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NASA NTRS1985

The application of artificial intelligence techniques to large distributed networks

Data accessibility and transfer of information, including the land resources information system pilot, are structured as large computer information networks. These pilot efforts include the reduction of the difficulty to find and use data, reducing processing costs, and minimize incompatibility between data sources. Artificial Intelligence (AI) techniques were suggested to achieve these goals. The applicability of certain AI techniques are explored in the context of distributed problem solving systems and the pilot land data system (PLDS). The topics discussed include: PLDS and its data processing requirements, expert systems and PLDS, distributed problem solving systems, AI problem solving paradigms, query processing, and distributed data bases.

Dubyah, R.↗

NASA NTRS1983

Preliminary airborne imaging spectrometer vegetation data

An airborne, pushbroom-style multispectral (1.2-2.4 micron) imaging sensor system known as Airborne Imaging Spectrometer (AIS) has been flown over the Pico Anticline region of the Santa Susana Mountains, California (U.S.A.). Initial interpretation of preliminary data sets suggest that the high spectral resolution data from AIS provide superior vegetation discrimination and mapping capabilities when compared with other imaging systems (MSS, TMS). AIS spectral data may also contain information concerning the state of health of both native and cultivated vegetation. Such capabilities (discrimination, mapping, and assessment of state of health) will be most useful in geobotanical assessment of this and other areas.

Rock, B. N.↗

NASA NTRS1985

Proceedings of the Airborne Imaging Spectrometer Data Analysis Workshop

The Airborne Imaging Spectrometer (AIS) Data Analysis Workshop was held at the Jet Propulsion Laboratory on April 8 to 10, 1985. It was attended by 92 people who heard reports on 30 investigations currently under way using AIS data that have been collected over the past two years. Written summaries of 27 of the presentations are in these Proceedings. Many of the results presented at the Workshop are preliminary because most investigators have been working with this fundamentally new type of data for only a relatively short time. Nevertheless, several conclusions can be drawn from the Workshop presentations concerning the value of imaging spectrometry to Earth remote sensing. First, work with AIS has shown that direct identification of minerals through high spectral resolution imaging is a reality for a wide range of materials and geological settings. Second, there are strong indications that high spectral resolution remote sensing will enhance the ability to map vegetation species. There are also good indications that imaging spectrometry will be useful for biochemical studies of vegetation. Finally, there are a number of new data analysis techniques under development which should lead to more efficient and complete information extraction from imaging spectrometer data. The results of the Workshop indicate that as experience is gained with this new class of data, and as new analysis methodologies are developed and applied, the value of imaging spectrometry should increase.

Vane, G.↗

NASA NTRS1985

Mineralogical Mapping in the Cuprite Mining District, Nevada

The airborne imaging spectrometer (AIS) has provided for the first time, the possibility to map mineralogical constituents in the Earth's surface and thus has enormously increased the value of remote-sensing data as a tool in the solution of geologic problems. The question addressed with AIS at Cuprite was how well could the mineral components at the surface of a hydrothermal alteration zone be detected, identified and mapped? The question was answered positively and is discussed. A relatively rare mineral, buddingtonie, that could not have been detected by conventional means, was discovered and mapped by the use of AIS.

Goetz, A. F. H.↗

NASA NTRS1985

High Resolution Spectrometry of Leaf and Canopy Chemistry for Biochemical Cycling

High-resolution laboratory spectrophotometer and Airborne Imaging Spectrometer (AIS) data were used to analyze forest leaf and canopy chemistry. Fundamental stretching frequencies of organic bonds in the visible, near infrared and short-wave infrared are indicative of concentrations and total content of nitrogen, phosphorous, starch and sugar. Laboratory spectrophotometer measurements showed very strong negative correlations with nitrogen (measured using wet chemistry) in the visible wavelengths. Strong correlations with green wet canopy weight in the atmospheric water absorption windows were observed in the AIS data. A fairly strong negative correlation between the AIS data at 1500 nm and total nitrogen and nitrogen concentration was evident. This relationship corresponds very closely to protein absorption features near 1500 nm.

Spanner, M. A.↗

NASA NTRS1985

Spectroscopy of Moses Rock Kimberlite Diatreme

Three types of remote sensing data (Airborne Imaging Spectroscopy (AIS), NS001, Zeiss IR-photographs) were obtained for the Moses Rock kimberlite dike in southern Utah. The goal is to identify and characterize the mantle derived mafic component in such volcanic features. The Zeiss and NS001 images provide information on the regional setting and allow units of the dike to be distinguished from surrounding material. A potential unmapped satellite dike was identified. The AIS data provide characterizing information of the surface composition of the dike. Serpentized olivine-bearing soils are (tentatively) identified from the AIS spectra for a few areas within the dike.

Pieters, C. M.↗

NASA NTRS1985

The role of artificial intelligence and expert systems in increasing STS operations productivity

Artificial Intelligence (AI) is discussed. A number of the computer technologies pioneered in the AI world can make significant contributions to increasing STS operations productivity. Application of expert systems, natural language, speech recognition, and other key technologies can reduce manpower while raising productivity. Many aspects of STS support lend themselves to this type of automation. The artificial intelligence section of the mission planning and analysis division has developed a number of functioning prototype systems which demonstrate the potential gains of applying AI technology.

Culbert, C.↗

NASA NTRS1985

A knowledge based software engineering environment testbed

The Carnegie Group Incorporated and Boeing Computer Services Company are developing a testbed which will provide a framework for integrating conventional software engineering tools with Artifical Intelligence (AI) tools to promote automation and productivity. The emphasis is on the transfer of AI technology to the software development process. Experiments relate to AI issues such as scaling up, inference, and knowledge representation. In its first year, the project has created a model of software development by representing software activities; developed a module representation formalism to specify the behavior and structure of software objects; integrated the model with the formalism to identify shared representation and inheritance mechanisms; demonstrated object programming by writing procedures and applying them to software objects; used data-directed and goal-directed reasoning to, respectively, infer the cause of bugs and evaluate the appropriateness of a configuration; and demonstrated knowledge-based graphics. Future plans include introduction of knowledge-based systems for rapid prototyping or rescheduling; natural language interfaces; blackboard architecture; and distributed processing

Gill, C.↗

NASA NTRS1985

Artificial intelligence - NASA

Artificial Intelligence (AI) represents a vital common space support element needed to enable the civil space program and commercial space program to perform their missions successfully. It is pointed out that advances in AI stimulated by the Space Station Program could benefit the U.S. in many ways. A fundamental challenge for the civil space program is to meet the needs of the customers and users of space with facilities enabling maximum productivity and having low start-up costs, and low annual operating costs. An effective way to meet this challenge may involve a man-machine system in which artificial intelligence, robotics, and advanced automation are integrated into high reliability organizations. Attention is given to the benefits, NASA strategy for AI, candidate space station systems, the Space Station as a stepping stone, and the commercialization of space.

Erickson, J. D.↗

NASA NTRS1985

Artificial intelligence - New tools for aerospace project managers

Artificial Intelligence (AI) is currently being used for business-oriented, money-making applications, such as medical diagnosis, computer system configuration, and geological exploration. The present paper has the objective to assess new AI tools and techniques which will be available to assist aerospace managers in the accomplishment of their tasks. A study conducted by Brown and Cheeseman (1983) indicates that AI will be employed in all traditional management areas, taking into account goal setting, decision making, policy formulation, evaluation, planning, budgeting, auditing, personnel management, training, legal affairs, and procurement. Artificial intelligence/expert systems are discussed, giving attention to the three primary areas concerned with intelligent robots, natural language interfaces, and expert systems. Aspects of information retrieval are also considered along with the decision support system, and expert systems for project planning and scheduling.

Moja, D. C.↗

NASA NTRS1986

Analysis of substrate and plant spectral features of semi-arid shrub communities in the Owens Valley, California

Airborne Imaging Spectrometer (AIS) data were analyzed to deduce plant density and species composition in three semi-arid shrub-dominated communities of Owens Valley, CA, occurring on either a sand, granite alluvium, or basalt substrate. The high-spectral resolution AIS data were related to spectra obtained with field portable spectrometers, which in turn were related to plant and soil characteristics of the communities. Many of the dominant species have unique spectral features which permit their identification in AIS pixel images. The canopy-induced shadow may be a major factor influencing substrate spectral properties during fall and winter, because of low sun angles. Moreover, changes in spectral signatures following dormancy and leaf senescence tend to decrease contrasts between the plant community and the geologic substrate, also suggesting that fall and winter are a difficult time of year for spectral analyses.

Ustin, S. L.↗

NASA NTRS1986

Rapid prototyping facility for flight research in artificial-intelligence-based flight systems concepts

The Dryden Flight Research Facility of the NASA Ames Research Facility of the NASA Ames Research Center is developing a rapid prototyping facility for flight research in flight systems concepts that are based on artificial intelligence (AI). The facility will include real-time high-fidelity aircraft simulators, conventional and symbolic processors, and a high-performance research aircraft specially modified to accept commands from the ground-based AI computers. This facility is being developed as part of the NASA-DARPA automated wingman program. This document discusses the need for flight research and for a national flight research facility for the rapid prototyping of AI-based avionics systems and the NASA response to those needs.

Duke, E. L.↗

NASA NTRS1986

Use of digital Munsell color space to assist interretation of imaging spectrometer data: Geologic examples from the northern Grapevine Mountains, California and Nevada

Techniques using Munsell color transformations were developed for reducing 128 channels (or less) of Airborne Imaging Spectrometer (AIS) data to a single color-composite-image suitable for both visual interpretation and digital analysis. Using AIS data acquired in 1984 and 1985, limestone and dolomite roof pendants and sericite-illite and other clay minerals related to alteration were mapped in a quartz monzonite stock in the northern Grapevine Mountains of California and Nevada. Field studies and laboratory spectral measurements verify the mineralogical distributions mapped from the AIS data.

Kruse, F. A.↗

NASA NTRS1985

Preliminary evaluation of the airborne imaging spectrometer for vegetation analysis in the Klamath National Forest of northeastern California

The experiences and results associated with a project entitled Preliminary Evaluation of the Airborne Imaging Spectrometer for Vegetation Analysis is documented. The primary goal of the project was to provide ground truth, manual interpretation, and computer processing of data from an experimental flight of the Airborne Infrared Spectrometer (AIS) to determine the extent to which high spectral resolution remote sensing could differentiate among plant species, and especially species of conifers, for a naturally vegetated test site. Through the course of the research, JPL acquired AIS imagery of the test areas in the Klamath National Forest, northeastern California, on two overflights of both the Dock Well and Grass Lake transects. Over the next year or so, three generations of data was also received: first overflight, second overflight, and reprocessed second overflight. Two field visits were made: one trip immediately following the first overflight to note snow conditions and temporally-related vegetation states at the time of the sensor overpass; and a second trip about six weeks later, following acquisition of prints of the images from the first AIS overpass.

Strahler, A. H.↗

NASA NTRS1986

Applications of artificial intelligence to scientific research

Artificial intelligence (AI) is a growing field which is just beginning to make an impact on disciplines other than computer science. While a number of military and commercial applications were undertaken in recent years, few attempts were made to apply AI techniques to basic scientific research. There is no inherent reason for the discrepancy. The characteristics of the problem, rather than its domain, determines whether or not it is suitable for an AI approach. Expert system, intelligent tutoring systems, and learning programs are examples of theoretical topics which can be applied to certain areas of scientific research. Further research and experimentation should eventurally make it possible for computers to act as intelligent assistants to scientists.

Prince, Mary Ellen↗

NASA NTRS1986

Evaluation of the airborne imaging spectrometer for remote sensing of forest stand conditions

Five pairs of plots were established in forest stands with one of each pair trenched and covered to prevent precipitation from reaching the tree roots. High winds and falling limbs destroyed the covers on three of the plots. The two remaining plots were in a red pine plantation and in a natural stand of sugar maple. Trees in both plots developed levels of moisture stress more than nine bars higher than control trees on the dates of overflights with the Airborne Imaging Spectrometer (AIS) and the Collins' Airborne Spectroradiometer (CAS). Hemispherical reflectance from stressed and control trees was measured with a Beckman DK2A spectrophotometer. On the day of the AIS overflight, stressed maple foliage was less reflective than the control from 1000 to 1300 nm, but more reflective at wavelengths longer than 1300 nm. Pine foliage was less reflective than the control from 1000 to 1600 nm, but the difference was small at wavelengths longer than 1350 nm. AIS data collected showed brightness values for both maple and pine to be lower than for the controls from 1000 to 1300 nm. CAS data were used to determine the gain in species identification accuracy obtainable with high spectral resolution data.

Olson, Charles E., Jr.↗

NASA NTRS1986

Artificial intelligence for Space Station automation: Crew safety, productivity, autonomy, augmented capability

Artificial intelligence (AI) R&D projects for the successful and efficient operation of the Space Station are described. The book explores the most advanced AI-based technologies, reviews the results of concept design studies to determine required AI capabilities, details demonstrations that would indicate the existence of these capabilities, and develops an R&D plan leading to such demonstrations. Particular attention is given to teleoperation and robotics, sensors, expert systems, computers, planning, and man-machine interface.

Firschein, O.↗

NASA NTRS1987

Raising the AIQ of the Space Station

Expert systems and robotics technologies are to be significantly advanced during the Space Station program. Artificial intelligence systems (AI) on the Station will include 'scars', which will permit upgrading the AI capabilities as the Station evolves to autonomy. NASA-Ames is managing the development of the AI systems through a series of demonstrations, the first, controlling a single subsystem, to be performed in 1988. The capabilities being integrated into the first demonstration are described; however, machine learning and goal-driven natural language understanding will not reach a prototype stage until the mid-1990s. Steps which will be taken to endow the computer systems with the ability to move from heuristic reasoning to factual knowledge, i.e., learning from experience, are explored. It is noted that the development of Space Station expert systems depends on the development of experts in Station operations, which will not happen until the Station has been used extensively by crew members.

Lum, Henry↗