-- 

NASA Reference Publication 1015 

LOAN COPY: RETUf 

AFWL TECHNICAL L, 

KfRTLAND AFB, N 

Procedures for Gathering Ground 

Truth Information for a Supervised 

Approach to a Computer-Implemented 

Land Cover Classification of 

Landsat-Acquired Multispectral 

Scanner Data 

Armond T. Joyce 

JANUARY 1978 

- 1 

NASA 

m 

1015 

c.1 

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NASA 
TECH LIBRARY KAFB, NM 

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OOb32i’3 

NASA Reference Publication 1015 

Procedures for Gathering Ground 

Truth Information for a Supervised 

Approach to a Computer-Implemented 

Land Cover Classification of 

Landsat-Acquired Multispectral 

Scanner Data 

Armond T. Joyce 

Lyndon B. Johnson Space Center 

Houston, Texas 

National Aeronautics 

. and Space Administration 

Scientific and Technical 

Information Office 

1978 

CONTENTS 

Section 

SUMMARY ................................ 

INTRODUCTION ............................. 

BACKGROUND AND BASIC THEORY ...................... 

FACTORS IN SELECTING TRAINING SAMPLE SITES .............. 

Categorization of Land Surface Features ............... 

Size and Shape of Training Sites .................. 

Number and Distribution of Training Sites .............. 

Homogeneity and Uniformity of Training Sites ............ 

OPERATIONS BEFORE IMPLEMENTING FIELD PROCEDURES ............ 

Site Preselection By Use of Aerial Photography ........... 

Site Referencing By Use of Prominent Surface Features ........ 

Concentration of Sites ....................... 

Acquisition of Aerial Photographs .................. 

Use of Prints or Maps ........................ 

Ground Truth Packages ........................ 

IMPLEMENTING FIELD PROCEDURES ..................... 

Organization of Field Personnel ................... 

Distribution of Responsibility ................... 

Timing of Ground Truth Gathering .................. 

Ground Truth Information Forms ................... 

Orientation of Field Personnel ................... 

Fieldwork .............................. 

Time Required and Cost ....................... 

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Section Page 

CONCLUDING REMARKS ........................... 26 

REFERENCES ............................... 28 

APPENDIX A - LAND COVER CONDITIONS IN MISSISSIPPI .' .......... 29 

APPENDIX B - DEFINITION OF MAJOR LAND COVER/VEGETATION TYPES ...... 31 

APPENDIX C - GROUND TRUTH DATA FORMS .................. 32 

APPENDIX D - TYPICAL INSTRUCTION SHEET ................. 41 

APPENDIX E - FOREST UNIFORMITY VERIFICATION PATTERN .......... 42 

APPENDIX F - TYPICAL REVISIT FORM ................... 43 

iv 
FIGURES 

Figure Page 

1 Statistical grouping of data from three training sites to 

develop statistics that encompass more variation in a 

land cover condition . . . . . . . . . . . . . . . . . . . . . 

2 Illustration of training sample site established to represent 

a sparse pine forest. 

(a) Adequate site . . . . . . . . . . . . . . . . . . . . . . . 

(b) Inadequate site . . . . . . . . . . . . . . . . . . . . . . 

3 Illustration of training sample site established to represent a 

dense oak-hickory forest 

(a) Adequate site . . . . . . . . . . . . . . . . . . . . . . . 

(b) Inadequate site . . . . . . . . . . . . . . . . . . . . . . 

4 Delineating training sample site by referencing location to 

prominent land surface features . . . . . . . . . . . . . . . 

9 

11 

11 

1: 

15 
PROCEDURES FOR GATHERING GROUND TRUTH INFORM(\TION FOR 

A SUPERVISED APPROACH TO A COMPUTER-IWLEMENTED LAND COVER 

CLASSIFICATION OF LANDSAT-ACQUIRED MULTISPECTRAL SCANNER DATA 

By Armond T. Joyce* 

Lyndon B. Johnson Space Center 

SUMMARY 

The accurate classification of land cover in the processing of 

satellite-acquired, multispectral scanner data requires the proper selection and assessment of training sample sites by field personnel. This 

report, one of a series by the NASA Earth Resources Laboratory in connection with a natural resource inventory project in Mississippi, describes 

the criteria involved in the selection of training sample sites, the orientation and training of field personnel, and the ground truth data forms 

and procedures for their use. The basic theory involved in the computer 

processing of land cover data is briefly presented. Although ground truth 

data for this project were acquired by local field personnel, suggested 

options include a ground truth field team devoted exclusively to that task. 

Experience from the project indicates that a field agent can assemble ground 

truth data for approximately six trainin sample sites per day, with operational costs estimated at approximately 3 0.06 for each square kilometer 

for the initial classification. 

INTRODUCTION 

This report provides the procedures for gathering ground truth information for a "supervised" approach to a computer-implemented land cover 

classification of Landsat-acquired multispectral scanner (MSS) data. The 

NASA Earth Resources Laboratory (ERL) has drawn heavily on experience 

gained during an applications system verification and transfer (ASVT) 

project in Mississippi, but an attempt is made to address alternatives to 

and deviations in procedures that may be appropriate for other situations. 

*NASA Earth Resources Laboratory, 1010 Gause Blvd., Slidell, La. 70458. 
The first step in planning a ground truth operation is to determine 

and define the major land cover categories that relate to the anticipated 

application. To ensure that the land cover categories are compatible with 

the data acquisition and processing technique, those factors that influence 

reflected energy, as measured by the Landsat MSS, must be addressed. The 

variations anticipated in each category must be listed so that training 

sample sites can be established to represent each source of variation. 

Appendix A shows a typical listing, established for Mississippi. It is 

important that each major land cover type in the listing be specifically 

defined and that the training sample site criteria be established. Appendix B provides definitions and criteria for the category terms used in 

appendix A and throughout this report. 

As an aid to the reader,. where necessary the original units of 

measure have been converted to the equivalent value in the Syst&ne 

International d'llnites (SI). The SI units are written first, and the 

original units are written parenthetically thereafter. 

BACKGROUND AND BASIC THEORY 

Although it is not essential for field personnel gathering ground 

truth information to understand all processes in the use of Landsat digital data for resource inventory, the quality of their work will be 

enhanced by an understanding of the basic principles involved. Therefore, 

some basic theory and principles are briefly addressed in this report; 

references 1 to 6 provide additional details. 

After the acquisition of computer-compatible tapes (CCT's) that 

contain the raw data acquired by the Landsat MSS, the first step in data 

processing during the ASVT project involved the use of a module of six 

computer programs, developed at the ERL, named PATREC (Pattern Recognition 

Analysis). The basic function of the PATREC computer programs is to effect 

a computer-implemented classification of each data cell1 (which represents 

0.45 hectare (1.1 acres) on the Earth's surface) for which data have been 

acquired. The classification process results in the identification of 

each of these areas as a particular land cover category (e.g., pine 

forest, soybean field, sand beach, etc.) that the computer has been 

programed to recognize. 

The computer programs that form the PATREC module relate to the 

"supervised" technique, and the classifier algorithm is based on maximum 

likelihood ratio calculation and Bayesian decision rules. (See refs. 3 

and 4 for additional theory and details.) The supervised technique 

requires that the location of a number of sites on which the land cover is 

known (e.g., a soybean field) be established in the Landsat data. The 

lA data cell is also referred to as a pixel, a data 

resolution cell in other literature, and relates to the 

of-view of the multispectral scanner. 

element, or a 

instantaneous f ield2 
area selected to contain a uniform, homogeneous land cover (e.g.; a soybean 

field that is uniform with respect to planting date, density, vigor, etc.) 

are called "training sample sites" because, in a simplistic sense, they are 

used as references to "train" the computer to "recognize" the same land cover 

elsewhere. It is the office and field activities associated with establishing the true ground cover composition of these training sample sites that 

are encompassed by the ground truth information gathering operation. 

FACTORS IN SELECTING TRAINING SAMPLE SITES 

A ground truth operations plan must address four basic factors in the 

selection of training sample sites: the categorization of land surface 

features, the size and shape of training sites, the number and distribution of sites, and the homogeneity and uniformity of the land cover. 

Categorization of Land Surface Features 

There are three general categories of land surface features that 

significantly affect the reflected energy measured by the MSS. The 

features are those that relate to the vegetation cover, those that apply 

to land surfaces that do not support vegetation of any significance, and 

those that pertain to the topography of the land surface. 

Vegetation cover.- Various elements of the vegetation cover influence 

the radiation measured by the MSS. These include plant species or species 

association, plant age and vigor, plant density, and understory 

vegetation. 

Plant species or species association: There are many characteristics 

unique to each plant species that affect the intensity and wavelength of 

reflected energy. Some of these are the size of the plant cells, the 

thickness of cell walls and intercellular air spaces, the leaf arrangement 

on the stem, the pignents present, the thickness and shape of the leaf. 

Some vegetation types such as agricultural crops, planted grasses, 

some forest plantations, and orchards are likely to consist of a single 

species; however, naturally occurring vegetation is usually a mixture of 

various species. Consequently, natural vegetation cover types are defined 

and named for the predominant species. For example, a forest may be termed 

a pine forest if 75 percent or more of the surface area of the tree crowns 

in the upper canopy were of pine trees. Therefore, a training sample site 

selected to represent a pine forest,'as defined in this example, could 

include sane hardwood trees provided they were uniformly intermingled with 

the pine trees and their crowns did not cover more than 25 percent of the 

total surface area. 

It is also possible to define and name a vegetation cover type consisting of two or three species that grow in association with one another 

but which together are predominant. For example, a forest cover may be 

called oak-hickory if oak and hickory grew together in an intermingled 

3 
manner and together constituted 75 percent or more of the surface area 

covered by tree crowns. Therefore, a training sample site established to 

represent the oak-hickory cover could include other species but should meet 

the criteria and be uniform with respect to how the various species were 

intermingled. Although forest vegetation was referred to in the previous 

examples, the same statements concerning vegetation-type criteria apply 

to marsh (nonforested wetlands) vegetation and brushland (multistemmed, 

woody shrubs) vegetation. 

Plant age and vigor: Although a given plant species is likely to be 

uniquely different in plant characteristics from another plant species, it 

is possible that some of those characteristics can change with plant age 

or plant vigor. For example, a young, vigorously growing plant may have 

less leaf water content or may have cells with thinner cell walls than an 

older, slow-growing plant of the same species. Consequently, the plant's 

age/vigor is the second variable that must be addressed, especially in the 

case of perennial vegetation. Of these two parameters, vigor (the rate at 

which the plant is growing) is the more important and is referred to in 

conjunction with age only because of the general correlation between vigor 

and age. 

In forested areas, there is likely to be a gradient from very young, 

vigorously growing forest stands to mature, stagnant, or even decadent 

forest stands as well as some all-aged stands. It is important that training sample sites be established to encompass all age/vigor variations in 

each vegetation type to be addressed; however, for practical purposes, it 

is recommended that the age/vigor categories be fairly broad. For example, 

such categories may include the following. 

1. Young forest stands that are on good sites with respect to soil 

and rainfall and, therefore, are growing at a fast rate, usually characterized by the profuse flushes of terminal branch growth during the 

spring 

2. Forest stands that have a moderate rate of growth because of site 

conditions and/or age 

3. Forest stands in which growth has slowed down appreciably because 

the trees are mature, near maturity, stagnated, or on poor site conditions 

As an example, in the case of Mississippi pine forests, the first category is likely to include plantations that are generally in the l- to lo-year 

age bracket and are not yet of commercial size; the second category would consist mainly of pulpwood-size trees, generally in the ll- to 30-year age 

bracket, but possibly would include some natural regeneration on poorer sites 

that was not yet of cotnnercial size; and the last category would include all 

other pine forest. In all three cases, the forest stand encompassed by each 

training sample site should be uniform in the criteria established for age 

and vigor and for vegetation cover. 

In the case of annual vegetation, such as an agricultural crop for 

which all fields in a given region are likely to have planted within a 2- 

to 3-week time span, the difference in age is not strongly correlated with 

vigor. Therefore, for agricultural crops, vigor is addressed with respect 

4 
to whether a particular crop is growing at the normal rate as opposed to 

growing under a stress caused by insect/disease infestations, inadequate 

moisture, or a lack of nutrients in the soil. If, at a particular time 

for which a classification is to be made, stress conditions are known to 

exist, training sample sites for a particular crop should be established 

both in fields in which plant growth is normal and in fields that are 

under stress. Because of likely differences in vigor, training sample 

sites for the same crop should be established for both irrigated and 

nonirrigated conditions, if both irrigation and dryland practices are 

intermingled for a given crop. Also, if there are other differences in 

practices, such as a particular grass species or species mix being grazed 

in one case and grown for hay in another case, separate training sample 

sites should be established for each. 

Plant density: A span of several weeks in the planting period for a 

particular agricultural crop could cause variation in plant density, the 

third principal variable in vegetative cover. Plant density is an important factor because the MSS is taking a measurement for a 0.45-hectare 

(1.1 acre) area. Consequently, if, in the case of row crops, both plants 

and exposed soil between the rows are visible from above, the measurement 

involves an integration of the energy reflected from plants and from 

exposed soi 1. If a wide difference in planting dates for a particular 

crop indicates that a significant variation in density could exist, then 

training sample sites should be established for each density category. For 

practical purposes, such density categories should be fairly broad; e.g., 

40 to 60 percent, 60 to 80 percent, and 80 to 100 percent. If the crop 

vegetation covers less than 40 percent of the surface, it is not likely 

that the specific crop could be identified. 

Vegetation density is also a factor in forest and brush vegetation, 

but it is recommended that the variation in density be addressed with two 

categories rather than three, as recommended for agricultural crops. For 

example, training sample sites established to address density variation in 

a pine forest may be categorized as sparse (20 to 65 percent of the surface 

covered by tree crowns) or dense (65 to 100 percent of the surface covered 

by tree crowns). 

Understory vegetation: The previous example leads to the fourth 

factor that must be considered in the major variations in vegetation that 

influence reflected energy, that of the understory vegetation. In the 

example of a sparse pine forest, there may be one condition in which a 

native grass would be visible from above through the gaps between the 

scattered trees; in another condition, the understory may be a brush species or species association rather than grass. In this example, it would 

be desirable to establish a training sample site for each of the two conditions. For Landsat data acquired during the.winter season, when deciduous 

forests are leafless, it is also desirable to establish a training sample 

site for dense, deciduous forest that has an evergreen understory species 

and another site for dense, deciduous forest that has a deciduous understory species. Separate training sample sites would also be appropriate 

for a leafless deciduous forest flooded with water and one without flooding. 

5 
Nonvegetated land cover.- Land surfaces that are essentially devoid 

of vegetation are those on which soil has been temporarily exposed and 

those of inert materials that do not support any, or support very little, 

vegetation. 

Temporarily exposed soil: During the spring, most cultivated areas 

are in sane stage of soil preparation. Consequently, criteria should be 

established for the condition of the exposed soil rather than for the 

anticipated crop. The three main variables to consider are the physical 

state of the surface, soil moisture, and soil type. As a minimum, training sample sites should represent the extremes, should they exist, and the 

various combinations of these three variables. For example, the extremes 

for the state of the surface would be a rough surface that may have 

resulted after plowing and a smooth surface that may have resulted after 

harrowing and/or planting. Soil moisture criteria should reflect the dry 

conditions of sOme fields and the very wet or waterlogged conditions that 

may exist jn other fields. Soil type extremes could range from the lightcolored, sandy soils of scme fields to dark-colored, clay soils of other 

fields. In the case of land used for cultivated crops, there may also be 

ground cover conditions other than green, growing vegetation or exposed 

soil that must be addressed at certain times of the year. One common condition is the stubble resulting from harvesting operations that may be 

widespread in early fall. Even though there should be no significant difference in energy reflected from dead stalks and debris from different 

crops, training samples should be established to represent various possible 

stubble conditions. For example, sites having stubble left after corn has 

been cut for silage (in which there is a low volune of stalk material left 

and considerable bare soil exposed) should be established separately from 

sites having stubble left from the harvesting of small grain. 

Inert materials: The general category of inert materials includes 

beaches, sand bars, mud flats, rock outcropping, extractive areas (e.g., 

gravel pits), asphalt, concrete, etc. Except for the topographical configuration, there is little variation within each of these land cover types; 

however, their basic characteristics may relate to different degrees of 

reflectivity in the four MSS-measured wavelengths. For example, concrete 

is highly reflective, whereas asphalt has low reflectivity. Consequently, 

training sample sites should be established to represent each of these 

inert materials that may be present. In some cases, inert materials may 

not exist in a pure enough form over an area large enough to serve as a 

training sample site, and, therefore, a site may be established to represent a particular complex. For example, a site that contains a heterogeneous mixture (concrete streets, gravel parking lots, metal roofs, etc.) 

would be appropriate in an area where these materials only exist in such 

a mixture. In the urban environment, sOme training sample sites may be 

termed "high density" to reflect a criteria requiring a pure or mixed form 

of inert materials with no vegetation intermingled; whereas, others may be 

termed "low density" to reflect a criteria permitting up to 35 percent of 

the total surface to be covered by isolated patches of vegetation (no 

larger than 31 meters (100 feet) in maximum dimension) with the remainder 

encompassed by pure or mixed forms of inert materials. High density may 

typify large urban commercial centers or industrial sites; low density may 

typify suburban residential areas with scattered trees partially overtopping the streets and houses. 

6 
Topography.- The topography (slope and aspect) is also a factar in 

establishing uniform, homogeneous training sample sites if there is pronounced topographical variation. It is recommended that slope categories 

be established for 0 to 10 percent slopes, 10 to 30 percent slopes, 30 to 

50 percent slopes, and 50 percent or greater slopes. Aspect is usually 

not considered in the training sample site criteria unless slopes are 30 

percent or greater, in which case aspect is categorized according to the 

four cardinal directions. Most steep slope conditions are in forested 

areas and are likely to be automatically categorized as to aspect in the 

course of applying criteria for defining the species or species association 

cover type. For example, in the western United States, a pronounced slope 

with a south aspect may support ponderosa pine, whereas a pronounced slope 

with a north aspect may support larch-Douglas fir. 

Size ahd Shape of Training Sites 

Size of site.- There are two principal factors that relate to the 

size ot the training sample site. One factor concerns the facility with 

which a training sample site can be located in the land surface image 

displayed on a cathode ray tube (CRT). The other factor relates to the 

number of data cells (0.45-hectare (1.1 acre) areas) required to develop 

valid statistics from the MSS measurements. 

Experience at ERL indicates that it is most desirable to establish 

training sample sites that are approximately 16 hectares (40 acres) in 

size. A site of this size can usually be located in the land surface 

image displayed on the .CRT without difficulty and will encompass around 30 

data cells, thereby providing a sample large enough to develop valid 

statistics. It is not recommended that training sample sites for natural 

vegetation be larger than approximately 65 hectares (160 acres) because 

of the difficulty in finding a site of that size that does not violate 

the criteria for uniformity. Conversely, the smaller the site, the higher 

the probability that it cannot be located in the CRT display. Also, the 

smaller the site, the lower the efficiency in developing valid statistics. 

For example, two 8-hectare (20 acre) sites with 15 data cells each could 

eventually be grouped in data processing to equal the 30 data cells encompassed by one 16-hectare (40 acre) site, but this would require twice the 

effort in field work and data analysis. Consequently, it is not recommended that training sample sites smaller than 16 hectares (40 acres) be 

established unless the particular land cover type in question does not 

exist except on areas smaller than that. In any event, it is recommended 

that 4 hectares (10 acres) be the absolute minimum size for reasons of 

efficiency, statistical validity, and probability of locational accuracy 

in the Landsat data. 

In view of this recommended restriction, it can be seen that land 

cover types that only occur on areas smaller than 4 hectares (10 acres) 

should be precluded from the list of land cover types that can be 

addressed with Landsat data. However, once valid statistics have been 

derived for a training sample and the spectral signature has been developed for a particular land cover type within a Landsat scene, the classification will be performed for each individual 0.45-hectare (1.1 acre) 

data cell in the Landsat scene (provided that cloud-free conditions permit 

7 
processing the four corresponding CCT's as a data set). Therefore, even 

though there may be some large areas in the 185- by 185-kilometer (115 by 

115 statute mile) scene within which a particular land cover type only 

occurs in units between 0.45 and 4 hectares (1.1 and 10 acres) in size, 

these units may be classified accurately through use of training sample 

sites of adequate size found elsewhere in the Landsat scene. 

Shape of site.- The shape of training sample sites is not crucial; 

however, in some cases, a square or rectangular training sample site may 

prove easier to locate in the Landsat data display. As will be further 

elaborated later in this report, locating sites in the CRT display of the 

land surface is often facilitated by visually projecting lines from prominent, easily identified surface features to two or more of the sides of 

a square or rectangular site. 

Number and Distribution of Training Sites 

From a theoretical point of view, only one training sample is needed 

to develop statistics and, subsequently, to perform a computer-implemented 

classification of the land cover feature that the particular training sample site was established to represent, provided that the training sample 

statistically represents the land cover type to be classified. However, 

for several reasons, it is recommended that.an attempt be made to establish at least three training sample sites for each land cover feature. 

First, it is possible that a training sample site could be lost 

either because its location cannot be established in the data or, infrequently, because its location coincides with scan line dropout (an electronic or transmission failure during which no measurements are recorded 

for all or part of the data cells on a particular scan line). 

Second, it may be necessary to discard a training sample because the 

statistics, once derived from the Landsat data, indicate that it is not a 

uniform, homogeneous land cover type from a spectral viewpoint. Such 

statistics may have resulted from human error during training sample site 

location either as delineated on field maps or located in the Landsat data, 

or they may be due to the basic nature of the particular land cover type. 

In any event, unless the problem can be corrected, the training sample 

should be discarded. Consequently, if only one training sample site had 

been established, data processing would have been interrupted by the need 

to redefine the boundary or by another field trip to establish a new site. 

Third, the analysis of the statistics is easier if the statistics from 

three or more sites established to represent the same land cover condition 

can be compared. For example, if the mean and the standard deviation calculated for one training sample are significantly different from those of the 

others, the analyst may discard that sample because those remaining better 

represent the particular land cover condition or he may carry that sample 

as a separate spectral subclass. 

only one training sample, he would 

However, if the analyst were dealing with 

have no basis of comparison and would 

have to accept the one sample as being representative; or, if he were dealing with two for which the statistics were substantially different, he 

would not know which of the two was more representative. 

8 
Finally, even though the statistics from three or more training samples established to.represent the,same land cover condition should not be 

substantially different, the means and standard deviations are usually not 

exactly the same. Consequently, the analyst may wish to group the three 

or more samples to create new statistics to develop a spectral signature 

that encompasses more variation in the particular land cover type. This 

approach is illustrated in figure 1, in which the three dashed-line 

ellipses represent statistically defined areas that encompass the measurements from all the data cells in each sample as they cluster around the 

means in the center of each ellipse. The solid-line ellipse constructed 

around the three dashed-line ellipses represents a hypothetical situation 

resul ing from a grouping of the statistics of the three individual samples. 5 Consequently, if the measurements for an unknown data cell fixed 

its location in the shaded area of figure 1 during classification, that 

data cell would be classified as pertaining to the particular land cover 

type; whereas, it would have been left uncategorized had each of the three 

training samples been carried as separate classes. In this hypothetical 

case, it can be seen that three grouped training samples would have 

resulted in a more accurate classification than one or two samples either 

held separately or grouped, if the 

grouped statistics more correctly estimated the true statistical population for the ground cover condition. 

A ground truth operation is usually oriented to a particular Landsat 

scene, which measures 185 by 185 kilometers (115 by 115 statute miles), 

an area covering approximately 3.4 

million hectares (8.5 million acres). 

The number of training sample sites 

needed within a particular scene varies with the number of land cover 

types to be classified within the 

scene and with the variations within each land cover type. As indicated by the listing in appendix A, 

there may be up to 11 major land 

cover categories in a State as large 

and varied as Mississippi, and it may 

be necessary to establish 80 or more 

training samples in order to address 

all variable conditions within these 

categories for all seasons of the 

year. However, ERL experience indicates that, within the area 

Ellipses encompassing data 

from three training samples 

LEllipse 

\ 

encompassing data after 

grouping of three training samples 

Additional statistically defined area 

encompassed by ellipse relating to 

grouped data 

Figure l.- Statistical grouping of 

data from three training sites to 

develop statistics that encompass 

more variation in a land cover 

condition. 

2The particular drawing of the solid-line ellipse in figure 1 was 

designed to illustrate the concept of grouping data and should not be 

taken to imply that the ellipse resulting from the grouping is always 

tangent to the individual sample boundaries. In fact, its position will 

depend on the confidence interval defined. 

9 
encompassed by a particular Landsat scene during the season for which ground 

truth is being gathered, there are likely to be 8 to 10 major land cover 

types for which around 30 to 50 training sample sites must be established 

to address the various land cover conditions. Consequently, since three 

or more training sample sites should be established for each land cover 

condition, 90 to 150 training sample sites may be established in the 3.4- 

million-hectare (8.5 million acre) area encompassed by each Landsat scene. 

Using the upper extreme of this example and assuming that each training 

sample site is 16 hectares (40 acres) in size, it is evident that the 

area required for the 150 training sample sites amounts to less than onethousandth of the 3.4 million hectares (8.5 million acres). 

One set of training sample sites would normally be established for 

the area encompassed by each Landsat scene because the.four CCT's relating 

to a scene are usually processed as a set. However, through an approach 

referred to,as signature extension, it may be possible to process more 

than the four CCT's from a Landsat scene as a set, thereby reducing the 

number oft training sample sites per scene. This possibility could arise 

if two or three cloud-free scenes (8 to 12 CCT's) of data have been acquired 

on a particular pass under fairly uniform atmospheric conditions. This 

situation is most often encountered when the passage of a strong cold weather 

front precedes a Landsat pass by 1 or 2 days. However, it is recommended 

that ground truth gathering activities be planned for the area encompassed 

by each scene, and that the concept of signature extension be considered 

only in respect to data processing efficiency after Landsat-acquired data 

have been assessed for quality. 

Homogeneity and Uniformity of Training Sites 

The fundamental requirement of the computer programs used to perform 

a land cover classification is that the statistics derived from the MSS 

data conform to a normal distribution. These statistical parameters are 

used to establish an elliptically shaped decision boundary, which, in 

turn, is based on a normal distribution. If statistics that do not 

reflect a normal distribution are used to define the decision boundary, 

the classification will be degraded. Therefore, it is necessary for a 

training sample site to reflect a uniform and homogeneous vegetation/land 

cover condition. The uniformity/homogeneity specification is made for 

those vegetation/land cover variables that influence the reflected and/or 

radiant energy being measured by the MSS. 

In figures 2 and 3, the large squares with solid lines represent 

training sample sites of approximately 16 hectares (40 acres). Within 

each large square, the dashed lines form small rectangular areas that 

represent the 0.45-hectare (1.1 acre) cells for which the MSS takes a 

measurement. The circles represent areas of tree crowns as viewed by the 

MSS. 

Fi ure 2 shows two training sample sites established to represent a 

sparse 9 20 to 65 percent crown coverage) pine forest (90 percent or more 

pine) with a native grass ground cover apparent in the gaps between the 

trees. The area shown in figure 2(b) is inadequate as a training site for 

a uniform, homogeneous, sparse pine forest for several reasons. In cell 3C, 

10 
1 2 3 4 5 6 1 2 3 4 5 6 

P - Pine H - Hardwood P - Pine H - Hardwood 

(a) Adequate site. (b) Inadequate site. 

Figure 2.- Illustration of training sample site established to represent a 

sparse pine forest. 

0 - Oak H - Hickory P - Pine 0 - Oak H - Hickory P - Pine 

(a) Adequate site. (b) Inadequate site. 

Figure 3.- Illustration of training sample site established to represent a 

dense oak-hickory forest. 

11 
a concentration of hardwood trees encompasses the entire cell; in cell 4D, 

the gap between trees is so large that the cell would reflect only the 

grass between the trees; and in cell 5E, the density of the pine trees is 

such that it exceeds the criteria for a sparse condition. Conversely, the 

site shown in figure 2(a) is adequate in reflecting a uniform, homogeneous 

condition. Pine trees are scattered throughout the site.in such a manner 

that a sufficient number fall in each cell without exceeding the criteria 

for a sparse forest. Even though a few hardwood trees are present, they 

do not occur in concentrations, they do not exceed 10 percent of the area 

covered by tree crowns, and there are no large gaps in the canopy. 

Figure 3 shows two training sample sites established to represent a 

dense oak-hickory forest (90 percent or more oak-hickory). The site in 

figure 3(b) is inadequate as a uniform, homogeneous site for two reasons. 

First, there is a concentration of pure oak in cells 2B and 3B; and, second, there is a concentration of pine in cell 4D. The site in figure 3(a) 

is an adequate representation of a uniform, homogeneous condition in that 

both oak and hickory trees occur in each cell in roughly the same proportion; and, even though a few pine trees are present, they are not concentrated and do not exceed 10 percent of the total area covered by tree crowns. 

With these examples, it is apparent that the main requirement for uniformity 

is that there be no condition within an area as large as 0.45 hectare (1.1 

acres) that differs substantially from the criteria that defines the land 

cover condition to be represented by the training sample site. Although 

figures 2 and 3 used forest vegetation as an example, the same criteria 

should be applied to other types of vegetation. For example, in a marsh 

(nonforested wetlands) or a pasture grass characterized as a species association with two or more intermingled species, none of the species should 

occur singly over an area as large as a cell. It is also recommended that 

a species occupying less than 25 percent of the area not be included in 

the name of multispecies associations; therefore, the species would not 

be considered in applying uniformity criteria. 

Agricultural crops are usually single species, but conditions of density and/or vigor may have a bearing on uniformity and homogeneity. For 

example, areas 0.4 hectare (1 acre) or larger in size having bare soil 

resulting from germination failure or having differences in vigor as a 

result of uneven fertilization or poor nutrient availability should not be 

permitted in a training sample site established to represent an otherwise 

healthy crop. 

Topographic features should also be uniform in respect to broad 

categories, as suggested previously in this report. For example, in 

mountainous terrain, a training sample site should not be established so 

that part is on a north aspect slope and part is on a south aspect slope 

if such slopes are greater than 30 percent. Also, slopes should not 

exceed the limits that have been defined for a slope category (e.g., 30 to 

50 percent). 

12 
OPERATIONS BEFORE IMPLEMENTING FIELD PROCEDURES 

A number'of oper-ations can be initiated prior to the actual field 

activities that will lend efficiency to the process of gathering ground 

truth data. These include the preselection of training sites by use of 

aerial photographs, notation of prominent surface features for location of 

sites, concentration of sites wherever possible, acquisition of aerial 

photography, the use of prints or maps, and the assembling of ground truth 

packages. 

Site Preselection By Use of Aerial Photography 

After the major land cover categories have been determined and the 

variations in conditions have been listed, the next step consists of using 

available aerial photography to preselect training sites according to 

predefined criteria for each land cover condition. Although preselecting 

training sample sites through aerial photointerpretation is not essential, 

it can be used to gain efficiency in ground truth activities. Any type of 

available aerial photography is adequate if it is not too old (within the 

last 5 years, under most conditions of land use change). However, ERL 

experience shows that preselection is most efficient with color infrared 

positive transparencies in roll form at scales of 1:60 000 to 1:120 000 

when interpreted under magnification. This efficiency is gained because 

large areas can be viewed on a single frame, the resolution at these 

scales is compatible with locating the training sample in Landsat data, 

and the logistical planning of field work is often facilitated when using 

one print that shows the road network for a large area. However, scales 

in the range of 1:15 000 to 1:30 000 can also be used with considerable 

efficiency and in sane cases can have certain other advantages in respect 

to the kind of detail that can be photointerpreted. 

In general, during a training sample site preselection process, the 

photointerpreter should not strive to photointerpret all details on which 

ground truth information is desired. For example, he may find and delineate potential training sample sites that meet the criteria for pine 

forest, hardwood forest, marsh, and brushland, but stop short of identifying the particular species or species association. He may also delineate 

potential training sample sites for cultivated areas or grassland that 

appear to be uniform, leaving the ultimate categorization to the field team. 

In essence, the photointerpreter strives to add efficiency by preselecting 

training sample sites that meet the general criteria so that field personnel 

(may also include the photointerpreter) can go directly to these preselected 

sites as opposed to canvassing the entire area in search of adequate training sample sites. Also, even if field personnel reject sane of the preselected sites and establish substitute sites while in the field, the overall 

operation is usually less time-consuming than it would have been had sites 

not been preselected. However, depending on the type, scale, and season 

of acquisition of available aerial photography, the photointerpreter may 

deal with certain variables of land cover more precisely than they can be 

dealt with on the ground. For example, color infrared positive transparencies acquired during the winter season at scales of 1:30 000 or larger 

can be used to determine density (crown closure) categories in pine forest 

13 
and/or the degree of the overstory mix between pines and leafless hardwoods 

as precisely and with much less effort than can be determined on the ground. 

Broad slope and aspect categories can also be efficiently determined through 

stereovision interpretation of forward overlapping photography. If the 

photointerpreter is not familiar with general land cover/vegetation types 

within the area of concern, a review of publications that give statistical 

information by county (such as those published by the U.S. Soil Conservation 

Service, the U.S. Forest Service, and the U.S. Crop Reporting Service) 

can be helpful. 

As potential training sample sites are located through photointerpretation, the boundaries of these preselected sites are usually delineated 

on transparent material overlaid on the original film for office records 

and on prints made for use in the field. If the original photography was 

color infrared or color, black-and-white prints are usually made for field 

use; and, if the original scale is smaller than 1:63 360 (1 centimeter = 

0.63 kilometer (1 inch = 1 statute mile)), a print enlarged to approximately that scale can be made to facilitate use in the field. As potential 

training sample sites are preselected and delineated, the photointerpreter 

writes a unique four- to six-digit letter/number identifier on the.print 

and overlay adjacent to the delineation of the site. This unique letter/ 

number identifier, to be explained later in this report, is used both for 

a cross reference to ground truth forms and for identification of the site 

during computer processing. 

Site Referencing By Use of Prominent Surface Features 

Another means of increasing overall efficiency by preselecting training sample sites through aerial photointerpretation is to delineate potential sites in such a manner that their locations are referenced to prominent surface features easily found in the field and detectable in the CRT 

display. This concept is illustrated in figure 4, in which the potential 

training sample site was so delineated that one side can be located by 

visually projecting from a road junction and another side can be located 

by visually projecting from a bend in a river. If such linear features 

are 15 meters (50 feet) or wider, they can almost always be used, subsequently, to locate the training sample site more easily in the Landsat 

image display. An even more effective use of this concept is to delineate 

potential training sample sites so that one or more sides are adjacent and 

parallel to straight-line interfaces between two different land cover types 

(e.g., forest and cropland), or, in the case of cropland and grassland, to 

delineate training sample sites in fields with one or more sides parallel 

and immediately adjacent to prominent roads. It is also often possible to 

project lines from the centers of two or more prominent nonlinear features 

such as small water bodies or built-up areas as references to the sides of 

potential training sample sites. Experience at ERL has shown that, if 

sufficient attention is given to this concept during preselection of sites 

and/or in field establishnent of sites, very few if any training sample 

sites are "lost" because their locations cannot be ascertained in the image 

display of Landsat data. 

14 
--I I I 

/ 

Junction of roads 

Figure 4.- Delineating training sample site by referencing location to 

prominent land surface features. 

During preselection of potential training samples, the photointerpreter should also observe the road network and, whenever possible, locate 

potential training sample sites so as to facilitate access and take best 

advantage of road networks. Site selection of this sort will lessen field 

work by reducing time spent in walking and in backtracking vehicle routes. 

Concentration of Sites 

A final means of gaining efficiency consists of establishing potential training sample sites in concentrated groups distributed throughout 

the area encompassed by a particular Landsat scene of interest. This can 

15 
be accomplished by having the photointerpreter begin by selecting 8 to 

12 aerial photographs, depending on scale, from all the photography 

available for the area encompassed by a particular Landsat scene. This 

selection can be made so that each photograph encompasses a variety of 

land cover types or in such a manner that each photograph focuses on a 

particular land cover type, depending on whether field teams are organized 

along multidisciplinary or disciplinary lines. In either case, it is desirable that each photograph covering an area of concentrated training 

sample sites fall completely within the area covered by one of the four 

CCT's in order to preclude the establishment of training sample sites at 

the abutment of tapes. Also, if field personnel are organized relative to 

political or management units (e.g., a county forester), it is also desirable that all or most of the photographs fall within that particular unit. 

If the photointerpreter does not encounter a sufficient number of 

potential training sample sites that meet the predetermined criteria for 

each land cover condition with the original selection of photographs, he 

can select additional photographs in areas that optimize the chances of 

finding sites relating to those land cover conditions lacking after the 

first iteration. The net effect of delineating potential training sample 

sites in concentrated groups distributed throughout the Landsat scene is 

the reduction of travel time between sites during field operations and, 

subsequently, the reduction of time required to locate training sample 

sites in the image display of Landsat data. 

Acquisition of Aerial Photographs 

At the present time, aerial photography that has been taken within 

the last 5 years and that is of a type and scale suitable for training 

sample site preselection is available for most of the United States. If 

not already in the possession of agencies planning Landsat ground truth 

information gathering operations, the existence and coverage of aerial 

photography acquired through various Federal programs can be ascertained 

through the Earth Resources Observation Systems (EROS) Data Center at Sioux 

Falls, S. Dak., operated by the U.S. Geological Survey. Landsat coverage 

for a particular area defined by latitude and longitude can be verified 

through and purchased from the EROS Data Center. If recent aerial photography is not available for a particular area of interest, it may be costeffective to acquire a limited amount of new aerial photography, especially 

in forest or marsh areas with poor accessibility. If the acquisition of 

new aerial photography is carefully planned, it should be possible to acquire sufficient aerial photography to gain cost-efficiency in ground truth 

information gathering operations by covering no more than 2 percent of the 

area with aerial photography (e.g., 36 frames or 12 sets of 23-centimeter 

(9 inch) format stereo triplets at 1:24 000 scale per Landsat scene). If 

the aerial photographs are to be used for ground truth information gathering 

activities for specialized (e.g., pine as compared to hardwood stratifications for forest inventory) rather than composite classifications, limited 

color infrared photography during the winter season at a time close to a 

cloud-free Landsat pass could completely eliminate the need and cost of 

field activities. 

16 
Use of Prints or Maps 

Even if aerial photography is not used for preselection of potential 

training sample sites, it is desirable to provide aerial photoprints to 

field personnel to be used as a map base on which to delineate the fieldestablished training sample sites. In case of the ASVT project to which 

this report relates, preselection of potential training sample sites 

through photointerpretation was accomplished within seven counties for 

which specific applications were demonstrated, but ground truth information gathering operations were conducted without preselection in the 

remaining 75 counties in the State of Mississippi. However, field personnel were provided with either aerial photoprints or photobase maps with 

broad land-cover-type delineations. The original photography was 

1:120 OOO-scale color infrared, but the prints were reproduced in black 

and white to reduce cost and then were enlarged to 1:60 000 to facilitate 

use in the field. A print of one photograph (16- by 16-kilometer (10 by 

10 mile) effective area) was provided for each of 65 counties, and a 

township-size (9.7- by 9.7-kilometer (6 by 6 mile)) photobase map with 

land cover types delineated was provided for 10 counties. The net effect 

was to concentrate established training sample sites in either 16- by 16- 

kilometer (10 by 10 mile) or 9.7- by 9.7-kilometer (6 by 6 mile) areas 

within each county. 

Inasmuch as the training sample sites were established by field personnel assigned to each respective county (e.g., County Extension agents 

and county foresters) or a management unit within a county (e.g., State 

park or game management unit), all field personnel were very familiar with 

their respective area of responsibility. In some cases, the field personnel 

were SO familiar with their areas that, after orientation b use of the 

photoprint, they could delineate satisfactory training samp Y e sites, each 

representing some specific vegetation/land cover condition in their area, 

and fill out corresponding ground truth forms without leaving their offices. 

It is mainly in this situation, in which field personnel are very familiar 

with a localized area, that training sample sites can be established efficiently without preselection through photointerpretation. 

Ground Truth Packages 

Prior to field implementation of a ground truth information gathering 

operation, ground truth packages should be assembled for field personnel. 

In the case of this ASVT project, the packages prepared for disciplinary 

personnel located in each county consisted of the following. 

1. An aerial photograph or a photograph-based land cover map 

2. A county map that shows the outline of the area encompassed by 

the aerial photograph 

3. Applicable blank ground truth forms (appendix C) 

4. An instruction sheet 

17 
5. A sheet defining letter symbols for each vegetation/land cover 

condition that could be characterized by a four-digit ccinbination of 

letters 

To show an example of letter sgbols, a pine forest that was old (more 

than 50 years) and sparse was characterized as PFOS. Field personnel were 

asked to write the appropriate letter symbols adjacent to each training 

sample site delineated on the photoprint. Instructions also called for a 

unique two-digit nunber to be added to the four letter characters as each 

training sample site was established and delineated on the photoprint. For’ 

example, 09 added to PFOS would mean that PFOSO9 was the ninth training 

sample established. This six-digit identifier was also used on the ground 

truth form (which contained additional information) corresponding to a 

particular training sample site as a cross reference. In the case of the 

seven counties for which preselection of sites through photointerpretation 

was conducted, the photographs taken to the field had from one to four 

letter spbols, depending on the degree to which photointerpretation of 

land cover conditions was possible, together with a unique two-digit nLsnber 

recorded adjacent to the delineation of the potential training sample site. 

When preselection of sites is made through photointerpretation and 

when the field team is not very familiar with the local area, it is 

recommended that the appropriate location of each preselected site be 

plotted with an "Xl' on a small-scale map such as a 1:250.000 topographic 

quad map. This map can then be used to assign field teams to specific 

areas outlined on the map with regard to site locations, the road network, 

and lodging facilities. 

IMPLEMENTING FIELD PROCEDURES 

Organization of Field Personnel 

Although there are various ways to organize a field team for gathering ground truth information, this section will focus only on what are 

considered to be two basic options. The first option is to organize the 

effort around field personnel employed by those agencies that are the 

anticipated users of the land cover classification. This option involves 

an organized effort in which each individual is responsible for establishing training sample sites within his local area for his area of specialty. 

For example, a county forester would only establish training sample sites 

to represent the various forest vegetation conditions within the county 

to which he is assigned. With this form of organization, each individual 

involved would use only a fraction of his time for the establishment and 

visitation of training sample sites and most work could be conducted in 

the course of carrying out routine activities as opposed to initiating 

a separate effort. 

The other option consists of employing a team representing several 

disciplines (e.g., a forester, an agronomist, and a botanist) whose 

primary responsibility would be to gather ground truth information. The 

possibility of this option is envisioned in a situation in which it would 

be feasible to make such a team part of the staff of a center that 

18 
Processes remotely sensed data. As will be explained later in this re art' 

it is believed that three disciplinary personnel could furnish ground P ruth 

information, for a State as large as Mississippi, using'80 percent of their 

time, leaving 20 percent for performing certain steps during data processing and for interfacing with disciplinary personnel from user agencies. 

There are advantages and disadvantages associated with each option. 

For the first option, one advantage involves the use of local field personnel's detailed knowledge of local vegetation/land cover conditions and 

road systems. In addition, it is believed that local field personnel, who 

eventually become users of the classification products, could make better 

use of the products by having been personally involved in their production. 

Finally, the use of local field personnel substantially reduces the funds 

'needed for travel and per diem expenses. The principal disadvantage in 

using a large nunber of local field personnel is that it involves substantial coordination. It is believed that the magnitude of the coordination 

effort for a State as large as Mississippi and with a comparable number 

of operating agencies would be such that a designated coordinator would 

expend at least 25 percent of his time in coordinating activities related 

to ground truth gathering. In addition, the effort required in time and 

cost to orient a large number of local field personnel in ground truthing 

procedures is substantial. 

Conversely, the second basic option discussed has the principal 

advantages that the little coordination required could be accomplished by 

the team itself, and the team could be formed by personnel already trained 

in the use of remotely sensed data. In addition, the team could give continuity to the total operation and could perform analysis during data processing more effectively than local field personnel. The main disadvantage 

of a small, centralized team is that the cost per training sample site 

established may be higher because of additional travel/per diem costs and 

because of some lost field time caused by the lack of familiarity with 

local road systems and conditions. 

Distribution of Responsibility 

When ground truth data are to be gathered by a large number of local 

field personnel, it is most important to have a well-conceived plan to 

distribute responsibility. However, because such a plan must consider the 

exact organization of field personnel to be involved, it is impossible to 

provide much more than general guidelines. 

Ideally, training sample sites that are established to relate to a 

particular Landsat scene should be distributed so that some are located 

on each of the four CCT's. Because there is.sane shifting in the Landsat 

coverage from pass to pass and because areas covered by individual CCTls 

may not encompass all of a land unit (e.g., a county) to which field personnel relate, it is impractical to assign responsibility for areas corresponding to each CCT. If field personnel are assigned to counties or management units within a county, such as a game management unit or a State 

park, a practical and simple manner of assigning responsibility is to request that each field person establish a given number of training sample 

sites in each land cover condition within his respective county. In the 

19 
ASVT project, county field personnel were requested to establish one 

training sample site for each vegetation/land cover condition occurring 

within the area encompassed by an aerial photograph or map selected for 

each county. Since field personnel were organized along disciplinary 

lines, the result was that county foresters established one training 

sample site for each forest vegetation condition, County Extension agents 

established one for each cropland and pasture condition, etc. 

The effect of supplying field personnel with one aerial photograph or 

township map within the respective county was to have some concentration 

of ground truth within each county, thereby saving time during the location of sites in the Landsat data at the same time that a distribution of 

ground truth throughout the Landsat scene was attained. The size of 

counties in Mississippi is such that, on the average, there are 12 counties 

within each Landsat scene; therefore, with a rule of one training sample 

site per land cover condition per county, 12 training sample sites for 

each land cover condition are theoretically possible. However, because 

all land cover conditions did not occur within the area covered by the 

aerial photograph or township map selected for each county, the actual 

outcome varied from three to eight land cover conditions per Landsat scene. 

In situations where the average size of counties is substantially 

larger or smaller than the average Mississippi county, the guideline used 

for the ASVT project could be adjusted accordingly. For example, in the 

case of smaller counties, if there were from 20 to 24 counties per Landsat 

scene, one may select one-half the counties for ground truth activities 

and still use the simple instruction of one training sample site for each 

land cover condition. The set of four CCT's for a given Landsat scene is 

usually processed as one data set; however, because of cloud problems, it 

may be necessary to use one CCT from one scene acquired on a given date 

and three CCT's from another scene acquired on a different date. Consequently, it is desirable to select counties for ground truthing so that 

established training sample sites are likely to occur in the areas encompassed by each CCT in the nominal Landsat scene. In addition, if there 

are different physiognomic areas within a particular Landsat scene, counties 

should be selected to be somewhat proportional to the area encompassed 

by each physiognomic unit. For example, in Mississippi, a Landsat scene 

may encompass both an alluvial plains agricultural area and an uplands 

area with mixed land use; in this case, counties would be selected to 

represent each of these two physiognomic units roughly in proportion to 

the extent of each. 

Timing of Ground Truth Gathering 

Although the gathering of ground truth can take place during any time 

of the year, it is desirable to restrict field activities to be within a 

prime time defined for each season, thereby avoiding transitions in 

respect to seasonal change and/or agricultural land use. For example, in 

the transition between winter and spring, forest vegetation may reflect a 

condition in which deciduous trees are neither leafless nor fully foliated; and agricultural fields may be in a state in which some fields show 

stubble from the previous crop, some are being plowed, and sOme are being 

planted. There are two reasons for avoiding transition periods: there 

20 
is a greater nunber'of land cover conditions with which to deal, and the 

ground truth information is valid only for a short period. For example, 

a field with stubble in early spring may be plowed within days after 

ground truth has been gathered. 

Although the commencement or termination of the phenomena associated 

with a given season may fluctuate somewhat from year to year because of 

weather patterns, the prime time for ground truth gathering for each 

season is generally considered to be a 4- to 6-week period that best 

typifies the following conditions in the season. 

Winter - Deciduous vegetation is leafless; green, growing winter 

crops or pastures cover at least 40 percent of the soil underneath; or, 

if in northern latitudes, snow covers the surface of cropland and pasture 

areas. 

Spring - New leaves on deciduous vegetation have fully foliated, but 

still have the color and texture of new leaves; land that will support 

crops during the summer is essentially exposed soil (plowed, disked, 

harrowed, planted, or germinated) but the vegetative portion of emergent 

plants covers less than 5 percent of the surface. 

Summer - All vegetation is in a green, growing state with crops 

and/or pastures planted during the previous spring covering at least 40 

percent of the soil underneath; in coastal marshlands, summer vegetation 

is predominant in those areas where there is a change in species predominance between spring and summer. 

Fall - Deciduous vegetation is in a state of growth decline with 

most forest and brushland foliage in sane stage of color change but not 

yet leafless; summer crops have been harvested, and most cropland reflects 

a stubble or plowed condition; pasture, range, and wetland grasses are in 

a yellowing or browning condition. 

Since the accuracy with which various land cover types can be classified varies between seasons, ground truth activities for specialized 

classifications should be performed during the prime time for the selected 

season. For example, ground truth for a classification of coastal marsh 

vegetation should be gathered during prime time for the summer; the best 

time to separate evergreen forest from other vegetation is during winter; 

the best time for an agricultural crop classification is during summer; 

etc. If a good canposite classification were desired with one set of data, 

a ground truth gathering operation during the spring season would be most 

appropriate. However, the best possible vegetation/land cover data base 

could be built up with a classification during each season, followed by 

subsequent updating as needed. 

Another consideration as to the timing of ground truth information 

gathering concerns the means by which field activity is initiated. There 

are several possible alternatives. One alternative is to make a GO/NO GO 

decision based on observations of cloud cover at the time of each satellite overpass during the prime time. In this situation a GO decision 

would be made for the first cloud-free or relatively cloud-free (95 to 100 

percent) pass, after which field personnel would be immediately notified 

21 
to gather ground truth information within JO to 15 days. This means of 

initiating field activity assures that ground truth will be close to the 

date for which Landsat data are acquired for processing, but requires a 

high degree of coordination between the weather observers, the decisionmaker, and the field personnel. In addition, this method limits the amount 

of time available for field personnel to perform their work. 

Another alternative is to preselect a scheduled Landsat pass date 

during prime time, then instruct field personnel to gather ground truth 

within a given number of days (e.g., 10 to 15 days) from that date. This 

alternative is easier to implement and gives the field personnel more 

flexibility in planning and conducting their activities to fit their own 

schedules, but has the disadvantage that the cloud condition may not be 

acceptable on the preselected overpass date. Of course, this does not 

preclude using ground truth acquired in this manner to process data from 

another pass closest to the preselected date. 

A third alternative is to initiate field activities to occur within 

a defined 6-week prime time period without regard to the satellite overpass dates. This instruction offers the greatest flexibility for field 

personnel to schedule and conduct their work and is the easiest to implement, but increases the chance that sOme training samples will have to be 

discarded during data processing because land cover conditions changed 

between ground truthing and Landsat data acquisition. The latter approach 

was followed in the ASVT project and resulted in a discard rate of only 3 

percent of all training samples because of an apparent change in conditions. 

Ground Truth Information Forms 

Ground truth information forms should be developed individually for 

each major land cover category or associated categories rather than as a 

single form for all land cover categories. For example, one form may be 

prepared for forest and brush vegetation, another for pasture and crops, 

another for urban areas, etc. Separate forms of this nature allow disciplinary field personnel to deal only with those forms pertinent to their 

responsibility, can be developed in a simpler format, and serve to reduce 

the total bulk of paperwork to be handled. Examples of forms used for 

this ASVT project are shown in appendix C. Another option in the development of forms concerns a "checkoff" versus a "fill in the blank" approach. 

A checkoff approach is preferred because it not only saves time but is much 

easier to use under field conditions. For most information, the checkoff 

approach is easy to develop and use; however, for some information, the 

fill-in-the-blank approach may be necessary. In the case of natural vegetation, unless the person developing the form is aware of all possible 

species associations in the area of concern, it is better to use a fillin approach so as not to preclude obtaining ground truth on sOme species 

associations. However, even when the checkoff approach is used, field 

personnel should be instructed to establish training sample sites for any 

land cover condition encountered that meets the established criteria, even 

though the condition is not indicated on the form. 

22 
Orientation of Field Personnel 

The ground truth package mentioned earlier contains an instruction 

sheet (see appendix D for an example) for field personnel that explains 

field procedures. However, it is desirable to hold orientation meetings 

with designated field personnel to deliver the package, review all details 

of its contents, outline areas of responsibility, discuss timing of ground 

truth gathering, etc. In the case of the ASVT project, orientation meetings were held at various locations throughout the State, usually in the 

district offices of each agency involved. Each meeting averaged approximately 3 hours; the first hour was used to explain the basics of satellite 

data acquisition and processing, and the last 2 hours were used to review 

the ground truth package contents, explain procedures and areas of responsibility, etc. However, if time and travel funds permit, it is preferable to 

assemble field personnel for orientation meetings at the location of the 

data processing equipment and provide them with a full day of orientation. 

This would allow a system demonstration, including the mechanics of locating 

training sample sites in the CRT display of the Landsat data image. Experience at ERL shows that such a demonstration gives field personnel a better 

feel for the process in which sites can be located through reference to 

other features in the image and visually emphasizes the need to establish 

sites that are uniform and homogeneous in respect to the particular land 

cover condition. 

Fieldwork 

The essence of ground truth fieldwork is to verify or establish the 

location of each training sample site that is uniform and homogeneous with 

respect to the particular land cover condition and to fill out a ground 

truth information form for each site. If potential training sample sites 

were preselected through photointerpretation, the field agent simply locates 

the delineated area on the ground, verifies that the area delineated is uniform and homogeneous, and makes the necessary observations to fill in the 

ground truth information form. If the delineated area is found to fail to 

meet the uniformity criteria, it may be discarded and another area selected. 

In other cases, it may be preferable to erase part or all of the delineated 

boundary and indicate.a new boundary, shifted slightly from the original 

delineation. In some cases, the photointerpreter may have delineated a 

uniform and homogeneous vegetation condition but may have interpreted it 

incorrectly. For example, the photointerpreter, using photography acquired 

during the winter season when hardwood trees were leafless, may have been 

misguided by an evergreen understory component (e.g., holly or wax myrtle) 

visually apparent through the leafless overstory and identified it as pine 

forest. In this case, the field person could simply change the letter/ 

number identifying symbol recorded by the interpreter to the correct symbol 

and fill out the form accordingly. 

In filling out the ground truth information form, the field person 

may take various approaches. If a trainin 

ricultural crop in a 16-hectare (40 acre) 9 

sample site delineates an agield bounded by roads on two or 

more sides, the field person may make most observations from a vehicle 

stopping only to make two or three spot checks by walking into the field. 

In the case of natural forest vegetation, he may use pacing and a hand 

23 
compass to keep his bearings as he follows scme pattern to assure adequate 

coverage (as suggested in appendix E) along which he stops occasionally to 

make visual observations. As the location and delineation of training sample sites and corresponding completion of the ground truth information form 

proceeds, it is extremely important that the letter/number identifier symbol (as described previously in this report) be recorded (both on the ground 

truth form and on the aerial photoprint or map) adjacent to the delineation 

of the corresponding training sample site. In addition, it is very helpful 

if field personnel staple all ground truth formsto the photoprint or map 

on which training sample sites corresponding to those forms are delineated. 

It is desirable to delineate training sample sites on recent aerial photoprints or photograph-based maps; however, if such are not available, training sample sites can be delineated on 7.5'-series (1:24 000) topography 

maps, or, in the absence of those, 15'-series (1:62 500) topography maps, 

provided that such maps are not grossly outdated. However, it is recommended that maps at scales smaller than 1:63 360 (1 centimeter = 0.63 kilometer (1 inch = 1 statute mile)) should not be used for training sample 

site delineation. 

If potential training sample sites are not preselected through photointerpretation, local field personnel may delineate sOme sites on aerial 

photographs or maps in the office based on their knowledge or on office 

records of the area. Visitation of these sites as well as delineation and 

visitation of additional sites can usually be performed in the course of 

the local field personnel's routine work. However, if the time period indicated for the ground truth gathering activity is short, a special effort 

may be required. After sites are located, the work proceeds in the same 

manner as described for sites preselected through photointerpretation. 

If personnel are employed exclusively for ground truth gathering, it 

is most efficient if they function as a team by meeting at the end of each 

day to keep a master list of training sample sites established and to plan 

the next day's activity. In this manner, preselected sites that may have 

been rejected or lost because of access problems may be substituted for by 

another teammember. 

The involvement of field personnel in producing a land cover classification with Landsat data may end with delivery of aerial photoprints 

with delineated training sample sites and corresponding ground truth information forms. However, it is desirable that field personnel also assist 

in the location of training sample sites in the Landsat data. Experience 

at ERL has shown that assistance from field personnel can save time, both 

through more rapid location of sites in the CRT display and in catching 

possible recording errors. 

Once training sample sites have been established for the first land 

cover classification, ground truth information for additional classifications can be obtained.with substantially less effort. Except in the case 

of agricultural land on which use changes from year to year, it is only 

necessary to ascertain that no drastic change has occurred since the first 

ground truth effort; consequently, ground truth forms can be greatly simplified. An example of a ground truth form prepared for revisits to training sample sites established for forest vegetation is shown in appendix F. 

24 
Time Required and Cost 

A tally of time required to make observations within a training 

sample site, to delineate the site on an aerial photograph or map, and to 

fill out the ground truth information form showed the following distribution for the ASVT project. 

Number of sites Time required 

93 5 to 15 minutes 

130 15 to 30 minutes 

117 30 to 60 minutes 

37 1 to 2 hours 

8 2 to 3 hours 

5 Over 3 hours 

There was a noticeable difference in time required for training sample 

sites for different land cover conditions. On an average, crop and 

pasture sites required 24 minutes per site, whereas forest and brushland 

sites required 43 minutes. It was not possible to keep account of travel 

time and expense (vehicle operation and depreciation costs) because most 

sites were established by field personnel during their routine activities. 

However, ERL experience shows that a field person can be expected to establish and provide ground truth on an average of six training sample 

sites per day when travel time between sites within the county is included. 

Consequently, for programmatic purposes, it can be estimated that 25 mandays (200 man-hours) would be required to address up to 150 training samples 

that may be established for one Landsat scene of 34 300 square kilometers 

(13 300 square statute miles). 

The cost of ground truth operations can be calculated by using a rate 

of $10.50/hr for the estimated 200 man-hours (to cover all salary, overhead, and operating costs) and dividing by the total area of the Landsat 

scene (34 300 square kilometers (13 300 square statute miles)). The calculation showed that ground truth operations would cost $O.O6/km2 ($O.l6/mi2) 

for the initial classification. This calculation is compatible with past 

ERL cost calculations, although they were derived in a research rather than 

an operational environment. The ERL-derived cost calculations indicate a 

range of from $O.O58/km2 ($O.l5/mi2) for the easiest ground truth gathering 

( i.e., recent aerial photography, field personnel familiar with area, easy 

access or terrain) to $O.l2/km2 ($0.3l/mi2) for difficult operations (aerial 

photography not available, field personnel unfamiliar with area, difficult 

access or terrain). Actually, it is unrealistic to charge all costs for 

the first ground truth effort against the initial classification because 

revisiting the established training sites for subsequent classifications 

requires far less effort. 

25 
In certain situations, such as when dealing with large, inaccessible marsh or wetlands areas, ground truth gathering can be more costly. 

It is in these conditions that selected coverage with aerial photography 

(if not already available) and/or the use of helicopters should be considered. However, even after comparisons are made with costs assuning 

access by boat, a higher cost for use of helicopters may be considered 

an adequate trade-off in view of the time required for a limited number 

of personnel to use boat transportation. 

Although ground truth for this project was acquired by local field 

personnel, it was mentioned earlier in this report that an option would 

be to use a ground truth team that would work almost exclusively for this 

purpose. Such an effort for a State as large as Mississippi may involve 

a breakout of work activity as follows. 

Activity Days 

Photointerpretation and prefield preparation 150 

Field work and travel within counties 180 

Postfield records and location of sites in 

Landsat data 

150 

Travel from central location to counties 

Total 

48 

528 

Allocating 220 workdays per year for each of three persons (e.g., an 

agronomist, a forester, and a botanist) results in a total of 660 days, 

leaving about 20 percent of their time for interface with users or other 

activities such as digitizing other information. However, it may be most 

desirable to use local field personnel for the first complete ground 

truthing effort, then use a full-time team of three for revisits and 

updating for subsequent classifications and specialized classifications. 

In this manner, the first ground truthing operation could be accomplished 

rapidly, and the field personnel, who eventually become users of the land 

cover classification, become familiar with the manner in which the classification is derived from Landsat-acquired data. 

CONCLUDING REM4RKS 

This report addressed ground truth information gathering procedures 

relative to performing a land cover classification using Landsat MSS digital data and the supervised approach to computer-implemented data processing. One difference between supervised and cluster analysis approaches 5s 

that, in the former, ground truthing takes place first so that the computer can be programed to recognize a land cover condition elsewhere; whereas, 

in the latter, classification takes place first, and then a ground truth 

operation is launched to determine the land cover condition that corresponds to each resulting class. Modified approaches employing unaided 

training sample selection and supervised classification are also in use. 

26 
However, since the same basic data are utilized and the basic principles 

involved in the measurement of reflected and/or emitted energy are the 

same, this report should have relevance to ground truthing activities 

irrespective of the approach to data processing. 

It is unlikely that new techniques will result in drastic changes to 

the procedures outlined in this report in the near future, However, techniques that are currently being developed and/or tested may cause some 

slight changes in ground truth gathering procedures. When raw data are 

registered to a given map projection so as to permit the development of 

techniques to allow automated location of training sample sites in the 

Landsat data, it will be necessary to determine the map coordinates 

(ref. 6) that define the location of each training sample site. If techniques currently under development and testing at ERL to define categories 

of mixed vegetation through distribution relationship analysis of classified data are successful, there may be no need to establish training sample 

sites for sane mixed vegetation categories (e.g., an oak-pine mix). Also, 

when techniques to merge land cover information from seasonal classifications into a master composite classification are perfected, it may be 

desirable to conduct ground truth activities during each season of the year 

in such a manner that each seasonal activity encompasses only those land 

cover categories that can be most accurately classified with Landsat data 

acquired during that particular season. 

This report was written primarily to help field personnel understand 

the principles and procedures involved in ground truth information gathering. However, it is believed that it would be beneficial for field personnel to familiarize themselves with aspects of data processing and analysis. Such familiarization would not only enhance their understanding of 

ground truth information gathering, but would also give them a better 

understanding of both the advantages and limitations of using land cover 

classifications derived from Landsat data. 

Lyndon B. Johnson Space Center 

National Aeronautics and Space Administration 

Houston, Texas, September 19, 1977 

177-52-89-00-72 

27 
REFERENCES 

1. Butera, M. K.: A Technique Development for the Determination of 

Louisiana Marsh Salinity Zones from Vegetation Mapped by Multispectral Scanner Data: A Comparison of Results from Satellite 

and Aircraft Data. NASA TM X-58203, 1977. 

2. Joyce, A. T. and Griffin, R. H.: The Use of Landsat Digital Data 

and Computer-Implemented Techniques for an Agricultural Application. 

NASA RP-1016. 

3. Whitley, Sidney L.: A Procedure for Automated Land Use Mapping 

Using Remotely Sensed Multispectral Scanner Data. NASA TR R-434, 

1975. 

4. Jones, Clay: Implementation of an Advanced Table Look-Up Classifier 

for Large Area Land-Use Classification. Proceedings of the Ninth 

International SFposium on Remote Sensing of Environment, vol. II, 

Environmental Research Institute of Michigan, Ann Arbor, Mich., Apr. 

1974, pp. 813-836. 

5. Whitley, S. L.: Low-Cost Data Analysis Systems for Processing 

Multispectral Scanner Data. NASA TR R-467, 1976. 

6. Graham, Marcellus H.: Digital Overlaying of the Universal Transverse . 

Mercator Grid with Landsat Data Derived Products. NASA TM 58200, 

1977. 

28 
APPENDIX A 

LAND COVER CONDITIONS IN MISSISSIPPI 

The major land cover categories and the various conditions of each 

category for the State of Mississippi are shown in table A-I. Such a 

listing is typical of that for a State as large and as varied as 

Mississippi. Some of the vegetation types listed do not occur at all 

times during the year; consequently, only a portion of the land cover 

conditions shown would be found during a ground truth operation conducted 

during a particular time of the year. Experience at ERL has shown that, 

once the vegetation types occurring at a given time of the year have been 

properly identified as to,variation in age, density, understory, and topography, there are typically around 30 to 50 land cover conditions bithin 

a 185- by 185-kilaneter (115 by 115 statute mile) area encompassed by a 

Landsat scene. 

The term "land cover condition" is used to refer to a particular combination of surface features that are likely to influence the reflected 

energy as measured with the MSS. For example, a land cover condition may 

be a combination of surface features described as "a sparse pine forest 

with a grass understory on the south aspect of a 30- to 50-percent slope." 

Another land cover condition may be described simply as "asphalt," implying 

that the material itself is the only feature expected to influence the 

reflected energy. Of course, a categorization of training sample sites as 

to land cover condition does not assure that each land cover condition categorized will be spectrally separable from all others. However, without 

such categorization, it would be impossible to attempt a computer-implemented 

classification of each land cover condition with Landsat digital data. 

29 
TABLE A-I. - MISSISSIPPI TRAINING SAMPLE MASTER FILE CATEGORY LIST 

Category Code Condition Category Code Condition 

Brushland BD 

Cropland 

Extractive 

Forest land 

BE 

BH 

BM 

CA 

CB 

cc 

CE 

CF 

CG 

E': 

CJ 

CK 

E! 

co 

CP 

'3 

CR 

:: 

cw 

cz 

EEGC 

EM 

EQ 

ES 

EZ 

FP 

HA 

HB 

HC 

HD 

HE 

HH 

HM 

HO 

HP 

F': 

PP 

PS 

Brush debris (e.g., recent 

clear-cut) 

Brush evergreen 

Brush deciduous 

Brush mixed 

Potatoes, sweet 

Grain sorghum 

Cotton 

Exposed soil, cleared 

Crop fallow 

Forage 

Peppers 

Plowed 

Disked 

Harrowed 

Wheat 

Corn 

Stubble 

Peanuts 

Cucumbers 

Rice 

Soybeans 

Oats 

Watermelons 

Field peas 

Clay extraction 

Gravel pit 

Strip mine, coal 

Quarry/limestone 

Sand pit 

Sand/gravel pit 

Oak-pine mix 

Elm-ash-cottonwood 

Maple-beech-birch 

Cypress-tupelo 

Leafless hardwood with 

deciduous understory 

Leafless hardwood with 

evergreen understory 

Oak-hickory 

Hardwood mixed 

Oak-gum-cypress 

Hardwood plantation 

Willow 

Loblolly-short leaf 

Pine plantation 

Longleaf-slash 

Inert materials IB 

I! 

IM 

IS 

Marshland 

Natural grassland 

Orchards 

Pasture 

and hayland 

Urban buildup 

Water 

DS 

JA 

JB 

JC 

JD 

JS 

ME 

SC 

SJ 

TY Typha 

NF Native field grass 

NW Native woodland grass 

0": 

OP 

Citrus 

Pecans 

Peaches 

IA 

IB 

:“D 

IE 

IF 

IO 

IT 

Alfalfa 

Bermuda 

Bahia 

Dallas 

Combination 

Fescue 

Other 

Temporary (e.g., ryegrass) 

UH 

UL 

WC 

WD 

wo 

2 

Barren or rock outcrops 

Building 

Hard surface (asphalt, concrete) 

Mud flat 

Sand beach or bar 

Baccharis halimifolia 

Baccharis halimlfolla/goldenrod 

Dlstlchlls splcata/Scirpus 

Juncus roemerianus/Spdrtina 

cyanosuroldes 

Juncus roemerlanus/Baccharis 

hallmlfolla 

Juncus roemerianus/Distichlis 

spicata 

Juncus roemerianus/Spartina 

alterniflora 

Juncus roemerlanus/Spartina 

patens 

CypjEleocharis cellolsa 

Spartina CyanosuroiaeslScirpus 

Spartina alterniflora/Juncus 

roemerianus 

High density 

Low density 

Catfish pond 

Deep lake, reservoir 

Other 

River 

Shallow lake, reservoir 
APPENDIX B 

DEFINITION OF t44JOR LAND COVER/VEGETATION TYPES 

The definitions and criteria of various land cover categories and 

conditions are included in the following list. 

Cropland - A specified unit area that is usually planted to an agronomic 

crop or grass on an annual basis after soil preparation 

Pasture/grassland - Specified unit area of which 90 percent or more of 

the surface covered with foliage is covered with foliage of grasses, 

generally used for grazing or hayland on other than an annual basis 

Forestland - Specified unit area of which 10 percent or more of the surface 

area is covered with foliage of trees 

Pine forest - Forest in which 66-2/3 percent or more of the area covered 

with foliage of trees is covered by foliage of evergreen trees as 

seen from above 

Hardwood forest - Forest in which 66-2/3 percent or more of the area 

covered with foilage of trees is covered by foliage of deciduous trees 

as seen from above 

Mixed pine/hardwood - Forest that does not meet the preceding criteria 

for evergreen or deciduous forest 

Brushland - Specified unit area of which 90 percent or more of the surface 

area covered with foliage is covered with foliage of multistemmed, 

perennial shrub species 

Forested wetlands - Forested areas that are seasonally flooded for prolonged 

periods (usually 3 months or more) and/or flooded because of diurnal 

tidal action directly or indirectly through water backup 

Marshland - Specified unit area that is frequently inundated for prolonged 

periods and contains plant species typical of nonforested wetlands 

covering 90 percent or more of its surface 

Species association - A vegetation type in'which two or more plant species 

grow intermingled, with the foliage of each species covering at least 

25 percent of the surface area as seen from above 

Sparse crown closure - Forested area in which 10 to 65 percent of the surface is covered by crowns (foliage and branches) of overstory trees 

when in leafed condition 

Dense crown closure - Forested area in which 65 to 100 percent of the surface is covered by crowns (foliage and branches) of overstory trees 

when in leafed condition 

31 
APPENDIX C 

GROUND TRUTH DATA FORMS 

Examples of the ground truth data forms used by field personnel for 

forest, brush, and.orchards; for crops and pasture; for extractive land 

uses; for urban areas; and for marsh vegetation are shown in figures C-l 

to C-5, respectively. 

32 
GROUND TRUTH DATA FOR FOREST, BRUSH, AND ORCHARDS 

TAKEN BY: _-------_~ DATE: 

TRAINING SAMPLE IDENTIFIER MAP OR AIR PHOTO INDEX d 

ESTIMATED FIELD SIZE: ft x ft. or ACRES 

LOCATION-~ 

County t f Section Township Range 

KIND OF VEGETATION (Check One) ( ) Natural Forest 

( ) Forest Plantation 

( ) Brush Vegetation 

IF NATURAL FOREST, INDICATE: 

(1) Major forest type (check one) 

( ) Maple-Beech-Birch 

( ) Oak-Hickory 

( > Oak-Gum-Cypress 

( ) Elm-Ash-Cottonwood 

( ) Loblolly-Shortleaf 

( > Longleaf-Slash 

( ) Aspen-Birch 

( ) Oak-Pine 

( ) Mixed Hardwood 

(2) Overstory Crown Closure 

( ) Dense (65% to lOa%) 

(3) Overstory species compos 

( ) Sparse (10% to 65%) 

ition (to nearest 25%) Species % 

(4) Understory species compostion ( to nearest 25%) Species % 

(5) Average age class of upper canopy trees (check one) 

( ) Less than 20 years ( ) 50 to 100 years 

( ) 20 to 50 years ( ) over 100 years 

(6) Average height class of upper canopy trees (check one) 

( ) Less than 20 feet ( ) 50 to 100 feet 

( ) 20 to 50 feet ( ) over 100 feet 

Figure C-l.- Ground truth form for forest,.brush, and orchards. 

33 
(FOREST, BRUSH, ORCHARDS CONTINUED) 

(7) Slope (Check One) 

( > 0% to 10% ( > 30% to 50% 

( > 10% to 30% ( ) 50% or more 

(8) Predominant Aspect (Check Onej 

( ) North ( ) South ( ) East ( ) West 

If Forested Wetlands are flooded at time of observation, indicate depth of water: 

( ) less than 1' ( ) 2' to 4' ( ) greater than 4' 

or if not flooded at time of observation indicate: 

( ) appears subject to flooding by water backup due to tidal action. 

( > appears to have been flooded for a prolonged period prior to observation. 

If Forest Plantation or Orchard, indicate: 

Species Average Age 

Spacing Average Height 

Row Direction 

If Brushland, indicate species composition to nearest 25%: 

(1) Species 

(2) Vegetation Density: 

( ) Sparse, 10% to 65% of surface covered. 

( ) Dense, 65% to 100% of surface covered. 

(3) If sparse density, ground level is: 

( ) Grass 

( ) Exposed earth 

( ) Other 

Figure C-l.- Concluded. 

34 

mm..-.... _-. -__- .--. -.--.*.I I..--.. mm.-..-. ..--....-.--.--. . ,,--_- . . ..---.... . ..--- .~ -...--. 
GROUND TRUTH DATA FOR CROPS AND PASTURE 

TAKEN BY 

TRAINING SAMPLE # 

ESTIMATED FIELD SIZE: ft x 

DATE 

MAP OR AIR PHOTO INDEX # 

ft. or ACRES 

LOCATION _- - 

County 174 --- l/4 Section Township Range 

GENERAL CONDITION(') 

DESCRIPTION (if not crop or pasture) 

CROP OR PASTURE SPECIES(2) VARIETY 

PLANTING TECHNIQUE (3) PLANT HEIGHT 

ROW WIDTH PHYSIOLOGICAL 

ROW DIRECTION VISUAL ASPECT 

PERCENT GROUND COVER ( ) 0% to 20% ( ) 40% to 60% 

( )20% to 40% ( ) 60% to 80% 

WEED INFESTATION (species & %, if greater than 20%) 

(if known) . 

to closest ft) 

STATE(4) 

5) 

( ) 80% to 100% 

DISEASE INFESTATION (kind & %, if greater than 20%). 

INSECT INFESTATION (kind & %, if greater than 20%) 

SOIL CONDITION@)_ _ _- 

SOIL MOISTURE(7) 

SOIL TYPE(8)(if available) _ 

OTHER COMMENTS (if needed) 

e.g. cro , pasture, stubble, plowed, fallow. 

e.g. soy 1 ean, bahia grass, etc. 

e.g. row, skip row, drilled, broadcast. 

e.g. flowering, heading, mature, etc. 

e.g. chlorotic, wilted, etc. . 

e.g. freshly cultivated, rough, smooth, etc. 

e.g. moist, dry, waterlogged, etc. 

series, texture, color, slope, etc. 

Figure C-2.- Ground truth form for crops and pasture. 

35 
GROUND TRUTH DATA 

Extractive Land Uses 

OBSERVATIONS MADE BY 

IDENTIFIER NO.* 

COUNTY 

DATE 

Approx. Size X (feet) or ~- 

acres. 

‘LOCATION (if known) 

Township Range Section Quarter Forty 

ACTIVITY TYPE () Sandpit 

( ) Gravel pit 

( ) Stone, dimension 

[ 1 ;o;e, crushed 

( ) Cement 

Clay 

Chert & Tripoli 

Lignite 

Heavy mineral 

Other 

Is area ( ) in-production or ( ) abandoned? 

If abandoned, is area ( ) barren or ( ) revegetated? 

Is the area likely to contain impounded water during all or a significant part of 

year ( ) yes ( ) no? 

How much time did it take to make observations and fill out this form 

(min. and/or hours) 

*Observations should only be made on extractive areas that are at least 600 feet 

by 600 feet, or approximately 10 acres. Once such an area is located, its 

location should be delineated on an aerial photo or map sheet with colored pen 

or pencil, and an identifier cross-reference number should be recorded on the 

aerial photo or map beside the delineated area and on the ground truth data 

form. 

Figure C-3.- Ground truth form for extractive land uses. 

36 
GROUND TRUTH DATA FORM FOR URBAN AREAS(') 

Training Sample ID No. ~ 

Collected by: Date: 

High Density Urban ( l(Z) 

Low Density Urban ( G3) 

If High Density Urban - Predominantly Concrete ( ) 

Predominantly Asphalt ( ) 

Predominantly Other ( ) 

Comments:(4) 

Inert Material Complex ( ) 

e.g., metal roof 

If Low Density Urban 

Main type of inert Material - Roof tops ( ) 

Concrete ( ) 

w&'~ ; ) 

Main type of vegetation - 

Grass (lawns) ( ) 

Pine trees ( ) 

Hardwood trees ( ) 

Mixed pine/hardwood ( ) 

(4) 

Mixed grass/trees ( ) 

Comments: ~__~ 

(1) An urban area training sample should be 1000 ft. by 1000 ft. or larger; however, 

if homogeneous areas of such dimensions cannot be located, areas of 500 feet by 

500 feet or larger (approx. a city block) are acceptable. 

(2) High Density Urban is defined as an area essentially devoid of vegetation; but 

with up to 10% covered with vegetation in small scattered parcels whose largest 

dimension is generally less than 100 feet. 

(3) Low Density Urban is defined as an area within which inert materials (roof 

tops, concrete, asphalt) are predominant; but with up to 45% of the surface 

covered with vegetation, including overtopping trees, occurring in small, 

scattered parcels with the maximum dimension of each parcel no greater than 

200 feet. 

(4) Appropriate comnents include identification of scenario, e.g., airport runway, 

industrial complex, downtown commercial area, etc.; height of buildings, e.g., 

one or two story, three to five story, 6 or more stories; pitch of roofs, e.g., 

flat, moderate angle, steep angle; or any other information pertinent to 

measurements made with overhead remote sensors. 

L. 

Figure C-4.- Ground truth form for urban areas. 

37 
GROUND TRUTH FORM FOR MARSH VEGETATION 

1. Sample number 

2. Date: 

3. Time: 

4. Vegetation type: 

. 

. 

. 

(1) pure stand (monotypic) . 

(a) species: . 

(2) intermixed (less than 6 vascular species present) . 

(a) dominant species: , . 

) intermixed (more than 6 vascular species present) . 

(a) dominant species: 9 . 

(NOTE: If a species comprises less than 5% of vegetation do not regard 

as major or dominant component.) 

(3 

5. Homogeneity: 

(1) sub-elements (defined) 

(a) vegetation differences (clumps, patches, zones) 

(b) barren areas 

(c) open water 

(d) sparse vegetation/barren 

(e) sparse vegetation/water 

(f) other (describe) 

(sub-elements (size) 

(a) less than 10 feet . 

(b) more than 10, but less than 20 - . 

(c) more than 20, but less than 40 . 

(d) more than 40, but less than 60 . 

I__ -.-- -. _, i ~-_ - 

Figure C-5.- Ground truth form for marsh vegetation. 

38 
(3) distribution (of sub-elements in study area). 

(a) evenly - 

(b) center _.-~--~__F .--~ 

(c) peripheral ~~~_ 

(4) density (of vegetation as % of surface area). 

(a) dense > 90 _=~~ 

(b) intermediate < 70 ~~ 

(cl sparse X50 I___~_= 

6. Height of plants (stands). 

(1) approximate height of major units: 

(a) species , height . 

(b) species , height . 

(c) species , height . 

(2) approximate height of minor units: 

(a) species , height 

(b) species , height 

7. Status of vegetation: 

(1) approximate (%) of dead-standing material. 

(a) major units (species) . 

(b) minor units (species) 

8. Stage of growth: 

(1) major units 

(a) dormancy (winter-no leaves) . 

(b) dormancy (winter-leaves dead-standing) . 

(c) seedlings . 

(d) immature _ 

. 

-- 

(e) mature . 

(f) anthesis 

Figure C-5.- Continued. 

39 
(9) vigor --.--- 

(1) excellent _ -.. -. _--- 

(2) fair 0 

(3) poor . 

9. Surface of substratum:' 

10. 

11. 

(1) covered by algae 

(2) covered by small vascular plants 

(3) covered by detritus 

(4) barren 

(5 1 substrate type 

(a) mud 

(b) sand 

(c) sandy/mud 

Water level. 

(1) standing on surface of marsh 

(a) covered by tidal water 

lb) covered by river overflow 

(cl combination of both (a & b) above 

(d) permanent or semi-permanent 

(2) Depth of water on marsh surface 

Comments: 

Figure C-5.- Concluded. 

40 
APPENDIX D 

TYPICAL INSTRUCTION SHEET 

The following is a typical instruction sheet for field personnel 

documenting ground truth information for agronomic crops and pastures. 

PROCEDURE FOR ESTABLISHING “TRAINING SAMPLE AREAS" AND 

DOCUMENTING "GROUND TRUTH" FOR AGRONOMIC CROPS AND PASTURES 

STEP #1 - Locate one typical field for each of the different crops and 

pastures that occur within the geographic area covered by the 

aerial photo (or photomap) provided. Each such field will be 

referred to as a "training sample area." (Note: a lo-acre 

field is the minimum size suitable for a training sample, but 

a larger field, 40 acres to 160 acres, is desirable.) 

STEP #2 - Outline each training sample area located in Step #1 with pen or 

pencil, assign a reference number to each (starting with the 

number one), and record the reference number on the aerial photo 

(or photo map) along side of each outlined field (training sample 

area).* 

STEP #3 - For each training area outlined and referenced on the aerial 

photo (or photomaps) in Step #2, fill out one "Ground Truth Data" 

form. Information on the form that is not readily available or 

not applicable can be so indicated in the appropriate blank. 

Record the index number of map or airphoto print on which the 

training sample is located in the upper right hand corner of the 

form. 

STEP #4 - Return all materials to project coordinator as soon as Step #l 

thru #3 are accomplished. This can take place between July and 

August; however, the earlier the better. 

*If scale is 1:62,500 (air photo), a 40-acre field is roughly l/4" X l/4" 

on the photo; if scale is 1:24,000 (township map), a 40-acre field is 

roughly 2/3 X 2/3" on map. 

Figure D-l.- Typical instruction sheet for ground truth field personnel. 

41 
APPENDIX E 

FOREST UNIFORMITY VERIFICATION PATTERN 

The following sketch illustrates the suggested coverage pattern to 

verify the uniformity of a forest vegetation training sample site of 

approximately 16 square hectares (40 acres) in size. 

Ck 

3. \ t + 

\ t + 

+ \ t + 

+ \ t + 

+ 

+ 

f\ + t\ + 

42 
APPENDIX F 

TYPICAL REVISIT FORM 

SITE IDENTIFIER CODE (6-7 digit code recorded on air photo or land use map) 

COUNTY: 

OBSERVATIONS MADE BY: DATE: 

Has the vegetation within the training sample area delineated on the air photo or land use map 

been altered during the last year? 0 yes 0 no 

If yes, what was the cause? El logging 

0 land clearing 

0 fire 

0 heavy insect or d.isease mortality 

0 other (indicate) 

In which month did the alteration occur (if known)? 

How much time did it take you to make observations and fill out this form (min. and/or hours)? 

43 
l.‘Report No. 2. Government Accession No. 

NASA RP- 1015 

t- 3. Reci pientCafalog[ 

4. Title and Subtitle PROCEDURES FOR GATHERING TRUTH INFORMA L- 5. Report Date 

TION FOR A SUPERVISED APPROACH TO A COMPUTER- January 1978 

IMPLEMENTED LAND COVER CLASSIFICATION OF LANDSAT- 6. Performing Orqanization Code 

ACQUIRED MULTISPECTRAL SCANNER DATA JSC- 12910 

7. Author(s) 8. Performing Organization Report No. 

Armond T. Joyce S-478 

10. Work Unit No. 

9. Performing Organization Name and Address 177- 52- 89- 00- 72 

Lyndon B. Johnson Space Center 11. Contract or Grant No. 

Houston, Texas 7’7058 

13. Type of Report and Period Covered 

1 2. Sponsoring Agency Name and Address 

National Aeronautics and Space Administration Reference Publication 

Washington, D. C. 20546 14.,Sponsoring Agancy Co& 

1 5. Supplementary Notes 

11 6. Abstract. 

Procedures for gathering ground truth information for a supervised approach to a 

computer- implemented land cover classification of Landsat- acquired multispectral scanner 

data are provided in a step-by-step manner. Criteria for determining size, number, 

uniformity, and predominant land cover of training sample sites are established. 

Suggestions are made for the organization and orientation of field team personnel, the 

procedures used in the field, and the format of the forms to be used. Estimates are made’ 

of the probable expenditures in time and costs. Examples of ground truth forms and 

definitions and criteria of major Iand cover categories are provided in appendixes. 

li 1. Key Words (Suggested by Author(s) 1 18. Distribution Statement 

Earth resources Ground truth STAR Subject Category: 

Pattern recognition Ground cover 43 (Earth Resources) 

Crop identification Landsat 

Land cover classifications Land use 

Multispectral band scanners 

16 ). Security Clanif. (of this report) 

Unclassified 

L 

20. Security Classif. lof this page) 21. No. of Pages 

Unclassified 47 

22. Price* 

$4.00 

*For sale by the National Technical Information Service, Springfield, Virginia 22151 

NASA-Langley, 1978 
National Aeronautics and 

Space Administration 

Washington, D.C. 

20546 

Official Business 

Penalw for Private Use, $300 

THIRD-CLASS BULK RATE Postage and Fees Paid 

National Aeronautics and 

Space Administration 

NASA451 

20 1 lCJ,E,SFGEN,010978 SOO903DS 740731 

DEPT OF TEE AIR FORCE 

AF WEAPONS LAEORATOBY i * A:TTN,: TECFINICAL LIBRARY (SUL) 

KIBTLAND APB NM 87117 

h.2 

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.: I l 

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