Large Area Crop Inventory Experiment (LACIE). An overview of the Large Area Crop Inventory Experiment and the outlook for a satellite crop inventory

The author has identified the following significant results. The most important LACIE finding was that the technology worked very well in estimating wheat production in important geographic locations. Based on working through the many successes and shortcomings of LACIE, it can be stated with confidence that: (1) the current technology can successfully monitor what production in regions having similar characteristics to those of the U.S.S.R. wheat areas and the U.S. hard red winter wheat areas; (2) with additional applied research, significant improvements in capabilities to monitor wheat in these and other important production regions can be expected in the near future; (3) the remote sensing and weather effects modeling technology approached used by LACIE is generally applicable to other major crops and crop-producing regions of the world; and (4) with suitable effort, this technology can now advance rapidly and could be widespread use in the late 1980's

The Large Area Crop Inventory Experiment (LACIE). Methodology for area, yield and production estimation, results and perspective

There are no author-identified significant results in this report

The Large Area Crop Inventory Experiment (LACIE). An application of remote sensing by multispectral scanners

There are no author-identified significant results in this report

Heat shield Patent

Compact heat shielding for interplanetary space vehicle

The Tactical and Strategic Value of Commodity Futures

Historically, commodity futures have had excess returns similar to those of equities. But what should we expect in the future? The usual risk factors are unable to explain the time-series variation in excess returns. In addition, our evidence suggests that commodity futures are an inconsistent, if not tenuous, hedge against unexpected inflation. Further, the historically high average returns to a commodity futures portfolio are largely driven by the choice of weighting schemes. Indeed, an equally weighted long-only portfolio of commodity futures returns has approximately a zero excess return over the past 25 years. Our portfolio analysis suggests that the a long-only strategic allocation to commodities as a general asset class is a bet on the future term structure of commodity prices, in general, and on specific portfolio weighting schemes, in particular. In contrast, we provide evidence that there are distinct benefits to an asset allocation overlay that tactically allocates using commodity futures exposures. We examine three trading strategies that use both momentum and the term structure of futures prices. We find that the tactical strategies provide higher average returns and lower risk than a long-only commodity futures exposure.

The ERTS-1 investigation (ER-600). Volume 5: ERTS-1 urban land use analysis

The Urban Land Use Team conducted a year's investigation of ERTS-1 MSS data to determine the number of Land Use categories in the Houston, Texas, area. They discovered unusually low classification accuracies occurred when a spectrally complex urban scene was classified with extensive rural areas containing spectrally homogeneous features. Separate computer processing of only data in the urbanized area increased classification accuracies of certain urban land use categories. Even so, accuracies of urban landscape were in the 40-70 percent range compared to 70-90 percent for the land use categories containing more homogeneous features (agriculture, forest, water, etc.) in the nonurban areas

The ERTS-1 investigation (ER-600): A compendium of analysis results of the utility of ERTS-1 data for land resources management

The results of the ERTS-1 investigations conducted by the Earth Observations Division at the NASA Lyndon B. Johnson Space Center are summarized in this report, which is an overview of documents detailing individual investigations. Conventional image interpretation and computer-aided classification procedures were the two basic techniques used in analyzing the data for detecting, identifying, locating, and measuring surface features related to earth resources. Data from the ERTS-1 multispectral scanner system were useful for all applications studied, which included agriculture, coastal and estuarine analysis, forestry, range, land use and urban land use, and signature extension. Percentage classification accuracies are cited for the conventional and computer-aided techniques

The ERTS-1 investigation (ER-600). Volume 6: ERTS-1 signature extension analysis, July 1972 - June 1973

Feature classification, spatially and temporally, was extended over the Houston test site area. The Earth Resources Technology Satellite (ERTS-1) multispectral scanner data from August, September, and October 1972, of five widely separated lakes were used as statistical training fields and test sites. Short term temporal (same day to 36 days) and moderately long term spatial (within and between three ERTS multispectral scanner frames) signature extensions have been verified with respect to large relatively homogeneous features. The most significant feature dependent variable affecting spatial and short term extension was water turbidity. Long term signature extension will require a model to compensate or modify the ERTS-1 multispectral scanner data for significant sun angle changes. The presence of atmospheric haze changed the absolute signature but always by approximately the same amount so that the measured water signature was always the same. The normally occurring variations in atmospheric haze conditions had no major effect on the water signatures in this study

An objective technique to estimate percentage of an ERTS-1 water boundary resolution element covered by water

The author has identified the following significant results. An objective technique was developed to measure the surface area of water bodies. Nineteen water bodies in the Houston and Galveston, Texas area were selected as a basis for the technique development. The actual surface area of each body was determined from rectified and enlarged NASA aircraft photography. A clustering algorithm was used to produce classification maps of the region from ERTS-1 data. Certain classes were identified as being 100% water. Other classes were identified as being mixtures of water with land or vegetation. The number of picture elements falling on each water body and its boundary were counted. A linear regression analysis was performed to relate the total number of picture elements and boundary elements counted to the actual surface area. The standard error of the estimate was 6.7 acres. The absolute error was not a function of the actual surface area of the water body

The ERTS-1 investigation. Volume 7: Erts-1 land-use analysis of the Houston area test site

There are no author-identified significant results in this report