. 1999). Precision is related 

to sample size, natural variability in the parameter, level of statistical significance, 

statistical power, and the level of observer bias in the method itself (MacDonald et al. 

1991). Because estimates of trend are made at each individual DMA, the principal sources 

of variability in sampling indicators are associated with measurement error (differences 

between repeat observations) and spatial variability within the DMA. If resampling is not 

always done at the low streamflow time of year, additional sources of temporal variability 

may be introduced. The MIM protocol has been designed so as to limit dependency upon 

streamflow. Still, some short-term climatic influences, such as a sudden cloudburst during 

the low-flow season, may introduce variation that must be accounted for. A reference 

reach subject to the same kinds of temporal variations is desirable in the latter case. The 

following describes how precision and accuracy were evaluated in testing this protocol. 

Precision: Imprecision, or the difference between repeat samples, arises from the bias of 

individual observers. It is critical to minimize the subjectivity associated with monitoring 

to reduce observer bias and to maximize precision. In a study of the precision of stream 

habitat measurements, Kaufmann et al. (1999) concluded:

“Measurements are more precise than visual estimates, but carefully-designed 

visual estimation procedures can be nearly as precise as measurements. To enhance 

precision, these visual observations are limited to measurable characteristics (e.g., 

cover or presence), rather than judgements of habitat quality, and they are made at 

multiple locations within a reach.”

Every effort has been made in the MIM protocol to ensure that visual observations are 

measurable. If a substantial amount of bias in sampling occurs, results may be imprecise 

and not useful for detecting change and/or compliance with specific targets for the 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

120APPENDIX F: TESTING PRECISION AND ACCURAC Y AND DETECTING CHANGEindicator (Elzinga et al. 1998). With respect to compliance, for example, if the stubble 

height allowable use criterion is 4 inches and the precision of the measurement is 

0.96 inches, an observation of 3.6 inches would not imply that the criterion was exceeded. 

With respect to trend, if the objective for streambank stability is to achieve 80 percent 

streambank stability and the precision is 8 percent, an observation of 85 percent does not 

mean that the objective has been met.

The coefficient of variation, a dimensionless index of variability between and among 

observers’ repeated samples, has been used to estimate precision (Kauffman et al. 1999, 

Coles-Ritchie et al. 2004, Heitke et al. 2008, and Roper et al. 2002). The CV is calculated as 

follows:

CV = sq root (crew variance)/mean*100

Where:

CV = the coefficient of variation

Crew Variance = variance on repeat samples

Mean = the mean value of the repeat samples

The CV may be expressed as a percentage and represents a proportion of the mean. If the 

square root of crew variance (standard deviation) is less than 20 percent of the mean 

(CV < 20), then by comparison, a CV of 30 would be less precise. For purposes of these 

tests, CV values greater than 20 and less than 33 are considered moderately precise, and 

values less than 20 are considered precise (Kaufmann et al. 1999). Kaufmann et al. (1999) 

were interested in detecting change by pooling data across many streams in a region. With 

the MIM protocol, the observer is more concerned with detecting change at a single site. 

For this reason, CV was examined site-by-site and not pooled regionally.

Values of CV less than 10 may be required to detect change for variables that may change 

slowly through time (vegetation erosion resistance, for example). Conversely, substrate 

values of CV less than 25 may be adequate for detecting change in variables more 

responsive to management, such as greenline-to-greenline width or percent streambank 

stability (Archer et al. 2004). 

Three different approaches have been used to assess the precision of MIM metrics. In the 

first approach, several test sites were established in Idaho and monitored over the years 

as part of the development of the MIM protocol. Repeat tests between and among the 

observers were used to evaluate precision at a limited number of sites (5). In the second 

approach, a much larger sample (30 sites) was obtained involving participants in a number 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX F: TESTING PRECISION AND ACCURAC Y AND DETECTING CHANGE

121

of regional training sessions at many locations in the Western United States over several 

years. Observers were instructed to repeat observations immediately after obtaining their 

first sample set to obtain a replicate using the same observers. The instructors would also 

sample the same reach to evaluate replication by different observers. It has been suggested 

that this approach may be biased by the fact that observers made the estimates at the 

same time they were learning the protocol. Such bias may result in better agreement, 

due to the immediacy of the training, or worse agreement, due to lack of experience with 

the rule sets. Also, this approach does not account for “revisit variance” across the sample 

season, which accounts for differences that may result from natural changes during that 

time period. In particular, streambank alteration and stability may change between the 

time grazing ends and the end of fall or onset of winter. Vegetation variables would not be 

expected to change dramatically during the sample season; however, the ability to identify 

plants could vary depending upon the presence of reproductive structures.

To further address revisit variance, in the third approach, a more controlled experiment 

was established to evaluate the variability among trained observers, consisting of three 

separate teams that visited eight monumented sites (PIBO sites) at different times in the 

same sampling season (late summer). Teams visited the sites at varying times within a 

2-month period during the low-flow season and independently relocated the sites. The 

advantage of these tests is that opportunities for assessing variation due to site revisits 

and within-seasonal variations are better. Results of these tests are presented in table F1. 

Differences among observers were often greatest at the authors’ test sites where indicators 

were being tested early in the development of the protocols and prior to refinements that 

included more detailed procedures. Training sites also tended to have higher differences 

as compared to the more controlled repeat sites likely because observers were not as 

proficient in the procedures and other details of the protocols.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

122APPENDIX F: TESTING PRECISION AND ACCURAC Y AND DETECTING CHANGE

Precision was also evaluated within and among observers to determine if same observer 

replicates would be less biased than those of different observers. Table F2 summarizes the 

coefficients of variation for same and different observers. 

Table F1. Mean Difference Among Observers at Authors’ Test Sites, at Training Sites, and at the PIBO 

Repeat Sites

Metric Test 

Sites (5)

Training 

Sites (30)

PIBO 

Repeat 

Sites (8)

All 

Sites

Average Stubble Height for All Species (in) 0.75 0.88 na 0.86

Streambank Alteration (%) 10.12 6.21 na 6.76

Woody Use All Species (%) 24.49 5.05 na 8.00

Streambank Stability (%) 8.82 8.16 5.61 8.23

Streambank Cover (%) 10.24 8.29 5.43 8.51

Percent Young 14.27 14.47 9.96 14.44

Percent Mature 15.00 14.18 9.44 14.30

Hydric Plants (%) 10.26 6.22 8.84 6.66

Winward Stability Rating (1-10) 0.97 0.42 0.50 0.48

Greenline Ecological Status (1-100) 14.20 10.51 6.93 10.93

Site Wetland Rating (1-100) 5.46 3.76 4.09 3.94

Greenline-to-Greenline 

Width (m) 

0.26 0.46 0.62 0.43

Woody Composition (%) 4.37 8.95 10.51 6.09

Hydric Herbaceous (%) 11.55 8.00 4.58 9.97

Average Height of Dominant Key Species 

(in)

1.20 1.47 na 1.44

Percent Fines 2.52 5.09 na 4.72

Median Particle Size (phi) 0.11 na na 0.11

Pool Frequency (pools per mile) na 22 na 22 

Mean Residual Pool Depth (m) na 0.01 na 0.01

* na: not available–data not collected.

*
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX F: TESTING PRECISION AND ACCURAC Y AND DETECTING CHANGE

123

Table F2. Percent Agreement and Coefficients of Variation (square root of crew variance divided by the 

mean times 100) for Repeat Sampling (observer agreement/variation)

Metric Agreement for 

categorical 

variables

CV 

same observers (30)

CV 

different observers (33)

Average Stubble Height for 

All Species (in)

10% 15%

Streambank Alteration (%) 18% 27%

Woody Use All Species (%) 23% 52%

Streambank Stability (%) 81%

Streambank Cover (%) 85%

Percent Seedlings + Young 20% 32%

Percent Mature 32%

Hydric Plants (%) 82%

Winward Stability Rating (1-10) 88%

Greenline Ecological Status (1-100) 74%

Site Wetland Rating (1-100) 77%

Greenline-to-Greenline Width (m) 6% 8%

Woody Composition (%) 79%

Hydric Herbaceous (%) 17% 38%

Average Height of Dominant Key Species (in) 17% 17% 23%

Percent Fines 7% 6%

Median Particle Size (phi) 5% 23%

Pool Frequency (pools per mile) 13%

Mean Residual Pool Depth (m) 13%

For categorical variables, agreement matrices were used to estimate observer agreement 

and differences by comparing rating results among repeat tests of the same DMAs. Table F3 

describes the results of that analysis for ecological status.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

124APPENDIX F: TESTING PRECISION AND ACCURAC Y AND DETECTING CHANGE Some variables are measured or estimated at each sample point, including stubble height, 

streambank alteration, greenline-to-greenline width, woody use, and substrate particle 

sizes. At the replicate test sites, monitoring indicators were recorded for the reach and 

revisits or resamples again recorded the same indicators. Because individual sample 

locations depend on the randomly selected starting point, revisit samples were likely not 

located at the same plot location. The combined standard deviations for all replications 

in a reach were then analyzed for sample standard deviation and 95 percent confidence 

intervals as presented in tables F4 and F5. By combining both site and revisit variability, 

these confidence intervals, or ranges of variation, are used to estimate the level of change 

that can be detected over time. 

Table F3. Agreement Matrix for Ecological Status (units within each cell represent the rating comparison 

from the test samples; e.g., 16 repeat tests agreed with an early ecological status rating)

Agreement Matrix - Ecological Status

Very Early Early Mid Late PNC

Very Early 7 1 0 0 0

Early 16 10 2 0

Mid 16 5 0

Late 14 1

PNC 1

# Tests: 73

Agreement: 74%

Table F4. Sample Size (N), Standard Deviation (SD), and 95 Percent Confidence Intervals (CI) for 

Substrate Particle Sizes from the Raw Data and Replicates

Stream Reach Substrate Particle Size (phi Φ)

N SD CI

Hardtrigger, Idaho, 08 400 2.4 0.24

Blanchard, Montana, 08 390 1.5 0.17

Fawn, Idaho, 08 400 1.7 0.17

Willow, Idaho, 07 600 2.1 0.17

Mean 448 1.9 0.2

Median 400 1.9 0.2
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX F: TESTING PRECISION AND ACCURAC Y AND DETECTING CHANGE

125

Table F5. Sample Size (N), Standard Deviation (SD), and 95 Percent Confidence Intervals (CI) for Four Greenline 

Metrics Using All Samples and Replicates

Stream Reach Stubble Height 

(inches)

Greenline-to-Greenline Width (m)Streambank 

Alteration (%)

Woody Species Use 

(%)

N SD CI N SD CI N SD CI N SD CI

Hardtrigger 08 74 2.3 0.53 135 0.85 0.1 125 33.8 5.9 32 28.1 9.7

Long Tom 1 46 3.4 0.99 81 0.94 0.2 80 36.8 8.1 21 1.1 0.5

Long Tom 2 41 2.5 0.76 95 0.87 0.2 103 30.3 5.8 122 27.5 4.9

Long Tom 5 58 1.4 0.37 89 2.0 0.4 89 23.4 4.9 82 17.1 3.7

Long Tom 6 70 2.8 0.66 87 0.97 0.2 87 31.4 6.6 262 28.2 3.4

NFHUM 1 80 1.17 0.3 80 40.7 8.9 32 21.1 7.3

NFHUM 2 25 5.1 2.01 74 2.25 0.5 80 26.2 5.7

NFHUM 4 68 1.4 0.34 80 1.39 0.3 81 33.7 7.3 26 32.7 12.6

Big Elk 107 3.2 0.61 130 1.34 0.2 132 35.3 6.0 30 29.1 10.4

Deadwood 250 10.1 1.3 150 2.3 0.4

Falls 229 1.34 0.2

Fawn 269 1.51 0.2

Morse 238 6.91 0.9

Panther 245 0.71 0.1

Pass 245 0.79 0.1

Pine 214 1.57 0.2

Hardtrigger 06 139 3.14 0.52 257 0.72 0.1 254 16.8 2.1 72 2.4 0.5

Marks RF1 79 1.5 0.33 43 1.76 0.5 81 2.2 0.5 15 12.9 6.5

Marks RF2 61 9.05 2.27 69 0.93 0.2 69 2.4 0.6 30 3.7 1.3

Marks 1 33 5.73 1.95 43 1.25 0.4 45 32.2 9.4 7 7.6 5.6

Marks 9 78 9.21 2.04 86 0.96 0.2 85 9.5 2.0 41 21.8 6.7

Marks 10 60 5.98 1.51 76 1.08 0.2 44 1.5 1.2

Marks 11 46 2.54 0.74 69 1.51 0.4 69 18.8 4.4 8 19.8 13.7

Marks 12 66 0.79 0.19 30 1.11 0.4 27 19.9 7.5 15 30.6 15.5

Haynes 1 50 1.14 0.3 49 25.9 7.3 7 9.8 7.2

Haynes 2 44 3.6 1.07 56 1.01 0.3 55 33.9 9.0 27 25.8 9.7

Haynes 3 48 1.14 0.3 47 27.3 7.8 46 28.1 8.1

Haynes 4 37 1.45 0.5 36 35.2 11.5 52 19.3 5.2

Haynes 5 17 2.7 1.3 55 0.84 0.2 55 30.9 8.2 37 36.6 11.8

Haynes 6 20 1.3 0.59 61 1.63 0.4 57 38.0 9.9 29 14.3 5.2

Shoshone 1 46 3.0 0.86 76 1.77 0.4 78 24.8 5.5 78 0.6 0.1

Shoshone 2 67 0.91 0.2 69 20.9 4.9 48 4.0 1.1

Shoshone 3 24 3.0 1.21 108 2.25 0.4 108 21.2 4.0 62 6.0 1.5

Shoshone 4 41 2.4 0.73 74 2.05 0.5 75 26.3 6.0
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

126APPENDIX F: TESTING PRECISION AND ACCURAC Y AND DETECTING CHANGE Sample Size Analysis: Another indicator of precision is often called the “bias” in statistics. 

Bias is the difference between the population mean of a measured indicator and the mean 

of a subsample of the population. This source of error is strongly influenced by the size 

of the sample. The more samples in the subsample, the closer the mean would be to the 

whole population mean. Thus larger samples come closer to the true mean value for the 

indicator. They produce a lower standard error or standard deviation from the mean. The 

larger a sample, the easier it is to detect a difference between sample means drawn from 

separate populations. A good statistic describing this effect is the confidence interval. It 

represents the range of values within which there is 95 percent certainty that the true 

value for the whole population lies. The confidence interval is calculated as a range from 

the sample mean, based on the standard deviation and the standard normal distribution. 

It can be described as follows:

CI = Ŷ +/- Z∞(σ/√N)

Where:

CI = confidence interval

Ŷ = the sample mean 

Z∞ = the upper critical value of the standard normal distribution, which is found in 

the table of the standard normal distribution

σ = the standard deviation

N = the sample size 

Table F5. Sample Size (N), Standard Deviation (SD), and 95 Percent Confidence Intervals (CI) for Four Greenline 

Metrics Using All Samples and Replicates continued.

Stream Reach Stubble Height 

(inches)

Greenline-to-Greenline Width (m)Bank Alteration (%) Woody Use (%)

N SD CI N SD CI N SD CI N SD CI

Shoshone 5 52 3.4 0.92 55 2.2 0.6 55 32.1 8.5

Shoshone 6 29 2.2 0.8 67 2.74 0.7 70 37.2 8.7 38 17.6 5.6

Shoshone 7 70 0.99 0.2 69 17.9 4.2 36 5.6 1.8

Shoshone 8 36 2.1 0.68 84 0.94 0.2 84 21.3 4.6 46 11.0 3.2

Shoshone 10 20 2.1 0.92 110 1.25 0.2 124 21.6 3.8 54 10.4 2.8

Range 0.2-

2.3

0.1-

1.3

0.5-

11.5

0.1-

15.5

Mean 0.96 0.3 6.0 5.7

Median 0.78 0.3 5.9 5.2
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APPENDIX F: TESTING PRECISION AND ACCURAC Y AND DETECTING CHANGE

127

Note that as sample size increases, the confidence interval decreases. In other words, 

the interval is closer to the mean. There are two components in the confidence interval: 

the confidence interval width (distance from the mean) and the confidence level 

(e.g., 90 percent, 95 percent, etc.).

Using this statistic, a target sample size can be estimated by solving for N at a preselected 

confidence interval width and confidence level and using field data collected at the DMA of 

interest. This produces the following equation:

N = (Z∞ )

2

(σ)2/(β)2

Where:

β = the desired precision level expressed as half of the 

maximum acceptable confidence interval width

This equation is provided in the Data Entry Module, giving the user the option of 

estimating sample size adequacy while collecting data in the field. The default precision 

levels provided in the module are based on confidence interval widths from field tests for 

the indicators at a 90 to 95 percent confidence level.

Results using this equation for estimating adequate sample sizes are summarized for 

several test sites in table F6.

Note that sample size adequacy varies considerably by stream. These differences reflect 

the unique characteristics of diversity associated with each site and emphasize the need to 

evaluate sample size at each site and to adjust the minimum number of samples collected 

accordingly.

Some metrics do not fit a normal probability distribution as needed to apply the standard 

normal coefficient in the equations above. Streambank alteration, for example, is heavily 

skewed to the left at most sites. Samples usually contain no or just one alteration, with two 

or more alterations less common.

Electronic data entry may be used to assess sample size levels while collecting field data. 

The MIM protocol uses a Microsoft Excel workbook, the Data Entry Module, which is 

designed for use with PDAs and that allows computation of sample sizes for various levels 

of confidence using the equation above.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

128APPENDIX F: TESTING PRECISION AND ACCURAC Y AND DETECTING CHANGETable F6. Estimates of Sample Size Needed at the 90 Percent Confidence Level and Precision (β) at 

10 Percent of the Mean

Stream

Streambank AlterationStreambank StabilityStubble HeightGGWWoodySpecies UseSubstrate

Beaver Creek, South Dakota 67 72 23 69 47 311

Big Creek, Utah 79 83 56 23 na 206

Darling Creek, Idaho 125 78 53 102 19 179

Ditch Creek, Idaho 135 81 51 61 15 242

Trout Creek, Oregon 69 45 62 68 47 245

Hardtrigger Creek, Idaho 47 53 29 99 11 384

Lawson Creek, Idaho 50 55 56 109 12 269

Long Tom Creek, Idaho 55 13 95 47 5 162

Blanchard Creek, Montana 15 22 60 91 19 74

Smart Creek, Montana 116 102 9 60 30 184

Telephone Creek, Idaho 31 39 22 59 81 346

Mill Creek, Oregon 38 5 28 21 14 149

Average 69 54 45 67 27 229

MAX 135 102 95 109 81 384

MIN 15 5 9 21 5 74

Using Precision to Evaluate Data and Detect Change: The ability to correctly evaluate 

data and detect change correctly is a critical step in the monitoring process. In natural 

stream and riparian systems, change is a relatively frequent natural process. The ability 

to isolate changes due to management requires that the method be robust, precise, and 

accurate and that some kind of reference to the disturbance events be available to calibrate 

changes due to natural disturbances. 

A good way to detect changes in long-term indicators or to detect a failure to meet shortterm indicators is to use a confidence interval around the mean that combines sources of 

variability or variations due to site complexity and revisit variance (observer plus withinseason variation). Table F7 summarizes the statistics from all test sites for each metric 

indicator, including the combined 95 percent confidence interval.

The sample sizes, N, represent the number of stream reaches used in the replication 

analysis for a given indicator. The numeric indicators, stubble height, streambank 

* na: not available–no woody plants present.

*
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APPENDIX F: TESTING PRECISION AND ACCURAC Y AND DETECTING CHANGE

129

Table F7. Sample Size(N), Standard Deviation(SD), and 95 Percent Confidence Intervals (CI) for the Metric 

Indicators for All Repeat Sites

Metric N SD CI

Average Stubble Height (in) 53 3.3 0.96

Streambank Alteration (%) 80 25.3 6.0

Woody Use (%) 52 5.7 5.2

Streambank Stability (%) 89 24.9 5.2

Streambank Cover (%) 89 21.8 4.5

Percent Seedlings + Young 66 29.7 7.2

Percent Mature 66 29.1 7.0

Hydric Plants (%) 89 22.2 4.6

Modified Winward Stability Rating (1-10) 89 0.8 0.16

Greenline Ecological Status (1-100) 89 27.7 5.75

Site Wetland Rating (1-100) 89 14.4 2.99

Greenline-to-Greenline Width (m) 109 0.3 0.3

Woody (%) 89 28.2 5.9

Hydric Herbaceous (%) 89 29.8 6.2

Percent Fines 9 17.8 11.6

Median Particle Size (mm) 9 41.2 26.9

Pool Frequency (pools per mile) 21 15.0 14.0

Residual Pool Depth (m) 21 0.23 0.06

alteration, greenline-to-greenline width, and woody use were analyzed using the statistics 

in table F4 and confidence intervals were derived from the mean for all reaches as shown 

at the bottom of that table. All other variables were analyzed from their reach summaries. 

For example, percent stable streambank is calculated from the total of all observations in 

a reach. The confidence interval for streambank stability was then determined from the 

sample size and standard deviation for all reaches.

The following examples demonstrate how to use table F6 in assessing short-term livestock 

grazing use indicators and in assessing change over time in a long-term condition indicator.

The streambank stability for a particular grazing unit has improved from 69 percent in 2007 

to 80 percent in 2010. Is this a statistically valid increase? Using table F7, the confidence 

interval around the estimate is plus and minus 5.2 percent. Figure F1 displays this interval 

around the two estimates for 2007 and 2010. Because these two intervals do not overlap, 

we would conclude that the increase is likely to have occurred. 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

130APPENDIX F: TESTING PRECISION AND ACCURAC Y AND DETECTING CHANGE In the second scenario, the manager has establish the allowable streambank alteration 

level for the grazing allotment at 30 percent (this does NOT imply that 30 percent is an 

appropriate criterion anywhere else). At the end of the grazing season, streambank 

alteration is estimated to be 31 percent. Was the allowable use criterion exceeded? The 

confidence interval for streambank alteration in table F7 is 6 percent. Therefore the 

operation did not exceed the upper range for the criterion, or 36 percent. On the other 

hand, it may have begun to exceed the criterion at the lower end of the range or at 

24 percent. It would be good for the manager to specify the range of confidence. One 

way to address variability is to make the lower end of the range the objective, and if it is 

reached, the operator then begins to move livestock off of the pasture. The upper end of 

the range could be used as a standard, which, if reached, might indicate a need to adjust 

the grazing practice.

Using reference sites, where the stream and riparian area are mostly absent of the 

management activity, is a good way to isolate management effects. This method assumes 

that both the DMA and the reference are concordant; that is, the indicator changes are in 

the same direction after a natural disturbance event.

An interesting example is the Big Elk Creek exclosure demonstration in north-central Idaho. 

Figure F2 displays the results of sampling the greenline-to-greenline width in each of two 

exclosures, one that has been fenced for 10 years and the other for 20 years. Widths for the 

unfenced, grazed pasture are also included in the figure for comparison to the exclosures. 

Confidence intervals around the mean, derived from table F7, are 0.3 meters. 

90

85

80

75

70

65

Stable Streambanks (%) 60

55

50

2006 2007 2008 2009

Mean = 69 (CI=+/-5.2%)

Mean = 80 (CI=+/-5.2%)

Year

2010 2011

Figure F1. Detecting change through time using 95 percent confidence intervals (CI) from 

table F7. Change here would be significant given that both intervals do not overlap.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX F: TESTING PRECISION AND ACCURAC Y AND DETECTING CHANGE

131

The overlap between grazed and the 10-year exclosure on Big Elk Creek suggests that 

there is no significant difference between these two livestock use treatments. There is, 

however, no overlap between the 20-year exclosure and either the 10-year exclosure or 

grazed pastures. This suggests that after 20 years of rest, the GGW decreased significantly 

as vegetation encroachment and streambank stability increased on the channel margins. 

This kind of before-and-after controlled impact design has been used commonly in relation 

to exclosures (Sarr 2002), but previous approaches have relied upon some test, such as 

the t test for comparing two means. The current approach uses combined sources of 

measurement error as the means of testing differences.

In the absence of a reference site comparison, the observer should assess indicators of 

natural disturbance to inform potential effects beyond those caused by management. 

Climate records, streamflow data, and records of fire, grasshopper invasions, etc. would be 

helpful. 

5

4.8

4.6

4.4

4.2

4.0

GGW (m)

3.8

3.6

3.4

3.2

3.0

Grazed 10 Year Exclosure 20 Year Exclosure

Figure F2. Using confidence intervals to define error bars around the average GGW for 

Big Elk Creek, Idaho.

MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATIONAPPENDIX G: GREENLINE-TO-GREENLINE WIDTH (GGW ) EXAMPLES

133

APPENDIX G: GREENLINE-TO-GREENLINE 

WIDTH (GGW) EXAMPLES *

Greenline-to-Greenline

Greenline

Width (GGW)

Vegetated

Broken Bank Greenline

Greenline

GGW

Greenline

Figure G1. The greenline-to-greenline width (GGW) is the horizontal distance between the greenlines 

on each side of the stream measured perpendicularly to the flow of the stream. It is the nonvegetated 

stream channel. When vegetated (at least 25 percent foliar cover) slump blocks or islands are encountered 

along the line, the vegetated portion is subtracted from the total width and only the nonvegetated 

portion of the width is recorded.

Figure G2. GGW is measured perpendicularly to the waterflow and from the rooted base on the 

greenline to the rooted base of plants on the greenline on the opposite side of the nonvegetated 

stream channel.

* The dashed line shown in some figures represents the greenline.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

134APPENDIX G: GREENLINE-TO-GREENLINE WIDTH (GGW ) EXAMPLES

Figure G3. The GGW at location “I” is the length of line “A” minus the length of segment “B.” The GGW at 

location “II” is the length of line “C” less the length of line “D.” The GGW at location “III” is the length 

of line “E.”

Figure G4. GGW is measured at regular intervals from one side of the stream at each plot location. Lines 

“A,” “B,” and “F” are the width of the nonvegetated stream channel measured perpendicularly to the 

waterflow direction. Line “C” shows a nonvegetated part above the steam. The GGW is measured between 

the greenlines. The GGW for line “D” is the total length of the line minus the distance on the island at “E.” 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATIONAPPENDIX G: GREENLINE-TO-GREENLINE WIDTH (GGW ) EXAMPLES

135

Figure G5. Line “A” is the total length of the GGW. The gravel bar has no vegetation. When the GGW 

crosses an island with at least 25 percent cover, the nonvegetated portion is calculated (total length 

of line “B” – line “C”) to determine the nonvegetated portion of the two channels. Photo - PIBO, 

U.S. Forest Service.

Figure G6. The slump block “A” is not attached to the streambank. The GGW is the total length of 

“B” less the width of the slump block.

MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX H: PLANT LIST INFORMATION

137

APPENDIX H: PLANT LIST INFORMATION

The plant list contained in the Data Entry Module and the Data Analysis Module (see 

section V) was developed from multiple sources. The list is neither comprehensive nor 

complete. Users should update plant information and add plants for their areas when 

additional data are available or plants are not in the list.

Modified Winward Greenline Stability Rating

The literature is lacking in riparian and wetland plant species characteristics such as 

their extent of root systems and belowground biomass (Boyd and Svejcar 2009). The 

modified Winward (2000) greenline stability ratings provided in this document are mostly 

the professional opinion of various authors, including the authors of this document 

(see table H1). As data become available, users should update the information in the tables.

Ecological Status Rating

The ecological status rating for individual plants was determined from the sources listed 

in the references. Ecological status is sometimes referred to as “successional status, 

successional stage, or seral stage” and refers to the relative position of individual plants or 

a plant community in relationship to climax. This is related to the tendency of a plant to 

occur either earlier or later in a successional progression and is based on its relative shade 

tolerance and persistence. Since riparian areas associated with streams are dynamic, plants 

of all seral stages may be present in a late seral riparian community. Winward 2000 and 

USDA, Forest Service, no date, provided much of the information used to determine the 

successional status of plants. 

The ecological status ratings for individual plants must be differentiated from the 

ecological status rating metric displayed for a site. The ecological status rating for a site is a 

summary metric that is calculated using individual ecological or successional status ratings 

and a weighted average of all plants recorded on the DMA according to their percent 

composition. 

Many woody riparian species (most species of willow, cottonwood, alder, dogwood, and 

birch) require bare ground or freshly deposited sediment for seeds to germinate and 

establish (USDA, Forest Service no date). These plants also tend to live a long time (50 years 

or more). Even though they are early seral for establishment, they are long lived. Therefore, 

they are considered late seral for the MIM protocol.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

138APPENDIX H: PLANT LIST INFORMATIONTable H1. Modified Winward (2000) Greenline Stability Rating

Reference Criteria Modified 

Winward 

Greenline 

Stability Rating 

for Individual 

Plant Species

Winward 

2000

Winward Greenline Stability Rating

1 to 3 Low (2)

4 to 6 Medium (5)

7 to 10 High (8.5)

Crowe 

and 

Clausnitzer

1997

Streambank Erosion

Poor = Low Low (2)

Fair = Moderate Medium (5)

Good = High High (8.5)

Excellent = High High (8.5)

Authors’ 

Criteria

Modified Winward Greenline Stability Rating - A relative value 

based on general rooting characteristics assigned by the authors or 

other referenced publications.

Forbs

Taproot and/or most roots, shallow (<15.2 cm) Low (2)

Fibrous roots, usually up to 30.5 cm Medium (5)

Rhizomatous roots, little indication of extensive fibrous roots Medium (5)

Rhizomatous roots, with extensive fibrous roots High (8.5)

Graminoids

Annual, biennial, and short-lived perennials Low (2)

Stoloniferous, caespitose, tufted, or short slender rhizomatous 

perennials (<1 m tall)

Low (2)

Slender or thin creeping rhizomes Medium (5)

Long, stout, well-developed creeping rhizomes High (8.5)

Woody Species

Taprooted species Low (2)

Short shrubs (<1 m tall) with shallow root systems Low (2)

Shallow to moderate root systems Medium (5)

Rhizomatous root system, generally shallow (<31 cm) Medium (5)

Root crown with spreading roots High (8.5)

Widespread root systems High (8.5)
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX H: PLANT LIST INFORMATION

139

The ecological status rating classes for individual plants are:

Early Seral (E) – All annual and short-lived (living less than 5 years) perennial plants 

tend to be replaced by plants that live longer. All noxious weeds and shallow-rooted 

perennial species that tend to be tolerant of grazing and other uses are classified 

as early seral.

Mid-Seral (M) – Perennial plants, mostly forbs that are not shade tolerant and tend to 

have fibrous root systems. These plants are usually replaced in a riparian community by 

long-lived plants.

Late Seral (L) – Plants that usually exist in the most stable riparian plant communities. 

They tend to stabilize streambanks and develop extensive root systems.

Wetland Status

The “1996 National List of Vascular Plant Species that Occur in Wetlands” 

(U.S. Army Corp of Engineers 1996) was used to establish the wetland rating. 
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

140APPENDIX H: PLANT LIST INFORMATION References Used to Develop Plant List in the Data 

Analysis Module

Boyd, C.S. and T.J. Svejcar. 2009. A technique for estimating root production. Rangeland 

Ecology and Management 62:198-202.

Brunsfeld, S.J. and F.D. Johnson. 1985. Field guide to the willow of east-central Idaho. 

Bulletin Number 39. College of Forestry, Wildlife, and Range Sciences, Forest, Wildlife, 

and Range Experiment Station, University of Idaho, Moscow, ID. 95 pp.

Cooke, S.S. (Editor). 1997. Wetland plants of western Washington and northwestern 

Oregon. Seattle Audubon Society, Seattle, WA. 417 pp.

Crowe, E.A. and R.R. Clausnitzer. 1997. Mid-montane wetland plant associations of the 

Malheur, Umatilla and Wallowa-Whitman National Forests. R6-NR-ECOL-TP-22-97. U.S. 

Department of Agriculture, Forest Service, Pacific Northwest Region, Portland, OR. 229 pp.

Elzinga, C., R. Rosentreter, J. Duft, H. Hudak, and A. Bahn. 1998 (unpublished). The ecology 

and management of riparian plants of the Great Basin (DRAFT). U.S. Department of the 

Interior, Bureau of Land Management, Boise, ID. 

Hansen, P.L., S.W. Chadde, and R.D. Pfister. 1988. Riparian dominance types of Montana. 

Misc. Publication No. 49. Montana Forest and Conservation Experiment Station, School 

of Forestry, University of Montana, Missoula, MT. 411 pp.

Hansen, P.L. and J.B. Hall. 2002. Classification and management of USDI Bureau of Land 

Management’s riparian and wetland sites in eastern and southern Idaho. Bitterroot 

Restoration, Inc., Corvallis, MT. 304 pp.

Hansen, P.L., R.D. Pfister, K. Boggs, B.J. Cook, J. Joy, and D.K. Hinckley. 1995. Classification 

and management of Montana’s riparian and wetland sites. Misc. Publication No. 54. 

Montana Forest and Conservation Experiment Station, School of Forestry, University of 

Montana, Missoula, MT. 646 pp.

Hoag, C., D. Tilley, D. Darris, and K. Pendergrass. 2008. Field guide for identification and 

use of common riparian woody plants of the Intermountain West and Pacific Northwest 

regions. U.S. Department of Agriculture, Natural Resources Conservation Service, Plant 

Materials Centers, Aberdeen, ID, and Portland, OR. 196 pp.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX H: PLANT LIST INFORMATION

141

Hurd, E.G., S. Goodrich, and N.L. Shaw. 1997. Field guide to Intermountain rushes. General 

Technical Report. INT-306. U.S. Department of Agriculture, Forest Service, Intermountain 

Research Station, Ogden, UT. 56 pp.

Hurd, E.G., N.L. Shaw, J. Mastrogluseppe, L.C. Smithman, and S. Goodrich. 1998. Field guide 

to Intermountain sedges. General Technical Report RMRS-GTR-10. U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Research Station, Ogden, UT. 282 pp.

Jankovsky-Jones, M., C. Murphy, and C. Coulter. 2001. Riparian and wetland plant 

associations of southwestern Idaho with a focus on the Bureau of Land Management’s 

Lower Snake River District. Idaho Department of Fish and Game, Idaho Conservation 

Data Center for the U.S. Department of the Interior, Bureau of Land Management, 

Denver, CO. 191 pp.

Johnson, C.G. Jr. 1998. Common plants of the inland Pacific Northwest. R6-NR-ECOL-04-98. 

U.S. Department of Agriculture, Forest Service, Pacific Northwest Region, Portland, OR. 394 pp.

Johnson, C.G. Jr. 2004. Alpine and Subalpine vegetation of the Wallowa, Seven Devils, and 

Blue Mountains. R6-NR-ECOL-TP-03-04. U.S. Department of Agriculture, Forest Service, 

Pacific Northwest Region, Portland, OR. 611 pp.

Kovalchik, B.L. 1987. Riparian zone associations, Deschutes, Ochoco, Fremont, and 

Winema National Forests. Region 6 Ecology Technical Paper 279-87. U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Region, Portland, OR. 171 pp.

Kovalchik, B.L. and R.R. Clausnitzer. 2004. Classification and management of aquatic, 

riparian, and wetland sites on national forests of eastern Washington: series 

descriptions. General Technical Report PNW-GTR-593. U.S. Department of Agriculture, 

Forest Service, Pacific Northwest Research Station, Portland, OR.

Larson, G.E. and J.R. Johnson. 1999. Plants of the Black Hills and Bear Lodge Mountains. 

B732. College of Agriculture and Biological Sciences, South Dakota Agricultural 

Experiment Station, South Dakota State University, Brookings, SD. 608 pp.

Lyons, C.P. 1999. Trees and shrubs of Washington. Lone Pine Publishing, Edmonton, 

Canada. 159 pp.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

142APPENDIX H: PLANT LIST INFORMATIONManning, M.E. and W.G. Padgett. 1995. Riparian community type classification for Humbolt 

and Toiyabe National Forests, Nevada and eastern California. R4-Ecol-95-01. U.S. 

Department of Agriculture, Forest Service, Intermountain Region, Ogden, UT. 306 pp.

McLaughlin, S.P. 2004. Riparian flora. In Riparian areas of the southwestern United States. 

Eds.: M.D. Baker, Jr., P.F. Pfolliott, L.F. DeBano, and D.G. Neary. Lewis Publishers, Boca 

Raton, FL. pp. 127-167.

New Mexico State University. No date. Riparian plants of New Mexico. 

http://aces.nmsu.edu/riparian/OBLList.html (accessed March 2009).

Padgett, W.G., A.P. Youngblood, and A.H. Winward. 1989. Riparian community type 

classification of Utah and southeastern Idaho. R4-Ecol-89-01. U.S. Department of 

Agriculture, Forest Service, Intermountain Region, Ogden, UT. 191 pp.

Patterson, P.A., K.E. Neiman, and J.R. Tonn. 1985. Field guide to forest plants of northern 

Idaho. General Technical Report INT-180. U.S. Department of Agriculture, Forest Service, 

Intermountain Research Station, Ogden, UT. 246 pp.

Pojar, J. and A. MacKinnon. 2004. Plants of the Pacific Northwest coast; Washington, 

Oregon, British Columbia and Alaska. Lone Pine Publishing, Edmonton, Canada. 528 pp.

U.S. Army Corps of Engineers. 1996. National list of vascular plant species that occur in 

wetlands. https://rsgis.crrel.usace.army.mil/NWPL_CRREL/docs/fws_lists/list96. .html

(accessed November 2009).

USDA, Forest Service. No date. Fire effects information system. 

http://www.fs.fed.us/database/feis/index.html (accessed March 2010).

USDA, Natural Resources Conservation Service. 2009. PLANTS database. 

http://plants.usda.gov/ (accessed March 2009).

U.S. Department of Agriculture. No date. Western wetland flora: Field office guide to plant 

species. U.S.D.A. Soil Conservation Service, West National Technical Center, Portland, 

Oregon. Jamestown, ND: Northern Prairie Wildlife Research Center Online. http://www.

npwrc.usgs.gov/index.htm (Version 16JUL1997) (accessed January 2010).

Utah State University. 2002. Manual of grasses for North America. 

http://herbarium.usu.edu/grassmanual/ (accessed March 2009).
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

APPENDIX H: PLANT LIST INFORMATION

143

Weixelman, D.A., D.C. Zamudio, and K.A. Zamudio. 1996. Central Nevada riparian field 

guide. R4-ECOL-96-01. U.S. Department of Agriculture, Forest Service, Intermountain 

Region, Ogden, UT.

Weixelman, D.A., D.C. Zamudio, and K.A. Zamudio. 1999. Eastern Sierra Nevada 

riparian field guide. R4-ECOL-99-01. U.S. Department of Agriculture, Forest Service, 

Intermountain Region, Ogden, UT.

Whitson, T.D., L.C. Burrill, S.A. Dewey, D.W. Cudney, B.E. Nelson, R.D. Lee, and R. Parker. 

2002. Weeds of the West. 9th Edition. The Western Society of Weed Science in 

cooperation with Western United States Land Grant Universities Cooperative Extension 

Services and the University of Wyoming. Newark, CA:. 626 pp.

Wilson, B.L., R.E. Brainerd, D. Lytjen, B. Newhouse, and N. Otting. 2008. Field guide to the 

sedges of the Pacific Northwest. Oregon State University Press. Corvallis, OR. 432 pp.

Winward, A.H. 2000. Monitoring the riparian resources in riparian areas. General Technical 

Report RMRS-GTR-47. U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station, Ogden, UT. 49 pp.

MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

GLOSSARY

145

GLOSSARY

Available woody species: The height to which large herbivores, e.g., cattle, sheep, 

horses, deer, elk, and moose, can graze on woody plant species. The taller the animal, 

the higher they can browse.

Bankfull stage (or level): The elevation of the bank where flooding begins. The bankfull 

level is associated with the streamflow that just fills the channel to the top of its banks 

where water begins to overflow onto the floodplain. This streamflow level is often 

associated with moving sediment, bar formation, and, generally, the work that forms 

the morphological characteristics of the stream channel (Wolman and Miller 1960).

Bench: A relatively flat area, more or less parallel to the stream, which may be a 

floodplain or terrace.

Current year’s leader or twig growth: That portion of the stems of woody plants that 

reflects the current year’s growth or that extends from the terminal buds of 2-year-old 

growth. Leaders represent the dominant trunk of a branch or stem; twigs represent the 

terminal parts of the branch or stem.

Designated monitoring area (DMA): A DMA, for the purposes of this protocol, is a 

permanently marked segment of stream that has been selected for monitoring. It 

refers to the specific sampling location that extends at least 110 m along the stream. 

Longer segments may be needed for monitoring larger streams (over 5.5 m greenline-to 

greenline width or GGW). For such streams, the DMA should be at least two meander 

wavelengths or approximately 20 times the GGW (Gordon et al. 2004). For example, a 

DMA on a stream segment with an average GGW of 8.3 m would be 8.3 m x 20, or 

166 m in length.

False bank: A slump block that is reattached to the streambank creating a new floodplain.

Fines: Substrate particles that are less than 6 mm in diameter.

Floodplain: The relatively flat area adjacent to a stream or lake that experiences 

occasional or periodic flooding. Dunne and Leopold (1978) defined the floodplain as the 

flat area adjoining a river channel constructed by the river in the present climate and 

overflowed at times of high discharge.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

146GLOSSARYFoliar cover: The amount of live plant parts, leaves, twigs, stems, and branches that 

covers the ground surface expressed as a percentage. Foliar cover is the shadow cast if 

the sun was directly overhead. 

Geomorphology: The study of landforms and the processes that form them.

Greenline: A linear grouping of perennial plants at or near the water’s edge along a 

stream channel.

Greenline-to-greenline width (GGW): The nonvegetated width of a stream channel 

measured from the greenline on one side of the channel perpendicular to the 

streamflow to the greenline on the opposite side.

Hoof shearing: A broken part of the streambank caused by the weight of a hoof or foot 

stepping on the streambank and causing it to break down. Shearing is usually the most 

obvious form of streambank disturbance caused by animals.

Island: Areas surrounded by water or bounded by a channel that is scoured frequently 

enough to keep perennial vegetation from growing at summer low flow.

Key species: Those plant species that are important (relatively common and desirable) in 

the plant community, are relatively palatable to livestock, and serve as indicators 

of change. 

Obvious streambank alterations: Those alterations to the streambank that are easily 

seen, clear to the eye, not to be doubted, or plain (Thorndike and Barnhart 1993).

Pool: A depression or deeper part of a stream channel that usually has slower 

moving water.

Riffle: A part of a stream that is locally steeper and shallower than adjacent reaches and 

has fast, shallow water causing choppiness on the water surface.

Riffle crest: The point at the lower end of a pool where water flows to a riffle.

Riparian complex: The overall geomorphology, substrate characteristics, stream gradient, 

and vegetation patterns along the stream.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

GLOSSARY

147

Scour line: The elevation of the ceiling of undercut banks at or slightly above the 

summer low-flow elevation or, on depositional banks, the lower limit of sod-forming 

or perennial vegetation.

Slump block: A block streambank that has completely detached and is within the 

stream channel.

Stratification: Dividing the length of a stream using specific criteria resulting in stream 

segments with similar characteristics.

Streambank alteration: Streambank disturbance caused by animals (e.g., elk, moose, 

deer, cattle, sheep, goats, and horses) walking along the streambanks or the margins of 

the stream. The animals’ weight can cause shearing that results in a breakdown of the 

streambank and subsequent widening of the stream channel. Streambank alteration 

also exposes bare soil, increasing the risk of erosion of the streambank. Animals walking 

in the channel margins may increase the amount of soil exposed to the erosive effects 

of water by breaking or cutting through the vegetation and exposing roots and/or soil. 

Excessive trampling causes soil compaction, resulting in decreased vegetative cover, less 

vigorous root systems, and more exposure of the soil surface to erosion.

Stream gradient: The gradient is the slope or amount of vertical drop along the stream 

channel per unit horizontal distance. It is usually expressed as a percentage.

Terrace: A level surface flanking, and more or less parallel to, a stream channel located 

upslope of the active floodplain. A terrace represents an abandoned floodplain that is 

not flooded, except possibly on rare occasions associated with extremely large floods.

Thalweg: A line joining the lowest points along the length of a streambed in its 

downward slope, defining its deepest channel.

Trampling: Animal-caused depressions in the soil surface or soil compaction along the 

streambank or crossing the channel.

MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

LITERATURE CITED

149

LITERATURE CITED

Allan, J.D. and M.M. Castillo. 2007. Steam ecology and function of running waters. 2nd 

edition. Springer, Dordrecht, The Netherlands. pp. 96-97.

Archer, E.K., B.B. Roper, R.C. Henderson, N. Bouwes, S.C. Mellison, and J.L. Kershner. 2004. 

Testing common stream sampling methods for broad-scale, long-term monitoring. 

General Technical Report RMRS-GTR-122. U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station. 

Bartholow, J.M. 2002. SSTEMP for Windows: Stream Segment Temperature Model (Version 

2.0). U.S. Geological Survey. http://www.fort.usgs.gov/products/software/SNTEMP/ 

(accessed 12 March 2010).

Bauer, S.B. and T.A. Burton. 1993. Monitoring protocols to evaluate water quality effects of 

grazing management on western rangeland streams. EPA 910/R-93-017. Idaho Water 

Resources Research Institute for U.S. Environmental Protection Agency. 

Bauer, S.B. and S.C. Ralph. 2001. Strengthening the use of aquatic habitat indicators in 

Clean Water Act programs. Fisheries 26(6):14–24. 

Bengeyfield, P. 2006. Managing cows with streams in mind. Rangelands 28(1):3–6.

Bohn, C. 1986. Biological importance of streambank stability. Rangelands 8:55–56.

Bonham, C.D. 1989. Measurements for terrestrial vegetation. John Wiley and Sons, Inc., 

New York.

Bunte, K. and S.R. Abt. 2001. Sampling surface and sub-surface particle size distributions in 

wadeable gravel- and cobble-bed streams for analysis in sediment transport, hydraulics, 

and streambed monitoring. General Technical Report RMRS-GTR-74. U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Experiment Station. 450 pp. 

Burton, T.A., S.J. Smith, and E.R. Cowley. 2008. Monitoring streambanks and riparian 

vegetation – multiple indicators. Version 5.0. U.S. Department of the Interior, Bureau of 

Land Management. Idaho State Office. Boise, ID.
MULTIPLE INDICATOR MONITORING (MIM) OF STREAM CHANNELS AND STREAMSIDE VEGETATION

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