Nevada Photo-Based Inventory Pilot 

(NPIP) Resource Estimates (2004–2005)

Tracey S. Frescino

Gretchen G. Moisen

Paul L. Patterson

Elizabeth A. Freeman

James Menlove

United States Department of Agriculture

Forest Rocky Mountain General Technical

Service Research Station Report RMRS-GTR-344 April 2016
Frescino, Tracey S.; Moisen, Gretchen G.; Patterson, Paul L.; Freeman, Elizabeth A.; 

Menlove, James. 2016. Nevada Photo-Based Inventory Pilot (NPIP) resource estimates (2004-2005). Gen. Tech. Rep. RMRS-GTR-344. Fort Collins, CO: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain Research Station. 59 p.

Abstract

The complex nature of broad-scale, strategic-level inventories, such as the Forest 

Inventory and Analysis program (FIA) of the USDA Forest Service, demands constant evolution and evaluation of methods to get the best information possible while continuously 

increasing efficiency. The State of Nevada is predominantly comprised of nonforested 

Federal lands with a small proportion of forest land dominated by non-commercial, woodland tree species. In 2004, a pilot study named the Nevada Photo-Based Inventory Pilot 

(NPIP) was initiated for Nevada using large-scale aerial photography to obtain ancillary 

information for inventory of forest land and lands not traditionally sampled by FIA, such 

as rangelands and riparian areas. Objectives of the pilot included development of photosampling protocol, statistical estimators, quality control procedures, and cost analyses. 

This report augments FIA’s standard forest reporting by providing new estimates on both 

forest and nonforest components of Nevada’s landscape to include area by cover type, 

prevalence of non-traditional elements such as disturbance and riparian, and an in-depth 

analysis of cover distributions. In addition, 250-m (820-ft) resolution maps of species’ distributions, live tree cover, and vegetation condition type are also provided.

Keywords: photo-based inventory, Nevada, photo interpretation, quality control, largescale aerial photography (LSP), species distribution maps, cover

Authors

Tracey S. Frescino, Forester/GIS Analyst, USDA Forest Service, Rocky Mountain 

Research Station, Interior-West Forest Inventory, Monitoring, and Assessment Program, 

Ogden, Utah.

Gretchen G. Moisen, Research Forester, USDA Forest Service, Rocky Mountain 

Research Station, Interior-West Forest Inventory, Monitoring, and Assessment Program, 

Ogden, Utah.

Paul L. Patterson, Statistician, USDA Forest Service, Rocky Mountain Research Station, 

Interior-West Forest Inventory, Monitoring, and Assessment Program, Ogden, Utah.

Elizabeth A. Freeman, Ecologist/Statistical Analyst, USDA Forest Service, Rocky 

Mountain Research Station, Interior-West Forest Inventory, Monitoring, and Assessment 

Program, Ogden, Utah.

James Menlove, Ecologist/Analyst, USDA Forest Service, Rocky Mountain Research 

Station, Interior-West Forest Inventory, Monitoring, and Assessment Program, Ogden, 

Utah.

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Contents

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

Methods.................................................................................................. 2

Data ................................................................................................... 2

Repeatability...................................................................................... 6

Estimation Strategies......................................................................... 6

Characteristics of Interest: Proportion of Condition or Cover.... 8

Characteristics of Interest: Percent Cover within Condition...... 8

Mapping Methodology ....................................................................... 9

Results.................................................................................................. 10

Photo-Based Estimates ................................................................... 10

Condition Class Status and Riparian Types.............................11

Forest/Nonforest Type ............................................................ 12

Tree Crown Density and Size Class ....................................... 14

Owner and Reserved Status................................................... 15

Disturbance............................................................................. 16

Cover ...................................................................................... 17

Cover by Condition Attributes ................................................. 19

Estimates by County and Ecoregion....................................... 24

Map Products................................................................................... 27

Quality and Accuracy Assessment................................................... 30

Discussion ............................................................................................ 32

Conclusions.......................................................................................... 35

References ........................................................................................... 36

Appendix............................................................................................... 39

All Rocky Mountain Research Station publications are published 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.

USDA Forest Service RMRS-GTR-344. 2016. 1

Introduction

Current budget declines along with the complexity of broad-scale, strategiclevel inventories, such as the U.S. Department of Agriculture, Forest Service, Forest 

Inventory and Analysis (FIA) program, demand constant evaluation and evolution of 

methods to get the best information possible and to continually increase efficiency. 

In 2004, Interior West (IW)-FIA launched a photo-based inventory pilot in the State 

of Nevada, referred to as NPIP, to test the use of GPS-controlled, large-scale aerial 

photography (LSP) for enhancing FIA’s existing annual inventory procedures. The 

purpose was to explore new information that could be gained from inexpensive LSP of 

forest land and lands not traditionally sampled by FIA, such as rangelands and riparian 

areas.

Inherent to a comprehensive vegetation inventory are species whose life-form 

properties are difficult to measure using traditional forestry methods. Land in the 

Interior Western United States poses a challenge given the prevalence of sparse 

canopy woodland. Other regions in the United States have similar situations, including: black spruce and willow in the Alaskan region, chaparral and alder in the Pacific 

Northwestern region, shrub-form hawthorn and serviceberry in the Northern Central 

region, and mangrove swamps in the Southern region. A national species list has been 

completed, but still needed is some type of alternative measurement for species that 

must be accounted for but do not require intensive volume related measures. Examples 

of these so-called “cover species” are: Tamarisk, Russian Olive, Evergreen oak or 

chaparral, and non-native tropical species.

A convergence of several issues, efforts, and needs over the 5 years previous to 

2004 led to this Nevada project. Over the past 20 years, FIA has gradually expanded 

from a “productive timberland” inventory to an “all forest land” inventory. Throughout 

this transition, the organization worked toward improving definitions of “tree” and 

“forestland,” as well as with its own responsibility for answering a multitude of questions about this “all forest land.” In 1994, Region 4 of the National Forest System 

(NFS) expressed the need for strategic-level information based on FIA’s sample 

grid on nonforest areas of National Forests. This led to the collection of a subset of 

FIA information on nonforest plots on the Bridger-Teton and Humboldt-Toiyabe 

National Forests. In 1998, the Bureau of Land Management (BLM) Rangeland 

Health Assessment Protocols were developed, and pilot projects launched in southern 

Colorado by the BLM and National Resources Conservation Service (Pellant and 

others 1999; Spaeth and others 1999). By 2000, IW-FIA was making maps of forest attributes at the State and national scales using models and other spatial layers 

(Ruefenacht and others 2004; Blackard 2005; Blackard and others 2008). A year later, 

a national FIA task group was formed to develop “consistent forestland definitions 

and inventory” and to resolve definitions associated with stocking versus cover, and 

species versus form versus use issues. Concurrently, the Remote Sensing Applications 

Center (RSAC), IW-FIA, and Region 1 of the NFS investigated the potential of using soft-copy photogrammetry to complement the field sample (Caylor and others 

2002). In 2003, the Criteria and Indicators for Sustainable Rangelands was published 

(O’Brien and others 2003). In 2004, IW-FIA developed new strategies to test and 

evaluate indicators for assessing rangeland status and health. Simultaneously, IW-FIA 
2 USDA Forest Service RMRS-GTR-344. 2016.

prepared to start annual inventory in Nevada after a lag of almost 20 years due to 

delayed funding.

Nevada is 84% publicly owned and only 14% forested with less than 10 million total acres of forest land. The State was a prime test area because of consistently 

delayed funding and because it is predominantly nonforested Federal lands, with 

the small amount of forest land dominated by woodland tree species. Woodland tree 

species are typically open-grown, slow-growing, and have large crowns, easily identifiable from above. These features provided a basis for analyzing alternative inventory 

methodologies to the traditional field sampling procedures.

One objective of this project was to develop several inter-related procedures, 

including: photo-sampling protocol, statistical estimators, quality control procedures, 

and methods to determine cost efficiencies. The pilot production work included 

acquisition and processing of the large-scale aerial photography, photo interpretation of sample locations, and organization of interpreted data. Previous reports have 

documented the photo sample procedures (Frescino and others 2009) and estimation 

equations (Patterson 2012). This report presents results from NPIP, including photobased estimates of all vegetation types, map products, and repeatability results.

Methods

Data

The sample area includes 63 million acres of the State of Nevada, including 

water but excluding land with restricted air space, such as land administered by the 

Department of Defense. The restricted area covers approximately 11 percent of the 

total area of the State. Global Positioning System- (GPS) controlled aerial photographs 

were acquired in 2004 and 2005, coincident with the FIA field data collected in the 

respective years. The survey design followed the systematic sampling design of the 

national FIA program (Reams and others 2005), consisting of one plot per regularly 

spaced hexagon distributed nationwide, each representing approximately 6,000 acres.

The State was pre-stratified into three initial strata using an existing, pixelbased, 250-m (820-ft) resolution map of predicted timberland forest, woodland forest, 

and nonforest lands (Blackard 2009; figure 1). Photos were acquired for all FIA locations within the timberland and woodland strata, totaling 1,455 plots, and one-tenth 

of the FIA locations within the nonforest stratum, totaling 877 plots, resulting in an 

overall total of 2,332 plots.

The NPIP sample design consisted of a dot grid within a 250-m (820-ft) radius 

circle covering approximately 20 ha (48 acres) of land. A total of 49 points per plot 

represented about 1 acre each (figure 2), the center point of which coincided with the 

FIA field plot center. Point generation and photo interpretation were accomplished 

using the Digital Mylar Image Sampler tool developed by the Remote Sensing 

Applications Center (RSAC) (Clark and others 2004). Each point was assigned a 

condition class, defined as an area of homogenous vegetation having similar characteristics (USDA 2006), and an object class identifying the object in the photograph the 

point fell on (Frescino and others 2009).
USDA Forest Service RMRS-GTR-344. 2016. 3

Figure 1—Pre-stratification layer for NPIP sample design and 

estimation (Blackard and others 2004).

Figure 2—NPIP plot design. The center point (0) is 

located at the theoretical center point of an FIA plot.
4 USDA Forest Service RMRS-GTR-344. 2016.

The photo estimates in this report are based on the interpreted objects (table 1), 

categorized by the condition in which the objects fell (table 2). Condition characteristics and delineation methods were based on FIA’s field inventory procedures (USDA 

Forest Service 2013), where separate conditions represent distinct changes occurring 

across the landscape in one or more of the following attributes: condition class status, 

condition type (forest and nonforest), stand size class, regeneration status, tree density, 

owner group, or reserved status (table 2). The first four attributes are distinguishable on an image. The last two attributes are not visible on a photograph and were 

Table 1—Object definitions.

Object Object type Description

Engelmann spruce Tree Picea engelmanii

Subalpine fir Tree Abies lasiocarpa

White fir Tree Abies concolor, Abies grandis

California red fir Tree Abies magnifica

Douglas-fir Tree Pseudotsuga menziesii

Great Basin bristlecone pine Tree Pinus longaeva

Limber pine Tree Pinus flexilis

Ponderosa pine Tree Pinus ponderosa

Jeffrey pine Tree Pinus jeffreyi

Washoe pine Tree Pinus washoensis

Sugar pine Tree Pinus lambertiana

Whitebark pine Tree Pinus albicaulis

Incense-cedar Tree Calocedrus decurrens

Quaking aspen Tree Populus tremuloides

Black cottonwood Tree Populus trichocarpa

Pinyon spp. Tree Pinyon spp. (Pinus edulis, Pinus discolor, Pinus monophylla)

Juniper spp. Tree Juniper spp. (Juniperus osteosperma, Juniperus scopulorum, etc.) 

Mountain-mahogany Tree Cercocarpus ledifolius

Gambel oak Tree Quercus gambelii

Other tree Tree Other non-tally tree species (not in list above)

Standing dead Tree Dead trees greater than 4.5 inches in height

Down dead Tree Down dead trees or stems 

Mortality Tree Trees that died within the last 5 years (Orange/Yellow color)

Sage complex Shrub Sage species (Artemisia spp.)

Other shrub Shrub Non-sage shrub species

Dry herbaceous Other veg A grass or forb living in dry conditions

Wet herbaceous Other veg A grass or forb living in moist conditions

Agriculture Other veg Managed vegetation for agricultural use

Pasture Other veg Managed vegetation for grazing use

Litter Other veg Organic debris on forest floor

Other vegetation Other veg Other vegetation not described above

Soil or rock Barren Bare soil or rock cover

Built (structure) Barren Cultural structure (business, residential, other human activity) 

Improved road Barren Roads or rights-of-way regularly maintained for long-term use

Unimproved road Barren Road not regularly maintained for long-term use

Water Barren Streams and canals more than 30 ft. wide; lakes and reservoirs

 more than 1 acre 

Other barren Barren Other barren land cover not described above

Snow or ice Unknown Snow or ice cover preventing view of object

Shadow Unknown Shadow preventing view of object

Other unknown Unknown Other phenomenon preventing view of object

Off photo Unknown Point falls off the photo
USDA Forest Service RMRS-GTR-344. 2016. 5

attributed using ArcGIS data extraction methods following the photo interpretation. Condition class status designates the forest and nonforest types, among others; 

stand size and regeneration status are only applied to forest conditions; and all other 

attributes are applied to both forest and nonforest conditions. The delineation of a 

condition based on the tree density attribute was slightly different than the FIA’s field 

inventory procedures, where a different condition is indicated based on a relative 

difference of over 50 percent density. Here, conditions are defined based on different 

categories of tree density characterizing the stand (Frescino and others 2009). Two 

other non-delineating condition attributes were recorded as ancillary information 

based on observations from the image: disturbance and treatment. Disturbance and 

treatments may not affect the entire condition, but the area affected must still be at 

least 1 acre in size and have a significant impact to the stand. These impacts may lead 

to changes in other condition-defining variables, but the boundaries do not necessarily 

coincide with condition boundaries. For more details, see Frescino and others (2009) 

or USDA (2013).

Another category of condition class status is riparian land. One of the objectives 

of NPIP was to take advantage of the large plot size to capture riparian land types. The 

current sample design and plot size of FIA does not adequately sample unique or less 

frequent linear features, such as riparian areas. Our classification system was designed 

to further identify riparian land types rather than using the field protocols from FIA’s 

forest inventory. Photo interpreters chose from three different riparian classes. These 

classes help to distinguish between defining riparian land based on FIA’s traditional 

field protocol versus using methods unique to NPIP. A riparian class with no associated type indicated that the area fully met the definition and size criteria of an FIA 

condition: greater than 1 acre in size and at least 120 ft wide (USDA, FS 2005). 

Table 2—Condition attributes. Owner group and reserved status were defined 

based on ArcGIS data extraction procedures.

Condition attribute Definition

Condition class status A classification of the land based on the current vegetation 

status.

Forest/Nonforest type A classification of forest or non-forest land based on the 

vegetation or vegetation communities that constitute the 

majority of cover on the site. Non-forest types include 

land use classes, such as cropland and developed 

lands. 

Stand size class A classification of forest land based on the predominant size 

class of all live trees. 

Regeneration status A classification of regeneration—natural or artificial.

Tree crown density A classification of forest land based on tree crown cover.

Owner group A classification of a land ownership.

Reserved status An identification of land withdrawn by law(s) prohibiting 

the management of land for the production of wood 

products.

Disturbance* The presence of a natural or human-caused disturbance. 

Treatment* The presence of a human-caused forest treatment.

*Ancillary information for condition. Not used for delineation. 
6 USDA Forest Service RMRS-GTR-344. 2016.

According to the FIA manual, an area may also be defined as riparian if it is <120 ft 

wide but ≥30 ft wide and has at least one forest condition with a shared segment of 

at least 120 ft. For NPIP, a riparian class with Type B represented this scenario. A 

riparian class of Type A was added to the photo-based procedure to represent areas 

similar to the Type B criteria but being adjacent to only nonforest conditions. For more 

details, see Frescino and others (2009).

It is also important to note costs and logistics associated with acquiring and 

interpreting these photos. The Aerial Photography Field Office maintains a vehicle for 

acquiring large-scale imagery in both film and direct-to-digital acquisition. For this 

project, costs per plot at the time of data collection follow and are based on collection 

of three photos (triplicates) per plot or stereo coverage. It cost $120 to capture a plot in 

triplicate if acquisition was processed directly to digital format, plus an additional $20 

per triplicate for scanning if not direct to digital; printing the triplicate cost $10. Photo 

interpretation cost $80 per plot, assuming a photo-interpreter can complete more than 

one plot per hour on average. Finally, quality control and quality assessment activities 

are estimated to cost $30 per plot to include quality checks and field validation. This 

brings the total to approximately $260 per photo-plot, contrasted to the average cost of 

$2000 per field plot.

Repeatability

Frescino and others (2009) provided details on quality control measures spanning photo-interpreter training, data compilation and edit checking, field validation, 

and interpreter repeatability. Here, we focus strictly on quality assessment, or repeatability and consistency between photo interpreters. Because White Pine County was 

used as a pilot area, plots had been photo interpreted by many interpreters, so these 

plots have been excluded from this repeatability analysis. Outside White Pine County, 

only two photo interpreters were used. For a repeatability analysis between these two 

interpreters, a random subset of 10 percent of the plots (188 plots) was selected. Of 

these, 118 plots originally interpreted by interpreter 1 were re-interpreted by interpreter 2, and 70 plots originally interpreted by interpreter 2 were re-interpreted by 

interpreter 1. These plots were assessed at their unedited and edited levels. Error matrices were used to compare both object and condition level plot data for several classes. 

A program was developed in R (R Core Team 2013) to generate the error matrices and 

produce summary statistics on the repeatability between photo interpreters.

Estimation Strategies

Three classes of population characteristics are of interest for this inventory:

1. Area of land classified as being in a particular condition; for example, proportion 

of land in pinyon/juniper forest type or proportion of land that is privately owned 

and in pinyon/juniper forest type

2. Percent cover by an object type across the landscape; for example, percent cover 

by bare ground or the percent cover by pinyon trees

3. Percentage of a condition that is occupied by a particular attribute; for example, 

percentage of bare ground in the pinyon/juniper forest type or the percentage of 

pinyon/juniper forest type covered by Juniper trees
USDA Forest Service RMRS-GTR-344. 2016. 7

Historically, FIA derives the properties of estimators based on the finite 

sampling paradigm, i.e., under the assumption of a finite population. However, this 

approach has the theoretical difficulties of specifying what the population unit is and 

whether area is subdivided into distinct non-overlapping population units. A different approach is to construct an estimator and derive its properties using the infinite 

sampling paradigm where the probability sample is a set of points from a continuous 

population (Cordy 1993). From an infinite sampling perspective, the NPIP sample is 

a set of clusters of sample points (the clusters are the photo-interpreted points within 

each photo-plot), with the clusters centered on the FIA plot centers. From this perspective, determining the probability of selection for a population element is difficult, 

more so for photo-plots that straddle a stratum boundary. Instead, one can consider 

the photo-plot as a support region for a measurement assigned to the FIA plot center, 

where a support region is the region over which the measurement is calculated (e.g., 

the proportion of bare ground on a photo-plot is the measurement assigned to the 

photo-plot center and is calculated on the support region of the photo-plot). In this 

case, the sample is the collection of FIA plot centers, and the 49 photo-interpreted 

points within each photo-plot comprise a separate point sample of the support region. 

An advantage of using a support region is that the independent samples from each stratum are maintained. The disadvantage is that the value assigned to the FIA plot center 

is an average of the attribute of interest over the entire support region and does not 

reflect the exact measure at that point. Patterson (2012) used Stevens and Urquhart’s 

(2000) results on support regions, along with Cordy’s (1993) extension of the HorvitzThompson estimator for an infinite universe, to construct unbiased estimators and 

unbiased estimated variances for the NPIP study.

For the purpose of estimation, the FIA quasi-systematic sample is treated as 

a simple random sample (Bechtold and Patterson 2005). We treat the NPIP sample 

as a pre-stratified sample, with a simple random sample within each stratum. For 

each 250-m (820-ft) radius photo-plot that was co-located with an FIA plot location, 

a systematic grid of 49 points was located within each photo-plot. Cochran (1977) 

shows that systematic sampling has a smaller variance than simple random sampling 

when units within systematic samples have greater variability than the population as 

a whole. We assume the systematic grid that was located within each photo-plot does 

not coincide with any systematic land feature within the photo-plot; hence, treating the 

systematic sample as a simple random sample produces a conservative estimate of the 

variance.

The temporal differences between photo acquisition dates were not taken into 

account in the estimation process, so the estimates are a blending of conditions in 2004 

and 2005. All the population characteristics can be stated in terms of proportions; for 

example, if R is the region of interest (e.g., a state, county, or ecoregion), then the area 

of land classified as being in condition c is equal to ||R||Pc, where Pc is the proportion 

of area classified as being in condition c and ||R||is the area of R.

The next two subsections are devoted to stating the estimators and estimated 

variances used for estimating the proportion of land by characteristic or type and the 

proportion of land within a condition that has a particular attribute. For details of the 

justifications of the stated statistical properties, see Patterson (2012).
8 USDA Forest Service RMRS-GTR-344. 2016.

Characteristics of Interest: Proportion of Condition or Cover

In this section, we cover estimation of the proportion of a region R that is classified as being in condition c or the proportion of R that is covered by object o. Suppose 

R has been partitioned into H subregions, R1, R2,..., RH and an independent sample 

S(Rh) of size nh is drawn from each Rh. A photo-plot is centered on each point shi in 

S(Rh) and a sample of size mhi is drawn from each photo-plot. For most photo-plots, 

mhi is equal to 49; for some photo-plots, location errors mean the photo-plot is not 

totally contained on the photo and mhi is less than 49. Let shij be the jth point of the ith

photo-plot in stratum h, and define the following two attributes of the sample:

Let

[1]

then an unbiased estimator of Pc is given by

[2]

An unbiased estimator of the variance of the estimator is given by

[3]

with the usual form used for calculation.

[4]

Characteristics of Interest: Percent Cover within Condition

The proportion of the condition c covered by attribute o is denoted by Po⊂c. 

Since spatial distribution of condition c is unknown, Po⊂c is the ratio of the proportion 

that is both classified as condition c and covered by object o to the proportion that is 

classified as condition c, that is Po⊂c = Po|c/Pc. Let shij be the jth point of the ith photoplot in stratum h and define

An estimator of Po⊂c is given by

[5]
USDA Forest Service RMRS-GTR-344. 2016. 9

where

[6]

An approximate variance is calculated using Taylor linearization (see Sarndal 

and others 1992: 172-175 and bottom p. 177-178).

An estimate of the approximate variance is obtained by estimating all the unknown quantities, that is

[7]

where

[8]

which for computational proposes can be expressed as

[9]

These equations have been incorporated into FIESTA (Forest Inventory 

Estimation for Analysis; Frescino and others 2012), a user-friendly R package that 

supports the production of estimates for forest resources based on procedures from 

Bechtold and Patterson (2005). FIESTA was developed as an alternative data retrieval 

and reporting tool that is functional within the R environment, allowing customized applications and compatibility with other R-based analyses. FIESTA generates 

estimates and percent sample errors of the estimates for area, population totals, and 

ratios while allowing user-defined boundaries, stratification schemes, and data filters. 

Estimates reported here were generated using the FIESTA package.

In the results reported here, the term “standard error” refers to the square root 

of the estimated variance of an estimator. The term “percent standard error” refers to 

100 times the ratio of the standard error and the estimator. It is important to remember 

that the results we report are error “estimates”; for example, estimated standard error. 

However, for simplicity’s sake, the “estimated” is not included in the descriptions.

Mapping Methodology

Maps were derived by modeling a number of response variables derived from 

the photo-plots, as functions of a set of 250-m (820-ft) resolution remotely sensed 

and topographic variables. We used the R package ModelMap (Freeman 2009) for 

model building and map production. The ModelMap package uses the randomForest 

package to construct models, and applies these models to spatially explicit predictor 

data layers. The response variables included two discrete variables of condition type 
10 USDA Forest Service RMRS-GTR-344. 2016.

groups, one continuous variable of percent live tree cover, and eight binary response 

variables of presence/absence for individual species or objects having high prevalence 

in the sample population, including: sagebrush, other shrub species, juniper, pinyon 

pine, mountain-mahogany, white fir, quaking aspen, and limber pine. In the case of the 

discrete variables of condition type groups, the type was determined from the condition having the majority of cover on a plot and then grouped into more general classes. 

Only plots with ≥55 percent homogenous cover of a condition type or type group were 

used for building the model. The percent cover of live trees for each plot is equal to 

the number of points with live tree cover on the plot divided by the total number of 

sampled points on the plot. In the case of the species or object presence variables, a 

species or object was classified as present if species or object occurred at one or more 

sampled points on a plot.

The predictor data set consisted of 16 raster layers of multi-temporal, remotely 

sensed imagery and digital topographic data for the State of Nevada. The remotely 

sensed imagery consisted of 250-m (820-ft) resolution, 16-day, cloud-free composites 

of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery for spring, 

summer, and fall of 2005, including the visible-red (RED) and near-infrared (NIR) 

bands and two vegetation indices: normalized difference vegetation index (NDVI) 

and enhanced vegetation index (EVI). Three different dates of each variable were 

used as a multitemporal approach to capture phenological differences that may occur 

among seasons. The topographic variables used were derived from the 90-m resolution, National Elevation Dataset produced from the United States Geological Survey 

(Gesch and others 2009). Elevation (meters) was re-sampled to a 250-m (820-ft) pixel 

size using the nearest neighbor algorithm in ArcMap (ESRI 2009). Slope (percent) 

and aspect (degrees) were derived from this 250-m (820-ft) elevation product using 

ArcGIS, and aspect northing and easting variables were calculated using sine and 

cosine functions, respectively, to convert aspect from a circular variable to a linear 

variable.

Random forests models were run using the R package ModelMap (Freeman 

2009) following explorations into the effectiveness of down-sampling for stratified 

designs and unbalanced prevalence (Freeman and others 2012). Map accuracy metrics 

for the binary species distribution maps included Percent Correctly Classified (PCC), 

sensitivity, specificity, and Kappa statistics, with thresholding based on maximizing Kappa (Freeman and Moisen 2008). Error matrices from the out-of-bag (OOB) 

predicted, and observed values, along with Kappa and user and producer’s errors, are 

presented with the discrete maps of condition types, while the continuous cover maps 

are accompanied by measures of root mean square error.

Results

Photo-Based Estimates

We first present estimates of acres or proportion of land by the condition attributes: condition class status, riparian within condition class status, forest and nonforest 

types, stand density and size class, owner and reserved status, and disturbance. We 

did not find any instances of a change in condition based on regeneration status, so 
USDA Forest Service RMRS-GTR-344. 2016. 11

this attribute was dismissed from the analysis. This may be a function of the ability of 

the photo-interpreter to see such changes or that it is a rare occurrence in the State of 

Nevada. Two other condition-based attributes were recorded based on observations 

from the photo: disturbance and treatment. We also saw little evidence of treatments, 

so we did not report those results. The next set of analyses focus on percent cover of 

specific objects the photo points fell on. These analyses capture percent cover by condition attributes and illustrate distributions of cover within conditions. For example, 

a forest condition may encompass several nonforest objects within its area, and conversely, a nonforest condition may contain forest objects within its area.

Condition Class Status and Riparian Types

The majority of the non-restricted land of the State of Nevada (63,288,883 acres) 

was classified as nonforest, constituting more than 80 percent of the land (figure 3). 

This is similar to findings from past assessments in which nonforest lands were also 

estimated as covering over 80 percent of the land (Born and others 1992). Estimated 

acres and associated percent standard error by condition class status category, including the riparian classes, are presented in table 3. Here, we see that, of the 11.7 million

acres of forest land, more than 27,000 acres are estimated as forested riparian lands, 

and of the 51.4 million acres of nonforest land, more than 81,000 acres are considered 

nonforest riparian land. The Type A riparian condition class added 31 plots to the 17 

plots that would have been sampled using only Type B classification used for FIA’s 

field protocol. This added approximately 26,000 acres to the total riparian estimate 

(table 3).

Figure 3—Area, in million acres, by condition class status 

across the State of Nevada. One standard error is indicated 

on each bar.
12 USDA Forest Service RMRS-GTR-344. 2016.

Forest/Nonforest Type

Forest and nonforest condition types characterize the ecosystem community 

that constitutes the majority cover of the site. The nonforest condition types include 

categories such as cropland and developed land. Figure 4 displays the number of acres 

estimated for four high-level condition types across the State of Nevada. It is obvious 

that the dominant vegetation group in Nevada consists of shrub life forms, estimated at 

more than 40 million acres, in correspondence with the majority of land estimated as a 

nonforest condition class previously examined (see Appendix, table A.1 for estimates 

and associated percent standard error). Figure 5 displays the acreage, in more detail, 

of all ecological condition types for Nevada. Again, the shrub condition types have 

the highest acreage followed by the pinyon-juniper type, covering more than 8 million 

acres of total land (Appendix, table A.2). Figure 6 displays the same information with 

different groupings (Appendix, table A.3).

Table 3—Estimated acres and percent standard error by condition class status and 

riparian type on all lands; Nevada, 2004–2005.

Condition class Condition subclass Acres Percent standard error

Forest total 11,719,094 3.58

Forest 11,691,265 3.59

Forest riparian 13,753 63.38

Forest riparian (Type A) 4,201 46.09

Forest riparian (Type B) 9,875 66.70

Nonforest total 51,370,692 0.83

Nonforest natural 50,846,659 0.87

Nonforest developed 442,640 28.76

Nonforest riparian 16,464 74.07

Nonforest riparian (Type A) 22,143 33.21

Nonforest riparian (Type B) 42,786 54.79

Noncensus water total 15,511 49.55

Census water total 183,586 52.64

Total 63,288,883 —

Figure 4—Area, in million acres, by vegetation condition type 

group across the State of Nevada. One standard error is indicated 

on each bar.
USDA Forest Service RMRS-GTR-344. 2016. 13

Figure 5—Area, in million acres, by condition type across the State of Nevada. One standard error 

is indicated on each bar.
14 USDA Forest Service RMRS-GTR-344. 2016.

Tree Crown Density and Size Class

A classification of tree crown density was assigned to a condition and used to 

distinguish different forested conditions (figure 7). The categories represent average 

percent canopy cover of trees within the defined condition. An additional category of 

“Nonstocked” designates land that was once forested but currently has less than 10 

percent cover of trees, such as areas that were burned or harvested. Most of the forested lands in Nevada have less than 50 percent cover of trees, with less than 300,000 

acres supporting greater than 75 percent cover of trees (Appendix, table A.4).

The stand size class of the stand was difficult to determine from aerial photographs. The FIA field method classifies a stand into one of six classes based on the 

Figure 6—Area, in million acres, by condition type group across the State of Nevada. One standard 

error is indicated on each bar.

Figure 7—Area, in million acres, by tree density class (percent cover) 

across the State of Nevada. One standard error is indicated on each bar.
USDA Forest Service RMRS-GTR-344. 2016. 15

size of the majority of trees in the stand. Because it is difficult to make this distinction 

from a photo, only three categories were used to identify the stand as mature trees 5 

inches diameter (DIA) and greater, a young stand of seedlings and saplings (less than 5 

inches DIA), or a nonstocked site that was once forested but currently has less than 10 

percent cover of trees. Table 4 shows acres of land by stand-size category. More than 

85 percent of the forested stands in Nevada are ≥5 inches DIA, but seedlings and saplings occupy more than 1 million acres of land. These latter lands are often considered 

encroachment areas where forest lands are “encroaching” the nonforest shrub lands 

along the foothills of the mountain ranges.

Table 4—Estimated acres and percent standard error by size 

class on all lands; Nevada, 2004–2005.

Size class Acres Percent standard error

Nonforest 51,543,869 0.82

Seedlings/Saplings 1,087,147 18.33

5 inch diameter and greater 10,130,172 3.54

Nonstocked 527,695 22.39

Total 63,288,883 —

Owner and Reserved Status

Two other condition-defining attributes are land owner group and reserved 

status. These attributes were determined using the most current geospatial layers available at the time of sampling (2004–2005). An owner category was extracted at each 

photo point, and conditions were assigned based on a grouping of four classes: Forest 

Service, Other Federal, State and Local Government, and Private Land. The original 

owner category was kept for ancillary information of owner status and included in 

the analysis. Acres of land across the non-restricted areas of Nevada, by owner group 

and reserved status, are provided in table 5. Most of Nevada’s land is non-reserved 

and administered by non-Forest Service Federal agencies, with the Bureau of Land 

Management making up nearly 70% of this area. Figure 8 shows the distribution of 

condition type groups by reserved status. The reserved land has more timber and 

woodland condition types than the nonreserved land (Appendix, table A.5).

Table 5—Acres by owner group and reserved status on all lands; Nevada, 

2004–2005.

 Reserved status 

Owner group Nonreserved Reserved Total

Forest Service 4,947,180 804,710 5,751,890 

Bureau of Land Management 41,916,978 999,920 42,916,898

Other Federal 635,352 3,208,840 3,844,192 

State and local government 207,098 83,610 290,708

Other 9,884,235 44,712 9,928,947 

Total 57,590,843 5,141,793 63,732,636
16 USDA Forest Service RMRS-GTR-344. 2016.

Disturbance

A disturbance class was assigned to a condition similar to FIA field protocol, if 

the affected area was greater than 1 acre in size, with >25 percent of the trees damaged or >50 percent of an individual species damaged. The disturbance classes were 

grouped into broad categories of Stress (insect/disease/drought), Fire, and Other to 

represent disturbances that can be distinguished from an aerial photograph. Table 6 

shows disturbance classes by condition class for the State of Nevada. Although the 

majority of disturbances occur on forest lands, several acres of fire were identified and 

estimated on nonforest lands.

Figure 8—Acres of condition type groups by reserved status: a. is 

Nonreserved land and b. is Reserved land. Notice the difference of 

scale. One standard error is indicated on each bar.

a

b
USDA Forest Service RMRS-GTR-344. 2016. 17

Cover

Each sample point was attributed with the type of object the point fell on, 

including either a tree object or a non-tree object. Figure 9 displays the total estimated 

percent cover for each object included in the pilot inventory for the entire State of 

Nevada (see Appendix, table A.6, for estimates with associated percent sample error). 

The figure is separated into tree objects (figure 9a; includes timber and woodland 

trees) and non-tree objects (figure 9b; includes shrubs, other vegetation, and barren objects). Considering the different scales, it is clear that, once again, the non-tree objects 

dominate the landscape of the State, with pinyon and juniper woodland species as the 

dominant cover for trees.

Figure 10 shows four different groupings of the photo-interpreted objects for the 

State of Nevada. The first grouping (figure 10a) is very broad, categorizing the objects 

into vegetated or non-vegetated classes; non-vegetated classes include soil or rock, 

man-made roads or structures, and water. This category makes up more than 45 percent of object types sampled in Nevada. The second grouping (figure 10b) separates 

the objects into four broad categories: tree, shrub, other veg and barren. Again, the 

barren or non-vegetated objects occupy more than 45 percent of the landscape while 

the vegetated objects are composed of mainly shrubs and other vegetation with less 

than 10 percent as trees. The third grouping (figure 10c) distinguishes the tree category 

as softwood or hardwood, while the forth grouping (figure 10d) differentiates the trees 

into timber, woodland, dead trees, and other trees; the shrubs into sagebrush and other 

shrubs; the other vegetation into herbaceous and other categories; and the barren into 

non-vegetated and water categories (Appendix, tables A.7-A.10).

Table 6–Estimated acres (percent standard error) disturbance and condition 

class group on all lands; Nevada, 2004–2005. Stress indicates insect, 

disease, or drought disturbance. 

Condition class Noncensus

Disturbance Forest Nonforest water Water Total

None 10,977,159 51,302,173 15,511 183,586 62,478,429

(3.65) (0.84) (49.55) (52.64) (0.23)

Stress 74,158 74,158

(24.99) — — — (24.99)

Fire 641,366 69,211 710,577

(19.77) (91.13) — — (19.91)

Other 25,719 25,719

(41.77) — — — (41.77)

11,718,402 51,371,383 15,511 183,586 63,288,883

Total (3.58) (0.83) (49.55) (52.64) (—)
18 USDA Forest Service RMRS-GTR-344. 2016.

a

b

Figure 9—Total percent cover of photo-interpreted objects across the State of Nevada: a. is Tree objects and b. 

is Non-tree objects. Notice the difference in scale. One standard error is indicated on each bar.
USDA Forest Service RMRS-GTR-344. 2016. 19

Cover by Condition Attributes

The previous section presented estimates characterizing the entire State of 

Nevada, excluding restricted space. The following section looks closer at the distribution of the sampled attributes within specific estimated categories. Figure 11 shows the 

general composition of tree attributes within the forest condition types sampled across 

the State. From this figure, we can also visualize the diversity of trees within these 

specific types in Nevada. For example, the Douglas-fir condition type is characterized 

by having just over 10 percent live tree cover, which is composed of four different tree 

species—white fir, Douglas-fir, limber pine, and pinyon pine—plus an additional 4 

percent cover of mortality trees (see Appendix, tables A.11a and A.11b for estimates 

and percent sample error). Figure 12 displays the composition of non-tree attributes 

within the sampled nonforest condition types for the State (see Appendix, tables A.12a 

and A.12b for estimates and percent standard errors, respectively). Tables A.13 and 

and A.14 present the estimates and percent standard errors of tree attributes within the 

nonforest conditions and the non-tree attributes within the forest conditions.

Figure 10—Total percent cover of photo-interpreted objects across the State of Nevada grouped in different classes: 

a. is two categories plus unknown; b. is four categories plus unknown distinguishing barren, shrubs, other vegetated, 

tree, and unknown; c. is six categories plus unknown; and d. is 10 categories plus unknown. One standard error is 

indicated on each bar.

a b

c d
20 USDA Forest Service RMRS-GTR-344. 2016.

Figure 11—Percent cover of tree objects within forest condition types across Nevada.

Figure 12—Percent cover of non-tree attributes within non-forest condition types across Nevada.
USDA Forest Service RMRS-GTR-344. 2016. 21

Figure 13 represents another picture of the composition of vegetation across 

Nevada, displaying the distribution of sampled attribute groups within each forested 

condition type (figure 13a) and nonforest condition (figure 13b). Just looking at 

the Douglas-fir condition type again, we can see that along with the approximate 

14 percent of live plus missing tree cover mentioned above, an estimated 3 percent of 

the attributes within this condition type are categorized as other shrub, 51 percent as 

herbaceous nonforest, 8 percent as other vegetation, and 23 percent as non-vegetated. 

Figure 13—Percent cover of object groups within estimated condition types across Nevada: a. is forest condition 

types and b. is non-forest condition types.

a

b
22 USDA Forest Service RMRS-GTR-344. 2016.

See Appendix tables A.15a and A.15b for estimates and associated percent sample 

error for forested condition types. Figure 14 focuses on a few different condition 

types and shows the attributes sampled by vegetation category for that condition type 

(Appendix, table A.14).

Figure 15 shows the proportion of conditions in each stand-size class as discernable from aerial photographs by forest condition type classes in Nevada. The 

seedlings/saplings stand-size class is often associated with regeneration in areas previously forested and disturbed or encroachment of tree-land into rangeland or alpine 

areas. More than 40 percent of the area estimated as spruce/fir condition types were 

classified as seedlings/saplings, which consist of trees less than 5 inches diameter. An 

estimated 27 percent of the Western juniper condition type is classified as seedlings/

saplings compared to only about 8 percent of the area meeting these conditions is 

within the pinyon/juniper condition type (See Appendix, table A.16).

Figure 14—Percent cover of objects within estimated condition types across Nevada, categorized by vegetative groups: 

a. is Bristlecone pine condition, b. is Mahogany woodland condition, c. is Sage shrub condition, and d. is Riparian zone 

condition.
USDA Forest Service RMRS-GTR-344. 2016. 23

Figure 16 (Appendix, table A.17) shows the distribution of crown density 

condition classes within the forest condition type classes. More than 40 percent of the 

aspen/cottonwood condition type has greater than 75 percent density of crown cover. 

All of the other forested types have less than 75 percent cover for the vast majority 

(>80 percent) of conditions. The distribution of disturbance classes within forest 

condition types across Nevada are shown in figure 17. The Jeffrey/ponderosa pine 

condition type class has the highest percentage of fire disturbance, with almost 10 percent of its area being identified as having burned. The disturbance category identifying 

insect/disease/drought affected about 5 percent of conditions in the spruce/fir and 

Figure 15—Percent of stand-size condition classes within forest condition types on forest land across Nevada. 

The stand-size class represents the majority of the stand as: 1) mature trees 5 inches diameter (DIA) and greater; 

2) young trees less than 5 inches DIA (Seedlings/Saplings); 3) or Nonstocked, once forested but currently less 

than 10 percent cover of trees.

Figure 16—Percent crown density condition classes within forest condition types on all lands across Nevada.
24 USDA Forest Service RMRS-GTR-344. 2016.

White fir/Douglas-fir condition type classes. Both the pinyon/juniper and mahogany 

condition types were found to have small amounts of human-caused disturbances 

(Appendix, table A.18).

Estimates by County and Ecoregion

This section presents a general picture of individual estimation units of counties 

and ecoregions within the State of Nevada (Figure 18). Figure 19 shows estimates 

of condition type groups by county or county groups. Here, we see that White Pine 

County and the county group consisting of Carson, Douglas, and Story Counties have 

the highest proportion of forest condition types for the State, whereas Humboldt county has the lowest proportion of forest condition types. The size (in acres) and number 

of plots sampled is shown in table 7 for a perspective on proportion. See Appendix 

table A.19 for estimates and associated sample errors.

Although county boundaries are the typical estimation unit for FIA, it is often 

more useful to look at more ecological boundaries, such as the U.S. Forest Service 

Ecological Subregions (ECOMAP Sections; Cleland et al. 2007; Figure 17). Figure 

20 shows the same condition type groups as above by ECOMAP Sections within 

the boundaries of Nevada, with the acres and number of plots by section displayed 

in table 8 (Appendix, table A.20). The Owyhee Uplands and Eastern Basin and 

Range Sections were combined to obtain a sufficient number of sample plots. The 

Sierra Nevada section has the greatest proportion of forest condition types, while the 

Northeastern Great Basin section has the lowest proportion.

Figure 17—Percent disturbance condition classes within condition types on all lands across Nevada.
USDA Forest Service RMRS-GTR-344. 2016. 25

Figure 18—NPIP estimation units of county boundaries and ECOMAP Sections (Cleland 2007).
26 USDA Forest Service RMRS-GTR-344. 2016.

Figure 19—Percent condition type groups by county or county group.

Table 7—Acres and number of FIA sample plots 

by county estimation unit,

County Acres # of plots

Carson_Douglas_Storey 650,885 46

Churchill 3,215,464 42

Clark 5,158,968 109

Elko 11,009,873 291

Esmeralda 2,296,992 65

Eureka 2,675,171 116

Humboldt 6,180,980 111

Lander 3,532,479 112

Lincoln 6,807,660 340

Lyon 1,295,517 49

Mineral 2,440,303 110

Nye 11,647,096 387

Pershing 3,883,165 78

Washoe 4,187,005 110

White Pine 5,693,898 364
USDA Forest Service RMRS-GTR-344. 2016. 27

Map Products

The percent live tree cover response variable was modeled with continuous 

random forest models, while the condition type group variables were modeled with 

categorical random forest models using the randomForest package (Liaw and Wiener 

2002) in R (R Core Team 2013). We used the suggested randomForest default values for the number of trees (ntree) and the number of predictor variables randomly 

sampled to test the number of potential predictors for each split in each decision tree 

(mtry). When looking at the number of trees to use in the forest, we examined graphs 

of OOB error as a function of number of trees to verify that the default of 500 trees 

was sufficient for each model. The number of predictors to test at each split defaults to 

the square root of the total number of predictor variables for categorical models and to 

one-third the number of predictors for continuous models.

Figure 20—Percent condition type groups by Cleland ECOMAP Sections (Cleland 2007).

Table 8—Acres and number of FIA sample plots by Cleland 

ECOMAP Sections.

Ecosection Acres # of plots

West Great Basin and Mountains 6,638,902 298

Southeastern Great Basin 13,584,709 425

Sierra Nevada 231,853 28

Owyhee Uplands/Eastern Basin and Range 2,283,025 46

Northwestern Basin and Range 5,937,030 125

Northern Mono 12,535,938 220

Northeastern Great Basin 4,983,660 103

Mono 2,200,925 153

Mojave Desert 8,636,213 155

East Great Basin and Mountains 13,733,774 777
28 USDA Forest Service RMRS-GTR-344. 2016.

We modeled two options for condition type. First, we modeled 12 condition type 

categories, with prevalence ranging from 1% to 55% of the sampled plots, with the 

rarest category only having 9 sampled plots. Second, we grouped the condition types 

into four categories: timber, woodland, shrub, and other nonforest. This brought the 

lowest prevalence categories (timber and other nonforest) up to 6% of the sampled 

plots, or 130 and 129 plots, respectively. In the case of the presence/absence models, the prevalence is shown in table 1, from Freeman and others (2012). Different 

modeling strategies were explored to accommodate the unbalanced prevalence of the 

response variables as well as the different sampling intensities per stratum (Freeman 

and others 2012).

The map of condition type with four groups is displayed in figure 21.a, with 

associated accuracy results in table 9. The Kappa value for this map was 0.76 with 

associated 0.012 standard deviation of Kappa. The map of percent live tree cover is 

shown in figure 21.b and its associate error is shown in figure 22, with root mean 

square error of 9.48, Pearson’s correlation at 0.85, and Spearman’s correlation at 0.86. 

Figure 21—Predicted maps from ModelMap: a. is Condition type with 

4 groups and b. is percent live tree canopy cover.

a
USDA Forest Service RMRS-GTR-344. 2016. 29

b

Table 9—Accuracy assessment error matrix for condition type map with four 

grouped categories.

Observed

Nonforest Shrub Timber Woodland Total User

Predicted

Nonforest 62 14 2 0 78 79%

Shrub 54 642 16 66 778 83%

Timber 1 8 59 14 82 72%

Woodland 12 58 53 1201 1324 91%

Total 129 722 130 1281 2262 PCC

Producer 48% 89% 45% 94% PCC 87%

Figure 21b
30 USDA Forest Service RMRS-GTR-344. 2016.

The map with 12 condition type groups and corresponding error matrix is shown in 

the Appendix figure A.1 and table A.21. The Kappa for this map was 0.64 with 0.013 

standard deviation. Figure 23.a and 23.b display maps of presence or species distributions for sage and pinyon pine. Presence (in green) is shown using the threshold based 

on maximized Kappa (Freeman and Moisen 2008). The threshold value and accuracy 

metrics for the eight binary, presence/absence models are presented in table 10. Maps 

of white fir, limber pine, quaking aspen, mountain-mahogany, and other shrub attributes are found in Appendix figure A.2.

Quality and Accuracy Assessment

A repeatability analysis, or quality assessment, involved a random sample 

(10 percent) of the photo-plots being interpreted by both interpreters. Error matrices 

were used to compare both object- and condition-level plot data for several classes. 

Table 11 shows results for forest type, seedling/sapling class, disturbance class, and 

percent cover class for tree and non-tree objects.

For accuracy assessment purposes, the only variable collected on FIA field plots 

that could be directly compared to photo interpretations was forest type. Photo calls 

agreed with FIA field calls 84.8 percent of the time with a sample error of 1.4 percent. 

Figure 22—Out-of-bag error for predicted live tree cover. 
USDA Forest Service RMRS-GTR-344. 2016. 31

Figure 23—Predicted maps of species presence: 

a. is pinyon pine and b. is sage. 

b

a
32 USDA Forest Service RMRS-GTR-344. 2016.

Table 10—The threshold used for mapping cut-off and accuracy assessment 

metrics for binary presence/absence models.

Threshold PCC Sensitivity Specificity Kappa

Quaking aspen 0.26 0.972 0.718 0.980 0.615

White fir 0.36 0.949 0.433 0.979 0.456

Limber pine 0.81 0.970 0.694 0.975 0.475

Mountain-mahogany 0.70 0.904 0.606 0.939 0.520

Juniper 0.43 0.864 0.923 0.796 0.724

Pinyon pine 0.54 0.889 0.896 0.881 0.778

Sage 0.58 0.701 0.763 0.601 0.364

Other shrub 0.71 0.756 0.831 0.416 0.230

Table 11—Percent agreement between two photo-interpreters for conditions 

and object group.

Condition/Object group Agreement (%) Percent standard error 

Forest Type 90.4 3.7

Seedlings/Saplings 75.4 4.4

Disturbance agent 90.5 4.0

Percent cover–trees 80.1 3.5

Percent cover–non-trees 81.1 1.1

Interestingly, FIA’s Measurement Quality Objectives (MQOs; Patterson and others 

2006) require field crews to accurately label forest type on plots no less than 84 percent of the time. Using field calls as truth, photo calls of forest type met these national 

MQO standards for forest type. It is not surprising that interpreting nonforest types 

from photos is less consistent. Distinguishing even between the predominant sage and 

other shrubs can be very challenging. Because of the very broad disturbance classes, 

repeatability was quite high for this attribute.

Discussion

It has been more than 20 years since the last comprehensive report of Nevada’s 

forest resources (Born and others 1992), and this report was based on field data collected over 30 years ago. Results presented here provide the most current look at 

quantity and spatial distribution of forests across the State, as well as the first ever 

summary of nonforest conditions that dominate this arid landscape. Information from 

this prototype will be used to investigate the potential for supplementing our traditional reports with photo-based information in the future.

The results illustrate how photo-based observations can augment FIA’s standard 

forest reporting in several ways. First, because of the larger plot footprint, photos 

are able to capture rare events that might be missed by the smaller field plots or by 

a smaller number of field plots (Patterson and Finco 2011). Photo-based estimates 

shown in figure 7 captured 19 different forest types. Though very rare on the landscape, cottonwood, Engelmann spruce, sugar pine, red fir, deciduous oak, and others 

were detected by these photo approaches. Similarly, riparian habitat is a limited 
USDA Forest Service RMRS-GTR-344. 2016. 33

resource type in the West that is extremely important in the arid landscape. Its small 

linear extent makes it difficult to measure with FIA’s systematic sample design. The 

size of the NPIP photo-plot footprint along with the added Type A riparian class 

provided an opportunity to sample riparian land more so than would be accomplished 

from the FIA sample design (figure 24). For FIA field protocol, riparian land that does 

not meet the standard condition criteria (at least 120 ft wide and at least 1 acre in size) 

Figure 24—NPIP photo plots with riparian lands sampled, adjacent to nonforest conditions, Type A. The NPIP sample plot is in 

yellow with sample points in black; FIA sample subplots are in 

red. Highlighted photo sample points are recorded riparian condition type. A. is Forested FIA plot location with riparian vegetation 

and b. is Nonforest FIA plot location with riparian vegetation.
34 USDA Forest Service RMRS-GTR-344. 2016.

must be adjacent to another forest condition to be considered riparian. Here, as an all 

condition inventory, we created a new class to capture riparian areas surrounded by 

nonforest lands, which accounted for 60 percent of the total riparian plots sampled and 

added more than 26,000 acres to the total estimate of riparian condition types.

Another rare condition on the landscape is forest disturbance, which is becoming 

increasingly important to the inventory and monitoring audience. Although it affects 

only a small percentage of a given forest each year, the cumulative effects through 

time can be dramatic. It is a complicated phenomenon in that it can impact canopy, 

understory, and forest floor. While field-based estimates can be constructed, they are 

limited to forested areas, a less intensive sample, and temporal challenges. The photobased estimates presented here illustrate observations made on disturbances that can 

be seen from the air, depicting the distribution of canopy-affecting fire, stress, and 

other disturbance agents on the different forest types across the State of Nevada.

Another way an aerial photo-based sample can augment FIA’s standard reporting 

is by enabling estimates over nonforest conditions where funding is unavailable for 

field visits. Area by nonforest condition type and type group provide a new summary 

of Nevada’s nonforest lands, depicting area of the landscape by 26 nonforest conditions, including prevalent conditions such as sage, other shrub, desert complex, and 

grass forb, as well as rare conditions such as idle farmland, cultural lands, and mining.

Aerial photography gives the interpreter a unique perspective, allowing aerial 

point sampling of various cover types over the landscape. The overall cover dominance of non-tree objects is quantified, with soil or rock being the predominant feature 

on the Nevada landscape. Similarly, the tree cover dominance of pinyon and juniper 

trees is illustrated. While cover estimates alone are interesting, the real value of 

photo-based estimation is that it enables analyses of the distribution of cover of many 

different attributes within both the forest and nonforest conditions. Understanding the 

distribution of tree species within different forest types leads to a better understanding 

of forest composition. Similarly, estimates of the distribution of cover of non-tree 

objects within the nonforest conditions of the State are a new addition to knowledge 

about this landscape.

This pilot provided a photo-based training dataset for mapping forest attributes 

at the same scale as the remote sensing imagery. FIA field plots measure trees on four 

1/20th-acre subplots distributed within 1 acre of ground. These data are often used to 

train models tasked with defining the relationship between truth on the ground and 

spectral data collected at 30-m, 90-m, 250-m (820-ft), and 500-m resolution. The mismatch between scale of ground data and scale of remotely sensed data can contribute 

a substantial amount of noise and confusion in predictive models. Here, the 250-m 

(820-ft) radius plot provided more scale-compatible training data for MODIS-based maps. 

The resultant maps from the predictive models derived using this photo-based training data are yet another example of new and unique ways to characterize vegetative 

characteristics. The four 250-m (820-ft) resolution maps of conditions and condition 

groups, live tree cover, individual tree species, sage, and other shrubs presented here 

provide a unique opportunity for more integrated analyses of the forest and nonforest 

components of Nevada’s landscape. Applications in wood products, wildlife habitat, 

water resources, carbon monitoring, insect and disease risk, fire, and others all require 

spatial depictions of the vegetative composition of an area.
USDA Forest Service RMRS-GTR-344. 2016. 35

One way to recoup the costs associated with acquiring and interpreting photos 

is to not send field crews to what are known as “checker” plots, that is, plots that are 

likely nonforested but the interpreter cannot be sure given available resolution photography. Larger-scale photography can eliminate more nonforested plots from being 

field visited. For example, in 2004, a simple test was conducted in Nevada where 77 

“checker” plots were photo-interpreted. Of these, 51 were determined to have some 

forest land requiring a field visit. However, twenty-six were determined to be nonforest with no field visit needed. We estimated per photo-plot costs to be $200, which 

means photo data collection for 77 plots cost $15,400. Field visit of nonforest plots 

in the Interior Western United States is estimated to be $2000 per plot, yielding a cost 

savings of $36,600 (Goeking and others 2009).

Integrating large-scale photography and other remote sensing data into the forest 

inventory observation process holds promise. Schroeder and others (2014) illustrated 

how a combination of Landsat observations coupled with National Agriculture 

Imagery Program (NAIP) photography and field observations give the most complete 

picture of disturbance on the Utah landscape. Cohen and others (2010) developed a 

Landsat-based observation system for monitoring landscape dynamics using historic 

Landsat imagery and NAIP photography in an integrative tool called TimeSync. In 

addition, FIA is in the process of adopting an Image Based Change Estimation (ICE) 

process wherein land use and land cover change observations are made on FIA plots 

using two dates of NAIP imagery (Webb and others 2012).

Finally, many interesting research issues arise from integrating photos into the 

forest inventory observation process. For example, questions about sample design 

arise such as whether to use two-phase versus two-step sample. The response design 

is riddled with questions such as appropriate photo-plot size, shape, or configuration; 

the configuration and number of dots; and other sampling tools such as transects, complete enumeration, or use of automated individual tree crown delineation algorithms. 

Much needs to be investigated regarding efficiency gains by adding photography, the 

potential for cost reduction in marginal forest land, the potential for modeling field 

attributes over photos, and application of nonparametric model-assisted estimation. 

Large-scale aerial photography offers potential in mapping forest attributes using 

photos as training data for the next round of FIA map products as well as a baseline for 

accuracy assessment for existing FIA map products. From an analytical perspective, 

it should be investigated whether there are FIA variables that might be better sampled 

from photos rather than from the ground.

Conclusions

FIA’s standard forest reporting can be augmented through the use of observations collected on large-scale aerial photography. Estimates of area by condition in 

the State of Nevada begin to capture attributes such as rare condition classes (e.g., 

riparian), disturbance agents, and less common forest types. The estimates targeting 

nonforest conditions reveal distributions within rangelands not previously seen using 

traditional forest inventory protocols. Exploring the distribution of cover of particular 

objects within a variety of conditions helps characterize both forested and nonforest 

landscapes in new ways. Photo-interpreted data provide an inexpensive source of 
36 USDA Forest Service RMRS-GTR-344. 2016.

training data for predictive models and maps of species distribution and cover. Costs 

of acquiring this valuable information can be offset through reducing the number of 

field visits to plots, as well as through potential gains in precision of map products.

References

Bechtold, W.A.; Patterson, P.L., eds. 2005. The enhanced Forest Inventory and 

Analysis program—national sampling design and estimation procedures. Gen. 

Tech. Rep. SRS-80. Asheville, NC: U.S. Department of Agriculture Forest Service, 

Southern Research Station.

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USDA Forest Service RMRS-GTR-344. 2016. 39

Appendix

Table A.1—Estimated acres and percent standard error 

by forest type group on all lands; Nevada, 2004–2005.

Forest type group Acres Percent standard error

Forest 11,718,402 3.58

Shrub 43,463,439 1.57

Grass/forb 3,916,176 10.90

Other nonforest 4,190,866 10.36

Total 63,288,883 —

Table A.2—Estimated acres and percent standard error by forest type and 

forest type group on all lands; Nevada, 2004–2005.

 Forest 

type group Forest type Acres Percent standard error

Forest Aspen 158,910 19.13

Forest Bristlecone pine 37,785 35.16

Forest Cottonwood 231 100.00

Forest Cottonwood/willow 13,738 63.22

Forest Deciduous oak 5,651 100.00

Forest Douglas-fir 8,848 62.94

Forest Engelmann spruce 3,575 61.76

Forest Jeffrey pine 33,446 36.74

Forest Juniper 1,350,752 15.08

Forest Limber pine 84,868 37.56

Forest Mahogany 459,343 14.04

Forest Pinyon/juniper 8,496,691 4.07

Forest Ponderosa pine 45,887 33.81

Forest Red fir 5,651 100.00

Forest Rocky Mountain juniper 12,830 72.07

Forest Subalpine fir 46,550 28.53

Forest Sugar pine 4,267 74.93

Forest Western juniper 633,713 24.98

Forest White fir 313,934 16.24

Forest Whitebark pine 1,730 100.00

Grass-Forb Grass/forb 3,916,176 10.90

Nonforest Beach/shoreline 301,508 41.50

Nonforest Census water 183,586 52.64

Nonforest Cropland 664,600 27.51

Nonforest Cultural 125,497 44.33

Nonforest Idle farmland 64,151 87.12

Nonforest Military 61,585 100.00

Nonforest Mining 154,279 50.91

Nonforest Noncensus water 15,511 49.55

Nonforest Pasture 298,942 40.78

Nonforest Rights-of-way 154,059 18.13

Nonforest Riparian zone 29,395 47.33

Nonforest Rock outcrops/scree 494,492 26.25

Nonforest Sand/dunes 1,384,340 19.35

Nonforest Unclassified agriculture 48,754 100.00

Nonforest Unclassified developed 16,679 100.00

Nonforest Unclassified other natural 171,679 52.34

Nonforest Wetland 21,811 100.00

Shrub Desert complex 5,158,841 10.42

Shrub Other shrub 17,524,996 4.74

Shrub Sagebrush 20,769,453 4.03

Shrub Unclassified shrub 10,149 63.67

Total 63,288,883 —
40 USDA Forest Service RMRS-GTR-344. 2016.

Table A.3—Estimated acres and percent standard error by forest type 

group on all lands; Nevada, 2004–2005.

Forest type group Acres Percent standard error

Agriculture/developed nonforest 1,588,545 17.41

Grass/forb 3,916,176 10.90

Sagebrush 20,769,453 4.03

Other shrub/unclassified 22,693,986 3.87

Other natural nonforest 2,551,115 13.58

Wetland-riparian nonforest 51,206 50.47

White fir/Douglas-fir 332,009 15.58

Spruce/fir 46,550 28.53

Limber/bristlecone pine 124,383 27.90

Jeffrey pine/ponderosa pine 83,600 24.19

Western juniper 633,713 24.98

Aspen/cottonwood 178,530 17.97

Mahogany 459,343 14.04

Pinyon/juniper 9,860,274 3.96

Total 63,288,883 —

Table A.4—Estimated acres and percent standard error by 

stand density classes on all lands; Nevada, 2004–2005.

Crown density Acres Percent standard error

Nonforest 51,543,869 0.82

Between 0 and 25 5,089,188 7.14

Between 26 and 50 4,527,686 4.38

Between 51 and 75 1,416,262 6.38

Between 76 and 100 252,572 18.98

Nonstocked 459,305 24.13

Total 63,288,883 —

Table A.5—Estimated acres and percent standard error by owner 

class on all lands; Nevada, 2004–2005.

Owner class Acres Percent standard error

Forest Service 6,021,413 7.20

National Park Service 522,390 32.11

Bureau of Land Management 45,194,167 1.75

Fish and Wildlife Service 1,042,659 23.12

Department of Defense 222,510 49.27

Department of Energy 62,868 100.00

Bureau of Reclamation 200,150 51.31

Nevada State 367,682 38.19

Tribal Land 887,758 26.31

Private 8,767,287 7.11

Total 63,288,883 —
USDA Forest Service RMRS-GTR-344. 2016. 41

Table A.6—Estimated percent cover and percent standard error by 

attribute class and vegetation group on all lands; Nevada, 2004–2005.

Percent Percent

Veg group Object cover standard error

Tree Black cottonwood 0.02 47.95

Tree Down dead 0.08 9.58

Tree Engelmann spruce 0.01 50.47

Tree Great Basin bristlecone pine 0.01 37.31

Tree Incense cedar 0.00 84.97

Tree Jeffrey pine 0.02 32.25

Tree Juniper spp. 1.57 5.36

Tree Limber pine 0.04 28.70

Tree Mortality 0.07 10.62

Tree Mountain mahogany 0.31 10.71

Tree Other hardwood 0.01 60.56

Tree Other softwood 0.01 40.75

Tree Pinyon spp. 2.24 3.72

Tree Ponderosa pine 0.01 29.88

Tree Quaking aspen 0.18 20.25

Tree Standing dead 0.15 9.61

Tree Subalpine fir 0.02 42.69

Tree Western juniper 0.32 14.38

Tree White fir 0.17 16.33

Shrub Other shrub 19.67 2.52

Shrub Sage complex 12.31 3.95

Other_veg Agriculture 1.10 26.79

Other_veg Dry herbaceous 14.41 3.77

Other_veg Litter 0.65 6.47

Other_veg Pasture 0.41 45.53

Other_veg Wet herbaceous 0.05 30.33

Barren Built (structure) 0.07 33.90

Barren Improved road 0.27 28.46

Barren Other barren 0.21 60.79

Barren Soil or rock 45.10 1.46

Barren Unimproved road 0.07 17.51

Barren Water 0.31 49.31

Unknown Other unknown 0.01 58.96

Unknown Shadow 0.09 11.46

Total 100.00 —

Table A.7—Estimated percent cover and percent 

standard error by attribute class on all lands; 

Nevada, 2004–2005.

Percent

Attribute class Percent cover standard error

Vegetated 53.85 1.23

Non-vegetated 46.03 1.44

Unknown 0.12 12.22

Total 100.00 —
42 USDA Forest Service RMRS-GTR-344. 2016.

Table A.8—Estimated percent cover and percent 

standard error by attribute class on all lands; 

Nevada, 2004–2005.

Percent

Attribute class Percent cover standard error

Tree 5.24 2.88

Shrub 31.99 1.70

Other veg 16.63 3.80

Barren 46.03 1.44

Unknown 0.12 12.22

Total 100.00 —

Table A.9—Estimated percent cover and percent 

standard error by attribute class on all lands; 

Nevada, 2004–2005.

Percent

Attribute class Percent cover standard error

Softwood 4.42 2.95

Hardwood 0.52 9.97

Unknown tree 0.30 6.33

Shrub 31.99 1.70

Other veg 16.63 3.80

Barren 46.03 1.44

Unknown 0.12 12.22

Total 100.00 —

Table A.10—Estimated percent cover and 

percent standard error by attribute class on all 

lands; Nevada, 2004–2005.

Percent

Attribute class Percent cover standard error

Timber 0.81 8.67

Woodland 4.12 3.09

Other tree 0.02 37.77

Dead tree 0.30 6.33

Sage shrub 12.31 3.95

Other shrub 19.68 2.52

Herbaceous 14.46 3.77

Other veg 2.17 16.42

Non-vegetated 45.72 1.44

Water 0.31 49.31

Unknown 0.12 12.22

Total 100.00 —
USDA Forest Service RMRS-GTR-344. 2016. 43 Table A.11a—Estimated percent cover of tree attributes within forest condition types on all lands; Nevada, 2004–2005. Tree attributes Forest type

Aspen

Black cottonwood

Bristlecone 

pine

Douglas-fir

Down dead

Engelmann 

spruce

Gambel 

oak

Incense 

cedar

Jeffrey pine

Juniper 

spp.

Limber 

pine

Lodgepole 

pine

Mortality

Mtn mahogany

Other 

hardwood

Other 

softwood

Pinyon spp.

Ponderosa 

pine

Red fir

Standing 

dead

Subalpine 

fir

Sugar pine

Western 

juniper

White fir

White pine

Whitebark 

pine Total

Aspen 55.41 0.81 0.00 0.00 0.36 0.51 0.00 0.00 0.07 0.88 0.44 0.00 0.36 0.44 0.07 0.07 0.00 0.07 0.00 0.80 0.15 0.00 0.00 2.20 0.00 0.00 62.65Bristlecone pine 0.00 0.00 18.92 0.31 0.92 0.00 0.00 0.00 0.00 0.00 5.49 0.00 0.61 0.92 0.00 0.00 0.00 0.00 0.00 0.61 0.00 0.00 0.00 1.22 0.00 0.00 29.00

Cottonwood 0.00 50.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 50.00

Cottonwood/willow 0.00 33.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.84 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 33.89

Deciduous oak 0.00 0.00 0.00 0.00 0.00 0.00 16.33 0.00 0.00 4.08 0.00 0.00 0.00 0.00 0.00 0.00 6.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 26.53Douglas-fir 0.00 0.00 0.00 5.26 0.00 0.00 0.00 0.00 0.00 0.00 1.32 0.00 3.95 0.00 0.00 0.00 1.32 0.00 0.00 0.00 0.00 0.00 0.00 2.63 0.00 0.00 14.47

Engelmann spruce 9.68 0.00 0.00 0.00 3.23 32.26 0.00 0.00 0.00 0.00 3.23 0.00 0.00 0.00 0.00 0.00 3.23 0.00 0.00 3.23 0.00 0.00 0.00 3.23 0.00 0.00 58.06

Jeffrey pine 0.00 0.00 0.00 0.00 0.69 0.00 0.00 0.00 17.93 0.00 0.34 0.00 1.03 0.00 0.00 0.00 0.00 0.00 1.03 0.34 0.00 0.00 0.00 0.69 0.00 0.00 22.07Juniper 0.00 0.00 0.00 0.00 0.26 0.00 0.00 0.00 0.00 14.74 0.00 0.00 0.13 0.22 0.00 0.00 0.49 0.00 0.00 0.31 0.00 0.00 0.00 0.05 0.00 0.00 16.20Limber pine 1.36 0.00 0.00 0.00 2.73 0.68 0.00 0.00 0.27 0.14 17.21 0.68 0.00 0.82 0.00 0.00 0.00 0.00 0.14 0.14 0.00 0.41 0.00 2.99 0.00 0.54 28.10

Mahogany 0.43 0.08 0.03 0.00 0.68 0.05 0.00 0.00 0.00 2.27 0.20 0.00 0.33 28.31 0.00 0.31 3.02 0.03 0.00 1.06 0.03 0.00 0.05 1.57 0.00 0.00 38.42

Pinyon/juniper 0.01 0.00 0.01 0.00 0.41 0.00 0.00 0.00 0.00 8.69 0.00 0.00 0.38 0.54 0.00 0.00 15.94 0.01 0.00 0.77 0.00 0.00 1.00 0.12 0.00 0.00 27.90

Ponderosa pine 0.00 0.00 0.00 0.25 0.75 0.00 0.00 0.00 0.00 0.50 0.00 0.00 0.00 0.50 0.00 0.25 3.27 15.87 0.00 0.75 0.00 0.00 0.00 3.77 0.00 0.00 25.92

Red fir 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 16.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 10.20 4.08 0.00 30.61Rocky Mountain juniper 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Subalpine fir 3.86 0.26 0.00 0.00 0.77 0.00 0.00 0.00 0.00 0.26 7.24 0.00 1.03 0.00 0.00 2.57 0.00 0.00 0.00 1.54 18.92 0.00 0.00 1.80 0.00 0.00 38.27

Sugar pine 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 8.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 10.81 0.00 8.11 0.00 0.00 27.03

Western juniper 0.00 0.00 0.00 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.07 0.00 0.00 0.29 0.00 0.00 0.25 0.00 0.00 16.98 0.00 0.00 0.00 17.69White fir 2.52 0.04 0.15 0.63 1.10 0.26 0.00 0.18 0.37 0.70 1.03 0.00 0.77 3.36 0.00 0.07 2.10 0.29 0.07 3.52 0.18 0.22 0.00 23.81 0.04 0.00 41.42

Whitebark pine 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 20.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 6.67 0.00 0.00 26.67
44 USDA Forest Service RMRS-GTR-344. 2016. Table A.11b—Percent standard error of estimated cover of tree attributes within forest condition types on all lands; Nevada, 2004–2005. Tree attributes Forest type

Aspen

Black cottonwood

Bristlecone 

pine

Douglas-fir

Down dead

Engelmann 

spruce

Gambel oak

Incense cedar

Jeffrey pine

Juniper spp.

Limber pine

Lodgepole 

pine

Mortality

Mtn mahogany

Other hardwood - Other soft

wood

Pinyon spp.

Ponderosa 

pine

Red fir

Standing 

dead

Sub-alpine fir

Sugar pine

Western 

juniper

White fir

White pine

Whitebark 

pine Total

Aspen 8.30 101.06 0.00 0.00 51.59 86.75 0.00 0.00 101.10 84.07 51.75 0.00 58.70 63.32 101.51 98.90 0.00 98.90 0.00 46.23 72.09 0.00 0.00 49.62 0.00 0.00 6.55Bristlecone pine 0.00 0.00 22.44 99.21 62.19 0.00 0.00 0.00 0.00 0.00 53.14 0.00 97.02 74.83 0.00 0.00 0.00 0.00 0.00 68.01 0.00 0.00 0.00 80.35 0.00 0.00 19.00

Cottonwood 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Cottonwood/willow 0.00 40.51 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 116.88 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 39.01

Deciduous oak 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Douglas-fir 0.00 0.00 0.00 49.35 0.00 0.00 0.00 0.00 0.00 0.00 100.36 0.00 49.35 0.00 0.00 0.00 111.54 0.00 0.00 0.00 0.00 0.00 0.00 111.55 0.00 0.00 29.26

Engelmann spruce 40.42 0.00 0.00 0.00 96.48 44.95 0.00 0.00 0.00 0.00 53.42 0.00 0.00 0.00 0.00 0.00 96.48 0.00 0.00 96.48 0.00 0.00 0.00 102.96 0.00 0.00 17.57

Jeffrey pine 0.00 0.00 0.00 0.00 89.33 0.00 0.00 0.00 29.14 0.00 92.75 0.00 59.45 0.00 0.00 0.00 0.00 0.00 68.94 94.61 0.00 0.00 0.00 92.75 0.00 0.00 20.02

Juniper 0.00 0.00 0.00 0.00 71.07 0.00 0.00 0.00 0.00 12.77 0.00 0.00 44.39 70.47 0.00 0.00 31.92 0.00 0.00 62.86 0.00 0.00 0.00 63.74 0.00 0.00 11.49

Limber pine 77.09 0.00 0.00 0.00 34.90 84.84 0.00 0.00 72.52 106.48 23.70 101.90 0.00 63.94 0.00 0.00 0.00 0.00 101.91 100.44 0.00 60.26 0.00 75.10 0.00 72.33 19.99

Mahogany 66.97 100.66 100.40 0.00 42.34 100.20 0.00 0.00 0.00 21.50 51.04 0.00 30.37 9.42 0.00 89.21 19.02 99.60 0.00 34.51 100.79 0.00 100.80 25.97 0.00 0.00 8.34Pinyon/juniper 49.26 58.54 100.00 0.00 9.34 0.00 0.00 0.00 0.00 4.45 100.00 0.00 11.82 12.82 70.37 58.54 3.28 70.37 0.00 10.09 0.00 0.00 13.21 27.52 0.00 0.00 2.47

Ponderosa pine 0.00 0.00 0.00 104.18 66.41 0.00 0.00 0.00 0.00 69.23 0.00 0.00 0.00 62.74 0.00 104.18 73.81 9.68 0.00 58.97 0.00 0.00 0.00 38.99 0.00 0.00 11.99

Red fir 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Rocky Mountain juniper 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Subalpine fir 52.90 100.97 0.00 0.00 67.69 0.00 0.00 0.00 0.00 97.32 37.85 0.00 58.55 0.00 0.00 60.20 0.00 0.00 0.00 44.62 37.41 0.00 0.00 59.88 0.00 0.00 15.76

Sugar pine 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 45.88 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 24.85 0.00 48.43 0.00 0.00 10.71

Western juniper 0.00 0.00 0.00 0.00 73.90 0.00 0.00 0.00 0.00 0.00 0.00 0.00 61.72 81.87 0.00 0.00 71.07 0.00 0.00 42.62 0.00 0.00 10.77 0.00 0.00 0.00 10.58

White fir 37.76 99.73 61.17 56.59 27.26 47.65 0.00 99.57 58.04 34.00 29.97 0.00 35.92 32.00 0.00 71.33 30.55 58.68 99.59 26.18 100.71 74.11 0.00 6.35 99.73 0.00 5.19

Whitebark pine 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
USDA Forest Service RMRS-GTR-344. 2016. 45 Table A.12a—Estimated percent cover of non-tree attributes within nonforest condition types on all lands; Nevada, 2004–2005. Non-tree attribute Forest type Sage shrub Other shrub Joshua tree Dry herb. Wet herb. Agri- culture Pasture Litter Other veg Soil or rock Built (structure) Improved road Unim- proved road Water Other barren Snow or ice Shadow Other unknown Beach/shoreline 0.00 0.85 0.00 1.70 0.00 0.00 0.00 0.00 0.00 97.02 0.00 0.43 0.00 0.00 0.00 0.00 0.00 0.00 Census water 0.00 0.00 0.00 0.00 1.40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 98.54 0.06 0.00 0.00 0.00 Cropland 0.00 0.39 0.00 2.70 0.00 95.95 0.00 0.00 0.00 0.77 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Cultural 0.19 2.80 0.00 14.84 0.00 0.00 0.00 0.56 2.07 29.33 17.75 11.64 0.09 0.00 11.93 0.00 1.03 0.00 Desert complex 0.18 27.07 0.13 7.91 0.00 0.00 0.00 0.03 0.03 64.34 0.08 0.03 0.05 0.00 0.00 0.00 0.03 0.10 Grass/forb 5.07 6.59 0.00 61.22 0.11 0.13 0.00 0.11 0.00 26.39 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.00 Idle farmland 0.00 0.00 0.00 0.00 0.00 86.00 0.00 0.00 0.00 14.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Military 0.00 2.08 0.00 12.50 0.00 0.00 0.00 0.00 0.00 10.42 4.17 70.83 0.00 0.00 0.00 0.00 0.00 0.00 Mining 0.15 0.91 0.00 8.47 0.00 0.00 0.00 0.00 0.00 44.74 0.00 2.49 0.00 0.83 42.41 0.00 0.00 0.00 Noncensus water 0.00 0.00 0.00 0.00 33.09 0.00 0.00 0.00 0.00 16.54 0.00 0.00 0.00 49.63 0.00 0.00 0.00 0.00 Other shrub 2.49 34.94 0.00 11.93 0.02 0.00 0.00 0.24 0.00 50.14 0.03 0.01 0.07 0.01 0.00 0.00 0.00 0.00 Pasture 0.00 2.58 0.00 6.87 0.00 0.00 86.27 0.00 0.00 4.29 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Rights-of-way 0.00 1.75 0.00 10.11 0.84 0.84 0.00 0.00 0.00 12.92 0.84 63.36 5.17 2.51 1.67 0.00 0.00 0.00 Riparian zone 0.00 52.77 0.00 18.65 17.48 0.00 0.00 0.00 0.00 9.13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Rock outcrops/scree 1.56 7.50 0.00 8.61 0.00 0.00 0.00 0.00 0.00 81.49 0.02 0.00 0.00 0.00 0.00 0.26 0.00 0.00 Sagebrush 29.49 16.61 0.00 14.19 0.02 0.00 0.00 0.25 0.00 38.99 0.02 0.03 0.06 0.00 0.00 0.01 0.00 0.01 Sand/dunes 0.37 5.66 0.00 1.11 0.00 0.00 0.00 0.00 0.00 92.76 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Unclassified agriculture 2.63 2.63 0.00 34.21 0.00 0.00 0.00 0.00 0.00 60.53 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Unclassified developed 0.00 7.69 0.00 15.38 0.00 0.00 0.00 0.00 0.00 76.92 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Unclassified other natural 0.00 5.37 0.00 27.65 0.00 0.00 0.00 0.00 0.00 66.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.75 Unclassified shrub 2.27 34.09 0.00 5.68 0.00 0.00 0.00 0.00 0.00 50.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Wetland 0.00 58.82 0.00 11.76 11.76 0.00 0.00 0.00 0.00 5.88 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
46 USDA Forest Service RMRS-GTR-344. 2016. Table A.12b—Percent standard error of estimated cover of non-tree attributes within nonforest condition types on all lands; Nevada, 2004–2005. Non-tree attribute Forest type Sage shrub Other shrub Joshua tree Dry herb. Wet herb. Agriculture Pasture Litter Other veg Soil or rock Built (structure) Improved road Unim- proved road Water Other barren Snow or ice Shadow Other unknown Beach/shoreline 0.00 98.05 0.00 72.45 0.00 0.00 0.00 0.00 0.00 2.05 0.00 104.37 0.00 0.00 0.00 0.00 0.00 0.00 Census water 0.00 0.00 0.00 0.00 99.98 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.43 113.06 0.00 0.00 0.00 Cropland 0.00 67.37 0.00 65.06 0.00 2.34 0.00 0.00 0.00 55.53 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Cultural 80.80 40.29 0.00 27.35 0.00 0.00 0.00 105.31 72.04 30.29 21.18 36.46 106.78 0.00 31.62 0.00 72.04 0.00 Desert complex 61.38 5.17 81.69 11.34 0.00 0.00 0.00 99.60 99.60 2.33 56.84 100.00 69.92 0.00 0.00 0.00 99.60 78.51 Grass/forb 20.37 14.56 0.00 4.03 52.75 100.00 0.00 67.69 0.00 7.80 0.00 0.00 74.26 0.00 0.00 0.00 0.00 0.00 Idle farmland 0.00 0.00 0.00 0.00 0.00 19.81 0.00 0.00 0.00 121.69 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Military 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mining 111.37 92.12 0.00 68.21 0.00 0.00 0.00 0.00 0.00 50.35 0.00 98.91 0.00 98.92 62.53 0.00 0.00 0.00 Noncensus water 0.00 0.00 0.00 0.00 50.13 0.00 0.00 0.00 0.00 64.50 0.00 0.00 0.00 23.55 0.00 0.00 0.00 0.00 Other shrub 12.22 2.52 0.00 6.84 57.27 0.00 0.00 31.65 0.00 2.25 49.83 70.75 32.20 101.37 0.00 0.00 100.00 0.00 Pasture 0.00 64.16 0.00 61.56 0.00 0.00 8.68 0.00 0.00 85.61 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Rights-of-way 0.00 66.80 0.00 41.44 97.55 97.55 0.00 0.00 0.00 36.77 91.34 12.81 45.81 95.81 70.20 0.00 0.00 0.00 Riparian zone 0.00 18.09 0.00 32.19 66.27 0.00 0.00 0.00 0.00 62.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Rock outcrops/scree 53.37 26.82 0.00 26.53 0.00 0.00 0.00 0.00 0.00 3.51 103.43 0.00 0.00 0.00 0.00 91.21 0.00 0.00 Sagebrush 2.77 4.44 0.00 5.31 56.02 0.00 0.00 24.69 0.00 2.29 56.02 59.68 31.35 0.00 0.00 96.12 100.00 100.00 Sand/dunes 62.41 25.52 0.00 48.53 0.00 0.00 0.00 0.00 0.00 2.01 97.41 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Unclassified agriculture 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Unclassified developed 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Unclassified other natural 0.00 47.21 0.00 52.59 0.00 0.00 0.00 0.00 0.00 18.32 0.00 0.00 0.00 0.00 0.00 0.00 0.00 84.12 Unclassified shrub 78.15 64.22 0.00 65.62 0.00 0.00 0.00 0.00 0.00 55.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Wetland 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
USDA Forest Service RMRS-GTR-344. 2016. 47 Table A.13a—Estimated percent cover of tree attributes within nonforest condition types on all lands; Nevada, 2004–2005. Attribute Forest type Quaking aspen Black cotton- wood Down dead Engel- mann spruce Incense cedar Jeffrey pine Juniper spp. Limber pine Mort- ality Mountain- mahogany Other hard- wood Other soft- wood Pinyon spp. Standing dead Sub- alpine fir Sugar pine Western juniper White fir Beach/shoreline 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Census water 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Cropland 0.00 0.19 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Cultural 0.00 0.00 0.00 0.00 0.09 1.86 0.00 0.00 0.09 0.00 4.14 1.03 0.09 0.09 0.00 0.28 0.00 0.09 Desert complex 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Grass/forb 0.00 0.03 0.04 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.00 0.00 0.13 0.04 0.00 0.00 0.00 0.00 Idle farmland 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Military 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mining 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Noncensus water 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.74 0.00 0.00 0.00 0.00 0.00 0.00 Other shrub 0.01 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 0.00 0.00 0.00 0.04 0.01 0.00 0.00 0.00 0.00 Pasture 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Rights-of-way 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Riparian zone 0.00 0.00 0.00 0.39 0.00 0.00 0.00 0.00 0.00 0.00 1.57 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Rock outcrops/ scree 0.00 0.00 0.02 0.00 0.00 0.00 0.35 0.00 0.02 0.00 0.00 0.00 0.14 0.02 0.00 0.00 0.00 0.00 Sagebrush 0.04 0.00 0.01 0.00 0.00 0.00 0.11 0.00 0.01 0.02 0.00 0.00 0.09 0.01 0.00 0.00 0.05 0.01 Sand/dunes 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Unclassified agriculture 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Unclassified developed 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Unclassified other natural 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.07 Unclassified shrub 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.14 0.00 1.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.68 Wetland 0.00 11.76 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
48 USDA Forest Service RMRS-GTR-344. 2016. Table A.13b—Percent standard error of estimated cover of tree attributes within nonforest condition types on all lands; Nevada, 2004–2005. Attribute Forest type Quaking aspen Black cotton- wood Down dead Engel- mann spruce Incense cedar Jeffrey pine Juniper spp. Limber pine Mort- ality Mountain- mahogany Other hard- wood Other soft- wood Pinyon spp. Stand- ing dead Sub- alpine fir Sugar pine West- ern juniper White fir Beach/shoreline 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Census water 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Cropland 0.00 94.87 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Cultural 0.00 0.00 0.00 0.00 107.32 67.85 0.00 0.00 105.60 0.00 33.40 98.71 108.18 108.18 0.00 83.36 0.00 107.32 Desert complex 0.00 0.00 0.00 0.00 0.00 0.00 70.32 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Grass/forb 0.00 99.70 85.53 0.00 0.00 0.00 103.45 0.00 91.60 103.45 0.00 0.00 97.39 85.27 0.00 0.00 0.00 0.00 Idle farmland 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Military 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mining 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Noncensus water 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 110.93 0.00 0.00 0.00 0.00 0.00 0.00 Other shrub 52.74 0.00 100.00 0.00 0.00 0.00 33.95 0.00 0.00 45.45 0.00 100.00 39.31 85.06 0.00 0.00 69.23 0.00 Pasture 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Rights-of-way 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Riparian zone 0.00 0.00 0.00 108.53 0.00 0.00 0.00 0.00 0.00 0.00 109.22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Rock outcrops/scree 0.00 0.00 103.43 0.00 0.00 0.00 78.40 0.00 103.00 0.00 0.00 0.00 66.64 102.58 0.00 0.00 0.00 0.00 Sagebrush 41.57 0.00 75.00 0.00 0.00 0.00 25.68 100.00 100.00 52.98 0.00 100.00 28.76 92.54 100.00 0.00 36.83 92.54 Sand/dunes 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Unclassified agriculture 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Unclassified developed 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Unclassified other natural 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 112.65 Unclassified shrub 0.00 0.00 0.00 0.00 0.00 0.00 0.00 110.67 0.00 78.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 110.67 Wetland 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
USDA Forest Service RMRS-GTR-344. 2016. 49 Table A.14a—Estimated cover of non-tree attributes within forest condition types on all lands; Nevada, 2004–2005. Non-tree attribute Forest type Built (structure) Dry herbaceous Improved road Joshua tree Litter Other barren Other shrub Other veg Sage shrub Shadow Snow or ice Soil or rock Unimproved road Wet herbaceous Aspen 0.00 7.48 0.00 0.00 3.38 0.00 13.10 0.00 2.86 1.61 0.00 8.77 0.00 0.15 Bristlecone pine 0.00 9.50 0.00 0.00 2.75 0.00 8.89 0.00 3.97 0.92 0.00 44.98 0.00 0.00 Cottonwood 0.00 0.00 0.00 0.00 0.00 0.00 50.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Cottonwood/willow 0.00 48.37 0.00 0.00 1.68 0.00 5.88 0.00 0.00 0.00 0.00 0.84 0.00 9.34 Deciduous oak 0.00 0.00 0.00 0.00 0.00 0.00 42.86 0.00 0.00 0.00 0.00 30.61 0.00 0.00 Douglas-fir 0.00 51.32 0.00 0.00 7.89 0.00 2.63 0.00 0.00 0.00 0.00 23.68 0.00 0.00 Engelmann spruce 0.00 9.68 0.00 0.00 0.00 0.00 0.00 0.00 6.45 9.68 0.00 16.13 0.00 0.00 Jeffrey pine 0.34 3.10 0.00 0.00 1.03 0.00 22.41 0.00 0.00 0.00 0.00 51.03 0.00 0.00 Juniper 0.02 15.65 0.01 0.00 1.19 0.01 14.88 0.00 12.79 0.27 0.00 38.97 0.01 0.01 Limber pine 0.00 4.91 0.00 0.00 1.90 0.00 5.59 0.00 9.85 0.54 1.09 47.88 0.00 0.14 Mahogany 0.03 5.74 0.00 0.00 3.20 0.00 16.36 0.00 6.48 0.93 0.00 28.84 0.00 0.00 Pinyon/juniper 0.02 7.70 0.02 0.00 2.78 0.60 7.32 0.00 8.18 0.43 0.00 44.97 0.05 0.02 Ponderosa pine 0.00 7.60 0.00 0.00 3.27 0.00 9.08 0.00 2.80 0.00 0.00 51.34 0.00 0.00 Red fir 0.00 0.00 0.00 0.00 10.20 0.00 22.45 0.00 0.00 0.00 0.00 36.73 0.00 0.00 Rocky Mountain juniper 0.00 40.00 0.00 0.00 0.00 0.00 10.00 0.00 0.00 0.00 0.00 50.00 0.00 0.00 Subalpine fir 0.00 11.07 0.00 0.00 4.41 0.00 14.23 0.00 3.89 0.00 0.00 28.13 0.00 0.00 Sugar pine 0.00 2.70 0.00 0.00 0.00 0.00 27.03 0.00 2.70 0.00 0.00 40.54 0.00 0.00 Western juniper 0.00 10.64 0.00 0.00 0.16 0.00 10.14 0.00 13.12 0.00 0.00 48.21 0.04 0.00 White fir 0.04 10.11 0.00 0.00 9.31 0.00 9.78 0.00 3.75 2.80 0.00 22.25 0.00 0.55 Whitebark pine 0.00 26.67 0.00 0.00 0.00 0.00 20.00 0.00 0.00 0.00 0.00 26.67 0.00 0.00
50 USDA Forest Service RMRS-GTR-344. 2016. Table A.14b—Percent standard error of estimated percent cover of non-tree attributes within forest condition types on all lands; Nevada, 2004–2005. Attribute Forest type Built (structure) Dry herbaceous Improved road Joshua tree Litter Other barren Other shrub Other veg Sage shrub Shadow Snow or ice Soil or rock Unimproved road Wet herbaceous Aspen 0.00 26.74 0.00 0.00 49.84 0.00 18.66 0.00 41.58 48.87 0.00 23.10 0.00 101.44 Bristlecone pine 0.00 33.99 0.00 0.00 55.11 0.00 28.20 0.00 65.53 73.59 0.00 14.14 0.00 0.00 Cottonwood 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Cottonwood/willow 0.00 35.29 0.00 0.00 90.77 0.00 85.14 0.00 0.00 0.00 0.00 113.23 0.00 52.88 Deciduous oak 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Douglas-fir 0.00 49.35 0.00 0.00 84.37 0.00 42.16 0.00 0.00 0.00 0.00 78.59 0.00 0.00 Engelmann spruce 0.00 75.65 0.00 0.00 0.00 0.00 0.00 0.00 102.96 23.61 0.00 25.15 0.00 0.00 Jeffrey pine 89.36 25.80 0.00 0.00 94.60 0.00 17.90 0.00 0.00 0.00 0.00 12.21 0.00 0.00 Juniper 71.93 18.71 100.00 0.00 30.31 100.00 15.19 0.00 14.77 40.74 0.00 5.88 100.00 100.00 Limber pine 0.00 40.53 0.00 0.00 54.10 0.00 23.15 0.00 39.27 55.22 81.59 16.87 0.00 105.81 Mahogany 100.00 15.91 0.00 0.00 18.05 0.00 16.98 0.00 21.32 38.52 0.00 11.09 0.00 0.00 Pinyon/juniper 29.36 5.79 69.86 100.00 5.76 99.37 5.79 58.54 7.37 15.35 0.00 2.08 18.49 38.42 Ponderosa pine 0.00 58.70 0.00 0.00 65.07 0.00 21.55 0.00 85.02 0.00 0.00 12.21 0.00 0.00 Red fir 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Rocky Mountain juniper 0.00 49.53 0.00 0.00 0.00 0.00 56.60 0.00 0.00 0.00 0.00 28.30 0.00 0.00 Subalpine fir 0.00 31.41 0.00 0.00 68.77 0.00 30.77 0.00 62.40 0.00 0.00 16.45 0.00 0.00 Sugar pine 0.00 45.88 0.00 0.00 0.00 0.00 24.85 0.00 45.88 0.00 0.00 17.59 0.00 0.00 Western juniper 0.00 27.90 0.00 0.00 48.11 0.00 23.65 0.00 25.46 0.00 0.00 7.99 102.47 0.00 White fir 99.73 11.84 0.00 0.00 13.63 0.00 19.39 0.00 49.28 26.41 0.00 14.87 0.00 51.41 Whitebark pine 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
USDA Forest Service RMRS-GTR-344. 2016. 51 Table A2.15a—Estimated cover of attribute groups within forest type on all lands; Nevada, 2004–2005. Attribute group Forest type Timber Woodland Other tree Dead tree Sage shrub Other shrub Herbaceous Other veg Non- vegetated Water Unknown Beach/shoreline 0.00 0.00 0.00 0.00 0.00 0.85 1.70 0.00 97.45 0.00 0.00 Census water 0.00 0.00 0.00 0.00 0.00 0.00 1.40 0.00 0.06 98.54 0.00 Cropland 0.19 0.00 0.00 0.00 0.00 0.39 2.70 95.95 0.77 0.00 0.00 Cultural 2.32 0.09 5.17 0.19 0.19 2.80 14.84 2.63 70.74 0.00 1.03 Desert complex 0.00 0.05 0.00 0.00 0.18 27.20 7.91 0.05 64.49 0.00 0.13 Grass/forb 0.03 0.14 0.00 0.11 5.07 6.59 61.33 0.24 26.48 0.00 0.00 Idle farmland 0.00 0.00 0.00 0.00 0.00 0.00 0.00 86.00 14.00 0.00 0.00 Military 0.00 0.00 0.00 0.00 0.00 2.08 12.50 0.00 85.42 0.00 0.00 Mining 0.00 0.00 0.00 0.00 0.15 0.91 8.47 0.00 89.65 0.83 0.00 Noncensus water 0.00 0.00 0.74 0.00 0.00 0.00 33.09 0.00 16.54 49.63 0.00 Other shrub 0.02 0.10 0.00 0.01 2.49 34.94 11.95 0.24 50.25 0.01 0.00 Pasture 0.00 0.00 0.00 0.00 0.00 2.58 6.87 86.27 4.29 0.00 0.00 Rights-of-way 0.00 0.00 0.00 0.00 0.00 1.75 10.95 0.84 83.96 2.51 0.00 Riparian zone 0.39 0.00 1.57 0.00 0.00 52.77 36.13 0.00 9.13 0.00 0.00 Rock outcrops/scree 0.00 0.49 0.00 0.07 1.56 7.50 8.61 0.00 81.52 0.00 0.26 Sagebrush 0.10 0.22 0.00 0.02 29.49 16.61 14.21 0.25 39.09 0.00 0.02 Sand/dunes 0.00 0.00 0.00 0.00 0.37 5.66 1.11 0.00 92.86 0.00 0.00 Unclassified agriculture 0.00 0.00 0.00 0.00 2.63 2.63 34.21 0.00 60.53 0.00 0.00 Unclassified developed 0.00 0.00 0.00 0.00 0.00 7.69 15.38 0.00 76.92 0.00 0.00 Unclassified other natural 0.07 0.00 0.00 0.00 0.00 5.37 27.65 0.00 66.17 0.00 0.75 Unclassified shrub 6.82 1.14 0.00 0.00 2.27 34.09 5.68 0.00 50.00 0.00 0.00 Wetland 11.76 0.00 0.00 0.00 0.00 58.82 23.53 0.00 5.88 0.00 0.00
52 USDA Forest Service RMRS-GTR-344. 2016. Table A.15b—Percent standard error of estimated percent cover of attribute groups within forest type on all lands; Nevada, 2004–2005. Attribute group Forest type Timber Woodland Other tree Dead tree Sage shrub Other shrub Herbaceous Other veg Non- vegetated Water Unknown Beach/shoreline 0.00 0.00 0.00 0.00 0.00 98.05 72.45 0.00 1.96 0.00 0.00 Census water 0.00 0.00 0.00 0.00 0.00 0.00 99.98 0.00 113.06 1.43 0.00 Cropland 94.87 0.00 0.00 0.00 0.00 67.37 65.06 2.34 55.53 0.00 0.00 Cultural 69.25 108.18 20.93 80.10 80.80 40.29 27.35 55.66 8.42 0.00 72.04 Desert complex 0.00 70.32 0.00 0.00 61.38 5.21 11.34 69.92 2.32 0.00 65.61 Grass/forb 99.70 93.28 0.00 50.35 20.37 14.56 4.03 62.59 7.75 0.00 0.00 Idle farmland 0.00 0.00 0.00 0.00 0.00 0.00 0.00 19.81 121.69 0.00 0.00 Military 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mining 0.00 0.00 0.00 0.00 111.37 92.12 68.21 0.00 7.77 98.92 0.00 Noncensus water 0.00 0.00 110.93 0.00 0.00 0.00 50.13 0.00 64.50 23.55 0.00 Other shrub 49.06 27.02 100.00 79.57 12.22 2.52 6.84 31.65 2.25 101.37 100.00 Pasture 0.00 0.00 0.00 0.00 0.00 64.16 61.56 8.68 85.61 0.00 0.00 Rights-of-way 0.00 0.00 0.00 0.00 0.00 66.80 43.92 97.55 6.46 95.81 0.00 Riparian zone 108.53 0.00 109.22 0.00 0.00 18.09 40.90 0.00 62.50 0.00 0.00 Rock outcrops/scree 0.00 62.58 0.00 62.43 53.37 26.82 26.53 0.00 3.51 0.00 91.21 Sagebrush 28.78 19.64 100.00 65.26 2.77 4.44 5.30 24.69 2.29 0.00 70.41 Sand/dunes 0.00 0.00 0.00 0.00 62.41 25.52 48.53 0.00 1.97 0.00 0.00 Unclassified agriculture 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Unclassified developed 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Unclassified other natural 112.65 0.00 0.00 0.00 0.00 47.21 52.59 0.00 18.32 0.00 84.12 Unclassified shrub 110.67 78.14 0.00 0.00 78.15 64.22 65.62 0.00 55.11 0.00 0.00 Wetland 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
USDA Forest Service RMRS-GTR-344. 2016. 53

Table A.16—Estimated percent of stand-size condition classes (percent 

standard error) within condition type groups on all lands; Nevada, 2004–

2005. The stand-size class represents the majority of the stand as: 1) mature 

trees 5 inches diameter (DIA) and greater; 2) young trees less than 5 inches 

DIA (Seedlings/Saplings); 3) or Nonstocked, once forested but currently less 

than 10 percent cover of trees.

Size class 

Forest type class DIA ≥5 inches Seedlings/saplings Nonstocked

Aspen/cottonwood 86.13 (6.38) 13.87 (39.65) 0.00 (0.00)

Jeffrey/ponderosa pine 94.33 (4.03) 5.67 (66.93) 0.00 (0.00)

Limber/bristlecone pine 98.61 (1.41) 1.39 (99.76) 0.00 (0.00)

Mahogany 91.24 (3.86) 6.44 (52.05) 2.31 (68.66)

Pinyon/juniper 86.59 (2.20) 8.40 (19.83) 5.01 (22.71)

Spruce/fir 59.22 (23.33) 40.78 (33.88) 0.00 (0.00)

Western juniper 71.54 (16.46) 26.85 (43.94) 1.61 (70.28)

White fir/Douglas-fir 95.86 (2.37) 0.35 (100.17) 3.80 (59.10)

Table A.17—Estimated percent of crown density condition classes (percent standard error) within condition 

type groups on all lands; Nevada, 2004–2005.

Crown density 

 Between Between Between Between

Forest type class Nonstocked 0 and 25 26 and 50 51 and 75 76 and 100

Aspen/cottonwood 0.00 (0.00) 5.84 (57.39) 13.90 (36.33) 36.38 (22.76) 43.88 (20.53)

Jeffrey/ponderosa pine 1.53 (91.85) 40.66 (25.02) 43.73 (26.14) 10.21 (66.33) 3.86 (96.15)

Limber/bristlecone pine 0.00 (0.00) 61.06 (18.82) 24.11 (36.44) 14.09 (48.28) 0.74 (100.04)

Mahogany 2.64 (61.48) 26.10 (29.49) 32.83 (17.60) 25.62 (22.64) 12.81 (29.05)

Pinyon/juniper 4.43 (24.48) 42.92 (5.11) 40.66 (4.66) 10.95 (7.31) 1.04 (34.24)

Spruce/fir 0.00 (0.00) 25.07 (46.11) 43.35 (31.95) 31.32 (43.79) 0.26 (103.73)

Western juniper 0.24 (102.87) 83.37 (9.22) 15.54 (48.91) 0.86 (102.26) 0.00 (0.00)

White fir/Douglas-fir 2.33 (73.73) 16.94 (23.81) 46.83 (16.26) 31.63 (21.31) 2.26 (59.11)
54 USDA Forest Service RMRS-GTR-344. 2016. Table A.18—Estimated percent of disturbance condition classes (percent standard error) within condition type groups on all lands; Nevada, 2004–2005. Disturbance Earth movement/ Insect/disease/ Forest type class Animal avalanche Fire Human-caused drought None Unknown Aspen/cottonwood 0.00 (0.00) 0.71 (99.97) 1.88 (73.56) 0.00 (0.00) 0.65 (72.32) 96.76 (1.70) 0.00 (0.00) Jeffrey/ponderosa pine 0.00 (0.00) 0.00 (0.00) 8.98 (72.19) 0.00 (0.00) 0.00 (0.00) 91.02 (7.13) 0.00 (0.00) Limber/bristlecone pine 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 99.17 (0.87) 0.83 (103.06) Mahogany 0.00 (0.00) 0.00 (0.00) 3.89 (47.36) 0.75 (99.99) 0.63 (71.24) 94.73 (2.27) 0.00 (0.00) Other nonforest 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 100.00 (0.00) 0.00 (0.00) Pinyon/juniper 0.03 (100.00) 0.00 (0.00) 5.85 (20.63) 0.11 (70.79) 0.52 (32.39) 93.43 (1.30) 0.06 (100.17) Shrub 0.00 (0.00) 0.00 (0.00) 0.16 (91.14) 0.00 (0.00) 0.00 (0.00) 99.84 (0.15) 0.00 (0.00) Spruce/fir 0.00 (0.00) 0.00 (0.00) 4.21 (99.90) 0.00 (0.00) 6.19 (97.90) 89.59 (8.15) 0.00 (0.00) Western juniper 0.00 (0.00) 0.00 (0.00) 1.96 (65.81) 0.00 (0.00) 0.24 (102.87) 97.80 (1.36) 0.00 (0.00) White fir/Douglas-fir 0.00 (0.00) 0.00 (0.00) 6.39 (43.08) 0.00 (0.00) 4.31 (42.57) 89.16 (3.73) 0.14 (101.15)
USDA Forest Service RMRS-GTR-344. 2016. 55

Table A.19—Estimated percent of condition type group (percent standard error) by 

county on all lands; Nevada, 2004–2005.

FORTYP1NM Forest Grass/forb Non-vegetated Shrub

Carson_Douglas_Story 57.77 (29.87) 4.88 (100.00) 18.27 (93.50) 19.08 (60.64)

Churchill 6.41 (25.46) 5.06 (79.55) 22.76 (35.61) 65.77 (12.80)

Clark 13.69 (18.84) 0 2.68 (65.03) 83.63 (3.63)

Elko 13.55 (13.06) 6.46 (26.33) 3.77 (32.26) 76.22 (3.35)

Esmeralda 7.52 (14.80) 0 7.03 (53.80) 85.46 (4.58)

Eureka 19.00 (17.64) 7.66 (49.47) 5.60 (56.93) 67.75 (8.10)

Humboldt 4.20 (37.53) 16.66 (20.54) 8.52 (27.36) 70.62 (5.74)

Lander 14.03 (16.78) 6.77 (48.34) 6.68 (45.81) 72.53 (6.41)

Lincoln 34.44 (6.82) 0.02 (65.45) 2.28 (48.11) 63.26 (3.96)

Lyon 12.90 (17.86) 4.43 (68.68) 13.54 (50.80) 69.13 (12.17)

Mineral 16.55 (15.89) 3.14 (73.70) 8.63 (44.72) 71.68 (7.13)

Nye 20.54 (4.41) 0.56 (77.99) 4.33 (36.07) 74.57 (2.48)

Pershing 8.42 (33.05) 21.92 (20.75) 10.24 (35.11) 59.43 (9.19)

Washoe 15.47 (22.23) 10.26 (30.82) 17.44 (22.77) 56.82 (9.17)

White Pine 45.69 (6.32) 3.46 (51.06) 2.60 (59.34) 48.25 (6.96)

Table A.20—Estimated percent of condition type group (percent standard error) by Cleland ECOMAP 

sections on all lands; Nevada, 2004–2005.

FORTYP1NM Forest Grass/forb Non-vegetated Shrub

East Great Basin and Mountains 37.01 (4.60) 3.73 (30.94) 4.29 (28.86) 54.97 (3.96)

Mojave Desert 10.08 (17.31) 0.20 (100.00) 2.51 (48.16) 87.21 (2.38)

Mono 45.44 (12.39) 3.77 (55.96) 8.26 (55.80) 42.53 (15.40)

Northeastern Great Basin 4.18 (31.24) 8.71 (32.67) 3.05 (45.99) 84.06 (3.86)

Northern Mono 6.23 (22.23) 15.80 (15.13) 14.85 (15.87) 63.12 (4.99)

Northwestern Basin and Range 8.74 (26.91) 10.45 (26.37) 11.35 (24.97) 69.46 (6.08)

Owyhee Uplands/Eastern Basin and Range 14.65 (38.86) 4.27 (74.73) 0.37 (100.00) 80.71 (7.71)

Sierra Nevada 74.49 (8.52) 2.04 (71.08) 10.06 (53.37) 13.41 (31.43)

Southeastern Great Basin 18.40 (7.21) 0.31 (96.52) 4.13 (32.24) 77.15 (2.38)

West Great Basin and Mountains 24.03 (8.21) 4.55 (44.14) 4.77 (39.46) 66.65 (4.78)
56 USDA Forest Service RMRS-GTR-344. 2016. Table A.21—Accuracy assessment error matrix for condition type map with 12 grouped categories. Agriculture/ developed nonforest Aspen/ cotton- wood Grass/ forb Jeffrey/ ponderosa Limber/ bristle- cone Maho- gany Other natural nonforest Other shrub Pinyon/ juniper Sage- brush Spruce/ Fir/ Douglas- fir Western juniper total User

Predicted

Agriculture/developed nonforest 15 0 3 2 0 0 0 0 0 1 0 0 21 71%

Aspen/cottonwood 0 2 0 0 0 2 0 0 0 0 1 0 5 40%

Grass/forb 2 0 23 0 0 0 0 2 1 10 0 0 38 61%

Jeffrey/ponderosa 1 0 0 0 0 0 0 0 0 0 1 0 2 0%

Limber/bristlecone 0 0 0 0 5 1 0 0 0 0 0 0 6 83%

Mahogany 0 2 0 2 4 21 0 1 2 3 6 0 41 51%

Other natural nonforest 1 0 0 0 0 0 23 2 0 1 0 0 27 85%

Other shrub 5 0 13 0 1 1 10 264 10 78 0 0 382 69%

Pinyon/juniper 3 1 9 6 0 23 2 15 1156 47 18 22 1302 89%

Sagebrush 0 3 13 0 0 3 1 89 48 199 3 7 366 54%

Spruce/fir/Douglas-fir 0 1 0 3 3 2 0 1 1 0 18 0 29 62%

Western juniper 0 0 0 0 0 0 0 0 1 3 0 8 12 67%

Total 27 9 61 13 13 53 36 374 1219 342 47 37 2231 PCC

Producer 56% 22% 38% 0% 38% 40% 64% 71% 95% 58% 38% 22% PCC 78%
USDA Forest Service RMRS-GTR-344. 2016. 57

Figure A.1–Predicted map of condition type group: 12 classes.
58 USDA Forest Service RMRS-GTR-344. 2016.

Figure A.2–Predicted maps of 

sampled attributes. a. Aspen; 

b. Limber pine; c. White fir.

a b

c
USDA Forest Service RMRS-GTR-344. 2016. 59

Figure A.3–Predicted maps of 

sampled attributes. a. Juniper; 

b. Mountain mahogany; c. Other 

shrub.

a b

c




United States Department of Agriculture

Forest Service

Rocky Mountain Research Station

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