Human and Robotic Mission to Small Bodies: Mapping, Planning and Exploration

This study investigates the requirements, performs a gap analysis and makes a set of recommendations for mapping products and exploration tools required to support operations and scientific discovery for near- term and future NASA missions to small bodies. The mapping products and their requirements are based on the analysis of current mission scenarios (rendezvous, docking, and sample return) and recommendations made by the NEA Users Team (NUT) in the framework of human exploration. The mapping products that sat- isfy operational, scienti c, and public outreach goals include topography, images, albedo, gravity, mass, density, subsurface radar, mineralogical and thermal maps. The gap analysis points to a need for incremental generation of mapping products from low (flyby) to high-resolution data needed for anchoring and docking, real-time spatial data processing for hazard avoidance and astronaut or robot localization in low gravity, high dynamic environments, and motivates a standard for coordinate reference systems capable of describing irregular body shapes. Another aspect investigated in this study is the set of requirements and the gap analysis for exploration tools that support visualization and simulation of operational conditions including soil interactions, environment dynamics, and communications coverage. Building robust, usable data sets and visualisation/simulation tools is the best way for mission designers and simulators to make correct decisions for future missions. In the near term, it is the most useful way to begin building capabilities for small body exploration without needing to commit to specific mission architectures

OCM 2023 - Optical Characterization of Materials : Conference Proceedings

The state of the art in the optical characterization of materials is advancing rapidly. New insights have been gained into the theoretical foundations of this research and exciting developments have been made in practice, driven by new applications and innovative sensor technologies that are constantly evolving. The great success of past conferences proves the necessity of a platform for presentation, discussion and evaluation of the latest research results in this interdisciplinary field

Polarimetric SAR as a Tool for Remote Sensing Salt Diapirs, Axel Heiberg Island, Nunavut

The costs and hazards associated with traditional geological mapping have driven rapid advancement of remote predictive mapping techniques using satellite data. However, few studies have implemented synthetic aperture radar for geology. This study uses quad-polarimetric RADARSAT-2 and PALSAR-1 data to produce circular polarization ratio images over Axel Heiberg Island, Nunavut, Canada. These images are used to characterize the radar properties of gypsum and anhydrite diapirs and secondary salt deposits that have been mapped using visible and near infrared, short wave infrared, and thermal infrared spectroscopy. Diapiric salt outcrops appear rough in radar at the C-Band and L-Band (cm-dm) scales, whereas the secondary salts appear smooth. Ground truthing in the field confirms that salt diapirs are rough from millimeter to meter scale, whereas secondary salt minerals are precipitating on smoother surfaces, like floodplains and hillslopes. These results show that radar can be used to differentiate between diapiric and secondary salt exposures

Unsupervised classification of vertical profiles of dual polarization radar variables

Vertical profiles of polarimetric radar variables can be used to identify fingerprints of snow growth processes. In order to systematically study such manifestations of precipitation processes, we have developed an unsupervised classification method. The method is based on k-means clustering of vertical profiles of polarimetric radar variables, namely reflectivity, differential reflectivity and specific differential phase. For rain events, the classification is applied to radar profiles truncated at the melting layer top. For the snowfall cases, the surface air temperature is used as an additional input parameter. The proposed unsupervised classification was applied to 3.5 years of data collected by the Finnish Meteorological Institute Ikaalinen radar. The vertical profiles of radar variables were computed above the University of Helsinki Hyytiala station, located 64 km east of the radar. Using these data, we show that the profiles of radar variables can be grouped into 10 and 16 classes for rainfall and snowfall events, respectively. These classes seem to capture most important snow growth and ice cloud processes. Using this classification, the main features of the precipitation formation processes, as observed in Finland, are presented.Peer reviewe

Using artificial neural networks to predict riming from Doppler cloud radar observations

Riming, i.e., the accretion and freezing of super-cooled liquid water (SLW) on ice particles in mixed-phase clouds, is an important pathway for precipitation formation. Detecting and quantifying riming using ground-based cloud radar observations is of great interest; however, approaches based on measurements of the mean Doppler velocity (MDV) are unfeasible in convective and orographically influenced cloud systems. Here, we show how artificial neural networks (ANNs) can be used to predict riming using ground-based, zenith-pointing cloud radar variables as input features. ANNs are a versatile means to extract relations from labeled data sets, which contain input features along with the expected target values. Training data are extracted from a data set acquired during winter 2014 in Finland, containing both Ka-and W-band cloud radar and in situ observations of snow-fall by a Precipitation Imaging Package from which the rime mass fraction (FRPIP) is retrieved. ANNs are trained separately either on the Ka-band radar or the W-band radar data set to predict the rime fraction FRANN. We focus on two configurations of input variables. ANN 1 uses the equivalent radar reflectivity factor (Ze), MDV, the width from left to right edge of the spectrum above the noise floor (spectrum edge width - SEW), and the skewness as input features. ANN 2 only uses Ze, SEW, and skewness. The application of these two ANN configurations to case studies from different data sets demonstrates that both are able to predict strong riming (FRANN > 0.7) and yield low values (FRANNPeer reviewe

Forest fire management using machine learning techniques

As per the latest survey produced by the Forest Survey, the forest cover is 19.27% of the geographic area. According to this report every country can meet the human needs of 16% of the world’s population from the 1% of the world’s forest resource. The Forest Survey said that 90% of the forest fires created by humans. They pose a threat not only to the forest wealth but also this leads to the main threat to biodiversity, a change in the ecosystem. The environment gets dry and twinges, which leads to produce flames in the forest. There are two types of forest fire i) Surface Fire and ii) Crown Fire iii) Ground Fire. Surface Fire: The forest fire starts its flame primarily as a surface fire, spreading along the ground with the help of dry grasses and so on. Crown Fire: It starts flame on the crown of the shrubs, bushes and trees and sustained on the surface. This type of fire is very dangerous because resinous material given off burning logs burn furiously. If there is a slope with fire then the fire spread from the top of the slope to the down. Ground fire occurs in the humus and peaty layers beneath the litter of under composed portion of forest floor with intense heat but practically no flame. Such fires are relatively rare and have been recorded occasionally at high altitudes in Himalayan fir and spruce forests. In Remote sensing field, the knowledge of surface temperature plays a vital role. By using brightness and emissivity feature, temperature mapping and analysis can be done. The surface temperature values are measured to detect the forest fire from the ASTER image. ASTER stands for Advanced Space borne Thermal Emission and Reflection Radiometer. ASTER image contains 5 thermal bands (wave length ranges from 8.125 μm to 11.65 μm) and these are used in comparison. To convert digital numbers to exoatmospheric radiance, ASTER thermal bands are used. The converted exoatmospheric radiance is then mapped into surface radiance using the Emissivity Normalization method

On Small Satellites for Oceanography: A Survey

The recent explosive growth of small satellite operations driven primarily from an academic or pedagogical need, has demonstrated the viability of commercial-off-the-shelf technologies in space. They have also leveraged and shown the need for development of compatible sensors primarily aimed for Earth observation tasks including monitoring terrestrial domains, communications and engineering tests. However, one domain that these platforms have not yet made substantial inroads into, is in the ocean sciences. Remote sensing has long been within the repertoire of tools for oceanographers to study dynamic large scale physical phenomena, such as gyres and fronts, bio-geochemical process transport, primary productivity and process studies in the coastal ocean. We argue that the time has come for micro and nano satellites (with mass smaller than 100 kg and 2 to 3 year development times) designed, built, tested and flown by academic departments, for coordinated observations with robotic assets in situ. We do so primarily by surveying SmallSat missions oriented towards ocean observations in the recent past, and in doing so, we update the current knowledge about what is feasible in the rapidly evolving field of platforms and sensors for this domain. We conclude by proposing a set of candidate ocean observing missions with an emphasis on radar-based observations, with a focus on Synthetic Aperture Radar.Comment: 63 pages, 4 figures, 8 table

The Comet Halley archive: Summary volume

The contents are as follows: The Organizational History of the International Halley Watch; Operations of the International Halley Watch from a Lead Center Perspective; The Steering Group; Astrometry Network; Infrared Studies Network; Large-Scale Phenomena Network; Meteor Studies Network; Near-Nucleus Studies Network; Photometry and Polarimetry Network; Radio Science Network; Spectroscopy and Spectrophotometry Network; Amateur Observation Network; Use of the CD-ROM Archive; The 1986 Passage of Comet Halley; and Recent Observations of Comet Halley