In this study we present a novel method for the automatic detection of minerals and elements using hyperspectral transmittance imaging microscopy measurements of complete thin sections (HyperTIM). This is accomplished by using a hyperspectral camera system that operates in the visible and near-infrared (VNIR) range with a specifically designed sample holder, scanning setup, and a microscope lens. We utilize this method on a monazite ore thin section from Steenkampskraal (South Africa), which we analyzed for the rare earth element (REE)-bearing mineral monazite ((Ce,Nd,La)PO4), with high concentrations of Nd. The transmittance analyses with the hyperspectral VNIR camera can be used to identify REE minerals and Nd in thin sections. We propose a three-point band depth index, the Nd feature depth index (NdFD), and its related product the Nd band depth index (NdBDI), which enables automatic mineral detection and classification for the Nd-bearing monazites in thin sections. In combination with the average concentration of the relative Nd content, it permits a destruction-free, total concentration calculation for Nd across the entire thin section.

The integration of imaging spectroscopy and aeromagnetics provides a cost-effective and promising way to extend the initial analysis of a mineral deposit. While imaging spectroscopy retrieves surface spectral information, magnetic responses are used to determine magnetization at both shallower and greater depths using 2D and 3D modeling. Integration of imaging spectroscopy and magnetics improves upon knowledge concerning lithology with magnetic properties, enhances understanding of the geological origin of magnetic anomalies, and is a promising approach for analyzing a prospective area for minerals having a high iron-bearing content. To combine iron diagnostic information from airborne hyperspectral and magnetic data, we (a) used an iron absorption feature ratio to model pseudo-magnetic responses and compare them with the measured magnetic data and (b) estimated the apparent susceptibility along the surface by some equivalent source modeling, and compared them with iron ratios along the surface. For this analysis, a Modified Iron Feature Depth index was developed and compared to the surface geochemistry of the rock samples in order to validate the spectral information of iron. The comparison revealed a linear increase in iron absorption feature depths with iron content. The analysis was performed by empirically modeling the statistical relationship between the diagnostic absorption features of hyperspectral (HS) image spectra of selected rock samples and their corresponding geochemistry. Our results clearly show a link between the spectral absorption features and the magnetic response from iron-bearing ultra/-mafic rocks. The iron absorption feature ratio of Fe3+/Fe2+ integrated with aeromagnetic data (residual magnetic anomaly) allowed us to distinguish main rock types based on physical properties. This separation matches the lithology of the Niaqornarssuit complex, our study area in West Greenland.

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 copper–gold–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.

Forage supply of savanna grasslands plays a crucial role for local food security and consequently, a reliable monitoring system could help to better manage vital forage resources. To help installing such a monitoring system, we investigated whether in-situ hyperspectral data could be resampled to match the spectral resolution of multi- and hyperspectral satellites; if the type of sensor affected model transfer; and if spatio-temporal patterns of forage characteristics could be related to environmental drivers. We established models for forage quantity (green biomass) and five forage quality proxies (metabolisable energy, acid/neutral detergent fibre, ash, phosphorus). Hyperspectral resolution of the Hyperion satellite mostly resulted in higher accuracies (i.e. higher R-2, lower RMSE). When applied to satellite data, though, the greater quality of the multispectral Sentinel-2 satellite data leads to more realistic forage maps. By analysing a three-year time series, we found plant phenology and cumulated precipitation to be the most important environmental drivers of forage supply. We conclude that none of the investigated satellites provide optimal conditions for monitoring purposes. Future hyperspectral satellite missions like EnMAP, combining the high information level of Hyperion with the good data quality and resolution of Sentinel-2, will provide the prerequisites for installing a regular monitoring service.

ABSTRACT This study investigates the usage of HyMAP airborne hyperspectral and Sentinel-2, ASTER and Landsat-8 OLI spaceborne multispectral data for detailed mapping of mineral resources in the Arctic. The EnMAP Geological Mapper (EnGeoMAP) and Iterative Spectral Mixture Analysis (ISMA) approaches are tested for mapping of mafic-ultramafic rocks in areas covered by abundant lichen. Using the Structural Similarity Index Measure (SSIM), the output classification results from airborne data are quantitatively compared to the available geological map and to the HyMAP reference data in case of using spaceborne dataset. Results demonstrate the capability of both airborne and spaceborne data to provide large-scale reconnaissance mapping of geologic materials over vast arctic regions where field access is limited. The distributions of three ultramafic units (dunite, peridotite, pyroxenite) and one mafic unit (gabbro) are mapped based on analyzing specific visible and near-infrared and short-wave-infrared spectral features. The extent of peridotite and dunite units mapped using both approaches is consistent with geological map, whereas pyroxenite abundance maps show different patterns in their distribution as compared to the geological map. The results suggest that EnGeoMAP method has a better performance than ISMA method for mapping the dunite unit, whilst ISMA performs better for mapping peridotite and pyroxenite rocks.

The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring.

In the face of rapid global change it is imperative to preserve geodiversity for the overall conservation of biodiversity. Geodiversity is important for understanding complex biogeochemical and physical processes and is directly and indirectly linked to biodiversity on all scales of ecosystem organization. Despite the great importance of geodiversity, there is a lack of suitable monitoring methods. Compared to conventional in-situ techniques, remote sensing (RS) techniques provide a pathway towards cost-effective, increasingly more available, comprehensive, and repeatable, as well as standardized monitoring of continuous geodiversity on the local to global scale. This paper gives an overview of the state-of-the-art approaches for monitoring soil characteristics and soil moisture with unmanned aerial vehicles (UAV) and air- and spaceborne remote sensing techniques. Initially, the definitions for geodiversity along with its five essential characteristics are provided, with an explanation for the latter. Then, the approaches of spectral traits (ST) and spectral trait variations (STV) to record geodiversity using RS are defined. LiDAR (light detection and ranging), thermal and microwave sensors, multispectral, and hyperspectral RS technologies to monitor soil characteristics and soil moisture are also presented. Furthermore, the paper discusses current and future satellite-borne sensors and missions as well as existing data products. Due to the prospects and limitations of the characteristics of different RS sensors, only specific geotraits and geodiversity characteristics can be recorded. The paper provides an overview of those geotraits.

The German Environmental Mapping and Analysis Program (EnMAP) will provide hyperspectral spaceborne data to the global geoscientific community. Here we present EnGeoMAP the EnMAP Geological Mapper and EnSoMAP the EnMAP Soil Mapper which are two geomapping tool provided in the EnMAP Box. They offer geologists and soil scientists worldwide a unique toolset for the analysis of future EnMAP data. The structure, workflow and results of EnGeoMAP and EnSoMAP are shown and discussed together with a brief overview on further developments. Synergies between EnMAP and other sensors are furthermore demonstrated.

In times of rapid global change, ecosystem monitoring is of utmost importance. Combined field and remote sensing data enable large-scale ecosystem assessments, while maintaining local relevance and accuracy. In heterogeneous landscapes, however, the integration of field-collected data with remote sensing image pixels is not a trivial matter. Indeed, much of the uncertainty in models that use remote sensing to map larger areas lies on the field data integration. In this study, we propose to use fine spatial resolution (5 x 5 m2) remote sensing data as auxiliary data for upscaling field-sampled aboveground carbon data to target (meso-scale, i.e., 30 x 30 m2) image pixels. In this process, we assess the effects of field data disaggregation and extrapolation, with and without the auxiliary data. We test this on three study sites in heterogeneous landscapes of the Brazilian savanna. We thus compare two methods that use auxiliary data—surface method, which uses a weighting layer, and regression method, which applies a regression model—with one method without auxiliary data—cartographic method. To evaluate our results, we compared observed vs. estimated aboveground carbon values (for known samples) at the pixel level. Additionally, we fitted a random forest regression model with the assigned carbon estimates and the target satellite imagery and assessed the influence of the fraction of extrapolated vs. sampled carbon values on model performance. We observed that, in heterogeneous landscapes, the use of fine spatial resolution remote sensing data improves the upscaling of field-based aboveground carbon data to coarser image pixels. We also show that a surface method is more suitable for spatial disaggregation, while a regression approach is preferable for extrapolating non-sampled pixel fractions. In our study, larger datasets, which included a higher proportion of estimated values, generally delivered better models of aboveground carbon than smaller datasets that are assumed to more reliably reflect reality. Our approach enables to link field and remote sensing data, which in turn enables the detailed mapping of aboveground carbon in heterogeneous landscapes over large areas through the optimized integration of field data and multi-scale remote sensing data.

The characterization of vegetation is a very important ecological task, especially in sensitive mountain areas, as alpine regions often respond to small short-term variations of abiotic and biotic components as well as long-term global changes. Spatial techniques, such as imaging spectroscopy, allow for detailed classification of different syntaxonomic categories of vegetation and their status. Based on the Airborne Prism Experiment (APEX) and simulated Environmental Mapping and Analysis Program (EnMAP) data, this study focused on subalpine and alpine vegetation mapping in the eastern part of the Polish Karkonosze National Park (KPN). The spatial resolution of APEX (3.12 m) enabled the classification of 21 vegetation communities, which was generalized into eight vegetation types. These types were identified on scaled-up APEX data, as both 252 bands from most of the spectral range and a spectrally reduced dataset of 30 minimum noise fraction (MNF) transforms, and compared to the simulated (30 m spatial resolution) EnMAP data using test areas extracted from the field survey derived reference non-forest vegetation map. After preprocessing, a pixel purity index (PPI) was calculated using the MNF image and then the training and validation pixels were selected with Support Vector Machine classification of vegetation communities carried out using different kernel functions: linear, polynomial, radial basis function, and sigmoid. The classification accuracy was obtained for 21 base classes, and the best result was achieved by using the linear function and 252 bands (overall accuracy (OA) of 74.39%). The next step was to classify the eight generalized vegetation types, and the OA for the APEX data reached 90.72% while EnMAP reached 78.25%. The results show the potential use of APEX and EnMAP imagery in mapping subalpine and alpine vegetation on a community and vegetation-type scales, within a diverse ecosystem such as the Karkonosze National Park.

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.

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.

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.

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 H 2 O 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.

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.

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.