Groundwater mounding: A diagnostic feature for mapping aquifer connectivity in hyper-arid deserts
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The author has identified the following significant results. In soil association mapping, computerized analysis of ERTS-1 MSS data has yielded images which will prove useful in the ongoing Cooperative Soil Survey program, involving the Soil Conservation Service of USDA and other state and local agencies. In the present mode of operation, a soil survey for a county may take up to 5 years to be completed. Results indicate that a great deal of soils information can be extracted from ERTS-1 data by computer analysis. This information is expected to be very valuable in the premapping conference phase of a soil survey, resulting in more efficient field operations during the actual mapping. In the earth surface features mapping effort it was found that temporal data improved the classification accuracy of forest classification in Tippecanoe County, Indiana. In water resources study a severe scanner look angle effect was observed in the aircraft scanner data of a test lake which was not present in ERTS-1 data of the same site. This effect was greatly accentuated by surface roughness caused by strong winds. Quantitative evaluation of urban features classification in ERTS-1 data was obtained. An 87.1% test accuracy was obtained for eight categories in Marion County, Indiana.
A variety of common eolian features on Mars have been identified from a survey of Mariner 9 and Viking orbiter images, and their regional and global distributions and orientations are discussed. Ten features have been mapped including: light and dark streaks, splotches, barchan and transverse dunes, crescentric and anomalous dunes, yardangs, wind grooves, and deflation pits. The north polar region shows a complex wind regime. Dunes and other ephemeral features reveal winds from the northwest and northeast. In the middle and low northern latitudes, northeasterly winds are the most effective winds. Southeast winds are the effective winds in most southern latitudes. Erosional features in bedrock indicate long-term and perhaps ancient wind trends, whereas depositional features may record relatively more recent winds. Deflation pits in the mantled terrain may contain the best record of both ancient and present-day winds.
Remote sensing provides a unique perspective on our dynamic planet, tracking changes and revealing the course of complex interactions. Long term monitoring and targeted observation combine with modeling and mapping to provide increased awareness of hydro-meteorological and geological hazards. Disasters often follow hazards and the goal of NASA’s Disasters Program is to look at the earth as a highly coupled system to reduce risk and enable resilience. Remote sensing and geospatial science are used as tools to help answer critical questions that inform decisions. Data is not the same as information, nor does understanding of processes necessarily translate into decision support for disaster preparedness, response and recovery. Accordingly, NASA is engaging the scientific and decision-support communities to apply remote sensing, modeling, and related applications in Communities and Areas at Intensive Risk (CAIR). In 2017, NASA’s Applied Sciences Disasters Program hosted a regional workshop to explore these issues with particular focus on coastal Virginia and North Carolina. The workshop brought together partners in academia, emergency management, and scientists from NASA and partnering federal agencies to explore capabilities among the team that could improve understanding of the physical processes related to these hazards, their potential impact to changing communities, and to identify methodologies for supporting emergency response and risk mitigation. The resulting initiative, the mid-Atlantic CAIR project, demonstrates the ability to integrate satellite derived earth observations and physical models into actionable, trusted knowledge. Severe storms and associated storm surge, sea level rise, and land subsidence coupled with increasing populations and densely populated, aging critical infrastructure often leave coastal regions and their communities extremely vulnerable. The integration of observations and models allow for a comprehensive understanding of the compounding risk experienced in coastal regions and enables individuals in all positions make risk-informed decisions. This initiative uses a representative storm surge case as a baseline to produce flood inundation maps. These maps predict building level impacts at current day and for sea level rise (SLR) and subsidence scenarios of the future in order to inform critical decisions at both the tactical and strategic levels. To accomplish this analysis, the mid-Atlantic CAIR project brings together Federal research activities with academia to examine coastal hazards in multiple ways: 1) reanalysis of impacts from 2011 Hurricane Irene, using numerical weather modeling in combination with coastal surge and hydrodynamic, urban inundation modeling to evaluate combined impact scenarios considering SLR and subsidence, 2) remote sensing of flood extent from available optical imagery, 3) adding value to remotely sensed flood maps through depth predictions, and 4) examining coastal subsidence as measured through time-series analysis of synthetic aperture radar observations. Efforts and results are published via ArcGIS story maps to communicate neighborhoods and infrastructure most vulnerable to changing conditions. Story map features enable time-aware flood mapping using hydrodynamic models, photographic comparison of flooding following Hurricane Irene, as well as visualization of heightened risk in the future due to SLR and land subsidence.
The majority of surface particles were found to be < 5 microns in diameter with increasing numbers close to the optical resolution limit of 0.3 microns. Acceleration grid EDS results show that the majority of materials appear to be from the SRC shell and SLA materials which include carbon-carbon fibers and Si-rich microspheres in a possible silicone binder. Other major debris material from the SRC included white paint, kapton, collector array fragments, and Al. Image analysis also revealed that SRC materials were also found mixed with the Utah mud and salt deposits. The EDS analysis of the acceleration grid showed that particles < 1 m where generally carbon based particles. Chemical cleaning techniques with Xylene and HF in an ultrasonic bath are currently being investigated for removal of small particles by the Genesis science team as well as ultra-pure water megasonic cleaning by the JSC team [4]. Removal of organic contamination from target materials is also being investigated by the science team with the use of UV-ozone cleaning devices at JSC and Open University [5]. In preparation for solar wind oxygen analyses at UCLA and Open University [1, 2], surface particle contamination on three Genesis concentrator targets was closely examined to evaluate cleaning strategies. Two silicon carbide (Genesis sample # 60001 and 60003) and one chemical vapor deposited (CVD) 13C concentrator target (60002) were imaged and mosaic mapped with optical microscopes. The resulting full target mosaic images and particle feature maps were subsequently compared with non-flight, but flight-like, concentrator targets and sample return capsule (SRC) materials. Contamination found on the flown concentrator acceleration grid was further examined using a scanning electron microscope (SEM). Energy dispersive X-ray spectroscopy (EDS) for particle identification was subsequently compared with the optical images from the flown targets. Figure 1 show that all three targets imaged in this report are fully intact and do not show any signs of material fractures. However, previous ellipsometry results and overview imaging of both flown SiC targets show a solar wind irradiation gradient from the center focal point to the outer edge [3]. In addition, due to the hard landing, each target has experienced varying degrees of impacts, scratches, and particle debris from the spacecraft and Utah impact site.
Mineral abundance maps of 18 minerals were made of the Cuprite Mining District using 1990 AVIRIS data and the Multiple Spectral Feature Mapping Algorithm (MSFMA) as discussed in Clark et al. This technique uses least-squares fitting between a scaled laboratory reference spectrum and ground calibrated AVIRIS data for each pixel. Multiple spectral features can be fitted for each mineral and an unlimited number of minerals can be mapped simultaneously. Quality of fit and depth from continuum numbers for each mineral are calculated for each pixel and the results displayed as a multicolor image.
Remotely sensed wide area synoptic data provides information on ocean dumping that is not readily available by other means. A qualitative approach has been used to map features, such as river plumes. Results of quantitative analyses have been used to develop maps showing quantitative distributions of one or more water quality parameters, such as suspended solids or chlorophyll a. Joint NASA/NOAA experiments have been conducted at designated dump areas in the U.S. coastal zones to determine the applicability of aircraft remote sensing systems to map plumes resulting from ocean dumping of sewage sludge and industrial wastes. A second objective is related to the evaluation of previously developed quantitative analysis techniques for studying dispersion of materials in these plumes. It was found that plumes resulting from dumping of four waste materials have distinctive spectral characteristics. The development of a technology for use in a routine monitoring system, based on remote sensing techniques, is discussed.
Low altitude black and white aerial photography is the prinicipal remote sensing tool for geologic investigations in West Virginia, although side looking radar and color infrared photography are also used. The first land use/cover map for the state was produced in color infrared and is being digitized. Linear features in Cabell and Wayne Counties, as revealed by LANDSAT, were evaluated to test the possible correlations with rock fractures and gas production from shales. A LANDSAT linear features map (1:250,000) was prepared for the entire state, also. Presently investigations are being made to understand karst and to predict areas that should not be used for development. Aerial photography and field mapping is being conducted to detect the location and causes of landslides.
A numerical representation of uncertain and incomplete sensor knowledge called Certainty Grids has been used successfully in several mobile robot control programs, and has proven itself to be a powerful and efficient unifying solution for sensor fusion, motion planning, landmark identification, and many other central problems. Researchers propose to build a software framework running on processors onboard the new Uranus mobile robot that will maintain a probabilistic, geometric map of the robot's surroundings as it moves. The certainty grid representation will allow this map to be incrementally updated in a uniform way from various sources including sonar, stereo vision, proximity and contact sensors. The approach can correctly model the fuzziness of each reading, while at the same time combining multiple measurements to produce sharper map features, and it can deal correctly with uncertainties in the robot's motion. The map will be used by planning programs to choose clear paths, identify locations (by correlating maps), identify well-known and insufficiently sensed terrain, and perhaps identify objects by shape. The certainty grid representation can be extended in the same dimension and used to detect and track moving objects.
The Galileo spacecraft completed its observations of Jupiter's volcanic moon Io in October 2001 with the orbit I32 flyby, during which new local (13-55 m/pixel) and regional (130-400 m/pixel) resolution images and spectroscopic data were returned of the antijovian hemisphere. We have combined a I32 regional mosaic (330 m/pixel) with lower-resolution C21 color data (1.4 km/pixel, Figure 1) and produced a geomorphologic map of the Culann-Tohil area of this hemisphere. Here we present the geologic features, map units, and structures in this region, and give preliminary conclusions about geologic activity for comparison with other regions to better understand Io's geologic evolution.
Future space missions require that spacecraft have the capability to autonomously navigate non-cooperative environments for rendezvous and proximity operations (RPO). Current relative navigation filters can have difficulty in these situations, diverging due to complications with data association, high measurement uncertainty, and clutter, particularly when detailed a priori maps of the target object or spacecraft do not exist. The goal of this work is to demonstrate the feasibility of random finite set (RFS) filters for spacecraft relative navigation and pose estimation. The approach is to formulate satellite relative navigation and pose estimation as a simultaneous localization and mapping (SLAM) problem, in which an observer spacecraft seeks to simultaneously estimate the location of features on a target object or spacecraft as well as its relative position, velocity and attitude. This work utilizes a filter developed using the framework of RFS which are well suited to multi-target SLAM operations, avoiding data association entirely. Relevant RPO scenarios with simulated flash LIDAR measurements are tested with a Probability Hypothesis Density (PHD) RFS filter embedded in a particle filter to obtain a feature map of a target and a relative pose estimate between the target and observer. Preliminary results show that an RFS-based filter can successfully perform SLAM in a spacecraft relative navigation scenario with no a priori map of the target. These results demonstrate the feasibility of RFS filtering for spacecraft relative navigation and motivate future studies which may expand to tracking space objects for space situational awareness, as well as relative navigation around small bodies.
Future space missions require that spacecraft have the capability to autonomously navigate non-cooperative environments for rendezvous and proximity operations (RPO). Current relative navigation filters can have difficulty in these situations, diverging due to complications with data association, high measurement uncertainty, and clutter, particularly when detailed a priori maps of the target object or spacecraft do not exist. The goal of this work is to demonstrate the feasibility of random finite set (RFS) filters for spacecraft relative navigation and pose estimation. The approach is to formulate satellite relative navigation and pose estimation as a simultaneous localization and mapping (SLAM) problem, in which an observer spacecraft seeks to simultaneously estimate the location of features on a target object or spacecraft as well as its relative position, velocity and attitude. This work utilizes a filter developed using the framework of RFS which are well suited to multi-target SLAM operations, avoiding data association entirely. Relevant RPO scenarios with simulated flash LIDAR measurements are tested with a Probability Hypothesis Density (PHD) RFS filter embedded in a particle filter to obtain a feature map of a target and a relative pose estimate between the target and observer. Preliminary results show that an RFS-based filter can successfully perform SLAM in a spacecraft relative navigation scenario with no a priori map of the target. These results demonstrate the feasibility of RFS filtering for spacecraft relative navigation and motivate future studies which may expand to tracking space objects for space situational awareness, as well as relative navigation around small bodies.
Future space missions require spacecraft to autonomously navigate non-cooperative environments for rendezvous and proximity operations (RPO). Current navigation filters used for RPO can have difficulty when optical sensors are used, due to complications with data association, high measurement uncertainty, and clutter. This paper provides an initial demonstration of the feasibility of using random finite set (RFS) filters for spacecraft relative navigation and pose estimation. Spacecraft relative navigation is formulated as a simultaneous localization and mapping (SLAM) problem, in which an observer spacecraft seeks to simultaneously estimate the location of features on a target object or spacecraft as well as its relative position, velocity and attitude. Simulated flash LIDAR measurements are processed using a Gaussian Mixture Probability Hypothesis Density (GMPHD) filter embedded in a particle filter to obtain a feature map of a target and a relative pose estimate between the target and observer over time. Results show that an RFS-based filter can successfully perform SLAM in a spacecraft relative navigation scenario.
A new unsupervised pattern classifier is introduced for on-line detection of abnormality in features of vibration that are used for fault diagnosis of helicopter gearboxes. This classifier compares vibration features with their respective normal values and assigns them a value in (0, 1) to reflect their degree of abnormality. Therefore, the salient feature of this classifier is that it does not require feature values associated with faulty cases to identify abnormality. In order to cope with noise and changes in the operating conditions, an adaptation algorithm is incorporated that continually updates the normal values of the features. The proposed classifier is tested using experimental vibration features obtained from an OH-58A main rotor gearbox. The overall performance of this classifier is then evaluated by integrating the abnormality-scaled features for detection of faults. The fault detection results indicate that the performance of this classifier is comparable to the leading unsupervised neural networks: Kohonen's Feature Mapping and Adaptive Resonance Theory (AR72). This is significant considering that the independence of this classifier from fault-related features makes it uniquely suited to abnormality-scaling of vibration features for fault diagnosis.
Since the launch of Landsat-1 28 years ago, remotely sensed data have been used to map features on the earth's surface. An increasing number of health studies have used remotely sensed data for monitoring, surveillance, or risk mapping, particularly of vector-borne diseases. Nearly all studies used data from Landsat, the French Systeme Pour l'Observation de la Terre, and the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer. New sensor systems are in orbit, or soon to be launched, whose data may prove useful for characterizing and monitoring the spatial and temporal patterns of infectious diseases. Increased computing power and spatial modeling capabilities of geographic information systems could extend the use of remote sensing beyond the research community into operational disease surveillance and control. This article illustrates how remotely sensed data have been used in health applications and assesses earth-observing satellites that could detect and map environmental variables related to the distribution of vector-borne and other diseases.
A correlation of known archaeologic sites with the mapped locations of the streamcourses is expected and may lead to new interpretations of early human history in the Sahara. The valley networks, faults, and other subjacent bedrock features mapped on the SIR-A images are promising areas for ground water and mineral exploration. Additionally, the analogies between the interplay of wind and running water in the geologic history of the Sahara and of Mars are strengthened by the SIR-A discoveries of relict drainage systems beneath the eolian veneer of Egypt and Sudan.
The case of the infrared-bright system NGC 6240 encapsulates in many ways the major issues in understanding processes in infrared-bright systems in general. Its morphology strongly suggests that this is a merger in progress, with energetic phenomena being triggered as evidenced by strong radio emission and optical emission lines as well as the strong far-infrared output. Evidence has been produced supporting various schemes for producing the energy being radiated by this object. Early work on the optical spectrum suggested a Seyfert 2 classification, but the ionization level and spatial extent of the emission implied a more distributed source of energy. IR line studies found evidence for young supergiants, and for immense amounts of H2 excited by low-velocity shocks, which might contribute to a large enhancement in star-formation rate. Finally, the radio-continuum morphology shows compact sources possibly associated with active nuclei in both remnants of the merger. This is one of the nearest IR-bright galaxies objects at high luminosity (Cz = 7500 km s(exp -1), so that spatially resolved studies can yield further insight into which of these processes are most important - star formation, nuclear activity, or shock excitation. The velocity field, linewidth distribution, and ionization structure of the gas in NGC 6240 was mapped with the Dense-Pak fiber-optic array on the Kitt Peak 4-m telescope. In all, 135 spectra were obtained through an array of 2 inch apertures, with considerable overlap between many of the adjacent apertures produced by offsetting the telescope between the exposures. As the strongest features with lambda less than 1.1 ma, H alpha and the adjacent (NII) lines were observed, along with the neighboring (O I) and (S II) features. Maps of velocity and linewidth were constructed by interpolating measurements from these spectra onto a regular grid, and show irregular patterns with large velocity gradients and, particularly, an extended region of large local (shear?) velocity width.
Distribution and flow of water masses at four depth intervals were determined by analyzing ERTS imagery through the use of optical models of classes of vertical oceanographic profiles. Data used for these models was obtained from shipboard measurements including surface spectral radiance, and optical and more conventional oceanographic depth profiles. The spectral models obtained were applied to radiance-contoured ERTS imagery in band 4, 5, 6, and 7. Features mapped by direct photointerpretation of ERTS imagery include submerged shoals, current streamlines, and location of possible upwellings, downwellings and submarine springs.