Global navigation satellite system (GNSS) signals are used throughout the world for position, navigation, and timing. As these signals travel to Earth's surface from Medium Earth Orbit they can encounter ionospheric scintillation caused by ionospheric plasma irregularities, especially at high latitudes. This ionospheric scintillation is detrimental to GNSS signal strength, accuracy, and confidence. Incoherent scatter radars like Resolute Bay Incoherent Scatter Radar (RISR) are excellent tools for measuring the ionospheric conditions that surround the path of incoming GNSS signals. This paper outlines a process that utilizes Systems Toolkit (STK) and Matrix Laboratory (MATLAB) to streamline the process of identifying valid future conjunctions between GPS signals and RISR radar beams to advance study of the impact of ionospheric scintillations on GNSS signals at high latitudes.

The United States Department of Defense (DoD) does not have an automated way in which they create Gridded Reference Graphics (GRGs). The current process is time intensive and requires expertise in numerous domains. We developed a two-step method using python scripting and machine learning to semi-automate the GRG development process. The two steps include 1) analyzing satellite imagery of the site to detect buildings and roads, 2) categorize and label buildings, and roads to meet DoD standards. The proposed methodology requires minimal expertise and enables the user to control various aspects of the workflow. Results revealed a 90% reduction in processing time when compared to the conventional approach.

Numerous locations across the Arctic have exhibited signs of landscape change due to permafrost thaw. However, due to the remoteness of much of the Arctic, ground-based measurements are difficult to make. Remotely sensed platforms provide promise for characterizing broad areas of the landscape. We analyzed landscape change associated with permafrost thaw (i.e., thermokarst development) near seasonal rivers and streams flowing from the Brooks Range to the Beaufort Sea on the North Slope of Alaska. This multidisciplinary project is investigating variability in arctic river chemistry, material fluxes, and thermokarst terrain change during summer season thaw. Photogrammetric topographic surveys were conducted at four selected study sites using a fixed-wing small unmanned aircraft system (sUAS). Ground control points (GCPs) were emplaced throughout the study areas and coordinates were established with post-processed Global Navigation Satellite System (GNSS) occupations. Digital imagery was acquired at an altitude of approximately 75 m above ground level (AGL) resulting in ground sample distances (GSDs) of around 1.3 cm. This paper presents terrain change results spanning a three-month period (June-September 2019) for one of the study sites and discusses some of the challenges of performing sUAS-based change detection in a remote arctic tundra environment.

The environment in which landmines are placed is heterogeneous. Such differences in soil type, packing and moisture, combined with changes in surface and climate conditions can oftentimes mask the presence of a mine. Understanding the impact of heterogeneity on heat and mass transfer behavior near landmines is paramount to properly identifying landmine locations for demining operations. This study investigates the impact of soil heterogeneity on soil moisture and temperature distributions around buried objects to increase understanding of environmental conditions most dynamic to mine detection performance. A ten-day field experiment was conducted with sensors monitoring atmospheric, surface, and subsurface conditions relative to four different conditions associated with landmine emplacement. Experimental results demonstrate distinct behaviors in soil moisture and temperature distributions above and around buried objects that change due to soil heterogeneity and different climate conditions (i.e., temperature and rain events).

GPS L-band signals are attenuated by vegetation, which makes it problematic to predict the quality of signal reception in forested areas. To predict GPS signal attenuation, a quantitative measure of the local forest structure and density is necessary. Terrestrial based hemispherical sky-oriented photographs (HSOPs) can be used to rapidly and remotely sample the structure and density of forest canopy. We report here the results of a study performed to determine the attenuation of GPS signals in forests, by correlating changes in the signal-to-noise ratio (SNR) of the received GPS signals under different canopies, using the observed canopy closure at the directed location of individual GPS satellite vehicles (SVs) derived from terrestrial photography. The results of this study verify that the loss of signal is strongly correlated with the local structure and density of the forest, and we demonstrate how the calculated canopy closure can be used to better predict the attenuation of the GPS signals. The results of this research also pertain to satellite communications, cellular signals, and perhaps the estimation of biomass from L-band radar.

During emergencies in urban areas, it is paramount to assess damage to people, property, and environment in order to coordinate relief operations and evacuations. Remote sensing has become the de facto standard for observing the Earth and its environment through the use of air-, space-, and ground-based sensors. These sensors collect massive amounts of dynamic and geographically distributed spatiotemporal data daily and are often used for disaster assessment, relief, and mitigation. However, despite the quantity of big data available, gaps are often present due to the specific limitations of the instruments or their carrier platforms. This chapter presents a novel approach to filling these gaps by using non-authoritative data including social media, news, tweets, and mobile phone data. Specifically, two applications are presented for transportation infrastructure assessment and emergency evacuation.

AbstractEmergency evacuations during the past decade have transitioned from landline analog to mobile digital communication devices. Over 88% of US citizens own a mobile phone, providing a tool to enable better communication between first responders and citizens in order to minimize risk to evacuees during no‐notice evacuations. During an emergency, evacuees rely on social media to communicate with family, friends, and coworkers, often finding accessibility to social media more reliable than trying to make a phone call. Federal, state, and local emergency operations centers have made limited use of social media or Internet‐based communications to provide an alternative means for citizens to request assistance or provide information. Mobile devices provide an alternative method of incident reporting and analysis through volunteered geographic information (VGI), which first responders can use to minimize risk to evacuees.

In order to coordinate emergency operations and evacuations, it is vital to accurately assess damage to people, property, and the environment. For decades remote sensing has been used to observe the Earth from air, space and ground based sensors. These sensors collect massive amounts of dynamic and geographically distributed spatiotemporal data every day. However, despite the immense quantity of data available, gaps are often present due to the specific limitations of the sensors or their carrier platforms. This article illustrates how nonauthoritative data such as social media, news, tweets, and mobile phone data can be used to fill in these gaps. Two case studies are presented which employ non-authoritative data to fill in the gaps for improved situational awareness during damage assessments and emergency evacuations.

Emergency services personnel face risks and uncertainty as they respond to natural and anthropogenic events. Their primary goal is to minimize the loss of life and property, especially in neighborhoods with high population densities, where response time is of great importance. In recent years, mobile phones have become a primary communication device during emergencies. The portability of cell phones and ease of information storage and dissemination has enabled effective implementation of cell phones by first responders and one of the most viable means of communication with the population. Using cellular location data during evacuation planning and response also provides increased awareness to emergency personnel. This article introduces a multi‐objective, multi‐criteria approach to determining optimum evacuation routes in an urban setting. The first objective is to calculate evacuation routes for individual cell phone locations, minimizing the time it would take for a sample population to evacuate to designated safe zones based on both distance and congestion criteria. The second objective is to maximize coverage of individual cell phone locations, using the criteria of underlying geographic features, distance and congestion. In summary, this article presents a network‐based methodology for providing additional analytic support to emergency services personnel for evacuation planning.