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At least 523 records · Page 29

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.↗

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.↗

Large Area Crop Inventory Experiment (LACIE). LANDSAT 3X gain study

The author has identified the following results. The maximum likelihood classifier on the ERIPS failed to show any improvement in accuracy when comparing high-gain LANDSAT data with the simulated normal-gain data. Even if an improvement in accuracy had been detected, the timespan within the crop growing season when the use of high-gain data could be advantageous is limited. A total of six sets of LANDSAT-1 imagery is available for the analysis of gain effects. Three of the sets are high-gain data in MSS bands 4 and 5, whereas the other three are simulated normal-gain. The four sets obtained over Imperial Valley have good supporting ground truth information for training; the two sets over Kansas do not. The use of various combinations of the six data sets is recommended in order to uncover the possible advantages of using the data.

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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.

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Large Area Crop Inventory Experiment (LACIE). LACIE phase 1 and phase 2 accuracy assessment

The author has identified the following significant results. The initial CAS estimates, which were made for each month from April through August, were considerably higher than the USDA/SRS estimates. This was attributed to: (1) the practice of considering bare ground as potential wheat and counting it as wheat; (2) overestimation of the wheat proportions in segments having only a small amount of wheat; and (3) the classification of confusion crops as wheat. At the end of the season most of the segments were reworked using improved methods based on experience gained during the season. In particular, new procedures were developed to solve the three problems listed above. These and other improvements used in the rework experiment resulted in at-harvest estimates that were much closer to the USDA/SRS estimates than those obtained during the regular season.

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Selected examples of dispersal of arthropods associated with agricultural crop and animal production

The economic importance of arthropods in agricultural production systems and the possibilities of using dispersal behavior to develop and manipulate control are examined. Examples of long and short distance dispersal of economic insect pests and beneficial species from cool season host reservoirs and overwintering sites are presented. Significant dispersal of these species often occurring during crop and animal production is discussed.

Henneberry, T. J.↗

Remote sensing applied to the evaluation of crop freeze protection devices

Thermal images from an aircraft-mounted scanner are used to evaluate the effectiveness of crop freeze protection devices. Fuel oil heaters, wind machines and irrigation systems are evaluated from flights at an altitude of 450 m over an experimental citrus grove of 1.5 hectares.

Sutherland, R. A.↗

Large Area Crop Inventory Experiment (LACIE). Development of procedure M for multicrop inventory, with tests of a spring-wheat configuration

The author has identified the following significant results. An outgrowth of research and development activities in support of LACIE was a multicrop area estimation procedure, Procedure M. This procedure was a flexible, modular system that could be operated within the LACIE framework. Its distinctive features were refined preprocessing (including spatially varying correction for atmospheric haze), definition of field like spatial features for labeling, spectral stratification, and unbiased selection of samples to label and crop area estimation without conventional maximum likelihood classification.

Horvath, R.↗

Global crop production forecasting data system analysis

The author has identified the following significant results. Findings led to the development of a theory of radiometric discrimination employing the mathematical framework of the theory of discrimination between scintillating radar targets. The theory indicated that the functions which drive accuracy of discrimination are the contrast ratio between targets, and the number of samples, or pixels, observed. Theoretical results led to three primary consequences, as regards the data system: (1) agricultural targets must be imaged at correctly chosen times, when the relative evolution of the crop's development is such as to maximize their contrast; (2) under these favorable conditions, the number of observed pixels can be significantly reduced with respect to wall-to-wall measurements; and (3) remotely sensed radiometric data must be suitably mixed with other auxiliary data, derived from external sources.

Castruccio, P. A.↗

Identification and area estimation of agricultural crops by computer classification of Landsat MSS data

Landsat Multispectral Scanner (MSS) data covering a three-county area in northern Illinois were classified using computer-aided techniques as corn, soybeans, or 'other.' Recognition of test fields was 80% accurate. County estimates of the area of corn and soybeans agreed closely with those made by the USDA. Results of the use of a priori information in classification, techniques to produce unbiased area estimates, and the use of temporal and spatial features for classification are discussed. The extendability, variability, and size of training sets, wavelength band selection, and spectral characteristics of crops were also investigated.

Bauer, M. E.↗

The Large Area Crop Inventory Experiment /LACIE/ - A summary of three years' experience

Aims, history and schedule of the Large Area Crop Inventory Experiment (LACIE) conducted by NASA, USDA and NOAA from 1974-1977 are described. The LACIE experiment designed to research, develop, apply and evaluate a technology to monitor wheat production in important regions throughout the world (U.S., Canada, USSR, Brasil) utilized quantitative multispectral data collected by Landsat in concert with current weather data and historical information. The experiment successfully exploited computer data and mathematical models to extract timely corp information. A follow-on activities for the early 1980's is planned focusing especially on the early warning of changes affecting production and quality of renewable resources and commodity production forecast.

Erb, R. B.↗

Econometric models for predicting confusion crop ratios

Results for both the United States and Canada show that econometric models can provide estimates of confusion crop ratios that are more accurate than historical ratios. Whether these models can support the LACIE 90/90 accuracy criterion is uncertain. In the United States, experimenting with additional model formulations could provide improved methods models in some CRD's, particularly in winter wheat. Improved models may also be possible for the Canadian CD's. The more aggressive province/state models outperformed individual CD/CRD models. This result was expected partly because acreage statistics are based on sampling procedures, and the sampling precision declines from the province/state to the CD/CRD level. Declining sampling precision and the need to substitute province/state data for the CD/CRD data introduced measurement error into the CD/CRD models.

Umberger, D. E.↗

Estimating crop proportions from remotely sensed data

The classification/pixel-count method for estimating the proportion of wheat in each segment is theoretically biased even if all distributional assumptions are met. Alternative ways to estimate crop proportions are examined and their performance testing is considered. Topics covered include general linear functional estimates, the method of moments, and maximum likelihood estimators.

Feiveson, A. H.↗

Feature extraction applied to agricultural crops as seen by LANDSAT

The physical interpretation of the spectral-temporal structure of LANDSAT data can be conveniently described in terms of a graphic descriptive model called the Tassled Cap. This model has been a source of development not only in crop-related feature extraction, but also for data screening and for haze effects correction. Following its qualitative description and an indication of its applications, the model is used to analyze several feature extraction algorithms.

Kauth, R. J.↗

Signature extension methods in crop area estimation

The Procedure B multispectral processing system is both multisegment and multistratum. It uses data from several LACIE-sized segments together and makes a proportion estimate for the entire group of segments as well as for the individual segments. In the clustering of data features, Procedure B produces multiple classes or strata rather than just two strata (as in Procedure 1), and performs stratified sampling on each of these mutliple strata in order to make a proportion estimate. Tests results for the components and for the overall performance of Procedure B are presented, and conclusions that can be drawn from these tests are discussed. The rationale for signature extension for crop area estimation is summarized.

Kauth, R. J.↗

The role of phenology in statistical crop acreage measurement

In order to achieve market acceptance, the accuracy of remote sensing systems needs to be increased from the historically achieved average level of approximately 80-85% to 96-98%, i.e., by a factor of at least three, preferably five. A theory of discrimination is developed based on the fine-grained spectral data from LACIE supersites. It is shown that significant improvements in discrimination accuracy are possible by exploiting the differentials of crop spectra occurring between different phenologic stages. The major effects of such techniques on data system design are examined with respect to recurrence frequency, data volume, and information extraction.

Castruccio, P. A.↗

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.↗

Dryland pasture and crop conditions as seen by HCMM

Ground truth, aircraft, and satellite data were examined in order to: (1) assess the capability for determining wheat and pasture canopy temperatures in a dryland farming region from HCMM data; (2) assess the capability for determining soil moisture from HCMM data in dryland crops (winter wheat) from adjacent range lands; and (3) determine the relationship of HCMM-derived soil moisture and canopy temperature values with the condition of winter wheat and dryland farming areas during the principal growth stages. The IR data were screened to include areas having greater than 60% pasture and surface temperatures were recalculated using the atmospheric correction factor calculated by the modified RADTRA model, and the July 29, 1978 IR data were analyzed. Screening the IR data improved the relationship for July 24/July 13 and October 7/August 31 temperature/API relationship. However the coefficient of determination was not improved in the July 29/July 13 relationship.

Rosenthal, W. D.↗