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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 433 records · Page 24
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NASA NTRS1976

Bayes estimation on parameters of the single-class classifier

Normal procedures used for designing a Bayes classifier to classify wheat as the major crop of interest require not only training samples of wheat but also those of nonwheat. Therefore, ground truth must be available for the class of interest plus all confusion classes. The single-class Bayes classifier classifies data into the class of interest or the class 'other' but requires training samples only from the class of interest. This paper will present a procedure for Bayes estimation on the mean vector, covariance matrix, and a priori probability of the single-class classifier using labeled samples from the class of interest and unlabeled samples drawn from the mixture density function.

Lin, G. C.↗
NASA NTRS1977

LACIE: A look to the future

The Large Area Crop Inventory Experiment (LACIE) is a project designed to demonstrate the applicability of remote sensing technology to monitor globally an important world food crop - wheat. The need for more timely and reliable monitoring of food and fiber supplies is discussed, and the monitoring systems currently utilized are reviewed. The fundamentals involved in assessing the impact of variable weather and economic conditions on wheat acreage, yield, and production are elucidated. The experiment's approach to production monitoring is described, and its status is reviewed. Examples of acreage and yield monitoring in the Soviet Union are used to illustrate the experiment's approach.

Macdonald, R. B.↗
NASA NTRS1977

LACIE - A look to the future

The Large Area Crop Inventory Experiment (LACIE) is a 'proof of concept' project designed to demonstrate the applicability of remote sensing technology to the global monitoring of wheat. This paper discusses the need for more timely and reliable monitoring of food and fiber supplies, reviews the monitoring systems currently utilized by the USDA and United Nations Food and Agriculture Organization in the United States and in foreign countries, and elucidates the fundamentals involved in assessing the impact of variable weather and economic conditions on wheat acreage, yield, and production. The experiment's approach to production monitoring is described briefly, and its status is reviewed as of the conclusion of 2 years of successful operation. Examples of acreage and yield monitoring in the Soviet Union are used to illustrate the experiment's approach.

Macdonald, R. B.↗
NASA NTRS1978

Remote sensing of crop type and maturity

A red-shift in the chlorophyll absorption edge of heading wheat and grain sorghum is clearly visible in high-spectral-resolution measurements made from a low-flying aircraft over Imperial Valley, California. The position of the absorption edge shifts progressively toward the longer wavelengths during the crop growth cycle. The red-shift of 7 to 10 nm can be measured by using 10 nm-wide spectral bands, centered at 745 nm and 785 nm. These two bands, plus a band in the pigment absorption region at 670 nm, contain enough information to identify wheat and grain sorghum in the heading stage, to indicate the degree of heading, and to indicate canopy density during the heading stage

Collins, W.↗
NASA NTRS1977

The Large Area Crop Inventory Experiment

Determination of wheat acreages in the central U.S., as well as the development and testing of yield models, is discussed in the framework of the Large Area Crop Inventory Experiment (LACIE). Particular attention is given to the goal of obtaining a 90% accuracy in yield forecasts in nine out of ten years. Current results of LACIE indicate that Landsat remote sensing data, in conjunction with information from agricultural meteorological surveys, can provide highly accurate early-season and at-harvest yield estimates in the principal wheat-growing regions of the world.

Macdonald, R. B.↗
NASA NTRS1977

Crop identification and area estimation by computer-aided analysis of Landsat data

This report describes the results of a study involving the use of computer-aided analysis techniques applied to Landsat MSS data for identification and area estimation of winter wheat in Kansas and corn and soybeans in Indiana. Key elements of the approach included use of aerial photography for classifier training, stratification of Landsat data and extension of training statistics to areas without training data, and classification of a systematic sample of pixels from each county. Major results and conclusions are: (1) Landsat data was adequate for accurate identification and area estimation of winter wheat in Kansas, but corn and soybean estimates for Indiana were less accurate; (2) computer-aided analysis techniques can be effectively used to extract crop identification information from Landsat MSS data, and (3) systematic sampling of entire counties made possible by computer classification methods resulted in very precise area estimates at county as well as district and state levels.

Bauer, M. E.↗
NASA NTRS1978

Large Area Crop Inventory Experiment (LACIE). Second-generation sampling strategy evaluation report

The author has identified the following significant results. The stratification procedure in the new sampling strategy for LACIE included: (1) correlation test results indicating that an agrophysical stratum may be homogeneous with respect to agricultural density, but not with respect to wheat density; and (2) agrophysical unit homogeneity test results indicating that with respect to agricultural density many agrophysical units are not homogeneous, but removal of one or more refined strata from any such current agrophysical unit can make the strata homogeneous. The apportioning procedure results indicated that the current procedure is not performing well and that the apportioned estimates of refined strata wheat area are often unreliable.

Basu, J. P.↗
NASA NTRS1978

Large Area Crop Inventory Experiment (LACIE). Detecting and monitoring agricultural vegetative water stress over large areas using LANDSAT digital data

The author has identified the following significant results. The Green Number Index technique which uses LANDSAT digital data from 5X6 nautical mile sampling frames was expanded to evaluate its usefulness in detecting and monitoring vegetative water stress over the Great Plains. At known growth stages for wheat, segments were classified as drought or non drought. Good agreement was found between the 18 day remotely sensed data and a weekly ground-based crop moisture index. Operational monitoring of the 1977 U.S.S.R. and Australian wheat crops indicated drought conditions. Drought isoline maps produced by the Green Number Index technique were in good agreement with conventional sources.

Thompson, D. R.↗
NASA NTRS1976

Large Area Crop Inventory Experiment (LACIE). Review of LACIE methodology, a project evaluation of technical acceptability

The author has identified the following significant results. Results indicated that the LANDSAT data and the classification technology can estimate the small grains area within a sample segment accurately and reliably enough to meet the LACIE goals. Overall, the LACIE estimates in a 9 x 11 kilometer segment agree well with ground and aircraft determined area within these segments. The estimated c.v. of the random classification error was acceptably small. These analyses confirmed that bias introduced by various factors, such as LANDSAT spatial resolution, lack of spectral resolution, classifier bias, and repeatability, was not excessive in terms of the required performance criterion. Results of these tests did indicate a difficulty in differentiating wheat from other closely related small grains. However, satisfactory wheat area estimates were obtained through the reduction of the small grain area estimates in accordance with relative amounts of these crops as determined from historic data; these procedures are being further refined.

Source record↗
NASA NTRS1978

LACIE (Large Area Crop Inventory Experiment) Programme

Large Area Crop Inventory Experiment was a three year experiment conducted jointly by the United States Department of Agriculture, NASA, and NOAA (National Oceanic and Atmospheric Administration) to determine if global wheat production can be forecast from LANDSAT data to within 90% of actual yield for each observed 9 years out of 10. Data for the third crop year (1976-1977) was compiled. The proportional area of cultivated land devoted to wheat was estimated based on a statistical sampling of LANDSAT data. Yield is presently estimated by NOAA using regression models developed from historical weather and yield information. Results are discussed.

Whitman, R. I.↗
NASA NTRS1979

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.↗
NASA NTRS1979

The classification and mensuration subsystem

From an operational standpoint, the most significant item the classification and mensuration subsystem (CAMS) had to overcome in providing the acreage component of the wheat production estimates for LACIE was the scope (segment volume processing required). Peak processing requirements per day increased from 16 to 20 for phase 1 with 700 total segments, to 35 to 40 per day for phase 2 with 1700 total segments, to 75 to 80 per day for phase 3 with 3000 total segments. Key issues regarding interrelationships between man and machines were identified during phase 1 using first generation technology. Procedure 1, tested and evaluated during phase 2 and continued through the initial phase 3 processing period for winter wheat, showed the need for software modification, procedures development, and analyst training. CAMS operations are described with emphasis on the training backgrounds of the analysts, the available data, and the labeling logic.

Abotteen, K. M.↗
NASA NTRS1979

Evaluation of several schemes for classification of remotely sensed data: Their parameters and performance

The author has identified the following significant results. Data sets for corn, soybeans, winter wheat, and spring wheat were used to evaluate the following schemes for crop identification: (1) per point Gaussian maximum classifier; (2) per point sum of normal densities classifiers; (3) per point linear classifier; (4) per point Gaussian maximum likelihood decision tree classifiers; and (5) texture sensitive per field Gaussian maximum likelihood classifier. Test site location and classifier both had significant effects on classification accuracy of small grains; classifiers did not differ significantly in overall accuracy, with the majority of the difference among classifiers being attributed to training method rather than to the classification algorithm applied. The complexity of use and computer costs for the classifiers varied significantly. A linear classification rule which assigns each pixel to the class whose mean is closest in Euclidean distance was the easiest for the analyst and cost the least per classification.

Scholz, D.↗
NASA NTRS1980

Evaluation of transition year Canadian test sites

The author has identified the following significant results. The spring small grain proportion accuracy in 15 Saskatchewan test sites was found to be comparable to that of the Large Area Crop Inventory Experiment Phase 3 and Transition Year results in the U.S. spring wheat states. Spring small grain labeling accuracy was 94%, and the direct wheat labeling accuracy was 89%, despite the low barley separation accuracy of 30%.

Payne, R. W.↗
NASA NTRS1980

Global crop forecasting

The needs for and remote sensing means of global crop forecasting are discussed, and key results of the Large Area Crop Inventory Experiment (LACIE) are presented. Current crop production estimates provided by foreign countries are shown often to be inadequate, and the basic elements of crop production forecasts are reviewed. The LACIE project is introduced as a proof-of-concept experiment designed to assimilate remote sensing technology, monitor global wheat production, evaluate key technical problems, modify the technique accordingly and demonstrate the feasibility of a global agricultural monitoring system. The global meteorological data, sampling and aggregation techniques, Landsat data analysis procedures and yield forecast procedures used in the experiment are outlined. Accuracy assessment procedures employed to evaluate LACIE technology performance are presented, and improvements in system efficiency and capacity during the three years of operation are pointed out. Results of LACIE estimates of Soviet, U.S. and Canadian wheat production are presented which demonstrate the feasibility and accuracy of the remote-sensing approach for global food and fiber monitoring.

Macdonald, R. B.↗
NASA NTRS1980

Canadian crop calendars in support of the early warning project

The Canadian crop calendars for LACIE are presented. Long term monthly averages of daily maximum and daily minimum temperatures for subregions of provinces were used to simulate normal daily maximum and minimum temperatures. The Robertson (1968) spring wheat and Williams (1974) spring barley phenology models were run using the simulated daily temperatures and daylengths for appropriate latitudes. Simulated daily temperatures and phenology model outputs for spring wheat and spring barley are given.

Trenchard, M. H.↗
NASA NTRS1980

Crop calendars for the US, USSR, and Canada in support of the early warning project

New crop calendars are produced for U.S. regions where several years of periodic growth stage observations are available on a CRD basis. Preexisting crop calendars from the LACIE are also collected as are U.S. crop calendars currently being created for the Foreign Commodities Production Forecast project. For the U.S.S.R. and Canada, no new crop calendars are created because no new data are available. Instead, LACIE crop calendars are compared against simulated normal daily temperatures and against the Robertson wheat and Williams barley phenology models run on the simulated normal temperatures. Severe inconsistencies are noted and discussed. For the U.S.S.R., spring and fall planting dates can probably be estimated accurately from satellite or meteorological data. For the starter model problem, the Feyerherm spring wheat model is recommended for spring planted small grains, and the results of an analysis are presented. For fall planted small grains, use of normal planting dates supplemented by spectral observation of an early stage is recommended. The importance of nonmeteorological factors as they pertain to meteorological factors in determining fall planting is discussed. Crop calendar data available at the Johnson Space Center for the U.S., U.S.S.R., Canada, and other countries are inventoried.

Hodges, T.↗
NASA NTRS1981

An analysis of haze effects on LANDSAT multispectral scanner data

Early season changes in optical depth change brightness, primarily along the soil line; and during crop development, changes in optical depth change both greenness and brightness. Thus, the existence of haze in the imagery could cause an unsuspecting analyst to interpret the spectral appearance as indicating an episodal event when, in fact, haze was present. The techniques for converting LANDSAT-3 data to simulate LANDSAT-2 data are in error. The yellowness and none such computations are affected primarily. Yellowness appears well correlated to optical depth. Experimental evidence with variable background and variable optical depth is needed, however. The variance of picture elements within a spring wheat field is related to its equivalent in optical depth changes caused by haze. This establishes the sensitivity of channel 1 (greenness) pixels to changes in haze levels. The between field picture element means and variances were determined for the spring wheat fields. This shows the variability of channel data on two specific dates, emphasizing that crop development can be influenced by many factors. The atmospheric correction program ATCOR reduces segment data from LANDSAT acquisitions to a common haze level and improves the results of analysis.

Johnson, W. R.↗