United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical 

Report RMRS-GTR-267

January 2012

Validating Visual Disturbance 

Types and Classes Used for 

Forest Soil Monitoring Protocols
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Page-Dumroese, D.S.; Abbott, A.M.; Curran, M.P.; Jurgensen, M.F. 2012. 

Validating visual disturbance types and classes used for forest soil 

monitoring protocols. Gen. Tech. Rep. RMRS-GTR-267. Fort Collins, 

CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 17 p.

Abstract

We describe several methods for validating visual soil disturbance classes 

used during forest soil monitoring after specific management operations. 

Site-specific vegetative, soil, and hydrologic responses to soil disturbance 

are needed to identify sensitive and resilient soil properties and processes; 

therefore, validation of ecosystem responses can provide information 

for best management practices in selecting appropriate harvest and site 

preparation techniques that limit long-term degradation and maintain 

site productivity and hydrologic function. Although research on forest 

managements affect on soil properties and plant growth responses has 

been conducted on a few sites, there is a need for additional site-specific 

validation data of soil visual disturbance attributes across the range of soil 

and forest conditions.

Keywords: soil productivity, vegetation, hydrologic function, adaptive 

management, soil disturbance type, soil disturbance severity 

class

Authors

Deborah S. Page-Dumroese, Research Soil Scientist, Rocky Mountain 

Research Station, Moscow, Idaho

Ann M. Abbott, Biometrician, Rocky Mountain Research Station, Moscow, 

Idaho

Michael P. Curran, Research Soil Scientist, British Columbia Forest 

Service, Nelson

Martin F. Jurgensen, Professor, School of Forest Resources and 

Environmental Science, Michigan Technological University, Houghton
Research Summary

We describe several validation approaches for developing sitespecific relationships among soil disturbance type and severity 

and vegetative growth, soil properties, and hydrologic response. 

For example, rutting caused by machine traffic can be classified in 

various severity classes (based on rut depth) that indicate whether 

the rutting is a concern for soil quality or hydrologic function and if 

it “counts” toward soil disturbance limits on a given soil. However, 

various site attributes, such as soil texture, slope, and soil moisture 

and/or temperature regime, will determine how plant growth and 

soil properties (including hydrology) are affected by management 

activities. Quantifying site-specific changes will provide managers 

with decisionmaking tools for assessing disturbance and vegetative 

growth relationships.

Through validation monitoring of actual soil and ecosystem response, resulting databases can be developed to help determine 

limits for site-specific thresholds when productivity and/or hydrologic function decline beyond acceptable limits. Validation information 

may be collected from retrospective studies, existing soil disturbance or related research, new experimental plots, or operational 

trials. Each approach has its own merits and disadvantages. For 

example, retrospective studies can provide long-term response 

data in the short term, but give little information on the actual disturbance at the time it was created. Current research studies, such as 

the North American Long-Term Soil Productivity study, often have a 

soil disturbance component and can be “data mined” or re-sampled 

to yield immediately useful, longer-term data on some aspects of 

soil disturbance response. However, the disturbance types used 

during study establishment may or may not be entirely compatible with current definitions of disturbance types and severity, or 

the practices that created the soil disturbance may no longer be 

used. New studies and operational trials are likely needed for soil 

disturbance types and soil/site conditions not covered in existing 

or retrospective studies. Because visual soil disturbance types are 

the integration of various disturbance processes (e.g., soil compaction and displacement combined), detailed measures of actual soil 

properties may be less important than monitoring the end-result response variables such as plant productivity and hydrologic function. 

Ultimately, validation of visual attributes of various disturbance levels will improve understanding of how site, soil, hydrologic function, 

and vegetation interact. In some cases, it may be desirable to relate 

vegetation responses to specific soil properties, but such studies 

will be more intensive and expensive, so they should be limited to a 

representative subset of study sites (soils and disturbance types).

We will outline a core set of soil properties and vegetation measures that can be used to document site quality changes after land 

management. Once sites are documented, longer-term monitoring 

efforts can further refine site-specific best management practices. 

As long as core soil properties and vegetation measurements are 

collected and linked to the visual disturbance classes, the adaptive 

management process can be used to ensure the visual classes 

are properly associated with changes in hydrologic function and/or 

above-ground productivity.
Contents

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

Validation Approaches.................................................................3

Using existing or retrospective studies ..................................3

New experimental plots or operational trials..........................5

Considerations for validation monitoring................................6

Sampling Design Examples.........................................................9

Fixed plot sampling................................................................9

Regeneration stocking surveys............................................10

Core Measurements ...................................................................10

Monumenting .......................................................................10

Understory vegetation..........................................................10

Overstory vegetation............................................................11

Soil measurements ..............................................................11

Soil Disturbance and Soil Quality Standards ..........................11

Modeling soil change ...........................................................12

Data storage ........................................................................12

Conclusions................................................................................12

Literature Cited...........................................................................13

Appendix. ....................................................................................15
USDA Forest Service RMRS-GTR-267. 2012. 1

Introduction

There are numerous classification systems for characterizing soil 

disturbance (e.g., Scott 2000; B.C. Ministry of Forests 2001; Heninger 

and others 2002). The classification system should include disturbance 

types and severity classes that are obvious concerns for knowledgeable 

users (e.g., deeps ruts, excavated/displaced areas, or berms). Other 

disturbance types may be of concern based on best available information (e.g., shallower or more extensive disturbances) and should be 

further validated with response variables that are ecologically relevant 

and that provide direct evidence of a change in a site’s capacity to grow 

vegetation or produce clean water (Curran and others 2005a). In forested 

ecosystems, soil disturbance is defined in terms of disturbance type and 

severity. Soil disturbance types, severity classes, and cumulative limits 

for disturbance should be related to how sensitive the soil is to long-term 

damage from the disturbance (Curran and others 2007). For example, 

ruts are defined disturbance types, which are then described under 

the conditions (dimensions and soil properties) they align with in the 

various disturbance severity classes. Severity classes help to define a 

disturbance type as being inconsequential, being of concern (and hence 

counting toward cumulative soil disturbance limits), or being severe 

enough to require remediation. The visual soil disturbance types, severity classes, and cumulative limits are usually developed within a given 

jurisdiction, based on best available information. For example, with the 

recent standardization of U.S. Department of Agriculture, Forest Service 

Forest Soil Disturbance Monitoring Protocol (FSDMP; Page-Dumroese 

and others 2009a and 2009b) and the use of other soil disturbance visual 

classifications systems (Howes and others 1983; Heninger and others 

2002; Province of British Columbia 2001; Curran and others 2007) by 

neighboring jurisdiction (e.g., industry, state lands, and Canadian forest 

lands), there is a need for site-specific assessments to further characterize forest soil disturbance types and their relationships to soil productivity 

and hydrologic responses. Although many forest soils are resilient to 

management activities, such as timber harvesting, thinning, or fire, others 

can be at risk of losing their productive and/or hydrologic capacity after 

vegetation management due to a sensitivity or limitation in their inherent 

soil properties (e.g., more sensitive texture, shallow forest floor, or thin 

mineral soil mantle; Burger and Kelting 1999). Thus, no set of standards, 

and thereby no monitoring system, is complete until data are collected 

that demonstrate the effect of various harvest methods on soil changes, 

as well as how vegetation growth or hydrologic function is affected on 

soils of differing sensitivity to disturbance. Validation monitoring tests the 

assumptions of soil disturbance types and severity classes, soil sensitivity (risk ratings), and cumulative soil disturbance limits for various soils 

to ensure that the monitoring criteria and standards are appropriate and 

that soil productivity and hydrologic function are maintained.

The FSDMP (Page-Dumroese and others 2009a, 2009b) and other 

visual assessment classifications (Howes and others 1983; Heninger 

and others 2002; Province of British Columbia 2001; Curran and others 

2007) use qualitative procedures to provide an efficient and cost-effective 

method for measuring soil disturbance. They simplify and standardize implementation (e.g., Have the prescribed soil practices been 
2 USDA Forest Service RMRS-GTR-267. 2012.

implemented?) and effectiveness monitoring (e.g., Was the prescribed 

soil practice effective in meeting management objectives?). However, 

more intensive measurements are needed for validation monitoring (e.g.,

What impact did management have on soil disturbance and/or vegetative 

growth?). Quantitative measures of soil disturbance include properties 

related to the degree of soil compaction (e.g., pore size distribution) 

and displacement, chemical properties, and organic matter content. 

Quantitative measures of these soil properties are more expensive to 

collect than qualitative data. However, when coupled with response 

variables, such as overall soil hydrologic function and/or vegetation 

productivity, these measures are appropriate for calibration of the qualita-

tive measures through a better understanding of the long-term effects 

of management activities. Both quantitative and qualitative information 

can give statistically valid answers to the question of “What does this 

data mean for site response?” A site’s disturbance response depends on 

many factors (e.g., climate and weather variables, competing vegetation, 

and pathogens), so it is important to characterize study sites to ensure 

control of these potentially confounding factors. Therefore, in a previous 

or retrospective study, it is possible that detrimental soil disturbance 

may not have resulted in an expected negative growth response due 

to compensating growth-limiting factors, such as reduced vegetation 

competition. Lack of a response could also be attributed to a favorable 

climate during the first 20 years of stand establishment. However, pru-

dence is important because more extreme climatic events are expected 

in the future, and such disturbance would be expected to have more 

negative implications for growth and hydrologic function (Westerling and 

others 2006).

Existing knowledge helps identify soil factors that are expected to influ-

ence vegetative responses (and hence form the basis of soil risk ratings 

for sensitivity to disturbance), including soil texture, soil development 

(organic matter enrichment, soil depth, and structural integrity), parent 

material, and topography. Therefore, it is critical to use validation moni-

toring on select groupings of areas (e.g., an area of similar soil textures 

with different harvest methods) that also include baseline sampling on a 

non-harvested control to characterize soil changes that may be occur-

ring due to more variable or changing climate. The first step in a local, 

regional, or national validation scheme is to develop these site groupings 

to enable efficient use of available validation resources (Curran and oth-

ers 2005a).

Visual classification systems define the attributes and severity classes of 

the disturbance (Heninger and others 1997; Page-Dumroese and others 

2009a). Each of these severity classes is designed to ensure consistency 

and repeatability among classifiers, as recommended in Curran and 

others (2005b). In order to validate the visual disturbance types that are 

linked to these classes and to determine site-specific vegetation and/or 

hydrologic response, direct evidence of a change in the site’s capacity to 

grow vegetation or its hydrologic function is required. Using a consistent 

method to classify harvest related disturbance across a gradient of soil 

and climate conditions is the most desirable method to track trends in oc-

currence and the effect of disturbance on vegetation growth (Curran and 
USDA Forest Service RMRS-GTR-267. 2012. 3

others 2005b). Once validation data have been obtained using a given 

set of visual criteria, the criteria limits should be reassessed through an 

adaptive management process so that additional site data can be collected, if needed (Figure 1).

Figure 1. Adaptive management processes for soil monitoring (after Curran and others 2005b; Curran and 

Maynard 2009).

Validation Approaches

Using existing or retrospective studies

In order to select the right attributes for monitoring and then make the 

correct management decisions about the results, definitive field experiments are necessary for validating the relationship of the indicators and 

disturbance classes and management activities to forest productivity or 

hydrologic function (Burger and Kelting 1999). Results from field experiments will increase the level of confidence in soil quality monitoring by 

supplementing this implementation and effectiveness monitoring data 

with more rigorously collected data (validation monitoring).

In many areas, a significant number of soil related research projects 

have been completed by graduate students, local researchers, and forest 
4 USDA Forest Service RMRS-GTR-267. 2012.

specialists. If these studies contain sufficient information about soil and 

vegetative response to forest management activities and can fit into a visual disturbance class system, then these data are appropriate to use for 

validation efforts. These studies are not likely to contain all the necessary 

information, but can supplement the information that is needed.

The North American Long-Term Soil Productivity (LTSP) Study (Powers 

and others 2005) provides one method for validating how changes 

in soil compaction and organic matter removal may affect vegetation 

growth. This controlled experiment uses moderately sized (0.4-ha) plots 

that bracket the extreme ranges of both compaction and organic matter 

removal, and provide valuable data on soil disturbances that result in a 

change in vegetative growth or hydrologic function. Although the LTSP 

study sites are geographically dispersed, they do not cover the full range 

of soil and vegetation found on National Forest, industry, private, or 

public lands. However, the LTSP plots can easily be linked to the four 

disturbance classes in the FSDMP (Table 1), and data from the LTSP 

network can supply some longer-term data on vegetative response to 

soil disturbance. Soil properties regularly monitored on LTSP sites (soil 

physical, chemical, and biological properties and associated vegetation 

production) can be used to infer some aspects of hydrologic function or 

site productivity changes.

Retrospective studies can be used to look at the progression of a site 

through time by looking backward to infer the effects of past treatments. The advantage of such studies is that they produce results much 

quicker than empirical studies. However, major disadvantages are: no 

control over the original experimental design, data collection, or use of 

a baseline (Curran 1988; Dyck and Mees 1990; Table 2). In addition, 

retrospective studies are more effective if they focus on treatments that 

trigger large ecosystem responses (Powers and van Cleve 1991), so, 

while these studies may be helpful for clearly identifiable previous disturbance (e.g., excavated trails), they may not be well suited for evaluating 

the subtle differences between various severities of soil disturbance 

classes and their resulting attributes. As with graduate theses, smallscale management, or research studies, the data collected through the 

Table 1. Examples of the relationship between the North American Long-Term Soil Productivity (LTSP) Study and the visual 

disturbance classes found in the FSDMP.

LTSP treatment FSDMP disturbance class

No compaction/bole only harvest (C0OM0) 0 – Forest floor intact, no signs of harvest impacts, natural 

 conditions

Moderate compaction/whole tree harvesting (C1/OM1) 1 – Dispersed wheel tracks, slight depressions evident, forest 

floor intact, no displacement

Moderate compaction/bole and forest floor removal 2 – Faint wheel tracks, forest floor layer missing, change in soil

 (C1/OM2) structure <30 cm deep, resistance of surface soil slightly 

 greater than natural conditions

Severe compaction/bole and forest floor removal 

 (C2/OM2) 3 – Wheel tracks or depressions highly evident, change in soil 

 structure extends beyond 30 cm deep, increased resistance 

deep into the soil profile, forest floor layers missing
USDA Forest Service RMRS-GTR-267. 2012. 5

use of retrospective study sites also need to fit into a visual disturbance 

class system so that inferences about vegetative growth or hydrologic 

function can be made.

New experimental plots or operational trials

New experimental trials or plots are usually installed to answer a wide 

array of questions that may or may not be important to land managers, 

whereas operational trials focus on issues that are of immediate concern 

and relevancy to management questions (Bulmer and Curran 2000). To 

begin a new experiment or operational trial, sites should be organized 

by soil factors that are expected to influence responses (and hence form 

the basis of soil risk ratings for sensitivity to disturbance). These factors 

include soil texture, soil development (organic matter enrichment, depth, 

and structural integrity), parent material, and topography (e.g., slope 

complexity and position and resulting soil moisture regime). Ideally, 

validation monitoring sites should be selected to fit into the overall site 

organizational scheme (sampling hierarchy) and should be selected to 

capture locally significant soil and site factors so that conclusions can be 

drawn about how each soil type or climatic regime responds to certain 

management practices. If possible, these sites should be selected based 

on the range of soil sensitivities to management impacts.

Once a site is selected, pre-harvest (or unharvested adjacent stands 

with similar soil, vegetation, slope, aspect, and climate) data should 

be collected for a reference baseline. We suggest that both pre- and 

post-harvesting monitoring be conducted according to the FSDMP 

(Page-Dumroese and others 2009a and 2009b) or other recognized 

visual disturbance class system, such as that proposed by Heninger and 

others (2002), or those in effect in other jurisdictions. The occurrence 

of each disturbance class and type should be surveyed and mapped or 

otherwise marked for further study (these sites can be flagged to enable 

relocation following random selection for study). Long-term validation 

monitoring locations within the activity area are then selected from the 

monitored sites. On-site replication is provided by selecting three to five 

(or more) study plots in each disturbance class and type, so that the variability of that disturbance class and type within an activity area can be 

measured.

Table 2. Summary of advantages and limitations to re-sampling past research sites (from Curran 1988).

Advantages Limitations

Feedback provided now for Data set must be questioned

⚫ Current management and policy ⚫ Measurements (before and after?)

⚫ Current research efforts ⚫ Methodology (documented?)

 ⚫ Missing data (for new use)

Capitalize on previous forest study investments Differing accuracy/precision of sample design

Increases scope of current studies Controls may be lacking or non-representative

⚫ Elapsed time since treatment

⚫ Range of site condition
6 USDA Forest Service RMRS-GTR-267. 2012.

Disturbance classes for most visual disturbance monitoring protocols 

are described based on a range of attributes—forest floor thickness, soil 

cover, displacement, compaction, erosion, rutting, burning, and soil structure—that need to be considered during validation monitoring. For each 

disturbance class and type within an activity area, each attribute must be 

quantified so that long-term recovery can be tracked. For example, these 

are the types of questions to answer:

⚫ What is the areal extent of displacement and/or rutting?

⚪ Record the dimensions of these features: ▖ Ruts ▖ Scalp or gouge depth and dimensions ▖ Remaining forest floor

⚫ What is the level of compaction?

⚪  Collect bulk density cores down to 30 cm.

⚫ What is the severity of burning?

⚪ Record the depth of char into the mineral soil or the amount of 

forest floor consumed in the fire.

⚫ How has soil structure changed?

⚪ Record the depth and thickness of platy/massive/puddled structure changes.

This is not an inclusive list, and local conditions, such as invasive species or erosion events (e.g., mass-wasting) may dictate other attributes 

to consider. Study sites can be placed within operational harvests, and if 

different logging techniques are used on the same soil, similar measurements can be taken in each area.

Below, we outline several methods for validation monitoring. While the 

actual field plots and layout may be different, a core set of measures 

needs to be taken on all plots to ensure that validation data are meaningful and useful. Once taken, validation monitoring data can be used to 

determine on which soil types certain harvest methods are more likely 

to be detrimental to vegetation growth and/or hydrologic function. This 

information can then be used to develop or modify best management 

practices on a given landscape and to develop risk rating systems for 

various soil and climate conditions. We use the basic concepts of the 

LTSP study, but provide a method for measuring smaller plots to aid in 

installation, monumenting, and measurement.

Considerations for Validation Monitoring

Validation monitoring involves establishment of controlled experiments 

with adequate levels of replication, and it follows an experimental design 

protocol that enables “roll-up” of data from across a range of sites at the 

local, regional, and even national level. Before beginning a validation 

monitoring program, consistent criteria should be established regarding 

permanent and temporary roads. Will roads be included in the visual 

class assessment? Will rehabilitated disturbance be assessed to ensure 

rehabilitation is successful? In addition, locating plots within an activity 

area should be done with care. To ensure applicability of data, provide 

replication by selecting at least three plots of the same disturbance 

type (e.g., ruts) and disturbance class (the categories of ruts in different 
USDA Forest Service RMRS-GTR-267. 2012. 7

disturbance classes) in each activity area. Site replication is provided 

by having several (at least three) sites with similar activity areas, that 

are located in geographically different areas, but on similar soil, slope, 

aspect, etc. (Figure 2). Sample replication within a site can be achieved 

by conducting implementation or effectiveness monitoring using a 

random GPS coordinate (Figure 3a), a random transect (Figure 3b), or 

other method that does not introduce bias into the sample site selection. 

Replication is often difficult to achieve in field studies but is essential for 

validation. Replication refers to the degree of similarity that exists or that 

can be obtained among experimental units (Hulburt 1984), and some 

time should be spent selecting adequate replicate sites. A survey may 

be carried out to identify candidate study plots, and a random subsample 

of the available plots can be used for studies that enable inferences to 

be made regarding the entire population of interest. Hence, selection of 

Figure 2. Example of numerous units available for validation monitoring within one area.
8 USDA Forest Service RMRS-GTR-267. 2012.

Figure 3b. Selection of soil monitoring points can 

be on a randomly selected transect within the 

harvested area.

Figure 3a. Example of 110 

random GPS points for 

disturbance monitoring. 

The green circle is an 

example of a smaller 

stratification with other 

survey points to see if 

higher disturbance existed 

around a landing used for 

in-block chipping.
USDA Forest Service RMRS-GTR-267. 2012. 9

the appropriate plots should be done with care and objectivity to yield 

the best possible inferences. Because these validation plots will be used 

to compare the current estimates of disturbance or productivity with 

any previous estimate(s), it is also important to ensure that validation 

plots cover the range of variability within disturbance types (how do the 

class 2 ruts compare to each other?) and among the different disturbance classes (how do the ruts compare across different disturbance 

classes?). Large and small plots are possible. For example, if only small 

disturbances are noted, numerous plots can be installed to determine 

variability and vegetation response to the soil disturbance. Large areal 

disturbances, such as skid trails, may have fewer study plots, but will 

have a larger area for plant growth. Therefore, the final number of plots 

will likely depend on the size and distribution of the soil disturbance and 

the presence of target vegetation species. Another consideration is the 

spatial relationship among smaller disturbances, such as individual ruts, 

and the response variables, such as a tree that is rooted both in the rut 

and in the surrounding area.

Sampling numerous small plots within each validation area helps provide 

a larger data set that can be used to determine within-site variability and 

to improve understanding of responses, such as plant growth in relation 

to smaller disturbances. This helps pinpoint the differences in growth-determining factors when forest practices are being monitored for changes 

in productivity (Curran 1988). Using numerous plots that have the same 

disturbance types and class will help interpret both short- and long-term 

validation data. If many small-plot disturbance data are available, vegetation growth is more easily paired with within-site variation, and greater 

statistical evaluation of the type and class data is possible.

Sampling Design Examples

Fixed plot sampling

Visual disturbance characteristics vary in size depending on the type of 

equipment used and the amount of disturbance. Either large or small 

plots can be used along with previous study sites, such as the LTSP 

installations, rehabilitation studies, graduate student research sites, skid 

road studies, and retrospective work (see “Using existing or retrospective 

studies”). Smaller plots and the LTSP study sites offer the advantage 

of being more uniform, but plot size should be designed to fit the data 

collected and needs to be related to the disturbances created by the 

harvest activities of interest. Harvest units should first be monitored for 

visual disturbance classes. The latitude/longitude of each monitoring 

point should be recorded and a marker should be placed on the ground 

so that random selection of a representative sample of each visual class 

(0-3) can be completed. Prior monitoring of the unit can help define the 

dimension of the plots. Optimum size of the plots will likely be determined 

by the attribute(s) of interest, but should be large enough to evaluate 

vegetative or hydrologic responses over time. Once a disturbance plot is 

located, it will be useful to designate a measurement plot (a smaller plot 

within the larger disturbance area) to avoid edge effects.
10 USDA Forest Service RMRS-GTR-267. 2012.

Regeneration stocking surveys

Regeneration surveys are used to track natural reforestation and the 

progress of any planted or seeded stock. These surveys are often 

scheduled as part of the stand work plan. This type of survey is easily 

adaptable to use of soil disturbance visual classifications and validation 

monitoring. During stocking surveys, visual soil classes can be assessed 

using a grid or concentric plots, thereby matching soil visual attributes 

with individual seedling growth. Soil and seedling assessments should 

be done in the first year or two after disturbance to ensure correct and 

complete description of both. Once the seedlings are measured and 

the visual disturbance classes are assessed, a subsample of plots can 

selected from the range of soil disturbance classes for long-term monitoring. Regeneration plots can be sampled with small (approximately 

0.001-ha), circular plots within a larger 0.1-ha plot. This makes it easier 

to assign only one visual disturbance class, which can be directly linked 

to one tree seedling and other understory information. Another method 

is to collect soil disturbance class information in each quadrant around 

the tree(s) of interest. Since these plots are associated with a seedling, 

tracking productivity in relation to the soil disturbance classes is relatively 

easy. Other soil measurements of interest can be obtained at the same 

time tree measurements are collected.

Core Measurements

Monumenting

Each visual disturbance type and class measurement plot should be 

mapped and monumented to allow for re-sampling at some interval (one 

yr, five yrs, etc.). At the very least, each plot should be marked in the 

field or noted on an air photo, or (preferably) GPS coordinates should 

be recorded. However, if those methods are not an option, plots can be 

established by marking one corner with rebar, metal conduit, PVC pipe, 

“pigtails”, etc., and then providing location surveys from established 

reference points on landings or road intersections. This method of plot 

location should be backed up with pin pricks on an air photo, hand-drawn 

maps, witness trees, or written directions, which will ensure the plots can 

be re-located. Monumenting plots and tagging trees also ensure that 

specific trees or vegetation are re-sampled over time (Curran 1988; Scott 

and others 1998). For specific details on how to establish and measure 

forestry plots, see Scott (1998).

Understory vegetation

Understory vegetation plots vary in size, but are usually 1-4 m in diameter. Vegetation should be documented by species. Ground cover 

estimates should be recorded for each species of understory vegetation 

or height and diameter by species should be measured. These data can 

be combined into cover classes if needed. Ocular estimates of ground 

cover can be also used, but consistent categories should be used and 

documented (Riegel and others 1995; van Hees and Mead 2000). For 

instance, cover classes use a percentage cover such as: (1) <0.1%, (2) 

0.1-1%, (3) 1-2%, (4) 3-5%, (5) 6-10%, (6) 11-20%, (7) 21-30%, and so 

on.
USDA Forest Service RMRS-GTR-267. 2012. 11

Overstory vegetation

Taller shrubs, seedlings, and trees need both height and diameter 

measurements. Height measures are taken with a meter stick or, for 

larger trees, a laser measuring device. Diameter can be measured with 

a caliper or diameter tape and should be at breast height once trees 

are tall enough (basal diameter is done at the start of monitoring). Site 

index should be determined on dominant tree species before the site is 

harvested to help document any changes that may occur as a result of 

the disturbance.

Soil measurements

The most basic soil measurement is the relationship of the visual 

disturbance class to the change in soil bulk density (Robinson 2011). 

A slide-hammer core sampler with a known volume is the easiest way 

to measure this, but rocky soils may require excavating and then filling 

the hole with foam to determine volume (Page-Dumroese and others 

1999) or another standard method. The forest floor (all organic horizons) 

depth should be measured, if present, as well as the depth of the surface 

mineral horizon (e.g., A horizon and E horizon). Surface horizon depth 

is variable, but the entire depth of those horizons should be determined 

so that changes associated with land management can be documented. 

Forest floor and surface mineral horizon depths are particularly important if displacement has occurred and can also provide information on 

the decomposition or accumulation rate of organic matter. If financially 

possible, the mineral soil and organic matter should be sent to a local 

laboratory for chemical and physical analyses. Organic carbon should 

be considered a minimum soil analysis because of its value in interpreting compaction results, but the appropriate analyses will depend on soil 

type and management concerns. For example, if loss of site potassium 

is a management concern, then potassium should be analyzed; or if the 

goal is carbon sequestration, then carbon and organic matter should be 

analyzed.

Soil Disturbance and Soil Quality Standards

When evaluating the effect of forest harvest disturbance (e.g., ruts, trails, 

scalps, or gouges) the size of these features can be linked to soil quality 

standards in place for a given jurisdiction, such as a U.S. Forest Service 

Region. For example, U.S. Forest Service Region 2 defines displacement as soil loss from a continuous area >9 m2. U.S. Forest Service 

Region 8 defines ruts as: (1) not exceeding 15 cm deep for a continuous 

distance of >15 m, (2) not exceeding 30 cm deep for >3 m, and (3) not 

exceeding 46 cm deep for any distance. These Regional Standards and 

Guidelines can help determine when a visual disturbance class might be 

detrimental, but standards and guidelines from all jurisdictions should be 

validated to ensure local soil and vegetation conditions are considered 

when determining alteration in soil or hydrologic conditions.

Interestingly, neighboring U.S. Forest Service Regions do not always 

have the same soil Standards and Guidelines, even though they often 

share similar soil types and vegetation. In British Columbia, which 
12 USDA Forest Service RMRS-GTR-267. 2012.

borders the two western-most Forest Service Regions, ruts are “counted” 

on moderate or higher compaction hazard sites when they exceed 5 

cm deep into the mineral soil and are 2 m in length, on all sites when 

they exceed 15 cm deep from the soil surface, and when any point is 

gouged deeper than 30 cm into the mineral soil or to bedrock (Curran 

and others 2007). In contrast, ruts in U.S. Forest Service Region 1 are 

counted when they are at least 5 cm deep on all sites. Although there are 

disparities between standards and guidelines, if similar soil attributes are 

measured during validation monitoring, then data can be more readily 

used at different scales and across different jurisdictions.

Modeling soil change

Reeves (2011) developed a method that uses historic soil monitoring 

data in a decision support tool for ground-based logging operations. It 

can be used anywhere that soil monitoring and harvest system data are 

collected. However, the key premise of this tool is that historic soil monitoring data were collected in a consistent manner.

Data storage

Data stored in a database can be used to inform land managers about 

the impacts of harvest and site preparation systems on changes in the 

visual disturbance classes and their severity. For the U.S. Forest Service, 

a national database for soil monitoring and validation data collected 

on National Forests will be completed by summer 2012. This database 

will store monitoring data collected pre- and post-harvest. In the future, 

there will be a place for validation monitoring, but until that is complete, 

these data must be stored on local computers or a central hard drive. 

Ultimately, routine soil monitoring and validation data can be used to 

develop a risk-rating system (Kimsey and others 2011; Reeves 2011; 

Reynolds and others 2011) that is sensitive to local soil and vegetation 

conditions.

Conclusions

Sustainable forest operations are a goal for most forestry organizations around the world. However, this goal will likely only be achieved 

by implementing monitoring and validation processes at the local level. 

Using a common soil monitoring protocol is one step toward determining the impacts of land management on the soil resource. However, 

individual attributes and the associated changes in vegetative production 

or hydrologic function must be validated on a site-specific basis. Using 

existing data, retrospective studies, regeneration surveys, and long-term 

studies or developing new studies are some of the ways that validation 

data can be collected. Identifying soil attributes necessary for maintaining 

productivity with a set of indicators that measure management-induced 

change from the desired baseline will help to meet the need for forest 

products while sustaining forest productivity.
USDA Forest Service RMRS-GTR-267. 2012. 13

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USDA Forest Service RMRS-GTR-267. 2012. 15

Appendix. See the following resources for further background information on 

monitoring, validation, statistics, and managing soil effects from land management.

Affleck, D.L.R. 2008. A line intersect distance sampling strategy for 

downed wood inventory. Canadian Journal of Forest Research 

38:2262-2273.

Affleck, D.L.R. 2010. On the efficiency of line intersect distance 

sampling. Canadian Journal of Forest Research 40:1086-1094.

Berch, S.M.; Dubé, S.; Hope, G.D. 2009. Best management practices for 

soil conservation in mountain pine beetle salvage operations. British 

Columbia Ministry of Forest and Range, Forest Science Program, 

Victoria, British Columbia Extension Note 91.

British Columbia Forest Science Program. 2002. Soil conservation and 

rehabilitation in British Columbia. Brochure No. 70. 20 p. Available: 

http://www.for.gov.bc.ca/hfd/pubs/Docs/Bro/Bro70.htm (accessed May 

24, 2011).

Curran, M.P. 1998. Skid trail rehabilitation (video). 53:02 min. British 

Columbia Ministry of Forests Research Program, Nelson, British 

Columbia. Includes field cards for summer and winter skid trail 

construction and rehabilitation. Contact Michael Curran at British 

Columbia Ministry of Forests, Lands and Natural Resource 

Operations, Kootenay-Boundary Region, Nelson, British Columbia.

Curran, M. 1999. Harvest systems and strategies to reduce soil and 

regeneration impacts (and costs). In: Impact of machine traffic on 

soil and regeneration. Proceedings of Forest Engineering Research 

Institute Canada (FERIC) Machine Traffic/Soil Interaction Workshop, 

held at Edmonton Alberta, Feb. 1999. FERIC Special Report No. SR133: 75-111.

Curran, M.; Davis, I.; Mitchell, B. 2000. Silviculture prescription data 

collection field handbook: Interpretive guide for data collection, site 

stratification, and sensitivity evaluation for silviculture prescriptions. 

British Columbia Ministry of Forests Land Management Handbook No. 

47. 156 p. Includes forms FS39A and B. Available: http://www.for.gov.

bc.ca/hfd/pubs/Docs/Lmh/Lmh47.htm (accessed March 13, 2011).

Curran, M.P.; Fraser, B.; Bedford, L.; Osberg, M.; Mitchell, B. 1993. 

Site preparation strategies to manage soil disturbance. Interior 

sites. Revised edition. British Columbia Ministry of Forests Land 

Management Handbook Field Guide Insert 2, Victoria. 26 p. Available: 

http://www.for.gov.bc.ca/hfd/pubs/Docs/Fgi/Fgi02.htm (accessed 

March 7, 2011).

Curran, M.; Dubé, S.; Bulmer, C.; Berch, S.; Chapman, B.; Hope, G.D.; 

Currie, S.; Courtin, P.; Kranabetter, M. 2009. Protocol for soil resource 

stewardship monitoring: cutblock level. Forest and Range Evaluation 

Program, British Columbia Ministry of Forest and Range and British 

Columbia Ministry of the Environment. Victoria, British Columbia. 
16 USDA Forest Service RMRS-GTR-267. 2012.

Available: http://www.for.gov.bc.ca/ftp/hfp/external/!publish/frep/

indicators/Indicators-Soils-Protocol-2009-May26-2009.pdf (accessed 

March 7, 2011—latest version contains general guidance on soil 

disturbance).

DeLuca, T.H.; Archer, V. 2009. Forest soil quality standards should be 

quantifiable. Journal of Soil and Water Conservation 64:117-123.

Geist, J.M.; Hazard, J.W.; Seidel, K.W. 2008. Juvenile tree growth on 

some volcanic ash soils disturbed by prior forest harvest. Res. Pap. 

RP-PNW-573. Portland, OR: U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Station. 22 p.

Kabzems, R.; Dube, S.; Curran, M.; Chapman, B.; Berch, S.; Hope, 

G.; Kranabetter, M.; Bulmer, C. 2011. Maintaining soil productivity in 

forest biomass chipping operations best management practices for 

soil conservation. British Columbia Forest Service, Forest Science 

Extension Note 98. Available: http://www.for.gov.bc.ca/hfd/pubs/Docs/

En/En98.htm (accessed March 13, 2011).

Kaiser, L. 1983. Unbiased estimation in line-intercept sampling. 

Biometrics 39:965-976.

Krzic, M.; Curran, M.P. 2005. Forest soils and tree nutrition. In: Forestry 

Handbook for British Columbia. University of British Columbia, Faculty 

of Forestry, Vancouver, British Columbia: 353-389.

Kvamme, C. 2010. A comparison of ground cover and frequency 

estimation methods for post-harvest soil monitoring. Thesis. Michigan 

Technological University, Houghton. 35 p.

Lewis, T.; Timber Harvesting Subcommittee. 1991. Developing timber 

harvesting prescriptions to minimize site degradation. British Columbia 

Forest Service Research Branch, Land Management Report 62. 

Available: http://www.for.gov.bc.ca/hfd/pubs/Docs/Mr/Lmr062.htm 

(accessed March 13, 2011).

Norris, D.; McLaughlin, J.; Curran, M. 1998. Armillaria root disease 

management guidelines for the Nelson Forest Region. British 

Columbia Ministry of Forests, Nelson Region, Resource Management 

Technical Report TR-014. 39 p. Available: http://www.for.gov.bc.ca/hfd/

pubs/RSI/FSP/Nelson/RNE_TR-014.htm (accessed March 7, 2011).

Province of British Columbia. 1999. Hazard assessment keys for 

evaluating site sensitivity to soil degrading processes guidebook, 

March 1999. Forest Practices Code of British Columbia. British 

Columbia Forest Service and British Columbia Ministry of 

Environment, Victoria, British Columbia. Available: http://www.for.gov.

bc.ca/tasb/legsregs/fpc/FPCGUIDE/HAZARD/HazardAssessKeysweb.pdf.

Sachs, D.; Heineman, J.; Mather, M.; Curran, M.; Simard, S. 2004. 

Expert system for site preparation and vegetation management in 

southern interior British Columbia. Available: http://www.myacquire.

com/spvegman/expertsystem/ (accessed March 7, 2011) (includes 

the SYTEPREP system growth limiting factor component that helps 

decide on site preparation treatment).
USDA Forest Service RMRS-GTR-267. 2012. 17

Sit, V.; Taylor, B., eds. 1998. Statistical methods for adaptive 

management studies. Research Branch, British Columbia Ministry of 

Forests, Victoria British Columbia Land Management Handbook 42. 

157 p. Available: http://www.for.gov.bc.ca/hfd/pubs/docs/lmh/lmh42.pdf

(accessed on May 24, 2011).

Strindberg, S.; Buckland, T. 2004. Zigzag survey designs in line transect 

sampling. Journal of Agricultural, Biological, and Environmental 

Statistics 9:443-461.

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