AEOLIAN SYSTEM DYNAMICS DERIVED FROM THERMAL INFRARED DATA

Thermal infrared (TIR) remote-sensing and field-based observations were used to study aeolian systems, specifically sand transport pathways, dust emission sources and Saharan atmospheric dust. A method was developed for generating seamless and radiometrically accurate mosaics of thermal infrared data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument. Using a combination of high resolution thermal emission spectroscopy results of sand samples and mosaic satellite data, surface emissivity was derived to map surface composition, which led to improvement in the understanding of sand accumulation in the Gran Desierto of northern Sonora, Mexico. These methods were also used to map sand transport pathways in the Sahara Desert, where the interaction between sand saltation and dust emission sources was explored. The characteristics and dynamics of dust sources were studied at White Sands, NM and in the Sahara Desert. At White Sands, an application was developed for studying the response of dust sources to surface soil moisture based on the relationship between soil moisture, apparent thermal inertia and the erosion potential of dust sources. The dynamics of dust sources and the interaction with sand transport pathways were also studied, focusing on the Bodele Depression of Chad and large dust sources in Mali and Mauritania. A dust detection algorithm was developed using ASTER data, and the spectral emissivity of observed atmospheric dust was related to the dust source area in the Sahara. At the Atmospheric Observatory (IZO) in Tenerife, Spain where direct measurement of the Saharan Air Layer could be made, the cycle of dust events occurring in July 2009 were examined. From the observation tower at the IZO, measurements of emitted longwave atmospheric radiance in the TIR wavelength region were made using a Forward Looking Infrared Radiometer (FLIR) handheld camera. The use of the FLIR to study atmospheric dust from the Saharan is a new application. Supporting data from AERONET and other orbital data enabled study of net radiative forcing

Measuring surface moisture on a sandy beach based on corrected intensity data of a mobile terrestrial LiDAR

Surface moisture plays a key role in limiting the aeolian transport on sandy beaches. However, the existing measurement techniques cannot adequately characterize the spatial and temporal distribution of the beach surface moisture. In this study, a mobile terrestrial LiDAR (MTL) is demonstrated as a promising method to detect the beach surface moisture using a phase-based Z&F/Leica HDS6100 laser scanner mounted on an all-terrain vehicle. Firstly, two sets of indoor calibration experiments were conducted so as to comprehensively investigate the effect of distance, incidence angle and sand moisture contents on the backscattered intensity by means of sand samples with an average grain diameter of 0.12 mm. A moisture estimation model was developed which eliminated the effects of the incidence angle and distance (it only relates to the target surface reflectance). The experimental results reveal both the distance and incidence angle influencing the backscattered intensity of the sand samples. The standard error of the moisture model amounts to 2.0% moisture, which is considerably lower than the results of the photographic method. Moreover, a field measurement was conducted using the MTL system on a sandy beach in Belgium. The accuracy and robustness of the beach surface moisture derived from the MTL data was evaluated. The results show that the MTL is a highly suitable technique to accurately and robustly measure the surface moisture variations on a sandy beach with an ultra-high spatial resolution (centimeter-level) in a short time span (12 x 200 m per minute)

Multispectral Resource Sampler: Proof of concept. Literature survey of bidirectional reflectance

A bibliography compiled in order to give a comprehensive review of previous work in scene bidirectional reflectance, particularly those studies relevant to the Multispectral Resource Sampler (MRS) is presented. The bibliography contains 124 abstracts. In addition a synthesis of the literature results is given along with background information concerning MRS

Airborne remote sensing of estuarine intertidal radionuclide concentrations

The ability to map industrial discharges through remote sensing provides a powerful tool in environmental monitoring. Radionuclide effluents have been discharged, under authorization, into the Irish Sea from BNFL (British Nuclear Fuels Plc.) sites at Sellafield and Springfields since 1952. The quantitative mapping of this anthropogenic radioactivity in estuarine intertidal zones is crucial for absolute interpretations of radionuclide transport. The spatial resolutions of traditional approaches e.g. point sampling and airborne gamma surveys are insufficient to support geomorphic interpretations of the fate of radionuclides in estuaries. The research presented in this thesis develops the use of airborne remote sensing to derive high-resolution synoptic data on the distribution of anthropogenic radionuclides in the intertidal areas of the Ribble Estuary, Lancashire, UK. From multidate surface sediment samples a significant relationship was identified between the Sellafieldderived 137Cs & 241Am and clay content (r2=0.93 & 0.84 respectively). Detailed in situ, and laboratory, reflectance (0.4-2.5mn) experiments demonstrated that significant relationships exist between Airborne Thematic Mapper (ATM) simulated reflectance and intertidal sediment grain-size. The spectral influence of moisture on the reflectance characteristics of the intertidal area is also evident. This had substantial implications for the timing of airborne image acquisition. Low-tide Daedalus ATM imagery (Natural Environmental Research Council) was collected of the Ribble Estuary on May 30th 1997. Preprocessing and linear unmixing of the imagery allowed accurate sub-pixel determinations of sediment clay content distributions (r2=0.8 1). Subsequently, the established relationships between 137Cs & 241Am and sediment grain-size enabled the radionuclide activity distributions across the entire intertidal area (92km2) to be mapped at a geomorphic scale (1.75m). The accuracy of these maps was assessed by comparison with in situ samples and the results of previous radiological studies within the estuary. Finally, detailed conclusions are made regarding radionuclide sinks and sources, and surface activity redistribution within the Ribble Estuary environment

Mineral identification using data-mining in hyperspectral infrared imagery

Les applications de l’imagerie infrarouge dans le domaine de la géologie sont principalement des applications hyperspectrales. Elles permettent entre autre l’identification minérale, la cartographie, ainsi que l’estimation de la portée. Le plus souvent, ces acquisitions sont réalisées in-situ soit à l’aide de capteurs aéroportés, soit à l’aide de dispositifs portatifs. La découverte de minéraux indicateurs a permis d’améliorer grandement l’exploration minérale. Ceci est en partie dû à l’utilisation d’instruments portatifs. Dans ce contexte le développement de systèmes automatisés permettrait d’augmenter à la fois la qualité de l’exploration et la précision de la détection des indicateurs. C’est dans ce cadre que s’inscrit le travail mené dans ce doctorat. Le sujet consistait en l’utilisation de méthodes d’apprentissage automatique appliquées à l’analyse (au traitement) d’images hyperspectrales prises dans les longueurs d’onde infrarouge. L’objectif recherché étant l’identification de grains minéraux de petites tailles utilisés comme indicateurs minéral -ogiques. Une application potentielle de cette recherche serait le développement d’un outil logiciel d’assistance pour l’analyse des échantillons lors de l’exploration minérale. Les expériences ont été menées en laboratoire dans la gamme relative à l’infrarouge thermique (Long Wave InfraRed, LWIR) de 7.7m à 11.8 m. Ces essais ont permis de proposer une méthode pour calculer l’annulation du continuum. La méthode utilisée lors de ces essais utilise la factorisation matricielle non négative (NMF). En utlisant une factorisation du premier ordre on peut déduire le rayonnement de pénétration, lequel peut ensuite être comparé et analysé par rapport à d’autres méthodes plus communes. L’analyse des résultats spectraux en comparaison avec plusieurs bibliothèques existantes de données a permis de mettre en évidence la suppression du continuum. Les expérience ayant menés à ce résultat ont été conduites en utilisant une plaque Infragold ainsi qu’un objectif macro LWIR. L’identification automatique de grains de différents matériaux tels que la pyrope, l’olivine et le quartz a commencé. Lors d’une phase de comparaison entre des approches supervisées et non supervisées, cette dernière s’est montrée plus approprié en raison du comportement indépendant par rapport à l’étape d’entraînement. Afin de confirmer la qualité de ces résultats quatre expériences ont été menées. Lors d’une première expérience deux algorithmes ont été évalués pour application de regroupements en utilisant l’approche FCC (False Colour Composite). Cet essai a permis d’observer une vitesse de convergence, jusqu’a vingt fois plus rapide, ainsi qu’une efficacité significativement accrue concernant l’identification en comparaison des résultats de la littérature. Cependant des essais effectués sur des données LWIR ont montré un manque de prédiction de la surface du grain lorsque les grains étaient irréguliers avec présence d’agrégats minéraux. La seconde expérience a consisté, en une analyse quantitaive comparative entre deux bases de données de Ground Truth (GT), nommée rigid-GT et observed-GT (rigide-GT: étiquet manuel de la région, observée-GT:étiquetage manuel les pixels). La précision des résultats était 1.5 fois meilleur lorsque l’on a utlisé la base de données observed-GT que rigid-GT. Pour les deux dernières epxérience, des données venant d’un MEB (Microscope Électronique à Balayage) ainsi que d’un microscopie à fluorescence (XRF) ont été ajoutées. Ces données ont permis d’introduire des informations relatives tant aux agrégats minéraux qu’à la surface des grains. Les résultats ont été comparés par des techniques d’identification automatique des minéraux, utilisant ArcGIS. Cette dernière a montré une performance prometteuse quand à l’identification automatique et à aussi été utilisée pour la GT de validation. Dans l’ensemble, les quatre méthodes de cette thèse représentent des méthodologies bénéfiques pour l’identification des minéraux. Ces méthodes présentent l’avantage d’être non-destructives, relativement précises et d’avoir un faible coût en temps calcul ce qui pourrait les qualifier pour être utilisée dans des conditions de laboratoire ou sur le terrain.The geological applications of hyperspectral infrared imagery mainly consist in mineral identification, mapping, airborne or portable instruments, and core logging. Finding the mineral indicators offer considerable benefits in terms of mineralogy and mineral exploration which usually involves application of portable instrument and core logging. Moreover, faster and more mechanized systems development increases the precision of identifying mineral indicators and avoid any possible mis-classification. Therefore, the objective of this thesis was to create a tool to using hyperspectral infrared imagery and process the data through image analysis and machine learning methods to identify small size mineral grains used as mineral indicators. This system would be applied for different circumstances to provide an assistant for geological analysis and mineralogy exploration. The experiments were conducted in laboratory conditions in the long-wave infrared (7.7μm to 11.8μm - LWIR), with a LWIR-macro lens (to improve spatial resolution), an Infragold plate, and a heating source. The process began with a method to calculate the continuum removal. The approach is the application of Non-negative Matrix Factorization (NMF) to extract Rank-1 NMF and estimate the down-welling radiance and then compare it with other conventional methods. The results indicate successful suppression of the continuum from the spectra and enable the spectra to be compared with spectral libraries. Afterwards, to have an automated system, supervised and unsupervised approaches have been tested for identification of pyrope, olivine and quartz grains. The results indicated that the unsupervised approach was more suitable due to independent behavior against training stage. Once these results obtained, two algorithms were tested to create False Color Composites (FCC) applying a clustering approach. The results of this comparison indicate significant computational efficiency (more than 20 times faster) and promising performance for mineral identification. Finally, the reliability of the automated LWIR hyperspectral infrared mineral identification has been tested and the difficulty for identification of the irregular grain’s surface along with the mineral aggregates has been verified. The results were compared to two different Ground Truth(GT) (i.e. rigid-GT and observed-GT) for quantitative calculation. Observed-GT increased the accuracy up to 1.5 times than rigid-GT. The samples were also examined by Micro X-ray Fluorescence (XRF) and Scanning Electron Microscope (SEM) in order to retrieve information for the mineral aggregates and the grain’s surface (biotite, epidote, goethite, diopside, smithsonite, tourmaline, kyanite, scheelite, pyrope, olivine, and quartz). The results of XRF imagery compared with automatic mineral identification techniques, using ArcGIS, and represented a promising performance for automatic identification and have been used for GT validation. In overall, the four methods (i.e. 1.Continuum removal methods; 2. Classification or clustering methods for mineral identification; 3. Two algorithms for clustering of mineral spectra; 4. Reliability verification) in this thesis represent beneficial methodologies to identify minerals. These methods have the advantages to be a non-destructive, relatively accurate and have low computational complexity that might be used to identify and assess mineral grains in the laboratory conditions or in the field

Proceedings of the Airborne Imaging Spectrometer Data Analysis Workshop

The Airborne Imaging Spectrometer (AIS) Data Analysis Workshop was held at the Jet Propulsion Laboratory on April 8 to 10, 1985. It was attended by 92 people who heard reports on 30 investigations currently under way using AIS data that have been collected over the past two years. Written summaries of 27 of the presentations are in these Proceedings. Many of the results presented at the Workshop are preliminary because most investigators have been working with this fundamentally new type of data for only a relatively short time. Nevertheless, several conclusions can be drawn from the Workshop presentations concerning the value of imaging spectrometry to Earth remote sensing. First, work with AIS has shown that direct identification of minerals through high spectral resolution imaging is a reality for a wide range of materials and geological settings. Second, there are strong indications that high spectral resolution remote sensing will enhance the ability to map vegetation species. There are also good indications that imaging spectrometry will be useful for biochemical studies of vegetation. Finally, there are a number of new data analysis techniques under development which should lead to more efficient and complete information extraction from imaging spectrometer data. The results of the Workshop indicate that as experience is gained with this new class of data, and as new analysis methodologies are developed and applied, the value of imaging spectrometry should increase

Electromagnetic characterization of barefaced terrain for oil sand exploration

The scant difference in the electromagnetic (EM) reflectivity of barefaced terrain often imposes challenges in differentiating between such terrain types and deployment of synthetic aperture radar to oil sand exploration. Microwave remote sensing has a proven ability to provide valuable information about targets. However to derive geoscientific information, a profound understanding of the EM interaction with terrain is vital. The challenge is to identify scattering characteristics relevant to oil sand fields. While various terrain identification methods and signature databases have been developed in the optical domain, only few examples of barefaced terrain discrimination in the microwave domain have been reported. In this thesis a three step multi-sensor approach has been used to identify EM signature of barefaced terrain encompassing homogeneous and heterogeneous materials, in the optical and microwave range. The combined method also led to the development of a large database of hyperspectral reflectivity, dielectric and backscattering data relevant to geointelligence analysis. The geochemical signature identification and prediction (GSIP) process required spectral data acquisition, chemometric model implementation and postprocessing to determine the spectral fingerprints and components of two strains of Nigerian oil sands. The results were compared with available hydrocarbon databases and four new features of Nigerian oil sands were observed. The dielectric discrimination statistical model (DDSM) involved three studies of the dielectric properties of oil sands and other barefaced terrain with different weight percentage of moisture and statistical processing of data to identify the 1 – 2 GHz and 5 – 7 GHz as most suitable frequency bands for microwave imaging. The GSIP and DDSM provided new empirical data on the geochemical and electrical behaviour of oil sand particularly the contrasting effects of bitumen, sand and moisture. Finally computer EM (CEM) models of barefaced terrain and sensors were used to identify the backscattering behaviour of the terrain for analysis in 2D/3D format. The results provided good agreement with classical surface roughness models particularly the Surface Perturbation and Kirchoffs Scattering model. They also enabled the investigation of the effect of wide variations in the sensor and terrain parameters on backscattering in order to evolve a radar signature necessary for identification of oil sand terrain for petroleum exploration. A laboratory scatterometer system (LSS) was developed and deployed in three imaging scenarios to verify aspects of the derived microwave EM signature of the terrain. The LSS measurements and the results from the CEMs were complimentary

Doctor of Philosophy

dissertationSuitable habitat for greater sage-grouse (Centrocercus urophasianus) has been greatly reduced over a relatively short ecological scale (1800s - Present). This reduction of habitat has had a negative impact on the current distribution and connectivity of the species. There has been work to map sage-grouse distribution at small ecological extents with fine resolution, and at broad extents and coarse resolutions. There is a current need to identify sage-grouse habitat at a fine ecological scale across a broad extent. This information will help researchers and land managers to better understand spatial patterns and connectivity associated with sage-grouse habitat and the processes that drive them. I focused my dissertation on testing the feasibility of developing broad spatial extent and fine resolution predictive habitat models for sage-grouse nest and brooding habitats. By using fine resolution mapping, I was able to capture more subtle variation in potential habitat; by using a broad extent I was able to apply these findings at a landscape scale. I also proposed a method of using nested ecological models blended together to predict potential habitat. In order to best predict habitat potential, multiple modeling techniques were applied (nonparametric multiplicative regression, maximum entropy distribution, random forest and generalized additive model). These methods were used to create independent sagebrush presence and total vegetation cover models and these were combined to create sage-grouse habitat predictive models. The statistical strength of each model was tested (logP, R2 and AUC) as well as their predictive ability (overall accuracy and RMSE ). The results of this work produced fine resolution (30m) models, predicted across a broad extent (Utah, 21.9 million ha). The overall accuracy for the final sagebrush model was 72%. The RMSE for the vegetation cover MODEL was between 6.6 and 7.6% cover. In addition to model creation, potential research and management applications for these models are discussed. These models will provide baseline habitat estimations that could be used for better understanding past distributions of sage-grouse and improving current and future management planning. Furthermore, these same techniques could be applied to other species across multiple spatial and temporal scales I would like to dedicate this dissertation to my wife, Marie Barrett Balzotti, who has always supported me in my educational pursuits. She has made sacrifices in her own career goals and provided valuable feedback and direction to this and my previous work