Statistical differentiation between malignant and benign prostate lesions from ultrasound images
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Premkumar, S. B.↗
Engineering topics
Houston, A. G.
Publications and source records attributed to Houston, A. G..
Crop acreage estimation using a Landsat-based estimator as an auxiliary variable
The problem of improving upon the ground survey estimates of crop acreages by utilizing Landsat data is addressed. Three estimators, called regression, ratio, and stratified ratio, are studied for bias and variance, and their relative efficiencies are compared. The approach is to formulate analytically the estimation problem that utilizes ground survey data, as collected by the U.S. Department of Agriculture, and Landsat data, which provide complete coverage for an area of interest, and then to conduct simulation studies. It is shown over a wide range of parametric conditions that the regression estimator is the most efficient unless there is a low correlation between the actual and estimated crop acreages in the sampled area segments, in which case the ratio and stratified ratio estimators are better. Furthermore, it is seen that the regression estimator is potentially biased due to estimating the regression coefficient from the training sample segments. Estimation of the variance of the regression estimator is also investigated. Two variance estimators are considered, the large sample variance estimator and an alternative estimator suggested by Cochran. The large sample estimate of variance is found to be biased and inferior to the Cochran estimate for small sample sizes.
Chhikara, R. S.↗
Preliminary Evaluation of TM for Soils Information
The capability of the LANDSAT TM for providing information for soil association maps and for detecting soil properties (variability within vegetated fields) was assessed using TM imagery of fields in Mississippi County, Arkansas that were planted with rice, cotton, and soybeans. Results indicate that the TM bands are providing information that is related to the soil properties within the field. Over large areas, these bands also appear to provide information that is related to the soil properties that are important to plant condition. While these results are only an indication of the information that TM can provide, they do indicate the TM data--especially, the mid-TR and thermal bands--show the capability for separating vegetated soil landscapes on a broad basis. The analysis at the field level with a growing crop also indicates that TM, with its additional and narrower bands and improved spatial and radiometric resolution is influenced by within field variability due to soils that has to be accounted for in the analysis of TM data.
Thompson, D. R.↗
Evaluation of crop acreage estimation methods using Landsat data as auxiliary input
The regression and ratio estimators are studied in the context of improving upon the ground survey estimates of crop acreages by utilizing Landsat data. The approach is to formulate analytically the estimation problem that utilizes ground survey data, as collected by the U.S. Department of Agriculture, and Landsat data, which provide a complete coverage for an area of interest, and then to conduct simulation studies. It is shown over a wide range of conditions that the regression estimator is the most efficient unless there is a low correlation between the actual and estimated crop acreages in the sampled area segments, in which case a ratio type estimator is superior. Estimation of the variance of the regression estimator is also investigated.
Chhikara, R. S.↗
Preliminary Evaluation of TM for Soils Information
Thematic mapper data acquired over Mississippi County, Arkansas, were examined for utility in separating soil associations within generally level alluvium deposited by the Mississippi River. The 0.76 to 0.90 micron (Band 4) and the 1.55 to 1.75 micron (Band 5) were found to separate the different soil associations fairly well when compared to the USDA-SCS general soil map. The thermal channel also appeared to provide information at this level. A detailed soil survey was available at the field level along with ground observations of crop type, plant height, percent cover and growth stage. Soils within the fields ranged from uniform to soils that occur as patches of sand that stand out strongly against the intermingled areas of dark soil. Examination of the digital values of individual TM bands at the field level indicates that the influence of the soil is greater in TM than it was in MSS bands. The TM appears to provide greater detail of within field variability caused by soils than MSS and thus should provide improved information relating to crop and soil properties. However, this soil influence may cause crop identification classification procedures to have to account for the soil in their algorithms.
Thompson, D. R.↗
Calibration or inverse regression: Which is appropriate for crop surveys using LANDSAT data?
Calibration and inverse regression estimators of crop proportions are investigated where the auxiliary variable is obtained from binary classification of multivariate LANDSAT data. The appropriate model relating classifier proportions and ground observed proportions for a given crop type is the calibration model. Under this model the inverse regression estimator is superior to the calibration estimator in estimating the crop acreage or proportion for a region of interest.
Chhikara, R. S.↗
Use of satellite data in agricultural surveys
The state-of-the-art of crop surveying by satellite is reviewed with an emphasis on the signature extension problem. Registration and preprocessing procedures are discussed with refereence to: normalization of the radiometric values of each scene for scene-to-scene differences; registration techniques, implemented at the NASA Johnson Space Center, capable of 0.5 pixel root-mean-square error; and current research in this direction. Data transformation and modeling techniques applied to the Landsat MSS images and a solution for the field-to-field variations of the greenness and brightness temporal trajectories are included. Finally, a review of the mixture decomposition method of labeling and estimating the areal proportions is given.
Hall, F. G.↗
Evaluation of Landsat Thematic Mapper for vegetated alluvium soils information
Landsat Thematic Mapper data acquired over Mississippi County, Arkansas, on August 22, 1982, were evaluated whether TM provides information that could be used for soil association maps and if soil properties (variability within vegetated fields) can be detected with the new bands on TM. It was found that TM data - especially the mid-IR and thermal bands - show the capability for separating vegetated soil landscapes on a broad basis. Analysis at the field level with a crop growing indicates that TM, with its additional and narrower bands and improved spatial resolution is influenced by within-field variability due to soils.
Thompson, D. R.↗
Monitoring global vegetation
An attempt is made to identify the need for, and the current capability of, a technology which could aid in monitoring the Earth's vegetation resource on a global scale. Vegetation is one of our most critical natural resources, and accurate timely information on its current status and temporal dynamics is essential to understand many basic and applied environmental interrelationships which exist on the small but complex planet Earth.
Macdonald, R. B.↗
Sampling, aggregation, and variance estimation for area, yield, and production in LACIE
An approximately 2% sampling error was achieved in LACIE by sampling only approximately 2% of the sampling frame. The sample design in the yardstick region for which historical data were available down to a substratum level to support missing data resulting from cloud cover provided the most accurate estimate possible. The implemented strategy provided data of sufficient quality and quantity to support required performance levels and also to satisfy the existing constraints. The allocation scheme appeared to provide the most efficient usage of the available data and gave segment coverage of major producing areas and thus improved the probability of an accurate estimate.
Hallum, C. R.↗
Accuracy assessment: The statistical approach to performance evaluation in LACIE
A statistical methodology was developed to check the accuracy of the products of the experimental operations throughout crop growth and to determine whether the procedures are adequate to accomplish the desired accuracy and reliability goals. It has allowed the identification and isolation of key problems in wheat area yield estimation, some of which have been corrected and some of which remain to be resolved. The major unresolved problem in accuracy assessment is that of precisely estimating the bias of the LACIE production estimator. Topics covered include: (1) evaluation techniques; (2) variance and bias estimation for the wheat production estimate; (3) the 90/90 evaluation; (4) comparison of the LACIE estimate with reference standards; and (5) first and second order error source investigations.
Houston, A. G.↗
Accuracy assessment system and operation
The accuracy and reliability of LACIE estimates of wheat production, area, and yield is determined at regular intervals throughout the year by the accuracy assessment subsystem which also investigates the various LACIE error sources, quantifies the errors, and relates then to their causes. Timely feedback of these error evaluations to the LACIE project was the only mechanism by which improvements in the crop estimation system could be made during the short 3 year experiment.
Pitts, D. E.↗
LACIE area, yield, and production estimate characteristics: Canada
Sampling segment allocation for Canada placed 283 segments within three provinces: Saskatchewan (170), Alberta (75), and Manitoba (38). The data base was comprised of five data sets: allocation, historical, ratio, LANDSAT, and yield. In-season area, yield, and production estimates were generated only during phase 2. These data are presented and analyzed.
Conte, D.↗
Accuracy and performance of LACIE area estimates
Results for the three crop years between 1974 and 1977 are presented in 25 tables for four regions of the U.S. Great Plains. Topics covered include error source analyses and special studies during each phase. Abnormal signature and boundary problems still under investigation are examined.
Potter, J. F.↗
Accuracy and performance of LACIE yield estimates in major wheat producing regions of the world
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.
Phinney, D. E.↗
Accuracy assessment in the Large Area Crop Inventory Experiment
The Accuracy Assessment System (AAS) of the Large Area Crop Inventory Experiment (LACIE) was responsible for determining the accuracy and reliability of LACIE estimates of wheat production, area, and yield, made at regular intervals throughout the crop season, and for investigating the various LACIE error sources, quantifying these errors, and relating them to their causes. Some results of using the AAS during the three years of LACIE are reviewed. As the program culminated, AAS was able not only to meet the goal of obtaining accurate statistical estimates of sampling and classification accuracy, but also the goal of evaluating component labeling errors. Furthermore, the ground-truth data processing matured from collecting data for one crop (small grains) to collecting, quality-checking, and archiving data for all crops in a LACIE small segment.
Houston, A. G.↗
Large Area Crop Inventory Experiment (LACIE). Effects of non-response including cloud cover on aggregation of wheat area in the US Great Plains
There are no author-identified significant results in this report.
Houston, A. G.↗