- 

NASA Reference Publication 1016 

The Use of Landsat Digital Data 

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Agricultural Application 

AFwL TECHNIcAL kleRA& KIRTuND AF& Na M 

. 

Armond T. Joyce and R. H. GrifKn II 

JANUARY 1978 
NASA Reference Publication 1016 

The Use of Landsat Digital Data 

and Computer-Implemented 

Techniques for an 

Agricultural Application 

Armond T. Joyce and R. H. Griffin II 

Lyndon B. Johnson Space Center 

Houston, Texas 

NASA 

National Aeronautics 

and Space Administration 

Scientific and Technical 

Intormation Office 

1978 

CONTENTS 

Section Page 

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

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

DATA PROCESSING PROCEDURES AND RESULTS ................. 

PRODUCT ADEQUACY ASSESSMENT ....................... 

ADDITIONAL COMMENTS ........................... 

CONCLUDING REMARKS ........................... 

REFERENCES ............................... 

APPENDIX A -GROUND TRUTH DATA FORMS .................. 

APPENDIX B -TOWNSHIP 171 . CODING DIAGRAM AND DENSITY PLOTS ...... 

APPENDIX C - COMPUTER REQUIREMENTS FOR LOW-COST DATA ANALYSIS 

SYSTEM .......................... 

1 

1 

2 

21 

26 

28 

30 

31 

38 

42 

. . . 

111 
TABLES 

Table Page 

I 

II 

III 

IV 

V 

VI 

Figure 

1 

2 

3 

4 

5 

6 

LAND COVER COMPILATION FOR TOWNSHIP 171 (T18N, R7W), WASHINGTON 

COUNTY............................. 

EXAMPLES OF POTENTIAL YIELDS OF COTTON AND SOYBEANS FOR TWO LEVELS 

OF AGRICULTURAL PRACTICE BY SOIL MAPPING UNIT . . . . . . . . . . 

EXAMPLE OF AGRICULTURAL YIELD INFORMATION OUTPUT 

(a) Class 1, soybeans . . . . . . . . . . . . . . . . . . . . . . 

(b) Class 2, cotton . . . . . . . . . . . . . . . . . . . . . . . 

SUMMARY OF AREA AND POTENTIAL COTTON YIELD FOR WASHINGTON 

COUNTY............................. 

SUMMARY OF AREA AND POTENTIAL SOYBEAN YIELD FOR WASHINGTON 

COUNTY.......................‘...... 

PERCENTAGE OF PIXELS AS CLASSIFIED WITHIN TRAINTNG SAMPLE AREAS 

BY LAND COVER CATEGORY . . . . . . . . . . . . . . . . . . . . . 

FIGURES 

Data processing flow diagram . . . . . . . . . . . . . . . . . . 

Portable image display system . . . . . . . . . . . . . . . . . . 

Geographic coverage of four classification tapes of Landsat scene 

E1736-15582 (acquired July 29, 1974) relative to the demonstration area (shaded area) . . . . . . . . . . . . . . . . . . . . 

Computer-implemented classification of Landsat digital data 

matched to Greenwood map sheet and reproduced at a scale of 

1:250000........................... 

Computer-implemented classification of Landsat digital data for 

the demonstration area, reproduced at a scale of 1:62 500 . . . 

Map of predominant land cover for each 400- by 400-meter (40 

acre) area, produced from land cover data from 50- by 50-meter 

cells on GEOREF tapes . . . . . . . . . . . . . . . . . . . . . 

12 

17 

18 

18 

19 

20 

27 

Page 

5 

6 

9 

10 

11 

16 

iv 
Figure 

7 

8 

9 

Location of various potentSal-yield categories of cotton as 

determined by soils data in Washington County, 

Mississippi . . . . . . . . . . . . . . . . . . . . . . . . . . 

Location of various potentEal-yield categories of soybeans as 

determined by soils data in Washington County, 

Mississippi . . . . . . . . . . . . . . . . . . . . . . . . . . 

General soils map of Washington County, made by film recording 

from DATABAS tapes, color-coded to reflect soils associations 

and series to which computer-stored mapping units 

pertain............................ 

22 

23 

24 
THE USE OF LANDSAT DIGITAL DATA 

AND COMPUTER-IMPLEMENTED TECHNIQUES 

FOR AN AGRICULTURAL APPLICATION 

By Armond T. Joyce* and R. H. Griffin II* 

Lyndon B. Johnson Space Center 

SUMMARY 

This report describes the use of a resource inventory system in 

combining Landsat-acquired data and soils information in a specific 

agricultural application. The data processing techniques are detailed 

in a step-by-step manner for use by agencies that have a need for assessment of specific crop potential and for production estimates of upcoming 

harvests in local areas. The system is designed so that data analysis 

can be performed for other natural resource applications in addition to 

the one described in this report. 

INTRODUCTION 

This report is one of a series that addresses a project conducted 

by the NASA Earth Resources Laboratory (ERL) and the State of Mississippi 

Office of Science and Technology in cooperation with other Mississippi 

State agencies. The overall project, entitled "Natural Resources Inventory System ASVT" (Applications System Verification and Transfer), has 

two facets. One facet is the transfer of technology associated with the 

use of Landsat digital data and computer-implemented techniques for 

resource inventory. The other facet is the demonstration of various specific applications for which the system has utility. This particular 

report addresses the use of Landsat digital data and computer-implemented 

techniques for a demonstration of an agricultural application. Other 

reports in this series will address applications such as wildlife habitat 

assessment, reforestation for erosion control, site selection, and coastal 

zone baseline inventory. 

Specifically, this report addresses the integration of information on 

the geographic location of agronomic crops, as derived from satellite data, 

with soils information digitized from U.S. Department of Agriculture (USDA) 

Soil Conservation Service county soils maps. It is anticipated that the 

-.------------- 

*NASA Earth Resources Laboratory, 1010 Gause Blvd., Slidell, La. 70458. 
integration of information on crops with information on soils will have 

utility for (1) baseline information that would aid the county agent in 

his routine work, (2) an assessment of the overall agricultural potential 

of a region, and (3) an estimation of the upcoming harvest for major crops 

in localized areas as a basis for decisions by local agricultural industries. 

For example, a cotton gin owner may decide to invest in new equipment or 

to provide for different transportation arrangements in preparation for 

an anticipated bumper crop in his area. In other words, it is not anticipated that the procedures and computerized system employed in this study 

would be used for nationwide or global crop production prediction but, 

rather, would be used to address selected areas considered to be critical 

to local economies, generally from one to six counties in a prime agricultural region. If the system were to be implemented by a State or a State 

agency. it is anticipated that the system would be used for various applications in addition to the agricultural application. In that event, the 

same soils information that was digitized for use in the agricultural application could also be used for forestry, wildlife management, site selection, 

and other applications. 

The demonstration area selected for use in the agricultural application 

was Washington County, which lies along the Mississippi River in west-central 

Mississippi. The entire county is in the highly productive, alluvial plains 

agricultural region of Mississippi. The major crops are soybeans and cotton, 

which together constituted 67 percent of all cropland and pasture in the 

county during the 1974 summer growing season. 

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

have been converted to the equivalent value in the Systeme International 

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

written parenthetically thereafter. 

DATA PROCESSING PROCEDURES AND RESULTS 

The intent of this report is to describe the use of a natural resources 

inventory system for an agricultural application in a step-by-step manner, 

corresponding to that in which data would actually be processed through the 

system. To facilitate this approach, the reader should periodically refer 

to figure 1. which shows the data processing flow. Also, to help the reader 

focus on the procedure itself, this report will not elaborate on the system 

details that are covered in other literature cited. 

After the acquisition of computer-compatible tapes (CCT's)l containing the raw data acquired by the Landsat multispectral scanner (MSS), the 

first step in data processing involved the use of an ERL-developed module 

of six computer programs named PATREC (Pattern Recognition Analysis) 

(ref. 3). The basic function of the PATREC programs is to generate a 

lcomputer-compatible tapes are available at the EROS Data Center, 

Sioux Falls, S.D., at a cost of $200 per set of four. Also see 

references 1 and 2. 

2 
computer-implemented classification of each pixel2 (representing 

0.44 hectare (1.1 acres) on the Earth surface) from data acquired by the 

MSS on the Landsat. This classification process identifies each pixel as 

some type of land cover category. The principal land cover categories of 

interest for this application demonstration were cotton and soybeans. 

The computer programs that make up the ERL PATREC module relate to 

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

maximum likelihood ratio calculation and Bayesian decision rules. (See 

refs. 4 to 6 for basic theory and details.) Use of the supervised technique requires that the location of a number of areas of known land cover 

(e.g., a soybean field) be established in the data. These areas are 

selected for uniform homogeneous land cover (e.g., a soybean field that 

is uniform in respect to planting date, density, vigor, etc.). They are 

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

eventually used to "train" the computer to recognize the same land cover 

elsewhere. 

The potential training sample areas are established independently 

from the data processing operation. They may be preselected by use of 

relatively recent (within 5 years) aerial photography for interpretation 

and subsequent ground verification, or they may be located through direct 

field observations. In this study, both methods were used. Forest training sample areas were preselected by photointerpretation of 2-year-old 

color infrared (IR) photography at a 1:120 000 scale and then were visited 

on the ground, whereas pasture and crop training samples were located 

through direct ground observations. Usually, the boundary of each potential 

training sample area is outlined on a recent black-and-white print of an 

aerial photograph or on a suitable map (e.g., 7.5'-series topographic map). 

The activity associated with ground observations is usually referred 

to as a "ground truth" operation and involves ascertaining whether the 

potential training sample area is uniform and homogeneous in respect to 

the land cover type that it was selected to represent. This activity 

involves recording certain observations about the training sample area on 

a ground truth data form. 

truth data forms.) 

(See appendix A for some examples of ground 

Usually, the size of each potential training sample area is approximately 16 hectares (40 acres). The number of training samples needed varies 

with the number of land cover categories to be classified and the variation 

within each category. However, as an example, if 12 land cover categories 

were to be classified within a 185- by 185-kilometer (115 by 115 statute 

mile) area that relates to a set of four CCT's from a particular Landsat 

scene, one may, as a rule-of-thumb, expect to encounter variation in each 

2A pixel is also referred to as a data cell, a data element, a 

resolution cell, and a picture element in other literature and relates to 

the instantaneous field-of-view of the multispectral scanner. 

3 
land cover category that may require the selection of 100 to 140 potential 

training sample areas.3 

The training sample areas for this application demonstration were 

established as part of a statewide activity to furnish ground truth for 

all applications being demonstrated during the project as well as for a 

quasi-operational test of the system on a State government computer. The 

exact procedures and details of the ground-truth activities for training 

sample area establishment for this project will be treated in a separate 

document. 

The total statewide effort was conducted by county agricultural 

extension agents, county foresters, wildlife biologists, botanists, park 

managers, and geologists in the various cooperating State agencies. A 

total of 189 State agency personnel were involved in the statewide 

ground-truth effort. Most of the field observations were made during the 

course of routine work by field personnel rather than as a separate effort. 

The potential training sample areas were related to the satelliteacquired data contained on CCT's through use of an image display device 

(activity A, fig. 1). Various types and makes of image display devices 

are available for this operation, but most display the image on a cathode 

ray tube (CRT) similar to that of a home television set. (See ref. 3 for 

several devices that have been used at the ERL.) The particular image 

display device used for this application demonstration was a portable, 

"stand alone" (not computer-interactive) device (fig. 2). 

As individual tapes were mounted and the image was displayed on the 

CRT, the operator matched the image on the CRT with the aerial photograph 

or map on which the training sample areas were outlined. To identify the 

location of a particular training sample area in the displayed digital 

data, the operator positioned a movable cursor (in the shape of a plus 

sign) on the CRT on each corner of the training sample and recorded the 

coordinates (scan line count and element count) of each corner. Each set 

of coordinates that referred to a particular training sample area was 

punched on cards for use in the implementation of the computer programs in 

the PATREC module. 

Activity B in figure 1 includes the implementation of six computer 

programs that perform different functions in the PATREC module (ref. 3). 

Activity B also includes both human and machine analysis to produce tapes 

labeled CLSTAP in figure 1. Tapes produced at this point contain computerimplemented classifications (land cover category) of each pixel (0.44-hectare 

(1.1 acre) area on the ground) on the tape. Each tape encompasses approximately 8500 square kilometers (2.1 million acres) and relates to the same 

3Using this example, one can calculate that 16 hectares (40 acres) 

times 140 training samples would amount to less than one one-thousandth of 

the 34 225 square kilometers (8.5 million acres) encompassed by one Landsat 

scene (four CCT's). 

4 
Letters@ throughBare used 

as references to specific 

activities addressed in 

the text of this report. 

Performs 

rectifica- 

@Digitize 

other 

information 

,O F Data base 

building 

program 

Acreage compi lation, 

change detection, 

spatial analysis, 

0 H Application 

programs 

7 

Crop production estimated, 

I 

wildlife habitant assessment, 

: 1 reforestation needs inventory, 

site selection 

Figure l.- Data processing flow diagram. 

5 
Figure 2.- Portable image display system. 

6 
185- by 46-kilometer (115 by 29 statute mile) geographic area contained 

on the original CCT's. However, the data contained on tapes produced at 

this point are not geometrically corrected to fit a given map projection. 

For activity C in figure 1, the CLSTAP tape is used as input, and two 

computer programs in the GEOREF (geographic referencing) module developed 

at ERL are used to rectify the data. The rectification involves registering 

each pixel to the universal transverse Mercator (UTM) projection (ref. 7). 

The procedure involves the determination of both UTM northing and easting 

coordinates and Landsat data scan line and element coordinates for 3 to 

10 points distributed over the four tapes in each Landsat scene. The 

operation was performed by visually matching the image displayed on 

the CRT with a map constructed with a UTM projection and determining the 

coordinates for 3 to 10 surface features (e.g., road intersections, 

bridges over water bodies) that are apparent on both the image and the 

map. The method involves the use of the control point coordinates that 

were input on cards and a formula involving a least-squares solution to 

perform the registration. In the course of registering each and every 

pixel to the UTM projection, the informational content that corresponds to 

each 0.44-hectare (1.1 acre) pixel is resampled and interpolated to fit a 

50- by 50-meter (0.62 acre) cell through the "nearest neighbor" approach. 

The rectification can be performed for an area of lo latitude by lo 

longitude (approximately 10 360 square kilometers (4000 square statute 

miles) during one computer run. In the course of rectifying data for a 

lo by lo area, which usually relates to portions of three or more CLSTAP 

tapes, all data are brought to one tape. The end result is a tape 

(indicated as GEOREF on fig. 1) that relates tq a 10 360-square-kilometer 

(4000 square statute mile) area and contains the land cover computerimplemented classification in 50- by 50-meter cells having sides oriented 

to the cardinal directions in a grid referenced to a UTM projection 

(ref. 7). 

The tapes produced in this manner are used for mapmaking (activity D, 

fig. 1), as a data source for various application algorithms (activity E, 

fig. l), and as a data source for data base building (activity F, fig. 1). 

The data on the tapes can also be displayed as a classified image on the 

CRT for visual analysis. In this application demonstration, a set of four 

tapes corresponding to Landsat scene El736-15582, containing data acquired 

by the satellite on July 29, 1974, was classified and used as input for 

rectification with the GEOREF computer program module. 

It would have been possible to rectify data for a lo by lo area 

selected so as to center over Washington County (the application demonstration area) and, thereby, encompass the entire demonstration area with only 

one GEOREF tape produced through one computer run. However, because the 

entire Landsat scene (four raw data tapes) that encompassed the demonstration 

area was processed, it was decided to make two computer runs to build two 

GEOREF tapes so as to be able to produce a small-scale map (1:250 000 scale) 

encompassing most of the satellite-acquired scanner data that were processed. 

For this operation, CLSTAP tapes 1, 2, and 3 were used to build a GEOREF 

tape for a lo by lo area covering the east half of the Greenwood map sheet 

(NI 15-9 of the U.S. Army Map Service series) at the 1:250 000 scale. A 

second GEOREF tape was built using CLSTAP tape 1 for that small portion of 

7 
the western half of the Greenwood map sheet that was encompassed by the 

Landsat scene. The geographic coverage of the four CLSTAP tapes relative 

to the Greenwood map sheet and to the demonstration area is shown in figure 3. 

The rectified land cover information on the two GEOREF tapes was recorded 

on film, through use of a digital film recorder loaded with a roll of 23- 

centimeter (9 inch) wide color negative film (activity D, fig. 1), at a 

scale of 1:250 000.4 Recording at this scale required two passes through 

the GEOREF tape corresponding to the east half of the Greenwood map sheet 

and one pass through the GEOREF tape containing partial data for the west 

half of the Greenwood map sheet, which resulted in three sections of exposed 

film on the roll. Subsequently, the roll of film was developed and printed, 

and the three sections were cut from the printed strip and matched to the 

Greenwood map sheet base. This layout was then photographed and reproduced 

at the 1:250 000 scale for project participants and in a format and size 

suitable for this report (fig. 4). 

In figure 4, the color assignment is completely arbitrary. Although 

red was assigned to cells classified as cotton, any color could have been 

assigned. In addition, since the human eye cannot deal comfortably with 

more than 10 colors, it is common practice to aggregate the specific land 

cover types that were classified, and for which information exists on the 

GEOREF tape, into broader categories during the digital film recording 

operation. In the case of the color-coded map product generated for 

figure 4, the two crops of special interest, cotton and soybeans, were 

assigned specific colors and other land cover types that were classified 

were grouped into the broad categories of water, forest, and other crops 

and pasture. However, it is possible to use the same GEOREF tapes used to 

produce the map product in figure 4 to create different products by simply 

providing different instructions when the digital data contained on the 

GEOREF tape are converted to a map product. One such alternative approach 

is the thematic (one-crop) map which was also produced during the project 

and will be shown later in this report. 

In addition to the flexibility for making various types of colorcoded maps with digital data on the GEOREF tapes, there is also the option 

of making maps at various scales. For example, the same GEOREF tapes used 

to make the 1:250 000 map (a reduced version of which is shown in fig. 4) 

were used to make a map of the demonstration area, Washington County, at 

a scale of 1:62 500 (fig. 5). This was accomplished by following the same 

procedure used for the 1:250 000 map but employing a larger expansion 

factor during film recording. In the film recording of digital data, the 

data are expanded electronically (e.g., an expansion factor of 2 outputs 

-------- ----- 

4See reference 3 for details on digital film recorders and an 

explanation of other means of producing color-coded maps from digital 

data. 

8 

II 
Figure 3.- Geographic coverage of four classification tapes of Landsat scene E1736-15582 

(acquired July 29, 1974) relative to the demonstration area (shaded area). 
‘I 

1 , 1 . .: ., 

: ‘. \ 

Figure 4.- Computer-implemented classification of Landsat digital data matched to 

Greenwood map sheet and reproduced at a scale of 1:250 000. 
SOY BEANS 

OTHER CROPS 

AND PASTURE 

Figure 5.- Computer-implemented classification of Landsat digital data for the 

demonstration area, reproduced at a scale of 1:62 500. 

11 
the initial data by a factor of 4). This does not involve any degradation 

of the image, such as occurs in a photographic enlargement process.5 

In addition to the use of the GEOREF tapes for making map products, 

the tapes can also be used as a data source for various application programs (activity E, fig. 1), which are used to extract and/or manipulate 

data on the tape for specific purposes. In this application demonstration, 

only one of these special-purpose computer programs, acreage compilation 

by land cover category, was demonstrated. 

In this computer program, the boundary of any circumscribed polygonal 

unit (e.g., a county, a watershed, a township) is defined by the UTM northing and easting coordinates. These coordinates are input by use of punched 

cards to allow a computer tally of the acreage encompassed by each land 

cover type classified within the circumscribed land unit. The line printer 

output shows the number of 50- by 50-meter cells, the percentage, and the 

land area of each land cover type in acres and square miles. The results 

of the output for a particular land unit are shown in table I. The circumscribed land unit, a township within the demonstration area of Washington 

County, is shown on figure B-l in appendix B. The program was also run 

for the entire area of Washington County, the results of which will be 

shown later in this report. 

TABLE I.- LAND COVER COMPILATION FOR TOWNSHIP 171 (Tl8N, R7W), 

WASHINGTON COUNTY 

Land cover Class- Element count Area, 

ification ha (acres; mi2) 

--- 

Number Percentage 

-------- -- -- 

Soybeans 

Cotton 

Bare Bermuda soil 

Other grass 

Oak-gum-cypress 

Inert materials 

1 7 643 20.49 1910 (4 720; 7.4) 

2 16 972 45.49 4241 

15 194 .52 

(10(;4;; 16:;; 

17 583 1.56 1:: (360; .6) 

19 9 587 25.69 

63: 2 073 5.56 

259 .69 

Total -- 37 311 100 9325 (23 040; 36) 

-- Pm- 

----------------- 

5The positional accuracy of the data, which in the case of the demonstration was 98 meters root mean square (rms) down track and 48 meters 

rms crosstrack, does not change with different scales selected for map 

products showing land cover. 

12 
Although not demonstrated in this application, various other specialpurpose computer programs exist that use GEOREF tapes as a source of data. 

Some of these include "change detection," which involves comparing two 

GEOREF tapes created with data acquired at different points in time so as 

to flag areas where land cover has changed; "distribution relationship 

analysis," which can be used to identify "mixed forest" areas; and "theme 

inventory," which involves determining the location and area of specific 

themes such as impounded water. 

The next major activity in the data processing flow (activity F, 

fig. 1) involves data base building, the purpose of which is to integrate 

land cover information from the GEOREF tapes with information that is 

digitized from other sources (activity G, fig. 1). It should be noted at 

this point that the objective of data base building is not to create a 

data base containing all conceivable information, but, rather, to create 

a data base to which the application programs will have efficient access 

(activity H, fig. 1). 

The design of the computer programs developed at ERL provides two 

options for data base building. One option is called the "gridded" 

option, in which the land cover information from the GEOREF tapes and any 

information digitized from other sources (e.g., soils maps) are assigned 

to cells that are subdivisions of the UTM grid in multiples of 50 meters. 

The other option, called the "nongridded" option, allows the UTM-gridded 

information on the GEOREF tapes to be input to the data base for units of 

the public land survey system (e.g., the subdivisions called "forties"6 of a 

given section) by identifying the center northing/easting UTM grid coordinates of each unit. Although either option may be used in addressing various 

applications for a particular land area that has been surveyed by the public 

land survey system, it is anticipated that the gridded option would usually 

be used for land areas surveyed by "metes and bounds."7 The advantage of 

using the nongridded option for public land surveyed areas has to do with 

the relationship of ownership to the use of land. For example, a farmer 

may buy a forty as defined by the boundaries of the NW&NW% sec. 33, T. 

9 s., R. 6 W. and subsequently decide to plant that entire forty to a 

specific crop. Likewise, a logging operation in a forested area is likely 

to be conducted for a specific forty as defined by the public land survey. 

However, since the size of the gridded data base cell is optional (in even 

multiples of 50 meters up to 400 by 400 meters), the advantage of the nongridded approach lessens as cell sizes smaller than 16 hectares (40 acres) 

are elected. For either option, gridded or nongridded, the desi n of the 

data base provides for storing up to 26 elements of information 9 variables) 

for each of the cells. 

It was anticipated that six of these variables would consist of land 

cover information extracted from GEOREF tapes, including four land cover 

classifications made with data acquired during each of the four seasons of 

-3-----w-- -----__1 

61'Forty" is a term used to refer to one of 16 parts of a section of 

land a)s defined by cornerstones set in the ground. 

Most land areas in the United States west of Ohio, with the exception of Texas, have been surveyed by the public land survey system. 

13 
1. Accuracy of the information other than the land cover information 

derived from sate llite-acquired data (e.g., soi 1s maps) 

2. Cost and effort involved in digitizing map source information for 

a particular cell size 

3. Size of the land area to be addressed relative to computer disc 

memory capacity, data storage, and retrieval time 

As mentioned previously, the size of the cell for the gridded option 

can be any multiple of 50 meters. The choice of cell size, made prior to 

imolementation. must take into account the combined effect of various 

factors such as the following. 

the year, one land cover classification derived by merging the four seasonal 

classifications, and one land cover classification used to address temporary 

phenomena such as flooding. The remaining 20 variables would include information other than land cover, such as soils, slope, elevation, and location. 

4. Accuracy required for the applications selected 

5. Decisions to be made on the ultimate use of the information 

It is anticipated that the choice selected will usually result in a DATABAS 

cell size of 200 by 200 meters (approximately 10 acres) or larger being 

chosen for statewide data bases. In the case of the Mississippi DATABAS 

design, a 16-hectare (40 acre) cell was chosen, which would result in 26 million elements of information (1 million cells times 26 variables) if 26 variables were to be stored for the entire State. This information could be 

stored on two CCT's, one each for the areas east and west of 900 longitude. 

No particular method is assumed for digitizing information other than 

land cover information (activity G, fig. 1). Anyone familiar with the 

process of digitizing land cover information (which is dynamic and everchanging) from maps would discount the use of manual techniques. However, 

this system does not involve digitizing land cover data from maps because 

the data are initially in digital form. Consequently, one may wish to employ manual techniques for digitizing such stable variables as soils, slope, 

elevation, aspect, and average annual rainfall, for which baseline information need be digitized only once. However, a system that is primarily based 

on the use of satellite-acquired digital data for land cover information can 

also include, as part of the system, a semiautomated method (X,Y digitizer) 

of digitizing other information, such as soils.8 In addition to digitizing 

_ll------- -w--e8This statement is not to imply that an either/or choice must be made 

in respect to use of satellite-acquired digital data for land cover information, because the data-base-building computer programs can be employed 

in such a manner that ground-acquired information can be input for small 

areas (e.g., urban areas, small parks, etc.), with reliance on satellite 

coverage for the bulk of the land area. 

14 
map source information, an X,Y digitizer could be used effectively for 

digitizing the northing/easting UTM coordinates that define areas of special 

interest for which compilations of acreage by land cover are to be made. 

It is not anticipated that agencies other than those engaged in nationwide 

digitizing of information would employ more sophisticated methods. 

For this particular application demonstration, the nongridded option 

was used. This involved determining the northing/easting UTM coordinate 

in the center of each forty as defined by the public land survey system. 

The computer program DATABAS takes the coordinates as card input and 

functions so that a forty midpoint is located on a GEOREF tape and a 7- by 

7-cell matrix of 50-meter cells around each midpoint is examined to 

determine the predominant land cover for each forty. 

Although production of a map was not needed for any of the application 

programs (activity H, fig. l), a map was produced by film recording to show 

the results of having aggregated the land cover information from the 50- by 

50-meter cells on GEOREF tapes to the forty for which land cover information 

was stored in the data base. The land cover data base information is shown 

in map format in figure 6, which can be compared with the map in figure 5. 

Appendix B includes black-and-white density plots of land cover in one township from both a GEOREF tape and a OATABAS tape to illustrate a low-cost 

means of map presentation (figs. B-l and B-2). In addition to the land 

cover information, the only other variable included on the OATABAS tape 

for this application demonstration was the soils information. The soils 

information was digitized from the USDA Soil Conservation Service county 

soils maps (ref. 8) by manual methods and punch card input. 

The final step in the data processing flow of this application 

demonstration was to use one of the special-purpose computer programs to 

which the data base was designed to feed information. In this case, the 

main function of the computer program used was to integrate soils and land 

cover (crop) information and, in the same procedure, to estimate the 

potential production for the upcoming harvest in the county. The estimate 

is made by determining both the crop and the soil that are predominant in 

each forty, then by referencing that integrated information to a computerstored table showing potential yield per 0.4 hectare (1 acre) by crop, 

soil, and management level. 

Table II shows 16 of the 56 soils mapping units that were encountered 

on the county soils maps. After the geographically referenced data base 

information on crop and soil has been tabulated and calu1ated.g the 

resulting information is output through a line printer to show summaries 

by township and county. Table III shows an example of the output for one 

of the townships in Washington County for both soybeans and cotton, and 

tables IV and V show the summaries for Washington County for cotton and 

soybeans, respectively. The Washington County cotton harvest (table IV) 

9In this application, management level B values (improved agricultural practices) were used for cotton; management level A values (normal 

agricultural practices), for soybeans. 

15 
LEGEND 

DECIDUOUS 

FOREST 

COTTON 

SOYBEANS 

OTHER CROPS 

AND PASTURE 

INERT AND 

UNCLASSIFIED 

WATER 

Figure 6.- Map of predominant land cover for each 400- by 4000meter (40 

acre) area, produced from land cover data from 50- by 50-meter cells 

on GEOREF tapes. 

16 
- 

TABLE II.- EXAMPLES OF POTENTIAL YIELDS OF COTTON AND SOYBEANS FOR 

TWO LEVELS OF AGRICULTURAL PRACTICE BY SOIL MAPPING UNIT 

Code 

Soil 

Type 

-~ - -~ -~ - 

Potential yield of - 

Cotton lint, kg/ha (lb/acre) Soybeans, kg/ha (bu/acre)a 

-- 

Level Ab Level Bc Level Ab Level Bc 

Alligator clay, level phase 196 (175) 

Alligator clay, nearly level phase 252 (225) 

Alligator clay, sloping phase 252 (225) 

Alligator silty clay loam, level 196 (175) 

Alligator silty clay loam, nearly level 252 (225) 

Alluvial land 

Beulah very fine sandy loam, nearly level 420-7375) 

Beulah very fine sandy loam, gently sloping 392 (350) 

9 Beulah very fine sandy loam, moderately 

10 

11 

12 

13 

shallow 504 (450) 

Bosket silty clay loam, nearly level 532 (475) 

Bosket very fine sandy loam, nearly level 644 (575) 

Bosket very fine sandy loam, gently sloping 532 (475) 

Bosket very fine sandy loam, moderately 

14 

15 

16 

shallow 644 (575) 785 (700) 

Bowdre silty clay, nearly level 364 (325) 504 (450) 

Bowdre silty clay loam, nearly level 364 (325) 504 (450) 

Borrow pit -- -- 

280 (250) 

420 (375) 

420 (375) 

280 (250) 

420 (375) 

504-(450) 

476 (425) 

617 (550) 

673 (600) 

785 (700) 

673 (600) 

'One bushel of soybeans = 27.2 kg (60 lb). 

bNormal agricultural practices. 

'Improved agricultural practices. 

272 (10) 

:44: 1;:{ 

272 (10) 

680 (25) 

544 (20) 

408 (15) 

508 (15) 

-- 
TABLE III.- EXAMPLE OF AGRICULTURAL YIELD INFORMATION OUTPUT 

(From Township 171, forties (16-hectare fields) 1 to 576) 

(a) Class 1, soybeans 

-_I--- - _------- ISoil type Occurrences Area, Potential yield, 

code ha (acres) kg( ';u' 

a 

11 

23 

31 

35 

40 

47 

48 

54 

6 

7 

: 

3: 

65 

1 

97 

113 

16 

32 

16 

567 

1052 

16 

(240) 

(::ij 

030) 

(40) 

(1400) 

(2Kz 

130 640 

114 310 

21 770 

43 550 

21 770 

421 020 

1 415 232 

27 216 

(4 800) 

(4(E; 

(l ,::,o; 

(14 000) 

Total -- 1910 (4720) (29 200) 

- 

(b) Class 2, cotton 

-- 

-- ---_ 

Soil type Occurrences Area, Potential lint yield, 

code ha (acres) kg (lb) 

--- - ---- --.- 

7 1 16 (40) 8 165 (18 000) 

11 141 2282 (5 

tz 

1 790 800 (3 948 000) 

13 12 700 

24 : ;; 16 330 '(:86 o,Eg 

279 2 332 49WN (182i{ 27 32220 660 I;; "oi{ 

31 18 291 (720) 212 280 (468 000) 

40 3555 3890 49(2 200) 748 204103 (1 4;; 0";;; 

47 ;: 178 

I~:~~ 

49 900 (110 000) 

48 50243 (g; 108 

52 : :: 

7 860 260 'g "o"o"o~ 

(80) 1 840 (40 000) 

54 6 97 (240) 65 320 (144 000) 

Total -- 4241.10 (10 480) 3 118 447.5 (6 875 000) 

aOne bushel = 27.2 kg (60 lb). 

18 
- 

TABLE IV.- SUMMARY OF AREA AND POTENTIAL COTTON YIELD FOR 

WASHINGTON COUNTY 

Soil type Occurrences Area, Potential lint yield, 

code ha (acres) kg (lb) 

- 

$ 

5 

i 

9 

10 

;: 

13 

14 

15 

17 

'1: 

E 

22 

23 

24 

25 

26 

27 

28 

29 

z: 

32 

34 

35 

36 

37 

38 

7: 

41 

42 

ti 

45 

46 

47 

48 

49 

50 

z: 

54 

55 

56 

19 

184 

894 

45 

9 

3 

7 

657 

10 

:: 

12 

50 

73 

4 

38 

7 

8: 

20 

a 

;: 

a 

14 

325 

15 

9 

382 

: 

21 

153 

156 

37: 

32 

iz 

38 

257 

364 

: 

116 

6 

6 

Total --- 

310 (760) 

* g86: (7 (160) 360) 

1 440 (3 560) 

50 (120) 

110 (280) 

10 630 

160 

""(2;;; 

280 (680) 

E (l (480) Oao) 

810 (2 000) 

130 (320) 

' 339200 (3 (800) 440) 

130 (320) 

260 (640) 

230 (560) 

150 (360) 

346: (160) 

(840) 

2 480 (6 120) 

2 53600 l6 (16’31 240) 

5 g9302 (14 aoo) (80) 

620 (1 520) 

4 160 (10 280) 

100 (240) 

56 310 (139 160) 

a 600 (190 000 

1 2;; ;"o; (2 760 000 

(40 000 

605 600 (1 335 000 

367 69 400 I% ::: 

29 900 (66 000 

76 200 (168 000 

8 344 300 (18 396 000 

108 900 (240 000 

215 900 (476 000 

220 500 (486 000 

98 000 (216 000 

703 100 (1 550 000 

1 092 700 (2 409 000 

58 loo (128 000 

568 800 (1 254 000 

101 600 (224 000 

7 300 (16 000 

78 000 (172 000 

108 900 (240 000 

116 100 (256 000 

232 200 (512 000 

I 238 300 (2 730 000 

94 300 (208 000 

152 10 400 0y:; "0:: 

3 a32 900 (a 450 000 

149 700 (330 000 

114 300 (252 000 

5 198 200 (11 460 000 

23 600 (52 000 

52 600 

276 200 [E io": 

1 388 000 (3 060 000 

1 061 400 (2 340 000 

272 200 (60 000 

3 020 900 (6 660 000 

15 400 

24 500 

204 100 (450 ooc 

523 900 I1 155 ooa 

172 400 

1 865 200 

29 000 

261 300 

al 600 

36 300 

1 262 800 

65 300 

70 800 

(380 ooc 

(4 '(is 00; 

;:;: "0:: 

(80 ooc 

'2(::: oOoOE 

(156 OOC 

35 all 600 (78 951 000) 

19 
- 

TABLE V.- SUMMARY OF AREA AND POTENTIAL SOYBEAN YIELD FOR 

WASHINGTON COUNTY 

- -- 

Soil type Occurrences Area, Potential yield, 

code ha (acres) kg((;b) 

a 

------ .-__ --- 

: 

4 

5 

ii 

10 

1': 

13 

14 

15 

17 

18 

19 

20 

22 

2243 

2'; 

PY 

3342 

35 

37 

38 

39 

:: 

42 

ti 

ii 

49 

:: 

54 

55 

56 

Total --- 

190 

203 

1: 

10 

4 

3; 

: 

14 

3 

14 

3 

: 

2252 

; 

16 

857 

4" 

27 

1 

1 

3 

52 

1 

43 

: 

601 

1111 

3 

10 

1 

93 

6 

1 

3 080 

3 290 

16 

160 

160 

60 

16 

490 

:; 

230 

50 

230 

50 

:i 

3 5:: 

110 

26600 

110 

1 380 

100 

60 

440 

16 

16 

8:: 

16 

700 

iFi 

9 730 

17 980 

50 

160 

16 

1 510 

100 

16 

(40 

(400 

(400 

'):i 

(l f:: 

(80 

(560 

(120 

\% 

(40 

(120 

(200 

(8( ;:i 

(160 

(640 

(280 

(3 (% 

(160 

(1 080 

144: 

(120 

(2 E 

(' K 

(120 

'24 040 

:44 440 

(120 

'g 

'3(E 

(40 

45 390 (112 160) 51 634 200 (1 897 200) 

2 068 420 

4 419 880 

10 890 

272 160 

54 430 

21 780 

21 780 

653 180 

21 780 

43 560 

228 610 

48 990 

457 230 

97 980 

32 670 

97 980 

27 220 

3 625 170 

114 310 

108 860 

348 360 

152 410 

1 850 690 

97 980 

87 120 

587 870 

21 780 

21 780 

65 340 

1 132 190 

16 330 

936 230 

16 330 

16 330 

6 552 260 

24 189 260 

81 650 

272 160 

27 220 

2 531 090 

195 960 

16 330 

(76 000) 

(162~~oooo~ 

'1; 0"::; 

(800) 

(800) 

(24 000) 

(800) 

(1 600) 

(8 400) 

(1 800) 

'1; 86;00/ 

1: iii{ 

(1 000) 

"i'b ;fJ;; 

(4 000) 

(12 800) 

(5 600) 

'Fi "6"oij 

(3 200) 

(21 600) 

iE; 

(2 400) 

(41 (3 

(34t% 

(600) 

(240 400) 

‘7 08;;; 

0: Ei 

(93 000) 

-- -- --- m-m-- 

aOne bushel = 27.2 kg (60 lb). 

20 
is estimated at 35 811 600 kilograms (78 951 000 pounds); the Washington 

County soybean harvest (table V) is estimated at 51 634 metric tons 

(1 897 200 bushels). In addition to its use for crop production estimation, the output showing crop and soil combinations by township (table III 

is one example) can be analyzed to determine how various soils are being 

utilized and to assess the general agricultural potential. 

Although additional mapmaking is not essential, this system can also 

be used to produce various types of maps from the information in the data 

base that may be desired for visual analysis. Figures 7 and 8 are examples 

of such maps and were made to show the inherent potential of the soils for 

producing cotton and soybeans. A separate color was assigned to each soil 

within a particular potential yield category. These potential yield categories are arbitrarily chosen; any particular range can be selected. The 

overlays to figures 7 and 8 show the location of each respective crop as 

determined from the satellite-acquired data. These thematic (one-crop) 

overlays were made by film recording from the GEOREF tapes; the crop in 

question was arbitrarily assigned a specific color, and all other land 

cover categories were assigned a common neutral color. This capability 

demonstrates the flexibility in making maps from digital data on CCT's. 

A comparison of the thematic overlay of cotton with the potential yield 

map (fig. 7) shows the close correlation of actual cotton lint production 

with a high-yield category (560 to 840 kg/ha (500 to 750 lb/acre)). This 

high correlation indicates that Washington County cotton farmers are very 

cognizant of the productivity of these soils for cotton. 

The essence of the Natural Resources Inventory System is the use of 

land cover information in a computerized system without reliance on mapped 

input or output. However, an example of the system's flexibility for mapmaking is shown in figure 9, which is a generalized soil association map 

of Washington County made by film recording from the DATABAS tapes. The 

basic information on soils mapping units stored on the tapes was assigned 

specific colors to show the soils associations and series to which the 

mapping units pertain. 

PRODUCT ADEQUACY ASSESSMENT 

The accuracy of the land cover classification was verified in several 

ways. First, the predominant land cover of every fifth forty in Washington 

County was determined through photointerpretation by using 1:120 OOO-scale 

color IR aerial photographs. The resulting categorization of each forty 

was then compared with the results that were extracted from the GEOREF tapes 

and read into the data base through use of the computer programs. During 

this comparison, each forty for which there was disagreement as to land 

cover category (as determined by the two methods) was flagged and, subsequently checked in the field to determine the actual land cover. In all cases, 

the field checks revealed that one of the two sources was correct (as opposed 

to neither one being correct), thereby substantiating that those forties in 

agreement and therefore, not field checked, had a very high probability of 

being categorized as the actual land cover. The total effort involved 2156 

of the 10 780 forties in the county, which constituted a 20-percent sampling. 

The results showed that 1722 or 92 percent of the forties categorized as 

21 
- Water 

r Red - Not suited 

-f 4 

Magmta - Less than 280 kg lint/ha (250 lb linl,acrc) 

. Cyan - 280 to 560 kg lint/ha (250 to 500 lb lint/acre) 

am&- 

Ycllo\v 

r 

- 560 to 840 kg lint/ha (500 to 750 lb lint/acre) 

w I .A Green - Greater than 840 kg lint/ha (750 lb lint/acre) 

Figure 7.- Location of various potential-yield categories of cotton as 

determined by soils data in Washington County, Mississippi. The corresponding overlay contained in the pocket in the inside back cover 

shows the location of cotton as determined from satellite-acquired data. 

22 
- - -.. . -.. ._-, “,..,, 

i 

.- - 

Dark blue - Water 

8 

+ 

Red - Nat suited 

Yellew - Less than 544 kg/ha QO bu/ac re) 

Light blue - 544 to 816 kg/ila 00 to 30 bu/acrc) 

Green - 816 to 1089 kgha 

Pink - Greater thnn 1089 kg/ha (40 bu/acre) 

00 to 40 tlu/acre) 

Figure 8.- Location of various potential-yield categories of soybeans as 

determined by soils data in Washington County, Mississippi. The corresponding overlay contained in the pocket in the inside back cover 

shows the location of soybeans as determined from satellite-acquired 

data. 

23 
-.. 

SHARKEY-TUNICA-BOWDRE 

ALLUVIAL LAND 

DOWLING-MHOON 

BORROW PITS 

DUNDEE-BOSKET-BEULAH-SOUVA 

MISSISSIPPI RIVER/LAKES 

COMMERCE-ROBINSONVILLE-CREVASSE 

FORESTDALE-ALLIGATOR 

AIRPORT-GREENVILLE 

SWAMP 

Figure 9.- General soils map of Washington County, made by fiJm recording 

from DATABAS tapes, color-coded to reflect soils associations and sertes 

to which computer-stored mapping units pertain. 

24 
cropland or pasture were correctly classified through the use of satelliteacquired MSS data and computer-implemented classification techniques. Of 

the 156 forties categorized as cropland or pasture that were incorrectly 

classified, 73 were misclassified as forest, 57 were misclassified as inert 

materials, and 26 were misclassified as water bodies. Of the 278 forties 

not in the cropland or pasture category, 93 were misclassified as crops 

or pastures. The combined number of commission and omission errors showed 

that 87 percent of the total number of forties were classified correctly. 

Because the aerial photography used for the accuracy check method 

described above was not acquired during the cotton and soybean growing seasons, two other methods were used to verify the accuracy of the cotton and 

soybeans classifications. First, the land area of each crop as compiled for 

the entire county through use of the acreage compilation computer program 

that uses the GEOREF tapes as a data source was compared with the county 

statistics for harvested acreage as published by the Mississippi Crop and 

Livestock Reporting Service (refs. 9 and 10). The results showed 45 351 

hectares (112 065 acres) of soybeans and 48 295 hectares (119 340 acres) 

of cotton tallied for the county from the GEOREF tapes. These figures can 

be compared with 49 655 hectares (122 700 acres) of soybeans and 45 730 

hectares (113 000 acres) of cotton reported for the county by the Mississippi 

Crop and Livestock Reporting Service publication.10 

The reader may note that the acreage determined from the GEOREF tapes 

is not the same as the acreage carried into the data base as shown in 

tables IV and V. This change took place during data base building when 

the computer made a tally of the land cover shown for individual 50- by 

50-meter (0.62 acre) GEOREF cells within the forty to determine, through 

plurality, the predominant land cover for the forty. The result was that 

the data base shows practically the same acreage for soybeans (45 390 

hectares (112 160 acres)) as was determined directly from GEOREF tapes 

(45 350 hectares (112 065 acres)), but the cotton acreage carried into the 

data base was 56 316 hectares (139 160 acres) versus the 48 295 hectares 

(119 340 acres) determined from GEOREF tapes. It is believed that this 

disparity is not a discrepancy but, rather, is related to the practice of 

skip-row plantin 

hectare (40 acre 3 

of cotton in Washington County. For example, if a 16- 

field is planted by alternating six rows of cotton and 

four skipped rows, the result is 9.7 hectares (24 acres) of cotton in a 

16-hectare field that is dedicated to cotton growing. Consequently, it 

is believed that the cotton acreage shown for the data base depicts the 

total acreage dedicated to cotton farming in Washington County; whereas, 

both the cotton acreage derived from GEOREF tapes and the area reported 

by the Crop and Livestock Reporting Service depict the net area. 

A second method used to verify the accuracy of the computerimplemented classification consisted of determining how the pixels within 

lOThe method of estimation employed by the Mississippi Crop and Livestock Reporting Service is designed to attain a specified accuracy at the 

State level; and, although the resulting statistics are published for 

counties, the accuracy at the county level is generally considered to be 

around +lO percent. 

25 
the training sample areas were eventually classified. The reader should 

understand that even though the training sample areas were used to "train" 

the computer to recognize the same land cover elsewhere in the data, the 

computer is not able to "recognize" which pixels were included in training 

sample areas when each pixel is systematically classified. Consequently, 

after the classification has taken place, it is possible to use a computer 

program that locates the original training sample areas in the data on the 

CLSTAP tapes and determines how each pixel was eventually classified. The 

results show that of the 111 pixels within cotton training sample areas 

90.1 percent were classified as cotton while 2.7 percent were misclassified 

as soybeans and 7.2 percent were misclassified as grass. Of the 261 pixels 

within soybean training samples, 98.8 percent were classified as soybeans, 

0.4 percent were misclassified as cotton, 0.4 percent were misclassified 

as grass, and 0.4 percent were misclassified as bare soil. The complete 

results of this tally, including all land cover categories classified, 

are shown in table VI. 

As a means of further substantiating the accuracy of the cotton and 

soybean classifications, the 1:62 500-scale map and the acreage compilations 

by townshi 

personnel. P l 

were evaluated by Mississippi Cooperative Extension Service 

Their conclusion was that the map and statistics, when viewed 

in relation to their knowledge of actual planting practices during the 

1974 crop season, appeared to be within the accuracy limits indicated in 

table VI. 

ADDITIONAL COMMENTS 

The main consideration during the development of computer programs 

and techniques used in this demonstration was to establish the hardware/ 

software system and associated procedures needed to use Landsat digital data 

and other digitized data (e.g., soils) to address specific applications. 

Consequently, no field studies were made to verify the accuracy of the 

crop yield information shown in table II or to certify the level of agricultural practices assumed for this application demonstration.12 However, 

the computer programs were designed to use card input so that information 

from other sources (such as the crop yield by soils or the yield information for a given soil, in table II), could be replaced by merely punching 

a new card (should more accurate information become available) without 

any need to make changes in the computer program. 

llA much more detailed evaluation dealing with map product adequacy, 

utility of statistical information, cost, etc. was performed by Cooperative 

Extension Service personnel. The results of this evaluation will be integrated with results of evaluations by other Mississippi State and local 

agencies and will be provided in the final report for this project. 

12Although no field studies were made for this purpose, there was no 

reason to suspect that the yield information or the level of agricultural 

management assumed was incorrect. 

26 
Similarly, it would also be possible to develop soils/yield data as 

shown in table II and card decks for 

both a "worse than average" and "better than average" weather situation 

by matching past records of actual 

yield by soil with historical weather 

1% records. If this were done, a deci- 

,E: sion could be made as to which card 

i' deck would be used based on field 

personnel assessment of the weather 

situation during the growing season 

for which data was being processed. 

During data processing, it would also 

be possible to make a decision as to 

the appropriate level of agricultural 

practices (level A, normal agricultural practices, or level B, improved 

agricultural practices) on a township 

basis as opposed to a decision on the 

county basis, as was made in this 

project. 

It should also be recognized 

that the data processing system and 

procedures used in this project could 

be used to address potential forest 

production by substituting a card 

deck with forest yield by soils information for the agronomic crop 

yield by soils information used in 

this application demonstration. In 

addition, projected forest production could be estimated if forest 

age were to be digitized and stored 

as one of the 30 variables provided 

for in the data base design. 

The costs for several possible 

configurations of system hardware are 

detailed in reference 3 and in appendix C. However, in summary, these 

capital investment costs would be 

less than $50 000 (image display device and electrostatic printerplotter) as a minimum if a computer 

and peripherals are already available. 

It is the objective of this project 

to document operating costs in the 

final report; however, tentative operating costs developed in a research 

environment indicate that the operating costs including field work, data 

processing (as indicated by the flow 

27 
diagram in fig. 1), and mapmaking are less than $0.39/km2 ($l/mi2) for the 

land cover information component. Furthermore, there is considerable potential for cost reductions through geographic signature extension, development of faster computer algorithms, and automated training-sample selection. 

One of the main advantages in both cost and time of the data processing 

system used for this application demonstration was the use of satelliteacquired digital data for land cover information, thereby, eliminating 

the need to digitize such dynamic information from a map or photographic 

base as is required by other approaches. 

The costs of digitizing other data, such as soils, from map sources 

vary according to the digitizing method used. Although the system used in 

this application demonstration does not presuppose any particular digitizing technique, even manual encoding from the map source is a feasible 

alternative for such static variables as soils, slope, elevation, and 

annual rainfall that would be digitized only once. 

Although it is believed that the utility of satellite data as reported 

for this application demonstration justifies the operational use of data generated by the satellites currently in orbit (Landsat I, launched July 1972, 

and Landsat II, launched January 1975), it is reasonable to assume that a 

decision to implement such a system would involve consideration of the use 

of Landsat C (scheduled for launch in February 1978) and Landsat D (currently 

proposed for launch in 1981). In this context, it should be recognized that 

the plan to provide the user with rectified raw data from Landsat C and D is 

conducive to an increase in classification accuracy through the sequential 

analysis of several sets of raw data acquired between planting time and 

advanced plant growth stages. Also, the addition of the thermal band to 

the Landsat C multispectral scanner and the proposed Landsat D thematic 

mapper scanner may prove to be useful for investigating vegetation stress 

(caused by insects, disease, or drought) as another factor to be considered 

in crop production estimation. 

CONCLUDING REMARKS 

Landsat multispectral scanner data in digital form was used in combination with digitized data from other sources in a computerized system to make 

estimates of soybean and cotton crop production for Washington County, 

MississiDDi. The effort was a demonstration uroiect for such forecasting 

on a loc'ai level (about one to six counties).' " 

Cross checks of the land-cover classifications and crop est 

were made with aerial color infrared photography, with crop stat 

the Crop Reporting Service, and with internal checks of the data 

itself. These tests all showed the computer-implemented categor 

to be of adequate accuracy. 

imations 

istics from 

against 

ization 

This report carefully described the steps needed to process the Landsat 

digital data and use it in combination with digitized data from other sources. 

Steps were shown for processing the combined data to produce thematic maps, 

28 
crop inventories, and estimates of crop production prior to harvest. More 

advanced uses were suggested. 

Costs and hardware required were noted. Ways of minimizing costs were 

suggested. 

1. 

Finally, it was demonstrated that this system can be implemented 

with resources and personnel existing at a state level. 

1-j 

fk : 

Lyndon B. Johnson Space Center 

IX National Aeronautics and Space Administration 

Houston, Texas, September 8, 1977 

177-52-89-00-72 

29 
REFERENCES 

1. Thomas, Valerie L.: Generation and Physical Characteristics of the 

ERTS MSS System Corrected Computer Compatible Tapes. NASA 

TM X-70426, July 1973. 

2. Earth Resources Technology Satellite Data Users Handbook. Goddard 

Space Flight Center, Greenbelt, Md., document 76SDS4258, 

Sept. 2, 1976. 

3. Whitley, Sidney L.: Low-cost Data Analysis Systems for Processing 

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

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

Remotely Sensed Multispectral Scanner Data. NASA TR R-434, Jan. 1975. 

5. Jones, Clay: Implementation of an Advanced Table Look-up Classifier 

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

International Symposium on Remote Sensing of the Environment, 

vol. II, Environmental Research Institute of Michigan, Ann Arbor, 

Mich., 1974, pp. 813-824. 

6. Eppler, W. G.: An Improved Version of the Table Look-up Algorithm 

for Pattern Recognition. Proceedings of the Ninth International 

Symposium on Remote Sensing of the Environment, vol. II, Environmental Research Institute of Michigan, Ann Arbor, Mich., 1974, 

pp. 793-812. 

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

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

Aug. 1977. 

8. Washington County, Mississippi. U.S. Department of Agriculture Soil 

Conservation Service, Soil Survey Series 1958, no. 3, May 1961. 

9. Cotton: Acreage, Yield, and Production by Counties, 1974. Mississippi 

Crop and Livestock Reporting Service, 1975. 

10. Soybeans: Acreage, Yield, and Production by Counties, 1974. Mississippi 

Crop and Livestock Reporting Service, 1975. 

30 
APPENDIX A 

GROUND TRUTH DATA FORMS 

This appendix provides samples of the ground truth data forms used 

for various applications programs. Figure A-l shows the form used for 

crops and pasture; figure A-2, forest or brush; figure A-3, extractive 

land uses; figure A-4, marsh areas. 

31 
GROUND TRUTH DATA FOR CROPS AND PASTURE 

TAKEN BY DATE 

TRAINING SAMPLE # MAP OR AIR PHOTO INDEX # 

ESTIMATED FIELD SIZE: ft x ft. or ACRES 

LOCATION 

County l/4 l/4 Section Township Range 

GENERAL CONDITION (1) 

DESCRIPTION (if not crop or pasture) 

CROP OR PASTURE SPECIES(2) VARIETY (if known) 

PLANTING TECHNIQ"E(3) PLANT HEIGHT (to closest ft) 

ROW WIDTH PHYSIOLOGICAL STATE (4) 

ROW DIRECTION VISUAL ASPECT(5) 

PERCENT GROUND COVER ( ) 0% to 20% ( ) 40% to 60% 

( )20% to 40% 

( ) 80% to 100% 

( ) 60% to 80% 

WEED INFESTATION (species & %, if greater than 20%) 

DISEASE INFESTATION (kind & %, if greater than 20%) 

INSECT INFESTATION (kind & %, if greater than 20%) 

SOIL CONDITION(6) 

SOIL MOISTURE(7) 

SOIL TYPE(8)(if available) 

OTHER COMMENTS (if needed) 

e.g. cro 

i 

, pasture, stubble, plowed, fallow. 

e.g. soy 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 A-l.- Ground truth data form for crops and pasture. 

32 
GROUND TRUTH DATA FOR FOREST/BRUSH VEGETATION 

TAKEN BY: DATE 

TRAINING SAMPLE # MAP-'OR AIR PHOTO INDEX* 

ESTIMATED FIELD SIZE: ft x ft. or ACRES 

LOCATION 

County l/4 l/4 Section Township Range 

KIND OF VEGETATION (check one) ( ) Natural Forest 

( ) Forest Plantation 

( ) Brush Vegetation 

If Natural Forest, indicate: 

(1) Major forest type (check one) 

Ma le-Beech-Birch 

11 Oa I: -Hickory [ ] L'~!i,Aslj~!k~~t'Y~~$ 

( ) Oak-Gum-Cypress ( ) Longleaf-Slash ( ) Mixed Hardwood 

(2) Species composition (to nearest 25%) Species % 

(3) Average age class of upper canopy 

trees (check one) 

( ) Less than 20 years ( ) 50 to 100 years 

( ) 20 to 50 years ( ) over 100 years 

(4) Average height class of upper canopy trees (check one) 

( ) Less than 20 feet ( ) 50 to 100 feet 

( ) 20 to 50 feet ( ) over 100 feet 

(5) Slope 

( ) O%tolo% ( ) 30% to 50% 

( ) 10% to 30% ( ) 50% or more) 

(6) Predominant Aspect 

( ) North ( ) South ( ) East ( ) West 

If Forest Plantation, indicate (1) Species (2) Spacing 

(3) Row Direction (4) Ave. age: (5) Ave. height: 

If Brush Vegetation, indicate species composition to nearest 25%. 

Species % Species % 

Figure A-2.- Ground truth data form for forest or brush. 

33 
GROUND TRUTH DATA 

Extractive Land Uses 

OBSERVATIONS MADE BY DATE 

IDENTIFIER NO.* Approx. Size X (feet) or 

-- 

acres. 

COUNTY 

'LOCATION (if known) _ 

ACTIVITY TYPE 

i 

t 

1 

Township Range Section Quarter Forty 

Sand pit 

Gravel pit 

Stone, dimension 

Stone, crushed 

Lime 

Cement 

( 1 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 A-3.- Ground truth data form for extractive land uses. 

34 
1. 

GROUHD TRUTH FORM FOR I'IARSH VEGETATION 

1. Sairple number . 

2. Date: . 

3. Time: . 

4. Vegetation type: 

(1) pure stand (iilonotypic) . 

(a) species: . 

(2) intermixed (less than G vascular species present) . 

(a) donlinant species: -' 

(3) intemixed (more than 6 vascular species present) 

(a) dominant species: , . 

(NOTE: If a species comprises less than 55 of vegetation do not regard 

as major or dominant component.) 

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) 11;ore than 20, but less tl!an 40 

(d) more than 40, but less than 60 

Fi‘gure A-4.- Ground truth data form for marsh areas. 

35 
(3) distributioli (of sub-clewnts in study area). 

(a) evenly ___- 

(b) center 

(c) peripheral 

(4) density (of vagetation as :L of surface arca). 

(a) dense > 90 

(b) in-temediate ( 70 

(c) sparse < 50 

-- 

. 

6. Height of plants (stands). 

(1) appt-oximate height of lilajor units: 

(a) species , heigh-t . 

(b) species , height 

(c) species , height . 

(2) approximate height of minor units: 

(a) species 3 height 

(b) species , height 

7. Status of vegetation: 

(1) approximate (Z) of dead-standing material. 

(a) major units (species) . 

(b) minor units (species) . 

a. Stage of growth: 

(1) lilajor units 

(a) dormancy (winter-no leaves) 

(b) dormancy (winter-leaves dead-standing) . 

(cl seedlings 

Cd) iiniiiature . 

(e) mature . 

(f) aniksis . 

Figure A-4.- Continued. 

36 
(9) vigor ---_-- ..- _____-___.__ ----- ._..- - --._ - .-.-- -.---._* 

('1) excel 1 cnt -.- .-- ------ - --- 

(2) fair -.-. _----.-- ----_- 

(3) poor ---- -._._ -~~_-_-_.----~* 

9. Surface of substratw: 

(1) covet-cd by algae . ----- 

(2) covered by s111al1 vascular plants --- -__--- -* 

(3) covered by detritus -- _---_-..------_* 

(4) barren _-. __._. -_--__- -- _-_--~-.. -* 

(5) substrate type 

(a) mud ---- ____---_--_--- 

(b) sand .- .--__- 

(cl sandy/wd ----- ---______- 

10. Hater level. 

(1) standing on surface of marsh --- 

(a) covered by tidal wa'm- 

.___ - 

(b) covered by river overflow --- 

(cl combination of both (a X II) above ___- 

(d) pemancni or semi-permncnt ------ -- 

(2) Depth of v:ater on marsh surface 

11. Conunen ts : 

Figure A-4.- Concluded. 

37 
APPENDIX B 

TOWNSHIP CODING DIAGRAM AND TOWNSHIP 171 DENSITY PLOTS 

A diagram of the coding for townships in Washington County, Mississippi, 

is shown in figure B-l. Figure B-2 shows a computer-implemented, 50-metercell density plot of land cover classifications in township 171; figure B-3 

shows a 16-hectometer (40 acre) cell density plot of the same area. 

38 
RlOW R9W R8W R7W R6W 

]f16 / 157 1 158 1 159 j 

RSW 

T18N 

\ 18’ I 182 I 183 184 i 

--- 

209 

T14N 

T17N 

Tl5N 

Figure B-l.- Diagram of township coding in Washington County, Mississippi. 

39 
Soybeans (1) i’fiffi%if Oak-gum-cypress (32) 

Cotton (2) ..::jiii;:;; Other (15 ,17,19 ,611 

Figure B-2.- Computer-implemented, 50-meter-cell density plot of land 

cover classifications in township 171. 

40 
m Soybeans (1) 

. . . . . . . . . . . . . . 

ZKIiiiCiJ Forest (32) 

- Cotton (2) ;;iii;;;i;;;; Other (15,17,19 ,61) 

. . . . . . _..-. 

Figure B-3.- Computer-implemented, 16-hectare (40 acre) cell density plot 

of land cover classifications in township 171. 

41 
APPENDIX C 

COMPUTER REQUIREMENTS FOR LOW-COST DATA ANALYSIS SYSTEM 

The following table shows the minimum and desired requirements for 

computer hardware in a low-cost data processing system. 

Characteristic Requirements 

__--- --- 

Minimum Desired 

Central processor unit with 

operator's console 

Memory 

Tape drives (computercompatible tape) 

Disk (rotating memory device) 

Line printer 

Electrostatic printer 

Card reader 

Floating-point hardware 

Microprogrammable writable 

control storage 

Operating executive system 

FORTRAN compiler 

Required 

16 000 16-bit words 

Two 7- or g-track 

drives 

12 000 000 

16-bit words 

Required 

Not required 

Required 

Not required 

Not required 

Not required 

Required 

Approximate cost (1975 prices) $75 000 to $80 000 

Required 

64 000 16-bit words 

(dual port) 

Two g-track drives, 

3.05 m/set (120 in/ 

set), 315 bytes/cm 

(800 bytes/in) 

46 000 000 

16-bit words 

Required 

Required 

Required 

Required 

Required 

Required 

Required 

$150 000 

42 
1. Report No. 2. Government Accession No. 

NASA RP-1016 

4. Title and Subtitle 

THE USE OF LANDSAT DIGITAL DATA AND COMPUTER-IMPLEMENTED 

TECHNIQUES FOR AN AGRICULTURAL APPLICATION 

7. Author(s) 

Armond T. Joyce and R. H. Griffin II 

9. Performing Organization Name and Address 

Lyndon 5. Johnson Space Center 

Houston, Texas 77058 

2. Sponsoring Agency Name and Address 

National Aeronautics and Space Administration 

Washington, D.C. 20546 

5. Supplementary Notes 

3. Recipient’s Catalog No. 

5. Report Date 

January 1978 

6. Performing Organization Cods 

JSC-12897 

8. Performing Organization R,eport No. 

s-477 

10. Work Unit No. 

177-52-89-00-72 

11. Contract or Grant No. 

13. Type of Report and Period Covered 

Reference Publication 

14. Sponsoring Agmcy Code 

6. Abstract 

Agricultural applications procedures are described for use of Landsat digital data and 

other digitized data (e.g., soils). The results of having followed these procedures are 

shown in production estimates for cotton and soybeans in Washington County, Mississippi. 

Examples of output products in both line printer and map formats are included, and a 

product adequacy assessment is made. 

7. Key Words (Suggested by Author(s)) 18. Distribution Statement 

Agriculture Earth resources 

Soils Pattern STAR Subject Category: 

Crop production estimates recognition 43 (Earth Resources) 

Information system Thematic mapping 

Remote sensor 

MUI tispectral scanner 

9. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price’ 

Unclassified Unclassified 47 $4.00 

*For sale by the National Technical Information Service, Springfield, Virginia 22161 

JSC Form 1424 (fled Nov 751 NASA - SC 

#7 U.S. GOVERNMENT PRINTING OFFICE: 1978-738-441 
.k + 
‘m 

mm 

- 

l .a%. 

. . >+‘I 7, ’ 1 

. L’ 

. 

. I’ i 

0 

.