Analyzing Remote Sensing Data in R: The landsat Package

Research and development on atmospheric and topographic correction methods for multispectral satellite data such as Landsat images has far outpaced the availability of those methods in geographic information systems software. As Landsat and other data become more widely available, demand for these improved correction methods will increase. Open source R statistical software can help bridge the gap between research and implementation. Sophisticated spatial data routines are already available, and the ease of program development in R makes it straightforward to implement new correction algorithms and to assess the results. Collecting radiometric, atmospheric, and topographic correction routines into the landsat package will make them readily available for evaluation for particular applications.

A multi-satellite orbit determination problem in a parallel processing environment

The Engineering Orbit Analysis Unit at GE Valley Forge used an Intel Hypercube Parallel Processor to investigate the performance and gain experience of parallel processors with a multi-satellite orbit determination problem. A general study was selected in which major blocks of computation for the multi-satellite orbit computations were used as units to be assigned to the various processors on the Hypercube. Problems encountered or successes achieved in addressing the orbit determination problem would be more likely to be transferable to other parallel processors. The prime objective was to study the algorithm to allow processing of observations later in time than those employed in the state update. Expertise in ephemeris determination was exploited in addressing these problems and the facility used to bring a realism to the study which would highlight the problems which may not otherwise be anticipated. Secondary objectives were to gain experience of a non-trivial problem in a parallel processor environment, to explore the necessary interplay of serial and parallel sections of the algorithm in terms of timing studies, to explore the granularity (coarse vs. fine grain) to discover the granularity limit above which there would be a risk of starvation where the majority of nodes would be idle or under the limit where the overhead associated with splitting the problem may require more work and communication time than is useful

Determination of spectral signatures of substances in natural waters

Optical remote sensing of water pollution offers the possibility of fast, large scale coverage at a relatively low cost. The possibility of using the spectral characteristics of the upwelling light from water for the purpose of ocean water quality monitoring was explained. The work was broken into several broad tasks as follows: (1) definition of a remotely measured spectral signature of water, (2) collection of field data and testing of the signature analysis, and (3) the possibility of using LANDSAT data for the identification of substances in water. An attempt to extract spectral signatures of acid waste and sediment was successful

Sampling system for wheat (Triticum aestivum L) area estimation using digital LANDSAT MSS data and aerial photographs

A procedure to estimate wheat (Triticum aestivum L) area using sampling technique based on aerial photographs and digital LANDSAT MSS data is developed. Aerial photographs covering 720 square km are visually analyzed. To estimate wheat area, a regression approach is applied using different sample sizes and various sampling units. As the size of sampling unit decreased, the percentage of sampled area required to obtain similar estimation performance also decreased. The lowest percentage of the area sampled for wheat estimation with relatively high precision and accuracy through regression estimation is 13.90% using 10 square km as the sampling unit. Wheat area estimation using only aerial photographs is less precise and accurate than those obtained by regression estimation

The auxiliary use of LANDSAT data in estimating crop acreages: Results of the 1975 Illinois crop-acreage experiment

The author has identified the following significant results. It was found that classifier performance was influenced by a number of temporal, methodological, and geographical factors. Best results were obtained when corn was tasselled and near the dough stage of development. Dates earlier or later in the growing season produced poor results. Atmospheric effects on results cannot be independently measured or completely separated from the effects due to the maturity stage of the crops. Poor classifier performance was observed in areas where considerable spectral confusion was present

Signature extension preprocessing for LANDSAT MSS data

There are no author-identified significant results in this report

Introduction to Thematic Mapper investigations. Section 1: Radiometry. Section 2: Geometry

An overview of papers which deal with radiometric characterization of the TM sensor is presented. Spectral characteristics are summarized. The geometric accuracy of TM are also examined. Aspects of prelaunch and post launch sensor performance, ground processing techniques, and error correction are also investigated

United States benefits of improved worldwide wheat crop information from a LANDSAT system

The value of worldwide information improvements on wheat crops, promised by LANDSAT, is measured in the context of world wheat markets. These benefits are based on current LANDSAT technical goals and assume that information is made available to all (United States and other countries) at the same time. A detailed empirical sample demonstration of the effect of improved information is given; the history of wheat commodity prices for 1971-72 is reconstructed and the price changes from improved vs. historical information are compared. The improved crop forecasting from a LANDSAT system assumed include wheat crop estimates of 90 percent accuracy for each major wheat producing region. Accurate, objective worldwide wheat crop information using space systems may have a very stabilizing influence on world commodity markets, in part making possible the establishment of long-term, stable trade relationships

Methods of extending signatures and training without ground information

Methods of performing signature extension, using LANDSAT-1 data, are explored. The emphasis is on improving the performance and cost-effectiveness of large area wheat surveys. Two methods were developed: ASC, and MASC. Two methods, Ratio, and RADIFF, previously used with aircraft data were adapted to and tested on LANDSAT-1 data. An investigation into the sources and nature of between scene data variations was included. Initial investigations into the selection of training fields without in situ ground truth were undertaken