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The scope of the three LACIE phases is discussed as well as the system efficiencies which had to be implemented to cope with the resulting LANDSAT data load. The methodologies used in system analysis, some of the specific data collected, and the inferences of these data and their implication on future systems are also discussed.
The implementation of direct access storage devices for LACIE is discussed with emphasis on the storage and retrieval of image data. Topics covered include the definition of the problem, the solution methodology (design decisions), the initial operational structure, and the modifications which were incorporated. Some conclusions and projections of future problems to be solved are also presented.
No estimates were generated for the U.S.S.R. during LACIE phase 1. Phase 2 effort was limited to two indicator regions: winter wheat areas where 385 segments were allocated, and spring wheat areas with 362 allocated segments. The level of activity for phase 3 was extended to the entire country which automatically increased the segment workload from 747 to 1947 segments. Production, area, and yield estimates, and their accuracy are discussed for phases 2 and 3 with emphasis on scope, sampling strategy, data base, LANDSAT data, yield analysis for winter and spring wheat, area and production analysis for winter and spring wheat, and technical issues and problems.
The LACIE yield models developed, implemented, and tested during the three phases of the experiment represent the first generation of models designed for the large-scale prediction of wheat production. The models are capable of supporting the stated project goal of being within 10 percent of the actual wheat production 90 percent of the time. The limitations of the models are inherent in their nature. The temporal resolution limits their ability to handle the erratic weather occurring in critical situations. The relatively large spatial resolution of the individual models limits the capture of localized but important episodic events.
An approach that may be useful in improving the estimates of the wheat acreages for the LACIE countries for each year by using the short-time series of estimates made in the sequence of consecutive years is presented. A simple 'synthesis' based method of variance component estimation is described. A general theorem concerning weighted least squares, referred to as the Aiken method, is proved.
The capability to acquire, process, and interpret remotely sensed multispectral measurements of the energy reflected and emitted from crops, soils, and other Earth surface features is considered. The LACIE Field Measurements Project is described including project objectives, the experimental approach, the data acquisition program, and selected results based on field data. The key accomplishments and results of the experiment and recommendations for future field research are summarized.
Not Available
Crop proportion estimators for determining crop acreage through the use of remote sensing were evaluated. Several studies of these estimators were conducted, including an empirical comparison of the different estimators (using actual data) and an empirical study of the sensitivity (robustness) of the class of mixture estimators. The effect of missing data upon crop classification procedures is discussed in detail including a simulation of the missing data effect. The final problem addressed is that of taking yield data (bushels per acre) gathered at several yield stations and extrapolating these values over some specified large region. Computer programs developed in support of some of these activities are described.
A review of results from the Uhuru satellite is presented. An intensive treatment of two subjects is given, rather than a broad review. First, Cyg X-1, a stellar X-ray source and a candidate for a black hole, is discussed; second, the X-ray source in the Perseus cluster of galaxies, which may be a cloud of hot intergalactic gas, is treated. In both cases, the train of logic used in establishing the nature of these objects is presented and evaluated. For both, while alternative explanations cannot be completely eliminated, they become more difficult to sustain when examined in detail, suggesting that the candidate explanations are more likely correct.
The program EPHEMS computes the orbital parameters for up to two vehicles orbiting the earth for up to 549 days. The data represents a continuous swath about the earth, producing tables which can be used to determine when and if certain land segments will be covered. The program GRID processes NASA's climatology tape to obtain the weather indices along with associated latitudes and longitudes. The program LUMP takes substrata historical data and sample segment ID, crop window, crop window error and statistical data, checks for valid input parameters and generates the segment ID file, crop window file and the substrata historical file. Finally, the System Error Executive (SEE) Program checks YES error and truth data, CAMS error data, and signature extension data for validity and missing elements. A message is printed for each error found.
Crop-weather models developed for wheat from an assembled base of approximately 45 years of climatic records and historic wheat yield and production data at the stratum level are described. Utilization of meteorological data acquired by the NOAA satellites is discussed.
The application of remote sensing technology by the U.S. Department of Agriculture (USDA) is examined. The activities of the USDA Remote-Sensing User Requirement Task Force which include cataloging USDA requirements for earth resources data, determining those requirements that would return maximum benefits by using remote sensing technology and developing a plan for acquiring, processing, analyzing, and distributing data to satisfy those requirements are described. Emphasis is placed on the large area crop inventory experiment and its relationship to the task force.
An experiment was designed to test the ability of the Classification and Mensuration Subsystem rework operations to improve wheat proportion estimates for segments that had been processed previously. Sites selected for the experiment included three in Kansas and three in Texas, with the remaining five distributed in Montana and North and South Dakota. The acquisition dates were selected to be representative of imagery available in actual operations. No more than one acquisition per biophase were used, and biophases were determined by actual crop calendars. All sites were worked by each of four Analyst-Interpreter/Data Processing Analyst Teams who reviewed the initial processing of each segment and accepted or reworked it for an estimate of the proportion of small grains in the segment. Classification results, acquisitions and classification errors and performance results between CAMS regular and ITS rework are tabulated.
It appears that the Large Area Crop Inventory Experiment over the Great Plains, can with a reasonable expectation, be a satisfactory component of a 90/90 production estimator. The area estimator produced more accurate area estimates for the total winter wheat region than for the mixed spring and winter wheat region of the northern Great Plains. The accuracy does appear to degrade somewhat in regions of marginal agriculture where there are small fields and abundant confusion crops. However, it would appear that these regions tend also to be marginal with respect to wheat production and thus increased area estimation errors do not greatly influence the overall production estimation accuracy in the United States. The loss of segments resulting from cloud cover appears to be a random phenomenon that introduces no significant bias into the estimates. This loss does increase the variance of the estimates.
There are no author-identified significant results in this report.
The author has identified the following significant results. This classification method appears to give accurate field center results and to give practical, statistically consistent and accurate estimates of crop proportions. The accuracy of this method is attributable to certain qualities of the particular clustering algorithm. These qualities are freedom from assumptions about Gaussian data, and the continual updating of distribution estimates, including updating the number of modes. This method is relatively tolerant of errors in the determination of crop type, as crop identity is used only for identifying clusters, and not for computing signatures.
A quantitative model determining the relationship between weather conditions and wheat yield in the U.S.S.R. was studied to provide early reliable forecasts on the size of the U.S.S.R. wheat harvest. Separate models are developed for spring wheat and for winter. Differences in yield potential and responses to stress conditions and cultural improvements necessitate models for each class.
Most of the variability in wheat production is due to weather fluctuations. Climatic differences within the region account for a large portion of the variability in yields for different parts of the region. Separate regression models were developed for each of the areas indicated.