Nina Boesche

Spaceborne mine waste mineralogy monitoring in South Africa, applications for modern push-broom missions: Hyperion/OLI and EnMAP/Sentinel-2

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 × 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.

Hyperspectral REE (Rare Earth Element) Mapping of Outcrops—Applications for Neodymium Detection

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.

Reduction of Uncorrelated Striping Noise—Applications for Hyperspectral Pushbroom Acquisitions

1. Helmholtz Center Potsdam, German Research Center for Geosciences, Telegrafenberg, Potsdam 14473, Germany; E-Mails: christian.mielke@gfz-potsdam.de (C.M.); daniel.scheffler@gfz-potsdam.de (D.S); nina.boesche@gfz-potsdam.de (N.B.); christin.lubitz@gfz-potsdam.de (C.L.); maximilian.brell@gfz-potsdam.de (M.B.); daniel.spengler@gfz-potsdam.de (D.S.); andreas.eisele@gfz-potsdam.de (A.E.); karl.segl@gfz-potsdam.de (K.S.); luis.guanter@gfz-potsdam.de (L.G.) 2. Helmholtz Center for Environmental Research-UFZ, Permoserstr 15, Leipzig 04318, Germany; E-Mail: angela.lausch@ufz.de

EnGeoMAP 2.0—Automated Hyperspectral Mineral Identification for the German EnMAP Space Mission

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.

New geometric hull continuum removal algorithm for automatic absorption band detection from spectroscopic data

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 8 nm 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 16 nm and a median difference of 25% for the feature shapes. The scale-space alpha hull technique shows a 23 nm 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.

Hyperspectral Rare Earth Element Mapping of Three Outcrops at the Fen Complex, Norway: Calcitic, Dolomitic, and Ankeritic Carbonatites

Imaging spectroscopy is widely used to identify the spatial distribution of surface cover materials via characteristic absorption features. The current study proposes a new approach to map calcitic, dolomitic, and ankeritic carbonatite outcrops in the Fen complex in Norway. Our method allows characterization of the outcrop mineralogy in a rapid and robust manner thanks to new spatiotemporal hyperspectral methods. To differentiate spectrally among the three different rock types and characterize their geochemical composition and rare earth element concentration, multitemporal series of hyperspectral images were collected and analyzed according to their spectral homogeneity. The mineral analysis was achieved using the iron feature depth indicator and the dolomite-carbonate feature depth estimation in accordance with the United States Geological Survey Tetracorder expert rule set. Spectroscopic identification of rare earth element–enriched zones consisted of a two-step approach: (1) spectral sharpening using Richardson Lucy deconvolution, and (2) knowledge-based retrieval of the rare earth element–indicative absorption depth. The resulting outcrop maps were evaluated using reference measurements from a handheld X-ray fluorescence device. In addition, rock samples were taken for further geochemical and petrographic work, which includes the elemental analysis of selected samples in inductively coupled plasma optical emission spectrometry. Our results show that hyperspectral remote sensing is a rapid and robust technique to map spatial distribution of the mineral and rare earth element content of carbonatite outcrops of calcitic, dolomitic, or ankeritic composition.

Translational Imaging Spectroscopy for Proximal Sensing

Proximal sensing as the near field counterpart of remote sensing offers a broad variety of applications. Imaging spectroscopy in general and translational laboratory imaging spectroscopy in particular can be utilized for a variety of different research topics. Geoscientific applications require a precise pre-processing of hyperspectral data cubes to retrieve at-surface reflectance in order to conduct spectral feature-based comparison of unknown sample spectra to known library spectra. A new pre-processing chain called GeoMAP-Trans for at-surface reflectance retrieval is proposed here as an analogue to other algorithms published by the team of authors. It consists of a radiometric, a geometric and a spectral module. Each module consists of several processing steps that are described in detail. The processing chain was adapted to the broadly used HySPEX VNIR/SWIR imaging spectrometer system and tested using geological mineral samples. The performance was subjectively and objectively evaluated using standard artificial image quality metrics and comparative measurements of mineral and Lambertian diffuser standards with standard field and laboratory spectrometers. The proposed algorithm provides highly qualitative results, offers broad applicability through its generic design and might be the first one of its kind to be published. A high radiometric accuracy is achieved by the incorporation of the Reduction of Miscalibration Effects (ROME) framework. The geometric accuracy is higher than 1 μpixel. The critical spectral accuracy was relatively estimated by comparing spectra of standard field spectrometers to those from HySPEX for a Lambertian diffuser. The achieved spectral accuracy is better than 0.02% for the full spectrum and better than 98% for the absorption features. It was empirically shown that point and imaging spectrometers provide different results for non-Lambertian samples due to their different sensing principles, adjacency scattering impacts on the signal and anisotropic surface reflection properties.

Evolution of temperature of a case-II water and an inland lake around the Weihai area, Shandong province between 1994 and 2014

The Weihai area in Shandong province has undergone intensive changes of the landscape over the past years. The city has expanded to a high percentage but is still characterized by a huge number of lakes and surrounding coastal waters (case-II). In the study, we investigated the temperature changes of those lakes and the coastal zones as well as of some features at land over the past 20 years. We were able to measure subtle changes and trends considering the background of global warming issues by 1.4 degree within this time span. First of all, advanced processing schemes are utilized to conduct the necessary geometric correction and radiometric calibration of the data. In a second step, data have to be converted from DN values to emittance and finally to temperature values. Data are analyzed, and results of temperature changes and trends of different lakes and coastal waters are displayed and visualized together with robust statistical data in digital form. Results are finally interpreted and may be compared to other data sources such as fixed measuring points on the ground or results at other regions eventually at different latitudes.

Urbanization of Weihai area in Shandong province analyzed by archived Landsat data

Intensive transformation of landscape has taken place in Weihai, Shandong province. This gave us the idea to investigate the urban development in this area by the use of archived satellite data, available for the last 30 years. We utilized advanced processing schemes to geometrically correct and spectrally calibrate about 60 cloud free frames of Landsat data available from 1984 to 2015. We used different classifiers to extract and model relevant parameters and provide information layers that display the changes and trends incl. robust statistics for the area. These information layers are finally analyzed and interpreted. Our findings confirm a tremendous increase of housing and industrial areas of 6.56% locally and 10% for the entire Weihai area within the last 10 years and also indicate new investments as drivers for the urbanization.

Rare earth element detection from near-field to space - samarium detection using the REEMAP algorithm

Hyperspectral rare earth elements detection in space borne and near-field acquired images becomes more and more important for global exploration. In comparison to classic exploration methods, the benefit of hyperspectral surveys is the fast and in-situ generation of spatial information. Current hyperspectral investigations do more and more include rare earth element mappings - one new tool for hyperspectral rare earth mapping is the REEMAP algorithm. So far it is trained for five rare earth elements (erbium, dysprosium, holmium, neodymium and thulium). Previous versions of REEMAP did not map samarium. The here presented study focusses on the extension of REEMAP to identify samarium and presents a detailed mapping of the samarium and dysprosium occurrences of a two-carbonatite units containing outcrop (rauhaugites - dolomitic carbonatites and rødbergites - hematitic carbonatites) at Fen Complex, Norway. Four absorption bands of samarium were scrutinized for their shape characteristics in order to extend REEMAP for the detection of samarium. REEMAP was extended with these newly defined filter parameters. The mapping result for the investigated outcrop show that two absorption bands proved to be robust enough to be used in the REEMAP algorithm. The two remaining absorption bands are superimposed by H2O absorptions and are therefore not recommended for space borne or near-field hyperspectral analyses. However, the resulting samarium map shows the two-rock units represented by different samarium concentration levels and revealed a gradual increase of samarium towards the top of the rauhaugites rock unit. This study shows that REEMAP can be trained for the detection of samarium, especially for two of the investigated absorption bands (1250 and 1567 nm), and that REEMAP helps for in-situ interpretations of REE ore distributions.