TY - JOUR A1 - Körting, Friederike Magdalena A1 - Köllner, Nicole A1 - Kuras, Agnieszka A1 - Bösche, Nina Kristin A1 - Rogass, Christian A1 - Mielke, Christian A1 - Elger, Kirsten A1 - Altenberger, Uwe T1 - A solar optical hyperspectral library of rare-earth-bearing minerals, rare-earth oxide powders, copper-bearing minerals and Apliki mine surface samples JF - Earth system science data : ESSD N2 - Mineral resource exploration and mining is an essential part of today's high-tech industry. Elements such as rare-earth elements (REEs) and copper are, therefore, in high demand. Modern exploration techniques from multiple platforms (e.g., spaceborne and airborne), to detect and map the spectral characteristics of the materials of interest, require spectral libraries as an essential reference. They include field and laboratory spectral information in combination with geochemical analyses for validation. Here, we present a collection of REE- and copper-related hyperspectral spectra with associated geochemical information. The libraries contain reflectance spectra from rare-earth element oxides, REE-bearing minerals, copper-bearing minerals and mine surface samples from the Apliki copper-gold-pyrite mine in the Republic of Cyprus. The samples were measured with the HySpex imaging spectrometers in the visible and near infrared (VNIR) and shortwave infrared (SWIR) range (400-2500 nm). The geochemical validation of each sample is provided with the reflectance spectra. The spectral libraries are openly available to assist future mineral mapping campaigns and laboratory spectroscopic analyses. The spectral libraries and corresponding geochemistry are published via GFZ Data Services with the following DOIs: https://doi.org/10.5880/GFZ.1.4.2019.004 (13 REE-bearing minerals and 16 oxide powders, Koerting et al., 2019a), https://doi.org/10.5880/GFZ.1.4.2019.003 (20 copper-bearing minerals, Koellner et al., 2019), and https://doi.org/10.5880/GFZ.1.4.2019.005 (37 copper-bearing surface material samples from the Apliki coppergold-pyrite mine in Cyprus, Koerting et al., 2019b). All spectral libraries are united and comparable by the internally consistent method of hyperspectral data acquisition in the laboratory. Y1 - 2021 U6 - https://doi.org/10.5194/essd-13-923-2021 SN - 1866-3508 SN - 1866-3516 VL - 13 SP - 923 EP - 942 PB - Copernics Publications CY - Katlenburg-Lindau ER - 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 - Mielke, Christian A1 - Bösche, Nina Kristine A1 - Rogass, Christian A1 - Kaufmann, Hermann A1 - Gauert, Christoph T1 - New geometric hull continuum removal algorithm for automatic absorption band detection from spectroscopic data JF - Remote sensing letters : an official journal of the Remote Sensing and Photogrammetry Society N2 - Modern imaging spectrometers produce an ever-growing amount of data, which increases the need for automated analysis techniques. The algorithms employed, such as the United States Geological Survey (USGS) Tetracorder and the Mineral Identification and Characterization Algorithm (MICA), use a standardized spectral library and expert knowledge for the detection of surface cover types. Correct absorption feature definition and isolation are key to successful material identification using these algorithms. Here, a new continuum removal and feature isolation technique is presented, named the 'Geometric Hull Technique'. It is compared to the well-established, knowledge-based Tetracorder feature database together with the adapted state of the art techniques scale-space filtering, alpha shapes and convex hull. The results show that the geometric hull technique yields the smallest deviations from the feature definitions of the MICA Group 2 library with a median difference of only 8nm for the position of the features and a median difference of only 15% for the feature shapes. The modified scale-space filtering hull technique performs second best with a median feature position difference of 16nm and a median difference of 25% for the feature shapes. The scale-space alpha hull technique shows a 23nm median position difference and a median deviation of 77% for the feature shapes. The geometric hull technique proposed here performs best amongst the four feature isolation techniques and may be an important building block for next generation automatic mapping algorithms. Y1 - 2015 U6 - https://doi.org/10.1080/2150704X.2015.1007246 SN - 2150-704X SN - 2150-7058 VL - 6 IS - 2 SP - 97 EP - 105 PB - Routledge, Taylor & Francis Group CY - Abingdon ER - TY - JOUR 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-Applications for Neodymium Detection JF - Remote sensing 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. Y1 - 2015 U6 - https://doi.org/10.3390/rs70505160 SN - 2072-4292 VL - 7 IS - 5 SP - 5160 EP - 5186 PB - MDPI CY - Basel ER - TY - JOUR A1 - Mielke, Christian A1 - Bösche, Nina Kristine A1 - Rogass, Christian A1 - Kaufmann, Hermann A1 - Gauert, Christoph A1 - de Wit, Maarten T1 - Spaceborne mine waste mineralogy monitoring in South Africa, applications for modern push-broom missions: Hyperion/OLI and EnMAP/Sentinel-2 JF - Remote sensing N2 - Remote sensing analysis is a crucial tool for monitoring the extent of mine waste surfaces and their mineralogy in countries with a long mining history, such as South Africa, where gold and platinum have been produced for over 90 years. These mine waste sites have the potential to contain problematic trace element species (e. g., U, Pb, Cr). In our research, we aim to combine the mapping and monitoring capacities of multispectral and hyperspectral spaceborne sensors. This is done to assess the potential of existing multispectral and hyperspectral spaceborne sensors (OLI and Hyperion) and future missions, such as Sentinel-2 and EnMAP (Environmental Mapping and Analysis Program), for mapping the spatial extent of these mine waste surfaces. For this task we propose a new index, termed the iron feature depth (IFD), derived from Landsat-8 OLI data to map the 900-nm absorption feature as a potential proxy for monitoring the spatial extent of mine waste. OLI was chosen, because it represents the most suitable sensor to map the IFD over large areas in a multi-temporal manner due to its spectral band layout; its (183 km x 170 km) scene size and its revisiting time of 16 days. The IFD is in good agreement with primary and secondary iron-bearing minerals mapped by the Material Identification and Characterization Algorithm (MICA) from EO-1 Hyperion data and illustrates that a combination of hyperspectral data (EnMAP) for mineral identification with multispectral data (Sentinel-2) for repetitive area-wide mapping and monitoring of the IFD as mine waste proxy is a promising application for future spaceborne sensors. A maximum, absolute model error is used to assess the ability of existing and future multispectral sensors to characterize mine waste via its 900-nm iron absorption feature. The following sensor-signal similarity ranking can be established for spectra from gold mining material: EnMAP 100% similarity to the reference, ALI 97.5%, Sentinel-2 97%, OLI and ASTER 95% and ETM+ 91% similarity. KW - mine waste KW - spatial extent KW - gold KW - platinum KW - South Africa KW - EnMAP KW - OLI KW - Hyperion KW - Sentinel-2 KW - iron feature depth (IFD) Y1 - 2014 U6 - https://doi.org/10.3390/rs6086790 SN - 2072-4292 VL - 6 IS - 8 SP - 6790 EP - 6816 PB - MDPI CY - Basel ER - TY - JOUR A1 - Heine, Iris A1 - Francke, Till A1 - Rogass, Christian A1 - Medeiros, Pedro Henrique Augusto A1 - Bronstert, Axel A1 - Förster, Saskia T1 - Monitoring seasonal changes in the water surface areas of reservoirs using TerraSAR-X time series data in semiarid northeastern Brazil JF - IEEE journal of selected topics in applied earth observations and remote sensing N2 - The 933 km(2) Bengue catchment in northeastern Brazil is characterized by distinct rainy and dry seasons. Precipitation is stored in variously sized reservoirs, which is essential for the local population. In this study, we used TerraSAR-X SM(HH) data for an one-year monitoring of seasonal changes in the reservoir areas from July 2011 to July 2012. The monitoring was based on acquisitions in the ascending pass direction, complemented by occasional descending-pass images. To detect water surface areas, a histogram analysis followed by a global threshold classification was performed, and the results were validated using in situ GPS data. Distinguishing between small reservoirs and similar looking dark areas was difficult. Therefore, we tested several approaches for identifying misclassified areas. An analysis of the surface area dynamics of the reservoirs indicated high spatial and temporal heterogeneities and a large decrease in the total water surface area of the reservoirs in the catchment by approximately 30% within one year. KW - Image classification KW - monitoring KW - radar imaging KW - remote sensing KW - synthetic aperture radar (SAR) Y1 - 2014 U6 - https://doi.org/10.1109/JSTARS.2014.2323819 SN - 1939-1404 SN - 2151-1535 VL - 7 IS - 8 SP - 3190 EP - 3199 PB - Inst. of Electr. and Electronics Engineers CY - Piscataway ER -