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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

Crop identification technology assessment for remote sensing (CITARS). Volume 6: Data processing at the laboratory for applications of remote sensing

The results of classifications and experiments for the crop identification technology assessment for remote sensing are summarized. Using two analysis procedures, 15 data sets were classified. One procedure used class weights while the other assumed equal probabilities of occurrence for all classes. Additionally, 20 data sets were classified using training statistics from another segment or date. The classification and proportion estimation results of the local and nonlocal classifications are reported. Data also describe several other experiments to provide additional understanding of the results of the crop identification technology assessment for remote sensing. These experiments investigated alternative analysis procedures, training set selection and size, effects of multitemporal registration, spectral discriminability of corn, soybeans, and other, and analyses of aircraft multispectral data.

Bauer, M. E.↗
NASA NTRS1975

The use of ERTS-1 multispectral imagery for crop identification in a semi-arid climate

Crop identification using multispectral satellite imagery and multivariate pattern recognition was used to identify wheat accurately in Greeley County, Kansas. A classification accuracy of 97 percent was found for wheat and the wheat estimate in hectares was within 5 percent of the USDA's Statistical Reporting Service estimate for 1973. The multispectral response of cotton and sorghum in Texas was not unique enough to distinguish between them nor to separate them from other cultivated crops.

Stockton, J. G.↗
NASA NTRS1976

Large Area Crop Inventory Experiment (LACIE). Phase 1: Evaluation report

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.

Source record↗
NASA NTRS1977

Crop identification and area estimation over large geographic areas using LANDSAT MSS data

The author has identified the following significant results. LANDSAT MSS data was adequate to accurately identify wheat in Kansas; corn and soybean estimates in Indiana were less accurate. Computer-aided analysis techniques were effectively used to extract crop identification information from LANDSAT data. Systematic sampling of entire counties made possible by computer classification methods resulted in very precise area estimates at county, district, and state levels. Training statistics were successfully extended from one county to other counties having similar crops and soils if the training areas sampled the total variation of the area to be classified.

Bauer, M. E.↗
NASA NTRS1976

Crop status evaluations and yield predictions

One phase of the large area crop inventory project is presented. Wheat yield models based on the input of environmental variables potentially obtainable through the use of space remote sensing were developed and demonstrated. By the use of a unique method for visually qualifying daily plant development and subsequent multifactor computer analyses, it was possible to develop practical models for predicting crop development and yield. Development of wheat yield prediction models was based on the discovery that morphological changes in plants are detected and quantified on a daily basis, and that this change during a portion of the season was proportional to yield.

Haun, J. R.↗
NASA NTRS1977

Large Area Crop Inventory Experiment (LACIE). Phase 2 evaluation report

Documentation of the activities of the Large Area Crop Inventory Experiment during the 1976 Northern Hemisphere crop year is presented. A brief overview of the experiment is included as well as phase two area, yield, and production estimates for the United States Great Plains, Canada, and the Union of Soviet Socialist Republics spring winter wheat regions. The accuracies of these estimates are compared with independent government estimates. Accuracy assessment of the United States Great Plains yardstick region based on a through blind sight analysis is given, and reasons for variations in estimating performance are discussed. Other phase two technical activities including operations, exploratory analysis, reporting, methods of assessment, phase three and advanced system design, technical issues, and developmental activities are also included.

Source record↗
NASA NTRS1978

Crop water-stress assessment using an airborne thermal scanner

An airborne thermal scanner was used to measure the temperature of a wheat crop canopy in Phoenix, Arizona. The results indicate that canopy temperatures acquired about an hour and a half past solar noon were well correlated with presunrise plant water tension, a parameter directly related to plant growth and development. Pseudo-colored thermal images reading directly in stress degree days, a unit indicative of crop irrigation needs and yield potential, were produced. The aircraft data showed significant within-field canopy temperature variability, indicating the superiority of the synoptic view provided by aircraft over localized ground measurements. The standard deviation between airborne and ground-acquired canopy temperatures was 2 C or less.

Millard, 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 NTRS1978

LACIE: An experiment in global crop forecasting

The author has identified the following significant results. Both the accuracy and efficiency with which LACIE crop survey estimates were made have shown significant improvement in three years. In the U.S. and U.S.S.R. winter wheat regions, the original accuracy goals were met or exceeded, with 90/90 estimates achieved in the United States 1.5 to 2 months before harvest. Additionally, all available accuracy parameters indicate 90/90 estimates for the U.S.S.R. total crop. Key technology problems were identified during phase 2 with spring wheat in the United States and Canada which prevented the attainment of 90/90 accuracies in these regions. Technology solutions developed and tested in phase 3 partly resolved these issues with a significant improvement realized in the accuracy of the spring wheat area estimates.

Macdonald, R. B.↗
NASA NTRS1978

Large Area Crop Inventory Experiment (LACIE). Executive summary

The author has identified the following significant results. The Large Area Crop Inventory Experiment (LACIE), completed June 30, 1978, has met the USDA at-harvest goals (90% accuracy with a 90% confidence level) in the US Great Plains and U.S.S.R. for two consecutive years. In addition, in the U.S.S.R., LACIE indicated a shortfall in the '76-'77 wheat crop about two months prior to harvest, thus demonstrating the capability of LACIE to make accurate preharvest estimates.

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

Cropping management using color and color infrared aerial photographs

The Universal Soil Loss Equation (USLE) is a widely accepted tool for erosion prediction and conservation planning. Solving this equation yields the long-term average annual soil loss that can be expected from rill and inter-rill erosion. In this study, manual interpretation of color and color infrared 70 mm photography at the scale of 1:60,000 is used to determine the cropping management factor in the USLE. Accurate information was collected about plowing practices and crop residue cover (unharvested vegetation) for the winter season on agricultural land in Pheasant Branch Creek watershed in Dane County, Wisconsin.

Morgan, K. M.↗
NASA NTRS1979

Accuracy and performance of LACIE crop development models

Of the three principal phenological crop calendar models evaluated for LACIE, Robertson's triquadratic model which predicts the rate of progression of wheat through its biological development, was selected. Daily maximum and minimum temperatures and day length are the input variables, and the principal output is a daily increment of development through six physiological growth stages. Because wheat corresponds differently to the environment during each growth stage, five different equations are required. The estimated and observed crop development data were compared in order to establish a measure of confidence in the model and to identify consistent discrepancies that would adversely affect LACIE operation. Although the model provided reliable estimates for various wheat growing regions of the world, it was found that there are still areas in need of further model improvement or development.

Woolley, S. K.↗
NASA NTRS1979

The vegetative index number and crop identification

A vegetative index number of numerical value was calculated from the digital values of the LANDSAT system to provide some measure of green growing vegetation. The usefulness of the green numbers for schemes in crop identification and acreage estimation is investigated and the Ashburn vegetation index (AVI) is compared with the Kauth-Thomas vegetation index (KVI) for crop identification schemes. Results of wheat acreage estimation using LACIE Procedure 1 and the AVI for eight sample segments are given. Tables show comparisons between the AVI and the KVI as well as visual results of the AVI.

Ashburn, P.↗
NASA NTRS1979

Manual LANDSAT data analysis for crop type identification

The process of manual identification of crop type by human analysts and problems associated in LACIE that were associated with manual crop identification measurement procedures are described. Research undertaken in cooperation with LACIE operations by the supporting research community to effect solutions to, or obtain greater understanding of the problems is discussed.

Hay, C. M.↗
NASA NTRS1979

Temporal resolution for crop discrimination estimated using J-M distance

A study is presented which examines techniques utilizing spectral/phenological data to classify and mensurate target crops at predetermined accuracies in order to estimate the temporal resolution requirements for a remote sensing system. An analysis is made of the physical target (crop) characteristics as they vary in time. A model is developed relating the frequency required to attain a particular probability of correct discrimination with N accumulated observations to that required with single observations. Finally, results for a multicrop spectral data set are shown to substantiate the theoretically-modeled results.

Baran, J. W.↗