TY - JOUR A1 - Mielke, Christian A1 - Rogass, Christian A1 - Bösche, Nina Kristine A1 - Segl, Karl A1 - Altenberger, Uwe T1 - EnGeoMAP 2.0-Automated Hyperspectral Mineral Identification for the German EnMAP Space Mission JF - Remote sensing N2 - Algorithms for a rapid analysis of hyperspectral data are becoming more and more important with planned next generation spaceborne hyperspectral missions such as the Environmental Mapping and Analysis Program (EnMAP) and the Japanese Hyperspectral Imager Suite (HISUI), together with an ever growing pool of hyperspectral airborne data. The here presented EnGeoMAP 2.0 algorithm is an automated system for material characterization from imaging spectroscopy data, which builds on the theoretical framework of the Tetracorder and MICA (Material Identification and Characterization Algorithm) of the United States Geological Survey and of EnGeoMAP 1.0 from 2013. EnGeoMAP 2.0 includes automated absorption feature extraction, spatio-spectral gradient calculation and mineral anomaly detection. The usage of EnGeoMAP 2.0 is demonstrated at the mineral deposit sites of Rodalquilar (SE-Spain) and Haib River (S-Namibia) using HyMAP and simulated EnMAP data. Results from Hyperion data are presented as supplementary information. KW - EnMAP KW - Hyperion KW - EnGeoMAP 2 KW - 0 KW - mineral mapping KW - imaging spectroscopy Y1 - 2016 U6 - https://doi.org/10.3390/rs8020127 SN - 2072-4292 VL - 8 SP - 392 EP - 414 PB - MDPI CY - Basel ER - TY - JOUR A1 - Brell, Maximilian A1 - Rogass, Christian A1 - Segl, Karl A1 - Bookhagen, Bodo A1 - Guanter, Luis T1 - Improving Sensor Fusion: A Parametric Method for the Geometric Coalignment of Airborne Hyperspectral and Lidar Data JF - IEEE transactions on geoscience and remote sensing N2 - Synergistic applications based on integrated hyperspectral and lidar data are receiving a growing interest from the remote-sensing community. A prerequisite for the optimum sensor fusion of hyperspectral and lidar data is an accurate geometric coalignment. The simple unadjusted integration of lidar elevation and hyperspectral reflectance causes a substantial loss of information and does not exploit the full potential of both sensors. This paper presents a novel approach for the geometric coalignment of hyperspectral and lidar airborne data, based on their respective adopted return intensity information. The complete approach incorporates ray tracing and subpixel procedures in order to overcome grid inherent discretization. It aims at the correction of extrinsic and intrinsic (camera resectioning) parameters of the hyperspectral sensor. In additional to a tie-point-based coregistration, we introduce a ray-tracing-based back projection of the lidar intensities for area-based cost aggregation. The approach consists of three processing steps. First is a coarse automatic tie-point-based boresight alignment. The second step coregisters the hyperspectral data to the lidar intensities. Third is a parametric coalignment refinement with an area-based cost aggregation. This hybrid approach of combining tie-point features and area-based cost aggregation methods for the parametric coregistration of hyperspectral intensity values to their corresponding lidar intensities results in a root-mean-square error of 1/3 pixel. It indicates that a highly integrated and stringent combination of different coalignment methods leads to an improvement of the multisensor coregistration. KW - Airborne laser scanning (ALS) KW - coregistration KW - direct georeferencing KW - imaging spectroscopy KW - multisensor KW - parametric georeferencing KW - preprocessing KW - ray tracing KW - rigorous geocoding KW - sensor alignment KW - sensor fusion Y1 - 2016 U6 - https://doi.org/10.1109/TGRS.2016.2518930 SN - 0196-2892 SN - 1558-0644 VL - 54 SP - 3460 EP - 3474 PB - Inst. of Electr. and Electronics Engineers CY - Piscataway ER - TY - GEN A1 - Mielke, Christian A1 - Rogass, Christian A1 - Boesche, Nina A1 - Segl, Karl A1 - Altenberger, Uwe T1 - EnGeoMAP 2.0 BT - automated hyperspectral mineral identification for the German EnMAP space mission N2 - Algorithms for a rapid analysis of hyperspectral data are becoming more and more important with planned next generation spaceborne hyperspectral missions such as the Environmental Mapping and Analysis Program (EnMAP) and the Japanese Hyperspectral Imager Suite (HISUI), together with an ever growing pool of hyperspectral airborne data. The here presented EnGeoMAP 2.0 algorithm is an automated system for material characterization from imaging spectroscopy data, which builds on the theoretical framework of the Tetracorder and MICA (Material Identification and Characterization Algorithm) of the United States Geological Survey and of EnGeoMAP 1.0 from 2013. EnGeoMAP 2.0 includes automated absorption feature extraction, spatio-spectral gradient calculation and mineral anomaly detection. The usage of EnGeoMAP 2.0 is demonstrated at the mineral deposit sites of Rodalquilar (SE-Spain) and Haib River (S-Namibia) using HyMAP and simulated EnMAP data. Results from Hyperion data are presented as supplementary information. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 365 KW - EnMAP KW - Hyperion KW - EnGeoMAP 2.0 KW - mineral mapping KW - imaging spectroscopy Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-400650 ER - TY - GEN A1 - Bösche, Nina Kristine A1 - Rogass, Christian A1 - Lubitz, Christin A1 - Brell, Maximilian A1 - Herrmann, Sabrina A1 - Mielke, Christian A1 - Tonn, Sabine A1 - Appelt, Oona A1 - Altenberger, Uwe A1 - Kaufmann, Hermann T1 - Hyperspectral REE (Rare Earth Element) mapping of outcrops BT - applications for neodymium detection N2 - In this study, an in situ application for identifying neodymium (Nd) enriched surface materials that uses multitemporal hyperspectral images is presented (HySpex sensor). Because of the narrow shape and shallow absorption depth of the neodymium absorption feature, a method was developed for enhancing and extracting the necessary information for neodymium from image spectra, even under illumination conditions that are not optimal. For this purpose, the two following approaches were developed: (1) reducing noise and analyzing changing illumination conditions by averaging multitemporal image scenes and (2) enhancing the depth of the desired absorption band by deconvolving every image spectrum with a Gaussian curve while the rest of the spectrum remains unchanged (Richardson-Lucy deconvolution). To evaluate these findings, nine field samples from the Fen complex in Norway were analyzed using handheld X-ray fluorescence devices and by conducting detailed laboratory-based geochemical rare earth element determinations. The result is a qualitative outcrop map that highlights zones that are enriched in neodymium. To reduce the influences of non-optimal illumination, particularly at the studied site, a minimum of seven single acquisitions is required. Sharpening the neodymium absorption band allows for robust mapping, even at the outer zones of enrichment. From the geochemical investigations, we found that iron oxides decrease the applicability of the method. However, iron-related absorption bands can be used as secondary indicators for sulfidic ore zones that are mainly enriched with rare earth elements. In summary, we found that hyperspectral spectroscopy is a noninvasive, fast and cost-saving method for determining neodymium at outcrop surfaces T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 350 KW - rare earth elements KW - imaging spectroscopy KW - neodymium KW - hyperspectral KW - HySpex KW - remote sensing KW - Fen complex Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-400171 ER - TY - JOUR A1 - Brell, Maximilian A1 - Segl, Karl A1 - Guanter, Luis A1 - Bookhagen, Bodo T1 - Hyperspectral and Lidar Intensity Data Fusion: A Framework for the Rigorous Correction of Illumination, Anisotropic Effects, and Cross Calibration JF - IEEE transactions on geoscience and remote sensing N2 - The fusion of hyperspectral imaging (HSI) sensor and airborne lidar scanner (ALS) data provides promising potential for applications in environmental sciences. Standard fusion approaches use reflectance information from the HSI and distance measurements from the ALS to increase data dimen-sionality and geometric accuracy. However, the potential for data fusion based on the respective intensity information of the complementary active and passive sensor systems is high and not yet fully exploited. Here, an approach for the rigorous illumination correction of HSI data, based on the radiometric cross-calibrated return intensity information of ALS data, is presented. The cross calibration utilizes a ray tracing-based fusion of both sensor measurements by intersecting their particular beam shapes. The developed method is capable of compensating for the drawbacks of passive HSI systems, such as cast and cloud shadowing effects, illumination changes over time, across track illumination, and partly anisotropy effects. During processing, spatial and temporal differences in illumination patterns are detected and corrected over the entire HSI wavelength domain. The improvement in the classification accuracy of urban and vegetation surfaces demonstrates the benefit and potential of the proposed HSI illumination correction. The presented approach is the first step toward the rigorous in-flight fusion of passive and active system characteristics, enabling new capabilities for a variety of applications. KW - Airborne laser scanning (ALS) KW - deshadowing KW - imaging spectroscopy KW - in-flight KW - mosaicking KW - pixel-level fusion KW - preprocessing KW - radiometric alignment KW - ray tracing KW - sensor alignment KW - sensor fusion Y1 - 2017 U6 - https://doi.org/10.1109/TGRS.2017.2654516 SN - 0196-2892 SN - 1558-0644 VL - 55 SP - 2799 EP - 2810 PB - Inst. of Electr. and Electronics Engineers CY - Piscataway ER - TY - THES A1 - Beamish, Alison Leslie T1 - Hyperspectral remote sensing of the spatial and temporal heterogeneity of low Arctic vegetation T1 - Hyperspektrale Fernerkundung der räumlichen und zeitlichen Heterogenität niedriger arktischer Vegetation BT - the role of phenology, vegetation colour, and intrinsic ecosystem components BT - die Rolle von Phänologie, Vegetationsfarbe und intrinsischer Ökosystemkomponenten N2 - Arctic tundra ecosystems are experiencing warming twice the global average and Arctic vegetation is responding in complex and heterogeneous ways. Shifting productivity, growth, species composition, and phenology at local and regional scales have implications for ecosystem functioning as well as the global carbon and energy balance. Optical remote sensing is an effective tool for monitoring ecosystem functioning in this remote biome. However, limited field-based spectral characterization of the spatial and temporal heterogeneity limits the accuracy of quantitative optical remote sensing at landscape scales. To address this research gap and support current and future satellite missions, three central research questions were posed: • Does canopy-level spectral variability differ between dominant low Arctic vegetation communities and does this variability change between major phenological phases? • How does canopy-level vegetation colour images recorded with high and low spectral resolution devices relate to phenological changes in leaf-level photosynthetic pigment concentrations? • How does spatial aggregation of high spectral resolution data from the ground to satellite scale influence low Arctic tundra vegetation signatures and thereby what is the potential of upcoming hyperspectral spaceborne systems for low Arctic vegetation characterization? To answer these questions a unique and detailed database was assembled. Field-based canopy-level spectral reflectance measurements, nadir digital photographs, and photosynthetic pigment concentrations of dominant low Arctic vegetation communities were acquired at three major phenological phases representing early, peak and late season. Data were collected in 2015 and 2016 in the Toolik Lake Research Natural Area located in north central Alaska on the North Slope of the Brooks Range. In addition to field data an aerial AISA hyperspectral image was acquired in the late season of 2016. Simulations of broadband Sentinel-2 and hyperspectral Environmental and Mapping Analysis Program (EnMAP) satellite reflectance spectra from ground-based reflectance spectra as well as simulations of EnMAP imagery from aerial hyperspectral imagery were also obtained. Results showed that canopy-level spectral variability within and between vegetation communities differed by phenological phase. The late season was identified as the most discriminative for identifying many dominant vegetation communities using both ground-based and simulated hyperspectral reflectance spectra. This was due to an overall reduction in spectral variability and comparable or greater differences in spectral reflectance between vegetation communities in the visible near infrared spectrum. Red, green, and blue (RGB) indices extracted from nadir digital photographs and pigment-driven vegetation indices extracted from ground-based spectral measurements showed strong significant relationships. RGB indices also showed moderate relationships with chlorophyll and carotenoid pigment concentrations. The observed relationships with the broadband RGB channels of the digital camera indicate that vegetation colour strongly influences the response of pigment-driven spectral indices and digital cameras can track the seasonal development and degradation of photosynthetic pigments. Spatial aggregation of hyperspectral data from the ground to airborne, to simulated satel-lite scale was influenced by non-photosynthetic components as demonstrated by the distinct shift of the red edge to shorter wavelengths. Correspondence between spectral reflectance at the three scales was highest in the red spectrum and lowest in the near infra-red. By artificially mixing litter spectra at different proportions to ground-based spectra, correspondence with aerial and satellite spectra increased. Greater proportions of litter were required to achieve correspondence at the satellite scale. Overall this thesis found that integrating multiple temporal, spectral, and spatial data is necessary to monitor the complexity and heterogeneity of Arctic tundra ecosystems. The identification of spectrally similar vegetation communities can be optimized using non-peak season hyperspectral data leading to more detailed identification of vegetation communities. The results also highlight the power of vegetation colour to link ground-based and satellite data. Finally, a detailed characterization non-photosynthetic ecosystem components is crucial for accurate interpretation of vegetation signals at landscape scales. N2 - Die arktische Erwärmung beeinflusst Produktivität, Wachstums, Artenzusammensetzung, Phänologie und den Reproduktionserfolg arktischer Vegetation, mit Auswirkungen auf die Ökosystemfunktionen sowie auf den globalen Kohlenstoff- und Energiehaushalt. Feldbasierte Messungen und spektrale Charakterisierungen der räumlichen und zeitlichen Heterogenität arktischer Vegetationsgemeinschaften sind limitiert und die Genauigkeit fernerkundlicher Methoden im Landschaftsmaßstab eingeschränkt. Um diese Forschungslücke zu schließen und aktuelle und zukünftige Satellitenmissionen zu unterstützen, wurden drei zentrale Forschungsfragen entwickelt: 1) Wie unterscheidet sich die spektrale Variabilität des Kronendaches zwischen dominanten Vegetationsgemeinschaften der niederen Arktis und wie verändert sich diese Variabilität zwischen den wichtigsten phänologischen Phasen? 2) Wie hängen Aufnahmen der Vegetationsfarbe des Kronendaches von hoch und niedrig auflösenden Geräten mit phänologischen Veränderungen des photosynthetischen Pigmentgehalts auf Blattebene zusammen? 3) Wie beeinflusst die räumliche Aggregation von Daten mit hoher spektraler Auflösung von der Boden- bis zur Satelliten-Skala die arktischen Vegetationssignale der Tundra und welches Potenzial haben zukünftige hyperspektraler Satellitensysteme für die arktische Vegetationscharakterisierung? Zur Beantwortung dieser Fragen wurde eine detaillierte Datenbank aus feldbasierten Daten erstellt und mit hyperspektralen Luftbildern sowie multispektralen Sentinel-2 und simulierten hyperspektralen EnMAP Satellitendaten verglichen. Die Ergebnisse zeigten, dass die Spätsai-son am besten geeignet ist um dominante Vegetationsgemeinschaften mit Hilfe von hyper-spektralen Daten zu identifizieren. Ebenfalls konnte gezeigt werden, dass die mit handelsüb-lichen Digitalkameras aufgenommene Vegetationsfarbe pigmentgesteuerte Spektralindizes stark beeinflusst und den Verlauf von photosynthetischen Pigmenten nachverfolgen kann. Die räumliche Aggregation hyperspektraler Daten von der Boden- über die Luft- zur Satelli-tenskala wurde durch nicht-photosynthetische Komponenten beeinflusst und die spektralen Reflexionsvermögen der drei Skalen stimmten im roten Spektrum am höchsten und im nahen Infrarotbereich am niedrigsten überein. Die vorliegende Arbeit zeigt, dass die Integration zeitlicher, spektraler und räumlicher Daten notwendig ist, um Komplexität und Heterogenität arktischer Vegetationsreaktionen in Reaktion auf klimatische Veränderungen zu überwachen. KW - hyperspectral remote sensing KW - Arctic tundra KW - vegetation KW - imaging spectroscopy KW - hyperspektral Fernerkundung KW - arktische Tundra KW - Vegetation KW - Spektroskopie Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-425922 ER - TY - THES A1 - Koerting, Friederike Magdalena T1 - Hybrid imaging spectroscopy approaches for open pit mining T1 - Hybride Ansätze der bildgebenden Spektroskopie für offene Tagebauten BT - applications for virtual mine face geology BT - Anwendungen für die virtuelle geologische Kartierung von Abbaufronten N2 - This work develops hybrid methods of imaging spectroscopy for open pit mining and examines their feasibility compared with state-of-the-art. The material distribution within a mine face differs in the small scale and within daily assigned extraction segments. These changes can be relevant to subsequent processing steps but are not always visually identifiable prior to the extraction. Misclassifications that cause false allocations of extracted material need to be minimized in order to reduce energy-intensive material re-handling. The use of imaging spectroscopy aspires to the allocation of relevant deposit-specific materials before extraction, and allows for efficient material handling after extraction. The aim of this work is the parameterization of imaging spectroscopy for pit mining applications and the development and evaluation of a workflow for a mine face, ground- based, spectral characterization. In this work, an application-based sensor adaptation is proposed. The sensor complexity is reduced by down-sampling the spectral resolution of the system based on the samples’ spectral characteristics. This was achieved by the evaluation of existing hyperspectral outcrop analysis approaches based on laboratory sample scans from the iron quadrangle in Minas Gerais, Brazil and by the development of a spectral mine face monitoring workflow which was tested for both an operating and an inactive open pit copper mine in the Republic of Cyprus. The workflow presented here is applied to three regional data sets: 1) Iron ore samples from Brazil, (laboratory); 2) Samples and hyperspectral mine face imagery from the copper-gold-pyrite mine Apliki, Republic of Cyprus (laboratory and mine face data); and 3) Samples and hyperspectral mine face imagery from the copper-gold-pyrite deposit Three Hills, Republic of Cyprus (laboratory and mine face data). The hyperspectral laboratory dataset of fifteen Brazilian iron ore samples was used to evaluate different analysis methods and different sensor models. Nineteen commonly used methods to analyze and map hyperspectral data were compared regarding the methods’ resulting data products and the accuracy of the mapping and the analysis computation time. Four of the evaluated methods were determined for subsequent analyses to determine the best-performing algorithms: The spectral angle mapper (SAM), a support vector machine algorithm (SVM), the binary feature fitting algorithm (BFF) and the EnMap geological mapper (EnGeoMap). Next, commercially available imaging spectroscopy sensors were evaluated for their usability in open pit mining conditions. Step-wise downsampling of the data - the reduction of the number of bands with an increase of each band’s bandwidth - was performed to investigate the possible simplification and ruggedization of a sensor without a quality fall-off of the mapping results. The impact of the atmosphere visible in the spectrum between 1300–2010nm was reduced by excluding the spectral range from the data for mapping. This tested the feasibility of the method under realistic open pit data conditions. Thirteen datasets based on the different, downsampled sensors were analyzed with the four predetermined methods. The optimum sensor for spectral mine face material distinction was determined as a VNIR-SWIR sensor with 40nm bandwidths in the VNIR and 15nm bandwidths in the SWIR spectral range and excluding the atmospherically impacted bands. The Apliki mine sample dataset was used for the application of the found optimal analyses and sensors. Thirty-six samples were analyzed geochemically and mineralogically. The sample spectra were compiled to two spectral libraries, both distinguishing between seven different geochemical-spectral clusters. The reflectance dataset was downsampled to five different sensors. The five different datasets were mapped with the SAM, BFF and SVM method achieving mapping accuracies of 85-72%, 85-76% and 57-46% respectively. One mine face scan of Apliki was used for the application of the developed workflow. The mapping results were validated against the geochemistry and mineralogy of thirty-six documented field sampling points and a zonation map of the mine face which is based on sixty-six samples and field mapping. The mine face was analyzed with SAM and BFF. The analysis maps were visualized on top of a Structure-from-Motion derived 3D model of the open pit. The mapped geological units and zones correlate well with the expected zonation of the mine face. The third set of hyperspectral imagery from Three Hills was available for applying the fully-developed workflow. Geochemical sample analyses and laboratory spectral data of fifteen different samples from the Three Hills mine, Republic of Cyprus, were used to analyse a downsampled mine face scan of the open pit. Here, areas of low, medium and high ore content were identified. The developed workflow is successfully applied to the open pit mines Apliki and Three Hills and the spectral maps reflect the prevailing geological conditions. This work leads through the acquisition, preparation and processing of imaging spectroscopy data, the optimum choice of analysis methodology, and the utilization of simplified, robust sensors that meet the requirements of open pit mining conditions. It accentuates the importance of a site-specific and deposit-specific spectral library for the mine face analysis and underlines the need for geological and spectral analysis experts to successfully implement imaging spectroscopy in the field of open pit mining. N2 - In dieser Dissertation wird die Machbarkeit und Anwendung moderner und eines eigen entwickelten Hybridverfahrens in der bildgebenden Spektroskopie für den Tagebau untersucht. Die Materialverteilung innerhalb einer Abbaufront unterscheidet sich oft innerhalb eines kleinen Maßstabs und variiert zudem innerhalb täglich zugeordneter Abbausegmente. Diese Veränderungen können für nachfolgende Verarbeitungsschritte relevant sein, sind aber vor dem Abbau nicht immer visuell erkennbar. Falsche Klassifizierungen des Materials führen zu Fehlverteilungen des abgebauten Materials, die minimiert werden müssen, um den energie-intensiven Materialtransport zu reduzieren. Mit Hilfe der bildgebenden Spektroskopie wird angestrebt, relevante Lagerstättenspezifische Materialien vor der Extraktion korrekt zuzuordnen und ein effizientes Materialhandling nach der Extraktion zu ermöglichen. Ziel dieser Arbeit ist die Parametrisierung der bildgebenden Spektroskopie für den Bergbau und die Entwicklung und Evaluierung eines Workflows zur spektralen Charakterisierung von offenem Bergbau mittels bodengebundener Sensorik. Dies wurde durch die Evaluierung bestehender Ansätze zur hyperspektralen Aufschlussanalyse erreicht, die auf Grundlage von Laborscans von Proben aus dem Eisernen Vierecks in Minas Gerais, Brasilien, durchgeführt wurde. Eine spektralen Abbaufrontanalyse wurde mithilfe von Daten eines aktiven und eines inaktiven Kupfer-Tagebaus in der Republik Zypern entwickelt. Der in dieser Arbeit vorgestellte Arbeitsablauf wird auf drei regionale Datensätze angewandt: 1) Eisenerzproben aus Brasilien (Labordaten); 2) Proben und hyperspektrale bildgebende Daten der Abbaufront aus dem Kupfer-Gold-Pyrit-Tagebau Apliki, Republik Zypern (Labor- und Abbaufrontdaten); und 3) Proben und hyperspektrale bildgebende Daten der Abbaufront aus der Kupfer-Gold-Pyrit-Lagerstätte Three Hills, Republik Zypern (Labor- und Abbaufrontdaten). Der hyperspektrale Labordatensatz von fünfzehn brasilianischen Eisenerzproben wurde zur Evaluierung verschiedener Analysemethoden und verschiedener Sensormodelle verwendet. Neunzehn gebräuchliche Methoden zur Analyse und Kartierung hyperspektraler Daten wurden im Hinblick auf ihre resultierenden Datenprodukte, die Genauigkeit der Kartierung und die Berechnungszeit der Analyse verglichen. Vier der evaluierten Methoden wurden für nachfolgende Analysen bestimmt: Der Spectral Angle Mapper (SAM), ein Support Vector Machine Algorithmus (SVM), der Binary Feature Fitting Algorithmus (BFF) und der EnMap Geological Mapper (EnGeoMap). Als nächstes wurden kommerziell erhältliche bildgebende Spektroskopiesensoren auf ihre Verwendbarkeit unter Tagebaubedingungen evaluiert. Ein schrittweises Reduzieren der Datenkomplexität, das sog. “downsampling” (die Verringerung der Anzahl der Bänder und gleichzeitige Erhöhung der Bandbreite jedes Bandes), wurde durchgeführt, um eine Vereinfachung der Sensorkomplexität ohne Qualitätseinbußen der Kartierungsergebnisse zu untersuchen. Der Einfluss der Atmosphäre, die im Spektrum zwischen 1300-2010nm sichtbar ist, wurde reduziert, indem der Spektralbereich aus den Daten für die Kartierung ausgeschlossen wurde. Dadurch wurde die Durchführbarkeit der Methode unter realistischen Tagebaubedingungen getestet. Dreizehn Datensätze, die auf den verschiedenen Sensoren basierten, wurden mit den vier vorher benannten Methoden analysiert. Der optimale Sensor für die spektrale Unterscheidung von Abbaufrontmaterial wurde als VNIR-SWIR-Sensor mit 40nm Bandbreite im VNIR- und 15nm Bandbreite im SWIR-Spektralbereich bestimmt, der atmosphärisch beeinflusste Spektralbereich wurde ausgeschlossen. Nun wurde der Datensatz von der Mine in Apliki verwendet, um die vorher bestimmten Analysen und Sensoren anzuwenden. Sechsunddreißig Proben wurden geochemisch und mineralogisch analysiert. Die Probenspektren wurden zu zwei Spektralbibliotheken zusammengestellt, die beide zwischen sieben verschiedenen geochemisch-spektralen Clustern unterscheiden. Die Reflexionsdaten wurden auf fünf verschiedene Sensoren heruntergerechnet. Diese fünf verschiedenen Datensätze wurden mit der SAM-, BFF- und SVM-Methode kartiert, wobei entsprechende Kartierungsgenauigkeiten von 85-72%, 85-76% bzw. 57-46% erreicht wurden. Ein Scan der Abbaufront von Apliki wurde verwendet, um den entwickelten Arbeitsablauf auf Daten unter realistische Bedingungen anzuwenden. Die Kartierungsergebnisse wurden auf der Grundlage der Feldbeprobung und einer geologischen Zonierungskarte der Abbaufront validiert. Die Abbaufront wurde mit SAM und BFF analysiert und die Analysekarten wurden auf einem von „Structure-from-Motion“ abgeleiteten 3D-Modell des Tagebaus visualisiert. Die kartographierten geologischen Einheiten und Zonen korrelierten gut mit der erwarteten Zonierung der Abbaufront. Ein dritter Datensatz stand für die Anwendung des entwickelten Arbeitsablaufs zur Verfügung. Geochemische Probenanalysen und Laborspektraldaten von fünfzehn verschiedenen Proben aus dem offenen Tagebau Three Hills in der Republik Zypern wurden zur Analyse eines Datensatzes der Abbaufron des Tagebaus verwendet. Dabei wurden Bereiche mit niedrigem, mittlerem und hohem Erzgehalt identifiziert. Der in der Arbeit entwickelte Arbeitsablauf konnte erfolgreich für die offenen Tagebaue Apliki und Three Hills angewandt werden. Die errechneten Spektralgeologischen Karten stellen die örtliche geologische Situation korrekt dar. Der entwickelte Arbeitsablauf erläutert die Erfassung, Aufbereitung und Verarbeitung von Daten aus der bildgebenden Spektroskopie und beschreibt die Wahl der Analysemethodik sowie die Verwendung robuster Sensoren, die den Anforderungen der Tagebaubedingungen entsprechen. Sie hebt die Bedeutung einer standort- und lagerstättenspezifischen Spektralbibliothek für die Analyse von Abbaufronten hervor und unterstreicht die nötige Einbindung von Experten im Bereich der Geologie und der Spektralanalyse für eine erfolgreiche Implementierung der bildgebenden Spektroskopie im Kontext des Abbaus von Material in offenen Tagebauten. KW - hyperspectral KW - imaging spectroscopy KW - porphyry copper deposit KW - Porphyrische Kupferlagerstätte KW - hyperspektral KW - Abbildende Spektroskopie KW - mine face mapping KW - Abbaufrontkartierung KW - open pit mining KW - offener Tagebau Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-499091 ER -