United States Department of Agriculture

Forest Rocky Mountain Resource Bulletin

Service Research Station RMRS-RB-23 March 2017

Colorado’s Forest Resources, 2004–2013

Michael T. Thompson, John D. Shaw, Chris Witt, Charles E. Werstak, 

Jr., Michael C. Amacher, Sara A. Goeking, R. Justin DeRose, Todd A. 

Morgan, Colin B. Sorenson, Steven W. Hayes, and Jim Menlove
All Rocky Mountain Research Station publications are produced by U.S. Forest Service employees and are in the 

public domain and available at no cost. Even though U.S. Forest Service publications are not copyrighted, they are 

formatted according to U.S. Department of Agriculture standards and research findings and formatting cannot be 

altered in reprints. Altering content or formatting, including the cover and title page, is strictly prohibited.

Thompson, Michael T.; Shaw, John D.; Witt, Chris; Werstak, Jr., Charles E.; Amacher, 

Michael C.; Goeking, Sara A.; DeRose, R. Justin; Morgan, Todd A.; Sorenson, Colin 

B.; Hayes, Steven W.; Menlove, Jim. 2017. Colorado’s forest resources, 2004–2013. 

Resour. Bull. RMRS-RB-23. Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 136 p.

Abstract

This report presents a summary of the most recent inventory of Colorado’s forests based 

on field data collected between 2004 and 2013. The report includes descriptive highlights 

and tables of area, numbers of trees, biomass, carbon, volume, growth, mortality, and removals. Most sections and tables are organized by forest type or forest-type group, species 

group, diameter class, or owner group. The report also describes the inventory’s design, 

inventory terminology, and data reliability. Results show that Colorado’s forest land covers 

22.9 million acres. Forty-nine percent (11.1 million acres) of this forest land is administered 

by the USDA Forest Service, and another 24 percent (5.6 million acres) is privately owned. 

The State’s most abundant forest type is pinyon/juniper, which covers more than 6 million 

acres. Engelmann spruce and other spruce species are the most abundant tree species by 

number of trees, and are also the most abundant by volume or biomass. Colorado’s forests 

contain 35.2 billion cubic feet of net volume in trees 5.0 inches diameter and larger. Gross 

growth of all live trees 5.0 inches diameter and larger averaged 559.0 million cubic feet per 

year. Average annual mortality totaled 704.2 million cubic feet per year, and net growth was 

therefore –145.2 million cubic feet per year.

Keywords: forest inventory, monitoring, mortality, forest products, wildlife habitat, fire

Cover photos: Top and bottom right: J.D. Shaw; bottom left: W.D. Shaw.
Acknowledgments

We would like to gratefully thank the dedicated Colorado State Forest Service 

field staff for collecting all of our inventory field data. We also extend special thanks to private landowners who graciously allowed our crews to access 

field plots located on their lands. The Rocky Mountain Research Station gratefully acknowledges the invaluable cooperation and assistance of the Rocky 

Mountain Region, Forest Service, U.S. Department of Agriculture; the Colorado 

State Forest Service and other Colorado Natural Resources Department personnel; the Colorado State Lands Office; the Colorado Department of Parks 

and Wildlife; the Bureaus of Land Management and Indian Affairs; and the 

National Park Service, U.S. Department of the Interior. 

i
ii

Authors

Michael T. Thompson is a Forester and a member 

of the Analysis Team with the Interior West Forest 

Inventory and Analysis Program at the USFS Rocky 

Mountain Research Station in Ogden, Utah. He 

holds a B.S. degree in Forestry from North Carolina 

State University.

John D. Shaw is a Biological Scientist and Analysis 

Team Leader with the Interior West Forest Inventory 

and Analysis Program at the USFS Rocky Mountain 

Research Station in Ogden, Utah. He holds B.S. and 

M.S. degrees in Natural Resources Management 

from the University of Alaska Fairbanks and a Ph.D. 

in Forest Ecology from Utah State University.

Chris Witt is an Ecologist and a member of the 

Analysis Team with the Interior West Forest Inventory 

and Analysis Program at the USFS Rocky Mountain 

Research Station in Ogden, Utah. He holds B.S. and 

M.S. degrees in Ecology from Idaho State University.

Charles E. Werstak, Jr., is a Biological Scientist and 

a member of the Analysis Team with the Interior West 

Forest Inventory and Analysis Program at the USFS 

Rocky Mountain Research Station in Ogden, Utah. 

He holds a B.S. degree in Geography from Western 

Illinois University and an M.S. degree in Geography 

from Utah State University.

Michael C. Amacher is a National Program 

Manager, Urban & Community Forestry at USFS 

Rocky Mountain Research Station in Logan, Utah. 

He also serves as the western soils Indicator Advisor 

in the Indicators of Forest Health program. He has 

B.S. and M.S. degrees in Chemistry and a Ph.D. 

in Soil Chemistry, all from The Pennsylvania State 

University.

Sara A. Goeking is a Biological Scientist and a 

member of the Analysis Team with the Interior West 

Forest Inventory and Analysis Program at the Rocky 

Mountain Research Station in Ogden, Utah. She 

holds a B.S. degree in Environmental Studies and 

an M.S. degree in Forest Ecology, both from Utah 

State University.

R. Justin DeRose is a Research Ecologist with 

the Interior West Forest Inventory and Analysis 

Program at the USFS Rocky Mountain Research 

Station in Ogden, Utah. He holds B.S. and M.S. degrees in Forestry from Utah State University and the 

University of Maine and a Ph.D. in Forest Ecology 

from Utah State University.

Todd A. Morgan is the Director of the Forest Industry 

Research Program at The University of Montana’s 

Bureau of Business and Economic Research in 

Missoula, Montana. He received a B.A. degree in 

Philosophy and a B.S. degree in Forest Science from 

The Pennsylvania State University and an M.S. degree in Forestry from The University of Montana.

Colin B. Sorenson is a Research Economist with 

the Forest Industry Research Program at The 

University of Montana’s Bureau of Business and 

Economic Research in Missoula, Montana. He holds 

a B.S. degree in Social Science-Monetary Policy 

with a secondary degree in Natural Resources 

and Environmental Science from Kansas State 

University and an M.A. degree in Economics from 

The University of Montana. 

Steven W. Hayes is a Research Forester with the 

Forest Industry Research Program at The University 

of Montana’s Bureau of Business and Economic 

Research in Missoula, Montana. He received a B.S. 

degree in Forest Resource Management and an M.S. 

degree in Forestry from The University of Montana.

Jim Menlove is an Ecologist and a member of the 

Analysis Team with the Interior West Inventory and 

Analysis Program at the USFS Rocky Mountain 

Research Station in Ogden, Utah. He holds a B.S. 

degree in Biology from the University of Utah and 

an M.S. degree in Zoology and Physiology from the 

University of Wyoming, both with an emphasis in 

ecology. 
Contents

Acknowledgments................................................................................................ i

Authors................................................................................................................. ii

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

Colorado’s Annual Forest Inventory...........................................................................1

Accessing Colorado’s Forest Inventory Data.............................................................1

Comparisons With Previous Periodic Inventories......................................................1

Overview of Standard and Supplemental Tables.......................................................2

Inventory Methods............................................................................................... 3

Plot Configuration ......................................................................................................3

Sample Design ..........................................................................................................4

Three-Phase Inventory ..............................................................................................4

Sources of Error.........................................................................................................8

Quality Assurance......................................................................................................9

Overview of Colorado’s Forests ...................................................................... 13

Ecoregion Provinces of Colorado ............................................................................13

Forest Land Classification .......................................................................................15

Forest Land Ownership ...........................................................................................15

Forest Type and Forest-Type Groups......................................................................16

Stand Age ................................................................................................................18

Numbers of Trees ....................................................................................................19

Tree Volume and Biomass.......................................................................................21

Forest Growth and Mortality ....................................................................................23

Stand Density Index (SDI) .......................................................................................28

Colorado’s Forest Resources .......................................................................... 31

Colorado’s Primary Wood Products Industry ...........................................................31

Removals for Timber Products ................................................................................33

The Pine Nut Resource of Pinyon-Juniper Woodlands............................................ 36

Snags as Wildlife Habitat.........................................................................................38

Old Forests ..............................................................................................................42

Understory Vegetation .............................................................................................44

Forest Soil Resources in Colorado ..........................................................................45

Current Issues in Colorado’s Forests ............................................................. 52

Colorado’s Mountain Pine Beetle Epidemic.............................................................52

Sudden Aspen Decline ............................................................................................55

Spruce Beetle in Colorado.......................................................................................59

Comparisons Between Colorado’s Periodic and Annual Forest Inventories............ 60

Fires in Colorado .....................................................................................................64

iii
Conclusions and Future Analysis.................................................................... 69

References ......................................................................................................... 73

Appendix A. Standard Forest Inventory and Analysis Terminology ............ 81

Appendix B. Standard Forest Resource Tables ............................................. 89

Appendix C. Colorado Forest-Type Groups and Forest Types, With 

Descriptions and Timber (T) or Woodland (W) Designations.............. 122

Appendix D. Tree Species Groups and Tree Species Measured in 

  Colorado’s Annual Inventory With Common Name, Scientific

Name, and Timber (T) or Woodland (W) Designation........................... 127

Appendix E. Volume, Biomass, and Site Index Equation Sources............. 128

Appendix F. Colorado Timber Products Output Tables and 

Colorado Soils Indicator Tables ............................................................. 129

iv
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 1

Introduction_______________________________________________

Colorado’s Annual Forest Inventory

The annual forest inventory of Colorado’s forests follows sampling procedures specified by Federal legislation and the national Forest Inventory Analysis 

(FIA) program. In 1998, the Agricultural Research Extension and Education 

Reform Act, also known as the Farm Bill, mandated that inventories be conducted 

throughout the forests of the United States on an annual basis. This annual system integrates FIA and Forest Health Monitoring (FHM) sampling designs into a 

mapped-plot design, which includes a nationally consistent plot configuration with 

four fixed-radius subplots; a systematic national sampling design consisting of one 

plot per approximately 6,000 acres; annual measurement of a constant proportion 

of permanent plots; data or data summaries within 6 months after yearly sampling 

is completed; and a State summary report after 5 years. The inventory strategy 

for the western United States involves measurement of 10 systematic samples, or 

subpanels, where one subpanel is completed each year and all subpanels are measured over a 10-year period. Each subpanel is pre-assigned to be surveyed during 

a specific calendar year, which is referred to as inventory year (see Appendix A for 

standard FIA terminology). The year in which each plot was actually surveyed is 

recorded as its measurement year.

Interior West Forest Inventory and Analysis (IWFIA) implemented the new 

annual inventory strategy in Colorado in 2002. The most recent annual report for 

Colorado (Thompson et al. 2010) was based on inventory data from 2002 to 2006. 

This report is based on the aggregated data collected in measurement years 2004 

to 2013. The aggregated dataset includes a total of 11,222 plots, where 3,949 plots 

contained at least one forested condition, 6,868 plots were entirely nonforest, and 

405 plots were not sampled.

Accessing Colorado’s Forest Inventory Data

Forest Inventory Analysis data are publicly available from the national FIA 

website at www.fia.fs.fed.us. This site includes data downloads; online tools that 

allow users to perform custom queries; and documentation of FIA’s field inventory protocols, database structure, and publications. For assistance with finding 

information on this site or with performing custom analyses, data users are encouraged to contact one of the members of the analysis team of the Interior West FIA 

Program who are listed as authors at the beginning of this report. Plot data may be 

downloaded in table form or summarized using a variety of online tools (http://fia.

fs.fed.us/tools-data/default.asp).

Comparisons With Previous Periodic Inventories

Data from new inventories are often compared with data from earlier inventories to determine trends in forest resources. However, for the comparisons 

to be valid, the procedures used in the two inventories must be compatible. Two 
2 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

previous periodic inventories of Colorado’s forest resources were completed in 

1983 and 1959 (Benson and Green 1987; Miller and Choate 1964). There are two 

significant factors that cause incompatibility between the 2013 annual inventory 

of Colorado and the previous periodic inventory completed in 1983 (Benson and 

Green 1987).

1. Inventory procedures: The first factor is that inventory procedures varied 

by major ownership category in the previous inventory. In 1983, lands managed by 

National Forest Systems (NFS) and Bureau of Land Management (BLM) were not 

inventoried by IWFIA. The forest inventory estimates such as forest area, volume, 

growth, and mortality were supplied to IWFIA by NFS and BLM. The IWFIA 

only measured inventory plots on lands owned by State agencies and private individuals. The 1983 report merged this inventory data from the different sources to 

describe the status and condition of Colorado’s forest resources. In the 2013 annual 

inventory, forest resource data were collected by IWFIA and Colorado State Forest 

Service on all lands meeting the definition of forest land regardless of ownership 

status or administrative status of the land. This included wilderness areas and other 

areas in reserved status. The 2013 inventory adhered to all national protocols such 

as plot configuration, field variables with nationally consistent meanings and measurements, and national precision standards. None of these national protocols were 

in place in 1983.

2. Definitions: The second factor is that many definitions of forest resource 

attributes have changed since 1983. The impact of these changes varies by inventory estimate. Forest land definitions, plot configuration, and procedures used to 

estimate forest type and stand size are some of the significant changes that have 

occurred since the previous inventory.

The 2013 inventory of Colorado’s forests marks an important shift from its 

predecessors, both in measurements scope and timeliness. With the ability to measure one 10-percent panel of the total sample locations each year, it is now possible 

and practical to monitor emerging resource issues by providing yearly “snapshot” 

updates and longer term trend analysis. The resulting improvements in timeliness, 

combined with the national effort to standardize national inventory procedures, 

have transformed Colorado’s forest inventory into a tool that can detect shortterm trends, address relevant issues, examine ecological relationships, and evaluate human activities that will shape the forests of Colorado for the future. A more 

detailed analysis of the differences between the periodic and annual inventories 

is discussed in the Comparisons between Colorado’s Periodic and Annual Forest 

Inventories section in the “Current Issues in Colorado’s Forests” chapter.

Overview of Standard and Supplemental Tables

Forest Inventory and Analysis produces a set of standard tables that incorporate most of the core FIA program, using both Phase 2 and 3 data. Appendix 

B presents tables B1–B37, which summarize annual forest inventory data collected in Colorado between 2004 and 2013 in terms of traditional FIA attributes. 

These tables encompass statistics for land area, numbers of trees, wood volume, 
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 3

biomass (oven-dry weight), growth, mortality, and sampling errors. Table B1 is 

the only table that includes all land cover types, and it summarizes the proportions 

of sample plots that were recorded as forest, nonforest, and nonsampled (e.g., due 

to inaccessibility). All other tables exclude nonforest land and therefore include 

only accessible forest land or timberland (see Appendix A for definitions). Table 

B37 shows sampling errors for area, volume, net growth, and mortality at the 67 

percent confidence level.

Inventory Methods__________________________________________

This chapter briefly describes five key aspects of the FIA program. The first 

four sections describe configuration of field plots, the national sample design, the 

three-phase inventory system, and sources of error, which are consistent among all 

States. The last section describes FIA’s quality assurance program and presents the 

results of quality assessments in the current forest inventory of Colorado.

Plot Configuration

The national FIA plot design consists of four 24-foot radius subplots configured as a central subplot and three peripheral subplots (USDA Forest Service 2013; 

see figure 1). Centers of the peripheral subplots are located at distances of 120 feet 

and at azimuths of 360 degrees, 120 degrees, and 240 degrees from the center of 

the central subplot. Each standing tree with a diameter at breast height (d.b.h.) for 

timber trees, or a diameter at root collar (d.r.c.) for woodland trees, of 5 inches or 

larger is measured on these subplots. Each subplot contains a 6.8-foot radius microplot with its center located 12 feet east of the subplot center on which each tree 

with a d.b.h./d.r.c. from 1 inch to 4.9 inches is also measured.

Figure 1—Plot configuration used by the Forest 

Inventory and Analysis program. Each plot consists of four subplots with a 24-foot radius. 
4 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

To enable division of the forest into various domains of interest for analysis, 

it is important that the tree data recorded on these plots are properly associated 

with stand-level data. In addition to the tree data recorded on FIA plots, data are 

also gathered about the condition class in which the trees are located. A condition 

class (or condition) is the combination of discrete landscape and forest attributes 

that define and describe the area associated with a plot. The six variables that define distinct condition classes are forest type, stand origin, stand size, ownership 

group, reserved status, and stand density (Bechtold and Patterson 2005). In some 

cases, the plot footprint spans two or more conditions if there is a distinct change 

in any of these six variables. For example, the four subplots on a plot may intersect 

both forest and nonforest areas, the plot may include distinct stands differentiated 

by forest type and/or stand size, or the plot may straddle a boundary between two 

different ownership groups. All three of these examples would result in more than 

one condition per plot. Field crews assign numbers to condition classes in the order 

they are encountered on a plot. Each tree is assigned the number of the condition 

class in which it stands to enable partitioning of tree data into meaningful categories for analysis.

Sample Design

Based on historic national standards, a sampling intensity of approximately 

one plot per 6,000 acres is necessary to satisfy national FIA precision guidelines 

for area and volume. Therefore, FIA divided the area of the United States into nonoverlapping, 5,937-acre hexagons and established one plot in each hexagon using 

procedures designed to preserve existing plot locations from previous inventories. 

These sample plots, designated as the Federal base sample, were divided into five 

spatially interpenetrating panels and 10 subpanels, where each panel consists of 

two subpanels. In the eastern United States, two subpanels are measured each year 

such that the inventory cycle is on a 5-year rotation, while in the western United 

States, including Colorado, one subpanel is measured each year and inventory 

cycles are completed on a 10-year rotation (Gillespie 1999). For estimation purposes, the measurement of each subpanel of plots can be considered an independent, equal probability sample of all lands in a State, or all plots can be combined 

to represent the State.

Three-Phase Inventory

The FIA conducts inventories in three phases. In Phase 1, remote sensing 

data are digitally analyzed to stratify each State into homogeneous groups such as 

forest and nonforest areas. Phase 2 relates to a permanent network of ground plots, 

where traditional inventory variables such as forest type and tree diameter are measured. In Phase 3, additional variables associated with forest and ecosystem health 

are measured on a subset of Phase 2 plots. The three phases of the enhanced FIA 

program are discussed in the following sections.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 5

Phase 1

Phase 1 uses remote sensing data to delineate homogeneous areas, or strata, 

throughout the entire State. Currently in the Interior West, only forest and nonforest strata are identified. The purpose of this delineation is to reduce the variance of 

FIA estimates through post-sampling stratification of field data. The initial Phase 1 

strata map consisted of forest, nonforest, and census water strata (see Appendix A 

for definitions), which were delineated at a spatial resolution of 250 meters using 

a combination of 2004 MODIS satellite imagery, other geospatial datasets, and 

plot-based calibration data (Blackard et al. 2008). Calibration data in Colorado 

consisted of periodic and annual inventory plot locations that had been classified 

as forest, nonforest, or census water, based on field surveys or human interpretation of aerial photographs prior to 2004. In Colorado, the census water stratum and 

nonforest stratum are combined.

In most Interior West States, post-sampling stratification is based solely on 

forest and nonforest strata under the assumption that any Phase 2 nonresponse 

plots occur randomly across the plot grid. Nonresponse plots are defined as plot 

locations that cannot be sampled by a field crew. They typically occur when landowners or managers do not grant permission for field crews to access plot locations 

on their lands, although some plots are not sampled due to hazardous conditions 

that may be permanent in nature (e.g., sheer cliffs) or temporary hazards (e.g., 

current wildfires or active logging operations). When nonresponse plots do not occur randomly across the plot grid, the estimates of forest attributes may be biased 

(Patterson et al. 2012). The overall nonresponse rate in Colorado’s forest inventory 

was relatively low at 3.6 percent. Future analyses will quantify the magnitude of 

the effect of nonresponse on FIA estimates, but for the purpose of this report, the 

effect is assumed to be small.

The FIA produces estimates at the scale of individual States, which can then 

be aggregated into regional estimates, as well as at smaller scales within each State. 

Within-State population estimates are constructed at two scales: survey units that 

are comprised of groups of counties, and smaller estimation units that represent 

individual counties. Colorado consists of five inventory units and 64 estimation 

units denoted as g, each containing ng ground plots. The area of each estimation 

unit is divided into strata of known size using the State’s stratification map, which 

divides the total area of the estimation unit into 250-meter pixels and assigns each 

pixel to one of H strata. Each stratum, h, within an estimation unit, g, then contains 

nhg ground plots where the Phase 2 attributes of interest are observed.

To illustrate, the area estimator for forest land within an estimation unit in 

Colorado is defined as:

H

h=1

i=1

nhg

nhg

Âg = ATg Ʃ Whg

Ʃ yihg

where
6 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

Âg = total forest area (acres) for estimation unit g,

ATg = total land area (acres) in estimation unit g,

 H = number of strata,

Whg= proportion of Phase 1 pixels in estimation unit g that occur in stratum h,

yihg = forest land condition proportion on Phase 2 plot i in stratum h in estimation 

unit g, and

 nhg = total number of Phase 2 plots in stratum h in estimation unit g.

Phase 2

Phase 2 pertains to FIA’s network of permanent plot locations, where each 

plot is assigned spatial coordinates and represents approximately 6,000 acres. To 

minimize inventory costs, plots that are obviously and entirely nonforest are not 

designated for field sampling, and these plots are recorded as nonforest. A human 

interpreter examines each plot location using digital imagery from the National 

Agriculture Imagery Program and distinguishes plots that potentially contain forest or wooded land from those that do not intersect any forest or wooded land. This 

process is known as prefield interpretation, and it was historically considered part 

of Phase 1 because both prefield interpretation and Phase 1 relied on remote sensing data. However, Phase 1 delineation of forest and nonforest strata occurs independently of current prefield interpretation of the Phase 2 grid. Therefore, prefield 

data collection is considered part of Phase 2 and not part of Phase 1.

The status of each plot in the Phase 2 grid is eventually assigned as accessible forest land, nonforest land, or not sampled. Plots that were not designated 

for field sampling by prefield interpreters are automatically recorded as nonforest 

plots. For plots that are designated for field sampling, field crews record the plot 

status as accessible forest land if (a) they can physically visit the plot location, and 

(b) the plot satisfies FIA’s definition of forest land (see Appendix A). Some field 

plots are recorded as nonforest because the field crew determines that they do not 

meet FIA’s definition of forest land. A field plot may be recorded as non-sampled 

if a field crew cannot safely measure the plot or if they cannot obtain permission 

to access the plot location.

Before visiting privately owned plot locations, FIA crews identify each plot’s 

ownership status by consulting county land records and then seek permission from 

private landowners to measure plots on their lands. Information about individual 

landowners and the existence of FIA plots on their property is considered confidential and is never shared with anyone, regardless of whether permission to access 

the plot location is granted. Table B1 (Appendix B) shows the total percentage of 

Phase 2 plot areas that represent forest, nonforest, and non-sampled conditions. 

Figure 2 illustrates the locations of FIA inventory plots that sampled forest land in 

Colorado.

Field crews record a variety of data on plot locations that contain accessible 

forest land. Crews locate the geographic center of the plot using geographic positioning system (GPS) receivers and then establish markers to facilitate relocation of the plot for future remeasurement. They record condition-level variables 

that include land use, forest type, stand origin, stand-size class, stand age, site 
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 7

productivity class, forest disturbance history, silvicultural treatment, slope, aspect, 

and physiographic class. Some of these area attributes are measured or observed 

(e.g., regeneration status), some are assigned by definition (e.g., ownership group), 

and some are computed from tree data (e.g., percent stocking). For each tree on 

the plot, field crews record a variety of attributes including species, live/dead status, diameter, height, crown ratio, crown class, damage, and decay status. The 

field procedures used in Colorado’s forest inventory are described in detail in the 

FIA field guide (USDA Forest Service 2013). Data analysts apply statistical models using field measurements to calculate additional variables such as volume and 

biomass for individual trees, as well as volume, biomass, growth, mortality, and 

number of trees per unit area.

Phase 3

The third phase of the enhanced FIA program focuses on forest and ecosystem health. The Phase 3 sample consists of a 1/16 subset of the Phase 2 plots, 

Figure 2—Map of Colorado illustrating approximate locations of FIA plots that sampled forest land, 2004–2013.
8 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

which equates to one Phase 3 plot for approximately every 96,000 acres. Phase 3 

plots include all the measurements collected on Phase 2 plots, plus an extended 

suite of ecological data pertaining to soil samples, down woody materials, lichen 

communities, tree crowns, and understory vegetation structure. Phase 3 measurements are obtained by field crews during the growing season. The entire suite of 

Phase 2 measurements is collected on each Phase 3 plot at the same time as the 

Phase 3 measurements.

Sources of Error

Sampling Error

The process of sampling (selecting a random subset of a population and calculating estimates from this subset) causes estimates to contain errors they would 

not have if every member of the population had been observed and included in 

the estimate. The 2004–2013 FIA inventory of Colorado is based on a sample of 

10,990 plots (not including 410 nonresponse plots) systematically located across 

the State. The total area of Colorado is 66.6 million acres, so the sampling rate is 

approximately one plot for every 6,062 acres.

The statistical estimation procedures used to provide the estimates of the 

population totals presented in this report are described in detail in Bechtold and 

Patterson (2005). Along with every estimate is an associated sampling error that 

is typically expressed as a percentage of the estimated value, but it can also be 

expressed in the same units as the estimate or as a confidence interval (the estimated value plus or minus the sampling error). This sampling error is the primary 

measure of the reliability of an estimate. An approximate 67 percent confidence 

interval constructed from the sampling error can be interpreted to mean that under 

hypothetical repeated sampling, approximately 67 percent of the confidence intervals calculated from the individual repeat samples would include the true population parameter if it were computed from a 100-percent inventory. The sampling 

errors for State-level estimates are presented in table B37 in Appendix B.

Because sampling error increases as the area or volume considered decreases, users should aggregate data categories as much as possible. Sampling errors 

obtained from this method are only approximations of reliability because homogeneity of variances is assumed. Users may compute statistical confidence for subdivisions of the reported data using the formula below:

SEs = SEt Xs

Xt

where

SEs = sampling error for subdivision of State total,

SEt

 = sampling error for State total,

Xs

 = sum of values for the variable of interest (area, volume, biomass, etc.) for 

subdivision of State total, and

 Xt = sum of values (area, volume, biomass, etc.) for State total.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 9

Measurement Error

Measurement errors are errors associated with the methods and instruments 

used to observe and record the sample attributes. On FIA plots, attributes such as 

the diameter and height of a tree are measured with specialized instruments and 

other attributes such as species and crown class are observed without the aid of an 

instrument. On a typical FIA plot, 30 to 70 trees are observed with 15 to 20 attributes recorded on each tree. In addition, many attributes that describe the plot and 

conditions on the plot are observed. Errors in any of these observations affect the 

quality of the estimates. If a measurement is biased—such as tree diameters consistently taken at a height other than 4.5 feet from the ground—then the estimates 

that use this observation (e.g., calculated volume) will reflect this bias. Even if 

measurements are unbiased, high levels of random error in the measurements will 

add to the total random error of the estimation process. To ensure that FIA observations meet the highest standards possible, a quality assurance program, described 

below, is integrated throughout all FIA data collection efforts.

Prediction Error

Prediction errors are associated with using mathematical models (such as volume models) to provide information about attributes of interest based on sample 

attributes. Area, number of trees, volume, biomass, growth, removals, and mortality are the primary attributes of interest presented in this report. The FIA estimates 

of area and number of trees are based on direct observations and do not involve 

the use of prediction models; however, estimates of volume, biomass, growth, and 

mortality used model-based predictions in the estimation process and are thus subject to prediction errors.

Quality Assurance

The FIA employs a Quality Assurance (QA) program to ensure the quality of 

all collected data. The QA program provides a framework to assure the production 

of complete, accurate, and unbiased forest information of known quality. There are 

two primary facets of FIA’s QA program: quality control and quality assessment.

The FIA’s quality control process operates via data quality inspectors, who 

assess individual field crews and then provide timely feedback to improve the 

crews’ performance. This is accomplished by means of hot checks and cold checks. 

During a hot check, an inspector accompanies a field crew to a plot and provides 

immediate feedback on the quality of their measurements. Cold checks occur when 

an inspector visits a recently completed plot, typically in possession of the original 

crew’s data but without the crew present, and then verifies each measurement and 

provides the crew an overall score as well as feedback on measurements that did 

not meet FIA specifications. On average, hot checks are done on 2 percent of all 

field-sampled plots and cold checks are done on 5 percent of field-sampled plots.

Quality assessment is the second facet of FIA’s QA program, and this process 

quantifies the overall precision of field measurements by comparing two independent measurements of the same plot. The independent measurements are collected 
10 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

by means of blind checks, where a regular field crew collects measurements and 

then a second crew collects a second set of measurements, without knowledge of or 

access to the first crew’s measurements (Pollard et al. 2006). Thus, these paired observations provide a means of assessing repeatability of FIA’s field measurements.

Quality control and quality assessment both require a data quality standard 

that defines the target level of precision for field measurements. The FIA program 

has specific Measurement Quality Objectives (MQOs) that enumerate data quality 

standards for individual field-measured variables. These data quality objectives 

were developed from knowledge of measurement processes in forestry and forest ecology as well as the requirements of the FIA program. Measurement quality 

objectives for each variable consist of a measurement tolerance and a compliance 

standard. Measurement tolerances define the acceptable range of variability between two independent observations, and compliance standards define the target 

percentage of observations that should be within the measurement tolerance when 

recorded by two independent observers. The practicality of these MQOs, as well 

as the measurement uncertainty associated with a given field measurement, can 

be tested by comparing the results of quality assessments using blind check data.

Quality assessment data for Colorado’s current inventory were collected between 2010 and 2013. The results of the QA analysis for this period are presented 

in tables 1 and 2. Table 1 describes tolerances for condition-level variables, and 

table 2 describes tree-level variables. Each variable and its associated measurement tolerance are followed by the percentage of total paired records that fall within one, two, three, and four times the tolerance. The last four columns show the 

number of observations that fell outside the tolerance. For example, table 1 shows 

that there were 64 paired records, representing 64 conditions that were measured 

independently by two field crews, for the variable “Forest Type.” About 91 percent 

of those records fell within the tolerance of having no errors. The percentage of 

observations that fall within the 1X tolerance level is referred to as the observed 

compliance rate, which can be compared to the compliance standard for each variable’s MQO to determine that variable’s performance. Compliance standards and 

measurement tolerances for FIA’s field measurements are listed within the field 

manual (USDA 2013).

The information in tables 1 and 2 shows variables with varying degrees of 

repeatability. For example, one condition-level regional variable that appears to be 

fairly repeatable is “Percent Bare Ground.” At the 1X tolerance level, its observed 

compliance rate was about 94 percent. This represents that 94 percent of 64 paired 

observations were within plus or minus 10 percent of each other. In contrast, the 

compliance rate for “Percent Crown Cover” was only 63 percent, which indicates 

that further training may be required to improve the repeatability of this variable. 

The compliance rate for “Habitat Type 1,” which has no tolerance variability, was 

only 55 percent. This low compliance rate warrants further investigation into the 

potential repeatability issues associated with evaluating habitat type, which can 

provide insight into successional status when combined with existing vegetation 

(such as tree numbers, size class, and species by habitat types or series). Habitat 

types are represented as a categorical value, and it is likely that the compliance 
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 11

rate for habitat types would be higher if we could consider habitat type groups (or 

groups of types that are very similar) in our quality assurance analysis.

The tree measurements that have the biggest influence on estimates of forest volume are tree species, tree diameter, and tree height. As shown in table 2, 

the compliance rate for the variable “Species” was 96 percent. The diameter variables “d.b.h.” and “d.r.c.” represent the respective diameters of timber and woodland tree species (see Appendix C) whereas timber species are measured at breast 

height (4.5 feet above ground level), and woodland species are measured near 

ground level at root collar. The 1X compliance rates for diameter were almost 90 

percent for both d.b.h., which has a tolerance of 0.1 inch, and almost 87 percent 

for d.r.c., which has a 0.2 inch per stem tolerance to allow for larger tolerances on 

multi-stemmed woodland trees. Tree height is represented by the variables “Total 

Length” and “Actual Length.” Both variables have a tolerance level of ±10 percent 

of the observed length, and compliance rates at the 1X level were about 85 percent and 86 percent, respectively. The variable “crown class” was the least repeatable tree-level variable, with a 1X compliance rate of only 4 percent; as with the 

condition-level variable “percent crown cover,” the compliance rate for “crown 

class” could likely be improved with additional clarification and training on the 

differences among crown class categories. The compliance rate for the variable 

“tree age” was also somewhat low at 31 percent. This is probably due to the difficulty of obtaining accurate tree ages. Several factors that might contribute to 

inconsistency among tree ages are (1) variation in age estimation when cores do 

Table 1—Results of quality assessment for condition-level variables from 71 conditions in Colorado. Variables that did not 

have any non-null values recorded on any QA plots are not shown; these include secondary and tertiary disturbances and 

treatments, owner industrial status, and regeneration species.

Variable Tolerance

Percentage of data 

within tolerance

Number of times data 

exceeded tolerance

Number 

of 

@1x @2x @3x @4x @1x @2x @3x @4x records

National core variables

Condition status No errors 98.6 1 71

Reserve status No errors 98.6 1 71

Owner group No errors 100.0 0 71

Forest type No errors 90.6 6 64

Stand size No errors 82.8 11 64

Tree density No errors 100.0 0 64

Disturbance 1 No errors 84.4 10 64

Disturbance year 1 ±1 yr 14.3 14.3 14.3 14.3 6 6 6 6 7

Treatment 1 No errors 98.4 1 64

Treatment year 1 ±1 yr 50.0 100.0 1 0 2

Physiographic class No errors 62.5 24 64

Regional variables

Percent crown cover ±10 % 62.5 85.9 93.8 96.9 24 9 4 2 64

Percent bare ground ±10 % 93.8 96.9 98.4 98.4 4 2 1 1 64

Habitat type 1 No errors 54.7 29 64

Habitat type 2 No errors 56.3 28 64
12 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

Table 2—Results of quality assessment for tree-level variables from 1,293 trees in Colorado.

Variable Tolerance

Percentage of data 

within tolerance

Number of times data 

exceeded tolerance

Number 

of 

@1x @2x @3x @4x @1x @2x @3x @4x records

National core variables

Diameter (d.b.h.) ±0.1 /20 in. 89.8 95.8 97.0 97.9 114 47 33 24 1,118

Diameter (d.r.c. using 

IW MQO) ±0.2 in*#stems 86.9 91.4 95.4 96.0 23 15 8 7 175

Azimuth ±10 o 97.5 99.2 99.5 99.7 16 5 3 2 645

Horizontal distance ±0.2 /1.0 ft 91.3 94.9 96.1 96.9 56 33 25 20 645

Species No tolerance 96.2 49 1,293

Tree status No tolerance 99.5 7 1,293

Rotten/Missing cull ±10 89.3 95.2 97.6 98.8 125 56 28 14 1,164

Total length ±10 85.2 95.9 98.5 99.2 192 53 19 10 1,293

Actual length ±10 85.5 96.4 98.6 99.3 144 36 14 7 996

Compacted crown ratio ±10 100.0 0 1,086

Uncompacted crown 

ratio (P3) ±10 76.8 93.8 97.8 99.1 222 59 21 9 958

Crown class No tolerance 4.1 1041 1,086

Decay class ±1 class 100.0 0 200

Cause of death No tolerance 76.9 24 104

Mortality year ±2 yr 76.9 96.2 99.0 100.0 24 4 1 0 104

Condition class No tolerance 99.0 13 1,293

Regional variables

Mistletoe ±1 class 98.4 98.8 99.4 99.8 17 13 7 2 1,086

Number of stems ±1 stem 96.6 97.7 99.4 100.0 6 4 1 0 175

Percent missing top ±10 97.0 97.0 97.0 97.0 35 35 35 35 1,164

Sound dead ±10 49.7 49.7 49.7 49.7 585 585 585 585 1,164

Form defect ±10 11.2 11.2 11.2 11.2 301 301 301 301 339

Current tree class No tolerance 95.3 61 1,293

Tree age ±5 30.6 41.7 50.0 52.8 25 21 18 17 36

Horiz dist-timberland ±0.2 /1.0 ft 98.2 99.3 99.5 99.6 20 8 6 4 1,118

Horiz dist-woodland ±0.2 /1.0 ft 73.1 82.3 86.9 89.7 47 31 23 18 175

 

not include tree center, or pith; (2) tree rings are too close together or too faint to 

read accurately in the field; and (3) some trees are too large to reach the center. 

These situations are particularly prevalent in old and slow-growing trees, and they 

could be mitigated through better field procedures and/or processing tree cores in 

a dedicated tree ring laboratory that uses sandpaper, microscopes, and sometimes 

modeling techniques to obtain more accurate age estimates.

As more blind check information becomes available, it might become apparent that either more intensive crew training is required, or that a variable’s MQO 

needs to be adjusted accordingly to better reflect the realistic expectation of quality 

for that variable. As a result, MQOs are used not only to assess the reliability of 

FIA measurements and their ability to meet current standards, but also to identify 
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 13

areas of improvement of data collection protocols and training. This ongoing process improves repeatability or may even lead to elimination of variables that prove 

to be unrepeatable.

Overview of Colorado’s Forests_______________________________

The following sections discuss the status and trends of Colorado’s forest 

land resources in terms of area, number of trees, volume and biomass, stand age, 

forest change components, growth, removals, and mortality, and stand density 

index (SDI) using annual data collected from 2004 through 2013. The sections 

“Ecoregion Provinces of Colorado,” Forest Land Classification,” and “Forest Land 

Ownership” include summaries of forest land, and non-forest lands; the remaining 

“Overview of Colorado’s Forests” sections will focus only on the forest land base.

Ecoregion Provinces of Colorado

Issues and events that influence forest conditions often occur across forest 

types, ownerships, and political boundaries. As a result, scientists, researchers, and 

land managers must also find a way to assess and treat these issues in a boundaryless way. Ecoregions are often used as a nonpolitical land division to help researchers study forest conditions. An ecoregion is a large landscape area that has relatively consistent patterns of topography, geology, soils, vegetation, climate, and 

natural processes (Shinneman et al. 2000). Many smaller ecosystems may reside 

within an ecoregion.

Colorado is at the confluence of seven ecoprovinces (Bailey 1995): (1) the 

Colorado Plateau Semi-Desert Province in the southwestern part of the State; 

(2) the Great Plains-Palouse Dry Steppe Province encompasses most of the eastern part of the State; (3) the Intermountain Semi-Desert Province in the northern 

part; (4) the Intermountain Semi-Desert and Desert Province in the west; (5) the 

Nevada-Utah Mountains-Semi-Desert Province in the northwest; (6) the Southern 

Rocky Mountain Steppe Province in the central and western part of the State; and 

(7) the Arizona-New Mexico Mountains Semi-Desert that occupies a very small 

portion in the southern region of Colorado (fig. 3).

The most prominent ecoprovince is the Southern Rocky Mountain Steppe, 

which contains the most forested area and greatest variety of forest types. This region is home to the Rocky Mountains, rugged glaciated mountains of over 14,000 

feet. Forests in this province are characterized by vegetational zonation, controlled 

by a combination of altitude, latitude, direction of prevailing winds, and slope exposure. The uppermost vegetational zone is characterized by alpine tundra and the 

absence of trees. Directly below it is the subalpine zone, dominated in most places 

by Engelmann spruce and subalpine fir. Below this area lies the montane zone, 

characterized by ponderosa pine and Douglas-fir. Fire disturbance regimes create 

stands of aspen or lodgepole pine in the subalpine and montane zones. Below the 

montane belt is the foothill zone. Dry rocky slopes abound in this province, and 
14 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

ponderosa pine and pinyon-juniper are the typical forest types found, depending 

on slope exposure.

The Great Plains-Palouse Dry Steppe Province is characterized by rolling 

plains and tablelands of moderate relief in a broad belt that slopes gradually eastward 

from an altitude of 5,500 ft (1,520 m) near the foot of the Rocky Mountains. This region, often referred to as the Great Plains grasslands, has scattered trees and shrubs, 

and it supports many species of grass. Forests are nearly nonexistent in this province.

The remaining four ecoregion provinces are characterized by dry rocky foothills, mesas, and plateaus. The predominate forest types in these regions are pinyon 

pine, juniper, or a mix of both commonly referred to as pinyon-juniper woodlands. 

The forests in these semi-desert regions are commonly associated with sagebrush 

communities.

Figure 3—Ecoregion map of Colorado.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 15

Forest Land Classification

Historically, FIA has used a nationally consistent standard for defining different categories of forest land. These categories were originally developed for 

the purpose of separating forest land deemed suitable for timber production from 

forest land that was either not suitable or unavailable for timber harvesting activity. 

The first division of forest land is unreserved forest land and reserved forest land. 

Unreserved forest land is considered available for harvesting activity where wood 

volume can be removed for wood products. Reserved forest land is considered 

unavailable for any type of wood utilization management practice through administrative proclamation or legislation.

Unreserved forest land is further divided into timberland and unproductive 

forests. Timberland is forest land capable of producing 20 cubic feet of wood per 

acre per year of trees designated as a timber species and not withdrawn from timber production. Unproductive forests, because of species characteristics and site 

conditions, are not capable of producing 20 cubic feet of wood per acre per year of 

trees designated as a timber species (see Appendix A for definitions).

Reserved forest land can also be divided by productivity. Some characteristics that contribute to productivity can be visibly obvious, such as the presence or 

absence of non-commercial species, rocky substrates, and high elevation. While 

these distinctions may be important to reserved area management concerns, for 

example their effect on visitor experience, wood production capability on reserved 

forest land is probably not the best way to discuss these issues.

The State of Colorado encompasses over 71.3 million acres (Appendix B, 

table B1). Thirty-two percent, or 22.9 million acres (Appendix B, table B2), of 

the area meets the definition of forest land (Appendix A). Unreserved forest land 

accounts for 87 percent of Colorado’s total forest land with 47 percent classified as timberland and 40 percent classified as unproductive. Twelve percent of 

Colorado’s forest land is reserved and nearly all of this reserved forest land is classified as productive (Appendix B, table B2).

Forest Land Ownership

Colorado’s largest manager of forest land is the USDA Forest Service’s 

National Forest System (NFS), which manages 11.1 million acres of forest land 

(Appendix B, table B2). This represents almost 49 percent of the State’s total forest 

land area (fig. 4). National Forest System land in Colorado consists of 10 different 

National Forests and two National Grasslands. Eighty-one percent of NFS forest 

land is classified as unreserved forest land of which the majority (84 percent) is 

timberland.

The other major government agency that controls a significant amount of 

forest land in Colorado is the Bureau of Land Management (BLM). Forest land 

administered by the BLM totals 5.0 million acres. The majority of BLM’s forest 

land—about 95 percent—is classified as unreserved. Only 13 percent of BLM’s 

forest land meets the criteria to qualify as timberland. Privately owned forest land 
16 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

totals 5.6 million acres. Private landowners are a diverse group in Colorado consisting of private individuals and corporations. All private forest land is in the 

unreserved owner class with 40 percent classified as timberland and 60 percent 

classified as unproductive. The remaining amount of forest land in Colorado is 

controlled by the National Park Service (NPS), State and local government, and 

the Department of Defense. Nearly 570,000 acres are controlled by State and local 

governments, another 366,000 acres are controlled by the NPS, and 103,000 acres 

are controlled by the Department of Defense.

Forest Type and Forest-Type Groups

Forest type is a classification of forest land based on the species forming a 

plurality of basal area of living trees growing in a particular forest. Forest type 

names may be based on a single species or groups of species. Forest types are an 

important measure of diversity, structure, and successional stage. The distribution 

of forest types across the landscape is determined by factors such as climate, soil, 

elevation, aspect, and disturbance history. The loss or gain of a particular forest 

type over time can help assess the impact of major disturbances related to fire, 

weather, climate, insects, disease, and human-caused disturbances such as timber 

harvesting or ecosystem restoration.

49%	

24%	

22%	

2%	

2%	 1%	

Forest	Service	

Private	

Bureau	of	Land	Management	

State	

Na8onal	Park	Service	

All	other	owners	

Figure 4—Percent distribution of forest land by owner group, Colorado 2004–2013.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 17

Forest types are aggregated into forest-type groups to simplify interpretation of large-scale forest trends. Colorado’s forests represent 11 forest-type groups 

that are further classified into distinct forest types, all of which are described in 

Appendix C. Some forest-type groups contain only one forest type, while other forest-type groups include several individual forest types. An example of a 

forest-type group with multiple forest types is the pinyon/juniper forest-type group, 

which consists of the Rocky Mountain juniper forest type, the pinyon/juniper forest type, and the juniper woodland forest type. As noted above, the distribution of 

forest types as well as individual tree species may vary among ecological provinces. Figure 5 shows the area occupied by each forest-type group in Colorado.

Colorado’s most abundant forest-type group is the pinyon/juniper group, 

which covers 6.4 million acres and accounts for 28 percent of total forest area in 

the State (Appendix B, table B3). Within this forest-type group, the pinyon/juniper 

woodland is the most abundant (4.7 million acres), followed by the juniper woodland type (1.1 million acres) and the Rocky Mountain juniper type (0.5 million 

acres). Next in abundance is the fir/spruce/mountain hemlock type group, which 

comprises 4.9 million acres and 21 percent of the State’s forest land. This diverse 

forest-type group consists of five forest types in Colorado where the Engelmann 

	-				 	1,000.0		 	2,000.0		 	3,000.0		 	4,000.0		 	5,000.0		 	6,000.0		 	7,000.0		

Exotic hardwoods group 

Elm / ash / cottonwood group 

Other western softwoods 

group 

Nonstocked 

Ponderosa pine group 

Douglas-fir group 

Lodgepole pine group 

Woodland hardwoods group 

Aspen / birch group 

Fir / spruce / mountain 

hemlock group 

Pinyon / juniper group 

Thousand	acres	

Figure 5—Area of forest land by forest-type group, Colorado, 2004–2013. See Appendix C for forest types and tree species 

included in each group.
18 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

spruce type accounts for the majority of area at 2.1 million acres. The aspen/birch 

type group is third in abundance at 3.4 million acres and is entirely comprised of 

the aspen forest type. Fourth in abundance at 2.4 million acres, the woodland hardwoods forest-type group is mainly comprised of the deciduous woodland type—a 

forest type where Gambel oak is the principal species. The lodgepole pine forest 

type is the fifth most abundant forest type totaling 1.6 million acres. The Douglasfir and ponderosa pine forest-type groups each occupy about 1.5 million acres. The 

remaining forest land in the State is classified as the nonstocked type group (0.7 

million acres), other western softwoods group (0.3 million acres), and the elm/ash/

cottonwood group (0.1 million acres).

Stand Age

The age structure of forest land provides insight into prospective shifts in 

stand structure and composition over time. On every FIA plot that samples forest 

land and includes suitable trees for increment core extraction, stand age is estimated based upon the average age of only those trees that fall within the calculated 

stand-size category. For example, suppose an FIA plot sampled a softwood forest 

type where about 30 percent of the live trees were in the large diameter stand-size 

(trees at least 9.0 inches d.b.h. and larger) and 70 percent were in the medium 

diameter size class (trees between 5.0 and 9.0 inches d.b.h.). The stand would be 

classified as a medium diameter stand size class, and therefore only the medium 

size trees would be used in determining stand age.

There are limitations to collecting data for stand age computation. Repeatable 

measurements of increment cores are difficult to collect from certain tree species, 

particularly woodland species or those that may be very long-lived. Stand age may 

not accurately depict the age structure of uneven-aged stands, which encompass 

multiple age classes. Stand ages are difficult to accurately determine for stands that 

are predominated by small-diameter tree species such as Gambel oak trees. Stand 

ages are not assigned to nonstocked conditions, which are stands that contain less 

than 10 percent stocking of live trees because of disturbance.

Table B6 (Appendix B) shows the area of forest land, by age class and foresttype group, with 20-year intervals representing stand age classes. Over 50 percent 

of Colorado’s forest land, or 11.5 million acres, is between 60 and 140 years of 

age. Stands between 80 and 100 years of age represent the largest single 20-year 

age class and comprise 3.5 million acres or 15 percent of Colorado’s forest land. 

Almost 13 percent of Colorado’s forest land, or 2.9 million acres, is in stands less 

than 20 years of age; 8 percent, or 1.8 million acres, is in stands over 200 years of 

age.

Considerable differences are apparent in stand age distribution among the 

major forest-type groups in the State (fig. 6). The pinyon/juniper and fir/spruce/

mountain hemlock forest-type groups have the most even distribution of forest 

area across all age class groups. The pinyon/juniper forest-type group has the largest proportion (17 percent) of forest area in stands over 200 years of age and also 

has the smallest proportion (3 percent) of forest area in stands less than 21 years of 
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 19

age. The lodgepole pine type group has the largest proportion (48 percent) of the 

State’s major forest-type groups in stands between 80 and 120 years of age.

Compared to the coniferous forest-type groups, aspen forests have a slightly 

higher percentage (28 percent) of area that is less than 61 years of age. Seventeen 

percent of aspen forest types are less than 21 years of age and only 1 percent 

of aspen types are in stands older than 160 years. Compared to other forest-type 

groups, the woodland hardwood group has the greatest proportion of its area in the 

youngest age classes: 68 percent is younger than 20 years and another 15 percent 

is in the 21 to 40 year age class.

Numbers of Trees

Estimates of the numbers of trees are beneficial to a variety of silvicultural, 

forest health, and habitat management applications. These estimates are typically 

combined with information about the size and species of trees to provide meaningful summaries of forest dynamics and stand structure. Younger forest stands usually consist of large numbers of small-diameter trees, whereas older forest stands 

contain small numbers of large-diameter trees. The FIA classifies individual tree 

species into species groups, and it also categorizes each species and species group 

as either softwood or hardwood (Appendix D).

0.0%	

10.0%	

20.0%	

30.0%	

40.0%	

50.0%	

60.0%	

70.0%	

80.0%	

Percent	distribu-on	

Stand	age	class	(years)	

Pinyon	/	juniper	group	

Fir	/	spruce	/	mountain	hemlock	group	

Aspen	/	birch	group	

Woodland	hardwoods	group	

Lodgepole	pine	group	

Figure 6—Distribution of forest land 

by stand-age class for major foresttype groups, Colorado, 2004-2013.
20 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

There is an estimated 11.9 billion live trees 1.0 inch in diameter or larger 

(Appendix B, table B10) on Colorado’s forest land area. Softwoods species total 

6.2 billion trees or 52 percent of the State’s live trees. The most abundant softwood species group was the Engelmann and other spruces group, which totaled 

1.5 billion trees and accounted for over 12 percent of the total number of live trees 

(fig. 7). Second in abundance was the western woodland softwood group, which 

totaled 1.4 billion trees or 12 percent of the total number of live trees. The third 

most abundant softwood species group was true fir, which totaled 1.3 billion trees 

and accounted for 11 percent of the total number of live trees. Lodgepole pine 

totaled over 1.0 billion trees and comprised 9 percent of all live trees in the State.

Hardwood species account for 5.7 billion trees, or 48 percent of Colorado’s 

live trees. The vast majority of hardwood species resides in the woodland hardwood species group, which totals 4.1 billion trees. This species group, comprised 

mainly of Gambel oak, also makes up the majority (34 percent) of all live trees in 

the State. The second most abundant hardwood species group is the cottonwood 

and aspen species group, which consists of 1.6 billion quaking aspens and a very 

small amount of three cottonwood species.

Figure 8 illustrates the number of live trees by diameter class for three major species groupings—softwood, cottonwood and aspen, and western woodland 

0	 500,000	 1,000,000	 1,500,000	 2,000,000	 2,500,000	 3,000,000	 3,500,000	 4,000,000	 4,500,000	

Douglas-fir	

Ponderosa	and	Jeffrey	pine	

True	fir	

Engelmann	and	other	spruces	

Lodgepole	pine	

Other	western	soEwoods	

Western	woodland	soEwoods	

CoHonwood	and	aspen	

Western	woodland	hardwoods	

Thousand	trees	

Species	groups	

Figure 7—Number of live trees 1.0 inch in diameter and larger on forest land, by species group, Colorado, 2004–2013.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 21

hardwoods—in Colorado. The distribution of live trees by diameter class indicates 

the typical inverse j-shape for softwoods and the cottonwood and aspen groups. 

The western woodland hardwoods species group has a very high proportion 

(97 percent) of live trees in the smallest diameter class (less than 5.0 inches in 

diameter) compared to the softwoods (57 percent) and cottonwood and aspen 

(62 percent).

Tree Volume and Biomass

The amount of cubic-foot volume of wood in a forest is important for determining the sustainability of current and future wood utilization. The forest products industry and forest managers are interested in knowing the tree species composition and size distribution, as well as the geographic location and ownership 

status, of available wood volume. Estimates of gross and net volume include only 

the merchantable portion or sawlog portion (e.g., cubic-foot or board-foot) of live 

trees 1 inch in diameter and larger. Net volumes are computed by deducting rotten, missing, or form defects from gross volume. Net volume is reported below 

as net volume of all live trees, net volume of growing-stock trees, net volume of 

sawtimber, and net volume of sawlogs. All of these terms are defined below as 

well as in Appendix A. Biomass estimates are based on gross volumes and describe 

0	

500,000	

1,000,000	

1,500,000	

2,000,000	

2,500,000	

3,000,000	

3,500,000	

4,000,000	

2	 4	 6	 8	 10	 12	 14	 16	 18	 20	 22+	

Thousand	trees	

Two-inch	diameter	class	

So.woods	

Co3onwood	and	aspen	

Western	woodland	hardwoods	

Figure 8—Number of live trees 

1.0 inch in diameter and larger on 

forest land, by diameter class and 

major species group, Colorado, 

2004–2013.
22 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

aboveground tree weight (oven-dry) by various components (merchantable bole 

and bark, tops and limbs, saplings). The sources of the equations used to estimate 

volume and biomass are documented in Appendix E.

Tables B12 through B16 illustrate the net volume of all live trees 5.0 inches 

in diameter and larger on Colorado’s forest land, by various discrete categories. 

The net volume of all live trees on Colorado’s forest land totals 35.2 billion cubic feet (Appendix B table B12). Almost 69 percent, or 24.2 billion cubic feet, 

is located on lands managed by the National Forest System. About 22 percent of 

the NFS-managed live volume exists on reserved lands and is unavailable for harvest. Privately owned forests contain 15 percent of the State’s total live volume, or 

5.5 billion cubic feet. Forest land controlled by the Bureau of Land Management 

accounts for 12 percent of total live volume, or 4.1 billion cubic feet.

The Engelmann and other spruces species groups contain the highest level of 

live tree volume at 11.1 billion cubic feet, or 32 percent of the total, of any species 

group (fig. 9). Second in abundance, the cottonwood and aspen species group accounts for 14 percent, or 5.1 billion cubic feet. The western woodland softwood, 

true fir, and lodgepole pine species groups account for 13, 12, and 11 percent of the 

total live volume, respectively.

0.0	 2,000.0	 4,000.0	 6,000.0	 8,000.0	 10,000.0	 12,000.0	

Western	woodland	hardwoods	

Other	western	so7woods	

Ponderosa	and	Jeffrey	pine	

Douglas-fir	

Lodgepole	pine	

True	fir	

Western	woodland	so7woods	

CoFonwood	and	aspen	

Engelmann	and	other	spruces	

Thousand	cubic	feet	

Species	group	

Figure 9—Net cubic feet volume of trees 5.0 inches in diameter or larger on forest land, by species group, Colorado, 

2004–2013.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 23

Growing-stock volume represents the live tree volume that is potentially 

available for harvest. The availability of timber volume for harvest is affected by three primary factors: reserved status, productivity, and merchantability. 

Timberland is defined as unreserved forest land capable of producing in excess of 

20 cubic feet per acre per year of wood at culmination of mean annual increment. 

Merchantable trees are those that are 5.0 inches in diameter or larger and contain, 

or have the potential to produce, an 8-foot sawlog that is reasonably free of defects. Growing-stock trees are live, merchantable trees that occur on timberland. 

Therefore, growing-stock volume on timberland represents the amount of timber 

that is potentially available for harvest. The net volume of growing-stock trees on 

timberland in Colorado totals 22.9 billion cubic feet (Appendix B, table B17), or 

65 percent of the total live volume on forest land.

The relationship between the growing stock-volume on timberland and all 

live volume on all forest land by species group indicates those tree species that 

have a higher likelihood of being harvested for timber products in Colorado. Over 

87 percent, or 2.3 billion cubic feet, of the total live volume in the ponderosa and 

Jeffery pine species group is growing stock volume on timberland. Based on this 

ratio, this species group has the highest proportion of live volume available for 

harvest in Colorado. This same ratio was 86 percent for the cottonwood and aspen 

species group. Eighty percent of the Douglas-fir live volume is potentially available for harvest followed by true fir (75 percent) and lodgepole pine (74 percent). 

In comparison, the Engelmann and other spruces species group—the most abundant species group in terms of live volume—ratio of available to all live volume 

was only 67 percent. Live volume is also reported for sawtimber trees, which are 

defined as softwood trees 9.0 inches in diameter or larger, or hardwood trees 11.0 

inches in diameter or larger (International 1⁄4-inch rule). The net volume of sawtimber trees on timberland totals 85.5 billion board feet (Appendix B, table B19).

The total weight of oven-dry above-ground biomass on Colorado’s forest 

land is 632 million tons. Sixty-six percent, or 418 million tons, occurs on NFS 

forest land. Although biomass is typically sold by green weight, the water content 

of wood is highly variable geographically, seasonally, and even across portions 

of a single tree. Therefore, live-tree inventory estimates of green biomass may be 

unreliable or even misleading. In contrast, oven-dry weight does not change due to 

fluctuations in tree water-content.

Forest Growth and Mortality

Forest vigor, sustainability, and timber supply are often assessed by what are 

referred to as forest change components: growth, mortality, and removals. The relationship among these three change components quantifies the change in tree volume over time. Growth is typically expressed as net annual growth and is defined 

as the gross, or total, average annual growth in tree volume minus the volume lost 

through mortality. Mortality is the average annual net volume of trees dying over a 

given time period due to natural causes and excludes the volume removed through 

harvesting. Tree mortality often occurs at low and predictable rates due to insects 
24 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

and disease, suppression by overstory trees, or advanced tree age. Occasionally, 

highly concentrated and localized losses occur due to insect and disease epidemics, 

wildfire, or severe weather events. Removals represent the net volume of growingstock trees removed from the inventory by harvesting or other cultural operations 

(such as timber-stand improvement), by land clearing, or by changes in land use 

(such as a shift to wilderness).

The three components of forest change—growth, mortality, and removals—

are typically analyzed using measurements of the same plot at two points in time. 

It is possible, however, to also estimate growth and mortality rates based on a 

single inventory, as described below. In contrast, removals cannot be reliably estimated without having two measurements of the same set of plots. The Colorado 

inventory did not begin remeasurement until 2012, and only 20 percent remeasurement is not sufficient to provide a reliable plot-based estimate of change components. Therefore recent removals can only be estimated using information about 

the amount of wood cut and processed by the forest products industry. Due to this 

difference in analysis methods, growth and mortality are analyzed and discussed 

separately from removals.

In Colorado, the procedures used to estimate tree growth and mortality depended on the remeasurement status of the plot. A remeasured or paired plot refers 

to a plot where a periodic inventory plot was established in the previous inventory 

(time 1), and the field crews were able to relocate the plot and account for all trees 

measured during the current inventory (time 2). In most cases, the previous and 

current plots are co-located. As of 2013, about 20 percent of all plots that sample 

forest land in Colorado were remeasured, so the same trees were measured at two 

points in time. For trees that were alive at time 1 and time 2, growth is calculated based upon the change in volume over the time interval between plot visits. 

The time interval between remeasured plot visits in Colorado averaged 10 years. 

Mortality volume is based upon the volume of any tree that qualifies as a mortality 

tree over the time interval between plot visits. A tree is classified as mortality if it 

was alive at time 1 but dead at time 2.

A new plot is a plot established for the first time where there was no previous 

co-located plot to be remeasured. On new plots, annual growth is estimated from 

a sample of increment core measurements based on the previous 10 years of radial 

growth. Mortality is estimated from trees that died in the 5 years prior to the year 

of measurement.

The annual estimate of gross growth of all live trees 5.0 inches in diameter 

and greater on forest land in Colorado totaled nearly 559.0 million cubic feet. This 

is the sum of growth on all survivor and ingrowth trees. Survivor trees are live trees 

5.0 inches and larger in diameter at time 1 and still alive at time 2 on remeasured 

plots, and live trees determined to be 5.0 inches and larger in diameter 10 years 

prior to the current measurement on new plots. Ingrowth trees are live trees 5.0 

inches and larger in diameter that grew over the 5.0-inch threshold between time 

1 and time 2 on remeasured plots or during the previous 10 years on new plots.

The average annual mortality of trees 5.0 inches and larger in diameter was 

704.2 million cubic feet (Appendix B, table B25). The difference between the 
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 25

live tree, or gross, growth and mortality indicates a net annual growth estimate 

of –145.2 million cubic feet on forest land in Colorado (see Appendix B, tables 

B21-B24). The –145.2 million cubic feet of net annual growth in Colorado signifies an inventory of live trees that is decreasing annually in the absence of trees 

removed from human-caused activities. High levels of tree mortality are offsetting 

gains from live tree growth.

Net annual growth varies considerably by major owner group. Figure 10 illustrates the relationship between annual gross growth, net growth, and mortality 

by owner group in Colorado. Annual mortality of all trees on forest lands managed 

by National Forest Systems totaled 578.3 million cubic feet (Appendix B, table 

B25) compared to –181.1 million cubic feet of net annual growth (Appendix B, 

table B21). In contrast, net annual growth was positive on privately owned forests: 

net growth totaled 38.5 million cubic feet compared to 54.1 million cubic feet of 

mortality.

Figure 11 illustrates the relationship between annual gross growth, net 

growth, and mortality for seven major inventory species in Colorado. Negative net 

growth was recorded for lodgepole pine, Engelmann and other spruces, and true 

fir species groups. Mortality was highest in the State for lodgepole pine at 257.5 

million cubic feet followed by the Engelmann and other spruces species group at 

-300.0	

-200.0	

-100.0	

0.0	

100.0	

200.0	

300.0	

400.0	

500.0	

600.0	

700.0	

Other	federal	 State	and	local	

government	

Undifferen?ated	

private	

Million	cubic	feet	

Ownership	group	

Gross	growth	

Net	growth	

Mortality	

Na?onal	Forest	System	

Figure 10—Annual gross growth, net growth, and mortality on forest land by ownership group, Colorado, 2004–2013.

National Forest
26 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

161.0 million cubic feet. The other four major species groups recorded positive net 

growth with cottonwood and aspen having the highest level of net annual growth 

at 38.9 million cubic feet. Net growth of Douglas-fir averaged 22.7 million cubic 

feet compared to 27.2 million cubic feet of mortality. Net growth of the ponderosa 

and Jeffery pine species group averaged 17.7 million cubic feet compared to 22.0 

million cubic feet of mortality.

Since high mortality is the driving force behind the large differences between 

gross and net growth, further examination of this change component by other resource attributes can help explain the factors behind the high level of tree volume 

estimated to have died. Significant differences were observed in per-acre estimates 

of mortality between major ownership groups and reserved statuses. Converting 

the state-level estimates of mortality into per-acre estimates removes the effect of 

differences in the amount of forest land controlled by different ownership groups. 

Across all ownerships, the per-acre estimate of annual mortality volume averages 30.8 cubic feet per year on forest land. Mortality on reserved forest land was 

significantly higher than unreserved land. Average annual mortality on reserved 

Figure 11—Annual gross growth, net growth, and mortality on forest land by seven major species groups, 

Colorado, 2004–2013.

-250.0	 -200.0	 -150.0	 -100.0	 -50.0	 0.0	 50.0	 100.0	 150.0	 200.0	 250.0	 300.0	

Ponderosa	and	Jeffrey	pine	

Douglas-fir	

Western	woodland	so>woods	

Co@onwood	and	aspen	

Engelmann	and	other	spruces	

Million	cubic	feet	

Species	group	

Mortality	

Net	growth	

Gross	growth	

Lodgepole	pine	

True	fir	 True fir
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 27

land averaged 62.9 cubic feet per acre, compared to 31.5 cubic feet per acre on 

unreserved forest land. Figure 12 illustrates per-acre estimates of mortality by 

two major owner categories and reserved status. Reserved lands managed by the 

National Forest System recorded the highest average level of per-acre mortality at 

72.1 cubic feet, which is over 12 times higher than the per-acre estimate recorded 

on unreserved land controlled by private landowners, other Federal agencies, and 

State agencies.

All trees classified as mortality trees are assigned a cause of death in the field. 

Drawing conclusions from mortality estimates by cause of death should be done 

with caution because the actual agent that caused a tree’s death may be difficult, if 

not impossible, to determine. The “other” cause of death category includes trees 

that have died due to reasons the field crews are unable to determine. Interactions 

between insects and diseases are complex and make identification of causal agents 

difficult. Figure 13 illustrates per-acre estimates of mortality by reserved status 

and cause of death. Mortality due to insects accounted for the majority (66.5 percent) of total mortality. Disease was the second leading contributor to mortality, 

0	

10	

20	

30	

40	

50	

60	

70	

80	

Na-onal	Forest	Service	 Other	federal,	state,	or	private	

Cubic	feet	per	acre	

Reserved	

Unreserved	

Figure 12—Average annual per-acre mortality on forest land by two major owner categories and reserved status, 

Colorado, 2004–2013.
28 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

accounting for 18.7 percent of total mortality. Fire accounted for 5.4 percent. The 

reasons behind the differences in levels of tree mortality by owner class and reserved status deserve further investigation. These differences have been observed 

in other State inventories (Goeking et al. 2014; Menlove et al. 2012; Witt et al. 

2012), suggesting that reserved National Forest System lands have a larger share 

of aging forest stands that are more susceptible to insect and disease. This assumption could be verified with additional analysis of stand age, structure, density, species composition, and management regimes.

Stand Density Index (SDI)

Stand density index (Reineke 1933) is a relative measure of stand density, 

based on quadratic mean diameter of the stand and the number of trees per acre. 

In the western States, silviculturists often use SDI as one measure of stand structure to meet diverse objectives such as ecological restoration and wildlife habitat 

(e.g., Lilieholm et al. 1994; Long and Shaw 2005; Shaw and Long 2007; Smith 

0	

10	

20	

30	

40	

50	

60	

Insects	 Disease	 Fire	 Weather	 Other	

Cubic	feet	per	acre	

Cause	of	death	

Reserved	

Unreserved	

Figure 13—Average annual per-acre mortality on forest land by reserved status and cause of death, Colorado, 

2004–2013.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 29

and Long 1987). Originally developed for even-aged stands, SDI can also be applied to uneven aged stands (Long and Daniel 1990; Shaw 2000). Stand structure 

can influence the computation of SDI, so the definition of maximum SDI must be 

compatible with the computation method. Stand density index was computed for 

each condition that sampled forest land using the summation method (Shaw 2000), 

and the SDI percentage was calculated using the maximum SDI for the forest type 

found on the condition.

Stand density index is usually presented as a percentage of a maximum SDI 

that is determined for each forest type. Maximum SDI is rarely, if ever, observed in 

nature at the stand scale because the onset of competition-induced (self-thinning) 

mortality occurs at about 60 percent of the maximum SDI. Within-stand variability 

of density results in the average stand density being well below that of the densest patches. A site is considered to be fully occupied at 35 percent of maximum 

SDI. Below about 25 percent of maximum SDI, individual trees are considered “free 

to grow.” At these lower densities, individual tree growth is maximized but stand 

growth is below potential, while at higher densities, individual tree growth is below 

potential, but stand growth is maximized (Long 1985). There are several reasons 

why stands may have a low SDI. Stands typically have low SDI following major 

disturbances, such as fire, insect attack, or harvesting. These stands remain in a lowdensity condition until regeneration fills available growing space. Stands that are 

over-mature can also have a low SDI, because growing space may not be reoccupied 

as fast as it is released by the mortality of large, old trees. Finally, stands that occur 

on very thin soils or rocky sites may remain at low density indefinitely, because limitations on physical growing space do not permit full site occupancy.

There has been substantial change in Colorado’s forests since annual inventory was started in 2002. Mountain pine beetle populations began to increase 

shortly afterward and reached epidemic levels in the late 2000s (see the Colorado’s 

Mountain Pine Beetle Epidemic section in this report; Thompson 2009; Thompson 

et al. 2010), killing a large proportion of lodgepole pines in the State. The pinyon 

ips beetle also killed many common pinyon trees (Shaw et al. 2005) in the early 

2000s. Drought has led to an increase in aspen mortality in some parts of the State 

(see the Sudden Aspen Decline section in this report; Worrall et al. 2008). In contrast, with a few exceptions fire has not had as proportionally great of an effect on 

forests of Colorado as it has in other Interior West States. Given the number of factors that result in mortality or reduced growth, we expected to find some change in 

relative density—in the form of overall lower SDI—in Colorado’s forests.

Because SDI is a condition-level variable, computation of SDI on individual 

conditions is sensitive to high scaling factors that are associated with small condition proportions (that is, conditions that occupy only a small fraction of the plot 

footprint). For this reason, the analysis of SDI presented here only uses conditions 

with a condition proportion of 0.5 or greater. This subset of plots was divided by 

measurement year, with plots measured from 2002 to 2007 representing early- or 

pre-disturbance conditions (n = 2,535) and plots measured from 2008 to 2013 representing late- or post-disturbance conditions (n = 2,411).
30 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

A statistical comparison, using a t-test, shows that the decrease in mean SDI 

is small (34.6 percent to 32.4 percent), but it is statistically significant (t = 3.58; P

< 0.001). In the previous FIA report on Colorado’s forests (Thompson et al. 2010), 

we noted that the SDI distribution would be “likely to skew toward lower-density 

stands in the coming years, because the most recent data suggests that there has 

been considerable mortality in conifers.” The anticipated change is apparent in 

figure 14, where the proportions of conditions in each SDI percentage class are 

compared for the two measurement periods. The number of conditions by SDI 

class has been normalized to account for the differing numbers of conditions in the 

two groups in order to make the classes more comparable. There is now a higher 

proportion of conditions in the “free to grow” class as compared to the earlier 

part of the inventory period, and somewhat lower proportions of conditions in the 

higher density classes.

The expected trajectory over the short term is a gradual increase in stand 

density. In the absence of disturbance or natural senescence of stands, unoccupied 

growing space will gradually be filled. However, disturbance-free periods are rare, 

and stands are continually maturing. Therefore, the average relative density of a 

population always remains well below the potential relative density. The result is 

that there are periods of increasing average density and periods of decrease. Only 

Figure 14—Distribution of condition-level SDI percentage in Colorado for two time periods: 2002–2007 and 2008–2013.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 31

about 2.4 percent of conditions have no trees present that are 1.0 inches diameter 

or greater—that is 0 percent of maximum SDI—although there may be seedlings 

present. This means the vast majority of stands have live stems remaining to immediately begin to occupy free growing space.

In cases where partial disturbance of the stands has resulted in lower relative density, and especially where the relative density is now in the “free to grow” 

range, the lower density can lead to higher growth rates of the remaining trees than 

would have occurred in the absence of disturbance. As a result, there is a compensating effect, where reduced competition causes relative density to rebound more 

rapidly than would be expected, based on pre-disturbance growth rates. Many residual trees in low-density stands should have increased vigor in response to lower 

competition, and they may be more resistant to further disturbance. Future plot 

measurements will record changes as residual trees and new regeneration recapture available growing space. In the absence of high rates of disturbance, relative 

density should begin to recover in the coming years.

Colorado’s Forest Resources_________________________________

Colorado’s Primary Wood Products Industry

The University of Montana’s Bureau of Business and Economic Research 

(BBER), in cooperation with the Interior West FIA program, conducts periodic censuses of Colorado’s primary forest products industry (Hayes et al. 2012; Sorenson 

et al. 2015). Censuses of Colorado’s primary wood products industry were conducted for calendar years 2002, 2007, and 2012, documenting the condition of 

the industry. Colorado’s industry consisted of 58 active manufacturers in 2012 

(table 3). Primary wood products manufacturers are firms that process timber into 

manufactured goods such as lumber and house logs, while the secondary industry 

further processes outputs from the primary industry into other value-added wood 

products, such as cabinets, doors, or furniture. The sawmill sector, manufacturing 

lumber and other sawn products, was the largest sector, operating 31 mills during 

2012; 12 facilities produced house logs and log homes, down dramatically from 

46 in 2002 and 19 in 2007. In both 2007 and 2012, there were also 15 facilities 

producing other primary products including excelsior, fuel pellets, posts, poles, log 

furniture, and biomass/energy.

Timber processors in Colorado received 89.2 million board feet (MMBF) 

Scribner of timber in 2012 (table 4), including 8.2 MMBF that was harvested 

Table 3—Active Colorado primary wood products facilities by product, 2002, 2007, and 2012.

Lumber

House logs and 

log homes Other products Total

2012 total 31 12 15 58

2007 total 30 19 15 64

2002 total 50 46 37 133
32 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

outside the State. While the overall 2012 timber receipts declined by 5 percent, 

receipts of “other products” nearly doubled from 2007 to 2012. This coincided 

with decreases in sawlog and house log receipts at Colorado mills. Public lands 

continued to provide a larger share of total timber receipts at 70 percent (63 

MMBF) in 2012, versus 52 percent (49 MMBF) in 2007 and 35 percent (29 

MMBF) in 2002. While private and tribal land provided a smaller share (30 

percent) of timber receipts in 2012 than in past years, 52 percent (1.6 MMBF) of 

house logs were sourced from private and tribal land. In 2007, just 15 percent of 

house logs came from private and tribal lands.

After a long period of declining output, lumber production at Colorado 

sawmills fell to 83 MMBF in 2002 before increasing to 116 MMBF in 2007 

and declining again to 95 MMBF in 2012. The number of sawmills in Colorado 

dropped from 50 in 2002 to 30 in 2007 and 31 in 2012. Of the 31 sawmills in the 

State in 2012, the eight largest mills produced an annual average of nearly 11 

MMBF of lumber, while 23 smaller sawmills produced an average of 386 MBF 

per mill during the year. On average, Colorado sawmills produced approximately 1.58 board feet of lumber for every board foot Scribner of timber processed, 

resulting in an average overrun of 58 percent in 2012. This continued the trend 

of increasing overrun through time, as overrun was 47 percent in 2002 and 54 

percent in 2007. Improved milling technologies and increased use of smaller 

diameter timber both contributed to increased overrun.

Sales from Colorado’s primary wood products industry during 2012 totaled nearly $87 million (table 5). Sawmill sales accounted for 45 percent ($39 

million) of total sales, house log and log home manufacturers accounted for 16 

percent (nearly $14 million), while other products accounted for 40 percent (over 

$34 million). Colorado was the leading market area for log homes, posts, poles, 

and log furniture, with in-state sales accounting for almost 39 percent of total 

sales. The North Central States were the leading market area for lumber and the 

second leading market area overall with 23 percent of sales from Colorado’s 

mills going to the region. Other Rocky Mountain States and the South followed 

with 9 and 8 percent of total sales, respectively.

Table 4—Timber received by Colorado primary wood products industry by ownership class 

and product, 2007 and 2012, MBF Scribner.

Ownership class Sawlogs

House 

logs

Other 

productsa All products

----------------------------2007----------------------------

Public timberland 36,917 5,275 6,790 48,982

Private and tribal timberland 38,192 927 5,740 44,859

Canada 30 30

All owners 75,109 6,232 12,530 93,871

----------------------------2012----------------------------

Public timberland 43,144 1,492 18,161 62,797

Private and tribal timberland 18,852 1,637 5,900 26,389

All owners 61,996 3,129 24,061 89,186

aOther products include energywood logs, fiber logs, posts, poles, furniture logs, and industrial fuelwood.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 33

The 95 MMBF of lumber produced by Colorado’s sawmills represented 

use of only 54 percent of the State’s annual sawmill production capacity in 2012 

(176 MMBF), which was a slight decline from 56 percent of capacity used in 

2007. Colorado timber processors produced 108,009 bone dry units (BDU) of 

mill residue in 2012, with 99 percent utilized, versus 98 percent of residue utilized in 2007. Sawmills produced the majority of mill residue, resulting in 0.99 

BDU per MBF of lumber in 2012, compared to 1.04 BDU per MBF of lumber 

in 2007.

The classification of forest industries used here follows the North American 

Industry Classification System (NAICS) available online via the U.S. Department 

of Commerce Census Bureau (USDC CB 2014). The forest industry can be 

found in four categories: NAICS 113—forestry and logging; NAICS 1153—forestry support activities; NAICS 321—wood product manufacturing; and NAICS 

322—paper manufacturing. These categories include employees that work in 

both the primary and secondary wood products sectors, as defined above.

Employment in Colorado’s forest industry, defined as the sum of employment in Forestry and Logging, Forestry Support, Wood Product Manufacturing, 

and Paper Manufacturing, has declined in recent years, though it remains an 

important source of jobs in many communities around the State. There were approximately 6,700 jobs in the industry in Colorado in 2012, compared to 9,100 

in 2002 and 9,250 in 2007 (Bureau of Economic Analysis 2014). About 2,050 

workers were employed in the “primary” industry—harvesting and processing 

timber or in private sector land management—during 2012, and the remaining 

component of the industry can be classified as secondary, employing approximately 4,650 workers in 2012. From 2007 to 2012, employment in Colorado’s 

forest industry declined nearly 30 percent overall. However, primary forest industry employment actually increased over that period, with the employment 

decrease coming entirely from the secondary industry.

Removals for Timber Products

Volume removed from forest inventory by timber harvesting, other cultural 

operations (such as timber-stand improvement), or land clearing is referred to as 

“removals.” Removals are an important indicator of the sustainability of forest 

management. Removals that exceed growth for extended periods can indicate 

Table 5—Finished product sales of Colorado’s primary wood products sectors, 2002, 2007, and 2012.

Sector 2002 2007 2012

------------Thousands of 2012 dollarsa------------

Sawmills 51,523 49,176 38,867

House logs and log homes 34,727 21,246 13,524

Other sectorsb 32,901 40,015 34,465

Total 119,151 110,437 86,856

aAll sales are reported f.o.b. the manufacturer’s plant.

bOther sectors include producers of posts, poles, log furniture, fuel pellets, biomass/energy, and excelsior.
34 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

over-harvesting. However, growth or mortality levels that greatly exceed removals can signal a potential need for increased vegetation management to decrease 

risks such as insect or disease outbreaks and wildfire, which can be associated with 

overstocking and high tree mortality.

Removals can come from two sources: the growing-stock portion of live 

trees (live trees of commercial species meeting specified standards of quality or 

vigor), or dead trees and other non-growing stock sources. The two types of removals addressed in this section are timber products harvested for processing by 

mills and logging residue (i.e., volume cut or killed during harvesting operations 

but not utilized). Removals, as reported here, are based on a census of Colorado’s 

primary forest products industry operating during 2012 (Sorenson et al. 2015 in 

preparation) and U.S. Energy Information Administration data for 2012 Colorado 

residential fuelwood consumption (EIA 2014).

Colorado’s 2012 timber harvest for industrial wood products (which does not 

include residential firewood) was approximately 82 million board feet (MMBF) 

Scribner (Sorenson et al. 2015), or about 21.6 million cubic feet (MMCF). Dead 

trees accounted for about 46.2 MMBF (56 percent). The 2012 harvest was about 

4.5 MMBF (5 percent) lower than the 2007 harvest (Hayes et al. 2012) but about 

3 percent higher than the 2002 harvest of 79.7 MMBF (Morgan et al. 2006). A 

complete time series of Colorado’s annual timber harvest is not available, although 

historic reports indicate somewhat higher harvest levels; for example the 1982 

harvest was over 103 MMBF (McLain 1985).

In Colorado, removals for all timber products (including firewood) totaled 

48 MMCF during 2012. Just 19 percent (9 MMCF) of removals for products came 

from growing stock, with over 39 MMCF coming from other sources, including 

dead trees and other non-growing stock sources. At 31.3 MMCF, fuelwood, including residential firewood, was the leading timber product, accounting for 65 percent of removals for products. Sawlogs, for producing lumber, accounted for 13.3 

MMCF, almost 28 percent. Logs for miscellaneous products (e.g., excelsior, log 

homes, and log furniture) accounted for 4 percent (2 MMCF); and logs for posts 

and poles accounted for the remaining 3 percent of removals for timber products.

Approximately 95 percent (45.7 MMCF) of removals for products consisted 

of softwood species. The largest volume (1.1 MMCF) of hardwoods was used for 

sawlogs, with smaller quantities used for miscellaneous products and fuelwood.

Total removals in Colorado during 2012 were 50.7 MMCF. This included 

the 48 MMCF used for timber products and 2.7 MMCF of logging residue left in 

the forest as slash. Growing-stock removals were 9.4 MMCF. About 95 percent 

(9 MMCF) of growing-stock removals were used to produce wood products, and 

less than 5 percent (0.4 MMCF) were left in the forest as slash and not utilized. 

Sawlogs were the largest component (65 percent) of growing-stock removals, followed by fuelwood (14 percent).

One-third (3.1 MMCF) of growing-stock removals came from private and 

tribal timberlands, while 61 percent (5.8 MMCF) came from national forests. 

About 5 percent of the volume removed from growing stock was from other public 

lands. Ponderosa pine accounted for 25 percent (2.4 MMCF) of growing-stock 
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 35

removals. Spruces also represented about 25 percent (2.3 MMCF), aspen (1.8 

MMCF), lodgepole pine (1.5 MMCF), and Douglas fir (1.1 MMCF) together accounted for 47 percent of growing-stock removals.

Total removals for timber products and logging residue in Colorado were 

estimated to have increased about 30 percent over the past 10 years (table 6), from 

38.6 MMCF in 2002 to 50.7 MMCF in 2012. However, most of this increase is attributable to increases in fuelwood consumption, rather than harvest for industrial 

timber products. The harvest of timber for industrial products increased less than 

5 percent from 2002 to 2012, while fuelwood consumption was estimated to have 

increased more than 60 percent (EIA 2014).

In Colorado, growing-stock removals for products and logging residue declined over the past decade (table 7), dropping from 12.5 MMCF in 2002 to 9.4 

MMCF in 2012. The annual average volume of growing-stock removals for products and associated logging residue was 10.5 MMCF for the 2002 to 2012 period.

Sustainability of Colorado’s forests depends, in part, on active management 

of lands available for timber production, a forest products industry capable of 

utilizing harvested material, and harvest levels that address the needs of society while maintaining the long-term productivity of the land. But Colorado’s 

commercial timber harvest volume has continued to decrease over the past decade; and the State’s forest products industry has been facing mill closures and 

Table 6—Colorado total removals for products and logging residue, 2002, 2007, and 2012.

2002 2007 2012

Million cubic feet

Sawlogs 12.272 13.853 13.293

Posts and poles 0.0405 1.253 1.383

Other products 3.281 3.485 2.033

Industrial products 15.958 18.591 16.709

Fuelwood 18.983 25.776 31.314

Total products 34.941 44.367 48.023

Logging residue 3.624 2.551 2.713

Total removals 38.564 46.918 50.736

Table 7—Colorado growing-stock removals for products and logging residue, 2002, 2007, and 2012.

2002 2007 2012 Annual average

Million cubic feet

Sawlogs 9.086 5.761 6.191 7.0

Posts and poles 0.305 0.831 0.488 0.5

Other products 2.459 2.459 0.997 2.0

Industrial products 11.850 9.051 7.676 9.5

Fuelwood 0.001 0.093 1.323 0.5

Total products 11.851 9.144 9.000 10.0

Logging residue 0.655 0.500 0.424 0.5

Total removals 12.505 9.644 9.424 10.5
36 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

curtailments even as markets have begun to improve. The decline in Colorado’s 

forest products industry has eroded the ability to actively manage forests and 

generate income for landowners to use towards activities like hazardous fuel 

reduction or forest restoration, which may not generate revenue. To ensure sustainable harvest levels for future generations, careful consideration should be 

given not only to growth, removals, and mortality across Colorado’s available 

timberlands, but also to the industry infrastructure and employees that conduct 

management activities and utilize harvested timber.

The Pine Nut Resource of Pinyon-Juniper Woodlands

Pinyon-juniper woodlands cover an estimated 4.7 million acres in Colorado, 

making it the most abundant forest type in the State by area covered. This woodland type usually consists of two-needle pinyon (Pinus edulis) and one or more 

species of juniper (Juniperus spp.). In Colorado, Utah juniper (J. osteosperma) is the most frequent cohabitant of pinyon-juniper woodlands, with Rocky 

Mountain juniper (J. scopulorum) and oneseed juniper (J. monosperma) occurring less often. Pinyon-juniper woodlands commonly occur in the mid-elevation 

belts between the lower grass/shrublands and either subalpine forests or tree line 

above (Lanner 1981). Trees from these woodlands have been utilized by indigenous peoples for thousands of years, providing them with building materials 

for basketry and clothing, hunting tools, shelter, fire wood, and medicine (Floyd 

and Kohler 1990; Janetski 1997). Pinyon and juniper trees continue to be used 

as fuel wood in many Native American communities in Colorado, making these 

woodlands a very important local resource for that reason alone. However, the 

most important utilization of pinyon-juniper woodlands has been (and continues 

to be) the abundant and nutritious seeds of the pinyon pine.

Pinyon pine seeds, or “pine nuts,” are an extraordinary food resource very 

high in protein and fats and containing all 20 amino acids required for human 

growth (Janetski 1997). Unlike many other food items used in the past by native 

cultures, pine nuts were able to be stored for several years, making it a critical 

food in winter, times of drought, and periods of game scarcity. Pine nuts continue 

to be an important cultural and economic staple of contemporary tribal communities in Colorado. Each year pine nuts supplement the diets and incomes of 

those who know how and where to collect, process, store and sell them. Pine nut 

production in the U.S. is estimated to be 400 to 500 tons per year, contributing 

to a $100 million domestic market for the seeds (Sharashkin and Gold 2004).

Two-needle pinyon generally begins producing seeds at around 25 years of 

age. Although important to wildlife at this stage, the numbers of seeds produced 

by these young trees are not economical to harvest. Not until tree-age reaches 75 

years or so do pinyon trees start producing pine nuts in sufficient quantities to 

harvest commercially. Two-needle pinyon trees reach maximum seed production 

around 160 years old and continue until roughly 300 years old, at which time 

production often falls off considerably (Lanner 1981). Thus, it is useful to know 

how many acres of pinyon-juniper are currently of sufficient age to provide a 
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 37

useful crop of pine nuts and how many acres are close to moving into and out of 

the most productive age-classes.

Forest Inventory Analysis data is used to estimate the extent and age distribution of Colorado’s pinyon-juniper woodlands and relate the estimates to potential 

seed production. Estimates were stratified by the age-class groups that reflect the 

varying seed productivity levels discussed above. In addition, the portion of each 

age-class that will change over the next 20 years (in the absence of natural or anthropogenic disturbance) is identified. This information is useful to resource managers interested in perpetuating pinyon-juniper woodlands that have the potential to 

produce large quantities of pine nuts. Many other environmental factors can affect 

the productivity of an individual tree or stand of pinyon pine (e.g., drought, insects, 

site quality, and disease) and these estimates speak only to the relative potential of a 

stand to produce pine nuts given their age.

An estimated 42 percent of Colorado’s pinyon-juniper woodlands currently 

reside in the 75 to 160 year age-class (fig. 15). This age-class represents fully 

mature trees that consistently yield harvest-worthy quantities of pine nuts. The 

second-most abundant age-class, at 40 percent, is 160 to 300 years. These stands 

have the greatest nut production of any age-class. Very few acres of Colorado’s 

pinyon-juniper woodland are expected to move from a productive class to an unproductive one (3 percent). However, many more acres of woodland are within 20 

years of moving into a more productive age-class. Barring any major disturbance, 

over 71 percent (466,388 acres) of the 35- to 74-year age-class will move into the 

75- to 160-year age-class, with a roughly equal number of acres (498,969) moving 

from this category to the older 161- to 300-year age-class (fig. 16). While most 

age-classes show a reduction in total acres over the next 20 years, the most productive 161- to 300-year age-class will see an estimated 19 percent net increase in area 

during this time.

These data suggest that in the absence of a major disturbance, Colorado’s 

pinyon-juniper woodland will potentially increase its pine nut output over the next 

20 years due to a large recruitment of stands into the most productive age-classes. 

This analysis assumes no disturbance and thus no increase in non-stocked pinyonjuniper woodlands and no decrease in other age-classes. It also does not consider 

effects of climate change that might have on production over the next two decades. However, should a major disturbance convert large areas of seed-producing 

woodlands into zero-aged (non-stocked) sites or climate change reduce the seed 

output of large stands of pinyon pine, net productivity in the State could remain flat 

or decrease. Pinyon-juniper woodlands become more susceptible to catastrophic wildfire, disease, and insect outbreaks as they age and become more heavily 

stocked. So as time passes, the likelihood and amplitude of a major disturbance in 

these more heavily stocked woodlands increases. In addition, as these woodlands 

become more productive in terms of pine nut production, they often become less 

valuable to many wildlife species due to changes in stand structure and understory 

plant composition and diversity (Miller et al. 2008). Therefore, there are trade-offs 

that need to be considered when managing pinyon-juniper woodlands for the pine 

nut resource.
38 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

Figure 15—Estimated acres of pinyon-juniper woodland by age-class defined by level of pine nut productivity, Colorado, 

2004–2013.

0	

500	

1,000	

1,500	

2,000	

2,500	

0-25	 26-75	 76-160	 161-300	 301+	

Thousand	acres	

Stand-age	class	(years)	

Snags as Wildlife Habitat

Standing dead trees (snags) provide important habitat in many of the forested ecosystems of Colorado. There are many organisms that utilize snags at some 

point in their life history, including bacteria, fungi, insects, rodents, cavity-nesting 

birds, bats, raptors, mustelids, and black bears. The diameter of a snag is an important variable to species that use snags as a nesting, roosting, or den site, as larger 

snags tend to have a longer retention time, provide better thermal insulation, and 

can provide better protection from predators than smaller snags (Bull et al. 1997; 

Laudenslayer et al. 2002).

There is a handful of bird species that act as primary excavators of nest sites 

for a suite of other birds and mammals. These birds create a cavity during one 

breeding season but often abandon it and create a new cavity the following year 

(Cline et al. 1980; Conner et al. 1975). The old cavities are often occupied by secondary cavity-nesting birds. Secondary cavity nesters do not excavate their own 

nest sites and are dependent on primary excavators for their nest cavities. The suitability of an old cavity for a secondary cavity-nester often depends on the species 

of primary cavity nester that created it.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 39

The Northern flicker (Colaptes auratus) and Williamson’s sapsucker 

(Sphyrapicus thyroideus) create different sized openings and cavities and can be 

found in a variety of forest types in Colorado (Scott et al. 1980). While flickers are 

found in abundance in many forest types and choose to nest in snags greater than 

19 inches d.b.h., Williamson’s sapsuckers in Colorado prefer mid to high elevation conifer forests containing aspen, where they choose dead or dying aspen trees 

greater than 7 inches d.b.h. for nesting (Crockett and Hadow 1975). Since these 

two species provide suitable nest sites for a wide variety of secondary-nesting 

species over a large ecological range, the frequency of their nest site preferences 

can be used to assess potential nest availability of most cavity-nesting birds in 

Colorado.

A general estimate of suitable snags was produced for all of Colorado’s primary and secondary cavity-nesting birds by estimating the number of snags meeting the size preferences for the two focal species described above. For this analysis, snags with diameters at least 20 inches d.b.h. in any forest type were used to 

quantify potential flicker nest sites, while aspen snags >7 inches d.b.h. found in 

Douglas-fir, spruce/fir, and ponderosa pine forest types were used to assess potential Williamson’s sapsucker nest sites.

Figure 16—Ratio of 

estimated acres of pinyonjuniper woodland that is ≤20 

years from moving into another age-class, Colorado, 

2004–2013. 

0	

500	

1,000	

1,500	

2,000	

2,500	

5-25	 55-75	 140-160	 280-300	

Thousand	acres	

Stand-age	class	(years)	

Acres	>	20	years	from	next	age-class	

Acres	<	20	years	from	next	age-class	 ≤
40 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

There are an estimated 19.4 million snags in Colorado that meet Northern 

flicker diameter preferences. Figure 17 shows the estimated number of snags in 

Colorado that meet the minimum diameter requirements for the Northern flicker 

by tree species group and forest-type group. Spruce species account for 44.6 percent of the snags suitable for flicker cavities with 8.7 million snags, the most of any 

tree species in Colorado. The vast majority of these snags are found in the spruce/

fir forest-type group; the spruce/fir forest-type group holds the most suitable flicker 

snags found in Colorado, with 10.5 million (54 percent) of the total. The disproportionate representation of spruce as a species and spruce/fir as a forest-type group is 

largely attributable to the fact that a large number of spruce attain 20 inches in diameter, and that the spruce beetle has caused a high level of mortality in the larger 

size classes of spruce. The Western woodland softwoods (which include pinyon 

pine and juniper spp.) and true fir species groups include the second and third most 

abundant numbers of snags, with 3.6 million and 2.7 million snags, respectively. 

The pinyon-juniper forest-type group is a distant second with 3.6 million snags. On 

a proportional basis the snag-forming species occurring within forest-type groups 

shows varying patterns (fig. 18). For most forest-type groups, the most abundant 

snags are the namesake species of the group (e.g., Engelmann spruce in the spruce/

fir group, ponderosa pine in the ponderosa pine group). However, this is not always the case. Most of the snags in the Woodland hardwoods group are actually 

conifers—over 60 percent are ponderosa pine, while the remainder are about equal 

parts of Douglas-fir and woodland softwoods such as common pinyon. The Aspen/

Birch group has a diversity of snag species, with aspen, Douglas-fir, Engelmann 

spruce, and true firs all well-represented by proportion.

0	

1	

2	

3	

4	

5	

6	

7	

8	

9	

spruce/	fir		 pinyon/	juniper		 Douglas-fir		 aspen/	birch		 other	forest	type	

groups	

Number	of	snags	>	20	in.	d.b.h.	(million)	

Forest-type	group	

	spruce	spp.	 True	fir	spp.	 Douglas-fir	 Western	woodland	so@wood	spp.	

Figure 17—Number of snags >20 inches d.b.h. by species group within forest-type group in Colorado, 2004–2013.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 41

Figure 19 displays the estimated number of aspen snags 7 inches d.b.h. or larger found in the forest types preferred by Williamson’s sapsucker by stand age class. 

The subalpine fir forest type contains the majority of these snags (4.1 million), followed by Engelmann spruce/subalpine fir (3.4 million), and Engelmann spruce (1.7 

million) respectively. In almost every case, the majority of suitable aspen snags are 

found in the older age classes. Only in ponderosa pine and blue spruce forests does 

one find more of these snags in forests younger than 100 years old.

The results of this analysis suggest that snags suitable for a large suite of 

cavity-nesting birds are found in a wide range of forest types and age-classes, but 

the majority is found in older, high elevation conifer forests. Exceptions to this 

general trend are the high number of snags found in the aspen/ birch and pinyon/juniper forest-type groups. Aspen forests are particularly important for some primary 

and secondary nesting birds because of the relationship between diseased aspen, 

primary excavators, and secondary nesters (Hart and Hart 2001).

Variables other than snag diameter, forest type, decay class, and stand-age 

need to be considered when predicting suitable wildlife habitat for forest-dwelling 

species. Proximity to forest edge and density of live trees is important to many 

cavity-nesting birds. The state of decay of a tree and its distance to foraging also 

plays a role in nest site suitability. Forest Inventory Analysis data can address 

many of these factors and there are current efforts to build predictive models for 

these species by using data collected by our crews. These models can be valuable 

tools for Federal and State land managers, as a large portion of the forests containing suitable snags occur on public lands.

0	

100,000	

200,000	

300,000	

400,000	

500,000	

600,000	

700,000	

spruce/	fir		 aspen/	birch		 ponderosa	pine		 woodland	hardwoods		 other	forest	type	

groups	

Number	of	snags	>	20	in.	d.b.h.	

Forest-type	group	

Lodgepole	pine	 CoBonwood	and	aspen	spp.	 Ponderosa	and	Jeffrey	pine	 Other	western	soHwood	spp.	

Figure 18—Proportions of snags >20 inches d.b.h. by species group and forest-type group in Colorado, 2004–2013. 
42 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

Old Forests

An important aspect of managing for ecologically sustainable and diverse 

ecosystems is the maintenance of forest stands representing the full range of forest succession. As forests mature, stand structure changes in ways that affect the 

stand’s ecological and habitat function. Historically, these last stages of forest 

growth have been difficult to define or describe. Terminology has included primeval, pristine, primary, late seral or successional, climax, mature, overmature, and 

old growth, among others (Helms 2004). Standardized definitions are problematic 

because the final structure and age of a given forest stand depends on many biological and physical components, such as climate and geology, dominant tree species, fire regimes, and others (Kaufmann et al. 2007; Vosick et al. 2007). Therefore, 

the forest structural indicators used to assess old forests may differ with changes 

in these components. In addition, the characteristics of old growth can change with 

the scale of observation, from patches to stands and landscapes (Kaufmann et al. 

2007). Some of the structural indicators of relatively old forests may include the 

size (diameter) and age of the oldest trees, the number of large and/or old trees per 

acre, overall stand density, canopy characteristics, and downed logs (Fiedler et al. 

2007; Helms 2004).

One approach for assessing old forests using FIA data simply defines old forests as those with a stand age of 150 years or older. Based on this threshold, about 

22 percent of Colorado’s forest land occurs in old forests (Appendix B, table B47). 

0	

500,000	

1,000,000	

1,500,000	

2,000,000	

2,500,000	

3,000,000	

<	50	 50-75	 76-100	 100+		

number	of	aspen	snags	>	7	in.	d.b.h.	

Age-class	(years)	

Ponderosa	pine	 Engelmann	spruce	 Engelmann	spruce	/	subalpine	fir	 Subalpine	fir	 Blue	spruce	

Figure 19—Estimated number of aspen snags >7 inches d.b.h. by forest-type and stand-age class in Colorado, 2004–2013.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 43

The percentage of land with a stand age of at least 150 years varies by forest-type 

group (fig. 20). Four forest-type groups have more than 20 percent of their total 

area in stands at least 150 years old. In descending order of their total area, these 

include the other western softwoods, pinyon/juniper, fir/spruce/mountain hemlock, 

and Douglas-fir forest-type groups. The group with the highest percent of old forests (48 percent) was the other western softwoods forest-type group, which consists of limber pine, bristlecone pine, and a small amount of southwestern white 

pine. Over 43 percent of the area covered by the pinyon/juniper forest-type group, 

or 2.7 million acres, occurs in stands at least 150 years old. The Douglas-fir and 

fir/spruce/mountain hemlock forest-type groups have percentages of stands that 

are 150 years old of 30 and 22 percent respectively. Although woodland hardwood 

forests cover more than 2.4 million acres in Colorado, less than 1 percent of their 

total area occurs in stands older than 150 years.

The large differences between forest-type groups illustrate the need for 

type-specific definitions for identifying old forests. Some tree species are longer 

lived, or typically grow larger, than others. Life histories of different species may 

affect how much area would be expected to be dominated by large, old trees of a 

given species. For example, a larger proportion of old forest might be expected 

Figure 20—Proportion of forest area by forest-type group 150 years and older, Colorado, 2004–2013.

0.00%	 10.00%	 20.00%	 30.00%	 40.00%	 50.00%	 60.00%	

Exo.c	hardwoods	group	

Nonstocked	

Woodland	hardwoods	group	

Aspen	/	birch	group	

Elm	/	ash	/	coEonwood	group	

Ponderosa	pine	group	

Lodgepole	pine	group	

Douglas-fir	group	

Fir	/	spruce	/	mountain	hemlock	group	

Pinyon	/	juniper	group	

Other	western	soOwoods	group	

Percentage	greater	than	or	equal	to	150	years	of	age	

Forest-type	group	
44 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

in limber or bristlecone pine than in aspen forest types. As noted above, the 

forest-type group that includes the limber and bristlecone pine forest types had 

the highest proportion of its area in old forests; in contrast, almost no aspen or 

cottonwood stands met the 150-year stand age criterion.

One caveat of this approach is that stand age does not portray the range of 

individual tree ages within a stand. Stand age is calculated as the mean age of 

trees from the stand-size class that has the plurality of stocking. This tends to 

diminish the significance of older trees by averaging tree ages of both old and 

young trees, so using stand age to identify old forests may exclude stands that 

include both very old and very young trees. To address this issue, other criteria 

have been applied to FIA data from Idaho, Montana, and Utah (respectively, 

Witt et al. 2012; Menlove et al. 2012; and Deblander et al. 2010), including a 

minimum density of trees that are at least 150 years old; minimum tree diameters; and minimum stand density (basal area per acre). These analyses found that 

using various criteria to identify old forests produced widely different results. 

Therefore, any analysis of old forests must use carefully selected criteria that 

represent the specific stand structure of interest. Future research may use the FIA 

database to validate or even help to establish surrogate measurements for defining old forest structure in different regions, by forest type, and under different 

site conditions.

Understory Vegetation

Understory vegetation contributes to the diversity, productivity, and habit 

structure of forest ecosystems. FIA collects understory vegetation data using two 

distinct protocols that characterize overall vegetation structure as well as species composition. Under the vegetation structure protocol, field crews record 

the height class and percent cover that is occupied by each of four plant growth 

habits: forbs, graminoids, shrubs, and understory trees (trees), which are defined 

in this analysis as trees less than 5 inches d.b.h. Under the species composition 

protocol, height class, growth habit, and percent cover are recorded for plant 

species that individually occupy at least 5 percent of the ground area. If more 

than four species occupy more than 5 percent cover, then only the most abundant 

four species per life form are recorded. Note that in Colorado, the threshold for 

recording individual species was lowered from 5 percent to 3 percent beginning 

in 2012 (USDA Forest Service 2006, 2011).

Figure 21 depicts the average percent cover of each plant growth habit within Colorado’s forest-type groups (the exotic hardwoods group was not included 

due to low sample size). Understory trees had equal or higher mean cover than 

other growth habits in half of the groups (pinyon/juniper, Douglas-fir, ponderosa 

pine, aspen/birch, and woodland hardwoods), while graminoid cover more or 

less equaled understory trees in three groups (pinyon/juniper, Douglas-fir, ponderosa pine) and dominated cover in three others (other western softwoods, Elm/

ash/cottonwood, and Nonstocked). Shrubs supplied the highest mean cover in 

just two groups (fir/spruce/mountain hemlock and lodgepole pine). Forbs had 
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 45

consistently higher mean cover in hardwood forest groups than softwoods but 

did not dominate understory cover in any group.

There were 745 individual plant species recorded on Colorado’s forest inventory plots. The most frequently recorded species within each growth habit are 

listed in table 8. Quaking aspen and Gambel oak seedlings and saplings were the 

most commonly encountered understory species across all growth habits, in terms 

of both frequency and mean percent cover. This suggests these two species are 

currently undergoing high regeneration and recruitment in the State. Colorado’s 

State grass, blue grama (Bouteloua gracilis), was one of the five most frequently 

encountered graminoid species and was recorded on 286 forested plots. On average, blue grama covered 11 percent of the plots where it occurred.

Forest Soil Resources in Colorado

Soils on the landscape are the product of five interacting soil forming factors: 

parent material, climate, landscape position (topography), organisms (vegetation, 

microbes, other soil organisms), and time (Jenny 1994). Many external forces can 

have a profound influence on forest soil condition and hence forest health. These 

include agents of change or disturbances to apparent steady-state conditions such 

as shifts in climate, fire, insect and disease activities, land use activities, and land 

management actions.

0	

5	

10	

15	

20	

25	

Pinyon/	

juniper	

Douglas-fir	 Ponderosa	

pine	

Fir/	spruce/	

mountain	

hemlock	

Lodgepole	

pine	

Other	western	

soBwoods	

Elm/	ash/	

coDonwood	

Aspen/	birch	 Woodland	

hardwoods	

Nonstocked	

Mean	percent	cover	

Forest-type	group	

Forbs	 graminoids	 trees	 shrubs	

Figure 21—Mean percent cover of understory vegetation by growth habit and forest-type group, Colorado, 2004–2013.
46 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

The Soil Indicator of forest health was developed to assess the status and 

trend of forest soil resources in the United States across all ecoregions, forest types, 

and land ownership categories. For this report, data were analyzed and are being 

reported by forest-type groups. This forest type stratification not only reflects the 

influence of forest vegetation on soil properties, but also the interaction of parent 

material, climate, landscape position, and time with forest vegetation and soil organisms. A complete listing of mean soil properties in Colorado, organized by forest type, is in the Soil Indicator core tables (Appendix F, tables F3-F10). These are 

least-squares means generated by the SAS GLMMIX data analysis software program. There are two sets of tables—one for each soil sampling visit, and each visit 

corresponds to a cycle of forest health indicator plot measurement and sampling.

Most soil samples collected in the first sampling cycle in Colorado were collected in 2000 through 2004, but some plots were sampled for the first time in later 

years (2005 to 2010). The second sampling cycle was done in 2005 through 2013 

(not yet complete), but there were not as many soil samples collected in the second cycle so some forest-type groups remain underrepresented in the resampling 

Table 8—The five most frequently recorded plant species in each growth habit and their average percent 

cover, Colorado, 2004–2013.

Growth habit Species Common name Number of plots Mean cover

Forbs Arnica cordifolia heartleaf arnica 402 31.0

Thalictrum fendleri 

Engelm. Fendler’s meadow-rue 338 8.1

Achillea millefolium common yarrow 284 6.7

Aster spp. aster 268 9.2

Fragaria virginiana Virginia strawberry 268 6.8

Graminoids Carex spp. sedge 706 11.0

Carex geyeri Geyer’s sedge 390 11.0

Festuca arizonica Arizona fescue 369 11.1

Poa spp. bluegrass 368 11.6

Bouteloua gracilis blue grama 286 11.0

Shrubs

Symphoricarpos 

oreophilus mountain snowberry 793 13.1

Juniperus communis common juniper 676 8.8

Cercocarpus montanus mountain mahogany 608 8.7

Artemisia tridentata big sagebrush 485 11.1

Vaccinium myrtillus L. whortleberry 453 17.9

Understory 

trees Quercus gambelii Gambel oak 991 24.1

Populus tremuloides quaking aspen 957 14.2

Pinus Engelmannii Engelmann spruce 849 9.6

Abies lasiocarpa subalpine fir 760 11.3

Pinus edulis two-needle pinyon 536 6.8
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 47

sequence. The total number of plots sampled for soil indicators is listed for each 

forest-type group in each set of tables. Some of the key soil properties were graphed 

by forest-type group in Colorado and are highlighted in the discussion below.

Forest soil resource data are available for eight forest-type groups in Colorado. 

These are the western hardwoods group (includes deciduous oak), pinyon/juniper 

group (includes Rocky Mountain juniper, juniper woodland, and pinyon/juniper 

woodland), ponderosa pine group, lodgepole pine group, Douglas-fir group, cottonwood/aspen group, spruce/fir group (includes white fir, blue spruce, Engelmann 

spruce, subalpine fir, and mixed Engelmann spruce/subalpine fir), and the highelevation, 5-needle, white pine group. Most of the soil samples represent the pinyon/juniper, cottonwood/aspen, and spruce/fir groups with a smaller sampling in 

the other forest-type groups in Colorado. The high-elevation white pine group was 

represented by the smallest number of soil samples.

Generally, soil moisture tends to increase with elevation and latitude in the 

Interior West (associated with cooler temperatures) and forest types tend to reflect this climatic gradient. Pinyon/juniper woodlands tend to occupy drier lowerelevation sites whereas the spruce/fir group is found in wetter environments at higher 

elevations. Aspen forests are found at similar elevations with Douglas-fir and spruce/

fir forests. There is considerable elevation overlap among the different forest-type 

groups and aspect also plays a key role in forest distribution and soil water content. 

When expressed in terms of megagrams of carbon (C) per hectare of forest area, soil 

C stocks also generally increase with elevation and/or soil moisture storage (fig. 22, 

top and fig. 23, top). There are two important exceptions to the generally observed elevation increase pattern for soil water content and soil C stocks in Colorado. Western 

hardwoods (deciduous oak woodlands) have higher soil water content and more soil 

C stocks than some other forest types found at similar to higher elevations. The highelevation pines tend to have both lower soil water content and soil C stocks than 

other higher elevation forest types. Overall, soils were significantly wetter during 

the second visit sampling cycle than during the first, reflecting the cyclical nature of 

precipitation patterns in the Interior West.

The soil C stock data shown in figure 22 top, are for all plots sampled during 

the first cycle of plot visits from 2000 through 2010 (n = 258) and for the second 

cycle of plot visits from 2005 through 2013 (n = 189), although there are no forest 

floor total C data available for samples collected in 2013 as yet. There was no significant change in soil C storage between the first and second soil sampling visits, 

although variability is high since a smaller number of plots were revisited for many 

forest types. Carbon stocks in the forest floor component of western hardwoods and 

pinyon/juniper are smallest of all the stocks measured because forest canopy of these 

forest types tend to be more open and there is less forest floor accumulation than in 

wetter higher-elevation forests. Among all forest types except lodgepole pine and 

high-elevation white pines, most soil C is stored in the top 10-cm of mineral soil, 

followed by the 10 to 20 cm increment, followed by forest floor. In the lodgepole and 

high-elevation white pine groups, soil C stocks in forest floor are as high as or higher 

than those in the 10 to 20 cm mineral soil increment. Overall, the pinyon/juniper 

group stores the least amount of C in Colorado forest soils.
48 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

Soil nitrogen (N) stocks show a more mixed response to climatic gradients in 

Colorado than do C stocks (fig. 22, bottom). The total N data include all sampled 

years except 2013. Aspen and western hardwood forests tend to store more N in the 

mineral soil than any other forest group in Colorado (fig. 22, bottom). Aspen forests 

store more N than Douglas-fir or spruce/fir forests, which often intermingle with aspen as forest succession proceeds. High N levels in aspen forest floor and soils lead 

to lower C/N ratios than those found in forest floor and soils under spruce/fir (data 

not shown). Since low C/N is a good indicator of relative organic matter decomposition rate, nutrient-rich aspen leaves decompose quickly and easily compared to tree 

needles of the spruce/fir group. There was no significant change in soil N storage between the first and second soil sampling visits, but again, variability is high because 

a smaller number of plots were revisited than were sampled the first time and total N 

data are not yet available for 2013.

Soil bulk density (weight of soil per unit volume) influences many other soil 

properties including porosity and water-holding capacity. In forest soils, bulk density 

tends to be controlled by soil organic matter content where bulk density decreases 

exponentially with increasing soil organic matter (O’Neill et al. 2005). In Colorado 

2000-13 Colorado FIA FHM Plots

Soil Organic C & Total N Stocks

Organic C, Mg/ha

0

10

20

30

40

50

60

10-20 cm

0-10 cm

Forest floor

Forest type

Western hardwoods 1Western hardwoods 2

Pinyon/Juniper 1Pinyon/Juniper 2

Ponderosa pine 1Ponderosa pine 2

Lodgepole pine 1Lodgepole pine 2

Douglas-fir 1Douglas-fir 2

Aspen 1Aspen 2

Spruce/Fir 1Spruce/Fir 2

High-elevation pines 1High-elevation pines 2

Total N, Mg/ha

0

1

2

3

10-20 cm

0-10 cm

Forest floor

Fig.	22.	

Figure 22—Distribution of organic carbon (top) 

and total nitrogen (bottom) stocks in Mg/ha in the 

forest floor, 0–10 cm, and 10–20 cm mineral soil 

depths in eight forest-type groups in Colorado. 

Soil samples were collected in 2000 through 2013 

(1st and 2nd plot visits) from western hardwoods 

(includes deciduous oak woodlands), pinyon/

juniper group (includes Rocky Mountain juniper, 

juniper woodland, and pinyon/juniper woodland), 

ponderosa pine, lodgepole pine, Douglas-fir, 

aspen, spruce/fir (includes white fir, blue spruce, 

subalpine fir, Engelmann spruce, and mixed 

Engelmann spruce/subalpine fir), and high-elevation, 5-needle white pine forest-type groups.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 49

forests, the lowest soil bulk densities tend to be found under aspen and spruce/fir 

forests (fig. 23, middle), and these forests have the highest organic C concentrations 

(fig. 23, top). Overall, soil bulk density was slightly, yet significantly, higher in the 

second visit samples compared to the first. The difference in visit means was about 

0.1 g/cm3 and is of no practical significance. It likely represents within-plot spatial 

variability because the same point cannot be sampled twice. High-elevation pines are 

often found on rockier sites with higher coarse fragment content (fig. 23, bottom). No 

significant difference in coarse fragment content was found between visits 1 and 2.

It is important to distinguish among forms of C in soils because the organic 

forms participate in a wide array of biogeochemical reactions including serving as 

substrate for microbial decomposition, thus contributing to atmospheric carbon dioxide (CO2). Inorganic forms (stored as carbonate minerals such as calcite [CaCO3]) 

tend to be more biologically inert but can be dissolved during physical, chemical, 

and biologically mediated mineral weathering reactions. In Colorado, appreciable 

amounts of soil C are stored in carbonate minerals under pinyon/juniper group forests (fig. 24, top). In contrast, the wetter, higher-elevation Douglas-fir, aspen, and 

spruce/fir forest soils store higher concentrations of organic C (fig. 24, top). Soil 

N concentrations tend to track organic C concentrations with more soil N found in 

higher-elevation forest-type groups (fig. 24, bottom) and also in western hardwoods. 

Ponderosa pine, lodgepole pine, and high-elevation pine soils have the lowest 

Soil Bulk Density,

g/cm3

0.8

1.0

1.2

1.4

visit 1

visit 2

Forest type Western hardwoods

Pinyon/Juniper

Ponderosa pine

Lodgepole pine

Douglas-fir

Aspen

Spruce/Fir

High-elevation pines

Coarse fragments

(>2-mm), %

0

10

20

30

40

50

60

visit 1

visit 2

2000-13 Colorado FIA FHM Plots

0 - 20 cm soil cores

Water content, %

0

5

10

15

20 visit 1

visit 2

Fig.	23.	

Figure 23—Soil water content (top), bulk density (middle), 

and coarse fragment (>2-mm) content (bottom) of the 

top 20-cm of mineral soil in eight forest type groups in 

Colorado.
50 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

concentrations of soil N (fig. 24, bottom). Although mean soil concentration of total 

N did not change between visits 1 and 2, mean organic C concentration was slightly 

higher in visit 1 samples than in visit 2 samples, which probably reflects within-plot 

sampling variability. This variability is of no practical significance since organic C 

stocks did not change between visits 1 and 2.

Soil pH is often closely related to the presence of carbonate minerals in soils. 

Thus, the higher pH forest soils are found under pinyon/juniper (fig. 24, top), the 

same forest-type group with relatively higher amounts of soil carbonates (fig. 24, 

top). These soils are near-neutral to alkaline. Lodgepole pine forests tend to occupy 

moderately acidic soils in Colorado. Higher elevation spruce/fir forests in Colorado 

are found on slightly acidic soils. All the forest soils in Colorado except those under 

lodgepole pine store appreciable amounts of exchangeable base cations as evidenced 

by the relatively high effective cation exchange capacities (ECEC) of these soils (fig. 

25, middle). The lower-elevation, higher pH soils under western hardwoods, pinyon/

juniper group, and ponderosa pine tend to have low levels of bicarbonate-extractable 

P (fig. 25, bottom). Bicarbonate-extractable P is used as a measure of bioavailable P 

for plant uptake. The pH, ECEC, and Olsen P levels of Colorado forest soils have not 

changed significantly between the first and second visits, suggesting that the overall 

nutrient status of these soils is being maintained.

2000-13 Colorado FIA FHM Plots

0 - 20 cm soil cores

Organic or

Inorganic C, %

0

1

2

3

4

Organic C

Inorganic (carbonate) C

Forest type Western hardwoods

Pinyon/Juniper

Ponderosa pine

Lodgepole pine

Douglas-fir

Aspen

Spruce/Fir

High-elevation pines

Total N, %

0.00

0.05

0.10

0.15

0.20

0.25 visit 1

visit 2

Fig.	24.	

Figure 24—Carbon forms (organic, carbonate) (top) and 

total nitrogen (bottom) in the top 20 cm of mineral soil in eight 

forest type groups in Colorado. In the top bar graph, visit 1 

stacked bars are on the left side of each forest type group 

tick mark, whereas visit 2 stacked bars are on the right side 

of each forest-type group tick mark.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 51

The Soil Quality Index (SQI) concept integrates 19 measured physical and 

chemical properties into a single value that serves as a means of tracking overall 

soil quality in time and space (Amacher et al. 2007). Lower values indicate increased risk of soils-related forest health decline. Spatial changes in SQI on the 

landscape can be used to identify areas of higher or lower overall soil quality 

and trends over time can be used to track potential declines in overall soil condition and thus provide an alert to potential declines in soils-related forest health. 

The highest SQI forest soils in Colorado are found under western hardwoods and 

the higher elevation Douglas-fir, aspen, spruce/fir, and high-elevation white pine 

forests. This reflects the overall higher organic matter content (except white pine 

forests) and higher productivity (higher nutrient content) of these soils. Aspen soils 

tend to have the highest nutrient content (especially N and K) and have the highest 

SQI values. This is also closely tied to the large effect of soil moisture in controlling overall forest productivity. Pinyon/juniper and lodgepole pine forests have 

the lowest SQI values. Overall in Colorado, lodgepole pine tends to occupy the 

lower pH, lower organic matter content, and lower nutrient content (less productive) soils. There has been no detectable change in the SQI for Colorado forest 

soils between the first and second visits (fig. 26).

2000-13 Colorado FIA FHM Plots

0 - 20 cm soil cores

Soil pH

5.0

5.5

6.0

6.5

7.0

7.5

8.0

visit 1

visit 2

ECEC, cmol(+)/kg

0

5

10

15

20

25

visit 1

visit 2

Forest type

Western hardwoods

Pinyon/Juniper

Ponderosa pine

Lodgepole pine

Douglas-fir

Aspen

Spruce/Fir

High-elevation pines

Olsen P, mg/kg

0

5

10

15

20

25

visit 1

visit 2

Fig.	25.	

Figure 25—Soil pH (top), effective cation exchange capacity (ECEC) (middle), and Olsen (pH 8.5, 0.5 M NaHCO3) 

extractable P (bottom) in the top 20 cm of mineral soil in eight 

forest-type groups in Colorado.
52 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

Current Issues in Colorado’s Forests___________________________

Colorado’s Mountain Pine Beetle Epidemic

Since the beginning of Colorado’s annual inventory in 2002 and the most 

recent annual inventory in 2013, extensive and widespread conifer mortality has 

occurred in Colorado (Thompson 2009). The single most significant episode occurred in lodgepole pine forests, where a mountain pine beetle (Dendroctonus 

ponderosae Hopkins) epidemic caused significant mortality of this species over 

extensive areas in Colorado. During the peak of the epidemic, there was significant concern over the visual impact and loss of commercially valuable lodgepole 

pine tree volume. Furthermore, forest managers in Colorado became concerned 

about the long-term impacts on sustainability, structure, and composition of future 

lodgepole pine forests.

There are more analysis options for monitoring forest change under the annual inventory system than the periodic inventory system. One analysis option 

considers the fact that the annual inventory is an interpenetrating design. This may 

be referred to as the independent panel option. Under this approach, each annual 

panel is analyzed independently and estimates are produced on a yearly basis. 

The independent panel option is a design where the n units (FIA sample plots) 

are divided into k = 10 panels, each panel containing m = n/k units. Panel 1 plots 

are measured in year 1, panel 2 plots are measured in year 2, and so on, such 

that all plots have been visited by the end of year 10. The panel cycle is repeated 

into perpetuity (Reams and Van Deusen 1999). The advantage of the independent 

panel option is that trends in inventory estimates such as tree growth and mortality, natural disturbances, and human-induced changes such as forest harvesting can be assessed almost immediately. The independent panel option does have 

2000-13 Colorado FIA FHM Plots

0 - 20 cm soil cores

Forest type

Western hardwoods

Pinyon/Juniper

Ponderosa pine

Lodgepole pine

Douglas-fir

Aspen

Spruce/Fir

High-elevation pines

Soil Quality Index (SQI), %

50

55

60

65

70

75

80

visit 1

visit 2

Fig.	26.	

Figure 26—Soil quality index (SQI) in the top 20 cm of 

mineral soil in eight forest-type groups in Colorado.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 53

certain limitations. Because each panel represents only about one-tenth of all plots 

in Colorado, the smaller number of plots results in a higher variance around the 

individual panel estimate. Mortality estimates, in particular, are subject to high 

variance because tree mortality is relatively infrequent. Tree mortality does not 

always occur on every plot. For situations such as insect and disease outbreaks 

where mortality may be spatially spotty, the signature of the event may not be 

adequately captured with a limited sample size. Another consideration of mortality 

estimates is that FIA compiles and reports tree mortality as an annual average over 

a measurement interval.

Independent panel estimates of live tree attributes are somewhat simpler to 

interpret than change estimates such as growth and mortality. An estimate of the 

number of live trees or live-tree volume can be generated for a specific year using 

only those plots measured in that year. Unlike change estimates, a live inventory 

estimate for a specific year is not an average—it is a valid estimate of a population total for that year. Furthermore, trends in changes of live inventory estimates 

can be generated for a series of individual years without the confounding effect of 

tracking a series of annual averages such as mortality estimates. Because of the 

abundance of live trees compared to mortality trees, the variance estimates are 

considerably lower resulting in a higher level of statistical confidence.

Lodgepole pines classified as live, ingrowth, and mortality were used in this 

analysis. Eleven panels representing estimates for individual measurement years 

were used to capture the beginning and end of the mountain pine beetle epidemic. 

Live lodgepole pines were classified as alive and standing at the time of the plot 

visit. Trees classified as ingrowth are live trees that are estimated to have grown 

over the 5-inch d.b.h. threshold for a given measurement year. Tree diameter 

growth for IWFIA plots measured for the first time is estimated from increment 

core measurements. For remeasured plots, plots with a visit at two points in time 

where the status of all trees are known at both points in time, diameter growth is 

obtained from the measurement of diameters at time 1 and time 2. Based on these 

measurements, a live tree’s diameter is projected or grown back in time for 1 year 

to obtain an initial diameter. Therefore, any live tree that has a current diameter of 

5.0 inches d.b.h. or larger and a projected initial diameter of less than 5.0 inches 

d.b.h. is classified as ingrowth.

Figure 27 illustrates the relationship between the average annual number of 

lodgepole pine trees classified as mortality and the number of ingrowth lodgepole 

pines by measurement year in Colorado. Beginning in 2002, the annual number of 

lodgepole pine mortality trees averaged 4.5 million trees compared to 10.5 million live lodgepole pines entering the 2002 inventory estimates by growing over 

the 5-inch d.b.h. threshold. During the years 2002–2005, the number of lodgepole 

pine ingrowth trees more than doubled the average number of mortality trees. In 

2006, believed to be the beginning of the epidemic, lodgepole mortality began to 

increase rapidly peaking in 2009 at an annual average of 38 million trees. This 

represents a more than eightfold increase compared to the 2002 mortality estimate. 

After 2007, lodgepole mortality began to rapidly outpace the ingrowth estimate. 

With the exception of 2008, where lodgepole pine ingrowth jumped to 22 million 
54 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

trees, the estimates of ingrowth over the 11 panels were relatively stable with no 

significant upward or downward trends noted.

Figure 28 compares trends in the number of live lodgepole pines by measurement year for two tree size classes: small diameter (5.0–8.9 inches d.b.h.) and large 

diameter (9.0 inches d.b.h. and larger). Small diameter lodgepole pines show fluctuation by measurement year but no significant trend over the 11 inventory panels. 

Numbers of large diameter lodgepole pines show a significant reduction over the 

time period. During the years 2003–2004, numbers of live large diameter lodgepole pines averaged about 215 million trees in Colorado. After 2009, numbers of 

live large diameter lodgepole pines averaged about 97 million trees.

Examination of the relationship between lodgepole pine ingrowth and annual 

mortality illustrates the advantage of using broad-scale inventory data to better 

quantify the actual net loss of trees due to a disturbance event over time. In typical 

non-epidemic periods with normal baseline tree mortality, losses of trees dying of 

natural causes are generally offset by an equal amount of tree growth. Inventories 

of an abundant species over a large geographic area usually remain relatively stable over time. As figure 27 illustrates, prior to the beetle epidemic, the number 

of new trees entering the inventory actually exceeded trees leaving the inventory 

0	

5	

10	

15	

20	

25	

30	

35	

40	

45	

50	

2002	 2003	 2004	 2005	 2006	 2007	 2008	 2009	 2010	 2011	 2012	

Million	trees	

Measurement	year	

Mortality	

Ingrowth	

Figure 27—Comparison between average annual numbers of lodgepole pine mortality trees and 

numbers of ingrowth lodgepole pine trees by measurement year in Colorado, 2002–2012. Error 

bars represent the 95% confidence interval.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 55

due to mortality by a significant margin. Also noted is that, despite the dramatic 

increase in lodgepole pine mortality that peaked in 2009, the numbers of ingrowth 

trees remained at about the same level between 2002 and 2012.

The effect of the mountain pine beetle epidemic in Colorado on residual 

lodgepole pine stand structure was pronounced. The number of live large-diameter 

lodgepole pine trees essentially declined by over 50 percent during the 11-year 

study period where the number of small diameter trees remained unchanged. The 

impact on stand structure resulted in an altered size class distribution of lodgepole 

pine forests in Colorado. During the years 2003–2005, the proportion of small 

diameter trees averaged 63 percent of the total number of lodgepole pine trees 

greater than or equal to 5.0 inches d.b.h. in Colorado. During the years 2010–2012, 

that same proportion of small diameter trees averaged 75 percent.

Sudden Aspen Decline

Quaking aspen is the most abundant and important hardwood species in 

Colorado. About 6.5 million acres in Colorado have aspen present—more acres 

than any other State in the western United States and almost twice as many as in 

Utah, which is second in aspen acreage. In Colorado, aspen is a valuable ecological 

component of the State’s landscape, occurring in pure forests as well as growing 

0	

100	

200	

300	

400	

500	

600	

2002	 2003	 2004	 2005	 2006	 2007	 2008	 2009	 2010	 2011	 2012	

Million	trees	

Measurement	year	

Small	diameter	

Large	diameter	

Figure 28—Numbers of live lodgepole pine trees 5.0 inches d.b.h. and larger by tree size 

class and measurement year in Colorado, 2002–2012. Error bars represent the 95% confidence interval.
56 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

in association with many conifer species. In addition to the tree’s desirable scenic 

value, the diversity of understory plants that occur under the aspen canopy supplies critical wildlife habitat, valuable grazing resources, and protection for soil 

and water.

Scientists, forest managers, and observers in the general public began noticing rapid mortality of aspen in multiple locales in southwestern Colorado beginning in 2004 (Worrall et al. 2008). The difference between this recently observed 

aspen mortality and the mortality typically observed in aspen stands is the suddenness of the phenomenon and the apparent lack of regeneration occurring in 

stands where the overstory mortality is unusually high. Evidence to date suggests 

that it is a decline disease incited by acute, warm drought (Anderegg et al. 2013a). 

Predisposing factors include low elevation, south and southwest aspects, droughty 

soils, open stands, and physiological maturity. The agents that commonly kill the 

stressed trees include Cytospora canker, two bark beetle species, poplar borer, 

and bronze poplar borer, but it has been shown that drought alone and the depletion of trees’ energy resources can kill aspen in the absence of insects and disease 

(Anderegg et al. 2012, 2013b).

Although aspen mortality is readily observable in some locations (e.g., Smith 

and Smith 2005), analysis of population trend at relatively large scales is the best 

way to assess whether or not the locally observed mortality occurrences are significantly affecting abundance of the overall population. Unfortunately, the early 

inventories of Colorado (Benson and Green 1987; Miller and Choate 1964) did 

not cover all aspen forests of Colorado. Therefore, the comparisons of periodic 

inventory results to more recent annual inventory data cannot be done for aspen as 

in other States, such as New Mexico (Goeking et al. 2014) and Utah (Werstak et 

al. 2016). In the first Colorado report based on annual inventory (Thompson et al. 

2010), which was based on 50 percent of the FIA plots in the State, there appeared 

to be no strong trend with respect to total aspen biomass in Colorado. However, the 

high variance associated with a partial inventory make it difficult to make strong 

conclusions. In addition, the absence of a strong trend in the early 2000s does not 

necessarily affect the current situation, given the high annual variation of weather 

patterns in the Interior West. Completion of a full cycle of plots and 2 years of remeasurement improve our ability to analyze the status of aspen in Colorado.

A forest population is analogous to a bank account that accrues interest. The 

volume of live trees represents an account balance. Any event that removes live 

trees, such as insect or disease mortality, or harvesting, can be thought of as account withdrawals. New trees coming into the inventory—in the case of volume, 

trees becoming large enough for volume to be calculated—are “deposits.” The 

“interest” comes in the form of growth of live trees. As opposed to a bank account, 

where the same interest rate applies to every dollar in the account, each tree has 

its own potential growth rate that is based on its size, age, the quality of the site it 

grows on, the presence of nearby competitors, and external factors such as temperature and precipitation. For example, it is well known that growth is lower in dry 

years than in wet years. Therefore, “interest” is never constant, even for a single 

tree. Another difference is that unlike a bank account balance, the volume of trees 
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 57

cannot increase indefinitely. Each site has a relatively fixed capacity, so the only 

way to increase the ceiling on the account balance is to add more acreage on which 

a species is growing. For forests this is a slow process, so we cannot observe that 

kind of change at the current length of the time scale of our observations. However, 

any loss of acreage—either by permanent land use change or a temporary reduction in acreage occupied by a species—will be reflected in the inventory almost 

immediately.

Using this analogy, it is possible to look at trends in the aspen population 

from several different aspects. In the Forest Growth and Mortality section, it has 

already been shown that the cottonwood and aspen species group, which is heavily 

dominated by aspen, showed positive net growth based on the last 10 years of annual inventory. In the bank account analogy, interest was greater than withdrawals. 

However, because the net growth calculations are based on plots collected over a 

period of time, they don’t show any year-to-year fluctuations that occur. In other 

words, while the 10-year average of net growth may be positive, the most current 

change in population might be negative net growth.

To look at these fluctuations, we calculated alternative metrics that explore 

different aspects of aspen growth and mortality. In the first case, aspen mortality 

in each year is expressed as a percentage of the pre-mortality volume (% mortality 

basal area = mortality basal area / [mortality basal area + live basal area]). Each 

point on the annual mortality line (red) in figure 29 represents a single year of measurements, and therefore provides one of the best views of year-to-year variation. 

Some of the variation is undoubtedly due to chance in sampling, such as measuring 

a few more recently burned plots than average in a given year, but the more general 

trend likely captures actual fluctuation.

The second line (blue) in figure 29 is essentially a population volume calculation, for only aspen, similar to what is presented in the Forest Growth and Mortality

section and elsewhere in this report. However, instead of presenting only the most 

recent 10 years of data, each point on the line includes data from years of data up 

to that inventory year. For example, 2013 includes years 2004–2013 (as elsewhere 

in this report), 2012 includes 2003–2012, etc. Because annual inventory started in 

Colorado in 2002, the earlier points on the line don’t include a full 10 years of data. 

Inventory year 2004 includes years 2002–2004, inventory year 2005 includes years 

2002–2005, etc. Points for 2002 and 2003–2003 are not shown due the high variability associated with using such small fractions of plots for volume estimation.

The two trend lines in figure 29, when compared, provide additional insight 

into recent aspen growth and mortality trends in Colorado. From 2002 to 2006, when 

aspen mortality averaged approximately 1.0 percent per year, it appears that there 

was a gradual increase in aspen volume. With the resurgence of drought conditions, 

mortality since 2007 has averaged closer to 1.4 percent. This increase in mortality 

rate appears to be coincident with the apparent decrease in aspen volume since 2006. 

One interpretation of these trends suggests that the “break-even” point of growth 

vs. mortality occurs between the two mortality levels shown in these data—perhaps close to 1.2 percent. This is a somewhat simplified view because, as mentioned 

above, growth rates are variable over time and the net of additions and removals 
58 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

from live volume is actually determined by the balance between growth and mortality—not just the rate of one or the other.

The question asked most often is: What is the long-term prospect for aspen in 

the Interior West? Based on the data currently available, it appears that under annual 

inventory (2002 to 2013), aspen transitioned from a period of positive net growth to 

a period of negative net growth, the latter of which is based on a steady, but gradual 

decline in the estimate of live volume. However, net growth remains positive, on 

average, for the whole time period on which the current inventory is based. If the 

slight decline observed in recent years were to continue at its present rate, it would 

take more than 50 years for the live volume of aspen in Colorado to be reduced to 

half of its present quantity. A steady decline for such a long period is highly unlikely 

as evidenced by the fact that in only 12 years of annual monitoring we have observed 

increases and decreases. The estimate of aspen volume in 2012 was almost identical 

to the estimate in 2004, which in the bank account analogy, means that the “interest” 

of past years has been “withdrawn” in recent years.

While it is impossible to predict future growth, some indicators suggest a possible return to positive net growth rates in the short term. Only about 30 percent of 

the area of Colorado was in some degree of drought by the end of water year 2014, 

and at the time of this writing (August 2015), only about 2 percent of Colorado is in 

drought conditions. Although it has been recently shown that growth does not fully 

rebound immediately after drought periods (Anderegg et al. 2015), favorable moisture conditions should help to reverse the most recent trends. In addition, live volume 

in younger stands that were regenerated during the late 1990s and early 2000s will 

0.0%	

0.5%	

1.0%	

1.5%	

2.0%	

2.5%	

3.0%	

	-		

	1,000		

	2,000		

	3,000		

	4,000		

	5,000		

	6,000		

2000	 2002	 2004	 2006	 2008	 2010	 2012	 2014	

Annual	mortality	(basal	area	basis)	

Net	volume	(million	cubic	feet)	

Measurement	/	Inventory	Year	

Live	Aspen	Volume	

Annualized	Aspen	Mortality	

Figure 29—Net live volume of aspen for inventory years 2004 to 2013, and annualized mortality rate 

for aspen in measurement years 2002 to 2013.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 59

come into the inventory soon as individual trees become large enough to compute 

volume. Because of the high level of interest and the value of aspen forests, the situation will continue to be analyzed in future reports.

Spruce Beetle in Colorado

Bark beetle activity has been a significant contributor to tree mortality in the 

western United States over the last few decades (Werstak et al. 2016). The spruce 

beetle (Dendroctonus rufipennis Kirby) is a bark beetle native to the Engelmann 

spruce forests of the Interior West. At endemic population levels the spruce beetle 

prefers mature Engelmann spruce in stands of high density (Schmid and Frye 1977). 

If spruce beetles can successfully mass-attack individual spruce, their populations 

may increase to epidemic levels where mortality levels of greater than 90 percent are 

possible at scales much larger than individual stands (Schmid and Frye 1977).

Engelmann spruce mortality resulting from spruce beetle attack was noted in 

the previous Colorado State report (2002–2006, Thompson et al. 2010); however, 

considerable additional mortality has occurred in the subsequent years (2007–2013). 

The average annual mortality for Engelmann spruce and Engelmann spruce/subalpine fir forest types jumped from 93.1 million cubic feet for the 2002–2006 period, 

to 161 million cubic feet for the 2004–2013 period (Thompson et al. 2010, table 

B27). Ninety-six percent of this mortality has occurred on National Forest Systems 

land. The area of Engelmann spruce and Engelmann spruce/subalpine fir forest type 

affected by insects, including damage to seedling and saplings, was just under nearly 

700,000 acres, which represents approximately 15 percent of the total area of these 

forest types in Colorado.

Mortality attributable to insects in the Engelmann spruce and Engelmann 

spruce/subalpine fir forest types exhibited an increasing trend over the reporting period with the largest percentage of mortality occurring from 2009 to 2013 (fig. 30).

Some insect-caused mortality of subalpine fir within the Engelmann spruce/subalpine fir forest type may be attributable to the western balsam bark beetle (Dryocoetes 

confuses Swaine), but species-level records of insects that cause mortality are not 

currently recorded. Individual tree mortality attributable to the spruce beetle occurred relatively uniformly across the State from 2004 through approximately 2009 

(fig. 31). Since 2010 the majority of the mortality has been centered on the San Juan 

and Rio Grande National Forests in the southwestern part of the State where there 

is an ongoing notable spruce beetle outbreak. Additionally, there appears to be an 

increase in recent Engelmann spruce mortality in the north part of the State on the 

Routt and Arapaho-Roosevelt National Forests.

Although the spruce beetle is thought to preferentially choose larger Engelmann 

spruce occurring in dense stands, this relationship breaks down during epidemics 

(DeRose and Long 2012; Hart et al. 2014). In Colorado, the area affected by spruce 

beetles occurred disproportionately higher in the 40 to 80 square foot basal area 

class, and lower than the overall population in the 120+ square foot class (fig. 32), 

confirming that stand density is probably not the limiting factor controlling spruce 

beetle outbreaks (Temperli et al. 2014). Interestingly, the distribution of spruce 

beetle-affected stands occurred uniformly across stand-age classes (fig. 33).
60 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

Comparisons Between Colorado’s Periodic and Annual Forest Inventories

One purpose of Colorado’s annual forest inventory is to provide information 

about changes in forest attributes over time. Prior to the implementation of the 

annual inventory, at least three plot-based periodic inventories were conducted in 

Colorado. If the definitions and methods used during the periodic inventories were 

compatible with those used during the annual inventory, we could quantify trends 

over the past 30 years. However, the sampling and field procedures used during 

the periodic inventories were different enough from those of the annual inventory 

to preclude reliable trend analysis. Therefore, direct comparisons of periodic and 

annual inventories in their entireties are not recommended and may even produce 

misleading results (Goeking 2015). This section describes the primary differences 

between the periodic and annual inventories; presents an appropriate method for 

comparing periodic and annual inventory data at plots that were measured during 

both inventories, or co-located plots; and summarizes some changes in forest attributes that have occurred at co-located plots.

The primary differences between Colorado’s periodic and annual forest inventories pertain to the operational definitions used during field data collection, 

plot design, estimation procedures, and sample design. Most of the periodic inventories used a variable-radius plot design with varying numbers of subplots. 

y	=	15.017x	+	41.002	

R2	=	0.39,	P	=	0.0525	

0	

50	

100	

150	

200	

250	

300	

350	

400	

2004	 2005	 2006	 2007	 2008	 2009	 2010	 2011	 2012	 2013	

Insect-caused	mortality	volume	(million	cubic	feet)	

Year	

Figure 30—Volume of Engelmann spruce mortality by inventory year, Colorado, 2004–2013.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 61

Figure 31—General locations of plots with spruce beetle-killed Engelmann spruce between 2004 and 

2013 in Colorado. 

Figure 32—Distribution 

of area by basal area 

classes of the combined 

Engelmann spruce and 

Engelmann spruce/

subalpine fir forest types 

for all stands and beetle 

affected stands, Colorado, 

2004–2013. 

0	

0.1	

0.2	

0.3	

0.4	

0.5	

0.6	

0.7	

0-40	 41-80	 81-120	 120+	

Frequency	

Basal	Area	(square	feet)	

All	spruce	

Beetle	affected	spruce	
62 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

In contrast, the plot design of the annual inventory consists of four fixed-radius 

subplots, as described in the Plot Configuration section of this report’s “Inventory 

Methods” chapter. Colorado’s periodic inventories also used an operational definition of “tree” that differentiated between tree-form and shrub-form trees. For 

example, pinyon pines that were less than 6 feet tall and were not expected to 

eventually produce a straight, 8-foot trunk section were not considered to be trees 

and were not measured, so they were not included in volume-based estimates such 

as biomass, growth, and mortality. In contrast, the annual inventory identifies 

trees strictly by their species, regardless of growth form. Therefore, trees on many 

woodland plots in the current annual inventory would not have been measured 

under previous definitions.

Estimation procedures also changed from using a combination of plot data 

and maps to using plot data almost exclusively, with maps being used only for 

post-stratification (Bechtold and Patterson 2005). For example, the first published 

statewide inventory of Colorado (Miller and Choate 1964) included area estimates 

that were based on delineation of stands on aerial photographs, while volume estimates were based on just over 2,000 ground plots. Sample designs also changed 

appreciably from samples that typically targeted specific ownership groups and 

varied in sample intensity to a spatially representative plot grid with consistent 

sample intensity across all forest types and management categories. For example, 

when the second periodic inventory was conducted between 1979 and 1984, sample plots were located almost exclusively on privately owned lands. The private 

plot data, on the other hand, were combined with information provided by individual National Forests and other Federal land management agencies to produce 

the estimates published by Benson and Green (1987; Thompson et al. 2010). In 

contrast, during the third periodic inventory in 1997, plots were measured only on 

the Grand Mesa, Uncompahgre, and Gunnison (GMUG) National Forests of westcentral Colorado.

0	

0.05	

0.1	

0.15	

0.2	

0.25	

0.3	

0.35	

0.4	

0.45	

0-20	 21-40	 41-60	 61-80	 81-100	 101-150	 151-200	 201+	

Frequency	

Stand	Age	

All	spruce	

Beetle	affected	spruce	

Figure 33—Distribution 

of area by stand age 

classes of the combined 

Engelmann spruce and 

Engelmann spruce/subalpine fir forest types for 

all stands and beetle affected stands, Colorado, 

2004–2013.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 63

Due to these differences in forest inventories over time, users of FIA data 

should be aware of appropriate methods of evaluating trends and avoid inappropriate methods. Examples of inappropriate comparisons between periodic and annual 

inventories range from comparing the statewide tree volume within a specific forest type to directly comparing the total area of forest land. Instead, an appropriate 

method of quantifying trends is to first identify forest plots that were measured 

during both periodic and annual inventories, and then assess trends at only those 

plots. FIA refers to such plot locations as co-located plots. Although different plot 

designs were used during the periodic and annual measurements of co-located 

plots, each plot design allows estimation of volume, growth, and mortality per 

acre as well as stand-level variables such as forest type. However, this approach 

also assumes that the information collected on forest plots was consistent among 

the two time periods being compared, so operational definitions must be consistent between the inventories. If this assumption is met, comparisons of multiple 

measurements at co-located plots are useful for quantifying trends in attributes on 

a per-acre basis, such as volume, mortality, growth, biomass, and number of trees 

per acre. This assumption can be met for the periodic inventory of the 1990s, but 

not for the earlier 1980s inventory. Therefore, valid comparisons of periodic versus 

annual plot data should include only the 1997 inventory of the GMUG National 

Forests.

Changes between Colorado’s most recent periodic inventory and the annual 

inventory have been presented by Goeking (2015) and are summarized here. The 

first measurements of co-located plots occurred in 1997, and the second measurements occurred between 2003 and 2012. Figure 34 shows the distribution of all 

plots in the 1997 periodic inventory, all plots in the annual inventory, and the 336 

co-located plots that were measured during both inventories. By comparing the 

proportional representation of each forest type during the periodic versus annual 

inventories in their entireties, Goeking (2015) determined that the 1997 inventory 

of the GMUG National Forests underrepresented pinyon/juniper forest types and, 

to a lesser extent, ponderosa pine and Douglas-fir types. Conversely, it overrepresented the following timber forest-type groups: aspen/birch, fir/spruce/mountain 

hemlock, and lodgepole pine. As a result of these biases among forest types, the 

1997 estimates of volume per acre of both live and dead trees were much higher, 

on average, than those produced by the annual inventory.

Mean total and live volume per acre at co-located plots decreased by 3 and 

5 percent, respectively, between 1997 and the annual measurements, and these 

decreases were statistically significant (Goeking 2015). In contrast, mean dead 

volume per acre increased by 14 percent. Mean mortality and net growth volumes, which are both expressed as cubic-foot volume per acre per year, showed 

contrasting but complementary changes. Mean net growth volume decreased 60 

percent from 32.8 to 13.2 cubic-feet per acre per year, while mean mortality volume increased by 78 percent from 12.8 to 22.8 cubic-feet per acre per year over 

the same period (77.6 percent; Goeking 2015). Because net growth equals gross 

growth minus mortality, part of the change in net growth reflects the large increase 

in mortality accompanied by an increase in gross growth. In contrast to the results 
64 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

from co-located plots, a comparison of all plots in both inventories indicated no 

change in either dead net volume or mortality (Goeking 2015), thus demonstrating that comparing periodic to annual inventories in their entireties may produce 

misleading results.

To investigate changes for individual tree species, we conducted additional 

analyses to quantify the mean annual mortality, mean annual net growth, live basal 

area, and dead basal area for several species. This analysis focused on co-located 

plots that were measured in 1997 and again during the most recent full cycle of 

Colorado’s annual inventory (2004–2013). Here we present the results for the five 

species that are most abundant, in terms of numbers of trees, in Colorado. In decreasing order of statewide abundance, these are Engelmann spruce, quaking aspen, subalpine fir, lodgepole pine, and Gambel oak.

Figure 35 shows that all major species except for subalpine fir showed positive net growth during both inventory periods, and all major species showed lower 

rates of net growth in the annual inventory than in 1997. Decreases in net growth 

appear to be at least partially attributable to increased mortality over this period; 

all species showed increased mortality between 1997 and the annual inventory. At 

the most recent measurements, all major species had less live basal area per acre, 

and more dead basal area per acre, than in 1997 (fig. 36). Given the recent negative 

net growth of subalpine fir (fig. 35), we could expect that subalpine fir will have 

less live basal area and more dead basal area during the second cycle of Colorado’s 

annual inventory.

The caveat of the co-located plot analysis presented here is that results cannot 

be scaled to the entire State and cannot overcome the limitations of the periodic 

sample design. For example, if the periodic inventory under-sampled a particular 

forest type, an analysis of co-located plots will still underrepresent that forest type 

and will instead exhibit trends that occurred on forest types that were sampled more 

representatively. Further, these results cannot be extrapolated beyond the GMUG 

National Forests. Nonetheless, this type of analysis provides the best information 

available on the direction of change in Colorado’s forests. As Colorado’s forest 

inventory continues into its second cycle and plots are remeasured at a consistent 

10-year interval, FIA’s ability to quantify trends in forest attributes will expand 

from analyses of co-located periodic plots to robust Statewide estimates of change 

based on the spatially representative annual plot grid.

Fires in Colorado

Fire is an important disturbance that influences the structure and dynamics 

of Colorado’s forests. In some forest types, such as ponderosa pine, fire can maintain open stands and stimulate the growth of grasses and forbs in the understory. 

Throughout the Interior West, a century of fire suppression has led to a buildup of 

fuels and stand densification, which may lead to uncharacteristically intense fires 

(Reinhardt et al. 2008). Areas that burn intensely may experience slow regeneration, but others may recover relatively quickly. For example, the area inside the 

boundary of the large 1910 fires in Idaho and Montana (Cohen and Miller 1978; 
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 65

Figure 34—Approximate locations of 

forested plots in (A) the 1997 periodic 

inventory; (B) a full cycle of the annual 

inventory; and (C) co-located plots measured during 1997 and again during the 

annual inventory.
66 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

Egan 2009; Pyne 2008) now carries about the same amount of live tree volume per 

acre as areas outside the fires. The mean stand age, however, is somewhat lower 

and the volume is generally distributed among smaller trees (Wilson et al. 2010).

During the period covered by this report there were numerous fires in 

Colorado. Some FIA plots within fire boundaries were measured before the fires 

occurred, and some were measured after. As a result, some fire perimeters contain 

both prefire and postfire plots, while others may contain only prefire or only postfire plots. Prefire plots represent the original conditions in areas that later burned, 

while only postfire plots provide insight into the short-term effects of fire. This 

means that normal data compilation methods cannot be used without introducing 

some element of temporal bias. These limitations on analysis will be reduced as 

more remeasurement data are acquired. However, there are some general analyses 

that can be conducted with the current data.

We used data from the Monitoring Trends in Burn Severity (MTBS) project, which is an interagency effort being conducted and maintained by the USDA 

Forest Service Remote Sensing Applications Center and the U.S. Geological 

Survey National Center for Earth Resources Observation and Science. The purpose of the MTBS project is to map the perimeters and severities of large wildland 

Figure 35—Mean annual mortality and mean annual net growth for the most abundant tree species of 

Colorado, as measured at co-located plots that were measured during the 1997 inventory (t1) and again between 2004 and 2013 (t2).

-15	

-10	

-5	

0	

5	

10	

15	

20	

25	

30	

35	

t1	 t2	 t1	 t2	 t1	 t2	 t1	 t2	 t1	 t2	

Engelmann	spruce	 Quaking	aspen	 Subalpine	fir	 Lodgepole	pine	 Gambel	oak	

Mortality	and	net	growth	(feet3/acre/year)	

Mortality	 Net	growth	
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 67

0	

20	

40	

60	

80	

100	

t1	 t2	 t1	 t2	 t1	 t2	 t1	 t2	 t1	 t2	

Engelmann	spruce	 Quaking	aspen	 Subalpine	fir	 Lodgepole	pine	 Gambel	oak	

Basal	area	()2/acre)	

Live	trees	 Dead	trees	

Figure 36—Mean basal area for major tree species of Colorado, as measured at co-located plots that were 

measured during the 1997 inventory (t1) and again between 2004 and 2013 (t2).

fires (including wildfire, wildland fire use, and prescribed fire) across all lands of 

the United States. In western States, the project maps all fires larger than 1,000 

acres (Eidenshink et al. 2007). The analysis presented here is based on fire perimeters identified by the MTBS program between 1984 and 2012 and FIA plot data 

collected between 2002 and 2013 in Colorado (Shaw et al. 2017).

The MTBS program delineated 336 fire perimeters from 289 different fires 

that burned just over 1.52 million acres in Colorado between 1984 and 2012. 

For fires overlapping State boundaries, we report only the portion occurring in 

Colorado. This acreage represents the sum of all burned areas, but about 4 percent 

of this area burned more than once. The unique burned area is just over 1.46 million acres. This is in contrast to other States and the Interior West in general, where 

approximately 20 percent of the area within MTBS boundaries burned more than 

once. These fires ranged in size from the minimum MTBS mapped area to 129,000 

acres (Hayman Fire), with an average size of about 5,000 acres. This average excludes the many smaller fires that occurred in Colorado, but which were below the 

MTBS minimum mapping threshold. In addition to the Hayman Fire, other large 

fires include the High Park Fire (90,769 acres), the Missionary Ridge Fire (68,919 

acres), and the West Fork Complex (53,216 acres).

These fire perimeters include 2 percent (211) of all the FIA plots in Colorado 

(10,813 plots) (fig. 37). Just over 64 percent of the plots within these fire perimeters are forest plots and about 36 percent are nonforest plots (table 9). Of the 

total land area burned by these fires, the largest proportion was on Federal land 
68 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

managed by the USDA Forest Service at 34 percent and accounts for nearly 50 

percent of the burned forest land. The second highest proportion of total land 

area burned was held by private landowners at 28 percent and accounts for nearly 20 percent of the burned forest land. The third highest proportion of total land 

area burned was on Federal land managed by the Bureau of Land Management 

at 20 percent, representing nearly 21 percent of the burned forest land (table 9).

Time since fire for the postfire forest plots ranged from almost immediately 

postfire to 26 years in Colorado (fig. 38). About 90 percent of plot visits occurred 

within 15 years of the most recent fire. Figure 39 shows the distribution of the 

total number and average number of acres burned by fire in Colorado between 

1984 and 2012 (MTBS 2014).

Figure 40 shows the distribution of average live basal area for postfire plots 

at time of measurement. Live basal area calculations include all live trees at 

least 5 inches in diameter and larger. Nearly one-half of the plots (47 percent) 

measured after the occurrence of fire had no live basal area. The remaining plots 

(53 percent) show a wide range of postfire live basal area. Because of the slow 

growth of most tree species sampled and the 5-inch minimum diameter used to 

calculate basal area, the amount of postfire basal area is influenced very little by 

new tree growth and therefore mostly reflects postfire residual trees.

The prefire and postfire average basal area for live trees and standing dead 

trees in Colorado clearly indicates that while fire kills some trees, it does not 

kill all trees (fig. 41). Further, the increase in standing dead basal area after fire 

indicates that trees are not completely consumed by fire. Another interesting 

observation is that the live basal area at burned plots was about 36 percent of 

the live basal area at unburned plots. This difference between live basal area at 

burned plots compared to unburned plots may be related to the forest type and 

stand structure in Colorado, which is dominated by pinyon/juniper woodlands. 

Because this comparison of burned and unburned plots used a space-for-time 

substitution, rather than remeasurement data from the same plots before and after 

fire, these results may be due to higher initial basal area at the burned plots. We 

do not yet have enough data to analyze whether prefire, high-density stands are 

more likely to be represented in our sample than low-density stands.

The analyses in this section should be considered only a first approximation of fire effects on Colorado’s forests. Although the results are generally consistent with expectations, the magnitude of fire-related mortality cannot yet be 

stated precisely. Nonetheless, the data confirm that within fire boundaries there 

has been only partial mortality. Additional data and analysis will be required 

to determine whether, for example, mortality is more or less evenly distributed 

among plots within the burned areas or if mortality tends to be all-or-none at 

the plot scale. Regeneration of seedling and sapling densities after fire loosely 

resembles what would be expected, but inconsistencies in the data still remain. 

Remeasurement data will be necessary to confirm fire related tree mortality and 

seedling and sapling regeneration.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 69

Conclusions and Future Analysis______________________________

Colorado’s 23 million acres of forest land are one of the more complex 

ecosystems of the Interior West, with a diverse mix of coniferous and deciduous 

tree species. The major forest-type groups in Colorado are the pinyon/juniper, fir/

spruce/mountain hemlock, aspen/birch, woodland hardwood, and lodgepole pine. 

Also comprising significant areas of forest land are Gambel oak, Douglas-fir, and 

ponderosa pine. The reason for this diversity is a physical landscape that ranges 

from flat plains and high plateaus to steep mountains and deep canyons. Within 

this landscape, a wide range of topographic, soil, and moisture regimes exist.

Figure 37—Distribution of FIA 

plots within MTBS burn perimeters, 

Colorado, 2004–2013.

Table 9—Proportions of total land area, forest area, and nonforest area 

burned by fire, by ownership, Colorado, 2004–2013.

Owner code Ownership Forest Nonforest All lands

11 Forest Service 49.5 6.1 33.9

21 NPS 33.0 3.9 3.3

22 BLM 20.5 17.5 19.4

24 DOD/DOE 4.8 19.0 9.9

31 State 2.4 11.5 5.7

40 Private 19.8 41.9 27.8

Total 100% 100% 100%
70 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

0	

50	

100	

150	

200	

250	

300	

350	

400	

450	

500	

Acres	(in	thousands)	

Year	

Total	Acres	Burned	

5-Year	Moving	Average	

(centered)	

Figure 39—Total number and average number of acres burned by fire at 5-year intervals, Colorado, 1984–2012. 

0	

5	

10	

15	

20	

25	

30	

35	

Number	of	plots	

Time	since	fire	(years)	

Figure 38—Number of years between fire and plot visit for postfire plots, Colorado, 2004–2013 and, MTBS, 

1984–2012.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 71

Most of Colorado’s forests are controlled by public agencies. Nearly half of 

all forest land is controlled by National Forest Systems and 24 percent is controlled 

by private landowners. The significant amount of public land points to a forest resource that must meet the diverse needs of people. These needs include shelter for 

people and wildlife, water quality, recreation, pollution control, and timber products that furnish jobs and strengthen local economies. The population of Colorado 

has been growing at a tremendous rate since 1990 in many of the mountain counties (Forstall 1995). This “mountain sprawl” has placed many homeowners very 

close to forest land. Naturally, people living in these settings are going to be concerned about anything that might endanger these forests and their homes, such as 

wildfire, insects, and disease.

The most significant estimates in Colorado’s forest inventory include high 

rates of mortality that are causing reductions in net growth in some of the major 

tree species in the State. Negative net growth was recorded for the lodgepole pine, 

Engelmann and other spruces, and true fir species groups. However, net growth 

was positive for most of the other major species groups in Colorado, including 

aspen. Insects and disease are the major contributing factors to the elevated levels 

of mortality that are likely related to multi-year weather patterns such as drought. 

Mortality was higher on National Forest lands than any other ownership category and mortality exceeds gross growth on National Forest lands in Colorado. 

Colorado’s commercial timber harvest volume has continued to decrease over the 

past decade; and the State’s forest products industry has been facing mill closures 

0	

10	

20	

30	

40	

50	

60	

70	

0	(or	“none”)	0.01	to	20.00	 20.01	to	

40.00	

40.01	to	

60.00	

60.01	to	

80.00	

80.01	to	

100.00	

100.01	to	

120.00	

120.01	to	

140.00	

More	than	

140.00	

Number	of	plots	

Post	live	basal	area	(square	feet	per	acre)	

Figure 40—Distribution of average live basal area for postfire plots at time of measurement, Colorado, 

2004–2013 and, MTBS, 1984–2012.
72 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

and curtailments even as markets have begun to improve. The decline in Colorado’s 

forest products industry has eroded the ability to actively manage forests and generate income for landowners to use towards activities such as hazardous fuel reduction or forest restoration, which may reduce future statewide mortality losses. 

To ensure sustainable harvest levels for future generations, careful consideration 

should be given not only to growth, removals, and mortality across Colorado’s 

available timberlands, but also to the industry infrastructure and employees that 

conduct management activities and utilize harvested timber.

The mountain pine beetle epidemic that occurred during the timespan this report covers in Colorado was considered to be catastrophic and unprecedented. The 

infestation peaked between 2004 and 2008 and is now considered to have ended. 

Forest managers have had serious concerns about the future of lodgepole pine, the 

primary affected species. However, comparing annual levels of lodgepole pine 

ingrowth against annual mortality as a metric for sustainability of the lodgepole 

resource indicates net mortality losses may not be as severe as initially thought. 

Nevertheless, the epidemic had an immediate effect on the structure and composition of lodgepole pine forests in Colorado. Beetles concentrated the attacks on 

older stands with large-diameter trees. The mortality associated with the epidemic 

0	

10	

20	

30	

40	

50	

60	

70	

80	

90	

Pre-fire	 Post-fire	

Basal	area	per	acre	(square	feet)	

Live	Basal	Area	

Dead	Basal	Area	

Figure 41—Estimates of prefire and postfire, live and dead average basal area per acre, Colorado, 

2004–2013 and, MTBS, 1984–2012.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 73

resulted in reshaping the lodgepole resource to a higher proportion of younger 

lodgepole pine forests comprised of smaller-diameter trees.

Also of concern is the relatively recent rapid mortality of aspen occurring in 

certain locales in Colorado—referred to as Sudden Aspen Decline. Based on the 

State’s annual inventory, aspen transitioned from a period of positive to negative 

net growth during the 2004 to 2013 inventory period. However, across the entire 

time period, average aspen net growth remained positive. No significant detectable reduction in aspen inventory occurred statewide although we acknowledge 

that certain regions in Colorado have experienced significant losses of aspen due 

to elevated mortality rates. Because there is so much interest and value of the 

aspen resource in Colorado, the situation will continue to be monitored in future 

inventories.

The information presented in this report points to opportunities for further 

analysis, investigation, and studies. The systematic interpenetrating panel design 

of the annual inventory presents opportunities to assess trends in inventory estimates never before possible with periodic inventories. Quantitative inferences 

about temporal trends require consideration of independent estimates of the population status each year, each of which uses completely different sample plots from 

different panels. Various time-series model-based estimation techniques have been 

explored (Czaplewski and Thompson 2009). These model-based estimators cannot only be used to track mortality events, they can lead to better monitoring of 

forest growth, live tree inventory, and tree harvest activity. Once the annual inventory effort extends into the second measurement cycle in Colorado, the power to 

detect significant effects related to tree mortality and other parameters of interest 

will increase substantially with estimates derived from the remeasured (paired) 

plots that will be available. What is clear is the need for accurate, consistent, longterm monitoring procedures for managers and researchers to study relationships 

between forest attributes and changing climate patterns.

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Appendix A. Standard Forest Inventory and Analysis Terminology

Average annual mortality—The average annual volume of trees 5.0 inches 

d.b.h./d.r.c. and larger that died from natural causes.

Average net annual growth—Average annual net change in volume of trees 5.0 

inches diameter at breast height (d.b.h.)/diameter at root collar (d.r.c). and larger 

in the absence of cutting (average annual gross growth minus average annual 

mortality).

Basal area (BA)—The cross-sectional area of a tree stem/bole (trunk) at the point 

where diameter is measured, inclusive of bark. BA is calculated for trees 1.0 

inch and larger in diameter and is expressed in square feet. For timber species, 

the calculation is based on d.b.h.; for woodland species, it is based on d.r.c.

Biomass—The quantity of wood fiber, for trees 1.0-inch d.b.h./d.r.c. and larger, 

expressed in terms of oven-dry weight. It includes above-ground portions of 

trees: bole/stem (trunk), bark, and branches. Biomass estimates can be computed for live and/or dead trees.

Board-foot volume—A unit of measure indicating the amount of wood contained 

in an unfinished board 1 foot wide, 1 foot long, and 1 inch thick. Board-foot 

volume is computed for the sawlog portion of a sawtimber-size tree; the sawlog 

portion includes the part of the bole on sawtimber-size tree from a 1-foot stump 

to a minimum sawlog top of 7 inches in diameter outside bark (d.o.b.) for softwoods, or 9 inches d.o.b. for hardwoods. Net board-foot volume is calculated 

as the gross board-foot volume in the sawlog portion of a sawtimber-size tree, 

less deductions for cull (note: board-foot cull deductions are limited to rotten/

missing material and form defect—referred to as the merchantability factor—

board-foot). Board-foot volume estimates are computed in both Scribner and 

International 1⁄4-inch rule and can be calculated for live and/or dead (standing 

or down) trees.

Census water—Streams, sloughs, estuaries, canals, and other moving bodies of 

water 200 feet wide and greater, and lakes, reservoirs, ponds, and other permanent bodies of water 4.5 acres in area and greater.

Coarse woody debris—Down pieces of wood leaning more than 45 degrees from 

vertical with a diameter of at least 3.0 inches and a length of at least 3.0 feet.

Condition class—The combination of discrete landscape and forest attributers 

that identify, define, and stratify the area associated with a plot. Such attributes 

include reserved status, owner group, forest type, stand size class, stand origin, 

and tree density.

Crown class—A classification of trees based on dominance in relation to adjacent 

trees in the stand as indicated by crown development and amount of sunlight 

received from above and the sides.

Crown cover (canopy cover)—The percentage of the ground surface area covered by a vertical projection of plant crowns. Tree crown cover for a sample site 
82 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

includes the combined cover of timber and woodland trees 1.0 inch d.b.h./d.r.c. 

and larger. Maximum crown cover for a site is 100 percent; overlapping cover 

is not double counted.

Cubic-foot volume (merchantable)—A unit of measure indicating the amount 

of wood contained in a cube 1-by-1-by-1 foot. Cubic-foot volume is computed 

for the merchantable portion of timber and woodland species; the merchantable 

portion for timber species includes that part of a bole from a 1-foot stump to a 

minimum 4-inch top d.o.b, or above the place(s) of diameter measurement for 

any woodland tree with a single 5.0-inch stem or larger or a cumulative (calculated) d.r.c. of at least 5.0 inches to the 1.5-inch ends of all branches. Net cubicfoot volume is calculated as the gross cubic-foot volume in the merchantable 

portion of a tree, less deductions for cull.

Diameter at breast height (d.b.h.)—The diameter of a tree bole/stem (trunk) 

measured at breast height (4.5 feet above ground), measured outside the bark. 

The point of diameter measurement may vary for abnormally formed trees.

Diameter at root collar (d.r.c.)—The diameter of a tree stem(s) measured at root 

collar or at the point nearest the ground line (whichever is higher) that represents 

the basal area of the tree, measured outside the bark. For multi-stemmed trees, 

d.r.c. is calculated from an equation that incorporates the individual stem diameter measurements. The point of diameter measurement may vary for woodland 

trees with stems that are abnormally formed. With the exception of seedlings, 

woodland stems qualifying for measurement must be at least 1.0 inch in diameter or larger and at least 1.0 foot in length.

Diameter class—A grouping of tree diameters (d.b.h. or d.r.c.) into classes of a 

specified range. For some diameter classes, the number referenced (e.g., 4”, 6”, 

8”) is designated as the midpoint of an individual class range. For example, if 

2-inch classes are specified (the range for an individual class) and even numbers 

are referenced, the 6-inch class would include trees 5.0 to 6.9 inches in diameter.

Diameter outside bark (d.o.b.)—Tree diameter measurement inclusive of the 

outside perimeter of the tree bark. The d.o.b. measurement may be taken at 

various points on a tree (e.g., breast height, tree top) or log, and is sometimes 

estimated.

Field plot/location—A reference to the sample site or plot; an area containing the 

field location center (LC) and all sample points. A field location consists of four 

subplots and four microplots.

• Subplot—A 1/24-acre fixed-radius area (24-foot horizontal radius) used to 

sample trees 5.0-inches d.b.h./d.r.c. and larger and understory vegetation.

• Microplot—A 1/300-acre fixed-radius plot (6.8-foot radius), located at the 

center of each subplot, used to inventory seedlings and saplings.

Fixed-radius plot—A circular sample plot of a specified horizontal radius: 1/300 

acre = 6.8-foot radius (microplot); 1/24 acre = 24.0-foot radius (subplot).
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 83

Forest industry land—Land owned by a company or an individual(s) operating a 

primary wood-processing plant.

Forest land—Land that has at least 10 percent cover of live tally tree species of 

any size, or land formerly having such tree cover, and not currently developed 

for a nonforest use. The minimum area for classification as forest land is 1 acre. 

Roadside, streamside, and shelterbelt strips of trees must be at least 120 feet 

wide to qualify as forest land. Unimproved roads and trails, streams and other 

bodies of water, or natural clearings in forested areas are classified as forest if 

less than 120 feet in width or 1 acre in size. Grazed woodlands, reverting fields, 

and pastures that are not actively maintained are included if the above qualifications are satisfied.

Forest type—A classification of forest land based on the species forming a plurality of live-tree stocking.

Gross growth—The annual increase in volume of trees 5.0 inches d.b.h. and larger 

in absence of cutting and mortality. Gross growth includes survivor growth, 

ingrowth, growth on ingrowth, growth on removals before removal, and growth 

on mortality prior to death.

Growing-stock trees—A live timber species, 5.0 inches d.b.h. or larger, with less 

than 2/3 (67 percent) of the merchantable volume cull, and containing at least 

one solid 8-foot section, now or prospectively, reasonably free of form defect, 

on the merchantable portion of the tree.

Growing-stock volume—The cubic-foot volume of sound wood in growing-stock 

trees at least 5.0 inches d.b.h. from a 1-foot stump to a minimum 4-inch top 

d.o.b. to the central stem.

Hardwoods—Dicotyledonous trees, usually broadleaf and deciduous.

Hexagonal grid (Hex)—A hexagonal grid formed from equilateral triangles for 

the purpose of tessellating the FIA inventory sample. Each hexagon in the base 

grid has an area of 5,937 acres (2,403.6 ha) and contains one inventory plot. 

The base grid can be subdivided into smaller hexagons to intensify the sample.

Indian Trust lands—American Indian lands held in fee, or trust, by the Federal 

Government, but administered for tribal groups or as individual trust allotments.

Inventory year—The year in which a plot was scheduled to be completed. Within 

each subpanel, all plots have the same inventory year. Inventory year may differ 

from measurement year.

Land use—The classification of a land condition by use or type.

Litter—The uppermost layer of organic debris on a forest floor; that is, essentially 

the freshly fallen, or only slightly decomposed material, mainly foliage, but also 

bark fragments, twigs, flowers, fruits, and so forth. Humus is the organic layer, 

unrecognizable as to origin, immediately beneath the litter layer from which it 

is derived. Litter and humus together are often termed duff.
84 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

Logging residue/products—

• Bolt—A short piece of pulpwood; a short log.

• Industrial wood—All commercial roundwood products, excluding fuelwood.

• Logging residue—The unused sections within the merchantable portions of 

sound (growing-stock) trees cut or killed during logging operations.

• Mill or plant residue—Wood material from mills or other primary manufacturing plants that is not used for the mill’s or plant’s primary products. Mill 

or plant residue includes bark, slabs, edgings, trimmings, miscuts, sawdust, 

and shavings. Much of the mill and plant residue is used as fuel and as the 

raw material for such products as pulp, palletized fuel, fiberwood, mulch, 

and animal bedding. Mill or plant residue includes bark and the following 

components:

• Coarse residue—Wood material suitable for chipping, such as slabs, edgings, 

and trim.

• Fine residue—Wood material unsuitable for chipping, such as sawdust and 

shavings.

• Pulpwood—Roundwood, whole-tree chips, or wood residues that are used for 

the production of wood pulp.

• Roundwood—Logs, bolts, or other round sections cut from trees.

Mapped-plot design—A sampling technique that identifies (maps) and separately 

classifies distinct “conditions” on the field location sample area. Each condition must meet minimum size requirements. At the most basic level, condition 

class delineations include forest land, nonforest land, and water. Forest land 

conditions can be further subdivided into separate condition classes if there are 

distinct variations in forest type, stand size class, stand origin, and stand density, 

given that each distinct area meets minimum size requirements.

Measurement year—The year in which a plot was completed. Measurement year 

may differ from inventory year.

Merchantable portion—For trees measured at d.b.h. and 5.0 inches d.b.h. and 

larger, the merchantable portion (or “merchantable bole”) includes the part of 

the tree bole from a 1-foot stump to a 4.0-inch top (d.o.b.). For trees measured 

at d.r.c., the merchantable portion includes all qualifying segments above the 

place(s) of diameter measurement for any tree with a single 5.0-inch stem or 

larger or a cumulative (calculated) d.r.c. of at least 5.0 inches to the 1.5-inch 

ends of all branches; sections below the place(s) of diameter measurement are 

not included. Qualifying segments are stems or branches that are a minimum of 

1 foot in length and at least 1.0 inch in diameter; portions of stems or branches 

smaller than 1.0 inch in diameter, such as branch tips, are not included in the 

merchantable portion of the tree.

Mortality tree—All standing or down dead trees 5.0 inches d.b.h./d.r.c. and larger 

that were alive within the previous five years (in most States); for the 2008 to 
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 85

2012 New Mexico inventory, this includes trees that were alive within the previous 10 years.

National Forest System (NFS) lands—Public lands administered by the Forest 

Service, U.S. Department of Agriculture, such as National Forests, National 

Grasslands, and some National Recreation Areas.

National Park lands—Public lands administered by the Park Service, U.S. Department of the Interior, such as National Parks, National Monuments, National 

Historic Sites (such as National Memorials and National Battlefields), and some 

National Recreation Areas.

Noncensus water—Portions of rivers, streams, sloughs, estuaries, and canals that 

are 30 to 200 feet wide and at least 1 acre in size; and lakes, reservoirs, and 

ponds 1 to 4.5 acres in size. Portions of rivers and streams not meeting the criteria for census water, but at least 30 feet wide and 1 acre in size, are considered 

noncensus water. Portions of braided streams not meeting the criteria for census 

water, but at least 30 feet in width and 1 acre in size, and more than 50 percent 

water at normal high-water level are also considered noncensus water.

Nonforest land—Land that does not support, or has never supported, forests, and 

lands formerly forested where tree regeneration is precluded by development 

for other uses. Includes areas used for crops, improved pasture, residential areas, city parks, improved roads of any width and adjoining rights-of-way, power 

line clearings of any width, and noncensus water. If intermingled in forest areas, 

unimproved roads and nonforest strips must be more than 120 feet wide, and 

clearings, etc., more than 1 acre in size, to qualify as nonforest land.

Nonindustrial private lands—Privately owned land excluding forest industry 

land.

Nonstocked stand—A formerly stocked stand that currently has less than 10 percent stocking but has the potential to again become 10 percent stocked. For 

example, recently harvested, burned, or windthrow-damaged areas.

Other Federal lands—Public lands administered by Federal agencies other than 

the Forest Service, U.S. Department of Agriculture, or the Bureau of Land Management, U.S. Department of the Interior.

Other private lands—Privately owned lands other than forest industry or Indian 

Trust.

Other public lands—Public lands administered by agencies other than the Forest 

Service, U.S. Department of Agriculture. Includes lands administered by other 

Federal, State, county, and local government agencies, including lands leased 

by these agencies for more than 50 years.

Poletimber-size trees—For trees measured at d.b.h, softwoods 5.0 to 8.9 inches 

d.b.h. and hardwoods 5.0 to 10.9 inches d.b.h. For trees measured at d.r.c., all 

live trees 5.0 to 8.9 inches d.r.c.

Primary wood-processing plants—An industrial plant that processes roundwood 

products, such as sawlogs, pulpwood bolts, or veneer logs.
86 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

Private lands—All lands not owned or managed by a Federal, State, or other public entity, including lands owned by corporations, trusts, or individuals, as well 

as Tribal lands.

Productive forest land—Forest land capable of producing 20 cubic feet per acre 

per year of wood from trees classified as a timber species (see Appendix D) on 

forest land classified as a timber forest type (see Appendix C).

Productivity—The potential yield capability of a stand calculated as a function 

of site index (expressed in terms of cubic-foot growth per acre per year at age 

of culmination of MAI). Productivity values for forest land provide an indication of biological potential. Timberland stands are classified by the potential 

net annual growth attainable in fully stocked natural stands. For FIA reporting, 

Productivity Class is a variable that groups stand productivity values into categories of a specified range. Productivity is sometimes referred to as “Yield” or 

“Mean annual increment (MAI).”

Removals—The net volume of sound (growing-stock) trees removed from the 

inventory by harvesting or other cultural operations (such as timber-stand improvement), by land clearing, or by changes in land use (such as a shift to wilderness).

Reserved land—Land withdrawn from management for production of wood products through statute or administrative designation; examples include Wilderness 

areas and National Parks and Monuments.

Sampling error—A statistical term used to describe the accuracy of the inventory estimates. Expressed on a percentage basis in order to enable comparisons 

between the precision of different estimates, sampling errors are computed by 

dividing the estimate into the square root of its variance.

Sapling—A live tree 1.0 to 4.9 inches d.b.h./d.r.c.

Sawlog portion—The part of the bole of sawtimber-size trees between a 1-foot 

stump and the sawlog top.

Sawlog top—The point on the bole of sawtimber-size trees above which a sawlog 

cannot be produced. The minimum sawlog top is 7 inches d.o.b. for softwoods 

and 9 inches d.o.b. for hardwoods.

Sawtimber-size trees—Softwoods 9.0 inches d.b.h. and larger and hardwoods 

11.0 inches and larger.

Sawtimber volume—The growing-stock volume in the sawlog portion of sawtimber-size trees in board feet.

Seedlings—Live trees less than 1.0 inch d.b.h./d.r.c.

Site index—A measure of forest productivity for a timberland tree/stand. Expressed in terms of the expected height (in feet) of trees on the site at an index 

age of 50 (or 80 years for aspen and cottonwood). Calculated from height-toage equations.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 87

Site tree—A tree used to provide an index of site quality. Timber species selected 

for site index calculations must meet specified criteria with regards to age, diameter, crown class, and damage.

Snag—A standing-dead tree.

Softwood trees—Coniferous trees, usually evergreen, having needle- or scale-like 

leaves.

Stand—A community of trees that can be distinguished from adjacent communities due to similarities and uniformity in tree and site characteristics, such as 

age-class distribution, species composition, spatial arrangement, structure, etc.

Stand density—A relative measure that quantifies the relationship between trees 

per acre, stand basal area, average stand diameter, and stocking of a forested 

stand.

Stand density index (SDI)—A widely used measure developed by Reineke (1933) 

and is an index that expresses relative stand density based on a comparison of 

measured stand values with some standard conditions; relative stand density 

is the ratio, proportion, or percent of absolute stand density to a reference level 

defined by some standard level of competition. For FIA reporting, the SDI for a 

site is usually presented as a percentage of the maximum SDI for the forest type. 

Site SDI values are sometimes grouped into SDI classes of a specified percentage range. Maximum SDI values vary by species and region.

Standing tree—To qualify as a standing dead tally tree, dead trees must be at least 

5.0 inches in diameter, have a bole that has an unbroken actual length of at least 

4.5 feet, and lean less than 45 degrees from vertical as measured from the base 

of the tree to 4.5 feet. Portions of boles on dead trees that are separated greater 

than 50 percent (either above or below 4.5 feet) are considered severed and are 

included in Down Woody Material (DWM) if they otherwise meet DWM tally 

criteria. For western woodland species with multiple stems, a tree is considered 

down if more than 2/3 of the volume is no longer attached or upright; cut and 

removed volume are not considered. For western woodland species with single 

stems to qualify as a standing dead tally tree, dead trees must be at least 5.0 

inches in diameter, be at least 1.0 foot in unbroken actual length, and lean less 

than 45 degrees from vertical.

Stand-size class—A classification of forest land based on the predominant diameter size of live trees presently forming the plurality of live-tree stocking. 

Classes are defined as follows:

• Sawtimber stand (large-tree stand)—A stand at least 10 percent stocked 

with live trees, in which half or more of the total stocking is from live trees 

5.0 inches or larger in diameter, and with sawtimber (large tree) stocking 

equal to or greater than poletimber (medium tree) stocking.

• Poletimber stand (medium-tree stand)—A stand at least 10 percent stocked 

with live trees, in which half or more of the total stocking is from live trees 
88 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

5.0 inches or larger in diameter, and with poletimber (medium tree) stocking 

exceeding sawtimber (large tree) stocking.

• Sapling/seedling stand—A stand at least 10 percent stocked with live trees, in 

which half or more of the total stocking is from live trees less than 5.0 inches 

in diameter.

• Nonstocked stand—A formerly stocked stand that currently has less than 10 

percent stocking but has the potential to again become 10 percent stocked. 

For example, recently harvested, burned, or windthrow-damaged areas.

Stockability (stockability factor)—An estimate of the stocking potential of a 

given site; for example, a stockability factor of 0.8 for a given site indicates that 

the site is capable of supporting only about 80 percent of “normal” stocking as 

indicated by yield tables. Stockability factors (maximum site value of 1.0) are 

assigned to sites based on habitat type/plant associations.

Stocking—An expression of the extent to which growing space is effectively utilized by live trees.

Timber species—Tally tree species traditionally used for industrial wood products. These include all species of conifers, except pinyon and juniper. Timber 

species are measured at d.b.h.

Timber stand improvement—A term comprising all intermediate cuttings or 

treatments, such as thinning, pruning, release cutting, girdling, weeding, or poisoning, made to improve the composition, health, and growth of the remaining 

trees in the stand.

Timberland—Unreserved forest land capable of producing 20 cubic feet per acre 

per year of wood from trees classified as a timber species (see Appendix D) on 

forest land designated as a timber forest type (see Appendix C).

Unproductive forest land—Forest land not capable of producing 20 cubic feet 

per acre per year of wood from trees classified as a timber species (see Appendix 

D) on forest land designated as a timber forest type and all forest lands designated as a woodland forest type (see Appendix C).

Unreserved forest land—Forest land not withdrawn from management for production of wood products through statute or administrative designation.

Wilderness area—An area of undeveloped land currently included in the Wilderness System, managed to preserve its natural conditions and retain its primeval 

character and influence.

Woodland species—Tally tree species that are not usually converted into industrial wood products. Common uses of woodland trees are fuelwood, fenceposts, 

and Christmas trees. These species include pinyon, juniper, mesquite, locust, 

mountain-mahogany (Cercocarpus spp.), Rocky Mountain maple, bigtooth maple, desert ironwood, and most oaks (note: bur oak and chinkapin oak are classified as timber species). Because most woodland trees are extremely variable 

in form, diameter is measured at d.r.c.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 89

Appendix B. Standard Forest Resource Tables

Table B1—Percentage of plot area by land status, Colorado, 2004–2013.

Table B2—Area of forest land, in thousand acres, by owner class and forest land 

status, Colorado, 2004–2013.

Table B3—Area of forest land, in thousand acres, by forest-type group and productivity class, Colorado, 2004–2013.

Table B4—Area of forest land, in thousand acres, by forest-type group, ownership 

group, and land status, Colorado, 2004–2013.

Table B5—Area of forest land, in thousand acres, by forest-type group and standsize class, Colorado, 2004–2013.

Table B6—Area of forest land, in thousand acres, by forest-type group and standage class, Colorado, 2004–2013.

Table B7—Area of forest land, in thousand acres, by forest-type group and stand 

origin, Colorado, 2004–2013.

Table B8—Area of forest land, in thousand acres, by forest-type group and primary disturbance class, Colorado, 2004–2013.

Table B9—Area of timberland, in thousand acres, by forest-type group and standsize class, Colorado, 2004–2013.

Table B10—Number of live trees (at least 1 inch d.b.h./d.r.c.), in thousand trees, on 

forest land by species group and diameter class, Colorado, 2004–2013.

Table B11—Number of growing-stock trees (at least 5.0 inches d.b.h.) on timberland by species group and diameter class, Colorado, 2004–2013.

Table B12—Net volume of live trees (at least 5 inches d.b.h./d.r.c.), in million cubic feet, by owner class and forest land status, Colorado, 2004–2013.

Table B13—Net volume of live trees (at least 5 inches d.b.h./d.r.c.), in million 

cubic feet, on forest land by forest-type group and stand-size class, Colorado, 

2004–2013.

Table B14—Net volume of live trees (at least 5 inches d.b.h./d.r.c.), in million 

cubic feet, on forest land by species group and ownership group, Colorado, 

2004–2013.

Table B15—Net volume of live trees (at least 5 inches d.b.h./d.r.c.), in million cubic 

feet, on forest land by species group and diameter class, Colorado, 2004–2013.

Table B16—Net volume of live trees (at least 5 inches d.b.h./d.r.c.), in million cubic 

feet, on forest land by forest-type group and stand origin, Colorado, 2004–2013.

Table B17—Net volume of growing-stock trees (at least 5 inches d.b.h.), in million cubic feet, on timberland by species group and diameter class, Colorado, 

2004–2013.
90 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

Table B18—Net volume of growing-stock trees (at least 5 inches d.b.h.), in million cubic feet, on timberland by species group and ownership group, Colorado, 

2004–2013.

Table B19—Net volume of sawtimber trees, in million board feet (International 

1/4 inch rule), on timberland by species group and diameter class, Colorado, 

2004–2013.

Table B20—Net volume of sawlog portion of sawtimber trees, in million cubic 

feet, on timberland by species group and ownership group, Colorado, 2004–

2013.

Table B21—Average annual net growth of live trees (at least 5 inches d.b.h./d.r.c.), 

in million cubic feet, by owner class and forest land status, Colorado, 2004–

2013.

Table B22—Average annual net growth of live trees (at least 5 inches d.b.h./d.r.c.), 

in million cubic feet, on forest land by forest-type group and stand-size class, 

Colorado, 2004–2013.

Table B23—Average annual net growth of live trees (at least 5 inches d.b.h./d.r.c.), 

in million cubic feet, on forest land by species group and ownership group, 

Colorado, 2004–2013.

Table B24—Average annual net growth of growing-stock trees (at least 5 inches 

d.b.h.), in million cubic feet, on timberland by species group and ownership 

group, Colorado, 2004–2013.

Table B25—Average annual mortality of trees (at least 5 inches d.b.h.), in million 

cubic feet, by owner class and forest land status, Colorado, 2004–2013.

Table B26—Average annual mortality of trees (at least 5 inches d.b.h./d.r.c.), in 

million cubic feet, on forest land by forest-type group and stand-size class, Colorado, 2004–2013.

Table B27—Average annual mortality of trees (at least 5 inches d.b.h./d.r.c.), in 

million cubic feet, on forest land by species group and ownership group, Colorado, 2004–2013.

Table B28—Average annual mortality of growing-stock trees (at least 5 inches 

d.b.h.), in million cubic feet, on timberland by species group and ownership 

group, Colorado, 2004–2013.

Table B29—Aboveground dry weight live trees (at least 1 inch d.b.h./d.r.c.), in 

thousand dry short tons, by owner class and forest land status, Colorado, 2004–

2013.

Table B30—Aboveground dry weight of live trees (at least 1 inch d.b.h./d.r.c.), 

in thousand dry short tons, on forest land by species group and diameter class, 

Colorado, 2004–2013.

Table B31—Area of forest land, in thousand acres, by inventory unit, county, and 

forest-land status, Colorado, 2004–2013.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 91

Table B32—Area of forest land, in thousand acres, by inventory unit, county, ownership group, and forest-land status, Colorado, 2004–2013.

Table B33—Area of timberland, in thousand acres, by inventory unit, county, and 

stand-size class, Colorado, 2004–2013.

Table B34—Area of timberland, in thousand acres, by inventory unit, county, and 

stocking class, Colorado, 2004–2013.

Table B35—Net volume of growing-stock trees (at least 5 inches d.b.h.), in million cubic feet, and sawtimber trees, in million board feet (International 1/4 

inch rule), on timberland by inventory unit, county, and major species group, 

Colorado, 2004–2013.

Table B36—Average annual net growth of growing-stock trees (at least 5 inches 

d.b.h.), in million cubic feet, and sawtimber trees, in million board feet (International 1/4 inch rule), on timberland by inventory unit, county, and major species 

group, Colorado, 2004–2013.

Table B37—Sampling errors (in percent) by inventory unit and county for area of 

timberland, volume, average annual net growth, average annual removals, and 

average annual mortality on timberland, Colorado, 2004–2013.
92 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

Percentage of sample

Accessible forest land

Unreserved forest land

Timberland 15.1

Unproductive 12.9

Total unreserved forest land 28.0

Reserved forest land

Productive 3.3

Unproductive 0.8

Total reserved forest land 4.1

Total accessible forest land 32.1

Nonforest and other areas

Nonforest land 63.5

Water 0.5

Census 0.3

Non-Census 0.2

Total nonforest and other areas 64.0

Nonsampled land

Access denied 3.2

Hazardous conditions 0.7

Other 0.0

Total nonsampled land 3.9

All land 100.0

Land status

Table B1—Percentage of plot area by land status, Colorado, 2004–2013.

All table cells without observations in the inventory sample are indicated by—. 

Table value of 0.0 indicates the percentage rounds to less 

than 0.1 percent. Columns and rows may not add to their totals due to rounding.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 93 Owner class Timberland Unproductive Total Productive Unproductive Total Forest Service National Forest 7,532 1,434 8,966 2,025 140 2,165 11,130 National Grassland — 25.7 25.7 — — — 25.7 Other Federal National Park Service — — — 203.9 161.9 365.8 365.8 Bureau of Land Management 671.9 4,083.2 4,755.1 36.6 231.9 268.6 5,023.6 Departments of Defense and Energy 11.2 91.4 102.6 — — — 102.6 State and local government State 199.7 294.0 493.7 74.5 1.5 75.9 569.6 County and Municipal 82.0 11.8 93.9 — — — 93.9 Private Undifferentiated private 2,248.6 3,331.2 5,579.8 — — — 5,579.8 All owners Total 10,745.2 9,271.2 20,016.3 2,340.0 535.0 2,875.0 22,891.3 Unreserved forests Reserved forests All forest land Table B2 All table cells without observations in the inventory sample are indicated by —. Table value of 0.0 indicates the acres round to less than 0.1 thousand acres. Columns and rows may not add to their totals due to rounding. —Area of accessible forest land, in thousand acres, by owner class and forest land status, Colorado, 2004–2013. Forest-type group 0-19 20-49 50-84 85-119 120-164 165-224 225+ Pinyon / juniper group 6,357.1 — — — — — — 6,357.1 Douglas-fir group 14.7 1,349.6 206.2 — — — — 1,570.5 Ponderosa pine group 19.0 1,387.7 136.8 — — — — 1,543.5 Fir / spruce / mountain hemlock group 38.5 2,419.0 2,147.5 325.1 6.0 — — 4,936.1 Lodgepole pine group 183.4 1,332.6 105.4 — — — — 1,621.4 Other western softwoods group 79.7 176.2 6.2 — — — — 262.0 Elm / ash / cottonwood group 9.5 81.5 27.0 6.0 — — — 123.9 Aspen / birch group 361.6 2,158.8 809.6 34.3 — — — 3,364.2 Woodland hardwoods group 2,442.0 — — — — — — 2,442.0 Exotic hardwoods group — 4.4 — — — — — 4.4 Nonstocked 300.6 321.8 43.8 — — — — 666.2 All forest-type groups 9,806.1 9,231.4 3,482.4 365.3 6.0 — — 22,891.3 Site-productivity class (cubic feet/acre/year) Total all classes Table B3—Area of accessible forest land, in thousand acres, by forest-type group and productivity class, Colorado, 2004–2013. All table cells without observations in the inventory sample are indicated by—. Table value of 0.0 indicates the acres round to less than 0.1 thousand acres. Columns and rows may not add to their totals due to rounding.
94 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 Other Other Other Other All Timber- forest Timber- forest Timber- forest Timber- forest forest Forest-type group land land land land land land land land land Pinyon / juniper group — 452.5 — 3,760.9 — 160.4 — 1,983.3 6,357.1 Douglas-fir group 770.9 130.0 204.8 4.6 73.8 1.7 378.1 6.6 1,570.5 Ponderosa pine group 668.4 38.0 105.1 20.7 50.1 7.8 647.0 6.4 1,543.5 Fir / spruce / mountain hemlock group 2,794.4 1,418.6 153.9 106.2 51.4 25.8 385.8 — 4,936.1 Lodgepole pine group 938.0 341.6 48.8 69.4 13.4 31.6 147.6 31.0 1,621.4 Other western softwoods group 124.5 78.5 7.6 17.4 — 4.8 22.3 6.9 262.0 Elm / ash / cottonwood group 3.1 — 1.7 3.4 23.1 3.5 86.6 2.6 123.9 Aspen / birch group 2,061.3 413.4 142.6 69.7 60.8 26.1 495.1 95.3 3,364.2 Woodland hardwoods group — 642.7 — 565.6 — 113.8 — 1,119.8 2,442.0 Exotic hardwoods group — — — — 4.4 — — — 4.4 Nonstocked 171.1 109.1 18.7 190.9 4.6 6.3 86.2 79.3 666.2 All forest-type groups 7,531.8 3,624.2 683.1 4,808.9 281.7 381.8 2,248.6 3,331.2 22,891.3 Table B4—Area of accessible forest land, in thousand acres, by forest-type group, ownership group, and land status, Colorado, 2004–2013. Forest Service Other Federal All table cells without observations in the inventory sample are indicated by —. Table value of 0.0 indicates the acres round to less than 0.1 thousand acres. Columns and rows may not add to their totals due to rounding. State and local government Undifferentiated private Large Medium Small All size Forest-type group diameter diameter diameter Nonstocked classes Pinyon / juniper group 5,622.0 499.4 235.7 — 6,357.1 Douglas-fir group 1,292.6 225.5 52.4 — 1,570.5 Ponderosa pine group 1,380.7 74.6 88.2 — 1,543.5 Fir / spruce / mountain hemlock group 3,664.3 811.1 460.7 — 4,936.1 Lodgepole pine group 580.2 775.5 265.6 — 1,621.4 Other western softwoods group 210.1 30.6 21.4 — 262.0 Elm / ash / cottonwood group 104.2 13.5 6.3 — 123.9 Aspen / birch group 822.7 1,818.3 723.2 — 3,364.2 Woodland hardwoods group 102.0 272.6 2,067.3 — 2,442.0 Exotic hardwoods group — — 4.4 — 4.4 Nonstocked — — — 666.2 666.2 All forest-type groups 13,778.9 4,521.0 3,925.3 666.2 22,891.3 Stand-size class Table B5 All table cells without observations in the inventory sample are indicated by —. Table value of 0.0 indicates the acres round to less 

than 0.1 thousand acres. Columns and rows may not add to their totals due to rounding.

—Area of accessible forest land, in thousand acres, by forest-type group and stand-size class, Colorado, 2004–2013.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 95 Non- All Forest-type group stocked 1-20 21-40 41-60 61-80 81-100 101-120 121-140 141-160 161-180 181-200 201+ classes Pinyon / juniper group — 165.6 145.6 382.1 611.4 699.8 695.4 700.9 578.9 626.0 650.6 1,100.9 6,357.1 Douglas-fir group — 46.2 7.8 39.9 174.5 371.9 273.5 231.9 146.5 132.8 56.5 89.0 1,570.5 Ponderosa pine group — 72.7 27.9 75.7 202.8 383.5 395.0 199.0 93.2 29.5 28.8 35.5 1,543.5 Fir / spruce / mountain hemlock group — 274.9 158.5 105.2 303.3 653.8 844.5 735.3 663.7 417.1 293.4 486.4 4,936.1 Lodgepole pine group — 109.2 68.9 80.9 214.2 355.3 426.1 168.2 63.2 33.7 18.6 83.1 1,621.4 Other western softwoods group — 5.8 4.4 6.2 17.5 32.3 17.2 40.8 37.7 37.9 32.1 30.3 262.0 Elm / ash / cottonwood group — 5.1 1.2 19.1 37.7 26.2 20.9 3.8 — 1.6 5.8 2.6 123.9 Aspen / birch group — 582.7 122.2 253.0 711.3 886.2 553.7 202.3 32.2 20.5 — — 3,364.2 Woodland hardwoods group — 1,654.9 377.0 58.7 98.1 123.4 59.3 44.4 19.4 6.6 — — 2,442.0 Exotic hardwoods group — 4.4 — — — — — — — — — — 4.4 Nonstocked 666.2 — — — — — — — — — — — 666.2 All forest-type groups 666.2 2,921.6 913.4 1,020.7 2,370.8 3,532.5 3,285.6 2,326.6 1,634.8 1,305.6 1,085.6 1,827.8 22,891.3 Stand-age class (years) Table B6 All table cells without observations in the inventory sample are indicated by—. Table value of 0.0 indicates the acres round to less than 0.1 thousand acres. Columns and rows may not add to their totals due to rounding. —Area of forest land, in thousand acres, by forest-type group and stand-age class, Colorado, 2004–2013. Natural Artificial All forest Forest-type group stands regeneration land Pinyon / juniper group 6,350.5 6.6 6,357.1 Douglas-fir group 1,570.5 — 1,570.5 Ponderosa pine group 1,536.9 6.6 1,543.5 Fir / spruce / mountain hemlock group 4,930.0 6.0 4,936.1 Lodgepole pine group 1,621.4 — 1,621.4 Other western softwoods group 262.0 — 262.0 Elm / ash / cottonwood group 123.9 — 123.9 Aspen / birch group 3,364.2 — 3,364.2 Woodland hardwoods group 2,442.0 — 2,442.0 Exotic hardwoods group 4.4 — 4.4 Nonstocked 659.5 6.6 666.2 All forest-type groups 22,865.3 25.9 22,891.3 Stand origin Table B7 All table cells without observations in the inventory sample are indicated by—. Table value of 0.0 indicates the acres round to less than 0.1 thousand acres. Columns and rows may not add to their totals due to rounding. —Area of forest land, in thousand acres, by forest-type group and stand origin, Colorado, 2004–2013.
96 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 Wild Domestic All forest Forest-type group None Insects Disease Fire animals animals Weather Vegetation Other Human Geological land Pinyon / juniper group 5,723.6 370.1 100.8 31.2 37.5 30.3 36.6 — — 11.3 15.8 6,357.1 Douglas-fir group 1,375.3 125.3 37.6 11.6 — 3.4 4.6 — 6.8 6.0 — 1,570.5 Ponderosa pine group 1,329.5 78.4 52.7 74.9 — 1.5 6.5 — — — — 1,543.5 Fir / spruce / mountain hemlock group 3,891.5 810.5 159.6 6.5 — 5.6 31.0 — — — 31.5 4,936.1 Lodgepole pine group 1,055.0 404.2 125.4 11.7 6.2 — 5.5 6.0 — — 7.4 1,621.4 Other western softwoods group 241.3 19.5 — — — — — — — — 1.3 262.0 Elm / ash / cottonwood group 103.8 — 7.2 — — 6.8 6.1 — — — - - 123.9 Aspen / birch group 2,519.4 263.6 378.8 43.6 68.3 57.3 19.2 — 6.4 — 7.6 3,364.2 Woodland hardwoods group 2,214.1 25.6 43.6 63.0 — 53.9 29.5 — 6.4 5.8 — 2,442.0 Exotic hardwoods group 4.4 — — — — — — — — — — 4.4 Nonstocked 493.3 51.2 1.6 110.8 1.6 — — 6.0 — — 1.7 666.2 All forest-type groups 18,951.0 2,148.5 907.2 353.3 113.6 158.8 139.1 12.0 19.6 23.1 65.1 22,891.3 Disturbance class Table B8 All table cells without observations in the inventory sample are indicated by—. Table value of 0.0 indicates the acres round to less than 0.1 thousand acres. Columns and rows may not add to their totals due to rounding. —Area of forest land. In thousand acres, by forest-type group and primary disturbance class, Colorado, 2004–2013. Large Medium Small All size Forest-type group diameter diameter diameter Nonstocked classes Douglas-fir group 1,176.7 209.0 41.8 — 1,427.6 Ponderosa pine group 1,307.9 74.6 88.2 — 1,470.6 Fir / spruce / mountain hemlock group 2,532.9 614.1 238.4 — 3,385.4 Lodgepole pine group 397.4 559.0 191.4 — 1,147.8 Other western softwoods group 132.5 16.0 5.9 — 154.4 Elm / ash / cottonwood group 98.1 13.5 2.9 — 114.5 Aspen / birch group 753.8 1,444.2 561.8 — 2,759.7 Exotic hardwoods group — — 4.4 — 4.4 Nonstocked — — — 280.7 280.7 All forest-type groups 6,399.3 2,930.3 1,134.9 280.7 10,745.2 Stand-size class Table B9 All table cells without observations in the inventory sample are indicated by—. Table value of 0.0 indicates the acres round to less than 0.1 thousand acres. Columns and rows may not add to their totals due to rounding. —Area of timberland, in thousand acres, by forest-type group and stand-size class, Colorado, 2004–2013.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 97 1.0- 3.0- 5.0- 7.0- 9.0- 11.0- 13.0- 15.0- 17.0- 19.0- 21.0- 25.0- 29.0- 33.0- All Species group 2.9 4.9 6.9 8.9 10.9 12.9 14.9 16.9 18.9 20.9 24.9 28.9 32.9 36.9 37.0+ classes Douglas-fir 154,435 93,186 68,680 55,473 39,474 27,456 15,820 12,075 8,301 4,450 4,004 941 734 185 36 485,249 Ponderosa and Jeffrey pine 63,828 43,206 36,113 33,755 33,887 26,221 18,683 11,115 6,225 4,412 4,121 1,533 308 204 40 283,651 True fir 663,800 291,537 152,560 95,088 59,682 34,747 20,962 10,643 6,470 3,525 2,704 978 392 39 37 1,343,163 Engelmann and other spruces 516,608 308,289 195,629 145,709 104,558 74,013 50,790 35,700 24,459 13,021 13,711 3,988 1,031 464 — 1,487,970 Lodgepole pine 279,488 284,136 204,528 128,937 70,790 32,218 15,233 5,632 1,973 631 112 — — — — 1,023,677 Other western softwoods 39,514 35,637 21,067 14,674 11,784 6,934 4,232 2,225 1,459 831 958 310 36 — — 139,663 Western woodland softwoods 449,580 256,996 173,019 136,435 104,994 87,750 63,982 51,397 35,750 25,475 26,300 10,646 4,358 1,464 976 1,429,122 2,167,252 1,312,987 851,596 610,071 425,169 289,339 189,702 128,786 84,636 52,346 51,910 18,396 6,859 2,356 1,089 6,192,494 Cottonwood and aspen 652,063 346,764 224,397 171,713 112,258 59,949 26,843 9,558 4,261 1,183 900 329 80 78 — 1,610,378 Other western hardwoods 2,051 3,550 840 165 205 167 284 80 39 — — — — — — 7,381 Western woodland hardwoods 3,551,063 441,238 83,519 15,478 4,279 1,712 433 157 39 37 — — — — — 4,097,953 4,205,177 791,553 308,756 187,357 116,742 61,828 27,560 9,795 4,338 1,220 900 329 80 78 — 5,715,713 6,372,429 2,104,539 1,160,352 797,427 541,911 351,167 217,262 138,581 88,975 53,566 52,811 18,725 6,939 2,434 1,089 11,908,207 Western hardwood species groups Other Table B10 All softwoods Hardwood species groups All hardwoods All species groups All table cells without observations in the inventory sample are indicated by —. Table value of 0 indicates the number of trees rounds to less than 1 thousand trees. Columns and rows may not add to their totals due to rounding. —Number of live trees (at least 1 inch d.b.h./d.r.c.), in thousand trees, on forest land by species group and diameter class, Colorado, 2004–2013. Diameter class (inches) Softwood species groups Western softwood species groups Other 5.0- 7.0- 9.0- 11.0- 13.0- 15.0- 17.0- 19.0- 21.0- 25.0- 29.0- 33.0- All Species group 6.9 8.9 10.9 12.9 14.9 16.9 18.9 20.9 24.9 28.9 32.9 36.9 37.0+ classes Douglas-fir 56,584 46,574 32,035 22,271 13,245 9,909 6,447 3,404 2,882 598 692 143 36 1,426,212 Ponderosa and Jeffrey pine 28,181 26,923 27,987 21,720 15,333 9,252 5,169 3,818 3,773 1,377 308 204 40 194,820 True fir 108,553 68,913 43,873 25,604 15,901 7,956 4,996 2,754 2,156 744 237 39 — 144,084 Engelmann and other spruces 129,262 97,357 69,624 49,063 33,919 24,090 16,371 8,316 8,715 2,273 492 308 — 281,725 Lodgepole pine 142,241 93,368 52,191 23,763 10,652 4,078 1,602 403 73 — — — — 439,790 Other western softwoods 12,231 8,551 7,783 3,595 2,479 1,040 852 381 392 119 — — — 328,371 477,053 341,685 233,493 146,015 91,528 56,324 35,437 19,077 17,992 5,110 1,729 694 76 37,421 Cottonwood and aspen 153,372 130,908 92,787 50,815 23,347 8,412 3,347 884 685 221 40 78 — 464,894 Other western hardwoods 193 — 43 — 39 40 — — — — — — — 314 153,565 130,908 92,830 50,815 23,385 8,451 3,347 884 685 221 40 78 — 465,208 630,618 472,593 326,323 196,830 114,913 64,775 38,784 19,961 18,676 5,331 1,769 772 76 1,891,420 Table B11 All hardwoods All species groups All table cells without observations in the inventory sample are indicated by —. Table value of 0 indicates the number of trees rounds to less than 1 thousand trees. Columns and rows may not add to their totals due to rounding. —Number of growing stock trees (at least 5 inches d.b.h.), in thousand trees, on timberland by species group and diameter class, Colorado, 2004–2013. Diameter class (inches) Softwood species groups Western softwood species groups All softwoods Hardwood species groups Western hardwood species groups
98 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 All Owner class Timberland Unproductive Total Productive Unproductive Total forest land Forest Service National Forest 17,904.5 920.2 18,824.8 5,330.7 107.7 5,438.4 24,263.2 National Grassland — 14.7 14.7 — — — 14.7 Other Federal National Park Service — — — 503.5 82.2 585.8 585.8 Bureau of Land Management 1,225.1 2,693.9 3,919.0 66.6 151.8 218.3 4,137.4 Departments of Defense and Energy 9.4 42.0 51.4 — — — 51.4 State and local government State 337.1 148.4 485.5 118.6 0.4 119.0 604.5 County and Municipal 107.0 2.9 109.8 — — — 109.8 PrivateUndifferentiated private 3,777.3 1,679.6 5,456.9 — — — 5,456.9 All owners 23,360.3 5,501.8 28,862.1 6,019.5 342.0 6,361.5 35,223.6 Table B12—Net volume of all live trees (at least 5 inches d.b.h./d.r.c.), in million cubic feet, by owner class and forest land status,Colorado, 2004–2013. Unreserved forests Reserved forests All table cells without observations in the inventory sample are indicated by —. Table value of 0.0 indicates the volume rounds to less than 0.1 million cubic feet. Columns and rows may not add to their totals due to rounding.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 99

Large Medium Small All size

Forest-type group diameter diameter diameter Nonstocked classes

Pinyon / juniper group 4,424.8 165.2 21.0 — 4,611.0

Douglas-fir group 2,528.6 276.2 29.3 — 2,834.1

Ponderosa pine group 2,140.1 55.9 36.5 — 2,232.4

Fir / spruce / mountain hemlock group 13,225.2 1,450.4 188.0 — 14,863.7

Lodgepole pine group 1,716.0 1,594.2 132.0 — 3,442.2

Other western softwoods group 353.8 18.6 4.1 — 376.4

Elm / ash / cottonwood group 199.8 4.3 0.4 — 204.5

Aspen / birch group 2,638.2 3,229.5 265.8 — 6,133.5

Woodland hardwoods group 90.5 114.8 287.0 — 492.3

Nonstocked — — — 33.5 33.5

All forest-type groups 27,316.9 6,909.1 964.1 33.5 35,223.6

Stand-size class

All table cells without observations in the inventory sample are indicated by —. Table value of 0.0 indicates the volume rounds to less than 0.1 million cubic 

feet. Columns and rows may not add to their totals due to rounding.

Table B13—Net volume of all live trees (at least 5 inches d.b.h./d.r.c.), in million cubic feet, on forest land by forest-type group and 

stand-size class, Colorado, 2004–2013.
100 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 Forest Other State and local Undifferentiated All Species group Service Federal government private owners Douglas-fir 1,973.1 413.3 102.5 668.5 3,157.4 Ponderosa and Jeffrey pine 1,277.1 134.1 96.5 1,080.2 2,588.0 True fir 3,523.1 154.5 60.8 352.8 4,091.4 Engelmann and other spruces 9,699.0 670.8 90.0 694.1 11,153.9 Lodgepole pine 3,113.6 288.9 83.3 379.3 3,865.1 Other western softwoods 453.3 32.8 6.0 62.3 554.4 Western woodland softwoods 298.4 2,842.1 105.0 1,263.5 4,508.9 20,337.7 4,536.4 544.1 4,500.8 29,919.0 Cottonwood and aspen 3,883.2 216.8 160.2 845.9 5,106.1 Other western hardwoods 0.8 2.5 4.8 1.9 9.9 Western woodland hardwoods 56.2 18.8 5.2 108.3 188.5 3,940.2 238.1 170.2 956.1 5,304.6 24,277.9 4,774.5 714.3 5,456.9 35,223.6 Western hardwood species groups Other Table B14 All softwoods Hardwood species groups All hardwoods All species groups All table cells without observations in the inventory sample are indicated by—. Table value of 0.0 indicates the volume rounds to less than 0.1 million cubic feet. Columns and rows may not add to their totals due to rounding. —Net volume of all live trees (at least 5 inches d.b.h./d.r.c.), in million cubic feet, on forest land by species group and ownership group, Colorado, 2004–2013. Ownership group Softwood species groups Western softwood species groups Other
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 101 5.0- 7.0- 9.0- 11.0- 13.0- 15.0- 17.0- 19.0- 21.0- 25.0- 29.0- 33.0- All Species group 6.9 8.9 10.9 12.9 14.9 16.9 18.9 20.9 24.9 28.9 32.9 36.9 37.0+ classes Douglas-fir 119 264 370 425 365 403 379 264 305 107 113 36 8 3,157 Ponderosa and Jeffrey pine 59 143 269 347 380 324 248 226 294 190 52 43 12 2,588 True fir 319 541 653 629 578 404 331 232 211 113 61 8 12 4,091 Engelmann and other spruces 418 824 1,169 1,394 1,457 1,513 1,378 926 1,295 517 166 96 — 11,154 Lodgepole pine 575 908 933 659 430 214 99 40 8 — — — — 3,865 Other western softwoods 31 63 95 85 79 52 49 30 47 20 3 — — 554 Western woodland softwoods 194 317 420 519 529 541 470 410 526 297 163 66 57 4,509 1,715 3,059 3,909 4,059 3,818 3,450 2,954 2,128 2,687 1,244 558 249 89 29,919 Cottonwood and aspen 453 962 1,236 1,075 704 321 179 62 68 28 7 13 — 5,106 Other western hardwoods 1 0 1 2 4 1 1 — — — — — — 10 Western woodland hardwoods 102 44 21 14 5 2 0 1 — — — — — 189 556 1,007 1,258 1,091 712 324 180 62 68 28 7 13 — 5,305 All species groups 2,271 4,066 5,167 5,150 4,530 3,775 3,133 2,190 2,755 1,272 565 261 89 35,224 Western hardwood species groups Other Table B15 All hardwoods All table cells without observations in the inventory sample are indicated by—. Table value of 0 indicates the volume rounds to less than 1 million cubic feet. Columns and rows may not add to their totals due to rounding. —Net volume of all live trees (at least 5 inches d.b.h./d.r.c.), in million cubic feet, on forest land by species group and diameter class, Colorado, 2004–2013. Diameter class (inches) Softwood species groups Western softwood species groups Other All softwoods Hardwood species groups
102 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

Natural Artificial All forest

Forest-type group stands regeneration land

Pinyon / juniper group 4,607.0 4.0 4,611.0

Douglas-fir group 2,834.1 — 2,834.1

Ponderosa pine group 2,218.8 13.7 2,232.4

Fir / spruce / mountain hemlock group 14,863.7 — 14,863.7

Lodgepole pine group 3,442.2 — 3,442.2

Other western softwoods group 376.4 — 376.4

Elm / ash / cottonwood group 204.5 — 204.5

Aspen / birch group 6,133.5 — 6,133.5

Woodland hardwoods group 492.3 — 492.3

Nonstocked 33.5 — 33.5

All forest-type groups 35,205.9 17.6 35,223.6

Stand origin

All table cells without observations in the inventory sample are indicated by —. 

Table value of 0.0 indicates the volume rounds to less than 0.1 million cubic feet. 

Columns and rows may not add to their totals due to rounding.

Table B16—Net volume of all live trees (at least 5 inches d.b.h./d.r.c.), in million cubic feet, on forest land by 

forest-type group and stand origin, Colorado, 2004–2013.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 103 5.0- 7.0- 9.0- 11.0- 13.0- 15.0- 17.0- 19.0- 21.0- 25.0- 29.0- 33.0- All Species group 6.9 8.9 10.9 12.9 14.9 16.9 18.9 20.9 24.9 28.9 32.9 36.9 37.0+ classes Douglas-fir 100 225 302 348 312 332 298 204 218 64 105 25 8 2,540 Ponderosa and Jeffrey pine 48 119 229 299 322 279 213 198 271 174 52 43 12 2,260 True fir 231 394 486 465 443 309 260 186 171 92 43 8 — 3,087 Engelmann and other spruces 286 567 797 953 991 1,051 943 599 832 298 78 69 — 7,464 Lodgepole pine 415 674 705 499 310 161 81 27 5 — — — — 2,878 Other western softwoods 19 39 66 47 49 25 30 16 22 8 — — — 323 1,099 2,016 2,587 2,612 2,427 2,157 1,825 1,230 1,520 636 278 145 20 18,552 Cottonwood and aspen 336 783 1,070 955 633 299 153 51 53 18 5 13 — 4,369 Other western hardwoods 0 — 0 — 1 1 — — — — — — — 2 337 783 1,071 955 633 300 153 51 53 18 5 13 — 4,371 1,436 2,800 3,657 3,567 3,060 2,457 1,978 1,281 1,572 654 283 158 20 22,923 Table B17 All hardwoods All species groups All table cells without observations in the inventory sample are indicated by —. Table value of 0 indicates the volume rounds to less than 1 million cubic feet. Columns and rows may not add to their totals due to rounding. —Net volume of growing stock trees (at least 5 inches d.b.h.), in million cubic feet, on timberland by species group and diameter class, Colorado, 2004–2013. Diameter class (inches) Softwood species groups Western softwood species groups All softwoods Hardwood species groups Western hardwood species groups
104 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 Forest Other State and local Undifferentiated All Species group Service Federal government private owners Douglas-fir 1,526.7 330.1 84.1 599.3 2,540.1 Ponderosa and Jeffrey pine 1,162.2 97.0 74.5 926.3 2,259.9 True fir 2,592.4 107.1 46.6 341.4 3,087.4 Engelmann and other spruces 6,323.4 385.7 65.9 688.8 7,463.8 Lodgepole pine 2,398.2 106.8 25.1 347.4 2,877.5 Other western softwoods 261.0 13.8 3.4 45.1 323.2 14,263.8 1,040.4 299.6 2,948.3 18,552.1 Cottonwood and aspen 3,346.5 148.4 134.6 739.4 4,368.9 Other western hardwoods 0.8 0.6 0.0 0.5 1.9 3,347.3 149.0 134.6 739.9 4,370.8 17,611.0 1,189.4 434.2 3,688.2 22,922.9 Table B18 All hardwoods All species groups All table cells without observations in the inventory sample are indicated by—. Table value of 0.0 indicates the volume rounds to less than 0.1 million cubic feet. Columns and rows may not add to their totals due to rounding. —Net volume of growing stock trees (at least 5 inches d.b.h.), in million cubic feet, on timberland by species group and ownership group, Colorado, 2004–2013. Ownership group Softwood species groups Western softwood species groups All softwoods Hardwood species groups Western hardwood species groups
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 105 9.0- 11.0- 13.0- 15.0- 17.0- 19.0- 21.0- 25.0- 29.0- 33.0- All Species group 10.9 12.9 14.9 16.9 18.9 20.9 24.9 28.9 32.9 36.9 37.0+ classes Douglas-fir 1,086 1,527 1,486 1,670 1,519 1,066 1,153 353 580 143 37 10,620 Ponderosa and Jeffrey pine 701 1,206 1,467 1,368 1,068 1,036 1,440 956 295 230 63 9,829 True fir 1,926 2,146 2,205 1,571 1,350 979 933 517 230 43 — 11,900 Engelmann and other spruces 3,240 4,559 5,087 5,596 5,135 3,326 4,830 1,753 458 413 — 34,395 Lodgepole pine 2,588 2,144 1,465 794 418 135 27 — — — — 7,570 Other western softwoods 175 156 192 105 139 72 104 36 — — — 979 9,716 11,738 11,901 11,104 9,629 6,614 8,485 3,615 1,563 828 100 75,293 Cottonwood and aspen — 4,312 3,036 1,467 770 249 258 71 19 41 — 10,224 Other western hardwoods — — 1 3 — — — — — — — 4 — 4,312 3,038 1,469 770 249 258 71 19 41 — 10,228 9,716 16,050 14,939 12,573 10,399 6,863 8,743 3,686 1,582 869 100 85,520 Table B19 All hardwoods All species groups All table cells without observations in the inventory sample are indicated by—. Table value of 0 indicates the volume rounds to less than 1 million board feet. Columns and rows may not add to their totals due to rounding. —Net volume of sawtimber trees, in million board feet (International 1/4 inch rule), on timberland by species group and diameter class, Colorado, 2004–2013. Diameter class (inches) Softwood species groups Western softwood species groups All softwoods Hardwood species groups Western hardwood species groups
106 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 Forest Other State and local Undifferentiated All Species group Service Federal government private owners Douglas-fir 1,210.0 250.6 58.2 451.0 1,969.8 Ponderosa and Jeffrey pine 966.9 79.9 59.4 741.5 1,847.7 True fir 1,824.2 71.6 32.6 236.7 2,165.1 Engelmann and other spruces 4,974.8 308.3 46.8 518.1 5,848.0 Lodgepole pine 1,402.4 36.7 15.1 175.0 1,629.2 Other western softwoods 200.5 9.2 2.4 33.3 245.5 10,578.8 756.3 214.6 2,155.7 13,705.3 Cottonwood and aspen 1,246.9 44.3 64.7 269.7 1,625.6 Other western hardwoods 0.6 0.5 — — 1.1 1,247.5 44.8 64.7 269.7 1,626.7 11,826.3 801.1 279.2 2,425.4 15,332.0 Table B20 All hardwoods All species groups All table cells without observations in the inventory sample are indicated by—. Table value of 0.0 indicates the volume rounds to less than 0.1 million cubic feet. Columns and rows may not add to their totals due to rounding. —Net volume of sawlog portion of sawtimber trees, in million cubic feet, on timberland by species group and ownership group, Colorado, 2004–2013. Ownership group Softwood species groups Western softwood species groups All softwoods Hardwood species groups Western hardwood species groups
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 107 All Owner class Timberland Unproductive Total Productive Unproductive Total forest land Forest Service National Forest -103.2 -1.4 -104.6 -74.7 -1.9 -76.5 -181.1 National Grassland — 0.1 0.1 — — — 0.1 Other Federal National Park Service — — — -8.2 0.4 -7.8 -7.8 Bureau of Land Management -3.7 8.7 5.0 0.7 0.2 0.8 5.8 Departments of Defense and Energy 0.1 -0.2 -0.1 — — — -0.1 State and local government State 4.1 -1.7 2.4 -4.4 0.0 -4.4 -2.0 County and Municipal 1.5 -0.1 1.4 — — — 1.4 PrivateUndifferentiated private 26.9 11.5 38.5 — — — 38.5 All owners -74.1 16.8 -57.3 -86.6 -1.3 -87.9 -145.2 Unreserved forests Reserved forests Table B21 All table cells without observations in the inventory sample are indicated by—. Table value of 0.0 indicates the volume rounds to less than 0.1 million cubic feet. Columns and rows may not add to their totals due to rounding. —Average annual net growth of all live trees (at least 5 inches d.b.h./d.r.c.), in million cubic feet, by owner class and forest land status, Colorado, 2004–2013. Large Medium Small All size Forest-type group diameter diameter diameter Nonstocked classes Pinyon / juniper group 13.0 2.5 0.5 — 15.9 Douglas-fir group 17.0 2.3 0.4 — 19.7 Ponderosa pine group 18.3 1.6 0.8 — 20.6 Fir / spruce / mountain hemlock group -44.0 -28.7 -54.7 — -127.4 Lodgepole pine group -1.4 -26.5 -35.1 — -63.0 Other western softwoods group 1.1 0.3 -1.1 — 0.3 Elm / ash / cottonwood group 1.7 0.2 0.0 — 1.8 Aspen / birch group 8.4 13.1 -9.5 — 12.0 Woodland hardwoods group 1.4 3.7 0.3 — 5.3 Nonstocked — — — -30.7 -30.7 All forest-type groups 15.4 -31.5 -98.5 -30.7 -145.2 Stand-size class Table B22 All table cells without observations in the inventory sample are indicated by—. Table value of 0.0 indicates the volume rounds to less than 0.1 million cubic feet. 

Columns and rows may not add to their totals due to rounding.

—Average annual net growth of all live trees (at least 5 inches d.b.h./d.r.c.), in million cubic feet, on forest land by forest-type group 

and stand-size class, Colorado, 2004–2013.
108 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 Forest Other State and local Undifferentiated All Species group Service Federal government private owners Douglas-fir 8.9 3.9 2.0 8.0 22.7 Ponderosa and Jeffrey pine 6.2 -0.5 -0.7 12.6 17.7 True fir -50.0 -0.2 0.9 -0.5 -49.9 Engelmann and other spruces -22.9 4.9 0.8 8.7 -8.5 Lodgepole pine -159.0 -18.3 -5.5 -2.5 -185.3 Other western softwoods 3.3 -0.8 0.0 0.7 3.1 Western woodland softwoods -1.7 7.3 0.0 3.0 8.6 -215.3 -3.7 -2.6 30.0 -191.6 Cottonwood and aspen 32.3 0.3 1.8 4.6 38.9 Other western hardwoods 0.0 0.1 0.1 0.0 0.2 Western woodland hardwoods 2.0 1.3 0.2 3.8 7.3 34.3 1.6 2.1 8.5 46.4 -181.1 -2.1 -0.6 38.5 -145.2 Western hardwood species groups Other Table B23 All softwoods Hardwood species groups All hardwoods All species groups All table cells without observations in the inventory sample are indicated by —. Table value of 0.0 indicates the volume rounds to less than 0.1 million cubic feet. Columns and rows may not add to their totals due to rounding. —Average annual net growth of all live trees (at least 5 inches d.b.h./d.r.c.), in million cubic feet, on forest land by species group and ownership group, Colorado, 2004–2013. Ownership group Softwood species groups Western softwood species groups Other
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 109 Forest Other State and local Undifferentiated All Species group Service Federal government private owners Douglas-fir 5.7 2.8 1.8 6.4 16.8 Ponderosa and Jeffrey pine 5.7 -1.0 0.8 9.8 15.4 True fir -39.3 -1.0 0.6 -0.9 -40.5 Engelmann and other spruces 9.9 3.3 0.6 8.6 22.4 Lodgepole pine -115.8 -8.5 0.1 -2.8 -126.9 -131.8 -4.1 3.9 21.7 -110.3 Cottonwood and aspen 25.8 -0.3 1.4 3.8 30.7 Other western hardwoods 0.0 0.0 0.0 0.0 0.0 25.8 -0.3 1.4 3.8 30.8 -106.0 -4.5 5.4 25.5 -79.6 Table B24 All hardwoods All species groups All table cells without observations in the inventory sample are indicated by—. Table value of 0.0 indicates the volume rounds to less than 0.1 million cubic feet. Columns and rows may not add to their totals due to rounding. —Average annual net growth of growing stock trees (at least 5 inches d.b.h.), in million cubic feet, on timberland by species group and ownership group, Colorado, 2004–2013. Ownership group Softwood species groups Western softwood species groups All softwoods Hardwood species groups Western hardwood species groups
110 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 All Owner class Timberland Unproductive Total Productive Unproductive Total forest land Forest Service National Forest 404.8 17.3 422.2 152.4 3.8 156.2 578.3 Other Federal National Park Service — — — 15.9 0.2 16.1 16.1 Bureau of Land Management 24.8 14.9 39.6 0.5 0.8 1.3 41.0 Departments of Defense and Energy 0.0 0.5 0.5 — — — 0.5 State and local government State 2.7 3.6 6.3 7.2 — 7.2 13.5 County and Municipal 0.6 0.2 0.7 — — — 0.7 PrivateUndifferentiated private 41.9 12.2 54.1 — — — 54.1 All owners 474.8 48.7 523.4 175.9 4.8 180.7 704.2 Unreserved forests Reserved forests Table B25 All table cells without observations in the inventory sample are indicated by —. Table value of 0.0 indicates the volume rounds to less than 0.1 million cubic feet. Columns and rows may not add to their totals due to rounding. —Average annual mortality of trees (at least 5 inches d.b.h./d.r.c.), in million cubic feet, on forest land by owner class and forest land status, Colorado, 2004–2013. Large Medium Small All size Forest-type group diameter diameter diameter Nonstocked classes Pinyon / juniper group 22.5 1.4 0.1 — 24.0 Douglas-fir group 19.4 4.2 0.3 — 23.9 Ponderosa pine group 14.7 0.1 0.0 — 14.9 Fir / spruce / mountain hemlock group 225.7 61.9 60.4 — 348.0 Lodgepole pine group 24.6 65.7 40.4 — 130.7 Other western softwoods group 3.0 0.1 1.2 — 4.3 Elm / ash / cottonwood group 1.5 0.0 — — 1.5 Aspen / birch group 34.9 64.3 16.7 — 116.0 Woodland hardwoods group 0.3 0.5 8.4 — 9.2 Nonstocked — — — 31.7 31.7 All forest-type groups 346.7 198.2 127.6 31.7 704.2 Stand-size class Table B26 All table cells without observations in the inventory sample are indicated by —. Table value of 0.0 indicates the volume rounds to less than 0.1 million cubic feet. Columns and rows may not add to their totals due to rounding. —Average annual mortality of trees (at least 5 inches d.b.h./d.r.c.), in million cubic feet, on forest land by forest-type group and stand- size class,Colorado, 2004–2013.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 111 Forest Other State and local Undifferentiated All Species group Service Federal government private owners Douglas-fir 20.7 2.6 0.5 3.5 27.2 Ponderosa and Jeffrey pine 11.9 2.6 2.3 5.3 22.0 True fir 121.1 3.6 0.6 9.1 134.5 Engelmann and other spruces 154.9 3.2 0.9 2.0 161.0 Lodgepole pine 214.9 25.2 7.3 10.1 257.5 Other western softwoods 2.5 1.4 0.2 0.2 4.2 Western woodland softwoods 4.2 14.2 1.0 9.6 29.0 530.1 52.7 12.7 39.8 635.3 Cottonwood and aspen 47.8 4.8 1.5 13.9 67.9 Other western hardwoods — — 0.0 0.0 0.0 Western woodland hardwoods 0.4 0.1 0.0 0.4 1.0 48.2 4.9 1.6 14.3 68.9 578.3 57.6 14.2 54.1 704.2 Western hardwood species groups Other Table B27 All softwoods Hardwood species groups All hardwoods All species groups All table cells without observations in the inventory sample are indicated by—. Table value of 0.0 indicates the volume rounds to less than 0.1 million cubic feet. Columns and rows may not add to their totals due to rounding. —Average annual mortality of trees (at least 5 inches d.b.h./d.r.c.), in million cubic feet, on forest land by species group and ownership group, Colorado, 2004–2013. Ownership group Softwood species groups Western softwood species groups Other
112 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 Forest Other State and local Undifferentiated All Species group Service Federal government private owners Douglas-fir 17.4 2.1 0.2 3.5 23.2 Ponderosa and Jeffrey pine 10.2 2.4 0.4 4.8 17.8 True fir 92.2 3.2 0.3 9.1 104.9 Engelmann and other spruces 79.9 1.1 0.4 2.0 83.4 Lodgepole pine 159.3 11.9 0.6 9.7 181.5 Other western softwoods 1.6 0.0 0.2 0.2 1.9 360.7 20.8 2.1 29.2 412.8 Cottonwood and aspen 40.6 3.9 1.3 12.1 57.8 40.6 3.9 1.3 12.1 57.8 401.3 24.7 3.3 41.3 470.6 Table B28 All hardwoods All species groups All table cells without observations in the inventory sample are indicated by —. Table value of 0.0 indicates the volume rounds to less than 0.1 million cubic feet. Columns and rows may not add to their totals due to rounding. —Average annual mortality of growing stock trees (at least 5 inches d.b.h.), in million cubic feet, on timberland by species group and ownership group, Colorado, 2004–2013. Ownership group Softwood species groups Western softwood species groups All softwoods Hardwood species groups Western hardwood species groups
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 113 All Owner class Timberland Unproductive Total Productive Unproductive Total forest land Forest Service National Forest 308,015 21,551 329,566 86,228 2,265 88,493 418,059 National Grassland — 204 204 — — — 204 Other Federal National Park Service — — — 8,405 1,872 10,277 10,277 Bureau of Land Management 22,357 54,772 77,129 1,212 3,076 4,287 81,416 Departments of Defense and Energy 168 696 863 — — — 863 State and local government State 6,161 3,281 9,442 2,177 19 2,196 11,638 County and Municipal 1,974 166 2,140 — — — 2,140 PrivateUndifferentiated private 68,946 38,494 107,440 — — — 107,440 All owners 407,621 119,163 526,784 98,021 7,231 105,252 632,036 Unreserved forests Reserved forests Table B29 All table cells without observations in the inventory sample are indicated by —. Table value of 0 indicates the aboveground tree biomass rounds to less than 1 thousand dry tons. Columns and rows may not add to their totals due to rounding. —Aboveground dry weight of all live trees (at least 1 inch d.b.h./d.r.c.), in thousand dry short tons, by owner class and forest land status, Colorado, 2004–2013.
114 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 1.0- 3.0- 5.0- 7.0- 9.0- 11.0- 13.0- 15.0- 17.0- 19.0- 21.0- 23.0- 25.0- 27.0- 29.0+ All Species group 2.9 4.9 6.9 8.9 10.9 12.9 14.9 16.9 18.9 20.9 22.9 24.9 26.9 28.9 classes Douglas-fir 417 1,485 2,503 5,443 7,509 8,569 7,301 8,011 7,509 5,206 3,700 2,308 1,173 915 3,063 65,113 Ponderosa and Jeffrey pine 105 416 1,101 2,625 4,877 6,240 6,776 5,748 4,385 3,993 3,442 1,731 1,890 1,433 1,878 46,640 True fir 1,545 4,121 4,749 7,919 9,473 9,048 8,273 5,776 4,793 3,348 2,048 1,070 1,153 586 1,230 65,131 Engelmann and other spruces 1,216 3,748 6,494 12,523 17,525 20,706 21,493 22,194 20,114 13,489 11,660 7,159 4,754 2,722 3,772 169,568 Lodgepole pine 1,109 5,620 9,516 14,706 14,912 10,439 6,760 3,347 1,538 620 105 25 — — — 68,698 Other western softwoods 77 389 573 1,115 1,683 1,486 1,387 913 850 524 365 454 259 87 46 10,208 Western woodland softwoods 1,084 2,209 3,113 5,139 6,936 8,814 9,110 9,593 8,453 7,430 5,707 4,111 3,119 2,529 5,388 82,734 5,554 17,987 28,048 49,471 62,914 65,303 61,099 55,582 47,641 34,610 27,026 16,859 12,348 8,272 15,378 508,092 Cottonwood and aspen 2,329 6,669 9,137 18,066 22,206 18,719 12,002 5,387 2,995 1,017 486 632 427 45 311 100,428 Other western hardwoods 7 113 21 7 24 29 75 22 17 — — — — — — 315 Western woodland hardwoods 12,731 6,872 1,955 847 397 261 85 41 5 9 — — — — — 23,202 15,068 13,654 11,113 18,919 22,628 19,009 12,162 5,449 3,017 1,026 486 632 427 45 311 123,944 All species groups 20,622 31,640 39,161 68,390 85,542 84,312 73,261 61,031 50,658 35,636 27,511 17,491 12,774 8,318 15,688 632,036 Western hardwood species groups Other Table B30 All hardwoods All table cells without observations in the inventory sample are indicated by —. Table value of 0 indicates the aboveground tree biomass rounds to less than 1 thousand dry tons. Columns and rows may not add to their totals due to rounding. —Aboveground dry weight of all live trees (at least 1 inch d.b.h./d.r.c.), in thousand dry short tons, on forest land by species group and diameter class, Colorado, 2004–2013. Diameter class (inches) Softwood species groups Western softwood species groups Other All softwoods Hardwood species groups
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 115

All

Timberland Unproductive Total Productive Unproductive Total forest land

Northern Front Range

Boulder 190.9 14.3 205.2 35.7 — 35.7 240.9

Clear Creek 108.5 22.3 130.9 25.3 5.6 30.8 161.7

Douglas 154.7 63.3 218.0 — 1.5 1.5 219.5

Elbert 31.4 2.6 34.0 — — — 34.0

El Paso 176.4 56.8 233.2 — — — 233.2

Gilpin 51.8 28.0 79.8 14.8 — 14.8 94.6

Jefferson 251.5 28.6 280.1 13.6 — 13.6 293.7

Lake 85.0 18.6 103.6 19.0 — 19.0 122.6

Larimer 536.4 96.1 632.5 238.8 12.4 251.2 883.8

Park 500.7 70.2 570.9 95.9 32.8 128.7 699.6

Teller 242.7 27.5 270.2 6.9 — 6.9 277.1

Total 2,330.0 428.4 2,758.4 450.0 52.2 502.2 3,260.7

Southern Front Range

Chaffee 248.2 148.8 397.0 20.3 — 20.3 417.3

Costilla 187.1 110.1 297.1 — — — 297.1

Custer 162.2 38.6 200.8 37.1 — 37.1 237.9

Fremont 183.6 485.2 668.8 27.4 6.8 34.2 703.0

Huerfano 134.5 266.7 401.3 36.6 — 36.6 437.9

Las Animas 197.0 727.6 924.6 6.8 — 6.8 931.3

Pueblo 26.4 156.3 182.7 7.2 — 7.2 189.9

Total 1,139.0 1,933.3 3,072.3 135.4 6.8 142.3 3,214.5

West Central

Alamosa — 12.0 12.0 10.5 6.0 16.5 28.6

Conejos 200.5 35.1 235.6 41.0 — 41.0 276.6

Eagle 344.0 290.5 634.5 107.3 12.0 119.2 753.7

Grand 567.2 48.1 615.3 147.0 3.3 150.3 765.6

Gunnison 783.3 146.1 929.4 247.1 51.1 298.2 1,227.6

Hinsdale 201.7 30.8 232.5 178.6 — 178.6 411.1

Jackson 281.3 12.4 293.7 130.1 — 130.1 423.7

Mineral 245.3 1.5 246.8 138.4 6.2 144.6 391.4

Pitkin 184.1 68.7 252.8 144.5 — 144.5 397.3

Rio Grande 198.9 54.1 253.0 — — — 253.0

Routt 624.3 161.7 786.0 100.3 6.4 106.7 892.7

Saguache 637.2 205.2 842.4 95.2 5.8 101.0 943.4

San Juan 85.1 — 85.1 20.7 — 20.7 105.8

Summit 148.7 3.4 152.1 65.2 — 65.2 217.3

Total 4,501.6 1,069.7 5,571.3 1,426.0 90.8 1,516.7 7,088.0

Western

Archuleta 366.8 283.5 650.3 66.5 — 66.5 716.8

Delta 143.4 186.3 329.8 — 11.3 11.3 341.1

Dolores 216.8 168.6 385.5 10.9 6.2 17.1 402.5

Garfield 406.6 723.7 1,130.3 121.3 — 121.3 1,251.6

La Plata 342.7 361.0 703.7 52.7 — 52.7 756.4

Mesa 321.8 880.3 1,202.1 — 90.9 90.9 1,293.0

Moffat 69.4 619.5 688.9 — 87.7 87.7 776.6

Montezuma 229.8 331.1 560.9 — 149.2 149.2 710.1

Montrose 163.0 762.3 925.2 — 33.0 33.0 958.2

Ouray 101.9 109.2 211.1 23.2 — 23.2 234.3

Rio Blanco 275.1 956.0 1,231.1 40.7 6.8 47.5 1,278.6

San Miguel 109.5 368.5 478.1 13.4 — 13.4 491.4

Total 2,746.9 5,750.1 8,497.0 328.7 385.1 713.8 9,210.8

Eastern

Adams 1.5 — 1,452.0 — — — 1.5

Arapahoe 5.7 — 5,728.0 — — — 5.7

Baca 7.5 23.9 31,357.0 — — — 31.4

Bent — 30.2 30,226.0 — — — 30.2

Denver 4.1 — 4,113.0 — — — 4.1

Logan 6.1 — 6,118.0 — — — 6.1

Morgan 2.9 — 2,855.0 — — — 2.9

Otero — 35.5 35,498.0 — — — 35.5

Total 27.7 89.6 117,346.0 — — — 117.3

All counties 10,745.2 9,271.2 20,016.3 2,340.0 535.0 2,875.0 22,891.3

Unreserved forests Reserved forests

Inventory unit and county

All table cells without observations in the inventory sample are indicated by—. Table value of 0.0 indicates the acres round to less than 0.1 thousand acres. 

Columns and rows may not add to their totals due to rounding.

Table B31—Area of accessible forest land, in thousand acres, by inventory unit, county and forest land status, Colorado, 2004–2013.
116 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

Other Other Other Other All

Inventory unit Timber- forest Timber- forest Timber- forest Timber- forest forest

land land land land land land land land land

Northern Front Range

Boulder 107.2 28.6 7.1 7.1 6.4 7.1 70.2 7.1 240.9

Clear Creek 60.4 47.6 — — 16.0 — 32.1 5.6 161.7

Douglas 134.4 1.5 — — 4.8 1.5 15.6 61.8 219.5

Elbert — — — — 6.0 — 25.4 2.6 34.0

El Paso 84.9 9.4 11.2 16.4 17.7 12.1 62.7 18.9 233.2

Gilpin 29.6 22.2 — — — 7.4 22.2 13.2 94.6

Jefferson 87.4 6.8 — — 47.7 17.9 116.4 17.5 293.7

Lake 36.7 30.4 10.1 3.5 — — 38.2 3.8 122.6

Larimer 345.5 224.0 6.2 92.8 16.2 14.5 168.6 16.0 883.8

Park 370.0 173.9 35.9 — 1.5 4.5 93.4 20.4 699.6

Teller 120.2 13.8 25.8 13.8 6.9 — 89.8 6.9 277.1

Total 1,376.1 558.1 96.4 133.6 123.0 65.0 734.5 173.9 3,260.7

Southern Front Range

Chaffee 218.5 85.0 1.6 58.5 6.3 6.3 21.8 19.3 417.3

Costilla — — — — — — 187.1 110.1 297.1

Custer 91.3 48.0 — — 6.2 6.2 64.6 21.6 237.9

Fremont 32.5 51.4 71.9 333.2 15.2 20.7 63.9 114.3 703.0

Huerfano 66.9 54.2 19.1 47.3 — 11.1 48.5 190.7 437.9

Las Animas 6.8 20.2 — 50.4 4.3 52.4 185.9 611.4 931.3

Pueblo 14.3 14.3 — 25.4 — 18.0 12.1 105.7 189.9

Total 430.4 273.0 92.6 514.7 32.0 114.7 584.0 1,173.1 3,214.5

West Central

Alamosa — 16.5 — 12.0 — — — — 28.6

Conejos 180.8 41.0 6.3 25.7 13.4 6.3 — 3.0 276.6

Eagle 295.8 188.0 18.9 149.1 — 12.0 29.3 60.6 753.7

Grand 423.1 96.4 38.1 83.8 11.8 — 94.1 18.3 765.6

Gunnison 614.1 329.3 56.9 52.5 — 12.2 112.3 50.4 1,227.6

Hinsdale 152.0 188.5 35.7 14.4 6.0 — 8.1 6.5 411.1

Jackson 203.6 88.6 23.5 — 5.5 52.5 48.7 1.4 423.7

Mineral 243.0 146.1 — — — — 2.3 — 391.4

Pitkin 157.7 182.0 — 12.5 6.2 — 20.1 18.7 397.3

Rio Grande 186.5 35.0 5.8 5.8 — 5.8 6.6 7.5 253.0

Routt 443.7 130.7 32.1 24.6 29.0 16.2 119.5 96.9 892.7

Saguache 545.6 174.8 74.4 96.5 1.6 1.6 15.6 33.3 943.4

San Juan 71.3 20.7 13.8 — — — — — 105.8

Summit 114.5 65.2 — — 6.4 — 27.9 3.4 217.3

Total 3,631.7 1,703.0 305.5 476.8 79.8 106.5 484.6 300.1 7,088.0

Western

Archuleta 264.2 145.4 — 6.6 — — 102.6 197.9 716.8

Delta 123.5 17.8 — 84.7 — 5.7 20.0 89.4 341.1

Dolores 206.0 88.5 10.9 58.1 — 1.6 — 37.5 402.5

Garfield 202.4 159.4 115.5 494.1 6.4 — 82.3 191.5 1,251.6

La Plata 295.4 93.3 — — 6.9 27.8 40.3 292.7 756.4

Mesa 276.5 182.6 — 652.9 — — 45.3 135.8 1,293.0

Moffat 27.2 1.8 14.5 610.5 6.1 6.1 21.7 88.8 776.6

Montezuma 190.4 41.5 6.5 155.8 6.5 34.1 26.3 249.1 710.1

Montrose 143.2 189.3 — 530.6 1.6 3.3 18.1 72.0 958.2

Ouray 52.1 40.3 14.3 34.6 — — 35.5 57.4 234.3

Rio Blanco 235.5 67.7 25.3 804.0 3.3 2.8 11.0 129.1 1,278.6

San Miguel 77.2 43.4 1.7 251.9 — 8.3 30.7 78.2 491.4

Total 2,093.6 1,071.0 188.7 3,683.9 30.9 89.6 433.8 1,619.5 9,210.8

Eastern

Adams — — — — — — 1.5 — 1.5

Arapahoe — — — — 5.7 — — — 5.7

Baca — 6.0 — — — 6.0 7.5 11.9 31.4

Bent — — — — — — — 30.2 30.2

Denver — — — — 4.1 — — — 4.1

Logan — — — — 6.1 — — — 6.1

Morgan — — — — — — 2.9 — 2.9

Otero — 13.1 — — — — — 22.4 35.5

Total — 19.0 — — 16.0 6.0 11.8 64.6 117.3

All counties 7,531.8 3,624.2 683.1 4,808.9 281.7 381.8 2,248.6 3,331.2 22,891.3

Table B32—Area of accessible forest land, in thousand acres, by survey unit, county, ownership group and forest land status, Colorado, 2004–2013.

and county

All table cells without observations in the inventory sample are indicated by—. Table value of 0.0 indicates the acres round to less than 0.1 thousand acres. 

Columns and rows may not add to their totals due to rounding.

Forest Service Other Federal State and local Undifferentiated

government private
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 117

Large Medium Small All size

diameter diameter diameter Nonstocked classes

Northern Front Range

Boulder 105.7 60.3 17.8 7.1 190.9

Clear Creek 60.4 43.3 4.8 — 108.5

Douglas 129.3 11.7 5.8 7.9 154.7

Elbert 28.4 — 3.0 — 31.4

El Paso 148.5 14.1 10.5 3.3 176.4

Gilpin 14.8 37.0 — — 51.8

Jefferson 160.6 24.9 28.4 37.5 251.5

Lake 55.9 20.2 8.9 — 85.0

Larimer 301.2 158.0 37.7 39.6 536.4

Park 265.7 113.8 91.3 29.9 500.7

Teller 193.8 19.7 18.9 10.3 242.7

Total 1,464.1 503.1 227.1 135.7 2,330.0

Southern Front Range

Chaffee 148.9 56.6 30.6 12.1 248.2

Costilla 126.3 35.4 25.4 — 187.1

Custer 108.0 18.6 27.9 7.7 162.2

Fremont 111.7 46.2 25.7 — 183.6

Huerfano 81.7 35.0 9.7 8.1 134.5

Las Animas 137.9 59.1 — — 197.0

Pueblo 19.3 — — 7.2 26.4

Total 733.8 250.9 119.2 35.1 1,139.0

West Central

Conejos 112.4 72.6 10.8 4.7 200.5

Eagle 190.7 92.3 47.9 13.1 344.0

Grand 201.8 210.0 137.5 17.9 567.2

Gunnison 495.1 221.4 53.4 13.4 783.3

Hinsdale 143.8 37.3 18.9 1.6 201.7

Jackson 62.0 139.9 76.7 2.8 281.3

Mineral 124.4 65.5 55.4 — 245.3

Pitkin 97.7 53.8 32.6 — 184.1

Rio Grande 122.6 54.5 21.9 — 198.9

Routt 236.2 314.8 54.3 19.0 624.3

Saguache 291.2 263.9 71.4 10.7 637.2

San Juan 56.7 17.4 5.5 5.5 85.1

Summit 81.1 37.4 23.8 6.4 148.7

Total 2,215.6 1,580.6 610.2 95.2 4,501.6

Western

Archuleta 331.9 29.9 5.0 — 366.8

Delta 81.6 50.5 11.3 — 143.4

Dolores 178.4 18.2 20.2 — 216.8

Garfield 299.4 68.5 33.9 4.8 406.6

La Plata 250.5 72.8 19.4 — 342.7

Mesa 189.2 89.0 43.7 — 321.8

Moffat 47.0 20.6 1.8 — 69.4

Montezuma 161.8 63.0 4.9 — 229.8

Montrose 115.2 34.6 13.2 — 163.0

Ouray 80.8 17.6 3.6 — 101.9

Rio Blanco 168.0 98.7 4.2 4.2 275.1

San Miguel 60.1 32.2 17.3 — 109.5

Total 1,963.9 595.6 178.4 8.9 2,746.9

Eastern

Adams — — — 1.5 1.5

Arapahoe 5.7 — — — 5.7

Baca 6.0 — — 1.5 7.5

Denver 4.1 — — — 4.1

Logan 6.1 — — — 6.1

Morgan — — — 2.9 2.9

Total 21.9 — — 5.8 27.7

All counties 6,399.3 2,930.3 1,134.9 280.7 10,745.2

Stand-size class

Inventory unit and county

Table B33—Area of timberland, in thousand acres, by inventory unit, county and stand-size class, Colorado, 2004–2013.

All table cells without observations in the inventory sample are indicated by —. Table value of 0.0 indicates the acres round to less 

than 0.1 thousand acres. Columns and rows may not add to their totals due to rounding.
118 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

Poorly Moderately Fully OverNonstocked stocked stocked stocked stocked All classes

Northern Front Range

Boulder 12.5 72.0 67.1 32.2 7.1 190.9

Clear Creek — 20.0 51.6 25.7 11.2 108.5

Douglas 7.9 47.5 64.3 35.1 — 154.7

Elbert — 12.0 15.0 4.5 — 31.4

El Paso 3.3 76.1 71.9 25.2 — 176.4

Gilpin — — 14.8 37.0 — 51.8

Jefferson 45.5 93.6 100.5 10.2 1.7 251.5

Lake — 16.7 34.2 19.0 15.1 85.0

Larimer 40.4 189.9 166.3 130.0 9.9 536.4

Park 29.9 142.4 191.1 134.7 2.6 500.7

Teller 15.5 113.9 89.3 22.3 1.7 242.7

Total 155.0 784.1 865.9 475.8 49.3 2,330.0

Southern Front Range

Chaffee 12.1 52.4 64.3 110.1 9.4 248.2

Costilla — 18.5 68.3 97.2 3.1 187.1

Custer 7.7 39.9 63.5 31.0 20.1 162.2

Fremont — 86.0 63.3 34.2 — 183.6

Huerfano 8.1 46.7 20.7 49.4 9.7 134.5

Las Animas 8.4 47.1 94.2 47.3 — 197.0

Pueblo 7.2 14.3 — 4.9 — 26.4

Total 43.4 304.9 374.3 374.1 42.3 1,139.0

West Central

Conejos 4.7 36.1 61.8 59.8 38.1 200.5

Eagle 19.1 72.3 79.2 145.8 27.6 344.0

Grand 17.9 126.5 234.5 170.7 17.5 567.2

Gunnison 15.9 198.4 232.9 274.3 61.8 783.3

Hinsdale 1.6 16.0 90.8 80.4 13.0 201.7

Jackson 2.8 52.8 122.3 93.7 9.7 281.3

Mineral 3.1 36.3 98.5 92.1 15.4 245.3

Pitkin — 37.3 37.5 84.3 25.0 184.1

Rio Grande — 41.0 63.2 61.2 33.5 198.9

Routt 19.0 124.7 259.8 188.8 32.1 624.3

Saguache 20.9 158.9 177.3 248.4 31.8 637.2

San Juan 5.5 9.7 24.9 33.2 11.8 85.1

Summit 6.4 15.2 52.5 69.9 4.8 148.7

Total 116.8 925.1 1,535.1 1,602.6 322.1 4,501.6

Western

Archuleta — 85.3 156.9 116.3 8.3 366.8

Delta — 33.5 22.6 81.0 6.4 143.4

Dolores 4.7 45.6 74.9 79.2 12.4 216.8

Garfield 16.1 152.7 139.0 78.3 20.5 406.6

La Plata — 81.4 123.4 110.1 27.8 342.7

Mesa 3.0 75.4 74.0 136.2 33.2 321.8

Moffat — 31.5 14.9 15.9 7.2 69.4

Montezuma — 72.3 63.2 84.6 9.8 229.8

Montrose — 37.9 50.9 72.5 1.6 163.0

Ouray — 27.1 26.6 40.8 7.4 101.9

Rio Blanco 4.2 40.4 114.4 111.0 5.1 275.1

San Miguel — 14.2 34.0 46.7 14.6 109.5

Total 28.0 697.3 894.8 972.5 154.3 2,746.9

Eastern

Adams 1.5 — — — — 1.5

Arapahoe — 5.7 — — — 5.7

Baca 1.5 — 6.0 — — 7.5

Denver — — — 4.1 — 4.1

Logan — — 6.1 — — 6.1

Morgan 2.9 — — — — 2.9

Total 5.8 5.7 12.1 4.1 — 27.7

All counties 349.0 2,717.1 3,682.1 3,429.1 568.0 10,745.2

Stocking class of growing-stock trees

Inventory unit and county

All table cells without observations in the inventory sample are indicated by—. Table value of 0.0 indicates the acres round to less than 0.1 thousand acres. 

Columns and rows may not add to their totals due to rounding.

Table B34—Area of timberland, in thousand acres, by inventory unit, county and stocking class, Colorado, 2004–2013.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 119

Inventory unit Other Soft Hard All Other Soft Hard All

Pine softwoods hardwoods hardwoods species Pine softwoods hardwoods hardwoods species

(In million cubic feet) (In million board feet)

Northern Front Range

Boulder 149.9 101.1 3.1 — 254.0 488.9 293.6 1.8 — 784.3

Clear Creek 101.7 147.5 6.8 — 256.1 232.8 589.2 5.7 — 827.7

Douglas 105.9 169.2 2.1 — 277.1 371.2 728.4 5.6 — 1,105.2

Elbert 52.8 — — — 52.8 247.9 — — — 247.9

El Paso 127.0 129.1 3.1 — 259.3 477.6 542.9 — — 1,020.5

Gilpin 73.2 42.0 7.8 — 123.0 124.9 152.7 — — 277.6

Jefferson 165.6 86.0 11.2 — 262.9 648.3 342.4 24.5 — 1,015.2

Lake 68.8 114.1 15.7 — 198.7 189.3 440.5 2.0 — 631.8

Larimer 522.2 207.7 17.5 — 747.4 1,397.5 696.1 10.1 — 2,103.7

Park 257.0 331.6 23.0 — 611.6 822.6 1,215.0 7.7 — 2,045.3

Teller 99.9 163.4 5.3 — 268.6 375.5 672.8 3.5 — 1,051.8

Total 1,724.2 1,491.7 95.7 — 3,311.5 5,376.5 5,673.7 60.8 — 11,111.0

Southern Front Range

Chaffee 159.1 356.0 32.5 — 547.6 496.8 1,490.8 78.1 — 2,065.7

Costilla 67.1 265.6 32.2 — 364.9 224.4 1,061.3 37.6 — 1,323.3

Custer 72.0 179.0 49.7 — 300.8 264.0 700.9 87.6 — 1,052.5

Fremont 66.7 164.0 7.9 — 238.6 264.2 612.1 3.3 — 879.6

Huerfano 59.8 116.3 45.1 — 221.2 167.2 486.3 103.5 — 757.0

Las Animas 103.0 170.4 33.2 — 306.7 314.4 590.6 71.5 — 976.5

Pueblo 15.0 10.5 0.5 — 26.0 63.2 38.8 1.6 — 103.5

Total 542.8 1,261.8 201.1 — 2,005.7 1,794.2 4,980.7 383.1 — 7,158.0

West Central

Conejos 24.3 410.2 101.1 — 535.6 96.1 1,870.2 130.2 — 2,096.5

Eagle 259.7 481.9 167.1 — 908.7 952.4 1,997.2 344.2 — 3,293.8

Grand 276.8 588.9 111.5 — 977.3 586.5 2,389.4 199.8 — 3,175.8

Gunnison 458.2 1,292.9 378.7 — 2,129.8 1,364.2 5,825.8 841.9 — 8,031.9

Hinsdale 12.8 483.0 75.7 — 571.5 55.4 2,259.0 161.1 — 2,475.4

Jackson 159.1 195.8 107.4 — 462.2 342.7 755.6 132.0 — 1,230.3

Mineral 21.5 399.3 125.8 — 546.6 71.6 1,765.9 242.4 — 2,079.9

Pitkin 84.1 301.8 104.6 — 490.6 243.6 1,342.7 290.9 — 1,877.2

Rio Grande 19.3 334.5 75.4 — 429.2 86.4 1,386.9 153.2 — 1,626.5

Routt 287.6 635.4 465.6 — 1,388.6 906.5 2,766.9 966.9 — 4,640.2

Saguache 340.1 572.4 213.4 — 1,125.9 874.1 2,155.0 249.2 — 3,278.3

San Juan 0.0 207.9 45.5 — 253.4 — 949.1 74.9 — 1,024.0

Summit 151.5 262.8 12.9 — 427.3 383.8 1,210.3 12.8 — 1,606.9

Total 2,095.0 6,166.7 1,984.9 — 10,246.6 5,963.1 26,674.1 3,799.5 — 36,436.7

Western

Archuleta 237.2 638.0 186.6 — 1,061.8 1,149.1 3,056.3 548.1 — 4,753.4

Delta — 303.6 134.5 — 438.1 — 1,476.4 341.4 — 1,817.8

Dolores 98.1 517.6 134.9 — 750.5 455.4 2,562.7 540.3 — 3,558.4

Garfield 6.3 639.0 170.6 — 815.8 30.7 2,801.0 457.8 — 3,289.5

La Plata 278.7 499.5 212.9 0.3 991.3 1,401.0 2,540.9 513.9 1.3 4,457.1

Mesa 50.3 398.4 344.0 2.4 795.1 249.1 1,825.7 981.5 9.1 3,065.3

Moffat 28.5 53.1 66.6 9.9 158.0 50.5 230.6 164.3 39.6 485.1

Montezuma 232.0 243.9 111.2 — 587.1 1,137.1 1,203.3 197.7 — 2,538.1

Montrose 58.0 142.3 201.5 — 401.8 270.9 641.4 668.7 — 1,581.1

Ouray — 230.4 64.6 — 295.0 — 1,143.6 194.8 — 1,338.4

Rio Blanco 66.5 334.9 305.2 0.2 706.8 298.3 1,400.0 881.8 — 2,580.1

San Miguel 43.1 170.7 98.4 — 312.3 201.8 704.6 289.8 — 1,196.1

Total 1,098.7 4,171.2 2,030.9 12.8 7,313.7 5,243.8 19,586.4 5,780.0 50.1 30,660.2

Eastern

Arapahoe — — 3.7 — 3.7 — — 16.8 — 16.8

Baca — — 15.4 — 15.4 — — 47.6 — 47.6

Denver — — 17.4 — 17.4 — — 60.3 — 60.3

Logan — — 9.0 — 9.0 — — 29.7 — 29.7

Total — — 45.4 — 45.4 — — 154.3 — 154.3

All counties 5,460.7 13,091.4 4,357.9 12.8 22,922.9 18,377.6 56,914.9 10,177.7 50.1 85,520.3 

and county

All table cells without observations in the inventory sample are indicated by --. Table value of 0.0 indicates the volume rounds to less than 0.1 million cubic or board feet. Columns and rows may not add to their totals due to rounding.

Table B35—Net volume of growing stock trees (at least 5 inches d.b.h.), in million cubic feet, and sawtimber trees, in million board feet (International 1/4 inch rule), on timberland by inventory unit, county, 

and major species group, Colorado, 2004–2013.

Growing stock Sawtimber

Major species group Major species group
120 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

Inventory unit Other Soft Hard All Other Soft Hard All

Pine softwoods hardwoods hardwoods species Pine softwoods hardwoods hardwoods species

(In million cubic feet) (In million board feet)

Northern Front Range

Boulder 0.3 2.0 0.2 — 2.5 8.2 7.8 0.1 — 16.0

Clear Creek 0.5 1.2 0.2 — 1.9 2.0 6.8 0.1 — 8.9

Douglas 0.0 1.5 -0.3 — 1.2 0.0 9.2 -1.1 — 8.0

Elbert 0.7 — — — 0.7 3.6 — — — 3.6

El Paso 1.8 2.0 0.1 — 3.9 9.7 11.9 -0.1 — 21.5

Gilpin 0.8 0.2 0.2 — 1.2 5.3 2.1 — — 7.5

Jefferson -0.2 1.5 0.2 — 1.5 2.3 8.5 0.5 — 11.3

Lake 1.1 1.5 0.3 — 2.8 9.3 7.4 0.1 — 16.8

Larimer 0.8 1.0 0.0 — 1.8 5.0 5.1 1.4 — 11.5

Park 2.8 4.3 -0.2 — 6.9 14.9 22.0 0.1 — 37.0

Teller -1.1 0.5 0.0 — -0.6 -5.2 6.6 0.0 — 1.5

Total 7.5 15.7 0.6 — 23.7 55.2 87.4 1.0 — 143.6

Southern Front Range

Chaffee 2.3 2.7 0.0 — 5.0 14.8 18.4 5.1 — 38.3

Costilla 0.7 3.2 0.6 — 4.5 5.4 22.4 0.6 — 28.4

Custer 0.8 1.6 0.1 — 2.5 3.6 8.0 3.8 — 15.4

Fremont 1.0 1.9 0.0 — 2.8 5.0 12.5 0.1 — 17.5

Huerfano -0.6 2.1 -0.7 — 0.7 0.3 12.2 -1.3 — 11.2

Las Animas 1.3 2.2 0.4 — 4.0 7.8 12.7 3.4 — 23.9

Pueblo 0.2 -1.7 0.0 — -1.4 1.3 -8.0 0.1 — -6.6

Total 5.7 12.0 0.4 — 18.2 38.0 78.4 11.8 — 128.1

West Central

Conejos 0.4 0.8 2.0 — 3.1 2.6 16.4 6.1 — 25.1

Eagle -3.3 -2.7 1.4 — -4.6 -11.4 7.6 18.7 — 14.8

Grand -75.7 -4.4 0.1 — -79.9 -275.8 -11.1 5.8 — -281.1

Gunnison 4.9 2.9 3.6 — 11.4 31.2 34.1 34.7 — 100.0

Hinsdale 0.0 1.6 1.2 — 2.8 0.1 9.3 13.4 — 22.8

Jackson -33.4 -4.1 0.9 — -36.6 -113.4 -19.9 2.8 — -130.4

Mineral 0.4 -19.2 2.0 — -16.8 3.2 -82.4 14.3 — -64.9

Pitkin -0.3 -4.7 1.2 — -3.7 0.8 -20.4 6.6 — -13.0

Rio Grande 0.2 1.6 0.3 — 2.1 0.8 22.8 5.0 — 28.6

Routt -21.8 0.2 1.8 — -19.8 -87.9 9.4 42.4 — -36.2

Saguache 3.4 3.9 3.3 — 10.6 10.2 29.5 12.0 — 51.7

San Juan 0.0 0.8 0.8 — 1.6 - - 8.7 1.2 — 9.9

Summit -7.4 2.1 0.1 — -5.2 -33.5 10.9 -0.3 — -22.9

Total -132.7 -21.1 18.6 — -135.2 -473.1 14.9 162.6 — -295.5

Western

Archuleta 2.7 -3.8 -0.1 — -1.2 16.7 -3.5 15.7 — 28.9

Delta — -3.7 0.3 — -3.4 — -11.4 2.0 — -9.4

Dolores 1.7 0.9 1.8 — 4.3 9.9 12.5 5.3 — 27.7

Garfield 0.0 3.1 0.0 — 3.2 0.1 23.1 17.3 — 40.5

La Plata 2.6 -3.9 0.3 0.0 -0.9 18.8 -14.5 25.1 1.4 30.7

Mesa 0.7 -2.1 0.0 0.0 -1.5 3.3 -3.6 27.8 0.0 27.5

Moffat -0.7 1.4 0.6 0.2 1.5 -6.4 8.3 1.2 1.0 4.1

Montezuma 3.8 -0.9 2.7 — 5.6 26.7 1.8 23.8 — 52.2

Montrose 0.7 -1.4 1.4 — 0.7 4.8 -2.1 22.6 — 25.3

Ouray — 0.1 0.1 — 0.2 — 8.1 3.4 — 11.5

Rio Blanco -1.5 2.0 3.2 -0.1 3.6 -8.0 14.1 34.7 — 40.9

San Miguel 0.5 0.4 0.1 — 1.0 2.2 0.8 5.6 — 8.6

Total 10.5 -7.9 10.5 0.2 13.2 68.1 33.5 184.5 2.4 288.4

Eastern

Adams — — -0.2 — -0.2 — — -0.5 — -0.5

Arapahoe — — 0.1 — 0.1 — — 0.8 — 0.8

Baca — — 0.5 — 0.5 — — 7.9 — 7.9

Denver — — 0.1 — 0.1 — — 0.5 0.0 0.5

Logan — — 0.0 — 0.0 — — -0.3 — -0.3

Total — — 0.5 — 0.5 — — 8.3 0.0 8.4

All counties -109.0 -1.4 30.6 0.2 -79.6 -311.9 214.2 368.3 2.4 273.0

and county

All table cells without observations in the inventory sample are indicated by —. Table value of 0.0 indicates the volume rounds to less than 0.1 million cubic or board feet. Columns and rows may not add to their totals due to rounding.

Table B36—Average annual net growth of growing stock trees (at least 5 inches d.b.h.), in million cubic feet, and sawtimber trees, in million board feet (International 1/4 inch rule), on timberland by 

inventory unit, county, and major species group, Colorado, 2004–2013.

Growing stock Sawtimber

Major species group Major species group
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 121

Inventory unit Forest Timberland

area area Volume Growth Removals Mortality Volume Growth Removals Mortality

Northern Front Range

Boulder 4.44 9.16 16.22 52.48 — 37.49 18.24 38.58 — 46.01

Clear Creek 8.14 15.22 22.26 50.92 — 39.26 27.68 49.95 — 47.24

Douglas 8.18 12.51 17.91 100.00 — 72.05 20.46 100.00 — 76.18

Elbert 38.79 41.28 53.75 47.71 — - - 57.12 50.19 — —

El Paso 7.75 10.48 13.11 15.66 — 43.85 14.44 16.20 — 50.77

Gilpin 1.65 26.73 31.17 58.06 — 48.57 45.10 44.64 — 72.38

Jefferson 6.61 8.59 12.62 100.00 — 44.84 13.65 76.88 — 52.66

Lake 9.79 17.33 27.97 20.88 — 83.29 35.92 30.76 — —

Larimer 3.02 7.28 10.80 100.00 — 24.19 12.90 100.00 — 25.92

Park 4.18 7.22 10.68 23.84 — 36.32 11.55 17.68 — 50.35

Teller 7.09 9.11 13.43 100.00 — 48.51 14.10 100.00 — 50.19

Total 1.84 3.23 4.97 24.62 — 14.85 5.72 18.53 — 17.33

Southern Front Range

Chaffee 4.61 10.45 16.40 26.92 — 36.34 19.86 24.58 — 48.34

Costilla 7.07 12.47 16.30 22.08 — 38.70 17.47 25.61 — 53.03

Custer 5.98 12.00 19.55 40.29 — 38.48 20.58 34.65 — 47.75

Fremont 3.53 15.75 20.52 20.35 — 41.68 22.33 23.68 — 54.33

Huerfano 5.86 18.21 24.64 100.00 — 48.65 30.04 68.05 — 56.95

Las Animas 4.93 15.50 18.91 24.50 — 36.01 21.00 24.96 — 51.04

Pueblo 12.52 46.13 61.33 100.00 — 94.38 64.82 100.00 — 95.53

Total 2.19 5.63 7.73 18.53 — 20.52 8.90 14.96 — 27.12

West Central

Alamosa 43.79 — — — — — — — — — —

Conejos 7.81 10.80 16.95 65.97 — 35.71 20.18 43.44 — 41.12

Eagle 3.83 9.88 13.06 100.00 — 25.16 14.37 100.00 — 25.70

Grand 2.84 5.66 9.90 -17.72 — 14.18 13.17 -18.71 — 14.38

Gunnison 3.06 5.79 8.99 29.93 — 16.66 10.73 24.50 — 23.21

Hinsdale 5.73 13.44 18.28 64.52 — 32.68 19.97 64.85 — 37.77

Jackson 4.00 9.25 13.71 -26.91 — 21.13 18.40 -30.82 — 23.75

Mineral 4.61 10.44 15.97 -39.36 — 26.09 18.05 -45.26 — 26.12

Pitkin 3.92 14.42 21.57 100.00 — 69.77 24.77 100.00 — 77.83

Rio Grande 7.01 9.42 15.18 76.11 — 26.83 17.18 35.64 — 31.36

Routt 3.62 5.94 9.75 -45.52 — 18.46 12.41 100.00 — 22.57

Saguache 3.37 6.02 9.04 26.36 — 23.31 10.53 26.32 — 28.71

San Juan 9.77 15.95 20.38 79.56 — 40.42 22.23 83.19 — 47.71

Summit 6.61 12.80 18.56 -71.94 — 34.16 23.88 -73.05 — 35.58

Total 1.22 2.35 3.70 -17.54 — 7.61 4.51 -35.56 — 8.48

Western

Archuleta 2.90 9.84 12.84 100.00 — 26.10 13.48 85.11 — 30.41

Delta 5.98 16.57 19.95 -78.79 — 31.77 22.82 100.00 — 35.89

Dolores 5.55 12.70 17.36 52.79 — 36.33 18.39 46.38 — 40.45

Garfield 3.21 10.02 14.41 100.00 — 32.09 15.92 54.25 — 36.76

La Plata 4.09 10.42 13.71 100.00 — 25.52 15.00 66.56 — 29.36

Mesa 2.95 11.92 16.17 100.00 — 24.04 18.09 45.25 — 30.72

Moffat 6.50 28.43 37.68 100.00 — 51.71 35.46 100.00 — 67.50

Montezuma 4.28 13.57 16.69 42.37 — 44.86 18.37 29.09 — 49.76

Montrose 3.35 18.14 21.91 100.00 — 36.19 23.89 51.14 — 48.73

Ouray 7.53 19.84 25.13 100.00 — 39.30 27.16 48.97 — 44.08

Rio Blanco 3.80 13.08 15.89 75.89 — 28.14 17.87 45.80 — 34.85

San Miguel 6.27 22.05 25.97 100.00 — 43.23 25.52 53.42 — 45.58

Total 1.26 3.91 5.07 71.79 — 9.54 5.52 18.71 — 11.34

Eastern

Adams 100.00 100.00 — 100.00 — 100.00 — 100.00 — 100.00

Arapahoe 100.00 100.00 100.00 100.00 — — 100.00 100.00 — —

Baca 42.51 82.39 100.00 100.00 — 100.00 100.00 100.00 — —

Bent 43.11 — — — — — — — — —

Denver 100.00 100.00 100.00 100.00 — — 100.00 100.00 — —

Logan 100.00 100.00 100.00 100.00 — 100.00 100.00 100.00 — 100.00

Morgan 100.00 100.00 — — — — — — — —

Otero 38.65 — — — — — — — — —

Total 21.44 41.94 55.38 95.58 — 62.75 54.44 95.31 — 72.72

All counties 0.76 1.68 2.52 -33.22 — 5.66 2.95 44.99 — 6.42

All table cells without observations in the inventory sample are indicated by—. Sampling errors that exceed 100% are reported as 100%.

Growing stock (on timberland) Sawtimber (on timberland)

and county

Table B37—Sampling errors (in percent) by inventory unit and county for area of timberland, volume, average annual net growth, average annual removals, and 

average annual mortality on timberland, Colorado, 2004–2013.
122 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

Appendix C: Colorado Forest-Type Groups and Forest Types, with 

Descriptions and Timber (T) or Woodland (W) Designations

Forest-type groups and forest types are usually named for the predominant 

species (or group of species) on the condition. In order to determine the forest type, 

the stocking (site occupancy) of trees is estimated by softwoods and hardwoods. 

If softwoods predominate, then the forest type will be one of the softwood types 

and if hardwoods predominate, then the forest type will be one of the hardwood 

types. Some other special stocking rules apply to individual forest types, and are 

described below.

Associate species are defined as those that regularly form the majority of 

the non-predominant species stocking of mixed-species conditions. These descriptions are applicable to the current inventory; species importance, including predominance in some cases, will vary for other States or inventory years. When 

species are listed, they are in decreasing order of overall forest type stocking.

ASPEN/BIRCH GROUP (T)

Aspen

Predominant species: quaking aspen

Associate species: Engelmann spruce, subalpine fir, lodgepole pine, Douglasfir, white fir, Gambel oak, ponderosa pine

Other species: blue spruce, Rocky Mountain bristlecone pine, limber pine, 

corkbark fir, Rocky Mountain juniper, Fremont cottonwood, narrowleaf 

cottonwood, water birch, Utah juniper, common or two-needle pinyon, 

southwestern white pine

DOUGLAS-FIR GROUP (T)

Douglas-fir 

Predominant species: Douglas-fir

Associate species: quaking aspen, ponderosa pine, limber pine, Engelmann 

spruce, white fir, Rocky Mountain juniper, lodgepole pine, Gambel oak, 

subalpine fir

Other species: Rocky Mountain bristlecone pine, blue spruce, common or twoneedle pinyon, corkbark fir, southwestern white pine, boxelder, narrowleaf 

cottonwood, Utah juniper, oneseed juniper

ELM/ASH/COTTONWOOD GROUP (T)

Cottonwood

Predominant species: narrowleaf cottonwood, plains cottonwood, Fremont cottonwood, eastern cottonwood

Associate species: blue spruce, quaking aspen, ponderosa pine

Other species: Utah juniper, white fir, Engelmann spruce, Rocky Mountain juniper, Douglas-fir, honeylocust, common or two-needle pinyon, boxelder, 

Gambel oak, subalpine fir 
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 123

Special rules: Stocking of cottonwoods must be at least 50 percent of total 

stocking.

Cottonwood/willow

Predominant species: narrowleaf cottonwood, Gambel oak

Associate species: none identified

Other species: ponderosa pine, blue spruce, quaking aspen

Special rules: Stocking of cottonwoods stocking is less than 50 percent, but predominant. In order to meet 50 percent hardwood stocking, other hardwoods 

must be present.

Sugarberry/hackberry/elm/green ash

Predominant species: boxelder

Associate species: none identified 

Other species: narrowleaf cottonwood, Rocky Mountain juniper

Special rules: Several species, mostly Eastern, are evaluated for this type. The 

only one of these species found in Colorado’s current inventory is boxelder.

EXOTIC HARDWOODS GROUP (T)

Other exotic hardwoods 

Predominant species: Siberian elm.

Associate species: none

Other species: none

Special rules: A “catch-all” type for non-native hardwood species.

FIR/SPRUCE/MOUNTAIN HEMLOCK GROUP (T)

Blue spruce

Predominant species: blue spruce

Associate species: quaking aspen, Douglas-fir, ponderosa pine, narrowleaf cottonwood, Engelmann spruce

Other species: subalpine fir, lodgepole pine, white fir, Gambel oak, Rocky 

Mountain juniper, Rocky Mountain bristlecone pine, limber pine

Engelmann spruce

Predominant species: Engelmann spruce

Associate species: subalpine fir, quaking aspen, lodgepole pine, Rocky Mountain bristlecone pine, Douglas-fir, corkbark fir

Other species: limber pine, blue spruce, white fir, ponderosa pine, narrowleaf 

cottonwood, water birch

Special rules: In order to use Engelmann spruce stocking predominance, subalpine fir and/or corkbark fir stocking must be less than 5 percent of the total. 

If subalpine fir and/or corkbark fir stocking is 5 percent or more, Engelmann 

spruce stocking must be at least 75 percent of total.

Engelmann spruce/subalpine fir 

Predominant species: Engelmann spruce, subalpine fir, corkbark fir
124 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

Associate species: quaking aspen, lodgepole pine, Douglas-fir, limber pine

Other species: Rocky Mountain bristlecone pine, blue spruce, white fir

Special rules: The combined stocking of Engelmann spruce with subalpine fir 

and/or corkbark fir is predominant. Stocking of both Engelmann spruce and 

subalpine fir/corkbark fir must each be between 5 and 74 percent of the total.

Subalpine fir

Predominant species: subalpine fir

Associate species: quaking aspen, Engelmann spruce, lodgepole pine, Douglasfir

Other species: blue spruce, Rocky Mountain juniper, limber pine, ponderosa 

pine, Gambel oak

Special rules: Both subalpine fir and corkbark fir are evaluated as subalpine 

fir. In order to use subalpine fir stocking predominance, Engelmann spruce 

stocking must be less than 5 percent of the total. If Engelmann spruce stocking is 5 percent or more, subalpine fir/corkbark fir stocking must be at least 

75 percent of total stocking.

White fir 

Predominant species: white fir

Associate species: Douglas-fir, quaking aspen, ponderosa pine, Engelmann 

spruce 

Other species: Rocky Mountain juniper, limber pine, lodgepole pine, boxelder, 

subalpine fir, Gambel oak, southwestern white pine, blue spruce

LODGEPOLE PINE

Lodgepole pine

Predominant species: lodgepole pine

Associate species: quaking aspen, Engelmann spruce, subalpine fir, Douglas-fir

Other species: limber pine, ponderosa pine, white fir, blue spruce, corkbark fir, 

Rocky Mountain bristlecone pine, Rocky Mountain juniper

NONSTOCKED

Nonstocked 

Predominant species: various, most commonly ponderosa pine, but many nonstocked conditions have no live-tree stocking.

Associate species: various, seldom more than two species on a condition

Other species: Complete species list: ponderosa pine, common or two-needle 

pinyon, Utah juniper, quaking aspen, lodgepole pine, Douglas-fir, Engelmann spruce, Gambel oak, subalpine fir, Rocky Mountain bristlecone pine, 

Fremont cottonwood, Rocky Mountain juniper

Special rules: Used when all live stocking is less than 10 percent. Implies disturbance, but may be used for sparse stands with no disturbance, especially 

with woodland species.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 125

OTHER WESTERN SOFWOODS GROUP (T)

Foxtail pine/bristlecone pine 

Predominant species: Rocky Mountain bristlecone pine

Associate species: quaking aspen, Engelmann spruce

Other species: limber pine, Douglas-fir, ponderosa pine, common or two-needle 

pinyon, lodgepole pine

Special rules: This is mostly an “either/or” forest type. Rocky Mountain bristlecone pine is the only applicable species that occurs in Colorado.

Limber pine 

Predominant species: limber pine

Associate species: Engelmann spruce, quaking aspen, Douglas-fir, lodgepole 

pine, ponderosa pine 

Other species: white fir, Rocky Mountain bristlecone pine, subalpine fir, Rocky 

Mountain juniper

Southwestern white pine 

Predominant species: southwestern white pine

Associate species: ponderosa pine 

Other species: Douglas-fir, quaking aspen, Gambel oak

PINYON/JUNIPER GROUP (W)

Juniper woodland

Predominant species: Utah juniper, oneseed juniper

Associate species: none identified

Other species: Rocky Mountain juniper, Gambel oak, Douglas-fir, ponderosa 

pine, eastern cottonwood

Special rules: Predominance of any combination of junipers other than Rocky 

Mountain juniper, and live pinyons are NOT present.

Pinyon/juniper woodland 

Predominant species: Utah juniper, common or two-needle pinyon, oneseed juniper

Associate species: Rocky Mountain juniper, Gambel oak

Other species: ponderosa pine, Douglas-fir, curlleaf mountain-mahogany, limber pine, narrowleaf cottonwood, white fir, Fremont cottonwood, Rocky 

Mountain bristlecone pine, quaking aspen

Special rules: Any combination of pinyons and junipers other than Rocky 

Mountain juniper predominate. Pinyons must be present.

Rocky Mountain juniper 

Predominant species: Rocky Mountain juniper

Associate species: common or two-needle pinyon, ponderosa pine, Gambel oak, 

Douglas-fir, oneseed juniper, Utah juniper

Other species: white fir, quaking aspen, narrowleaf cottonwood, blue spruce, plains 

cottonwood, limber pine
126 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

PONDEROSA PINE GROUP (T)

Ponderosa pine 

Predominant species: ponderosa pine

Associate species: Douglas-fir, Gambel oak, Rocky Mountain juniper, quaking 

aspen, common or two-needle pinyon 

Other species: white fir, lodgepole pine, limber pine, Engelmann spruce, Rocky 

Mountain bristlecone pine, narrowleaf cottonwood, blue spruce, oneseed 

juniper, southwestern white pine, subalpine fir

WOODLAND HARDWOODS GROUP (W)

Cercocarpus woodland

Predominant species: curlleaf mountain-mahogany

Associate species: Gambel oak

Other species: Rocky Mountain juniper, common or two-needle pinyon, Utah 

juniper, Douglas-fir

Deciduous oak woodland 

Predominant species: Gambel oak

Associate species: ponderosa pine, common or two-needle pinyon, Rocky 

Mountain juniper, Douglas-fir, quaking aspen, Utah juniper

Other species: white fir, oneseed juniper, narrowleaf cottonwood, limber pine, 

Rocky Mountain bristlecone pine, curlleaf mountain-mahogany, Engelmann 

spruce, subalpine fir, plains cottonwood
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 127

Appendix D: Tree Species Groups and Tree Species Measured in 

Colorado’s Annual Inventory with Common Name, Scientific Name, 

and Timber (T) or Woodland (W) Designation

HARDWOODS

Cottonwood and aspen group (T)

Eastern cottonwood (Populus deltoides)

Fremont cottonwood (Populus fremontii)

Narrowleaf cottonwood (Populus angustifolia)

Plains cottonwood (Populus deltoides ssp. monilifera)

Quaking aspen (Populus tremuloides)

Other western hardwoods group (T)

Boxelder (Acer negundo)

Honeylocust (Gleditsia tricanthos)

Water birch (Betula occidentalis)

Woodland hardwoods group (W)

Gambel oak (Quercus gambelii)

Curlleaf mountain-mahogany (Cercocarpus ledifolia) 

SOFTWOODS

Douglas-fir group (T)

Douglas-fir (Pseudotsuga menziesii)

Engelmann and other spruces group (T)

Blue spruce (Picea pungens)

Engelmann spruce (Picea engelmannii)

Lodgepole pine (T)

Lodgepole pine (Pinus contorta)

Other western softwoods group (T)

Limber pine (Pinus flexilis)

Rocky Mountain bristlecone pine (Pinus aristata)

Southwestern white pine (Pinus strobiformis)

Ponderosa and Jeffrey pines group (T)

Ponderosa pine (Pinus ponderosa)

True fir group (T)

Corkbark fir (Abies lasiocarpa var. arizonica)

Subalpine fir (Abies lasiocarpa)

White fir (Abies concolor)

Woodland softwoods group (W)

Common or two-needle pinyon (Pinus edulis)

Oneseed juniper (Juniperus monosperma)

Rocky Mountain juniper (Juniperus scopulorum)

Utah juniper (Juniperus osteosperma)
128 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

Appendix E: Volume, Biomass, and Site Index Equation Sources

Volume

Chojnacky (1985) was used for bigtooth maple, curlleaf mountain-mahogany, 

Gambel oak, and singleleaf pinyon pine volume estimation.

Chojnacky (1994) was used for common or two-needle pinyon pine, Rocky Mountain juniper, and Utah juniper volume estimation.

Edminster et al. (1980) was used for ponderosa pine volume estimation in northeastern Utah.

Edminister et al. (1982) was used for aspen volume estimation in northeastern 

Utah.

Hann and Bare (1978) was used for aspen, blue spruce, Douglas-fir, Engelmann 

spruce, Great Basin bristlecone pine, limber pine, lodgepole pine, ponderosa 

pine, subalpine fir, and white fir volume estimation in southwestern Utah.

Kemp (1956) was used for Fremont and narrowleaf cottonwood volume estimation.

Myers (1964) was used for limber and lodgepole pine volume estimation in northeastern Utah.

Myers and Edminister (1972) was used for blue spruce, Douglas-fir, Engelmann 

spruce, subalpine fir, and white fir volume estimation in northeastern Utah.

Biomass

Chojnacky (1984) was used for curlleaf mountain mahogany biomass estimation.

Chojnacky (1992) was used for bigtooth maple and Gambel oak biomass estimation.

Chojnacky and Moisen (1993) was used for all juniper and pinyon species biomass 

estimation.

Van Hooser and Chojnacky (1983) was used for all timber (T) species biomass 

estimation.

Site Index

Brickell (1970) was used for blue spruce, Douglas-fir, Engelmann spruce, Great 

Basin bristlecone pine, limber pine, lodgepole pine, ponderosa pine, and subalpine fir site index estimation.

Edminster et al. (1985) was used for aspen and Fremont and narrowleaf cottonwood site index estimation.

Stage (1966) was used for white fir site index estimation. (Original equations were 

reformulated by J. Shaw; documentation on file at U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Inventory Monitoring, 

Ogden, UT.)
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 129

Appendix F: Colorado Timber Products Output Tables and Colorado 

Soils Indicator Tables

Table F1—Total roundwood output (in thousand cubic feet) by product, species group, and source of material, 

Colorado, 2012.

Source of material

Growing-stock trees

Product and species group Sawtimber Poletimber Other sources All sources

Sawlogs

Softwood 4,509 591 7,040 12,140

Hardwood 965 126 62 1,153

Total 5,474 717 7,102 13,293

Veneer logs

Softwood 0 0 0 0

Hardwood 0 0 0 0

Total 0 0 0 0

Pulpwood

Softwood 0 0 0 0

Hardwood 0 0 0 0

Total 0 0 0 0

Composite panels

Softwood 0 0 0 0

Hardwood 0 0 0 0

Total 0 0 0 0

Poles and posts

Softwood 5 477 895 1,377

Hardwood 0 6 0 6

Total 5 483 895 1,383

Other miscellaneous

Softwood 344 45 771 1,160

Hardwood 538 70 264 873

Total 882 116 1,035 2,033

Total industrial products

Softwood 4,858 1,113 8,706 14,677

Hardwood 1,503 203 326 2,032

Total 6,361 1,316 9,032 16,709

Fuelwood (including residential)a

Softwood 1,164 152 29,691 31,007

Hardwood 6 1 300 307

Total 1,170 153 29,991 31,314

All products

Softwood 6,022 1,266 38,397 45,685

Hardwood 1,508 204 626 2,338

Total 7,531 1,469 39,023 48,023 

a Includes residential fuelwood consumption reported by U.S. Energy Information Administration http://www.eia.gov/state/

seds/seds-data-complete.cfm?sid=US#Consumption. 
130 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 Table F2—Volume of timber removals (in thousand cubic feet) by source of material, species group, and removal type, Colorado, 2012. Source of material Growing stock Other sources All sources Removal type Softwoods Hardwoods Total Softwoods Hardwoods Total Softwoods Hardwoods Total Roundwood products Saw logs 5,100 1,091 6,161 7,040 62 7,102 12,140 1,153 13,293 Veneer logs 0 0 0 0 0 0 0 0 0 Pulpwood 0 0 0 0 0 0 0 0 0 Composite products 0 0 0 0 0 0 0 0 0 Fuelwood (including residential)a 1,317 6 1,323 29,691 300 29,991 31,007 307 31,314 Posts, poles, and pilings 482 6 488 895 0 895 1,377 6 1,383 Miscellaneous products 389 608 997 771 264 1,035 1,160 873 2,033 Total roundwood products 7,288 1,712 9,000 38,397 626 39,023 45,685 2,338 48,023 Logging residues 330 94 424 1,992 298 2,289 2,322 392 2,713 Total timber removals 7,618 1,806 9,424 40,389 924 41,313 48,006 2,730 50,736 a Includes residential fuelwood consumption reported by U.S. Energy Information Administration http://www.eia.gov/state/seds/seds-data-complete. cfm?sid=US#Consumption.
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 131 Table F3—Mean water, carbon, and nitrogen contents of forest floor and soil cores by forest type, Colorado, visit 1, 2000–2010. Forest Water Organic Inorganic Total floor Organic Total Soil layer contenta carbon carbon nitrogen massa carbon nitrogen Number C/N Forest type cm of plots % % % % ratio Mg/ha Mg/ha Mg/ha Western hardwoods Forest floor 31 17.21 29.55 — 1.127 26.7 11.17 3.361 0.129 groupb 0–10 22 8.36 3.09 0.33 0.244 12.6 — 23.746 1.908 10–20 22 9.56 2.00 0.29 0.157 12.9 — 17.314 1.360 Pinyon/Juniper groupc Forest floor 61 8.32 25.29 — 0.731 35.8 8.71 2.226 0.066 0–10 49 5.00 1.95 0.41 0.156 12.5 — 16.588 1.328 10–20 39 5.65 1.18 0.47 0.095 12.5 — 10.403 0.843 Ponderosa pine Forest floor 27 12.50 33.99 — 0.770 44.6 14.86 4.974 0.117 0–10 23 4.40 2.20 0.21 0.125 17.7 — 15.647 0.884 10–20 20 3.94 1.08 0.14 0.057 19.0 — 9.015 0.474 Lodgepole pine Forest floor 24 47.90 37.99 — 0.939 41.2 31.47 12.062 0.293 0–10 20 11.98 3.07 0.17 0.102 30.4 — 17.902 0.594 10–20 20 7.34 0.95 0.11 0.026 36.1 — 8.327 0.230 Douglas-fir Forest floor 15 19.33 30.53 — 0.869 37.0 21.69 6.679 0.192 0–10 13 5.83 3.51 0.20 0.206 17.4 — 19.844 1.146 10–20 10 6.93 1.40 0.22 0.088 16.6 — 10.137 0.616 Cottonwood/aspen Forest floor 37 27.19 29.57 — 1.145 26.0 20.93 6.172 0.242 group 0–10 29 10.51 4.05 0.31 0.275 14.9 — 26.781 1.846 10–20 28 7.83 2.11 0.23 0.159 13.1 — 17.214 1.317 Spruce/fir groupd Forest floor 52 36.99 33.99 — 1.038 34.1 27.87 9.611 0.295 0–10 36 16.24 4.99 0.25 0.237 20.7 — 19.888 0.967 10–20 24 12.21 2.24 0.19 0.109 20.3 — 12.848 0.630 High-elevation white Forest floor 3 9.25 24.36 — 0.618 41.7 10.55 2.987 0.072 pine group 0–10 2 5.04 2.96 0.34 0.119 25.0 — 10.680 0.437 10–20 2 4.92 1.91 0.15 0.065 29.6 — 8.783 0.303 a Water content and forest floor mass are reported on an oven-dry weight basis (105 degrees C). b Western hardwoods group includes deciduous oak woodland. c Pinyon/juniper group includes Rocky Mountain juniper, juniper woodland, and pinyon/juniper woodland. d Spruce/fir group includes white fir, blue spruce, Engelmann spruce, subalpine fir, and mixed Engelmann spruce/subalpine fir. 
132 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

Table F4—Mean physical and chemical properties of soil cores by forest type, Colorado, visit 1, 2000-2010.

Bray 1 Olsen

Soil Bulk Coarse extractable extractable

layer Number SQI density fragments pH phosphorus phosphorus

of 

Forest type cm plots % g/cm3 % H2O CaCl2 mg/kg mg/kg

Western hardwood 0–10 22 72 1.07 23.92 6.73 6.28 18.3 11.5

group 10–20 22 66 1.29 26.61 6.68 6.16 10.3 5.0

Pinyon/juniper group 0–10 49 64 1.15 20.09 7.30 6.83 6.4 6.7

10–20 39 58 1.30 27.25 7.40 6.87 3.5 2.3

Ponderosa pine 0–10 23 67 1.19 31.90 6.36 5.76 19.4 8.6

10–20 20 57 1.48 39.62 6.54 5.84 11.1 3.6

Lodgepole pine 0–10 20 63 0.91 27.18 5.30 4.71 19.0 13.6

10–20 20 56 1.35 33.78 5.47 4.77 22.4 10.3

Douglas-fir 0–10 13 71 0.97 36.46 6.80 6.35 28.4 16.8

10–20 10 64 1.26 42.19 6.80 6.26 22.5 11.5

Cottonwood/aspen 0–10 29 72 0.91 20.58 6.25 5.73 19.4 9.1

group 10–20 28 68 1.16 23.91 6.28 5.70 15.3 8.6

Spruce/fir group 0–10 36 72 0.75 30.97 5.93 5.39 18.6 16.3

10–20 24 64 1.12 37.74 5.79 5.20 14.5 7.9

High-elevation white 0–10 2 69 1.08 51.88 7.12 6.59 10.8 4.0

pine group 10–20 2 65 1.28 56.31 6.65 6.18 9.2 4.9

SQI = Soil Quality Index

Table F5—Mean exchangeable cation concentrations in soil cores by forest type, Colorado, visit 1, 2000–2010.

1 M NH4Cl Exchangeable cations

Soil

layer Number Na K Mg Ca Al ECEC

of

Forest type cm plots mg/kg cmolc/kg

Western hardwood 0–10 22 16 279 290 3266 0 20.13

group 10–20 22 26 229 272 3056 3 18.74

Pinyon/juniper 0–10 49 14 201 194 3517 2 20.19

group 10–20 39 24 148 200 3493 1 19.92

Ponderosa pine 0–10 23 10 167 209 1945 2 11.96

10–20 20 14 107 158 1493 8 9.22

Lodgepole pine 0–10 20 17 161 118 1020 64 7.58

10–20 20 16 108 85 672 78 5.40

Douglas-fir 0–10 13 4 255 289 3416 -1 20.58

10–20 10 88 217 361 3054 6 19.44

Cottonwood/ 0–10 29 8 324 254 2591 4 16.02

aspen group 10–20 28 14 253 184 1927 4 12.08

Spruce/fir group 0–10 36 30 290 274 2666 61 18.14

10–20 24 29 178 228 1718 73 13.77

High-elevation 0–10 2 21 401 186 2475 1 15.27

white pine group 10–20 2 14 427 212 2259 0 14.23
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 133

Table F6—Mean extractable trace element concentrations in soil cores by forest type, Colorado, visit 1, 2000-2010.

1 M NH4Cl extractable

Soil

layer Number Mn Fe Ni Cu Zn Cd Pb S

of

Forest type cm plots mg/kg

Western hardwood 0–10 22 5.2 0.01 0.05 0.00 0.02 0.09 0.05 511.1

group 10–20 22 6.0 0.32 0.03 0.04 0.07 0.04 0.14 489.8

Pinyon/juniper group 0–10 49 4.9 0.18 0.06 0.00 1.85 0.15 0.26 166.3

10–20 39 3.4 0.07 0.01 0.05 0.03 0.02 0.20 129.7

Ponderosa pine 0–10 23 24.0 0.04 0.04 0.00 0.41 0.12 0.14 7.1

10–20 20 10.2 0.14 0.03 0.04 0.26 0.06 0.11 6.3

Lodgepole pine 0–10 20 47.0 7.01 0.06 0.00 1.25 0.10 0.46 9.8

10–20 20 8.9 6.14 0.02 0.03 1.44 0.05 0.47 6.4

Douglas-fir 0–10 13 13.4 0.22 0.02 0.00 0.16 0.12 0.21 16.2

10–20 10 5.9 0.01 0.06 0.01 0.02 0.02 0.14 44.8

Cottonwood/aspen 0–10 29 13.9 0.18 0.12 0.00 0.09 0.08 0.15 9.5

group 10–20 28 9.7 0.41 0.01 0.00 0.16 0.03 0.15 17.6

Spruce/fir group 0–10 36 33.0 3.10 0.06 0.00 0.66 0.12 0.22 8.1

10–20 24 13.3 2.57 0.06 0.03 0.93 0.07 0.38 27.8

High-elevation white 0–10 2 7.8 0.65 0.00 0.00 0.00 0.00 0.01 7.6

pine group 10–20 2 6.0 0.00 0.00 0.00 0.00 0.01 0.09 22.8
134 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 Table F7—Mean water, carbon, and nitrogen contents of forest floor and soil cores by forest type, Colorado, visit 2, 2005–2013. Forest Soil Water Organic Inorganic Total floor Organic Total layer Number contenta carbon carbon nitrogen massa carbon nitrogen of C/N Forest type cm plots % % % % ratio Mg/ha Mg/ha Mg/ha Western hardwoods Forest floor 21 21.99 22.56 — 0.189 13.9 13.77 24.25 1.644 groupb 0–10 20 11.32 2.62 0.18 0.116 12.2 — 12.75 0.973 10–20 18 11.57 1.52 0.18 0.127 12.6 — 14.88 1.202 Pinyon/juniper groupc Forest floor 49 8.70 25.12 — 0.091 11.4 10.44 9.66 0.852 0–10 47 5.13 1.55 0.38 0.124 19.8 — 22.01 1.098 10–20 44 6.17 1.01 0.53 0.063 18.6 — 10.39 0.550 Ponderosa pine Forest floor 20 18.93 28.41 — 0.081 26.8 25.55 16.84 0.645 0–10 20 9.31 2.52 0.15 0.045 26.3 — 8.91 0.342 10–20 20 8.13 1.19 0.10 0.185 15.9 — 21.62 1.235 Lodgepole pine Forest floor 13 43.59 35.99 — 0.111 16.5 42.65 13.66 0.885 0–10 13 14.09 2.14 0.08 0.224 15.2 — 24.28 1.555 10–20 11 6.20 1.19 0.09 0.105 15.5 — 14.14 0.835 Douglas-fir Forest floor 9 28.37 28.79 — 0.196 19.3 40.57 21.57 1.165 0–10 7 8.66 2.96 0.26 0.102 17.8 — 11.74 0.675 10–20 7 7.37 1.72 0.21 0.041 18.7 — 9.46 0.315 Cottonwood/aspen Forest floor 25 37.62 27.22 — 0.047 13.2 34.55 8.83 0.483 group 0–10 24 12.39 3.34 0.17 0.189 13.9 — 24.25 1.644 10–20 21 9.47 1.77 0.13 0.116 12.2 — 12.75 0.973 Spruce/fir groupd Forest floor 28 53.65 27.36 — 0.127 12.6 48.77 14.88 1.202 0–10 30 18.99 3.43 0.17 0.091 11.4 — 9.66 0.852 10–20 28 14.15 1.66 0.10 0.124 19.8 — 22.01 1.098 High-elevations white Forest floor 1 7.31 43.33 — 0.063 18.6 26.04 10.39 0.550 pine group 0–10 2 4.55 1.69 0.07 0.081 26.8 — 16.84 0.645 10–20 2 5.88 1.33 0.06 0.045 26.3 — 8.91 0.342 a Water content and forest floor mass are reported on an oven-dry weight basis (105 degrees C). b Western hardwoods group includes deciduous oak woodland. c Pinyon/juniper group includes Rocky Mountain juniper, juniper woodland, and pinyon/juniper woodland. d Spruce/fir group includes white fir, blue spruce, Engelmann spruce, subalpine fir, and mixed Engelmann spruce/subalpine fir. 
USDA Forest Service Resour. Bull. RMRS-RB-23. 2017 135

Table F8—Mean physical and chemical properties of soil cores by forest type, Colorado, visit 2, 2005–2013.

 Bray 1 Olsen

Soil Bulk Coarse extractable extractable

layer Number SQI density fragments pH phosphorus phosphorus

of

Forest type cm plots % g/cm3 % H2O CaCl2 mg/kg mg/kg

Western hardwood 0–10 20 71 1.22 22.87 6.42 5.94 24.2 9.7

group 10–20 18 61 1.45 32.94 6.53 5.97 10.7 4.6

Pinyon/juniper group 0–10 47 61 1.23 19.77 7.35 6.91 7.8 5.7

10–20 44 55 1.41 28.39 7.42 6.93 1.8 2.8

Ponderosa pine 0–10 20 69 1.29 28.50 6.26 5.71 18.6 9.7

10–20 20 59 1.52 39.37 6.38 5.78 12.6 6.0

Lodgepole pine 0–10 13 59 1.07 23.78 5.23 4.67 18.7 11.0

10–20 11 54 1.26 34.18 5.33 4.73 19.1 11.7

Douglas-fir 0–10 7 70 1.14 37.80 6.75 6.32 41.8 19.4

10–20 7 62 1.38 38.60 6.83 6.35 27.1 13.4

Cottonwood/aspen 0–10 24 72 1.03 26.72 6.17 5.76 28.0 15.0

group 10–20 21 66 1.26 30.51 6.25 5.76 16.6 8.6

Spruce/fir group 0–10 30 70 0.95 27.25 5.54 5.13 22.1 16.5

10–20 28 62 1.27 35.82 5.52 4.98 13.8 10.5

High-elevation white 0–10 2 66 0.98 37.59 7.01 6.40 18.0 10.1

pine group 10–20 2 73 1.25 41.45 6.84 6.37 14.9 11.2

SQI = Soil Quality Index

Table F9—Mean exchangeable cation concentrations in soil cores by forest type, Colorado, visit 2, 

2005–2013.

1 M NH4Cl exchangeable cations

Soil

layer Number Na K Mg Ca Al ECEC

of

Forest type cm plots mg/kg cmolc/kg

Western hardwood 0–10 20 40 248 307 2670 0 17.34

group 10–20 18 32 194 290 2533 3 16.40

Pinyon/juniper 0–10 47 45 165 171 3187 0 18.50

group 10–20 44 48 134 168 3293 1 18.91

Ponderosa pine 0–10 20 12 181 221 1887 1 11.89

10–20 20 20 140 187 1702 2 10.62

Lodgepole pine 0–10 13 39 109 115 882 74 7.03

10–20 11 37 49 92 625 89 5.64

Douglas-fir 0–10 7 13 354 242 2823 2 16.92

10–20 7 18 204 201 2522 1 15.12

Cottonwood/aspen 0–10 24 33 264 214 2078 4 13.58

group 10–20 21 38 237 167 1724 2 11.13

Spruce/fir group 0–10 30 35 176 216 2127 30 14.04

10–20 28 57 140 188 1475 45 10.93

High-elevation white 0–10 2 15 483 242 2329 0 15.22

pine group 10–20 2 34 692 340 2669 0 18.31
136 USDA Forest Service Resour. Bull. RMRS-RB-23. 2017

Table F10—Mean extractable trace element concentrations in soil cores by forest type, Colorado, visit 2, 2005-2013.

1 M NH4Cl extractable

Soil

layer Number Mn Fe Ni Cu Zn Cd Pb S

of

Forest type cm plots mg/kg

Western hardwood 0–10 20 7.7 0.36 0.02 0.00 0.02 0.04 0.12 476.7

group 10–20 18 6.0 0.18 0.05 0.00 –0.02 0.03 0.16 443.0

Pinyon/juniper group 0–10 47 2.4 0.17 –0.01 0.00 1.43 0.07 0.08 289.3

10–20 44 1.9 0.10 0.00 0.01 –0.10 0.01 0.11 507.8

Ponderosa pine 0–10 20 13.3 0.38 0.00 0.00 0.22 0.08 0.08 2.6

10–20 20 12.0 0.40 0.04 0.01 –0.01 0.03 0.15 2.5

Lodgepole pine 0–10 13 22.2 11.17 0.02 0.00 0.40 0.04 0.76 4.3

10–20 11 10.3 11.98 0.00 -0.01 –0.51 0.01 0.41 2.6

Douglas-fir 0–10 7 12.1 0.23 0.00 0.00 0.12 0.06 0.10 –3.6

10–20 7 4.6 0.02 0.00 0.00 0.02 0.03 0.05 –22.0

Cottonwood/aspen 0–10 24 16.7 0.59 0.03 0.00 0.18 0.06 0.19 16.2

group 10–20 21 9.8 0.04 0.00 0.00 –0.05 0.03 0.09 12.5

Spruce/fir group 0–10 30 54.1 5.59 0.04 0.00 0.43 0.11 1.76 17.6

10–20 28 18.4 4.96 0.09 0.01 0.05 0.06 0.16 19.3

High-elevation white 0–10 2 6.3 0.00 0.00 0.00 0.00 0.00 0.06 8.8

pine group 10–20 2 7.2 0.14 0.00 0.00 0.00 0.00 0.12 9.2

In accordance with Federal civil rights law and U.S. Department of Agriculture (USDA) 

civil rights regulations and policies, the USDA, its Agencies, offices, and employees, and 

institutions participating in or administering USDA programs are prohibited from discriminating based on race, color, national origin, religion, sex, gender identity (including gender 

expression), sexual orientation, disability, age, marital status, family/parental status, income 

derived from a public assistance program, political beliefs, or reprisal or retaliation for prior 

civil rights activity, in any program or activity conducted or funded by USDA (not all bases 

apply to all programs). Remedies and complaint filing deadlines vary by program or incident.

Persons with disabilities who require alternative means of communication for program information (e.g., Braille, large print, audiotape, American Sign Language, etc.) should contact 

the responsible Agency or USDA’s TARGET Center at (202) 720-2600 (voice and TTY) or 

contact USDA through the Federal Relay Service at (800) 877-8339. Additionally, program 

information may be made available in languages other than English.

To file a program discrimination complaint, complete the USDA Program Discrimination Complaint Form, AD-3027, found online at http://www.ascr.usda.gov/complaint_filing_cust.html and at any USDA office or write a letter addressed to USDA and provide in 

the letter all of the information requested in the form. To request a copy of the complaint 

form, call (866) 632-9992. Submit your completed form or letter to USDA by: (1) mail: U.S. 

Department of Agriculture, Office of the Assistant Secretary for Civil Rights, 1400 Independence Avenue, SW, Washington, D.C. 20250-9410; (2) fax: (202) 690-7442; or (3) email: 

program.intake@usda.gov. 

To learn more about RMRS publications or search our online titles:

www.fs.fed.us/rm/publications

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