United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical

Report RMRS-GTR-41

November 1999

Ground Sample Data

for the Conterminous

U.S. Land Cover

Characteristics Database

Robert Burgan

Colin Hardy

Donald Ohlen

Gene Fosnight

Robert Treder
Rocky Mountain Research Station

324 25th Street

Ogden, UT 84401

Contents_______________________

Page

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

Sampling Design and Statistical Summary ......................... 2

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

Results............................................................................. 4

Vegetation Labels ............................................................... 8

How to Obtain the Data Set .............................................. 13

References ........................................................................ 13

The Authors ___________________

Robert Burgan—USDA Forest Service, Rocky Mountain

Research Station, PO Box 8089, Missoula MT 59807

Tel. 406-728-0172; e-mail: firebug@centric.net

Colin Hardy—USDA Forest Service, Rocky Mountain

Research Station, PO Box 8089, Missoula MT 59807

Tel. 406-329-4978; Fax 406-329-4877;

e-mail: chardy/rmrs_missoula@fs.fed.us

Donald Ohlen—Science and Application Branch, USGS

EROS Data Center, Sioux Falls, SD 57198

Tel. 605-594-6026; FAX 605-594-6529;

e-mail: ohlen@edcmail.cr.usgs.gov

Gene Fosnight—United Nations Environmental Program,

Global Resource Information Data Set,

USGS EROS Center, Sioux Falls, SD 57198

Tel. 605-594-6051; FAX 605-594-6529;

e-mail: fosnight@edcsns1.cr.usgs.gov

Robert Treder—Statistical Sciences, Inc. 1700 Westlake

Ave. N., Seattle, WA 98109

Tel. 206-283-8802; FAX 206-283-6310;

e-mail: bob@statsci.com

Abstract ____________________________________________

Burgan, Robert; Hardy, Colin; Ohlen, Donald; Fosnight, Gene; Treder, Robert. 1999. Ground sample data

for the Conterminous U.S. Land Cover Characteristics Database. Gen. Tech. Rep. RMRS-GTR-41.

Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 13 p.

Ground sample data were collected for a land cover database and raster map that portray 159 vegetation

classes at 1 km2 resolution for the conterminous United States. Locations for 3,500 1 km2 ground sample

plots were selected randomly across the United States. The number of plots representing each vegetation

class was weighted by the proportionate coverage of each vegetation class. Field data collected for 2,560

of these plots included dominant vegetation species; coverage, height, and diameter estimates of major

tree species; coverage, depth, and density estimates for shrubs and grasses; and photographs of the

sample plots. These data were compared to vegetation labels assigned in the USGS, EROS Data Center’s

Land Cover Characteristics (LCC) database. The vegetation types were comparable in most cases, but

the ground data provided labels ordered according to decreasing vegetation frequency. A formal accuracy

assessment of the LCC map was not attempted. All data are available on a CD-ROM from the EROS Data

Center.

Keywords: vegetation types, vegetation mapping, satellite data

Acknowledgments ______________

Data collectors came from many land management agencies

including the U.S. Forest Service, Bureau of Land Management, Soil Conservation Service, Texas Parks and Wildlife

Department, U.S. Fish and Wildlife Service, and numerous

State forestry offices. Without their cooperation we could not

have compiled this database.

The use of trade or firm names in this publication is for reader information and does not imply

endorsement by the U.S. Department of Agriculture or any product or service
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-41. 1999 1

Ground Sample Data for the

Conterminous U.S. Land Cover

Characteristics Database

Robert Burgan

Colin Hardy

Donald Ohlen

Gene Fosnight

Robert Treder

Introduction ______________________________________________________

A land cover characteristics (LCC) database and an associated raster map

layer, which may serve a variety of land management functions, have been

produced by the U.S. Geological Survey’s EROS Data Center (EDC) and the

University of Nebraska-Lincoln (Loveland and others 1991). The database

consists of 159 classes that identify seasonally distinct regions of land cover,

attributes that define the characteristics of each class, seasonality, and site

characteristics. The LCC database can be remapped through different

definitions of land cover and environmental parameters for use in simulation

models of processes such as global climate change.

Cover classes were derived using seasonal profiles of Normalized Difference Vegetation Index (NDVI) data from the Advanced Very High Resolution

Radiometer (AVHRR) sensor deployed on polar-orbiting satellites operated

by the U.S. Department of Commerce, National Oceanic and Atmospheric

Administration. The data resolution is nominally 1.1 km. AVHRR coverages

were taken from March through October 1990. Daily acquisitions made

nearly cloud-free data possible.

The LCC classes provide a framework for assessment of spatial vegetation

patterns. Rather than being based on defined mapping units in a land cover

classification scheme, the seasonal land cover classes serve as summary

units for both descriptive and quantitative attributes. Regions are composed

of relatively homogenous land cover associations that exhibit unique phenology and have common levels of primary production. The vegetation labels

assigned to each class are generally to the level of species or genus, or both.

Examples of vegetation classes include class 107 (lodgepole pine, ponderosa

pine, western white pine), class 92 (maple, birch, beech), and class 90 (maple,

birch, white pine, red pine).

The database accompanying the map provides ancillary characteristics

like elevation, climate, ecoregion, land use/land cover, and major land

resource area data for each class. While there are problems with these data

(Zhu and Yang 1996) they remain the best current source for broad-scale

vegetation mapping. The land cover map reasonably portrays vegetation

patterns, but the actual vegetation names associated with each LCC class are

not adequate for derivation of secondary map products.
2 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-41. 1999

The LCC database is a flexible data set of land characteristics. It incorporates the concept of regionalizing several data layers, thus implying a

mixture of land cover types within each class. Land cover regions can be

translated to various classification schemes, thereby meeting the requirements of various applications such as endangered species management,

forest planning, and ecosystem management. However, it must be recognized that at 1 km resolution, each pixel contains a mosaic of different cover

types.

The Forest Inventory and Analysis project of the USDA Forest Service,

Southern Forest Experiment Station (Zhu and Evans 1992), produced a map

similar to the LCC map but it had just 26 classes that address only forested

areas within the conterminous United States while the LCC map has 159

classes covering all vegetation types.

The Fire Behavior Research Work Unit of the USDA Forest Service, Rocky

Mountain Research Station, worked with the EROS Data Center to improve

understanding of, and vegetation labels for, each LCC class. We used the

LCC database and field data to develop a national fuels map for the next

generation fire danger/behavior system (Burgan and others 1998).

We discuss the LCC classes rather than assess their accuracy. An alternative validation method (the multivariate composite estimator) for these data

presented by Zhu and others (1996) discussed the rationale against performing a conventional accuracy assessment. There are ambiguities within and

among the vegetation descriptors for the LCC categories. Indeed, one

objective in development of the LCC database was to produce “flexible results

that are not application specific” (Loveland and Ohlen 1993). These ambiguities confound a primary tenet of conventional accuracy assessments that the

reference data (ground-truth observations) correspond to a predefined classification scheme (the LCC vegetation categories). In their development of

the LCC database, Loveland and others (1991) defined vegetation labels

subsequent to the classification process to suggest probable vegetation types

within each land cover class.

A probability proportional to size sampling design was chosen to represent

the variability in the classes. Cost considerations prevented sufficient

sample size for many classes. The smaller size classes were dropped from the

analysis. Statistical summaries and graphical representations were compiled, and original LCC vegetation category labels were refined to the extent

allowed by the ground-truth data.

The purposes of this paper are to: (1) describe the sampling design and

statistical summarization of the dataset, (2) present an improved list of

major vegetation-type labels for the LCC database, and (3) provide information on how to access the data.

Sampling Design and Statistical Summary_____________________________

Methods

Plot Selection and Identification—For the national field verification

process, 3,500 1 km2 ground-truth plots were selected. The sample size was

determined by estimating what was possible to accomplish in a reasonable

amount of time. These plots were distributed proportionally to the number

of land pixels in each respective LCC class. That is:

Plots per LCC class = (Pixels per LCC class/total land pixels)*3,500 (1)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-41. 1999 3

Ground-truth plots were located by randomly selecting 700 USGS 7.5

minute quadrangle maps, from a total of 45,000 such maps, within the

bounds of the conterminous United States (fig. 1). To achieve the proportionality calculated by equation 1, five 1 km2 plots were randomly located within

each quadrangle map. There were two constraining rules for selection of a

pixel as a plot: (1) the selected plot must consist of a single pixel within a

group of three or more adjacent pixels of the same LCC class; and (2) LCC

classes covering less than 5 percent of the total LCC map area were excluded

from sampling.

A plot location overlay was prepared for each 7.5 minute quadrangle map

by outlining the locations of the five 1 km2 plots on a mylar sheet and then

placing it on the quadrangle map.

Data Collection—Numerous State and Federal agencies provided field

personnel with the majority coming from wildland fire management offices.

Knowledge of local plant species was the primary requirement. USGS 7.5

minute quadrangle maps and mylar overlays, black and white National High

Altitude Aerial Photos (1:80,000) or National Aerial Photos (1:40,000), a

study plan, and a set of annotated training slides were provided to field

personnel. They transferred the boundaries of each 1 km2

 plot from the

USGS quadrangle map to an aerial photo, then delineated the primary

vegetation types within each plot area. Vegetation types that covered less

than 10 percent of the plot were not delineated. The sum of the general plot

vegetation type coverages totaled 100 percent.

 Plot data were recorded on two resolution levels. An overview was based on

field assessment of the general character of the entire plot with respect to

primary vegetation cover and geomorphology. Within the plot overview,

landform was entered as flat plains, highly dissected plains, rolling, or

mountainous. Elevation was recorded for the plot center. Soil color was

recorded as white, grey, tan, red, brown, or black. Land use was entered as

Figure 1—USGS 7.5 minute quads were randomly distributed across the

United States. There are five ground-truth plots per quad. Plus signs

represent quads for which ground-truth data were collected. Dots represent quads for which ground-truth data were not obtained.
4 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-41. 1999

cropland, forestry, military, mining, or ranching. Ocular estimates were

made of the proportion of the 17 dominant cover classes plot within each plot:

• Conifer — <30 percent crown closure

• Conifer — 30 to 60 percent crown closure

• Conifer — >60 percent crown closure

• Mixed — <30 percent crown closure

• Mixed — 30 to 60 percent crown closure

• Mixed — >60 percent crown closure

• Deciduous — <30 percent crown closure

• Deciduous — 30 to 60 percent crown closure

• Deciduous — >60 percent crown closure

• Shrub

• Grass

• Shrub/grass

• Agriculture

• Water

• Nonvegetated

• Forbs

• Mosses/lichens

Field personnel then walked into a representative portion of the vegetation

type that covered the highest percentage of the 1 km2 plot area and recorded

the following information for the second level of resolution:

• Trees: Percentage cover, average height, and average diameter

of up to four major species.

• Shrubs: Morphology, density, percentage cover, and depth of up to

four major species.

• Subshrubs: Percentage cover and depth of two major subshrubs.

• Forbs: Percentage cover and depth.

• Mosses: Percentage cover and depth.

• Grass: Morphology, density, percentage cover, depth, and whether

annual or perennial.

• Agriculture: Percentage cover and whether or not irrigated, for up to

four crop types.

Grass and shrub density estimates were made by comparing actual

vegetation with photo-keys of known grass and shrub types and density

classes (Burgan and Rothermel 1984). With the exception of depth measurements, all data were obtained through ocular estimates.

Up to four photos were taken of a representative site within each plot. A

selected set of these photos was scanned and recorded on a CD-ROM.

The field data from each plot were entered into a relational database, then

transferred to the statistical package Splus as input for displaying graphical

and tabular summaries for each land cover class. Splus was developed by

Statistical Sciences, Inc. The graphical summaries are also on the CD-ROM.

Results

We received and analyzed data for 2,560 of the 3,500 randomly selected

field sample plots. The number of observations completed per LCC category

is illustrated by the frequency distribution in figure 2. No data were recorded

for 33 of the 159 LCC categories—this was the result both of an a priori

exclusion of LCC categories occupying less than 5 percent of the conterminous
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-41. 1999 5

U.S. and the inability to acquire data from some field plots. The maximum

number of plots completed for any LCC category was 90. Only 4 percent of the

159 LCC categories were represented by more than 75 plots, which is the

minimum number recommended for an accuracy assessment (Congalton

1991). Another 4 percent of the LCC categories had 51 to 75 plots; 13 percent

had 26 to 50 plots; 30 percent had 6 to 25 plots; and 27 percent had only 1 to

5 plots per LCC category.

Although a map accuracy assessment was not possible, the plot data were

summarized graphically. Examples of all available graphs is beyond space

limitations, so they are summarized in table 1. Four types of graphs were

utilized: box plots with whiskers, bar charts, line graphs, and polar plots.

Sample graphic outputs for LCC class 122 are presented in figures 3 to 6.

Figure 3 summarizes overall plot data from the plot overview form. These

data relate to the entire 1 km2 ground-truth plot and reflect plot coverage

estimates by cover type as obtained from type mapping on the aerial photos.

The graphical summary includes bar graphs showing the number of plots on

which each of the 17 general cover types dominate (fig. 3a), the number of

plots where the cover types exists (fig. 3b), and the median percent cover

where the cover type exists (fig. 3d). Figure 3c is a box plot showing the

proportion of the plot in each cover type. The box plots with whiskers show

the median value as a dot within the box; the ends of the box designate the

first and third quartiles. The whiskers above and below the box represent 1.5

times the inner quartile range, which given a normal distribution, should

include 95 percent of the data. Outliers are shown as dots beyond the

whiskers.

Figure 4 summarizes data observed within a representative area of the

dominant vegetation type, as recorded for four dominant tree species on the

Number of plots observed

0 10 20 30 40 50 60 70 80 90

Number of LCC categories

35

30

25

20

15

10

5

0

Figure 2—Frequency distribution of the number of land cover characteristics (LCC) class categories (y-axis) that had a given number of

observations (x-axis). Example: 33 LCC classes had no observations, 11 classes had 1 observation, 2 classes had 9 observations,

and so forth.
6 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-41. 1999

Table 1—Statistical summary graphics available for most land cover classes.

Graphic subject Graph type

Number of plots where cover type is dominant Bar chart

Number of plots where cover type exists Bar chart

Proportion of plot covered by cover type Box plo

Median percent cover where cover type exists Bar chart

Number of plots by land use class Bar chart

Number of plots by soil color Bar chart

Number of plots by land form Bar chart

Number of plots by major land resource area Bar chart

Number of plots by ecological region Bar chart

Number of plots by land use/land cover Bar chart

Number of plots by USGS 7.5 minute quadrangle map Bar chart

Plot elevation (ground-truth and LCC determination) Box plot

NDVI quartiles for 8 monthly periods Box plot

1990 NDVI plot profiles for 8 monthly periods Line graph

1990 NDVI median absolute deviations about the median Line graph

Duration of growing season by year Box plot

Time integrated new growth NDVI by year Box plot

Time integrated growing season NDVI by year Box plot

Maximum baseline NDVI by year Box plot

Onset of growing season by year Polar plot

End of growing season by year Polar plot

Period of maximum NDVI by year Polar plot

NDVI value at onset of growing season by year Box plot

NDVI value at end of growing season by year Box plot

Maximum NDVI value by year Box plot

Rate of senescence by year Box plot

Rate of greenup by year Box plot

Percentage distribution of neighboring pixels by LCC class Bar chart

Plot count of neighboring pixels having same LCC class Bar chart

Plot count of major tree species Bar chart

Proportional representation of major tree species Box plot

Mean height of major tree species Box plot

Mean diameter of major tree species Box plot

Plot count of major shrub species Bar chart

Shrub density class by species Box plot

Proportional representation of major shrub species Box plot

Average shrub height by major shrub species Box plot

Plot count by shrub stem and leaf morpholgy class Bar chart

Shrub density class by stem and leaf morphology class Box plot

Proportional representation by stem and leaf morphology class Box plot

Average shrub height by stem and leaf morphology class Box plot

Plot count of subshrub species Bar chart

Proportional representation of subshrubs by species Box plot

Average subshrub height by species Box plot

Plot count of forb coverage by percent ranges Box plot

Plot count of forb depth by height ranges Box plot

Plot count of moss and lichen coverage by percent ranges Box plot

Plot count of moss and lichen depth by height ranges Box plot

Plot count of grass morphology classes Box plot

Plot count of annual and perennial grass types Box plot

Grass density class for continuous and clumped grasses Box plot

Proportion of continuous and clumped grasses Box plot

Average grass depth by continous and clumped grasses Box plot

Plot count by dominant agricultural crop type Bar chart

Proportional representation by dominant agricultural crop type Box plot

Percent irrigated by crop type Bar chart
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-41. 1999 7

Figure 3—Example, graphical summary of plot overview data.

Figure 4—Example, graphical summary of data observed within a

representative area of the dominant vegetation type.
8 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-41. 1999

upper portion of the dominant cover data form. The information is presented

as a graphical summary of the number of plots by tree species (fig. 4a), the

proportional occurrence by species (fig. 4b), and the mean height and

diameter (fig. 4c, 4d).

Figures 5 and 6 summarize NDVI-related data rather than groundsampled information (Reed and others 1994). The box plots of figure 5

summarize statistics on the length of the growing season, in number of

biweekly periods (fig. 5a); the sum of biweekly Normalized Difference

Vegetation Index (NDVI) values during the period of increasing vegetation

greenness, by year (fig. 5b); the sum of biweekly NDVI values during the

entire growing season, by year (fig. 5c); and the maximum NDVI for the land

cover class, by year (fig. 5d).

Figure 6 is a polar plot illustrating when greenup occurred. The point at

which the radius line meets the circle indicates the composite period and the

percentage number indicates how often greenup occurred for the most

frequently occurring composite period. For example, for LCC class 122, in

1991, the onset of greenup for 41 percent of the samples occurred in composite

period 3, which is late January to early February. Other numbered nodes

within the circle show the relative amount of greenup that occurred during

those composite periods, with the center of the circle indicating zero greenup.

For example, since node 2 is about 3⁄4 of the distance from the center of the

circle to the edge, greenup occurred 3⁄4 as often in the mid-January composite

period as in composite period 3, the most frequent greenup composite period.

The sum of the greenup percentages for all the nodes is 100.

Vegetation Labels _________________________________________________

In addition to providing statistical summaries of the ground-sampled data,

a major objective of the study was to improve the vegetation labels attached

to each land cover class in EDC’s original LCC database. This was accomplished by using Splus routines to determine the four most frequent primary

vegetation types within each land cover class. The results, in table 2, provide

a comparison between the LCC map vegetation labels and the groundobserved primary vegetation types. The species listed in the LCC map

Figure 5—Graphical summary, growing season length for an LCC class.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-41. 1999 9

Figure 6—Polar plots in the database indicating the

timing of greenup by year for each land cover class.

Table 2—Comparison between LCC map labels and ground observed primary vegetation types.

LCC map Ground sampled

LCC vegetation description No. plots vegetation type

1 Spring wheat 11 Wheat

2 Soybean, corn, cotton 3 Sorghum

3 Small grains, mixed row crops 19 Wheat

4 Small grains 7 Wheat

5 Small grains, mixed row crops 42 Wheat

6 Mixed row crops, small grains 5 Wheat

7 Mixed crops (wheat, sorghum, corn) 6 Corn

8 Soybeans, cotton, rice, corn 0 No data

9 Winter wheat 29 Wheat

10 Soybeans, cotton, rice, corn 13 Beans

11 Corn, soybeans 77 Corn

12 Small grains, row crops, pasture 41 Hay

13 Small grains, row crops, pasture 18 Corn

14 Wheat, sorghum, corn, alfalfa 23 Corn

15 Soybeans, cotton, corn 3 Rice

16 Corn, soybeans, alfalfa, flax 5 Corn

17 Corn, soybeans 78 Corn

18 Mixed cropland, pasture, woodland 21 Corn

19 Soybeans, corn, cotton 0 No data

20 Corn, soybeans, pasture 27 Rice, soybeans

21 Corn, soybeans, loblolly pine 0 No data

22 Irrigated pasture 0 No data

23 Irrigated pasture 2 Wheat

24 Irrigated pasture 0 No data

25 Irrigated pasture 4 Wheat

26 Irrigated pasture 0 No data

27 Irrigated pasture 8 Wheat

28 Irrigated pasture 0 No data

(con.)
10 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-41. 1999

29 Irrigated pasture 3 Wheat

30 Irrigated pasture 0 No data

31 Irrigated pasture 0 No data

32 Irrigated pasture 0 No data

33 Sorghum, small grains, irr. ag. 0 No data

34 Irr. ag., mixed row crops 15 Wheat

35 Bluestem, grama, small grains 45 Wheat

36 Grama, buffalograss, wheat 52 Wheat

37 Small grains, sorghum, blue grama 12 Wheat

38 Wheatgrass, needleandthread, peas 6 Hay

39 Bluestem, wheatgrass, wheat 25 Wheat

40 Riparian woods, irr. ag., grass 4 Pasture

41 Soybeans, cotton, rice, corn 2 Cotton

42 Corn, soybeans, sorghum, irr. ag. 23 Corn

43 Mixed pine, oak, soybeans, corn 4 Cotton

44 Pasture, small grains, Douglas-fir 0 No data

45 Soybeans, corn, peanuts, cotton 14 Cotton

46 Maple, birch, beech, corn 77 Corn

47 Oak, hickory, pine, corn 39 Corn

48 Citrus, pasture, slash pine 0 No data

49 Loblolly pine, slash pine, corn 44 Corn

50 Pine, oak, soybeans, corn 51 Corn

51 Pasture, hay, corn, soybeans 40 Corn

52 Forage crops, hay, woodlots 85 Corn

53 Oak, pine, soybeans, corn, cotton 10 Cotton

54 Soybeans, cotton, rice, corn, oak 0 No data

55 Wheatgrass, needlegrass 0 No data

56 Wheatgrass, needlegrass 64 Perennial and annual grass

57 Bluestem, blue grama 6 Perennial grass, wheat

58 Blue grama, wheatgrass 14 Perennial and annual grass,

cotton

59 Wheatgrass, blue grama 58 Perennial and annual grass,

wheat

60 Wheatgrass, fescue, big sage 6 Perennial grass

61 Wheatgrass, blue grama, big sage 60 Perennial and annual grass,

wheat

62 Annual grasses, manzanita, oak 12 Annual grass, wheat

63 Wheatgrass, needlegrass 2 Perennial grass

64 Wheatgrass, needlegrass 26 Perennial grass, wheat

65 Bluestem, indiangrass 20 Perennial grass, alfalfa

66 Bur sage, saltbush, greasewood 13 Shadscale, greasewood,

sagebrush

67 Greasewood, sage 32 Shadscale, sage, ragweed,

juniper

68 Creosote, mesquite, saltbush 4 Annual and perennial grass

69 Creosote, saltbush, sand sage 0 No data

70 Dropseed, sand sage, creosote 2 Unidentified shrubs and grass

71 Saltbush, greasewood, big sage 25 Low sagebrush, big sage

72 Greasewood, sage, rabbitbrush 48 Big sage, low sage, juniper

73 Sand sage, creosote, ricegrass 3 Mesquite

74 Big sage, rabbitbrush, wheatgrass 20 Sage, mesquite, juniper,

live oak

75 Greasewood, sage, wheatgrass 43 Sage, rabbitbrush, juniper,

pinyon

76 Sand sage, blue grama, wheatgrass 9 Perennial grass

77 Creosote, sand sage, grama 3 Mesquite, perennial grass

Table 2—Con.

LCC map Ground sampled

LCC vegetation description No. plots vegetation type

(con.)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-41. 1999 11

78 Big sage, rabbitbrush, wheatgrass 15 Big sage, perennial grass

79 Sand sage, creosote, dropseed 6 Cotton, perennial grass

80 Blue grama, big sage, saltbush 43 Sage, yucca, juniper,

pinyon pine

81 Sand sage, oak, blue grama 27 Mesquite, juniper, mimosa,

82 Grama, buffalograss, creosote 3 Agriculture

83 Grama, buffalograss, creosote 0 No data

84 Annual grasses, manzanita, oak 4 Annual and perennial grasses

85 Annual grasses, manzanita, oak 0 No data

86 Bluestem, sand sage, oak, juniper 24 Mesquite, live oak, cenizo

87 Oak, juniper, bluestem, mesquite 0 No data

88 Oak, Bluestem, mesquite, juniper 36 Mesquite, juniper, oak, algerita

89 Oak, bluestem, juniper 23 Foresteria, mesquite, juniper,

oak

90 Maple, birch, white and red pine 4 Maple

91 Oak, maple, ash, pasture 15 Aspen, birch, oak, hazel

92 Maple, birch, beech 3 Maple, birch, basswood

93 Beech, birch, maple, oak, pasture 50 Maple, oak, dogwood

94 Oak, hickory, mixed cropland 26 Oak, pine, hickory

95 Oak, hickory, poplar, beech 89 Hickory, oak

96 Loblolly, longleaf, slash pines 2 Aspen, subalpine fir

97 Aspen, mountain shrubs 0 No data

98 Loblolly, longleaf, slash pines 66 Loblolly, sweetgum, longleaf,

oak

99 Fir, spruce 10 Subalpine fir, Engelmann spruce

100 Douglas-fir, pacific silver fir 0 No data

101 W. white, ponderosa pine, Douglas-fir 0 No data

102 Lodgepole pine, fir, spruce 26 Lodgepole, Douglas-fir,

Engelmann spruce

103 Lodgepole pine, ponderosa pine 0 No data

104 Pondersa, lodgepole pine, Douglas-fir 2 Engelmann spruce

105 Lodgepole, ponderosa pine, Douglas-fir 0 No data

106 Ponderosa, lodgepole, juniper 13 Ponderosa, Douglas-fir, juniper

107 Lodgepole, ponderosa pine 0 No data

108 Ponderosa, pinyon pines, juniper 0 No data

109 Ponderosa, lodgepole pines 12 Douglas-fir, Lodgepole pine,

fir

110 Douglas, pacific silver fir 0 No data

111 Ponderosa, lodgepole, juniper 17 Ponderosa, Douglas-fir,

white oak

112 Douglas-fir, sitka spruce, hemlock 3 Lodgepole, Douglas-fir,

western larch

113 Lodgepole, ponderosa pines 3 Douglas-fir, lodgepole pine

114 Ponderosa, pinyon pines, juniper 0 No data

115 Ponderosa, lodgepole, Douglas-fir, oak 0 No data

116 Ponderosa, lodgepole, Douglas-fir 9 Douglas-fir, noble fir, hemlock

117 Ponderosa, lodgepole, Douglas-fir 0 No data

118 Ponderosa pine, Douglas-fir 0 No data

119 Douglas-fir, ponderosa, sugar pines 7 Ponderosa pine

120 Ponderosa pine, Douglas-fir, oak 4 Douglas-fir, ponderosa pine

121 Ponderosa pine, Douglas-fir, oak 0 No data

122 Douglas-fir, w. hemlock, w. redcedar 22 Douglas-fir, alder, western

redcedar

123 W. Hemlock, w. redcedar, Douglas-fir 0 No data

124 Ponderosa, sugar pines, Douglas-fir 8 Douglas-fir, California loral, oak

125 Douglas-fir, w. hemlock, oak 7 Douglas-fir, black oak

Table 2—Con.

LCC map Ground sampled

LCC vegetation description No. plots vegetation type

(con.)
12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-41. 1999

126 Grasses, ponderosa, lodgepole 0 No data

127 Sage, annual grasses, oak, pine 16 Pinyon pine, juniper, mtn.

mahogany

128 Ponderosa, lodgepole pine, sage 0 No data

129 Ponderosa, pinyon pines, juniper 2 Ponderosa pine

130 Pinyon, ponderosa pines, juniper 14 Douglas-fir, ponderosa pine,

spruce

131 Ponderosa, w. white pine, grasses 12 Douglas-fir, aspen

132 Pinyon, ponderosa pines, juniper 0 No data

133 Maple, beech, birch, Jack pine 0 No data

134 Maple, birch, beech, spruce, fir 11 Red spruce, e. white pine,

aspen

135 Spruce, pine, wetlands, n. hdwds 0 No data

136 Oak, maple, ash, beech, birch 40 Maple spp., aspen, balsam fir

137 Beech, birch, maple, hemlock, fir 46 Maple, hemlock, birch, beech

138 Slash, longleaf pines, wetlands 3 Baldcypress, slash pine

139 Loblolly, slash pines, oak, Gum 90 Loblolly pine, sweetgum, oak

140 Loblolly, longleaf, slash pines 75 Loblolly pine, red oak,

sweetgum

141 Southern pines, oak, gum, poplar 79 Loblolly pine, sweetgum,

mixed hardwood

142 Lodgepole pine, Douglas-fir, aspen 12 Douglas-fir, spruce,

lodgepole pine

143 Lodgepole, w. white, ponderosa 0 No data

144 Lodgepole, w. white, ponderosa 2 Douglas-fir

145 Lodgepole, w. white, ponderosa 2 Lodgepole pine

146 Ldogepole pine, Douglas-fir, aspen 4 Douglas-fir, aspen

147 Ponderosa, lodgepole pine, aspen 0 No data

148 Ponderosa, lodgepole pine, aspen 0 No data

149 Water 0 No data

150 Fresh/saltwater marsh 0 No data

151 Fresh/saltwater marsh 0 No data

152 Fresh/saltwater marsh 0 No data

153 Fresh/saltwater marsh, cypress 3 Pondcypress

154 Fresh/saltwater marsh, oak, gum 0 No data

155 Barren or sparsely vegetated 27 Larrea, ragweed, sage,

coleogyne

156 Lodgepole pine, Douglas-fir, aspen 5 Lodgepole pine, spruce, fir

157 Alpine tundra 0 No data

158 Alpine tundra 0 No data

159 Alpine tundra 0 No data

Table 2—Con.

LCC map Ground sampled

LCC vegetation description No. plots vegetation type

vegetation description column have no implied abundance levels—they

simply are likely to exist to some extent in the land cover class. However, the

species in the ground-sample vegetation description are listed in order of

abundance. In many cases the species correspondence between the two lists

is close, but the ground-truth list is more informative because the species are

listed in decreasing order of abundance. The number of plots in each LCC

class are also provided.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-41. 1999 13

How to Obtain the Data Set _________________________________________

The ground-sample data set is available on a CD-ROM that contains: (1) an

ASCII FoxPro database file of data entered into the field forms, (2) an Splus

routine that will provide, for each land cover class, numerous statistical

summaries and graphs (users of this routine must have Splus installed on a

workstation), and (3) an HTML document that provides easy access to JPG

files of slides scanned for many plots, and GIF files of all the graphical

summary statistics for each LCC class (about 2,500 graphics). The HTML

document makes the graphical summaries available to those not having

access to Splus on a workstation. This CD-ROM is available to climate

modelers, ecologists, the remote sensing community, and others who have an

interest in broad-scale vegetation data. It can be ordered from the Customer

Services Branch of the U.S. Geological Survey’s EROS Data Center, Sioux

Falls, SD 57198. Phone: 605-594-6151; email: custserv@edcmail.cr.usgs.gov

References _______________________________________________________

Burgan, Robert E.; Rothermel Richard C. 1984. BEHAVE: Fire behavior prediction and fuel modeling

system—FUEL subsystem. NFES 0275. National Wildfire Coordinating Group, National Interagency

Fire Center, 3837 S. Development Ave., Boise, ID. 126 p.

Burgan, Robert E.; Klaver, Robert W.; Klaver, Jacqueline M. 1998. Fuel models and fire potential from

satellite and surface observations. International Journal of Wildland Fire. 8(3): 159-170.

Congalton, R. G. 1991. A review of assessing the accuracy of classifications of remotely sensed data.

Remote Sensing of Environment. 37: 35-46.

Loveland, Thomas R.; Merchant, James W.; Ohlen, Donald O.; Brown, Jesslyn F. 1991. Development of

a land cover characteristics database for the conterminous U.S. Photogrammetric Engineering and

Remote Sensing. 57(11): 1453-1463.

Loveland, T. R.; Ohlen, D. O. 1993. Experimental AVHRR land data sets for environmental monitoring

and modeling. In: Goodchild, M. F.; Parks, B. O.; Steyaert, L. T., eds. Environmental modeling with GIS.

New York: Oxford University Press: 379-385.

Reed, Bradley C.; Brown, Jesslyn F.; VanderZee, Darrel; Loveland, Thomas R.; Merchant, James W.;

Ohlen, Donald O. 1994. Measuring phenological variability from satellite imagery. Journal of Vegetation Science. 5: 703-714.

Zhu, Zhiliang; Evans, David L. 1992. Mapping midsouth forest distributions: AVHRR satellite data and

GIS help meet RPA mandate. Journal of Forestry. 90(12): 27-30.

Zhu, Zhiliang; Ohlen, D. O.; Czaplewski, R. L.; Burgan, R. E. 1996. Alternative method to validate the

seasonal land cover regions of the conterminous United States. In: Mowrer, H. Todd; Czaplewski,

Raymond L.; Hamre, R. H., tech. coords. Spatial accuracy assessment in natural resources and

environmental sciences: second international symposium; 1996 May 21-23; Fort Collins, CO. RM-GTR277. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and

Range Experiment Station: 409-418.

Zhu, Zhiliang; Yang, L. 1996. Characteristics of the 1 km AVHRR data set for North America.

International Journal of Remote Sensing. 17(10): 1915-1924.

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