An SMP Soft Classification Algorithm for Remote Sensing

This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR) algorithm, a semiautomated classification algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classification containing inherently more information than a comparable hard classification at an increased computational cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel algorithm development work here. Experimental results of applying parallel CIGSCR to an image with approximately 10^8 pixels and six bands demonstrate superlinear speedup. A soft two class classification is generated in just over four minutes using 32 processors

A review of parallel computing for large-scale remote sensing image mosaicking

Interest in image mosaicking has been spurred by a wide variety of research and management needs. However, for large-scale applications, remote sensing image mosaicking usually requires significant computational capabilities. Several studies have attempted to apply parallel computing to improve image mosaicking algorithms and to speed up calculation process. The state of the art of this field has not yet been summarized, which is, however, essential for a better understanding and for further research of image mosaicking parallelism on a large scale. This paper provides a perspective on the current state of image mosaicking parallelization for large scale applications. We firstly introduce the motivation of image mosaicking parallel for large scale application, and analyze the difficulty and problem of parallel image mosaicking at large scale such as scheduling with huge number of dependent tasks, programming with multiple-step procedure, dealing with frequent I/O operation. Then we summarize the existing studies of parallel computing in image mosaicking for large scale applications with respect to problem decomposition and parallel strategy, parallel architecture, task schedule strategy and implementation of image mosaicking parallelization. Finally, the key problems and future potential research directions for image mosaicking are addressed

Advancements in Measuring and Modeling the Mechanical and Hydrological Properties of Snow and Firn: Multi-sensor Analysis, Integration, and Algorithm Development

Estimating snow mechanical properties – such as elastic modulus, stiffness, and strength – is important for understanding how effectively a vehicle can travel over snow-covered terrain. Vehicle instrumentation data and observations of the snowpack are valuable for improving the estimates of winter vehicle performance. Combining in-situ and remotely-sensed snow observations, driver input, and vehicle performance sensors requires several techniques of data integration. I explored correlations between measurements spanning from millimeter to meter scales, beginning with the SnowMicroPenetrometer (SMP) and instruments applied to snow that were designed for measuring the load bearing capacity and the compressive and shear strengths of roads and soils. The spatial distribution of snow’s mechanical properties is still largely unknown. From this initial work, I determined that snow density remains a useful proxy for snowpack strength. To measure snow density, I applied multi-sensor electromagnetic methods. Using spatially distributed snowpack, terrain, and vegetation information developed in the subsequent chapters, I developed an over-snow vehicle performance model. To measure the vehicle performance, I joined driver and vehicle data in the coined Normalized Difference Mobility Index (NDMI). Then, I applied regression methods to distribute NDMI from spatial snow, terrain, and vegetation properties. Mobility prediction is useful for the strategic advancement of warfighting in cold regions. The security of water resources is climatologically inequitable and water stress causes international conflict. Water resources derived from snow are essential for modern societies in climates where snow is the predominant source of precipitation, such as the western United States. Snow water equivalent (SWE) is a critical parameter for yearly water supply forecasting and can be calculated by multiplying the snow depth by the snow density. In this work, I combined high-spatial resolution light detection and ranging (LiDAR) measured snow depths with ground-penetrating radar (GPR) measurements of two-way travel-time (TWT) to solve for snow density. Then using LiDAR derived terrain and vegetation features as predictors in a multiple linear regression, the density observations are distributed across the SnowEx 2020 study area at Grand Mesa, Colorado. The modeled density resolved detailed patterns that agree with the known interactions of snow with wind, terrain, and vegetation. The integration of radar and LiDAR sensors shows promise as a technique for estimating SWE across entire river basins and evaluating observational- or physics-based snow-density models. Accurate estimation of SWE is a means of water security. In our changing climate, snow and ice mass are being permanently lost from the cryosphere. Mass balance is an indicator of the (in)stability of glaciers and ice sheets. Surface mass balance (SMB) may be estimated by multiplying the thickness of any annual snowpack layer by its density. Though, unlike applications in seasonal snowpack, the ages of annual firn layers are unknown. To estimate SMB, I modeled the firn depth, density, and age using empirical and numerical approaches. The annual SMB history shows cyclical patterns representing the combination of atmospheric, oceanic, and anthropogenic climate forcing, which may serve as evaluation or assimilation data in climate model retrievals of SMB. The advancements made using the SMP, multi-channel GPR arrays, and airborne LiDAR and radar within this dissertation have made it possible to spatially estimate the snow depth, density, and water equivalent in seasonal snow, glaciers, and ice sheets. Open access, process automation, repeatability, and accuracy were key design parameters of the analyses and algorithms developed within this work. The many different campaigns, objectives, and outcomes composing this research documented the successes and limitations of multi-sensor estimation techniques for a broad range of cryosphere applications

Load balancing techniques for I/O intensive tasks on heterogeneous clusters

Load balancing schemes in a cluster system play a critically important role in developing highperformance cluster computing platform. Existing load balancing approaches are concerned with the effective usage of CPU and memory resources. I/O-intensive tasks running on a heterogeneous cluster need a highly effective usage of global I/O resources, previous CPU-or memory-centric load balancing schemes suffer significant performance drop under I/O- intensive workload due to the imbalance of I/O load. To solve this problem, Zhang et al. developed two I/O-aware load-balancing schemes, which consider system heterogeneity and migrate more I/O-intensive tasks from a node with high I/O utilization to those with low I/O utilization. If the workload is memory-intensive in nature, the new method applies a memory-based load balancing policy to assign the tasks. Likewise, when the workload becomes CPU-intensive, their scheme leverages a CPU-based policy as an efficient means to balance the system load. In doing so, the proposed approach maintains the same level of performance as the existing schemes when I/O load is low or well balanced. Results from a trace-driven simulation study show that, when a workload is I/O-intensive, the proposed schemes improve the performance with respect to mean slowdown over the existing schemes by up to a factor of 8. In addition, the slowdowns of almost all the policies increase consistently with the system heterogeneity

Review Article: Global Monitoring of Snow Water Equivalent Using High-Frequency Radar Remote Sensing

Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 × 106 km2 of Earth\u27s surface (31 % of the land area) each year, and is thus an important expression and driver of the Earth\u27s climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (∼ −13 % per decade) as Arctic summer sea ice. More than one-sixth of the world\u27s population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth\u27s cold regions\u27 ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of water stored as snow on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations are not able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high-socio-economic-value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-band synthetic aperture radar (SAR) for global monitoring of SWE. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimeter-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modeling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, density, and layering. We describe radar interactions with snow-covered landscapes, the small but rapidly growing number of field datasets used to evaluate retrieval algorithms, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and application communities on progress made in recent decades and sets the stage for a new era in SWE remote sensing from SAR measurements

Détection de la stratigraphie en milieu de neige sèche à l'aide d'un radar à onde continue modulé en fréquence (FMCW) de 24 GHz

Considérant l’augmentation en popularité des activités hivernales dans l’arrière-pays, il est important de s’assurer qu’elles puissent être pratiquée en toute sécurité. Il devient alors impératif de mieux comprendre le rôle des interfaces de neige problématiques menant à des instabilités qui influence le danger d’avalanche. La cueillette des données géophysiques de la neige en terrain avalancheux demeure toutefois restreinte due aux contraintes logistiques, temporelles et financières limitant l’accès à l’immense territoire. L’objectif de ce mémoire est donc de développer une méthode automatisée et rapide de détection des interfaces à grand contraste de propriétés nivales pouvant potentiellement mener à de l’instabilité à l’aide d’un radar à onde continue modulée en fréquence (FMCW) de 24 GHz. Les vents forts, les événements de pluie-sur-neige, les évènements de gel-dégel hivernaux et les longues périodes de froid de la péninsule Gaspésienne entraînent souvent la formation d’un manteau neigeux complexe intéressant pour l’étude de ces interfaces instables. Ce projet de recherche se concentre sur le développement d’une méthode empirique de détection de ces interfaces à l’aide de données in-situ recueillies dans le territoire des Chic-Chocs au Québec. Les mesures radar ont suivi deux protocoles différents : 1) acquisition de données en mode mobile pour comprendre l’interaction et la sensibilité de l’onde radar avec la neige et ainsi optimiser les paramètres de l’instrument pour d’éventuelles études de variabilité spatiales, et 2) acquisition de données en mode fixe pour évaluer le potentiel du dispositif radar à étudier la variabilité temporelle de la stratigraphie du manteau neigeux et ainsi mieux comprendre la persistance des interfaces à grands contrastes et le rôle que joue la météorologie dans leur développement. Plus spécifiquement, le principe du radar est de quantifier le contraste diélectrique entre les différentes couches de neige. En établissant un seuil sur l’amplitude radar et en connaissant la vitesse de propagation du signal dans différentes strates de neige, il est possible de corréler la profondeur des pics d’amplitude avec les interfaces potentiellement instables. Les données de comparaison in-situ utilisées initialement pour bien comprendre la signature du signal radar proviennent de profils de neige manuels et d’un Snow Micro Penetrometer (SMP). Ces données ont aussi servi à la validation des résultats et à établir la performance du dispositif radar. Lors de la validation, les mesures radar ont démontré un bon potentiel pour l’étude de la variabilité spatiale et temporelle en détectant 80% des interfaces identifiées manuellement, et ce, avec une erreur de positionnement vertical de 3 cm.Abstract : Considering the increased popularity for backcountry mountain recreation activities, problematic snowpack interfaces are currently of great interest given their impact on snow stability. As such, the identification of interface vertical locations in the snowpack and their spatial variability is essential for avalanche danger forecasting. The Gaspé Peninsula specific climate (strong winds, rain-on-snow events, winter thaw and prolonged very cold temperatures) often leads to a complex snowpack development, where the need of improved monitoring is important. The goal of this research is to asses an automated method to detect contrasted snow interfaces (i.e. contrasted layers) using a 24 GHz Frequency Modulated Continuous Wave (FMCW) portable radar. Based on different in-situ configurations (upward and downward looking), we compared the radar amplitude signals with in-situ snow geophysical measurements, including Snow Micro Penetrometer. Radar measurements have been done following two different protocols: 1) mobile radar looking-up and down in order to understand the radar-snow wave interactions and optimize its parameters for spatial variability assessment of contrasted snow layers and 2) fixed radar mounted on a tripod looking down to evaluate its potential to study snow stratigraphy temporal variability in one fixed location. Results show good agreements with compared validation data with 80% of manually identified interfaces detection and a vertical positioning error of 3 cm. The presented FMCW radar appears to have a good potential for spatial and temporal variability assessment of snowpack stratigraphy

Development and Application of Tools for Avalanche Forecasting, Avalanche Detection, and Snowpack Characterization

Avalanche formation is a complex interaction between the snowpack, weather, and terrain. However, detailed observations typically can only be made at a single point and must be extrapolated over the slope or regional scale. This study aims to provide avalanche forecasters with tools to evaluate the snowpack, avalanche hazard, and avalanche occurrence when manual observations are not feasible. Avalanches that occur within the new storm snow are a prevalent problem for the avalanche forecasters with the Idaho Transportation Department (ITD) along Highway 21. We have implemented a real time SNOw Slope Stability (SNOSS) model that provides an index to the stability of that layer. SNOSS has been run real time starting during the winter of 2011/2012 with model results outputted to a webpage for easy viewing by avalanche forecasters. To further improve the accuracy of SNOSS, the model was evaluated with a large database of avalanches from the Utah Department of Transportation (UDOT). Using weather data and SNOSS results, the probability of an avalanche day producing a natural direct action avalanche was calculated using a Balanced Random Forest (BRF). In the future, we hope that the BRF can provide a probability of an avalanche occurrence given the current weather and snowpack conditions that can be utilized by avalanche forecasters in their normal operations. The concern for avalanche forecasters with highway operations is the threat of an avalanche releasing and hitting a highway. Infrasound generated by an avalanche moving downhill can be detected and tracked using array processing techniques. This will allow avalanche forecasters to evaluate the avalanche hazard more effectively by determining when and where avalanches have occurred. An avalanche detection system has been developed to detect avalanches in near real time using infrasound arrays. The system processes the infrasound data on-site, automatically detects events, and classifies the events using multiple neural networks. If an avalanche has been detected, the system will transmit the necessary information over satellite to be viewed by avalanche forecasters on a webpage