Mapping of Hydrothermal Alteration in Mount Berecha Area of Main Ethiopian Rift using Hyperspectral Data

Airborne Imaging Spectroradiometer for Applications (AISA) Hawk data was used to identify and map hydrothermal alteration mineralogy in Mount Berecha area of Main Ethiopian Rift valley. The Airborne image mapping was coupled with laboratory analysis involving reflectance spectroscopic measurements with the use of ASD FieldSpec for mineral and rock samples. The study was based in the shortwave infrared wavelength (SWIR) region. Laboratory spectra acquired from field data analysis served as guide in selecting image endmembers which were used as input in Spectral Angle Mapper (SAM) classification for mineral mapping. SWIR spectroscopy was able to detect the main very fine grained mineral assemblages which occur in the study area, including kaolinite, halloysite, opal, montmorillonite, nontronite, calcite, K-alunite, palygorskite, MgChlorite, zoisite, illite and mixtures of these minerals. SAM classification algorithm gives the overall classification of the alteration minerals of Berecha area and was used to generate the surficial mineral map of the study area. Berecha alteration is related to low sulfidation system and the most widespread alteration effects are represented essentially in advanced argillic alteration assemblage consisting mainly of kaolinite + opal + smectite + alunite which is likely of steam heated origin. Keywords: Hyperspectral, Imaging Spectrometry, AISA Hawk, Berecha, ASD FieldSpec, Spectral Angle Mappe

The Geochemical Data Imaging and Application in Geoscience: Taking the Northern Daxinganling Metallogenic Belt as an Example

Geochemical data were predominantly expressed by vector format, the research on geochemical data visualization, i.e., raster data format, was not paid proper attention. A total of 39 geochemical elements in 1:200,000 regional geochemical exploration data were rasterized to form images, and then a geochemical image database was generated. This article has carried out the study on geochemical imaging within Daxinganling metallogenic belt. The metallogenic belt had once carried out the regional geochemical survey, the sampling density was 1 site/4 km2, and 39 geochemistry elements including the microelement and trace element have been analyzed. Quintic polynomial method was used to implement the geochemical data interpolation, and the cell size of formed geochemical elemental image is 1 km. The images of the geochemical elements were processed by image enhancement methods, and then hyperspectral remote sensing data processing method was used for prospecting target selection, lithology mapping, and so on. The interpreted results have been verified in practice. All the abovementioned suggested a good development prospect for the rasterized geochemical images. Finally the author puts forward using rasterize geochemical images in combination with other geological, geophysical, and remote sensing data to make better use of the geochemical data and be more extensively applied in the geoscience

Evaluation of spectral similarity indices in unsupervised change detection approaches

Unsupervised change detection (UCD) is a subject of Remote Sensing whose objective is to detect the differences between two multi-temporal images. In some cases, spectral similarity indices have been used as the comparison block in algorithms of UCD. The aim of this paper is to show in a quantitative way the performance of four spectral similarity indices in the correct identification of changes. Comparison is performed in terms of precision (overall accuracy and kappa index) over medium and high-resolution images (SPOT-5: Satellite Pour l'Observation de la Terre and Quickbird), with a reference obtained through a post-classification method (based on Support Vector Machines, SVM). The results show dependence on the automatic thresholding technique, as well as on the classes associated with the change.La detección de cambios de forma no-supervisada (UCD) es un área de teledetección, cuyo objetivo consiste en encontrar las diferencias entre dos imágenes multi-temporales. En algunos casos, los índices de similitud espectral son utilizados como bloque de comparación de UCD. El objetivo de este documento consiste en analizar de forma cuantitativa el desempeño de cuatro índices de similitud espectral en la correcta identificación de cambios. La evaluación se realiza en términos de la precisión (mediante la precisión global e índice kappa) utilizando imágenes de media y alta resolución (SPOT-5: Satélite Para la Observación de la Tierra y Quickbird), así como una imagen de cambio de referencia obtenida a través de un método de post-clasificación (basado en Máquinas de Soporte Vectorial, SVM). Los resultados obtenidos presentan dependencia con la técnica automática de umbralización, así como con las clases asociadas con el cambio

Applied Geochemistry with Case Studies on Geological Formations, Exploration Techniques and Environmental Issues

Geochemistry has become an essential subject to understand our origins and face the challenges that humanity will meet in the near future. This book presents several studies that have geochemistry as their central theme, from the description of different geological formations, through its use for the characterization of contaminated sites and their possible impact on ecosystems and human health, as well as the importance of geochemical techniques as a complement to other current scientific disciplines. Through the different chapters, the reader will be able to approach the world of geochemistry in several of its subfields (e.g. environmental, isotope, or biogeochemistry) and learn through practical cases

Analyse multi-échelle des spectres de réflectance dans un environnement minier

Les mines à ciel ouvert requièrent une gestion optimale des résidus miniers, tant pour améliorer les temps d‘exploitation, comme pour prévenir des dommages environnementaux. Les stériles provenant de la fosse font partie de ces résidus miniers. Lorsque ces stériles sont déposés en surface ils peuvent générer un drainage minier acide (DMA). L’industrie minière et la communauté en général ont un grand intérêt à éviter et remédier les conséquences de ce drainage. La télédétection par satellite permettrait de cartographier l’étendue de toute la mine (la fosse et les haldes à stériles). La télédétection à très basse altitude permettrait une discrimination plus précise des lithologies (types de roche) grâce à une résolution spatiale plus fine. Ainsi, cette discrimination à basse altitude pourrait valider la cartographie satellitaire et faciliter les opérations minières. Afin de contribuer à la cartographie de la surface de la mine Canadian Malartic, une méthodologie visant une analyse multi-échelle des spectres de réflectance de surface a été formulée. Les étapes sont: 1) l’analyse de la variabilité des mesures de spectrométrie et des images, 2) l’analyse de la correspondance entre les images multi-échelles et 3) l’analyse de l’effet d’échelle sur les cartographies réalisées par les approches Spectral Angle Mapper (SAM) et Mixture Tuned Matched Filtering (MTMF). Les capteurs imageurs Worldview-3 (satellitaire) et Pika II (hyperspectral à bord d’un Aéronef Sans Pilote) ont fourni les images. Les systèmes de spectrométrie ASD et Ocean Optics ont fourni les spectres de réflectance des échantillons. Les résultats de l’analyse de variabilité spectrale des échantillons de surface montrent que les spectres de réflectance sont très corrélés et que leurs valeurs sont très proches (RMS ≤ 0,09). Ce qui suggère que la surface minière est très homogène. Un effet d’échelle n’a pas été observé sur les spectres de réflectance multi-échelle (tous les coefficients r > 0,93). Quant à l’analyse de la variabilité spectrale des images multi-échelles, les histogrammes et l’autocorrélation spatiale par l’Indice de Moran montrent que l’image satellite Worldview-3 (WV-3, 120 cm/pixel) et l’image Pika II (10 cm/pixel) ont une faible variabilité spectrale. Cependant, il a été constaté un effet d’échelle sur la cartographie par SAM et MTMF. Il a été constaté plus précisément qu’au fur et à mesure que l’échelle augmente (diminution de la résolution spatiale), certaines classes sont sous ou surestimées. Ce qui pourrait avoir des effets pratiques sur l’application au triage de matériel extrait de la fosse.Abstract: Open pit mines require optimal management of mine tailings, both to improve operating times and to prevent environmental damage. Waste rock from the pit is part of these tailings and deposited on the surface they can generate acid mine drainage (AMD). The mining industry and the community in general have a great interest in avoiding and remedying the consequences of this drainage. Satellite remote sensing could map the extent of the entire mine (pit and waste dumps). Remote sensing at very low altitude would allow more precise discrimination of lithologies (rock types) through a finer spatial resolution. Thus, this low-level discrimination could validate satellite mapping and facilitate mining operations. In order to contribute to the mapping of the surface of the Canadian Malartic Mine, a methodology for multi-scale analysis of surface reflectance spectra has been formulated. The steps are: 1) the analysis of the variability of spectrometry measurements and images, 2) the analysis of the correspondence between multi-scale images and 3) the analysis of the effect of scale on cartographyies made using Spectral Angle Mapper (SAM) and Mixture Tuned Matched Filtering (MTMF) approaches. The imaging sensors Worldview-3 (satellite) and Pika II (hyperspectral sensor aboard a drone) provided the images. The ASD and Ocean Optics spectrometry systems provided the reflectance spectra of the samples. The results of the spectral variability analysis of the surface samples show that the reflectance spectra are highly correlated and that their values are very close (RMS ≤ 0.09). This suggests that the mining surface is very homogeneous. Scale effect was not observed on multi-scale reflectance spectra (all coefficients r> 0.93). As for spectral variability analysis of multi-scale images, histogram and spatial autocorrelation by the Moran Index show that the satellite image (WV-3 of 120 cm / pixel) and the Pika II image of 10 cm / pixel have low spectral variability. However, there was a scale effect on SAM and MTMF mapping. It has been found more specifically that as the scale increases (decrease in spatial resolution), some classes are under or overestimated. This could have practical effects on the accuracy of sorting material extracted from the pit

DEVELOPING INNOVATIVE SPECTRAL AND MACHINE LEARNING METHODS FOR MINERAL AND LITHOLOGICAL CLASSIFICATION USING MULTI-SENSOR DATASETS

The sustainable exploration of mineral resources plays a significant role in the economic development of any nation. The lithological maps and surface mineral distribution can be vital baseline data to narrow down the geochemical and geophysical analysis potential areas. This study developed innovative spectral and Machine Learning (ML) methods for mineral and lithological classification. Multi-sensor datasets such as Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Observing (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), Sentinel-1, and Digital Elevation Model (DEM) were utilized. The study mapped the hydrothermal alteration minerals derived from Spectral Mapping Methods (SMMs), including Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and SIDSAMtan using high-resolution AVIRIS-NG hyperspectral data in the Hutti-Maski area (India). The SIDSAMtan outperforms SID and SAM in mineral mapping. A spectral similarity matrix of target and non-target classes based optimum threshold selection was developed to implement the SMMs successfully. Three new effective SMMs such as Dice Spectral Similarity Coefficient (DSSC), Kumar-Johnson Spectral Similarity Coefficient (KJSSC), and their hybrid, i.e., KJDSSCtan has been proposed, which outperforms the existing SMMs (i.e., SAM, SID, and SIDSAMtan) in spectral discrimination of spectrally similar minerals. The developed optimum threshold selection and proposed SMMs are recommended for accurate mineral mapping using hyperspectral data. An integrated spectral enhancement and ML methods have been developed to perform automated lithological classification using AVIRIS-NG hyperspectral data. The Support Vector Machine (SVM) outperforms the Random Forest (RF) and Linear Discriminant Analysis (LDA) in lithological classification. The performance of SVM also shows the least sensitivity to the number and uncertainty of training datasets. This study proposed a multi-sensor datasets-based optimal integration of spectral, morphological, and textural characteristics of rocks for accurate lithological classification using ML models. Different input features, such as (a) spectral, (b) spectral and transformed spectral, (c) spectral and morphological, (d) spectral and textural, and (e) optimum hybrid, were evaluated for lithological classification. The developed approach has been assessed in the Chattarpur area (India) consists of similar spectral characteristics and poorly exposed rocks, weathered, and partially vegetated terrain. The optimal hybrid input features outperform other input features to accurately classify different rock types using the SVM and RF models, which is ~15% higher than as obtained using spectral input features alone. The developed integrated approach of spectral enhancement and ML algorithms, and a multi-sensor datasets-based optimal integration of spectral, morphological, and textural characteristics of rocks, are recommended for accurate lithological classification. The developed methods can be effectively utilized in other remote sensing applications, such as vegetation/forest mapping and soil classification

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

Illumination Invariant Deep Learning for Hyperspectral Data

Motivated by the variability in hyperspectral images due to illumination and the difficulty in acquiring labelled data, this thesis proposes different approaches for learning illumination invariant feature representations and classification models for hyperspectral data captured outdoors, under natural sunlight. The approaches integrate domain knowledge into learning algorithms and hence does not rely on a priori knowledge of atmospheric parameters, additional sensors or large amounts of labelled training data. Hyperspectral sensors record rich semantic information from a scene, making them useful for robotics or remote sensing applications where perception systems are used to gain an understanding of the scene. Images recorded by hyperspectral sensors can, however, be affected to varying degrees by intrinsic factors relating to the sensor itself (keystone, smile, noise, particularly at the limits of the sensed spectral range) but also by extrinsic factors such as the way the scene is illuminated. The appearance of the scene in the image is tied to the incident illumination which is dependent on variables such as the position of the sun, geometry of the surface and the prevailing atmospheric conditions. Effects like shadows can make the appearance and spectral characteristics of identical materials to be significantly different. This degrades the performance of high-level algorithms that use hyperspectral data, such as those that do classification and clustering. If sufficient training data is available, learning algorithms such as neural networks can capture variability in the scene appearance and be trained to compensate for it. Learning algorithms are advantageous for this task because they do not require a priori knowledge of the prevailing atmospheric conditions or data from additional sensors. Labelling of hyperspectral data is, however, difficult and time-consuming, so acquiring enough labelled samples for the learning algorithm to adequately capture the scene appearance is challenging. Hence, there is a need for the development of techniques that are invariant to the effects of illumination that do not require large amounts of labelled data. In this thesis, an approach to learning a representation of hyperspectral data that is invariant to the effects of illumination is proposed. This approach combines a physics-based model of the illumination process with an unsupervised deep learning algorithm, and thus requires no labelled data. Datasets that vary both temporally and spatially are used to compare the proposed approach to other similar state-of-the-art techniques. The results show that the learnt representation is more invariant to shadows in the image and to variations in brightness due to changes in the scene topography or position of the sun in the sky. The results also show that a supervised classifier can predict class labels more accurately and more consistently across time when images are represented using the proposed method. Additionally, this thesis proposes methods to train supervised classification models to be more robust to variations in illumination where only limited amounts of labelled data are available. The transfer of knowledge from well-labelled datasets to poorly labelled datasets for classification is investigated. A method is also proposed for enabling small amounts of labelled samples to capture the variability in spectra across the scene. These samples are then used to train a classifier to be robust to the variability in the data caused by variations in illumination. The results show that these approaches make convolutional neural network classifiers more robust and achieve better performance when there is limited labelled training data. A case study is presented where a pipeline is proposed that incorporates the methods proposed in this thesis for learning robust feature representations and classification models. A scene is clustered using no labelled data. The results show that the pipeline groups the data into clusters that are consistent with the spatial distribution of the classes in the scene as determined from ground truth

Analysis of Remote Sensing Methods for Studying the Occurrence of Hydrocarbons.

Import 23/07/2015Dizertačná práca je zameraná na vývoj procedúry diaľkového prieskumu Zeme pre detekciu lokalít pravdepodobného výskytu organických uhľovodíkov, resp. výskytu možnej kontaminácie spôsobenej ropnými látkami v podmienkach mierneho pásma. Procedúra bola vyvíjaná, overovaná a následne aplikovaná s použitím družicových dát z dvoch lokalít: Nesyt - Česká republika a Santa Barbara – Kalifornia, USA. In-situ merania boli z dôvodu kalibrácie a validácie metódy realizované v mieste aktívneho prírodného ropného prameňa v obci Korňa (Slovenská republika) a na ložisku uhľovodíkov Nesyt pri meste Hodonín, kde je známy výskyt starých ekologických záťaží spôsobených nedostatočnou likvidáciou ťažobných vrtov a sond. Ako doplnkové boli použité in-situ a letecké dáta z lokality Santa Barbara - Kalifornia, čo je oblasť s dokumentovaným rozsiahlym výskytom uhľovodíkových priesakov. Využívané boli per-pixel metódy pre analýzy multi a hyperspektrálnych družicových dát. Na základe skúmania a testovania techník pre detekciu uhľovodíkových látok boli identifikované metódy potenciálne vhodné pre aplikáciu v podmienkach mierneho pásma na lokalitách Čiech a Slovenska (mapovanie spektrálneho uhla SAM, výpočty hydrokarbónového indexu a hydrokarbónovej detekcie, identifikácia uhľovodíkmi ovplyvnenej vegetácie) a pre implementáciu do navrhovanej procedúry. Finálna procedúra bola vyvíjaná a overovaná pomocou družicových a leteckých dát z lokality Santa Barbara a následne aplikovaná na družicové dáta z oblasti Nesyt. Na základe výsledkov práce možno konštatovať, že boli stanovené techniky pre detekciu uhľovodíkových látok s použitím družicových dát spolu s detailným popisom teoretického pozadia. Ďalej bola úspešne zostavená a overená spektrálna knižnica (databáza kalibračných dát) vhodná pre ďalšie použitie v klasifikačných postupoch s cieľom identifikovať prítomnosť uhľovodíkov. Na základe výsledkov testovania metód v lokalite Santa Barbara bola vyvinutá procedúra aplikovaná na územie Nesyt, kde existuje predpoklad prítomnosti uhľovodíkových kontaminácií. Poznatky zistené v práci majú význam pri podpore použitia inovatívnych metód DPZ a ich potenciálnej integrácie do postupov v oblasti detekcie možných výskytov únikov ropných látok v okolí nedostatočne sanovaných ropných sond a ťažobných vrtov, pre sledovanie a analýzy kontaminácií organickými uhľovodíkmi a je prínosom k problematike detegovania a riešenia starých ekologických záťaží na území Českej a Slovenskej republikyThesis is focused on development of a procedure based on remote sensing technologies for the detection of probable occurrences of hydrocarbons, or the presence of possible contamination caused by hydrocarbon substances in the area of temperate Earth climate zone. Developed methodology was tested and verified using satellite imagery from two sites: Nesyt – Czech Republic and Santa Barbara – California, USA. In order to calibrate and validate the method, in-situ measurements were conducted in the location of natural hydrocarbon seepage in Korňa village (Slovak Republic) and at hydrocarbon deposit Nesyt near the city of Hodonín, a location with documented presence of old ecological hazards caused by insufficiently remediated and liquidated boreholes and production wells. The airborne and in-situ data from Santa Barbara were utilized as auxiliary data. Per-pixel algorithms for analysis of multi and hyperspectral Earth observation imagery were applied. Based on the analysis and tests of methods suitable for hydrocarbon occurrence detection, suitable methods for application in locations in Czech and Slovak Republic (spectral angle mapping SAM, calculations of hydrocarbon index and hydrocarbon detection, identification of the vegetation “stressed” by hydrocarbons) and for their implementation into the proposed procedure, were identified. Final procedure was developed and verified using satellite and airborne data for the territory of Santa Barbara site and afterwards applied on satellite imagery from Nesyt site. According to the achieved results it can be stated that methods for the detection of the occurrences of hydrocarbons using satellite imageries accompanied with the detailed description of theoretical background of algorithms applied were determined together with successful testing of the methods and creation of the procedure for detection of probable occurrences of hydrocarbons. Important thesis outcome is the successful establishment of a spectral library (database with calibration data) suitable for further application in data classification for identifying the occurrence of hydrocarbons. Based on the results of testing the remote sensing methods on Santa Barbara territory, the developed procedure was applied on Nesyt site, where possible contamination caused by hydrocarbons is assumed. The emphasis of the thesis is on the geological application of reflectance spectroscopy and multi/hyperspectral image analysis for the detection of probable occurrence of hydrocarbons. The practical outcome of the study is the possible integration of innovative methods into the procedures for hydrocarbon exploration activities. The application field lies in the detection of possible occurrences of oil contaminations in the vicinity of insufficiently remediated boreholes and wells, in the observation and analysis of hydrocarbon seepages, and it also contributes to the problem of detection and remediation of old ecological hazards in the areas of the Czech Republic and Slovakia.Prezenční541 - Institut geologického inženýrstvívyhově