Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone

The application of GIS-based binary logistic regression for slope failure susceptibility mapping in the Western Grampian Mountains, Scotland

Slope failure has resulted in significant disruption to the Scottish road network in recent years and failure processes are widely considered to pose a very real risk to both infrastructure and road users. The manifestation of proposed regional climate variations could increase the hazard posed by landslide and debris flow activity within upland environments. It is therefore in the interests of decision makers and land managers to delineate the susceptibility of these areas to failure activity. The availability of accurate and high resolution geophysical data presents an opportunity to conduct a susceptibility analysis of proposed risk areas based on existing sites of failure. It is considered that failure sites are identifiable prior to activity and that events are triggered by external forcing in the form of excessive antecedent precipitation conditions. Binary logistic regression analysis is utilized to identify independent geophysical parameters that have been most associated with instances of past failure events. This technique facilitates the delineation of locations characterized by key parameter conditions most inductive to failure given the occurrence of an external trigger. It is proposed that when exposed to external forcing these locations are most susceptible to failure. To identify these locations is paramount to the successful application of any monitoring and/or preventative strategy. A Geographical Information System (GIS) is the ideal platform from which to undertake such a susceptibility analysis as it facilitates the precise identification of key independent parameter data associated with recorded instances of existing failure locations. The preparation, storage, extraction and analysis of intrinsic geophysical parameters promotes the development of a consistent modelling approach which can be applied to additional regions in the future

Hypotheses for the Origin of the Hypanis Fan-Shaped Deposit at the Edge of the Chryse Escarpment, Mars: Is it a Delta?

We investigated the origin of the fan-shaped deposit at the end of Hypanis Valles that has previously been proposed as an ExoMars, Mars 2020, and human mission candidate landing site, and found evidence that the landform is an ancient delta. Previous work suggests that the deposit originated from a time of fluvial activity both distinct from and prior to catastrophic outflow, and crater counting placed the deposit’s age at  ≥ 3.6 Ga. We found over 30 thin sedimentary strata in the proposed delta wall, and from our slope analysis conclude that the fluvial sequence is consistent with a lowering/retreating shoreline. We measured nearly horizontal bedding dip angles ranging from 0° to 2° over long stretches of cliff and bench exposures seen in HiRISE images and HiRISE stereo DTMs. From THEMIS night IR images we determined that the fan-shaped deposit has a low thermal inertia (150-240 Jm-2 K-1 s-1/2) and the surrounding darker-toned units correspond to thermal inertia values as high as 270-390 Jm-2 K-1 s-1/2. We interpret these findings to indicate that the fan-shaped deposit consists mostly of silt-sized and possibly finer grains, and that the extremely low grade and large lateral extent of these beds implies that the depositional environment was calm and relatively long-lived. We interpret the geomorphology and composition as incompatible with an alluvial fan or mudflow hypothesis. From our stratigraphic mapping we interpret the order of events which shaped the region. After the Chryse impact, sediment filled the basin, a confined lake or sea formed allowing a large delta to be deposited near its shoreline, the water level receded to the north, darker sedimentary/volcanic units covered the region and capped the light-toned deposit as hydro-volcanic eruptions shaped the interior of Lederberg crater, freeze/thaw cycles and desiccation induced local fracturing, and finally wrinkle ridges associated with rounded cones warped the landscape following trends in degraded crater rims and existing tectonic features. The ancient deltaic deposit we observe today was largely untouched by subsequent catastrophic outflows, and its surface has been only moderately reshaped by over 3 billion years of aeolian erosion

Spatio-temporal modeling of soil characteristics for soilscape reconstruction

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A Preliminary Assessment of Tidal Flooding along the New Hampshire Coast: Past, Present and Future

This report presents the results of a preliminary study that examines several critical coastal issues for New Hampshire including sea level fluctuations (past, present and future), shoreline migrations, and tidal flooding. Included are: 1) an analysis of sea level changes over the Holocene and resulting shoreline migrations, 2) an assessment of low-lying areas with elevations below selected tidal flooding datums in coastal areas, and 3) an assessment of increases in low-lying areas that are potentially at risk to tidal flooding over the next century due to sea level rise

Mapping Mangrove Extent and Change: A Globally Applicable Approach

This study demonstrates a globally applicable method for monitoring mangrove forest extent at high spatial resolution. A 2010 mangrove baseline was classified for 16 study areas using a combination of ALOS PALSAR and Landsat composite imagery within a random forests classifier. A novel map-to-image change method was used to detect annual and decadal changes in extent using ALOS PALSAR/JERS-1 imagery. The map-to-image method presented makes fewer assumptions of the data than existing methods, is less sensitive to variation between scenes due to environmental factors (e.g., tide or soil moisture) and is able to automatically identify a change threshold. Change maps were derived from the 2010 baseline to 1996 using JERS-1 SAR and to 2007, 2008 and 2009 using ALOS PALSAR. This study demonstrated results for 16 known hotspots of mangrove change distributed globally, with a total mangrove area of 2,529,760 ha. The method was demonstrated to have accuracies consistently in excess of 90% (overall accuracy: 92.293.3%, kappa: 0.86) for mapping baseline extent. The accuracies of the change maps were more variable and were dependent upon the time period between images and number of change features. Total change from 1996 to 2010 was 204,850 ha (127,990 ha gain, 76,860 ha loss), with the highest gains observed in French Guiana (15,570 ha) and the highest losses observed in East Kalimantan, Indonesia (23,003 ha). Changes in mangrove extent were the consequence of both natural and anthropogenic drivers, yielding net increases or decreases in extent dependent upon the study site. These updated maps are of importance to the mangrove research community, particularly as the continual updating of the baseline with currently available and anticipated spaceborne sensors. It is recommended that mangrove baselines are updated on at least a 5-year interval to suit the requirements of policy makers

Ski areas, weather and climate: Time series models for New England case studies

Wintertime warming trends experienced in recent decades, and predicted to increase in the future, present serious challenges for ski areas and whole regions that depend on winter tourism. Most research on this topic examines past or future climate-change impacts at yearly to decadal resolution, to obtain a perspective on climate-change impacts. We focus instead on local-scale impacts of climate variability, using detailed daily data from two individual ski areas. Our analysis fits ARMAX (autoregressive moving average with exogenous variables) time series models that predict day-to-day variations in skier attendance from a combination of mountain and urban weather, snow cover and cyclical factors. They explain half to two-thirds of the variation in these highly erratic series, with no residual autocorrelation. Substantively, model results confirm the backyard hypothesis that urban snow conditions significantly affect skier activity; quantify these effects alongside those of mountain snow and weather; show that previous-day conditions provide a practical time window; find no monthly effects net of weather; and underline the importance of a handful of high-attendance days in making or breaking the season. Viewed in the larger context of climate change, our findings suggest caution regarding the efficacy of artificial snowmaking as an adaptive strategy, and of smoothed yearly summaries to characterize the timing-sensitive impacts of weather (and hence, high-variance climate change) on skier activity. These results elaborate conclusions from our previous annual-level analysis. More broadly, they illustrate the potential for using ARMAX models to conduct integrated, dynamic analysis across environmental and social domains

Discrete and Distributed Error Assessment of UAS- SfM Point Clouds of Roadways

Perishable surveying, mapping, and post-disaster damage data typically require efficient and rapid field collection techniques. Such datasets permit highly detailed site investigation and characterization of civil infrastructure systems. One of the more common methods to collect, preserve, and reconstruct three-dimensional scenes digitally, is the use of an unpiloted aerial system (UAS), commonly known as a drone. Onboard photographic payloads permit scene reconstruction via structure-from-motion (SfM); however, such approaches often require direct site access and survey points for accurate and verified results, which may limit its efficiency. In this paper, the impact of the number and distribution of ground control points within a UAS SfM point cloud is evaluated in terms of error. This study is primarily motivated by the need to understand how the accuracy would vary if site access is not possible or limited. In this paper, the focus is on two remote sensing case studies, including a 0.75 by 0.50-km region of interest that contains a bridge structure, paved and gravel roadways, vegetation with a moderate elevation range of 24 m, and a low-volume gravel road of 1.0 km in length with a modest elevation range of 9 m, which represent two different site geometries. While other studies have focused primarily on the accuracy at discrete locations via checkpoints, this study examines the distributed errors throughout the region of interest via complementary light detection and ranging (lidar) datasets collected at the same time. Moreover, the international roughness index (IRI), a professional roadway surface standard, is quantified to demonstrate the impact of errors on roadway quality parameters. Via quantification and comparison of the differences, guidance is provided on the optimal number of ground control points required for a time-efficient remote UAS survey