ANDREWS. BENSON WILLIAMC. DRAEGER LAWRENCER. PETTINGER University of California* Berkeley, Calif. 94720 Ground and Use

These are essential components of an agricultural resource . INTRODUCTION sions based on their efforts to perform re- HE IMPORTANCE OF collecting meaningful gional agricultural inventories in Maricopa T and timely ground data for resource in- County, Arizona, using space and high-alti- ventories which employ remote sensing tech- tude aerial photography, as part of an ongoing niques is often discussed, and appropriately NASA-sponsored research project (Draeger, so. However, those wishing to conduct inven- et a]., 1970). This paper draws upon examples tories frequently fail to devote as much time from this research. However, much of the dis- to the planning of field activities which occur cussion is relevant to other disciplines for in conjunction with an aircraft mission as which ground data is important. they do in planning the flight itself. As a re- sult. adequate remote sensing data mav be collected,-but no adequate sipporting infor- Preliminary evaluation of the geographical

ABSTRACT:During the past two years, extensive studies have been conducted out in the Phoenix, Arizona area to ascertain the degree to which sequential high- altitude aircraft and spacecraft imagery can contribute to operational agri- cultural crop surveys. Data collected on the ground within the test site con- stituted an essential component of the three phases of the survey: (I)farniliariza- tion with the area and design of preliminary evalz~ationexperiments, (2) train- in,g of interpreters, and (3)providing the basis upon which image interpretation estimates can be adjusted and evaluated. This paper will discuss the problems encountered when gathering extensive sequential agricultural ground data, re- quirements for such data in terms of amount and timing, methods of collecting, handling and compiling such data in a useful form, and the use of ground data in the survey evaluation process. mation is available to permit its meaningful area for which an agricultural inventory is evaluation. Unless care is taken in the speci- planned must be made in light of the capabil- fication of ground data collection efforts, the ities of available remote sensing systems. Two resulting inventory may be of minimal value. categories of information must be known for Ground data is used in all three phases of this evaluation: (1) the optimum remote sen- an agricultural resource survey. First, some sing specifications for crop discrimination and field work may be necessary in the survey identification, and (2) the sequential pattern planning stage. At this point, preliminary of crop development and the geographical evaluations are made to determine how the distribution of each crop type. If this infor- survey might best be organized. Secondly, mation is available at the beginning of the ground data must be used to train image in- planning stage, additional ground data may terpreters (both human and electronic) and to not be needed to complete the survey design. judge their competence. Finally, accurate ground However, if the available information is in- data is crucial for evaluating operational sur- adequate, certain kinds of data, both from zrey results and adjusting interpretation esti- existing records and from limited field work, mates. must be obtained. The authors have come to these conclu- Specifications for remote-sensing imagery * Forest Remote Sensing Laboratory 1159 - -

1160 PHOTOGRAMMETRIC ENGINEERING, 1971

JFMAMJJASOND...... - These two particular areas were chosen for Alfalfa several reasons: (1) each was contiguous, ...... making the collection of data within each plot Barley & Wheat ...... - a convenient process; (2) they contained rep- Sugar Beets ------resentative examples of each of the important Cotton ...... , ------...... ---- crop types grown in the Phoenix area; (3) ...... planting nearly 400 separate fields were contained in -growth --- harvest the two plots, providing enough examples for FIG. 1, This crop summarizes the de- training and testing of interpreters; and (4) velopment patterns of five major crop types in the both were easily reached by vehicle so that Maricopa County test site. The duration of each they could be field checked in one day by a of the three main phases of development (planting, growth and harvest) is indicated. It was prepared two-man crew. As larger areas (or a greater using field data and published crop status reports number of areas which were for Maricopa County. This kind of information is separated) could not be investigated (eco- used to select optimum dates for discrimination of nomic constraint), little information regarding each crop type 0x1 aerial and space photography. relative distribution of crop type could be obtained. However, as stated above, the ob- are based on the objectives of the survey. The jective in selecting the two pilot plots was to specifications should consider: (1) the sensor evaluate image characteristics and relate system capabilities (camera systems, max- them to survey planning. A regional imum flight altitude, etc.), (2) interpretation of plots would have been taken if information procedures to be used (prints us. transparen- concerning variability of crop distribution had cies, human us. machine interpretation), (3) been desired. film-filter types and (4) resolution require- In order to ascertain optimum film-filter ments for identification of each crop type. If combinations and dates for identifying par- the factors that affect interpretation accuracy ticular crop types, air photo missions over 1 (film type, resolution, contrast, etc.) and the the test areas were flown at approximately proper time(s) of year for obtaining imagery monthly intervals, and extensive photo in- have not been determined, either from pre- terpretation tests were conducted using the vious experience with similar conditions or photography thus obtained. An essential ele- from published surveys, then some prelimi- ment in any photo interpretation testing pro- nary testingof film types and dates of imagery cedure is the collection of accurate, timely is necessary. ground data. Data concerning crop distribution and de- velopment can often be derived from current INTERPRETERTRAINING records of agricultural advisors and published The accuracy of a survey that employs re- agricultural literature and , or through the actual compilation of ground data. Oneof the most useful forms for present- ing such crop development information is the crop calendar (Figure 1). For each crop type, the calendar indicates the major periods of crop development (for example, planting, growth and harvest) as they occur through the year. From such a calendar one can select the time(s) of the year for maximum discrim- ination of particular crop types based on their sequence of development relative to all other crops. For example, in the Maricopa County sur- vey, information regarding crop development patterns was not available. Therefore, two FIG.2. TWOtest sites, initially selected for study field plots-16 square miles and 22 square on space and sequential high altitude aerial pho- tographs during 1969, are outlined on this black- miles-were selected in which crop develop- and-white enlargement of a portion of Apollo 9 ment could be monitored (Figure 2). Data Infrared Ektachrome frame AS9-26-3801 of the collected from these plots at the time of photo Phoenix, Arizona area. Detailed crop information was collected for the 16-square-mile area (left) and missions were correlated with theaerial photos the 22-square-mile area (right) at the time of each to determine which dates and film-filter flight. Phoenix is in the upper left, and Mesa in the binations could best be used for the survey. center of this frame. GROUND DATA COLLECTION AND USE 1161

mote-sensing techniques depends heavily on not vary significantly whether plots were 1, the training of interpreters. In turn, the suc- 4 or even 8 square miles in size. Thus plot cess of a training effort depends completely size was determined solely on an economical on accurate ground data. Only where the basis. Based on experience, it was known that ground data is absolutely correct can the in- a plot size of 8 square miles was too large for terpreter be presented with examples repre- an adequate number to be easily ground- senting the complete of crop variation. checked in a reasonable time period, and that In addition, once the training phase has been if the plots were only one square mile in size, completed, it may still be a difficult task for field crews would spend a disproportionate an interpreter to satisfy himself that he has time traveling between plots relative to the learned the identifying characteristics of a time spent collecting data. The decision was crop. If some of the training examples he had made to use four-square-mile plots (two miles studied were incorrectly identified initially by by two miles) because they were large enough field crews, his task becomes impossible. He to contain a representation of the major crops will believe that there is greater variability growing in the particular area where the plot within a crop type than actually exists, and was located, yet small enough that many he may also become more confused as to plots could be visited each day. Thirty-two differences between crop types. plots were chosen because this number could be completely field-checked within a two-day period by a team of three persons (economic After the basic decisions as to image type constraint), enough data would be provided and date of photography have been made, the for adequate statistical analyses (statistical next step in the preparation for an operational constraint), and all major crop types would survey is to establish permanent field plots be represented in one or more of the plots. from which data can be collected. These data The allocation of sample plots to a survey are used to determine the accuracy of the sub- area is often made more statistically efficient sequent photo interpretation estimates and if the area is stratified into relatively homo- for the later adjustment of these estimates. geneous areas. For stratification to be useful, it The two pilot plots established for the pre- must be possible to delineate strata bound- liminary familiarization of Maricopa County aries such that the variability of the charac- were not adequate for conducting an agricul- teristic being estimated within strata is less tural survey based on photo interpretation than the variability between strata. Assum- that was to be county-wide in scope. These ing that stratification is found to be appro- plots did not give any real indication of the priate, a common method of plot allocation variation in crop-tone signatures that could is that of proportional allocation whereby the exist due to differences in soil types or crop- proportion of the total plots allocated to a ping techniques, localized insect and/or path- stratum is based on the proportion of the total ogen" infestations. and other factors which survey area falling within that stratum. The might occur elsewhere in the survey area and appropriate number of plots can then be dis- not be represented in the pilot plots. In addi- tributed within the stratum in some random tion, it was possible that not all of the major fashion. crop types were represented in the two pilot For the Maricopa County survey all stra- plots, thus making a complete operational in- tification was done on Apollo 9 Infrared Ekta- ventory impossible. Therefore, prior to the chrome photograph AS9-26-3801 (see Figure operational survey a larger number of per- 3). All cropland in the county was stratified manent field plots had to be established. into eight homogeneous units strictly on the There is no set formula for determining the basis of appearance on the photo. The strata size and number of field plots that should be boundaries were then transferred toa 1:250,000 established. Usually a compromise among the topographic map, and the 32 plots were allo- various constraints placed upon the agency cated to the strata on a proportional area doing the survey will determine the size and basis. The plot centers were chosen by ran- number. The most common constraints are domly selecting section corners. Thus plot statistical (e.g., enough plots of the right size boundaries coincided with surveyed section to provide sufficient data for analysis and ad- boundaries, providing for convenient tabula- justment) and economical (e.g., the number tion of both field and photo interpret~tion of man-hours that can be spent on field check- data. Later analysis showed that there was no ing). For the Maricopa County survey, pre- statistical difference in the variation between vious studies had indicated that the strata with regard to proportion of various of the proportion of crops per square mile did crop types. Nevertheless, the strata were re- FIG.3. This black-and-white copy of a portion of an Apollo 9 Infrared Ektachrome photograph (AS9-26-3801) shows the eight strata that were delineated for the Maricopa County agricultural survey. Als? shown are the 32 four-square-mile field plots that were allocated to these strata on a proportional area basls. tained because they provided a convenient Ground data are used to obtain the final wav to subdivide the countv into more man- results of an agricultural survey, such as the ageable areas of study. one discussed here, in the following manner. In planning the actual photo interpretation Each interpreter's crop estimate for the field for a survey involving large areas (such as an plots within his area and the actual acreages entire county), the question arises as to for these fields as determined by on-the-ground whether interpreting a sample of the area will surveys are totalled. The ratio of the total provide a satisfactory estimate of crop acre- actual field plot acreages to the total interpreta- age, or if 100 percent interpretation will be tion estimated acreages is calculated. This required. Using data obtained in the field, ratio is used to adjust the individual inter- calculations can be made to determine the preter's crop estimate by the formula: variability in the distribution of major field crops. Once this variability is known, it is possible to determine a sample size which where YI is the estimate of total crop acreage would satisfy the accuracy requirements. In within an interpreter's area, YPIis the initial order to estimate the acreage of wheat in photo interpretation of acreage within an in- Maricopa County with a of terpreter's area, and R is the correction ratio f 10 percent of the total acreage using a plot as derived from the field plots (Freese, 1962). size of four square miles, a 75-percent photo The category estimates for the individual interpretation sample would have been re- interpreters are summed to form a total es- quired. Because such a large sample size was timate. errors are calculated for the required, it was decided that it would be crop estimate by each interpreter as well as more efficient to carry out a 100-percent for the total estimate in order to give an in- photo interpretation of the agricultural areas. dication of the accuracy of the crop estimates. As a rule of thumb, if the required sample In calculating the combined statistics, each size is greater than 30 percent of the total interpreter's data is handled as an individual acreage being surveyed, it is more efficient to stratum. The sampling error is calulated by: require 100-percent interpretation. Also, 100- Sampling error percent = s;/P X 100 percent interpretation ensures that every per- manent field plot is interpreted, thus giving where Sy is the standafd error of the estima- a more powerful statistical analysis. ted crop acreage, and Y is the estimated crop GROUND DATA COLLECTION AND USE 1163

R UN STRATUM NUMBER ? 2 CROP CATEGORY ? 110 STRATUM SIZE ? 117 SAMPLE SIZE ? 9 PHOTO INTERPRETER STRATUM ESTIMATE ? 24042

PHOTO INTERPRETER CELL ESTIMATE--X ? 496? 70? 625? 882? 730? 972? 240? 32? 413 GROUND CELL DATA--Y ? 6861 72? 504? 810? 795? 914? 240? 188? 408 I STRATUM NUMBER 2 CROP CATEGORY ADJUSTED STRATUM ESTIMATE 24888.3 VARIANCE PHOTO INTERPRETER CELL ESTIMATE--X 114520 VARIANCE GROUND CELL DATA--Y 93024.6' COVARIANCE XY 98451.7 REGRESSION ESTIMATOR 1.~7352 VARIANCE REGRESSIOM ESTIMATOR 1222.(41 REGRESSION ESTIMATOH 34.9572 VARIANCE OF STRATUM ESTIMATE I.h7278E+7 STANDARD DEVIATION OF STRATUM ESTIMATE 4U89.97 SAMPLING ERROR--% 16-4333 CORRELATION COEFFICIENT -953856 - FIG.4. This Basic language computer printout shows how ground data and photo interpretation data inputs are used to adjust photo interpretation stratum estimates to yield the final adjusted stratum acreage e;stimate. The regression estimator is used to adjust the photo interpretation crop estimate by the formula Yr = YPIX R: 686 + 72 + 504 4- . . . $408 24888.3 acres = 24042 acres X 496 + 70 + 625 + . . . + 413 where 6 is the estimate of total crop acreage within an interpreter's area, YPIis the initial photo interpre- ter's estimate within an interpreter's area, and R is the regression estimator which is calculated by divid- ing the sum of the actual crop acreages on field plots within the area (Y-input) by the interpreter's esti- mates of crop acreages for the same plots (X-input).

acreage. Figure 4 shows in detail the calcula- Repeated ground checking of one or more of may tion of the corrected estimates as performed the field plots by different field enumerators give an indication of ground survey accuracy. How- for the Arizona survey. ever, the field enurneratots are probably trained in a similar manner by the same agency. Therefore, a GROUNDDATA ACCURACY lack of discrepancies between their data does not all statistical analyses and photo inter- necessarily ensure a high degree of accuracy in data collection, but only a high degree of precision which pretation estimate adjustments depend on can lead to biased results. field data, these data must be accurate with If it seems that a field has been incorrectly respect to existing ground conditions or the identified on the ground, positive crop identifica- survey results become meaningless. B~~~~~~ tion often can be obtained from sequential pho- tographs, because the tone signatures of most crops of the importance of accuracy, one or more change in a distinctive pattern over a period of of the following techniques should be used to time. If only single-data photography is available, evaluate the collected field data: however,. . positive identification may be question- able. In most agricultural areas some govern- As a continuous inventory of all crops re- mental agencies maintain current maps of areas quires collection of data periodically throughout under cultivation. If these maps contain enough the year, searching the data bank for an illogical detailed information they can provide an inde- crop sequence of individual fields may indicate pendent check on one's ground data. A problem errors in ground enumeration. arises, however, when discrepancies occur between two independent data sources, and these differences must be resolved to determine which is correct. For example, in Maricopa County the local irrigation In addition to the considerations discussed district prepares a spring crop and livestock report previously, several factors which can greatiy which requires, among other things, the collection influence the value of the ground-data col- of detailed crop acreage data by section, the data being recorded field by field on individual maps. lection effort should be discussed. These are: As might be expected, some discrepancies occurred methods of collecting data, the amount and between the irrigation district maps and those pre- timing required, and techniques for compiling pared by the Forestry Remote Sensing Laboratory and handling data so that it can be used in (Figure S), but these discrepancies were resolved with the aid of high resolution color aerial pho- the most efficient fashion. tography. To increase the of data collection, CELL 2-1 DATE 7-20-70 CREW sL1*/

FIG. 5. This map contains field data collected for one of the four-square-mile field plots in Maricopa County at the time of a NASA high altitude overflight. The coded fraction in each field is explained in Figure 6. Computer storage of survey data, collected at the time of each flight on a field by field basis, facilitates sequential analysis of crop patterns as well as evaluation of photo interpretation results (see Figure 7). field personnel should be provided with a map if the initial number of field plots is large or of each field plot that shows individual field may be expanded in the future, and/or re- boundaries and assigns a permanent number peated data collection is planned, it is worth- to each field (Figure 5). Information such as while to institute a data collection system crop category, stage of maturity and condi- compatible with computers at the onset. In- tion, percent ground cover, crop height, and asmuch as more than 2,500 fields were present row direction (if any) can then be annotated in the 32 four square mile plots in Maricopa directly on the map using some relevant cod- County (comprising more than 80,000 acres), ing system (Figures 5 and 6). The crop cate- field data were punched on computer cards in gory code used in the Maricopa County sur- order to facilitate access to this information. vey is an adaptation of a coding system orig- Programs were written which made it pos- inally developed by the U. S. Government sible to compile data by stratum, field plot, for categorizing land use (U. S. Urban Re- crop type, and date. Thus data were available newal Administration, 1965) and subse- not only for each date of photography, but quently refined for specific use in agricultural for the sequential changes in crop type and land use mapping by researchers at the Uni- condition through the growing season as well. versity of California, Riverside Campus An example of the computer printout for a (Johnson, et al., 1969). Not only is such a few fields from one field plot appears in Fig- code easy to learn, but it is compatible with ure 7. computer programs that can store and com- The extent of ground data that have to be pile data. collected is determined by the survey ob- If very small quantities of data are to be jectives. For the Maricopa study, the only collected and handled, an automated data data required were those needed to evaluate storage procedure is not required. However and adjust photo interpretation estimates, GROUND DATA COLLECTION AND USE 1165

CATEGORY CODE (PARTIAL) Category 100 Field and Seed Crops \133-3 Yition 111 Barley 114 Sorghum (grain) 118 Wheat 133 Alfalfa 142 Sugar Beets % Cover Height Row Direction 151 Cotton 200 Vegetable Crops CONDITION CODE 300 Fruit and Nut Crops 1. Seeded 331 Grapefruit 2. Young 335 Orange 3. Mature 400 Livestock 500 Animal Specialties 4. Dry (not harvested) 600 Pasture and Ranaelands 5. Cut Back (e.g., alfalfa)

610 Pasture 4-. COVER - CODE 620 Rangelands 700 Horticultural Specialties I. 80-100% 800 Non-producing and Transit ion 2' 50-80% Cropland !. 20-50%- -." 4. 5-ZUX 810' Fallow - . -" 820 Plowed 5. u-5x 850 Harvested HEIGHT: Indicate average 860 Prepared crop height in feet and 900 Other Uses tenths. 910 Urban 'ODE 920 Farmhouses and Farm-related Structures 1. N-S 930 Agricultural-related 2. E-W Activities 3. NW-SE 940 Native Vegetation 4. NE-SW

FIG. 6. The fraction at the top of this page represents a typical field code as recorded by ground crews gathering information pertaining to the field plots. The example shown is a mature alfalfa field one foot in height, with 5C-80-percent ground cover and rows running in a north-south direction. The complete category code is quite lengthy and therefore not reproduced here. Only the major headings (100, 200, etc.) and a few sub-headings (which are common to the Phoenix area) are presented. This system provides a useful method of abbreviated data recording, and is compatible with the computer storage capability that has been developed.

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FIG. 7. A portion of the Maricopa County ground data printout for two fields (34 and 35) as docu- mented on 20 dates between March, 1969, and November, 1970, is reproduced here. Asdata from only four dates can be tabulated on each computer card, six cards are needed to contain all the data collected to date. The key to each set of field data appears in Figure 6. Blanlc entries appear if there are no changes from the previous month (status code 1 no change). The data above indicate that field 34 has been planted to cotton (151) during 1969 and 1970, whereas field 35 contained alfalfa (133) in 1969 and was planted to grain sorghum (114) in summer, 1970. This system can easily accommodate field divisions and consolidations which occur from month to month, and new information can be added as subsequent data is required. and included crop type, acreage, stage of ground readings at or near the time the im- growth, percent ground cover and crop condi- agery is acquired. tion. However, if the survey objectives had Careful evaluation of all the data require- necessitated other types of imagery (e.g., ments for future analysis and of the capability therma! infrared), then perhaps soil moisture to collect timely and accurate field data will and radiometer measurements would have result in the selection of the optimal ground been required in addition to the more general data techniques. crop information. Coordinating the ground data collection efforts with aircraft overflights should also be evaluated with respect to the survey objec- Draeger, W. C., L. R. Pettinger and A. S. Renson. tives. Given the objectives of the Arizona 1970. "A semi-operational agricultural inventory using small-scale aerial photography." I11 "Anal- study, the collection of field data at the in- ysis of remote sensing data for evaluating vege- stant of photography was unnecessary and, tation resources," R. N. Colwell, et al. Annual in fact, would have been physically impos- Progress Report. NASAEarth Resources Survey Program. Forestry Remote Sensing Laboratory. sible. Intervals of a day or two between the University of California, Berkeley. 171 p. time of photo mission and time of data col- Freese, F. 1962. Elementary forest sampling. Agri- lection were easily tolerated. The only normal cultural Handboolr No. 323. U. S. Department of changes that may occur during these intervals Agriculture. 91 p. Johnson, C. W., et al. 1969. "A system of regional are those which involve harvesting or soil agricultural land use mapping tested against preparation. Changes in field appearance due small-scale Apollo 9 color-infrared photography to these factors can be inferred by studying in the Imperial Valley." USDI Status Report 111, the imagery and relating it to field notes. Technical Report V, Contract 14-08-0001-10674. University of California, Riverside. 96 p. However, if more transient phenomena are U. S. Department of Commerce, Urban Renewal being monitored (e.g., soil moisture or sur- Administration. 1965. Standard use coding man- face temperature), there is a real need for ual. 111 p.

Symposium Proceedings Available Price per copy Price per copy to Members to Nonmembers Annual March Meetings 1968-428 pages, 1969-415 pages, 1970-769 pages, 1971-817 pages. $2.50 ea. $5 .OO ea.

Fall Technical Meetings Portland, Oregon, 1969. 363 pages, 39 papers Denver, Colorado, 1970. 542 pages, 33 papers San Francisco, 1971, 770 pages, 71 papers* 2.50 ea. 5.00 ea. Remote Sensing 21 selected papers, 1966, 290 pages

Third Biennial Workshop Color Aerial Photography in the plant Sciences and Related Fields, 20 papers, 288 Pages 5.00 10.00 Close-Range Photogrammetry, 1971 33 papers, 433 pages American Society of Photogrammetry, 105 N. Virginia Ave., Falls Church, Virginia 22046. * Includes papers from Symposium on Computational Photogrammetry.