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Produced by the NASA Center for Aerospace Information (CASI)
NASA CONTRACTOR REPORT 177327

(E85-10054 NASA-CG-177327) MULTI CIIUP A5EA Ne5-15251

ESTIMATION IN ICANG USING LCIUCN

(Technicolor uover_nmeut Services, Inc.)

J7 p do 403/MP A01 CSCL 06C Unclas

G3/43 00054

Crop Area Estimation

in Idaho Using EDITOR

E.J. Sheffner

^, EG ^g9A

CONTRACT NAS2-11101

December 1989

s^`^IYWWl^ I"""L''l"O^W6^'^

s

t!
NASA CONTRACTOR REPORT 177327

Crop Area Estimation

in Idaho Using EDITOR

I

f

a

E.J. Sheffner

Technicolor Government Services

Moffett Field, California

Prepared for

Ames Research Center

under Contract NAS2-11101

PJASA

National Aeronautics and

Space Administration

Ames Research Center

Moffett Feld, California 94035
,

Table of Contents

Introduction

Approach

Stratification

Study Area

Cover Types

Data Processing System

Approach Summary

Data Processing

Ground Data Preparation

Stratification

Segment Data

Landsat Data Preparation

Date Selection

Registration

Landsat to Ground Calibration

Selection of test and Training Sets

Digital Data Processing

Clustering

Classification and Aggregation

Estimation

Results

Discussion

Summary

References

Acknowledgments

Page

1

3

3

4

5

7

7

9

9

9

10

12

12

13

13

13

15

15

19

20

25

25

26

29

31

i
List of Tables

Page

Table 1. USDA Strata Definitions for Idaho Area Frame 9

Table 2. Number of Pixels and Percent Cover by Strata

and County 10

Table 3. Segments in the Four County Study Site 11

Table 4. Population (N) and Sample Size (n) - Strata

15 and 25 12

Table 5. Segment Assignments - Independent Training

and Test Sets 15

Table 6. Number of Pixels Packed for Training by

Cover Type and Training Set 16

Table 7. Number of Training Categories by Cover

Type and Training Set 17

Table 8. Percent Correct/Percent Commission Error 18

Table 9. Aggregation Totals, in Acres, for Strata

15 and 25 - Gooding County 19 

Table 10. Aggregation Totals, in Acres, for Strata

15 and 25 - Jerome County 19

Table 11. Aggregation Totals, in Acres, for Strata

15 and 25 - Lincoln Countv 20

Table 12. Aggregation Totals, in Acres, for Strata

15 and 25 - Minidoka County 20

Table 13. Acreage Estimates - Direct Expansion of

Ground Data 21 

Table 14. Acreage Estimates - Standard Procedure 23 

Table 15. Acreage Estimates - Jackknife Procedure 24

Table 16. Regression Estimate Parameters and Relative

Efficiency (RE) - Gooding, Jerome, Lincoln,

Minidoka Counties Combined 25

Table 17. Regression Estimate Parameters and Relative

Efficiency (RE) by County - Strata 15 27

ii
List of Figures

Page

Figure 1. Snake River Plain and the Four County

r;h rnl i Vi 1 , G

iii
Introduction

This report desribes work performed at NASA Ames Researcl:

Center on a multi- crop area estimation project in the central

Snake River Plain of Idaho in partial fulfillment of contract

NAS2-11101.

In 1982, H.A. Anderson, Director of the Idaho Image

Analysis Facility (IIAF) of the Idaho Department of Water

Resources (IDWR), approached the National Aeronautics and apace

Administration (NASA) with a request for assistance. Two of

IDWR's responsibilities are to monitor the extent of irrigated

land and to estimate, on an annual basis, the demand for water

for irrigation from ground and surace water sources. In order to

meet the Department's information needs, IIAF has been using

remotely sensed data, especially Landsat digital data and image

products. The first large scale project incorporating Landsat

multispectral scanner (MSS) data undertaken by IIAF was an

irrigated lands survey of the Snake River Plain - the premier

agricultural region in Idaho and one of the major areas of

irrigated agriculture in the United States. The irrigated lands

survey was completed in 1982. By the end of that year, IIAF was

seeking ways to increase the amount of information it could

obtain through digital data processing and requested that NASA

assist it in the research effort required.

Anderson approached NASA with a research plan consisting of

five tasks including:

e develop a procedure to detect change in irrigated

lands, i.e. land going into and out of irrigation,

• develop a procedure for multi-crop labelling and area

estimation in southern Idaho,

• determine the separability in spectral response of

irrigated and non-irrigated grain and alfalfa,

• determine the separability in spectral response of

irrigated land and natural wetland, and

• determine the relationships among biomass, spectral.

response and water-consumptive use and the extent to which those

relationships can be used to improve the accuracy of

water-consumptive use models.

The first task addresses the need for IDWR to locate sources

of water application. The latter four tasks are directed at

improving the accuracy of the input data in the water-consumptive

use model utilized by IIAF to estimate demand for irrigation

water.

NASA responded to IIAF's request for assistance by providing

research support in FY-84 on the change detection and multi-crop

area estimation tasks. The research was conducted at the Ames

Research Center (ARC) by the Technology Application Branch with

funding from the Western Regional Test and Evaluation (WRETE)

program. The following report describes the work undertaken at

awe
ARC between January 1983 and June 1984 on the multi-crop area

estimation task.

Although the area estimation task was initiated by IIAP, the

character of the work was influenced by the US Department of

Agriculture (USDA). USDA influence came from two sources.

Concurrent with the Idaho WRETE project, the Remote Sensing

Branch (RSB)-Statistical Research Division, Statistical Reporting

:; ­ rvirn or I,h„ USnA was working with ARC, the California

UiepoiLmonL or Wafer Resources and the Remote Sensing Research

Program (RSRP) of the University of California on the California

Cooperative Remote Sensing Project - a program to improve

multi-crop area estimation and mapping in California. Because of

the existing cocperation between ARC and RSB, the Idaho-WRETE

project had access to the USDA sampling frame for Idaho and

assistance with the operation of the EDITOR software system. The

second source of USDA influence came from the Boise, Idaho office

of the Statistical Reporting Service (SRS). SRS supplied the

ground data for the multi-crop task as part of a working

agreement between SRS and IDWR to determine if the same

multi-crop area estimation procedure could meet the needs of both

agencies and, therefore, save each agency the expense of

generating the data independently.

2
Approach

The approach to the multi-crop area estimation task evolved

as decisions were made on the nature of key elements in the data

analysis procedure. These elements included stratification,

study area, cover types, data processing system and area

estimation procedure.

stratification

The IIAF plan recommended evaluation of two candidate

stratification and sampling schemes as the first phase in the

development of a research approach to the multi-crop task. The

recommendation was made because stratification prior to sampling

can improve the efficiency of a crop area estimation procedure by

reducing the number of samples required and the variance of the

estimates. The two stratifications evaluated were the IIAP

stratification and the USDA sampling frame for Idaho.

The IIAF stratification is based on land use only or

location and field size. It was used in the 1980 irrigated lands

survey and IIAP was hopeful that the it would be suitable for

multi-crop area estimation. Twenty-four strata were defined in

the Snake River Plain. Strata definitions included large fields

- lower Snake River Plain, small fields- Boise region, rangeland,

wetlands, etc. Strata boundaries were drawn by inspection on

1:250000 scale Landsat color composite images. Sample units were

6.25 sq.km . (2.5 sq.mi.).

The USDA sample frame is a stratification bases] on

agricultural land use. Sample units, called segments, are

generally one square mile in size, but the sample unit size may

vary by stratum. Strata boundaries extend beyond the area of

interest for estimation, and strata definitions are not statespecific.

Evaluation of the USDA and IIAF stratifications was

performed under the direction of Dr. R.W. Thomas (ASAP) using a

tool for experimental design dr.veloped at the University of

California, Berkeley, called the survey planning model (SPM)

[11,12]. SPM determines the number of sample units required and

the cost to generate an area estimate given the requirements for

estimate accuracy, Landsat-to-ground correlations for the crop

types of interest, sample unit size, a digital map of the strata

boundaries, a digital map of the class boundaries, and estimated

cost per sample unit to collect ground data.

A complete data set for analysis with SPM was available for

the IIAF stratification only, because the USDA sampling frame for

Idaho was revised in 1982 and strata boundaries for the revised

frame were not available at the time of the SPM evaluation.

Consequently, sample size and number of sample units were

3
evaluated using the IIAF stratification and class map. The

output from SPM indicated that estimate costs wouldnotvary

S, 

significantly if either the USDA or IIAF sample unit size were

used [10]. Nevertheless, the number of sample units required to

generate crop acreage estimates within acceptable limits of error

was too large to be economical using the IIAF stratification.

Although the USDA stratification was not fully evaluated with

SPM, the stratification and sample size were selected for the

multi-crop task.

Study Area

The Snake River Plain is an elongated U-shaped region in

southern Idaho (Figure 1). The region increases in elevation

from the west, about 2000' at the Oregon border, to the east,

approximately 5200' at Ashton. The plain is confined within high

mountains to the north and south and varies in width between four

and sixty miles. Sagebrush and grass are the predominant natural

vegetation. The climate is arid to semi-arid, and the soils have

developed on highly permeable extrusive igneous rock.

Irrigation, from ground water and surface sources, is extensive.

Agriculture in the plain varies by field size and crop type.

IIAF selected three study sites that captured the variation in

the region (Figure 1). In the Boise area, site I, field size is

small, cover types are numerous and "ranchettes," houses on 5 to

40 acres of idle land or land used for pasture or crops, are

common. Site II, near Twin Falls, is a mixture of large and

small fields. It has less variation in cover types than site I

and more field crops such as sugarbeets and potatoes. Field size

is largest and variation in cover types least in site III,

centered on Blackfoot.

The research plan from IIAF suggested developing and testing

a multi-crop area estimation procedure using data from the three

study sites. For the research performed in 1983, a four county

study site was selected that included most of site II. The four

county site was selected for the following reasons:

a in the midst of the research year, the Technology

Applications Branch at Ames retired its two primary interactive

computing systems and brought new systems on-.line. Scheduling

delays caused by the systems switch made it doubtful that work on

three study sites could be completed on time.

• site II contained a good mix of field sizes and a

good representation of the crop types of interest to IDWR and

USDA.

• SRS collected ground sample data in 1983 from the

three sites as part of its annual June Enumerative Survey (JES).

SRS was able to collect additional data in site II so that there

was sufficient ground data from that site to develop independent

training and test sets for digital data processing.

4
The boundary of the study site was determined by the

estimation unit of interest to SRS and the location of the

Landsat frame covering the site. Because SRS was interested

primarily in county level acreage estimates, the study site wan

defined as Gooding, Jerome, Lincoln and Minidoka Counties (Fiqure

L). The four counties fall entirely within Landsat 4 path/row

40/30.

Cover Types

The 1'SAF research proposal specified five cover types of

interest: potatoes, small grains (wheat, barley and oats),

alfalfa, row crops (corn, sugarbeets, and beans), and other

irrigated crops. Because SRS was also interested in potatoes an:]

sugarbeets, those two crops were designated the crops of primary

interest. The multi-crop task was able to generate estimates for

potatoes, sugarbeets, alfalfa, smat.l grains, corn and beans.

4

5
Figure 1.

Snake River Plain and the Four County Study Site

"'>;'•.`'` Snake River Plain r7 Proposed Study Silos - IIAF

1 -Boise

2 - Twin Falls

3 - Blackfoot

Four County Study Sits

--..- l.andeat 4 Frame 4030

6
Data Processing System

The IIAF plan did riot specify a software system to use i,i

the area estimation procedure but required the software to b^

compatible with the IIAF data processing system. EDITOR was thv,

software system chosen for the task.

EDITOR was developed by USDA in the mid-1970's to improva

the accuracy of its large scale acreage estimates of major crops

in the Midwest. The system generates a regression estimate using

ground sample units (segments), and corresponding Landsat MSS

i data from a maximum of two acquisitions. Segments are selected]

through a ' stratifted random sampling procedure, and a L+Inds•3t

pixel classifier is compiled and tested using data from all the

segments. Descriptions of the development and operation of

EDITOR are given in Ozga, et. al.[8] and Hanuschak, et. al.

C3,4]. During the time that work on the multi-crop task was

performed, EDITOR resided on a PDP-10 at Bolt, Beranek, and

Newman(BBN) in Cambridge, Mass. and was accessible to ARC

through the ARPANET.

EDITOR was selected for data processing and area estimation

in the multi-crop task because it m.jt the project specifications

and was the best system available for the task at ARC in FY-84.

EDITOR software is currently being re-written in a "portable"

format, and it is intended that most, if not all, EDITOR

functions will be transferrable to the IIAF system. Due to the

system changes at ARC described above, EDITOR was the only

reliable software system available that could generate the

estimates for the four county study site and meet the schedul.e

given in the research plan.

Use of EDITOR in Idaho was encouraged by RSB and SRS in

order to evaluate the performance of standard EDITOR processing

and the use of EDITOR for acreage estimation at the county level.

The standard EDITOR procedure uses data from all sampled segments

to build the classifier and test the results. Recent work on the

California Cooperative Remote Sensing Project suggested that a

significant bias is introduced by training and testing with the

same data [5]. The experimental design for the multi-crop task

in Idaho was modified to include an EDITOR-based procedure that

used independent training and testing.

Approach Summary

Through the efforts of personnel from IIAF, USDA, ARC an.l

RSRP, a plan was developed for multi-crop area estimation that

contained the following elements:

• Stratification and sampling were accomplished using

the USDA sampling frame and sample segments.

* The study site was in the central Snake River Plain -

Gooding, Jerome, Lincoln and Minidoka Counties.
* Two dates of landsat MSS data from the 1983 grawinj

season were required.

• Two procedrrs for data processing would be tsstad.

One procedure would follow standard EDITOR processing for a

multi-date data set. The other procedure would use EDrni

software but would employ independent training and test data

sots.

e County level and study site acreage estimates would

be generated for potatoes, sugarbeets and other crops of interest

with sufficient representation to make estimation feasible.

8
Data Processing

Ground Data Preparation

The EDITOR system requires both ground data and Landsat MSH

data. The ground data consisted of digital maps of the segments

and stratification, and digital data files containing

descriptions of the contents of each field within each each

segment, a segment catalog file identifying the stratum from

which each segment was drawn and a frame unit file Listing the

number of sample segments in each stratum by county.

Stratification

Six strata were defined by USDA in the area frame revised in

1982. Strata definitions are given in Table 1. Virtually all

agriculture in the four county test site was located in strata 13

and 25. Table 2. lists the percent of land in each county

occupied by the each stratum. Strata boundary information was

recieved at ARC on maps, scale 1 inchm = 2 miles. The boundari.e:;

were digitized using EDITOR software and a standard USDA

digitizing tablet .linked to BBN via the ARPANET.

Table 1.

USDA Strata Definitions for Idaho Area Frame

Stratum 15 - General crops; 50% or more cultivated along the

Snake River. All irrigated, intensively cultivated land in Canyon, Ada, Owyhee, Elmore, Gooding,

Twin Falls, Lincoln, Jerome, Minidoka, Cassia,

Po, er, Bannock, Caribou, Bingham, Bonneville,

T ton, Madison, Jefferson, Fremont, Clark, and

Butte Counties. This stratum should contain virtually all potatoes and sugarbeets.

Stratum 25 - 15-49% cultivated used in conjunction with stratum

15.

Stratum 31 - Agri-urban; more than 20 dwellings per square

mile, residential mixed with agricultural.

Stratum 32 - Residential/Commercial; more than 20 dwellings per

square mile. No agriculture present.

Stratum 42 - Rangeland; Jess than 15% cultivated.

Stratum 62 - Water bodies larger than one square mile.

9

y.•YL.^ ^ fY..r L..' ^ ^ rs .:.
Tab1.0 2.

Number o[ Pixels and

Pot'Oent c'nvvr by St r.itnm .111 11 ,•dill y

County Stratum Pixels % Cover

Gooding 15 171924 29.2

25 69199 11.8

31/32 2160 .4

42 345192 58.6

Jerome L5 212519 44.2

25 127406 26.5

31/32 4998 1.0

42 135221 28.1

62 601 L

Lincoln 15 106224 13.8

25 78762 10.3

31/32 1945 .2

42 580289 75.6

Minidoka 15 257674 62.4

25 52428 12.7

31/32 9145 2.2

42 93765 22.7

Segment Data

The ground data compiled for the multi-crop task differed

from a standard EDITOR data set in two ways that affected the

results of the task - the sampling rate at the county level was

higher, and the type of information collected from each segment

varied.

All sample unit (segment) data for the task was collected

by, and supplied to ARC through, the SRS office in Boise. SRS

collects segment data every June as part of the June Enumerative

Survey (JES). The JES was not designed for county level

estimation, and the number of segments sampled in the study site

in 1983 was only twenty. In order to improve the chances of

generating reasonable regression estimates at the county level,

SRS agreed to collect data from twelve additional segments in the

study site. The additional segments were JES segments from

previous surveys; however, the amount of information collected

on the additional segments was not as extensive asthat collectedd

on the JES segments from 1983 and included field boundaries and

crop labels only.

10
Table 3. lists the 32 segments from which data was

collected for the multi-crop task with their stratum and counLI

location. The segments numbered 2000 and 3000 were JGS segmenta

in 1983. The segments numbered 6000 and 9000 were JES segments

in previous years, rotated out in 1983, and surveyed for use ii

the multi-crop task only.

Table 3.

Segments in the Four County Study Site

Segment County Stratum

number

2078 Gooding 15

2081 Minidoka 15

2082 Jerome 15

2090 Gooding 15

2093 Minidoka 15

2094 Lincoln 15

2105 Jerome 15

2106 Jerome 15

2141 Minidoka 15

2142 Lincoln 15

2153 Jerome 15

2165 Minidoka 15

2174 Gooding 15

2177 Minidoka 15

2253 Lincoln 25 Total. by Stratum:

2254 Lincoln 25 Stratum 15 = 27

2263 Minidoka 25 Stratum 25 = 5

3129 Minidoka 15 Total by County:

3130 Jerome 15 Gooding = 6

3189 Jerome 15 Jerome = 9

Lincoln = 6

6102 Minidoka 15 Minidoka = 11

6213 Lincoln 15

6216 Gooding 15

6217 Gooding 15

6218 Gooding 15

6245 Lincoln 25

9088 Jerome 25

9091 Jerome 15

9093 Jerome 15

9098 Minidoka 15

9101 Minidoka 15

9102 Minidoka 15

ll
Field boundary data was inscribed on large scale aerial

photography and the boundaries transferred by IIAF to 1:24000

scale maps. Copies of the maps were sent to ARC for digitizing.

Segment data was digitized directly from the maps using EDITOR

software to create segment network files.

Cove: type information andreported field size were sent to

ARC separately. Ground truth files were created manually using

the ground truth file creating and editing routine in EDITOR.

Reported field size was included in the file when available, i.e.

only for the 1983 JES segments. Digitized acreage was

substituted for reported acreage for the previous years segments.

A segment catalog file and a frame unit file were compiled

manually using EDITOR routines. The frame unit counts wer.

supplied by SRS-RSB. Table 4. lists the frame unit information.

Table 4.

Population (N) and Sample Size (n) - Strata 15 and 25

Stratum County Total

Gooding Jerome Lincoln Minidoka

N n N n N n N n N n

15 218 6 272 8 140 3 331 LO 961 27

25 42 0 81 1 54 3 32 1 209 5

Total 260 6 353 9 194 6 363 11 1170 32

Landsat Data Preparation

Date Selection

Landsat MSS data have been shown to be most effective for

discrimination of crop types when the data are obtained at or

near the period in the crop phenology when infrared response is

at its peak [2]. The crops of interest in the multi-crop task

reach peak infrared response at two different times in L•he

growing season. Small grains (wheat, barley, and oats) are

planted in early spring, reach maximum infrared response in early

summer and are harvested by the end of July. Other field crops,

such as potatoes and sugarbeets, are planted in early summer and

reach maximum infrared response in mid-summer. Consequently, it

was desirable to obtain at least two acquisitions of MSS data to

perform the multi- crop task. Since EDITOR software can process

up to eight channels of data, a maximum of two Landsat

acquisitions was sought. The preferred dates were late May and

mid-August.

A satisfactory May acquisition for frame 4030 was obtained,

but the earliest mid-summer acquisition with a low percentage of

cloud cover was &sptember 1. The September date was ased

12
^i

y ' although it was considered a marginal date and, subsequently,

proved to be too late in the season forgood summer crop

separability. The Landsat acquisitions used for the multi-crop

{- task were%

f

Frame Scene ID Date

4030 4031617491 28 May 83

4041217491 1 Sept 83

Registration

Before the Landsat MSS data were extracted for classiEier

training, the two Landsat acquisitions were registered to a

common point-line coordinate system. The scene-to-scene

registration was accomplished using a block corre;ation technique

in EDITOR. The May acquisition was selected as the base :late.

Output from the registration procedure was two eight channel

tapes containing the eight registered bands. The data set was

split at column 1800 so that the record would not exceed B13N

system limitations, and the first 1599 lines removed because they

were north of the study site. Registration was confirmed by

visual inspection of gray scale maps of blocks of data from bands

on both dates. Field patterns were observed to ensure that .i

proper overlay had been achieved.

Y

Landsat to Ground Calibration

The final step in Landsat data preparation was to generate a

Landsat-to- ground calibration file. The file is used to locate

the digitized ground segment and stratum data in the Landsat

scene. MSS data from the 28 May acquisition was displayed on a

CRT using a HP3000 system and IDIMS software. At least one point

was located on each 7.5' quad in the four county area and the

f point/column coordinates recorded. The points were digitized and

the corresponding Landsat coordinates entered. EDITOR software

was used to generate a second order polynomial. relating the

ground data to Landsat coordinates with a mean square error of

less than .5 pixels.

Selection of Test and Training Sets

The approach to the multi-crop task specified using all

segments to train the classifier and test the results and using

independent training and test sets of segments to accomplisl-i the

same. Because there were only 32 segments in the sample,

dividing the sample in half, and using half for training and half

for testing, was not considered feasible, and a "modified

jackknife" approach was adopted to define the independent

training and test set.

13
The available segments were divided into three groups, anci

three training and three test sets were created. Each training

set contained two thirds of the segments, while the remaining

third were reserved for testing. Each test set contained

unique list of segments, but half the segments in each training

set were also used in one other training set. Following

classification, the segment results from the three test sets wera

combined to form the independent test set used in the regression

estimates.

Sugarbeets and potatoes were the crops ok primary interest

and served as the starting point for development of the training]

sets. Three segments contained at least one potato field larger

than 100 acres and five segments contained at least one sugarbeet

field of similar size. The three segments with the large potato

fields were placed one in each training set using a random number

table to select the segments. The five segments with large

sugarbeet fields (there was no duplication with the first three

segments) were placed similarly in the three training sets, but

the segment with the largest field of sugarbeets was placed on

its own. Nine segments contained at least one sugarbeet field

with no field larger than 100 acres, and seven segments contained

potato fields with the same size limit (three segments were on

both lists). The segments with the small sugarbeet fields ware

randomly selected and placed into the three sets, three to a set.

The segments containing potatoes which had not already been

assigned by virtue of their sugarbeet fields were randomly

selected and placed in the three sets. The remaining segments,

none of which contained either sugarbeets or potatoes, wera

randomly selected and assigned, in turn, to one of the sets.

Segment assignments for the training and test sets are shown in

Table 5.

14
Table 5.

Segment Assignments - Independent Training and Test Sets

Training Set A Test Set A Training Set B Test Set B

2078 2093 6213 2105 6213 2078

2105 2165 2253 2093 2253 2165

2177 2142 2081 2142 2081 2177

9102 9091 2141 9091 2141 9102

9088 3129 9101 3129 9101 9088

2106 6102 2153 6102 2153 2106

2090 6217 2254 6217 2254 2090

9093 2082 3130 2082 3130 9093

2263 3189 6218 3189 6218 2263

2094 6245 6245 2174 2094

6216 2174 9098 6216

9098

Training Set C Test Set C

2078 6213 2105 2174

2165 2253 2093 9098

2177 2081 2142

9102 2141 9091

9088 9101 3129

2106 2153 6102

2090 2254 6217

9093 3130 2082

2263 6218 3189

2094 6216 6245

Digital Data Processing

Clustering

Landsat data from all cover types was packed using EDITOR

software. In the packing process, the data for a given cover

type is gathered from all segments and placed in a disk file.

The geometric relationship among the pixels is lost. Packed

files were created for each cover type by training set. Two

packed files were created for each test set; one file contained

the data from all cover types within the segments excluding

background pixels (the NB file), and the second file contained

the data from all cover types within the segments excluding the

background pixels, a one pixel border on the edge of each field,

and fields i;aentified as bad fields in the ground data files (the

-NB file). Table 6. lists the cover types found in the training

sets and the number of pixels packed by cover type and training

group.

15
Table 6.

Number of Pixels Packed for

Training by Cover Type and Training Set*

Standard Training Set Training Set Training Set

Cover Procedure A B C

Alfalfa 1232 694 874 896

Corn 680 503 378 479

Farmstead 19 19 16 3

Sugurbeets 974 695 656 597

Wasteland 140 101 94 85

Wheat** 3437 2176 2505 2035

Pasture 989 831 845 302

Idle 1337 991 613 742

Potatoes 721 462 517 432

Wild hay 15 15 15 5

Other crops 5 0 5 7

Other hay 7 1 6 0

Range 1990 1454 563 1963

Beans 328 244 271 141

Onions 63 63 63 0

Unknown 155 134 63 113

Total 12092 83B3 7484 7800

*background and field border pixels not packed

**small grains

Standard EDITOR processing uses the algorithm CLASSY to

cluster data, i.e. to group pixels with similar spectral

characteristics [7]. USDA experience with CLASSY has shown that

the algorithm does not perform well with fewer than 100 pixels;

consequently, 100 pixels was used as the minimum number for

CLASSY clustering. CLASSY does not require the operator to

specify any parameter settings other than the maximum number of

iterations to perform on the data. The program was run on the

Cray 1S at ARC.

Wasteland, farmstead and onions fell below the minimum pixel

count for CLASSY clustering but were felt to be represented

sufficiently in the data set to warrant inclusion in the

classifier. Clustering was performed on those three cover types

at BBN using the EDITOR version of ISOCLAS. Unlike CLASSY,

ISOCLAS requires the operator to set three parameters - the

number of clusters to create in the data set, the number of

16
iterations to perform, and the minimum cluster disLancf!

acceptable before clusters will be combined. The cover type:3

wild hay, other hay and other crops wore not clustered becausn

too few pixels remained in the training set after field border

pixels were removed. All clustering was performed by cover type.

Statistics from the CLASSY clustering -- means, variances

and covariances -- were file transferred to BBN and reformatted

into EDITOR format statistics files, one statistics file for eacli

cover type in each training group. The crop specific statistics

files from CLASSY and ISOCLAS were combined by training group

using the statistics file editing program in EDITOR. Clusters

with fewer than 10 pixels were deleted from the file. Four

classifiers were compiled in the above manner - one for the

standard procedure and one for each of the three training groups

in the modified jackknife procedure. The cover types included in

the classifiers and the number of clusters in each category are

listed in Table 7.

Table 7.

Number of Training Categories

by Cover. Type and Training Set

Standard Training Set Training Set Training Sel

Cover Procedure A B C

Alfalfa 7 2 3 6

Corn 6 5 4 6

Sugarbeets 5 6 8 6

Wasteland 2 2 2 2

Wheat* 13 11 10 to

Pasture 7 2 9 2

Idle 6 2 6 5

Potatoes 6 6 2 2

Range 8 8 2 9

Beans 6 4 5 3

Onions 3 3 3 3

Farmstead 1 1 1 1.

Total 70 53 55 55

*small grains

17
Classification and Aggregation

Maximum likelihood classifications were performed on the Ntl

} and -NB files for all test groups using the appropriate traininq

statistics. In addition, each of the three sets of training

statistics was used to generate a maximum likelihood

classification of the entire scene.

Percent correct classified was calculated from the

classifications of the -NB files. The results from the standard

procedure and the independent training sets are given in Table 8.

The test with the standard procedure performed better than the

modif• Led jackknife (as expected) because, in the standard set,

a.11. segments were used for training and testing. The percent

correct and commission error give an impression of the amount of

confusion among cover types. The variation in percent correct

among the three independent test groups may be indicative of the

limited number of pixels available for training.

Table 8.

Percent Correct/Percent Commission Error

Standard Independent Training and Testing

Cover Procedure Group A Group B Group C

Alfalfa 87.2/8.5 74.4/6.6 91.3/36.7 83.0/16.2

Corn 80.4/28.4 61.0/63.1 38.0/36.4 20.0/79.6

Farmstead 73.7/72.0 ---- 66.7/89.5 87.5/64.1

Sugarbeets 84.1/14.5 78.9/12.4 80.0/34.5 76.3/38.0

Wasteland 31.4/75.4 0.0/100.0 27.3/97.2 52.7/83.9

Small grain 89.2/5.8 90.4/11.5 62.5/22.3 77.1/5.8

Pasture 76.7/26.5 55.1/70.0 44.4/93.7 3.1/78.5

Idle 76.9/11.7 27.5/25.8 67.7/27.4 65.5/38.9

Potatoes 77.9/38.6 60.1/53.6 22.4/74.0 39.5/51.L

Range 82.9/16.1 65.9/29.6 9.0/25.0 37.0/98.6

Beans 66.5/41.1 51.2/29.6 50.0/88.0 20.3/73.4

i

Onions 85.7/51.4 ---- ---- 92.1/46.3

Overall

% Correct 81.4 70.6 42.0 55.2

18
1

The total number of pixels classified in each cover type by

county and stratum was required prior to estimating acreages.

The EDITOR aggregation program was employed to intersect the full

scene classified images with the mask files containing county and

stratum boundaries. The output from the aggregation program was

a table listing the number of pixels from each spectral class in

each stratum within each county. The class totals ~.sere summed by

cover type to generate the required numbers.

The total acreage numbers for the modified jackknife

procedure required an additional step because there were three

separate classifications of the four county area, and, therefore,

three "independent" totals for each cover type. The totals were

averaged to obtain the number used as input in the regression

estimator.

The results of the aggregation for the independent and

dependent data sets are listed in Tables 9 to 12.

Table 9.

Aggregation Totals, in Acres,

for Strata 15 and 25 - Gooding County

Standard Independent Training and Testing Jackknife

Cover Procedure Group A Group B Group C (Mean A+B+C)

15 25 15 25 15 25 15 25 15 25

Alf, 27365

Corn 9742

Sgb. 3698

Bns. 5357

Smgr. 22881

Pot. 11796

4896

2347

410

766

3768

2579

25035

11868

4722

3762

24905

14816

4312

3398

564

474

4007

3216

29401

9172

8169

5888

21616

6181

5246 317G6

1746 10696

1134 5512

1271 2426

3856 25642

1152 2967

5657

2175

741

387

4374

523

28734

10578

6134

4025

24057

7988

5071

2439

813

2052

4079

1G30

Table 10.

Aggregation Totals, in Acres,

for Strata 15 and 25 - Jerome County

Standard Independent Training and Testing Jackknife

Cover Procedure Group A Group B Group C (Mean A+B+C)

15 25 15 25 15 25 1S 25 15 25

Alf. 33497 5936

Corn 9445 1857

Sgb. 8352 1934

Bns. 9681 1503

Smgr. 42203 3695

Pot. 13179 3131

31751

12105

9431

7114

44271

15569

5561

2897

1991

1057

10851

2771

33930 6210

9393 1620

13758 2895

10349 2223

41034 10833

7739 1366

36421

10382

10198

7175

44391

5590

6520

2132

2378

787

10602

1015

34034 6097

10627 2219

10303 1601

8212 1355

43232 10750

9633 1717

19
c

a.

Table 11.

Aggregation Totals, in Acres,

for Strata 15 and 25 - Lincoln County

Standard Independent Training and Testing Jackknife

Cover Procedure Group A Group 8 Group C (Mean A+B+C)

15 25 15 25 15 25 15 25 15 25

Alf. 15484 3830

Corn 3574 1129

Sgb. 3228 519

8ns. 1192 510

Smgr. 14194 6109

Pot. 5009 1526

for

Standard

Cover Procedure

15 25

Alf. 22303 982

Corn 8187 764

Sgb. 27772 2048

8ns. 10751 560

Smgr. 69994 5715

Pot. 14851 1017

11841 2870 17633 4318 18194 4378 15899 3865

5323 2186 3544 810 3591 1202 4152 1399

3350 517 4687 893 3746 692 3927 2104

1394 350 2422 809 1330 340 1715 500

16091 5181 L4810 4928 15870 5154 15590 5088

6467 525 2765 700 1296 3G8 2509 531,

Table 12.

Aggregation Totals, in Acres,

Strata 15 and 25 - Minidoka County

independent Training and Testing Jackknife

Group A Group 8 Group C (Mean A+13+C)

15 25 15 25 15 25 L5 25

20459 897 21919 1130 23872 1061 22083 1020

11128 1570 7249 520 9327 952 9234 1014

28495 1963 33967 2509 31172 2173 31210 1562

8141 453 11657 665 6696 382 8831 385

75013 6233 69988 6317 72884 6047 72629 G199

16127 1189 9707 728 7277 583 11037 833

Estimation

Two types of estimates were generated for each cover type --

direct expansion of the ground data and regression. The direct

expansion estimates were generated using EDITOR software and the

ground truth files. Since digitized acreage was substituted for

crop acreage in the twelve segments not from the JES in 1.983, the

direct expansion estimate, given in Table 13. may be slightly

inflated.

20
Table 13.

Acreage Estimates - Direct Expansion of Ground Data

Stratum 15 Stratum 25* Strata 154.25*

County Cover Estimate CV(%) Estimate CV(%) EsLimate CV($)

Gooding Alfalfa 18075 28.23 -- -- 21558 35.83

Corn 20695 19.54 -- -- 24693 29.45

Sugarbeets 0 -- -- -- 0 --

Beans 1231 62.67 -- -- 1469 (4.55

Small grain 28412 37.36 -- -- 33886 45.42

Potatoes 11314 50.57 -- -- 13494 55.25

Jerome Alfalfa 32755 11.50 -• -- 39284 22.46

Corn 8381 53.01 -- -- 9668 61.09

Sugarbeets 3672 69.93 -- -- 6577 32.37

Beans "6319 47.78 -- -- 21132 56.00

Snall gr. 39276 25.30 -- -- 40561 20.10

Potatoes 3889 91.08 -- -- 4487 99.64

Lincoln Alfalfa 13351 49.97 3925 63.64 17277 41.23

Corn 2613 98.92 86 97.18 2699 95.81

Sugarbeets 7000 98.92 4655 97.18 11755 70.82

Beans 5292 98.92 0 5292 98.92

Small gr. 25223 30.71 11509 79_21 36732 32.57 

Potatoes 5338 98.92 0 5338 98.92

Mini.loka Alfalfa 28906 29.08 --- -- 28618 33.31

Corn 8162 42.24 -- -- 8137 46.28

Sugarbeets 30150 31.84 30059 35.98 

Beans 6193 55.67 6174 59.90

Small gr. 62012 19.24 -- -- 67306 19.01

Potatoes 12952 30.11 -- -- 12912 34.30

Above Alfalfa 93263 12.61 10713 57.46 103976 12.77

Four Corn 39781 22.10 2,,00 98.80 39982 22.00

Combined Sugarbeets 41604 32.11 13539 74.05 55143 30.89

Beans 31079 35.42 0 -- 31079 35.42

Small gr. 154870 12.76 41377 47.73 196248 14.24

Potatoes 33154 25.97 0 -- 33154 29.97 

*If no estimate is listed for stratum 25 (--), less than 2

segments were present from that stratum in that county, and

the combined estimate was generated by pooling strata 15

and 25.

21
Y[kc] = acreage of crop c

b[Oc],b[lc] analysis district

parameters

X[kc] - number of pixels

county k

V[kc] = county effect on

county k

E[kc] - random error

in county k

single variable regression

classified to crop c in

the regression for crop c in

The Landsat to ground correlations for the regression

estimates came from the classifications of the NO files.

Following classification, segment total files were created for

each of the four classifications. The segment total files were

crop specific. Each file contained a list of segments, the

stratum assignment for each segment, and the number of pixels of

the given cover type in the segment from the ground data and the

Landsat classification. The segment total files from the

modified jackknife procedure contained only about 1/3 of the

segments and had to be combined using a text editor to put the

data in a format suitable for estimation.

The regression estimates were generated using a procedure

under study by USDA. The procedure employs the Dattesse/Fuller

estimator to calculate small area (county) estimates from data

covering a larger region [1]. The Dattesse/Fu.11er model is:

Y[kc] = b[Oc] + b[lc] (X[kc]) + VLkc] + E[kc]

where:

The characteristics of the model are described in Sigman and

Walker [9], and its use in an agricultural inventory similar to

the one described in this paper is given in Holko [4]. The

Dattese/Puller program was applied to the four county area

because county estimates were de-,red. The program works on one

cover type at a time. It requires as input an EDITOR segment

total file, the segment catalog and frame unit files, the number

of frame units (sample units) by county and stratum, and the

aggregated total of pixels (or acres) of the cover type of

interest by county and stratum. Output from the program includes

the following options: estimate of the mean, estimate of the

total, b0, bl and r-square by county and combined by stratum.

The Dattesse/Puller estimates were generated for the standard and

modified jackknife procedures. Results are given in tabels 14

and ].5.

22
Table 14.

Acreage Estimates - Standard Procedure*

Stratum 15 Stratum 25 Strata 15+25

County Cove Estimate CV(%) Estimate CV(%) Estimate CV(S)

Gooding Alfalfa 29376 7.05 GOG6 6.69 35443 5.95

Corn 11654 14.18 672 84.67 12372 14.17

Sugarbeets 1140 237.72 586 40.9G 1727 157.50

Beans 3755 62.23 -- -- 3755 --

Small gr. 24912 11.88 3446 19.41 28378 10.69

Potatoes 9281 28.20 -- -- 9281 --

Jerome Alfalfa 34091 6.47 6415 .85 40507 5.45

Corn 2919 59.68 477 90.57 3396 52.86

Sugarbeets 8109 35.65 2930 1.96 11040 26.20

Beans 16423 15.41 -- -- 16423 --

Small gr. 41443 7.GO 3642 72.46 4508G 9.11

Potatoes 5724 48.57 -- -- 5724 --

Lincoln Alfalfa 17825 10.35 4858 .42 22684 8.13

Corn 2008 70.42 287 81.18 2296 62.41

Sugarbeets 3915 63.65 527 4.65 4488 55.53

Beans -- -- -- -- -- --

Small gr. 18043 14.91 5419 18.62 23463 12.25

Potatoes 819 339.44 -- -- 81.9 --

Minidoka Alfalfa 24672 9.80 1279 1.64 25952 9.32

Corn 4065 46.27 229 75.11 4295 43.98 

Sugarbeets 35009 9.69 2360 .96 37369 9.08 

Beans 11494 24.25 -- -- 11494 --

Small gr. 69911 5.08 6032 16.10 75943 4.85

Potatoes 6495 48.25 -- -- 6495 --

Above Alfalfa 105669 4.07 18619 2.23 124586 3.48

Four Corn 20684 17.85 1667 80.98 22316 17.58

Combined Sugarbeets 48174 11.93 6450 3.92 54625 10.44

Beans 29561 6.53 0 -- 29561. --

Small gr. 154311 3.95 18561 19.95 172872 4.13

Potatoes 22320 25.63 0 -- 22320 --

*Estimates and errors were calculated using the Battese/Ful.l.er

estimator in the USDA-SRS program DFB; delta=l.

23
Table 15.

Acreage Estimates - Jackknife Procedure*

Stratum 15 Stratum 25 Strata 15+25

County Cover Estimate CV($) Estimate CV(3) Estimate CV($)

Gooding Alfalfa 25605 12.77 7049 12.26 32655 10.36

Corn 19991 17.45 244 .81 20236 17.24

Sugarbeets 294 1274.83 875 29.49 1169 321.39

Beans -- -- -- -- -- --

Small gr. 29434 15.29 3994 8.89 33428 13.50

Potatoes 9769 4.25 -- -- 9769 --

Jerome Alfalfa 32987 10.43 6675 17.25 39662 9.15

Corn 7463 50.90 216 1.90 7679 53.21

Sugarbeets 5892 67.00 1159 1.55 7142 --

Beans 17505 30.54 -- -- 17505 --

Small gr. 42519 11.49 11210 3.61 53729 9.12

Potatoes 1-616 123.64 -- -- 3616 --

Lincoln Alfalfa 16120 17.97 5824 12.29 21945 13.37

Corn 2315 131.54 112 .14 2427 131.19

Sugarbeets 3666 93.94 2633 .27 6299 54.69

Beans 1651 285.71 -- -- 1651 --

Small gr. 20055 20.67 5201 3.27 25257 16.43

Potatoes 2229 169.54 -- -- 2229 --

Minidoka Alfalfa 2!+893 14.72 1455 28.59 27348 14.02

Corn 5375 81.30 103 .15 5479 79.76

Sugarbeets 35245 13.34 2015 .35 37261 12.62

Beans 7042 82.28 -- -- 7042 --

Small gr.. 71480 7.85 6072 2.64 77508 7.24

Potatoes 10694 48.80 -- -- 10694 --

Above Alfalfa 100605 8.23 21003 11.49 121611 5.79

Four Corn 35164 21.89 677 1.11 35823 21.48

Combined Sugarbeets 45188 17.43 6683 5.49 51872 15.20

Beans 24309 -4.87 -- -- 24309 --

Small gr. 163489 5.81 26434 2.58 189923 5.01

Potatoes 26304 35.57 -- -- 26304 --

*Estimates and errors were calculated using the Battese/Fuller

estimator in the USDA-SRS program DFB; delta=l.

24
Results

Discussion

Acreage estimates at the county level and for the four

counties combined were generated using three methods -- direct

expansion of the ground data, the standard procedure and the

modified jackknife procedure. The standard procedure performed

the best for the four county combined estimates i.e. errors for

all crops were lowest and Landsat-to-ground correlations

(R-squares) were best (tables 13-16). Acreage estimates, errors

and R-squares from the modified jackknife procedure were similar

to those from the standard procedure for three cover types --

sugarbeets, alfalfa and small grain.

Table 16.

Regression Estimate Parameters and Relative Efficiency (RE)*

- Gooding, Jerome, Lincoln, Mini.doka Counties Combined

Standard Procedure

Stratum 15 Stratum 25 Strata 15+25

Cover b0 bl r2 RE b0 bl r2 RE b0 bl r2

Alf. 12.1 .94 .89 8.41 4.3 1.29 .99 29.48 9.2 .98 .90

Corn 16.4 1.24 .84 5.53 -.8 .30 .51 1.02 -22.1 1.31 .84

Sgb. -1.6 1.23 .88 8.35 1.6 1.15 .99 827.91 -.B 1.20 .91

ens. -29.4 2.10 .81 4.77 -- -- -- -- -29.4 2.10 .81

Smgr. 6.3 .99 .91 25.29 -12.3 1.06 .99 73.50 1.6 1.01 .94

Pot. -24.2 1.02 .65 2.18 -- -- -- -- -24.2 1.02 .65

Jackknife Procedure

Stratum 15 Stratum 25 Strata 15+25

Cover b0 bl r2 RE b0 bl r2 RE b0 bl r2

Alf. 23.5 .77 .74 3.48 -1.7 1.44 .95 11.12 20.3 .81 .76

Corn 6.6 .79 .16 1.09 -.6 .11 .97 15.88 1.8 .84 .22

Sgb. -12.7 1.11 .68 2.84 -3.0 1.29 .99 352.30 -12.4 1.17 .76

Bns. -1.6 1.14 .14 1.06 -- -- -- -- -2.9 1.14 .17

Smgr 16.9 .94 .80 5.64 -8.13 1.08 .99 113.15 9.2 .99 .87

Pot. 6.2 .63 .12 1.05 -- -- -- -- .8 .69 .17

*RE = ground variance/regression variance

-- = could not be calculated due to zero value in one term

Although the errors and R-squares from the modified

jackknife procedure for corn, beans and potatoes were less

satisfactory than those from the standard procedure, they may be

more representative of the informational content of Landsat data,

because the same data was not used for training and testing the

classifier. In that regard, the relative efficiencies for the

jackknife procedure given in Table 16. are more conservative

indicators of what can be expected when Landsat data is used in

this type of an inventory than the relative efficiencies shown

25
for the standard proceduze.

Both regression procedures improved upon the precision of

direct expansion of the ground data for the large acreage crops

in the four county combined estimates. When discussing the

results from a similar jackknife procedure performed with Landsat:

data from California, Holko C5) noted that the best results were

obtained when independent training was done on cover types with

large acreage. That result was duplicated in this experiment.

The poor R-squares and large error terms for corn, beans and

potatoes in the modified jackknife procedure may be attributable

to inadequate training or the date of the second Landsat

acquisition. Display of segment data from the September 1

acquisition revealed that most of the potato fields were either.

turned (vine death), partially turned, or harvested - well beyond

the biostage desirable for acreage estimation with Landsat. An

earlier acquisition might improve the performance of the

classifier, but a definitive statement to that effect cannot be

made based on the results from this study.

Regression parameters and relative efficiency for county

level estimates, strata 15, are shown in Table 17. The results,

at the county level, parallel those for the four counties

combined, i.e. both regression procedures improved upon direct

expansion for the large acreage crops. However,even in stratum

15, only Minidoka and Jerome Counties had enough segments, 10 and

8 respectively, to attempt comparisons between the standard and

modified jackknife procedures.

Regression parameters at the county level could not be

generated for stratum 25 because of the limited sample size.

26
Table 17.

Regression Estimate Parameters and Relative Efficiency (RE)*

by County - Strata 15

Standard Jackknife

Procedure Procedure

County Cover b0 bl r2 RE b0 bl r2 RE

Gooding Alfalfa 27.7 .78 .74 2.28 50.4 .37 .38 .97

Corn -3.5 1.27 .73 2.24 150.0 -1.04 .43 1.06

Sugarbt -- -- -- -- -- -- -- --

Beans -1.5 .38 .58 1.43 5.4 .01 .00 .60

Smallgrn -18.9 1.23 .96 14.54 3.1 1.21 .78 2.74

Potato -36.1 1.39 .78 2.73 34.9 .36 .03 .62

Jerome Alfalfa 17.0 .88 .89 6.69 5.0 .93 .92 8.64

Corn -38.6 1.37 .84 4.46 -55.8 1.95 .76 3.01

Sugarbt 7.7 .34 .06 .76 9.4 .14 .01 .72

Beans -31.6 2.54 .98 30.76 -12.8 2.44 .32 1.05

Smallgrn -44.7 1.29 .98 55.13 -78.7 1.53 .80 3.68

Potato -32.8 1.14 .91 8.02 -16.5 .83 .22 .91

Lincoln Alfalfa 13.0 1.05 .87 -- -4.3 1.13 .98 --

Corn -21.6 1.39 .94 -- 6.8 .36 .01 --

Sugarbt -3.1 1.29 .99 -- -9.9 1.21 1.00 --

Beans -18.6 1.42 .98 -- -44.0 2.28 .90 --

Smallgrn 72.1 .70 .78 -- 55.9 .83 .72 --

Potato -23.9 .92 .32 -- 7.2 .51 .32 --

Mini- Alfalfa 3.2 1.04 .96 17.22 7.5 1.02 .93 12.12

doka Corn -11.7 1.05 .93 10.39 -13.7 .93 .57 1.79

Sugarbt 10.7 1.12 .83 4.38 5.1 1.07 .65 2.20

Beans -53.1 2.91 .49 1.53 30.5 -.48 .05 .82

Smallgrn 21.3 .89 .96 21.03 29.5 .84 .95 15.14

Potato -7.5 .73 .60 1.96 10.7 .64 .19 .96

*RE = ground sample variance /regression variance

-- = could not be calculated due to zero value in one term

County level estimates would probably improve if additional

training were made available either by increasing the JES

sampling rate further or by devising an alternative procedure for

collecting training and test data. The California Cooperative

Remote Sensing Project is preparing an experiment in which JES

segments will be used for testing the classifier, but the

training data will be collected along transects. The transect

data will provide more training at a lower cost than JES data and

will allow all the JES data to be reserved for testing. A

similar methodology could be applied in Idaho where a shorter

growing season and less phenological variability in a given crop

at a given time should make collecting an adequate training set a

manageable problem.

27
Summary

1. Two methods for generating regression estimates using

Landsat data from four counties in the central Snake River Plain

produced significantly better estimates for large acreage crops

than direct expansion of the ground data. The improvement was

noted in the estimates for the counties combined and for county

level estimates, although the county level estimates are suspect

because of the small number of samples.

$ 2. The results of this test support the contention that

training and testing on the same data set introduces a bias in

the results.

3. A 508 increase in the JL'S sampling rate was required to

obtain independent training and testing data sets that were, at

best, marginally sufficient in size. If additional work of this

type is to be performed in Idaho, the acquisition of adequate

training and test data sets will have to be addressed.

3

4. A late August, early September Landsat acquisition

appears to be too late in the growing season for differentiation

of summer crops in the central Snake River Plain.

28
v

J ^ References

[1] Battese, G.E., W.A. Fuller. "Prediction of County Crop Areas

Using Survey and Satellite Data." Survey Section Proceedings,

American Statistical Association Annual Meeting, Detroit,

Michigan. 1981

[2] Colwell, R.N. et. al. (editor). Manual of Remote Sensing

(2nd edition). American Society of Photogrammetry, Falls

Church Virginia. 1983

E

[3] Hanusehak, G.A., R.D. Allen and W.H. Wigton. "Integration of

Landsat Data into the Crop Estimation Program of USDA's Statistical Reporting Service." Machine Processing of Remotely

Sensed Data Symposium, Purdue University, West Lafayette,

Indiana. 1982

[4] Hanuschak, G.A., M. Craig, M. Ozga, R. Luebbe, P. Cook,

D. Kleweno and C. Miller. "Obtaining Timely Crop Area Estimates Using Ground-Gathered and LANDSAT Data." USDA ESCS

Technical Bulletin No. 1609. 1979

[5]Holko, M.L. "1982 Results for the California Cooperative

Remote Sensing Project." Statistical Reporting Service Staff

Report No. AGES840305, Washington, D.C. 1984

[6] Idaho Image Analysis Facility - Project Plan for FY83 

[7] Lennington,R.K, and M.E. Rassback. "CLASSY - An Adaptive Maximum Likelihood Clustering Algorithm." Proceedings of the

LACIE Symposium, Houston, Texas. 1972

[8] Ozga, M., S.R. Faerman and R.S. Sigman. "Editor Multitemporal

System." USDA, ESCS. 1979

[9] Sigman, R. and G. Walker. "The Use of Landsat for County Estimates of Crop Acres: Evaluation of the Huddleston-Ray and

Battese-Fuller Estimators." USDA, SRS, Washington, D.C. 1982

[10] Thomas, R.W. personal communication

[11] Wensel, L.C., S.J. Titus and R.W. Thomas. "Sample Allocation

in Multiresource Inventories." Proceeding, Society of American

Foresters Workshop on Forest Resource Inventories. Colorado

State University, Ft. Collins. 1979

29
[12] Wensel, L.C., M. Griksson, R.W. Thomas and D.R. Taylor.

"Survey Planning Model - User's manual. Part of the final

report for USFS, Rocky Mountain Station Cooperative Agreement No.16-935-Ca and NASA Johnson Space Center Contract No.

NAS9-15987. Department of Forestry and Resource Management and

Remote Sensing Research Program, University of California,

Berkeley. 1981

30
Acknowledgments

The author wishes to express his gratitude to:

The National Aeronautics and Space Administration, under

whose auspices this research was done.

Hal N. Anderson, Director of the Idaho Image Analysis

Facility, for initiating this project and providing the

inspiration to bring it to a successful conclusion.

Ethel Bauer, NASA-Ames Research Center, for making this

project happen and giving the author the opportunity to

work on it.

Richard Sigman, Marty Holko, Paul Zuttermeister, and

Martin Ozga, USDA, Statistical Reporting Service, Remote

Sensing Branch, Washington, for their support, for supplying the USDA sample frame data, and for their willingness

to answer innumerable questions about EDITOR.

Dick Max and Doug Hensley, Statistical Reporting Service,

Boise, for providing the ground data and reviewing the work

while in progress.

Tony Morse, Idaho Image Analysis Facility, for reviewing the

final draft.

Dr. Randy Thomas, Remote Sensing Research Program, University

of California, Berkeley, for sharing his expertise in the

design of agricultural inventories while supervising the

design of this project, and for always being there when called

upon.

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