The recent proliferation of small unmanned aerial systems (sUAS) along with development of the photogrammetric processing techniques known as Structure from Motion (SfM) and Multi-View Stereo (MVS) have made the use of sUAS collected imagery more readily accessible to a variety of users. These sUAS platforms provide a mechanism for collecting overlapping imagery over targeted landscapes followed by production of dense 3D point clouds, digital surface models (DSMs), digital terrain models (DTMs), and high-resolution orthomosaics. For a study site located in the Pohakuloa Training Area (PTA) on the big island of Hawai’i, a vegetation height raster was generated by differencing the photogrammetrically-derived DSM and DTM surfaces. The photogrammetric aerial survey was conducted using a Skydio X2D quadcopter carrying a 12-megapixel RGB camera payload. Two imagery acquisitions were conducted at altitudes of approximately 60m and 120m above ground level (AGL) resulting in ground sample distances (GSDs) of approximately 2.3cm and 5.0cm, respectively. An accuracy assessment of the resulting vegetation height raster was conducted using in-situ measurements collected using terrestrial laser scanning (TLS) data. This paper presents an assessment of how well the technique of differencing photogrammetrically-derived DSM and DTM surfaces captures vegetation height, the effect of variable flying height on vegetation height determination, and the challenges of extracting vegetation height from sUAS-based photogrammetric surveys.

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.

Lead contamination in soil is a human health hazard common in residential communities that pre-date regulatory bans on lead in both gasoline and paints. New remote sensing tools allow for quicker and more affordable sampling, but there is still a challenge in interpreting the data, visualizing the results, and communicating the relevance for response and remediation. Our work builds on previous studies analyzing soil lead concentrations at West Point, NY. The federal installation and college campus hosts residential neighborhoods with historic homes that were painted with lead paint in the past and are adjacent to high traffic roadways. Previous research established several areas where the lead concentrations significantly exceeded the U.S. Environmental Protection Agency (EPA) recommended safe concentrations for soil, but further exploration was necessary to refine those results. We targeted one location where a 2019 measurement indicated lead in excess of 1200 mg/kg. We used an X-Ray Fluorescence (XRF) meter to collect 73 soil lead concentrations between the road and the home, logging the locations using ArcGIS Collector. Results indicate localized lead concentrations with distinctive patterns that may provide clues to the origin of the contamination. Our analysis suggests that in situ measurements are effective to characterize concentrations but conclusions on the severity of lead contamination should not be made using widely spaced transect investigations. The XRF, combined with geospatial visualization methods, is a quick and inexpensive way to investigate neighborhood-scale soil lead contamination and refine the potential remediation response.

Extensive gaps in terrestrial laser scanning (TLS) point cloud data can primarily be classified into two categories: occlusions and dropouts. These gaps adversely affect derived products such as 3D surface models and digital elevation models (DEMs), requiring interpolation to produce a spatially continuous surface for many types of analyses. Ultimately, the relative proportion of occlusions in a TLS survey is an indicator of the survey quality. Recognizing that regions of a scanned scene occluded from one scan position are likely visible from another point of view, a prevalence of occlusions can indicate an insufficient number of scans and/or poor scanner placement. Conversely, a prevalence of dropouts is ordinarily not indicative of survey quality, as a scanner operator cannot usually control the presence of specular reflective or absorbent surfaces in a scanned scene. To this end, this manuscript presents a novel methodology to determine data completeness by properly classifying and quantifying the proportion of the site that consists of point returns and the two types of data gaps. Knowledge of the data gap origin can not only facilitate the judgement of TLS survey quality, but it can also identify pooled water when water reflections are the main source of dropouts in a scene, which is important for ecological research, such as habitat modeling. The proposed data gap classification methodology was successfully applied to DEMs for two study sites: (1) A controlled test site established by the authors for the proof of concept of classification of occlusions and dropouts and (2) a rocky intertidal environment (Rabbit Rock) presenting immense challenges to develop a topographic model due to significant tidal fluctuations, pooled water bodies, and rugged terrain generating many occlusions.

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).

The concentration of heavy metals, specifically lead, in soil may create unsafe environmental conditions. Unsafe conditions may occur based upon previous exposure to lead, such as particulate pollution from leaded gasoline. Accumulation of lead in the soil is especially concerning due to the detrimental physiological effects soil lead has on populations within residential neighborhoods. This study investigates the efficacy of an X-ray fluorescence (XRF) sensor compared to use of an inductively coupled plasma (ICP) laboratory instrument to measure soil lead concentration through a comparison of 87 soils samples. Findings note a strong correlation between both measurement methods. Additionally, 206 samples were evaluated to visualize soil lead concentrations throughout the residential West Point area. The highest soil lead concentrations are along the former route 9W, at locations associated with buildings that pre-date 1940.

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.

In this paper GPS (Global Positioning System)-based methods to measure L-band GPS Signal-to-Noise ratios (SNRs) through different forest canopy conditions are presented. Hemispherical sky-oriented photos (HSOPs) along with GPS receivers are used. Simultaneous GPS observations are collected with one receiver in the open and three inside a forest. Comparing the GPS SNRs observed in the forest to those observed in the open allows for a rapid determination of signal loss. This study includes data from 15 forests and includes two forests with inter-seasonal data. The Signal-to-Noise Ratio Atmospheric Model, Canopy Closure Predictive Model (CCPM), Signal-to-Noise Ratio Forest Index Model (SFIM), and Simplified Signal-to-Noise Ratio Forest Index Model (SSFIM) are presented, along with their corresponding adjusted R2 and Root Mean Square Error (RMSE). As predicted by the CCPM, signals are influenced greatly by the angle of the GPS from the horizon and canopy closure. The results support the use of the CCPM for individual forests but suggest that an initial calibration is needed for a location and time of year due to different absorption characteristics. The results of the SFIM and SSFIM provide an understanding of how different forests attenuate signals and insights into the factors that influence signal absorption.

Understanding how Global Positioning System (GPS) signals are influenced by vegetation structure allows for the determination of how specific technologies might be affected in certain forest environments. This study presents three different models that predict signal loss in a natural deciduous forest using fisheye photography. Relationships between terrestrial-based hemispherical sky-oriented photo (HSOP) measurements and GPS signal-to-noise ratios (SNRs) are explored. ArcGIS is used for image processing of HSOPs to rapidly estimate canopy closure (CC) at particular angles from zenith in forested areas. The difference between the observed SNR of GPS L-band signals under forest canopies to those observed in the open determines signal loss. CC values at different zenith angles inside the forest during four seasons are used to model signal attenuation. This article presents a canopy closure predictive model (CCPM), a model that includes the CCPM and incorporates the difference between the CC value in any season minus the CC in the winter, and a model that includes a seasonal component. The three models presented in this article yield adjusted R2 values between 0.60 and 0.62 and root mean square error range of 3.21 to 3.28 dB.

We depend on numerous technologies that use microwave signals. The reception of these signals is degraded by reflection, absorption, and scattering due to propagation through vegetation. An understanding of how these signals are influenced by vegetation structure allows for the determination of how specific technologies may be affected in certain forest environments. This study presents a model that predicts signal loss in forested areas using novel methods. We explore the relationships between forest parameters from traditional mensuration techniques and terrestrial-based hemispherical sky oriented photos (HSOPs), and GPS signal-to-noise ratios (SNRs). HSOPs can be used to rapidly estimate leaf area index (LAI) and canopy closure (CC) values at particular angles from zenith in forested areas. The relationships between changes in the observed SNR of received GPS L1-band signals under forest canopies and forest parameter estimates calculated using HSOPs and traditional forest measurements are used to model signal attenuation. Using ordinary least squares regression modeling, we present the Canopy Closure Predictive Model (CCPM). The CCPM outlines the key forest parameters used with an adjusted R2 of 0.71 and RMSE of 2.78 dB. The resulting CCPM predicts signal attenuation while using only the minimum number of statistically-significant parameters which, conveniently, are taken from sky oriented photos and GPS receivers allowing for simple and rapid replication.

This study proposes a novel method to predict Global Positioning System (GPS) signal loss in forested areas. We explore the relationships between forest parameters mensurated using traditional techniques, terrestrial-based hemispherical sky-oriented photos (HSOPs), GPS signal-to-noise ratios (SNRs), and individual GPS signal dropouts. Microwave signals suffer from reflection, absorption, and scattering while propagating through vegetative media, which cause signal attenuation and therefore deteriorate signal reception. HSOPs can be used to rapidly sample the leaf area index (LAI) and gap fractions at particular angles from zenith in forested areas. Changes in the observed SNR of received GPS L-band signals under forest canopies are correlated with forest parameter estimates calculated using HSOPs and traditional forest measurements to establish approximate descriptions of signal attenuation. Using ordinary least squares regression a predictive model is presented. We outline the key forest parameters used with resulting R-2 values ranging from 0.613 to 0.830 and RMSEs ranging from 1.94 to 4.11 decibels. In this study, simple models are presented that effectively predict signal attenuation while using only the minimum number of statistically significant parameters making the results easy to replicate.

In this study, we propose a novel method to predict microwave attenuation in forested areas by using airborne Light Detection and Ranging (LiDAR). While propagating through a vegetative medium, microwave signals suffer from reflection, absorption, and scattering within vegetation, which cause signal attenuation and, consequently, deteriorate signal reception and information interpretation. A Fresnel zone enveloping the radio frequency line-of-sight is applied to segment vegetation structure occluding signal propagation. Return parameters and the spatial distribution of vegetation from the airborne LiDAR inside Fresnel zones are used to weight the laser points to estimate directional vegetation structure. A Directional Vegetation Density (DVD) model is developed through regression that links the vegetation structure to the signal attenuation at the L-band using GPS observations in a mixed forest in North Central Florida. The DVD model is compared with currently-used empirical models and obtained better R2 values of 0.54 than the slab-based models. Finally, the model is evaluated by comparing with GPS observations of signal attenuation. An overall root mean square error of 3.51dB and a maximum absolute error of 9.38dB are found. Sophisticated classification algorithms and full-waveform LiDAR systems may significantly improve the estimation of signal attenuation.

Introduction Interteaching offers instructors an alternative teaching method that provides the teacher a full lesson plan run by the students with feedback on the student population’s grasp of lesson material toward the end of the lesson. We examined the use of interteaching with 2 through 4 year undergraduates to determine if interteaching is more effective for long-term concept retention than traditional lecture methods. These objectives were accomplished in a surveying course as well as a psychology course using multiple-choice, as well as, math problems focused on key concepts. We compared sections that had interteaching to those that did not and also established a comparison of each section when each had the same teaching technique. In the surveying course three class sections (n = 33) went through the first 15 lessons using the standard lecture format and then answered questions on the first test. Using the results of the first test as a base comparison, over the next 25 lessons 1 of the 3 sections consisted of the lecture while the other 3 sections received interteaching classes. In the psychology course two sections (n=30 and n=43) were selected. Utilizing an alternating treatments design each section received instruction on the same materials; however, the sections alternated between the interteaching methods and traditional lecture methods. We evaluated long-term concept retention by linking exam and quiz questions performance and compared the results of the interteaching method to the standard lecture method. We hypothesized that students would perform better on test and quiz questions when they received interteaching on that block of material than those that had standard lecture. Upon completion of the study, in every case the interteaching average result was as good as, if not better than tradition teaching method results.

One of the great challenges facing humankind in the 21st century is how to deal with the global climate, today and in the future. Seasonal swings in climate, with their droughts, floods and storms, are responsible for major natural disasters that, at their worst, wreak death, famine, loss of livelihood, epidemics and displacement of populations, as well as vast losses of personal and State-owned belongings. On top of that, the vast consensus of reputable scientific opinion, as represented in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, states that the climate is changing and will change significantly further. In general, these changes will be for the worse, with the most severe effects likely in developing and Least Developed Countries—precisely those countries with the least ability to adapt.

The L-band signals broadcast by GPS satellites are attenuated by vegetation, making it problematic, if not impossible, to predict the performance of the system in forested areas without some quantitative measure of the structure and density of the local forest canopy. Airborne laser swath mapping (ALSM) observations can be used to rapidly and remotely sample the structure and density of forested areas. 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 three dimensional structure and density information about each canopy derived from ALSM observations. 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 ALSM point cloud can be used to better predict the attenuation of the GPS signals. The results of this research also pertain to other modes of microwave transmission in forested areas, including satellite and cellular telephony, and the estimation of biomass from L-band radar.