In this thesis, the development and methodology of a new, consolidated BYU/NIC Antarctic Iceberg Tracking Database is presented. The new database combines data from the original BYU daily iceberg tracking database derived from scatterometers, and the National Ice Center's weekly Antarctic iceberg tracking database derived from mostly optical and infrared sensors. Using this data, interpolation methods and statistical analyses of iceberg locations are discussed. The intent of this database is to consolidate iceberg location data in order to increase accessibility to users.Active microwave remote sensing instruments are used to track tabular icebergs and provide a daily estimate of their positions and sizes. A consolidated data set of these positions from several different instruments is valuable to ensure accurate positional data. The scatterometer iceberg positional record began with the Seasat-A Satellite Scatterometer (SASS) and is continued with the Quick Scatterometer (QuikSCAT) and Advanced Scatterometer (ASCAT) data sets.A reliable method of automatically estimating Antarctic iceberg contours and sizes from satellite data is desirable to help better understand patterns in iceberg formation and behavior. Starting from scatterometer images, this thesis develops a method of using the relatively constant backscatter values across the surface of an iceberg to derive a contour of its shape. Contours are then used to find an angle of rotation between images taken on successive days. This method produces size estimates that are within 10% of the area given by the National Ice Center (NIC). The size estimates and rotation angles are included in the new consolidated database.

The Southern Pine Beetle, or SPB, (Dendroctonus frontalis Zimmerman) is a bark beetle that is endemic to the Southern forest ecosystem. Beetle populations remain stable for years at a time; however, for reasons not fully understood the beetle undergoes accelerated population growth on approximate ten-year cycles, culminating in increased beetle activity. Although most trees can withstand a beetle assault when populations are reduced, healthy trees, and even pine species that are not generally attacked can become a host tree for millions of beetles during infestations. Much of Appalachia was paralyzed by an unusually heavy snowstorm on January 27, 1998, resulting in major power failures and tremendous tree damage. Environmental hazards such as this storm have historically been a factor with southern pine beetle populations. This study hypothesizes that the blizzard of January 27, 1998, significantly increased the southern pine beetle population. The evidence, however, did not support this hypothesis.

The Pile Mound survey includes magnetometry paired with targeted ground-penetrating radar (GPR) and electromagnetic induction (EMI) surveys of the mound and testing of associated features over the ca. 6.5 ha site. The GPR survey discovered six rock features (five large rock features within the mound and one marking the outside of the mound). Knowledge of mounds in the Upper Cumberland Plateau (UCP) is lacking—the closest other studied sites are at the Corbin Site, Croley-Evans, Bell Site, and Beasley Mounds, approximately 75 – 100 km away. However, the most similar mound construction is found at Corbin and Cherokee sites, some 175 – 275 km away. In addition, the associated ceramic assemblage appears to reflect more similarity to the East Tennessee Valley rather than the Middle Cumberland region. These data provide a unique opportunity to better understand the Mississippian occupation in the UCP of Tennessee.

abstract: The seasonal deposition of CO2 on the polar caps is one of the most dynamic processes on Mars and is a dominant driver of the global climate. Remote sensing temperature and albedo data were used to estimate the subliming mass of CO2 ice on south polar gullies near Sisyphi Cavi. Results showed that column mass abundances range from 400 - 1000 kg.m2 in an area less than 60 km2 in late winter. Complete sublimation of the seasonal caps may occur later than estimated by large-scale studies and is geographically dependent. Seasonal ice depth estimates suggested variations of up to 1.5 m in depth or 75% in porosity at any one time. Interannual variations in these data appeared to correlate with dust activity in the southern hemisphere. Correlation coefficients were used to investigate the relationship between frost-free surface properties and the evolution of the seasonal ice in this region. Ice on high thermal inertia units was found to disappear before any other ice, likely caused by inhibited deposition during fall. Seasonal ice springtime albedo appeared to be predominantly controlled by orientation, with north-facing slopes undergoing brightening initially in spring, then subliming before south-facing slopes. Overall, the state of seasonal ice is far more complex than globally and regionally averaged studies can identify. The discovery of cryovolcanic features on Charon and the presence of ammonia hydrates on the surfaces of other medium-sized Kuiper Belt Objects suggests that cryovolcanism may be important to their evolution. A two-dimensional, center-point finite difference, thermal hydraulic model was developed to explore the behavior of cryovolcanic conduits on midsized KBOs. Conduits on a Charon-surrogate were shown to maintain flow through over 200 km of crust and mantle down to radii of R = 0.20 m. Radii higher than this became turbulent due to high viscous dissipation and low thermal conductivity. This model was adapted to explore the emplacement of Kubrik Mons. Steady state flow was achieved with a conduit of radius R = 0.02 m for a source chamber at 2.3 km depth. Effusion rates computed from this estimated a 122 - 163 Myr upper limit formation timescale. / Dissertation/Thesis / Doctoral Dissertation Geological Sciences 2019

Recently, the use of unmanned aerial vehicles (UAVs) has increased in popularity across several industries. Most notable, however, is the impact that this technology has had in research at academic institutions worldwide. As the technology for UAVs has improved, with that comes easier to operate, more accessible equipment. UAVs have been used in various types of applications and are quickly becoming a preferred method of studying and analyzing a site. Currently, the most common use of a UAV is to monitor a location of interest to a researcher that is difficult to gain access to otherwise. The UAV can be altered to meet the needs of any given project and this versatility has contributed to their popularity. Often, they are equipped with a type of remote sensor that can gather information in the form of images, sounds, heat, or light. Once data has been gathered from a site, it is processed and modified, allowing it to be studied and analyzed. A process known as Structure from Motion (SfM) creates a 3D digital terrain model from camera images captured through the use of a UAV. SfM is a common method of processing the vast amount of images that are taken at a site and the 3D model that it creates is a helpful resource for analysis. These digital models, while useful, are oftentimes created at an unknown accuracy. This research presents a comparative study of the accuracies obtained when different parameters are applied during the SfM process. The results present a comparison of the time required to process a particular model and the accuracy that the model had. Depending on the application and type of project, a desired level of accuracy can be obtained in the presented amount of time. This particular study used a landslide as the site of interest and captured the imagery using a helicopter UAV.

Spaceborne microwave sensors are capable of estimating various properties of many geophysical phenomena, including the age and extent of Arctic sea ice and the relative soil moisture over land. The measurement and classification of such geophysical phenomena are used to refine climate models, localize and predict drought, and better understand the water cycle. Data from the active Ku-band scatterometers, the Quick Scatterometer (QuikSCAT), and the Oceansat-2 Scatterometer (OSCAT), are here used to classify areas of first-year and multiyear Arctic sea ice using a temporally adaptive threshold on reported radar backscatter values. The result is a 15-year data record of daily ice classification images. An additional ice age data record is produced using the C-band Advanced Scatterometer (ASCAT) and the Special Sensor Microwave Imager Sounder (SSMIS) with an alternate classification methodology based on Bayesian decision theory. The ASCAT/SSMIS classification methodology results in a record which is generally consistent with the QuikSCAT and OSCAT classifications, which conclude in 2014. With multiple ASCAT and SSMIS sensors still operational, the ASCAT/SSMIS ice classifications can continue to be produced into the future. In addition to ice classification, ASCAT is used to estimate the relative surface soil moisture at high-resolution (4.45 — 4.45 km per pixel). The soil moisture estimates are obtained using enhanced resolution image reconstruction techniques and an altered version of the Water Retrieval Package (WARP) algorithm. The high-resolution soil moisture estimates are shown to agree well with the existing lower resolution WARP products while also revealing finer details.

Malgré les efforts nationaux et internationaux, le paludisme reste un problème de santépublique majeur dans de nombreux pays et les systèmes de santé ont des difficultés pourévaluer son poids réel, le risque de transmission des plasmodiums et leur répartitiongéographique. Pourtant, l’hétérogénéité spatiale du paludisme peut être importante et dans cecontexte de concentration du risque, la lutte contre la maladie peut gagner à être focaliséedans certains lieux et certaines périodes. D’une part, l’environnement est un déterminantmajeur de la biodiversité du paludisme à cause du caractère vectoriel de la transmission et despréférences bioécologiques des vecteurs. D’autre part, les satellites en orbite peuvent fournirdes données environnementales, climatiques et météorologiques qui ont déjà été utilisées pourl’étude de maladies infectieuses. La « télé-épidémiologie » a été définie comme une approcheintégrée visant à associer des données médicales, épidémiologiques ou entomologiques deterrain à des données environnementales obtenues par satellites, en s’appuyant sur lacompréhension et la mesure des mécanismes physiques et biologiques qui sont en jeu. Dansles villes, il a déjà été possible de mettre en évidence, en utilisant des données satellites à deséchelles appropriés, des associations entre des éléments urbains cartographiés et desindicateurs paludométriques. Chez des voyageurs, dans l’objectif d’une évaluation du risquede contracter le paludisme ou de l’efficacité de mesures prophylactiques, il serait utile depouvoir évaluer et prédire les niveaux d’exposition à la transmission dans les différenteslocalités parcourues. L’objectif général des travaux de la présente thèse était d’identifier desfacteurs environnementaux mesurables par satellite et utilisables pour l’évaluation du risquede paludisme chez les voyageurs d’une part et en milieu urbain d’autre part.Tout d’abord, des données de télédétection ont été utilisées pour évaluer les niveauxd’expositions au risque de paludisme d’une population de militaires, dans le cadre d’un travailsur l’estimation de leurs facteurs de risque d’accès palustre. Les résultats ont montré que,même en prenant en compte les facteurs de confusion de l’âge et de l’observance de lachimioprophylaxie, l’environnement était le principal facteur associé au risque de survenued’accès palustres.En parallèle, une large collecte de données entomologiques a été effectuée pendant cinq ansdans la ville de Dakar et a permis de mettre en évidence une très forte hétérogénéité spatiale ettemporelle de la transmission du paludisme dans la ville. Les informations collectées ont étécentralisées dans une base de données géoréférencée (SIG - Système d’InformationGéographique) contenant toutes les variables entomologiques, environnementales,météorologiques, biologiques et physiques relevées sur le terrain ou par satellites.Puis un travail de modélisation du risque entomologique dans la capitale du Sénégal, basé surles données collectées sur le terrain et sur des données environnementales issues d’imagessatellites a été mené. Une première étape a permis de mettre en évidence les évolutions deszones à risque de transmission et d’affirmer que le pourcentage de la population dakaroise àfort risque de transmission avait diminué entre 1996 et 2007. Une deuxième étape a conduit àl’élaboration de 1) une carte des gîtes larvaires accompagné d’un indice de productivité dansla ville de Dakar, 2) une carte des densités d’agressivité des anophèles adultes, puis 3) cescartes étaient rendues dynamiques, c'est-à-dire que les variations temporelles liées auxvariations de leurs déterminants météorologiques étaient prises en compte.Les résultats des travaux de thèse ont montré que des données de télédétection associées à unegrande quantité de données de terrain peuvent permettre l’ajustement de modèles prédictifs etla construction de cartes de risque entomologique, en milieu urbain ou pour des populationsmobiles. / Despite national an international efforts, malaria remains a major public health in manycountries and sanitary systems are hindered by the lack of information on the actual burden ofmalaria, on the plasmodium transmission risk and on their geographical distribution.Nevertheless, spatial heterogeneity can be important and in this context, malaria control couldbe improved if could be focused in place and time. On one hand, the environment is a majordeterminant of malaria biodiversity, because of the vectorial transmission and the vectorsbioecological preferences. On another hand, orbiting satellites can provide environmental,climatic and meteorological data that already have been used for the study of infectiousdiseases. “Tele-epidemiology” has been defined as an integrated approach aiming atassociating medical, epidemiological or entomological ground data, with remotely-sensedenvironmental data, based on the in depth comprehension and measurement of the involvedphysical and biological mechanisms. In cities, it has already been possible to highlightassociations between mapped urban settings and malariometric indices, using satellite data atappropriate scales. Among travellers, in the objective to evaluate malaria risk or efficacy ofprophylactic devices, it would be useful to evaluate and predict transmission levels in thevisited places. The objective of the present thesis was to identify environmental factors thatcould be remotely-sensed and that could be used in the evaluation of malaria risk amongtravellers on one hand and in urban settings on the other hand.First, remotely-sensed data have been used to evaluate levels of exposure to malaria risk ofmilitaries, in the scope of a study on their risk factors for clinical malaria. Results haveshowed that, even when taking into account age and compliance to chemoprophylaxis asconfusion factors, the environment was the factor the most strongly associated to clinicalmalaria risk.In parallel, an extensive entomological study has been conducted during five years in Dakarand allowed demonstrating a strong spatial and temporal heterogeneity of malariatransmission in the city. Collected information were centralized in a georeferenced database(GIS - Geographic Information System) containing all entomological, environmental,meteorological, biological and physical data collected on the field or by remote sensing.Finally, modelling of entomological risk in the capital city of Senegal was undertaken, basedon data collected on the ground and environmental data issued from satellites. A first stepshowed the evolution of malaria transmission risk areas and allowed declaring that thefraction of human population that was at high risk for transmission decreased between 1996and 2007. A second step led to the development of 1) a map of the breeding sites with aproductivity indicator in Dakar city, 2) a map of aggressive adult Anopheles densities, and 3)a dynamic aspect was added to those maps, taking into account the variations of theirmeteorological determinants.The results of the work undertaken in this thesis demonstrated that remotely-sensedinformation, associated with a large amount of ground data, allow to adjust predictive models and to draw entomological risk maps, in urban settings or for moving populations

Space-borne remote sensing datasets have the potential to allow us to progress towards global scale flood prediction systems. However, these datasets are limited in terms of space-time resolution and accuracy, and the best use of such data requires understanding how uncertainties propagate through hydrological models. An unbiased investigation of different datasets for hydrological modeling requires a parsimonious calibration-free model, since calibration masks uncertainties in the data and model structure. This study, which addresses these issues, consists of two parts: 1) the development and validation of a multi-scale distributed hydrological model whose parameters can be directly linked to physical properties of the watershed, thereby avoiding the need of calibration, and 2) application of the model to demonstrate how data uncertainties propagate through the model and affect flood simulation across scales. I based the model development on an interactive approach for model building. I systematically added processes and evaluated their effects on flood prediction across multiple scales. To avoid the need for parameter calibration, the level of complexity in representing physical processes was limited by data availability. I applied the model to simulate flows for the Cedar River, Iowa River and Turkey River basins, located in Iowa. I chose this region because it is rich in high quality hydrological information that can be used to validate the model. Moreover, the area is frequently flooded and was the center of an extreme flood event during the summer of 2008. I demonstrated the model's skills by simulating medium to high-flow conditions; however the model's performance is relatively poor for dry (low flow) conditions. Poor model performance during low flows is attributed to highly nonlinear dynamics of soil and evapotranspiration not incorporated in the model. I applied the hydrological model to investigate the predictability skills of satellite-based datasets and to investigate the model's sensibility to certain hydro-meteorological variables such as initial soil moisture and bias in evapotranspiration. River network structure and rainfall are the main components shaping floods, and both variables are monitored from space. I evaluated different DEM sources and resolution DEMs as well as the effect of pruning small order channels to systematically decreasing drainage density. Results showed that pruning the network has a greater effect on simulated peak flow than the DEM resolution or source, which reveals the importance of correctly representing the river network. Errors on flood prediction depend on basin scale and rainfall intensity and decrease as the basin scale and rainfall intensity increases. In the case of precipitation, I showed that simulated peak flow uncertainties caused by random errors, correlated or not in space, and by coarse space-time data resolution are scale-dependent and that errors in hydrographs decrease as basin scale increases. This feature is significant because it reveals that there is a scale for which less accurate information can still be used to predict floods. However, the analyses of the real datasets reveal the existence of other types of error, such as major overall bias in total volumes and the failure to detect significant rainfall events that are critical for flood prediction.

Floods are a significant threat to communities around the world and require substantial resources and infrastructure to predict. Limited local resources in developing nations make it difficult to build and maintain dense sensor networks like those present in the United States, creating a large disparity in flood prediction across borders. To address this disparity, I operated the Iowa Flood Center Top Layer model to predict floods in Puerto Rico without relying on in-situ data measurements. Instead, all model forcing was provided by satellite remote sensing datasets that offer near-global coverage. I used three datasets gathered via satellite remote sensing to build and operate watershed streamflow models: elevation data obtained by the Space Shuttle Endeavour through the Shuttle Radar Topography Mission (SRTM), rainfall estimates gathered by a constellation of satellites through the Global Precipitation Measurement Mission (GPM), and evapotranspiration rate estimates collected by Moderate Resolution Imaging Spectroradiometer (MODIS) sensors aboard the Aqua and Terra satellites. While these satellite remote sensing datasets make observations of nearly the entire world, their spatiotemporal resolution is coarse compared to conventional on-the-ground measurements. Hydrologic models were assembled for 75 basins upstream of streamflow gages monitored by the United States Geologic Survey (USGS). Model simulations were compared to real-time measurements at these gages. Continuous simulations spanning 58 months achieve poor Nash Sutcliffe Efficiency and Klinge Gupta Efficiency of -112.0 and -0.5, respectively. The sources of error that influence model performance were investigated, underlining some limitations of relying solely on satellite data for operational flood prediction efforts.

Les acquisitions de télédétection ont des caractéristiques complémentaires en termes de résolution spatiale et temporelle et peuvent mesurer différents aspects de la couverture neigeuse (propriétés physiques de surface, type de neige, etc.). En combinant plusieurs acquisitions, il devrait être possible d'obtenir un suivi précis et continu de la neige. Cependant, cet objectif se heurte à la complexité du traitement des images satellites et à la confusion possible entre les différents matériaux observés. Plus particulièrement, l’accès à l’information fractionnelle, c’est-à-dire à la proportion de neige dans chaque pixel, nécessite de retrouver la proportion de l’ensemble des matériaux qui se trouvent dans celui-ci. Ces proportions sont accessibles via des méthodes d’inversions ou démélange spectral se basant sur la résolution spectrale des images obtenues. Le défi général est alors d’arriver à exploiter correctement les différentes informations de natures différentes qui nous sont apportées par les différentes acquisitions afin de produire des cartes d’enneigement précises. Les objectifs de la thèse sont alors au nombre de trois et peuvent se résumer par trois grandes interrogations qui permettent de traiter les différents points évoqués:- Quelles sont les limitations actuelles de l’état de l'art pour l’observation spatiale optique de la neige ?- Comment exploiter les séries temporelles pour s’adapter à la variabilité spectrale des matériaux ?- Est-il possible de généraliser la fusion de données pour une acquisition multimodale à partir de capteurs optiques ?Une étude complète des différents produits de neige issus du satellite MODIS est ainsi proposée, permettant l’identification des nombreuses limitations dont la principale est le haut taux d’erreurs lors de la reconstitution de la fraction (environ 30%). Parmi ces résultats sont notamment identifiés des problèmes liés aux méthodes de démélange face à la variabilité spectrale des matériaux. Face à ces limitations nous avons exploité les séries temporelles MODIS pour proposer une nouvelle approche d’estimation des endmembers, étape critique du démélange spectral. La faible évolution temporelle du milieu (hors neige) est alors utilisée pour contraindre l’estimation des endmembers non seulement sur l’image d’intérêt, mais également sur les images des jours précédents. L’efficacité de cette approche bien que démontrée ici reste sujette aux limitations de résolution spatiale intrinsèques au capteur. Des expérimentations sur la fusion de donnée, à même de pouvoir améliorer la qualité des images, ont par conséquent été réalisées. Devant les limitations de ces méthodes dans le cas des capteurs multispectraux utilisés, une nouvelle approche de fusion a été proposée. Via la formulation d’un nouveau modèle et sa résolution, la fusion entre des capteurs optiques de tous types peut être réalisée sans considération de recouvrement spectral. Les différentes expérimentations sur l’estimation de cartes de neige montrent un intérêt certain d’une meilleure résolution spatiale pour isoler les zones enneigées. Ce travail montre ainsi les nouvelles possibilités de développement pour l’observation de la neige, mais également les évolutions de l’utilisation combinée des images satellites pour l’observation de la Terre en général. / Remote sensing acquisitions have complementary characteristics in terms of spatial and temporal resolution and can measure different aspects of snow cover (e.g., surface physical properties and snow type). By combining several acquisitions, it should be possible to obtain a precise and continuous monitoring of the snow. However, this task has to face the complexity of processing satellite images and the possible confusion between different materials observed. In particular, the estimation of fractional information, i.e., the amount of snow in each pixel, requires to know the proportion of the materials present in a scene. These proportions can be obtained performing spectral unmixing. The challenge is then to effectively exploit the information of different natures that are provided by the multiple acquisitions in order to produce accurate snow maps.Three main objectives are addressed by this thesis and can be summarized by the three following questions:- What are the current limitations of state-of-the-art techniques for the estimation of snow cover extent from optical observations?- How to exploit a time series for coping with the spectral variability of materials?- How can we take advantage of multimodal acquisitions from optical sensors for estimating snow cover maps?A complete study of the various snow products from the MODIS satellite is proposed. It allows the identification of numerous limitations, the main one being the high rate of errors during the estimation of the snow fraction (approximately 30%).The experimental analysis allowed to highlight the sensitivity of the spectral unmixing methods against the spectral variability of materials.Given these limitations, we have exploited the MODIS time series to propose a new endmembers estimation approach, addressing a critical step in spectral unmixing. The low temporal evolution of the medium (except snow) is then used to constrain the estimation of the endmembers not only on the image of interest, but also on images of the previous days. The effectiveness of this approach, although demonstrated here, remains limited by the spatial resolution of the sensor.Data fusion has been considered aiming at taking advantage of multiple acquisitions with different characteristics in term of resolution available on the same scene. Given the limitations of the actual methods in the case of multispectral sensors, a new fusion approach has been proposed. Through the formulation of a new model and its resolution, the fusion between optical sensors of all types can be achieved without consideration of their characteristics. The various experiments on the estimation of snow maps show a clear interest of a better spatial resolution to isolate the snow covered areas. The improvement in spectral resolution will improve future approaches based on spectral unmixing.This work explores the new possibilities of development for the observation of snow, but also for the combined use of the satellite images for the observation of the Earth in general.