Applications of ISES for vegetation and land use

Remote sensing relative to applications involving vegetation cover and land use is reviewed to consider the potential benefits to the Earth Observing System (Eos) of a proposed Information Sciences Experiment System (ISES). The ISES concept has been proposed as an onboard experiment and computational resource to support advanced experiments and demonstrations in the information and earth sciences. Embedded in the concept is potential for relieving the data glut problem, enhancing capabilities to meet real-time needs of data users and in-situ researchers, and introducing emerging technology to Eos as the technology matures. These potential benefits are examined in the context of state-of-the-art research activities in image/data processing and management

Terrain analysis using radar shape-from-shading

This paper develops a maximum a posteriori (MAP) probability estimation framework for shape-from-shading (SFS) from synthetic aperture radar (SAR) images. The aim is to use this method to reconstruct surface topography from a single radar image of relatively complex terrain. Our MAP framework makes explicit how the recovery of local surface orientation depends on the whereabouts of terrain edge features and the available radar reflectance information. To apply the resulting process to real world radar data, we require probabilistic models for the appearance of terrain features and the relationship between the orientation of surface normals and the radar reflectance. We show that the SAR data can be modeled using a Rayleigh-Bessel distribution and use this distribution to develop a maximum likelihood algorithm for detecting and labeling terrain edge features. Moreover, we show how robust statistics can be used to estimate the characteristic parameters of this distribution. We also develop an empirical model for the SAR reflectance function. Using the reflectance model, we perform Lambertian correction so that a conventional SFS algorithm can be applied to the radar data. The initial surface normal direction is constrained to point in the direction of the nearest ridge or ravine feature. Each surface normal must fall within a conical envelope whose axis is in the direction of the radar illuminant. The extent of the envelope depends on the corrected radar reflectance and the variance of the radar signal statistics. We explore various ways of smoothing the field of surface normals using robust statistics. Finally, we show how to reconstruct the terrain surface from the smoothed field of surface normal vectors. The proposed algorithm is applied to various SAR data sets containing relatively complex terrain structure

Land use, urban, environmental, and cartographic applications, chapter 2, part D

Microwave data and its use in effective state, regional, and national land use planning are dealt with. Special attention was given to monitoring land use change, especially dynamic components, and the interaction between land use and dynamic features of the environment. Disaster and environmental monitoring are also discussed

LANDSAT land cover analysis completed for CIRSS/San Bernardino County project

The LANDSAT analysis carried out as part of Ames Research Center's San Bernardino County Project, one of four projects sponsored by NASA as part of the California Integrated Remote Sensing System (CIRSS) effort for generating and utilizing digital geographic data bases, is described. Topics explored include use of data-base modeling with spectral cluster data to improve LANDSAT data classification, and quantitative evaluation of several change techniques. Both 1976 and 1979 LANDSAT data were used in the project

Army-NASA aircrew/aircraft integration program. Phase 5: A3I Man-Machine Integration Design and Analysis System (MIDAS) software concept document

This is the Software Concept Document for the Man-machine Integration Design and Analysis System (MIDAS) being developed as part of Phase V of the Army-NASA Aircrew/Aircraft Integration (A3I) Progam. The approach taken in this program since its inception in 1984 is that of incremental development with clearly defined phases. Phase 1 began in 1984 and subsequent phases have progressed at approximately 10-16 month intervals. Each phase of development consists of planning, setting requirements, preliminary design, detailed design, implementation, testing, demonstration and documentation. Phase 5 began with an off-site planning meeting in November, 1990. It is expected that Phase 5 development will be complete and ready for demonstration to invited visitors from industry, government and academia in May, 1992. This document, produced during the preliminary design period of Phase 5, is intended to record the top level design concept for MIDAS as it is currently conceived. This document has two main objectives: (1) to inform interested readers of the goals of the MIDAS Phase 5 development period, and (2) to serve as the initial version of the MIDAS design document which will be continuously updated as the design evolves. Since this document is written fairly early in the design period, many design issues still remain unresolved. Some of the unresolved issues are mentioned later in this document in the sections on specific components. Readers are cautioned that this is not a final design document and that, as the design of MIDAS matures, some of the design ideas recorded in this document will change. The final design will be documented in a detailed design document published after the demonstrations

Estimation of physical variables from multichannel remotely sensed imagery using a neural network: Application to rainfall estimation

Satellite-based remotely sensed data have the potential to provide hydrologically relevant information about spatially and temporally varying physical variables. A methodology for estimating such variables from multichannel remotely sensed data is presented; the approach is based on a modified counterpropagation neural network (MCPN) and is both effective and efficient at building complex nonlinear input-output function mappings from large amounts of data. An application to high-resolution estimation of the spatial and temporal variation of surface rainfall using geostationary satellite infrared and visible imagery is presented. Test results also indicate that spatially and temporally sparse ground-based observations can be assimilated via an adaptive implementation of the MCPN method, thereby allowing on-line improvement of the estimates