Monitoring vegetation- and geodiversity with remote sensing and traits

Geodiversity has shaped and structured the Earth's surface at all spatio-temporal scales, not only through long-term processes but also through medium- and short-term processes. Geodiversity is, therefore, a key control and regulating variable in the overall development of landscapes and biodiversity. However, climate change and land use intensity are leading to major changes and disturbances in bio- and geodiversity. For sustainable ecosystem management, temporal, economically viable and standardized monitoring is needed to monitor and model the effects and changes in vegetation- and geodiversity. RS approaches have been used for this purpose for decades. However, to understand in detail how RS approaches capture vegetation- and geodiversity, the aim of this paper is to describe how five features of vegetation- and geodiversity are captured using RS technologies, namely: (i) trait diversity, (ii) phylogenetic/genese diversity, (iii) structural diversity, (iv) taxonomic diversity and (v) functional diversity. Trait diversity is essential for establishing the other four. Traits provide a crucial interface between in situ, close-range, aerial and space-based RS monitoring approaches. The trait approach allows complex data of different types and formats to be linked using the latest semantic data integration techniques, which will enable ecosystem integrity monitoring and modelling in the future. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'.

Effect of Spatial and Spectral Scaling on Joint Characterization of the Spectral Mixture Residual: Comparative Analysis of AVIRIS and WorldView-3 SWIR for Geologic Mapping in Anza-Borrego Desert State Park

A geologic map is both a visual depiction of the lithologies and structures occurring at the Earth's surface and a representation of a conceptual model for the geologic history in a region. The work needed to capture such multifaced information in an accurate geologic map is time consuming. Remote sensing can complement traditional primary field observations, geochemistry, chronometry, and subsurface geophysical data in providing useful information to assist with the geologic mapping process. Two novel sources of remote sensing data are particularly relevant for geologic mapping applications: decameter-resolution imaging spectroscopy (spectroscopic imaging) and meter-resolution multispectral shortwave infrared (SWIR) imaging. Decameter spectroscopic imagery can capture important mineral absorptions but is frequently unable to spatially resolve important geologic features. Meter-resolution multispectral SWIR images are better able to resolve fine spatial features but offer reduced spectral information. Such disparate but complementary datasets can be challenging to integrate into the geologic mapping process. Here, we conduct a comparative analysis of spatial and spectral scaling for two such datasets: one Airborne Visible/Infrared Imaging Spectrometer-Classic (AVIRIS-classic) flightline, and one WorldView-3 (WV3) scene, for a geologically complex landscape in Anza-Borrego Desert State Park, California. To do so, we use a two-stage framework that synthesizes recent advances in the spectral mixture residual and joint characterization. The mixture residual uses the wavelength-explicit misfit of a linear spectral mixture model to capture low variance spectral signals. Joint characterization utilizes nonlinear dimensionality reduction (manifold learning) to visualize spectral feature space topology and identify clusters of statistically similar spectra. For this study area, the spectral mixture residual clearly reveals greater spectral dimensionality in AVIRIS than WorldView (99% of variance in 39 versus 5 residual dimensions). Additionally, joint characterization shows more complex spectral feature space topology for AVIRIS than WorldView, revealing information useful to the geologic mapping process in the form of mineralogical variability both within and among mapped geologic units. These results illustrate the potential of recent and planned imaging spectroscopy missions to complement high-resolution multispectral imagery-along with field and lab observations-in planning, collecting, and interpreting the results from geologic field work.

Experimental Analysis on the Ultraviolet Imaging of Oil Film on Water Surface: Implication for the Optimal Band for Oil Film Detection Using Ultraviolet Imaging

Passive ultraviolet (UV) imaging is a potential way to detect thin oil film on water surface due to the high reflectivity of oil film under UV bands. This study conducted an outfield imaging experiment on oil film under the UV bands at 300 nm, 310 nm, and 330 nm. The obtained images were visually compared and quantitatively analyzed using imaging quality index (IQI). The experimental results indicated that the UV images obtained under 300 nm are not able to distinguish between oil film and clean water; those obtained under 310 nm achieve high contrast between oil film and clean water, but low average gray value; those obtained under 330 nm have high IQI and thus may be the optimal wavelength for UV imaging of oil film on water surface. This study provides a guidance to the choices of bands for the oil film detection using passive UV imaging method.

Remote Sensing of Geomorphodiversity Linked to Biodiversity—Part III: Traits, Processes and Remote Sensing Characteristics

Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed.

Thermal-Infrared Spectral Feature Analysis and Spectral Identification of Monzonite Using Feature-Oriented Principal Component Analysis

Rock spectral analysis is an important research field in hyperspectral remote sensing information processing. Compared with the spectra in the short-wave infrared and visible–near-infrared regions, the emittance spectrum of rocks in the thermal infrared (TIR) region is highly significant for identifying some major rock-forming minerals, including feldspar, biotite, pyroxene and hornblende. Even for the same rock type, slight differences in mineral composition generally result in varying spectral signatures, undoubtedly increasing the difficulty in discriminating rock types on the Earth’s surface via TIR spectroscopy. In this study, amounts of monzonite samples from different regions were collected in the central part of Hunan Province, China, and emission spectra at 8–14 μm were measured using a portable thermal infrared spectrometer. The experimental result illustrates 13 remarkable feature positions for all the monzonite samples from different geological environments. Furthermore, by combining the extracted features with the principal component analysis (PCA) method, feature-oriented PCA was applied to establish a model for identifying monzonite accurately and quickly without performing spectral library matching and spectral deconvolution. This study provides an important method for rock type identification in the TIR region that is helpful for the rock spectral analysis, geological mapping and pixel unmixing of remote sensing images.

A Novel Method for Hyperspectral Mineral Mapping Based on Clustering-Matching and Nonnegative Matrix Factorization

The emergence of hyperspectral imagery paved a new way for rapid mineral mapping. As a classical hyperspectral classification method, spectral matching (SM) can automatically map the spatial distribution of minerals without the need for selecting training samples. However, due to the influence of noise, the mapping accuracy of SM is usually poor, and its per-pixel matching method is inefficient to some extent. To solve these problems, we propose an unsupervised clustering-matching mapping method, using a combination of k-means and SM (KSM). First, nonnegative matrix factorization (NMF) is used and combined with a simple and effective NMF initialization method (SMNMF) for feature extraction. Then, k-means is implemented to get the cluster centers of the extracted features and band depth, which are used for clustering and matching, respectively. Finally, dimensionless matching methods, including spectral angle mapper (SAM), spectral correlation angle (SCA), spectral gradient angle (SGA), and a combined matching method (SCGA) are used to match the cluster centers of band depth with a spectral library to obtain the mineral mapping results. A case study on the airborne hyperspectral image of Cuprite, Nevada, USA, demonstrated that the average overall accuracies of KSM based on SAM, SCA, SGA, and SCGA are approximately 22%, 22%, 35%, and 33% higher than those of SM, respectively, and KSM can save more than 95% of the mapping time. Moreover, the mapping accuracy and efficiency of SMNMF are about 15% and 38% higher than those of the widely used NMF initialization method. In addition, the proposed SCGA could achieve promising mapping results at both high and low signal-to-noise ratios compared with other matching methods. The mapping method proposed in this study provides a new solution for the rapid and autonomous identification of minerals and other fine objects.

An Open Integrated Rock Spectral Library (RockSL) for a Global Sharing and Matching Service

Minerals and rocks are important natural resources that are formed over a long period of geological history. Spectroscopy is the basis of the identification and characterisation of rocks and minerals via proximal sensing in the field or remote sensing systems with multi- and hyper-spectral capabilities. However, spectral data is scattered around different institutions worldwide and stored in various formats, resulting in poor data usability and an unnecessary waste of time and information. To improve the usability and performance of mineral spectral data, we developed an integrated open mineral spectral library (Rock Spectral Library, RockSL). Shared spectral data and related information were collected worldwide, and data cleaning measures were performed to retain the qualified spectra and merge all qualified data (raster, vector, and text formats) in a common framework to establish a reliable and comprehensive digital data set for an easy sharing and matching service. A software system was developed for the RockSL to manage, analyse, and apply the spectral data of minerals and rocks. We demonstrate how the information encoded in RockSL can determine the species of unknown rocks and describe specific mineral compositions. We also provide a reference scheme of the work chain and present key technologies for building different spectral libraries in diverse fields using RockSL. New contributions to RockSL are encouraged for this work to be improved to provide a better service and extend the applications of geo-sciences. This article introduces the characteristics of RockSL and demonstrates an experimental application.

Solar photovoltaic module detection using laboratory and airborne imaging spectroscopy data

Over the past decades, solar panels have been widely used to harvest solar energy owing to the decreased cost of silicon-based photovoltaic (PV) modules, and therefore it is essential to remotely map and monitor the presence of solar PV modules. Many studies have explored on PV module detection based on color aerial photography and manual photo interpretation. Imaging spectroscopy data are capable of providing detailed spectral information to identify the spectral features of PV, and thus potentially become a promising resource for automated and operational PV detection. However, PV detection with imaging spectroscopy data must cope with the vast spectral diversity of surface materials, which is commonly divided into spectral intra-class variability and inter-class similarity. We have developed an approach to detect PV modules based on their physical absorption and reflection characteristics using airborne imaging spectroscopy data. A large database was implemented for training and validating the approach, including spectra-goniometric measurements of PV modules and other materials, a HyMap image spectral library containing 31 materials with 5627 spectra, and HySpex imaging spectroscopy data sets covering Oldenburg, Germany. By normalizing the widely used Hydrocarbon Index (HI), we solved the intra-class variability caused by different detection angles, and validated it against the spectra-goniometric measurements. Knowing that PV modules are composed of materials with different transparencies, we used a group of spectral indices and investigated their interdependencies for PV detection with implementing the image spectral library. Finally, six well-trained spectral indices were applied to HySpex data acquired in Oldenburg, Germany, yielding an overall PV map. Four subsets were selected for validation and achieved overall accuracies, producer's accuracies and user's accuracies, respectively. This physics-based approach was validated against a large database collected from multiple platforms (laboratory measurements, airborne imaging spectroscopy data), thus providing a robust, transferable and applicable way to detect PV modules using imaging spectroscopy data. We aim to create greater awareness of the potential importance and applicability of airborne and spaceborne imaging spectroscopy data for PV modules identification.

Numerically modelling the reflectance of a rough surface covered with diesel fuel based on bidirectional reflectance distribution function

Oil spills have become a problem that negatively affects the oceanic environment and maritime transportation. Optical remote sensing technology is a potential method to monitor oil spills by analyzing the reflectance spectra of oil-polluted and clean water surface. In this paper, a numerical model for the reflectance of a rough oil surface is constructed by combining Fresnel reflection and bidirectional reflectance distribution function (BRDF). The way that visible light is reflected from the rough diesel fuel surface is quantitatively described and discussed based on the reflection theory of electromagnetic waves. The simulation result of the proposed model shows reasonable agreement with experimental measurements. With reliable prediction and a low computational complexity, the proposed model is expected to provide a theorical basis for rapid detection of oil spills on rough sea surfaces using optical remote sensing technology.

Hyperspectral and Lidar Data Applied to the Urban Land Cover Machine Learning and Neural-Network-Based Classification: A Review

Rapid technological advances in airborne hyperspectral and lidar systems paved the way for using machine learning algorithms to map urban environments. Both hyperspectral and lidar systems can discriminate among many significant urban structures and materials properties, which are not recognizable by applying conventional RGB cameras. In most recent years, the fusion of hyperspectral and lidar sensors has overcome challenges related to the limits of active and passive remote sensing systems, providing promising results in urban land cover classification. This paper presents principles and key features for airborne hyperspectral imaging, lidar, and the fusion of those, as well as applications of these for urban land cover classification. In addition, machine learning and deep learning classification algorithms suitable for classifying individual urban classes such as buildings, vegetation, and roads have been reviewed, focusing on extracted features critical for classification of urban surfaces, transferability, dimensionality, and computational expense.

Hydrothermal Alteration of the Ocean Crust and Patterns in Mineralization With Depth as Measured by Micro-Imaging Infrared Spectroscopy

Processes for formation, cooling, and altering Earth's ocean crust are not yet completely understood due to challenges in access and sampling. Here, we use contiguous micro-imaging infrared spectroscopy to develop complete-core maps of mineral occurrence and investigate spatial patterns in the hydrothermal alteration of 1.2 km of oceanic crust recovered from Oman Drilling Project Holes GT1A, GT2A, and GT3A drilled in the Samail Ophiolite, Oman. The imaging spectrometer shortwave infrared sensor measured reflectance of light at wavelengths 1.0-2.6 μm at 250-260 μm/pixel, resulting in >1 billion independent measurements. We map distributions of nine key primary and secondary minerals/mineral groups-clinopyroxene, amphibole, calcite, chlorite, epidote, gypsum, kaolinite/montmorillonite, prehnite, and zeolite-and find differences in their spatial occurrences and pervasiveness. Accuracy of spectral mapping of occurrence is 68%-100%, established using X-ray diffraction measurements from the core description. The sheeted dikes and gabbros of upper oceanic crust Hole GT3A show more pervasive alteration and alteration dominated by chlorite, amphibole, and epidote. The foliated/layered gabbros of GT2A from intermediate crustal depths have similarly widespread chlorite but more zeolite and little amphibole and epidote. The layered gabbros of the lower oceanic crust (GT1A) have remnant pyroxene and 2X less chlorite, but alteration is extensive within and surrounding major fault zones with widespread occurrences of amphibole. The results indicate greater distribution of higher temperature alteration minerals in the upper oceanic crust relative to deeper gabbros and highlight the importance of fault zones in hydrothermal convection in the lower ocean crust.

Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery

Clustering methods unequivocally show considerable influence on many recent algorithms and play an important role in hyperspectral data analysis. Here, we challenge the clustering for mineral identification using two different strategies in hyperspectral long wave infrared (LWIR, 7.7–11.8 μm). For that, we compare two algorithms to perform the mineral identification in a unique dataset. The first algorithm uses spectral comparison techniques for all the pixel-spectra and creates RGB false color composites (FCC). Then, a color based clustering is used to group the regions (called FCC-clustering). The second algorithm clusters all the pixel-spectra to directly group the spectra. Then, the first rank of non-negative matrix factorization (NMF) extracts the representative of each cluster and compares results with the spectral library of JPL/NASA. These techniques give the comparison values as features which convert into RGB-FCC as the results (called clustering rank1-NMF). We applied K-means as clustering approach, which can be modified in any other similar clustering approach. The results of the clustering-rank1-NMF algorithm indicate significant computational efficiency (more than 20 times faster than the previous approach) and promising performance for mineral identification having up to 75.8% and 84.8% average accuracies for FCC-clustering and clustering-rank1 NMF algorithms (using spectral angle mapper (SAM)), respectively. Furthermore, several spectral comparison techniques are used also such as adaptive matched subspace detector (AMSD), orthogonal subspace projection (OSP) algorithm, principal component analysis (PCA), local matched filter (PLMF), SAM, and normalized cross correlation (NCC) for both algorithms and most of them show a similar range in accuracy. However, SAM and NCC are preferred due to their computational simplicity. Our algorithms strive to identify eleven different mineral grains (biotite, diopside, epidote, goethite, kyanite, scheelite, smithsonite, tourmaline, pyrope, olivine, and quartz).

Potentials of Airborne Hyperspectral AVIRIS-NG Data in the Exploration of Base Metal Deposit—A Study in the Parts of Bhilwara, Rajasthan

In this study, we have processed the spectral bands of airborne hyperspectral data of Advanced Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) data for delineating the surface signatures associated with the base metal mineralization in the Pur-Banera area in the Bhilwara district, Rajasthan, India.The primaryhost rocks of the Cu, Pb, Zn mineralization in the area are Banded Magnetite Quartzite (BMQ), unclassified calcareous silicates, and quartzite. We used ratio images derived from the scale and root mean squares (RMS) error imagesusing the multi-range spectral feature fitting (MRSFF) methodto delineate host rocks from the AVIRIS-NG image. The False Color Composites (FCCs) of different relative band depth images, derived from AVIRIS-NG spectral bands, were also used for delineating few minerals. These minerals areeither associated with the surface alteration resulting from the ore-bearing fluid migration orassociated with the redox-controlled supergene enrichments of the ore deposit.The results show that the AVIRIS-NG image products derived in this study can delineate surface signatures of mineralization in 1:10000 to 1:15000 scales to narrow down the targets for detailed exploration.This study alsoidentified the possible structural control over the knownsurface distribution of alteration and lithocap minerals of base metal mineralizationusing the ground-based residual magnetic anomaly map. This observationstrengthens the importance of the identified surface proxiesas an indicator of mineralization. X-ray fluorescence analysis of samples collectedfromselected locations within the study area confirms the Cu-Pb-Zn enrichment. The sulfide minerals were also identified in the microphotographs of polished sections of rock samples collected from the places where surface proxies of mineralization were observed in the field. This study justified the investigation to utilize surface signatures of mineralization identified using AVIRIS-NG dataand validated using field observations, geophysical, geochemical, and petrographical data.

Spectroscopic and theoretical investigation on the origin of color in jarosites

This work aims to understand the origin of the electronic spectra of Fe³⁺ (d⁵), Cr³⁺ (d³), and V³⁺ (d²) containing jarosites. The electronic spectrum of the Fe-jarosite is currently assigned to spin forbidden transitions. This work shows that the spectra essentially arise due to the tetragonal distortion of the coordination symmetry of the Fe³⁺ ion in the jarosite crystal, and thereby obviates the need for invoking spin forbidden transitions. The absorption spectra of Cr- and V-jarosite are also assigned to transitions predicted for the tetragonal distortion of the metal ion coordination. The electronic term symbols are worked out using the correlation diagram and Tanabe-Sugano diagram for orbital splitting for all three systems employing ab initio and DFT methods. The bandgaps were computed and corroborated with the experimentally measured values to support the low symmetry at the metal center.

WorldView-3 mapping of Tarmat deposits of the Ras Rakan Island, Northern Coast of Qatar: Environmental perspective

This study characterizes the spectral behavior of tarmats and maps the tarmat deposits found along the coast of Ras Rakan Island off Qatar using WorldView-3 (WV-3) sensor data. The laboratory spectra of tar materials showed diagnostic absorptions features at 0.6 and 1.1 μm in the visible and near-infrared (VNIR) and 1.52, 1.73, 2.04, and 2.31 μm in the short wave infrared (SWIR) region. The panchromatic grayscale image and FCC showed the tarmat deposit as a linear warp feature between beach and water. The mapping of deposits using WV-3 data by decorrelation stretch and Linear Spectral Unmixing (LSU) methods discriminated the tarmats from the sandy soil, vegetation and sabkha features in a different tone. The capability of WV-3 sensor and the potential of image processing methods were verified by mapping the tar distribution of the Ras Ushayriq and NE of Al Ruwais.

Hierarchical Spatial-Spectral Feature Extraction with Long Short Term Memory (LSTM) for Mineral Identification Using Hyperspectral Imagery

Deep learning models are widely employed in hyperspectral image processing to integrate both spatial features and spectral features, but the correlations between them are rarely taken into consideration. However, in hyperspectral mineral identification, not only the spectral and spatial features of minerals need to be considered, but also the correlations between them are crucial to further promote identification accuracy. In this paper, we propose hierarchical spatial-spectral feature extraction with long short term memory (HSS-LSTM) to explore correlations between spatial features and spectral features and obtain hierarchical intrinsic features for mineral identification. In the proposed model, the fusion spatial-spectral feature is primarily extracted by stacking local spatial features obtained by a convolution neural network (CNN)-based model and spectral information together. To better exploit spatial features and spectral features, an LSTM-based model is proposed to capture correlations and obtain hierarchical features for accurate mineral identification. Specifically, the proposed model shares a uniform objective function, so that all the parameters in the network can be optimized in the meantime. Experimental results on the hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) in the Nevada mining area show that HSS-LSTM achieves an overall accuracy of 94.70% and outperforms other commonly used identification methods.

Automatic Estimation of Crop Disease Severity Levels Based on Vegetation Index Normalization

The timely monitoring of crop disease development is very important for precision agriculture applications. Remote sensing-based vegetation indices (VIs) can be good indicators of crop disease severity, but current methods are mainly dependent on manual ground survey results. Based on VI normalization, an automated crop disease severity grading method without the use of ground surveys was proposed in this study. This technique was applied to two cotton fields infested with different levels of cotton root rot in south Texas in the United States, where airborne hyperspectral imagery was collected. Six typical VIs were calculated from the hyperspectral imagery and their histograms indicated that VI normalization could eliminate the influences of variable field conditions and the VI value range variations, allowing a potentially broader scope of application. According to the analysis of the obtained results from the spectral dimension, spatial dimension and descriptive statistics, the disease grading results were in general agreement with previous ground survey results, proving the validity of the disease severity grading method. Although satisfactory results could be achieved from different types of VI, there is still room for further improvement through the exploration of more VIs. With the advantages of independence of ground surveys and potential universal applicability, the newly proposed crop disease grading method will be of great significance for crop disease monitoring over large geographical areas.

Montmorillonite Estimation in Clay–Quartz–Calcite Samples from Laboratory SWIR Imaging Spectroscopy: A Comparative Study of Spectral Preprocessings and Unmixing Methods

Clay minerals play an important role in shrinking–swelling of soils and off–road vehicle mobility mainly due to the presence of smectites including montmorillonites. Since soils are composed of different minerals intimately mixed, an accurate estimation of its abundance is challenging. Imaging spectroscopy in the short wave infrared spectral region (SWIR) combined with unmixing methods is a good candidate to estimate clay mineral abundance. However, the performance of unmixing methods is mineral-dependent and may be enhanced by using appropriate spectral preprocessings. The objective of this paper is to carry out a comparative study in order to determine the best couple spectral preprocessing/unmixing method to quantify montmorillonite in intimate mixtures with clays, such as montmorillonite, kaolinite and illite, and no-clay minerals, such as calcite and quartz. To this end, a spectral database is built with laboratory hyperspectral imagery from 51 dry pure mineral samples and intimate mineral mixtures of controlled abundances. Six spectral preprocessings, standard normal variate (SNV), continuum removal (CR), continuous wavelet transform (CWT), Hapke model, first derivative (1st SGD) and pseudo–absorbance (Log(1/R)), are applied and compared with reflectance spectra. Two linear unmixing methods, fully constrained least square method (FCLS) and multiple endmember spectral mixture analysis (MESMA), and two non-linear unmixing methods, generalized bilinear method (GBM) and multi-linear model (MLM), are compared. Global results showed that the benefit of spectral preprocessings occurs when spectral absorption features of minerals overlap for SNV, CR, CWT and 1st SGD, whereas the use of reflectance spectra performs the best when no overlap is present. With one mineral having no spectral feature (quartz), montmorillonite abundance estimation is difficult and gives RMSE higher than 50%. For the other mixtures, performances of linear and non-linear unmixing methods are similar. Consequently, the recommended couple spectral preprocessing/unmixing method based on the trade-off between its simplicity and performance is 1st SGD/FCLS for clay binary and ternary mixtures (RMSE of 9.2% for montmorillonite–illite mixtures, 13.9% for montmorillonite–kaolinite mixtures and 10.8% for montmorillonite–illite–kaolinite mixtures) and reflectance/FCLS for binary mixtures with calcite (RMSE of 8.8% for montmorillonite–calcite mixtures). These performances open the way to improve the classification of expansive soils.

Optimized Lithological Mapping from Multispectral and Hyperspectral Remote Sensing Images Using Fused Multi-Classifiers

Most available studies in lithological mapping using spaceborne multispectral and hyperspectral remote sensing images employ different classification and spectral matching algorithms for performing this task; however, our experiment reveals that no single algorithm renders satisfactory results. Therefore, a new approach based on an ensemble of classifiers is presented for lithological mapping using remote sensing images in this paper, which returns enhanced accuracy. The proposed method uses a weighted pooling approach for lithological mapping at each pixel level using the agreement of the class accuracy, overall accuracy and kappa coefficient from the multi-classifiers of an image. The technique is implemented in four steps; (1) classification images are generated using a variety of classifiers; (2) accuracy assessments are performed for each class, overall classification and estimation of kappa coefficient for every classifier; (3) an overall within-class accuracy index is estimated by weighting class accuracy, overall accuracy and kappa coefficient for each class and every classifier; (4) finally each pixel is assigned to a class for which it has the highest overall within-class accuracy index amongst all classes in all classifiers. To demonstrate the strength of the developed approach, four supervised classifiers (minimum distance (MD), spectral angle mapper (SAM), spectral information divergence (SID), support vector machine (SVM)) are used on one hyperspectral image (Hyperion) and two multispectral images (ASTER, Landsat 8-OLI) for mapping lithological units of the Udaipur area, Rajasthan, western India. The method is found significantly effective in increasing the accuracy in lithological mapping.

Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics

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.

Comparison of Methods for Modeling Fractional Cover Using Simulated Satellite Hyperspectral Imager Spectra

Remotely sensed data can be used to model the fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil in natural and agricultural ecosystems. NPV and soil cover are difficult to estimate accurately since absorption by lignin, cellulose, and other organic molecules cannot be resolved by broadband multispectral data. A new generation of satellite hyperspectral imagers will provide contiguous narrowband coverage, enabling new, more accurate, and potentially global fractional cover products. We used six field spectroscopy datasets collected in prior experiments from sites with partial crop, grass, shrub, and low-stature resprouting tree cover to simulate satellite hyperspectral data, including sensor noise and atmospheric correction artifacts. The combined dataset was used to compare hyperspectral index-based and spectroscopic methods for estimating GV, NPV, and soil fractional cover. GV fractional cover was estimated most accurately. NPV and soil fractions were more difficult to estimate, with spectroscopic methods like partial least squares (PLS) regression, spectral feature analysis (SFA), and multiple endmember spectral mixture analysis (MESMA) typically outperforming hyperspectral indices. Using an independent validation dataset, the lowest root mean squared error (RMSE) values were 0.115 for GV using either normalized difference vegetation index (NDVI) or SFA, 0.164 for NPV using PLS, and 0.126 for soil using PLS. PLS also had the lowest RMSE averaged across all three cover types. This work highlights the need for more extensive and diverse fine spatial scale measurements of fractional cover, to improve methodologies for estimating cover in preparation for future hyperspectral global monitoring missions.

Integrated Hyperspectral and Geochemical Study of Sediment-Hosted Disseminated Gold at the Goldstrike District, Utah

The Goldstrike district in southwest Utah is similar to Carlin-type gold deposits in Nevada that are characterized by sediment-hosted disseminated gold. Suitable structural and stratigraphic conditions facilitated precipitation of gold in arsenian pyrite grains from ascending gold-bearing fluids. This study used ground-based hyperspectral imaging to study a core drilled in the Goldstrike district covering the basal Claron Formation and Callville Limestone. Spectral modeling of absorptions at 2340, 2200, and 500 nm allowed the extraction of calcite, clay minerals, and ferric iron abundances and identification of lithology. This study integrated remote sensing and geochemistry data and identified an optimum stratigraphic combination of limestone above and siliciclastic rocks below in the basal Claron Formation, as well as decarbonatization, argillization, and pyrite oxidation in the Callville Limestone, that are related with gold mineralization. This study shows an example of utilizing ground-based hyperspectral imaging in geological characterization, which can be broadly applied in the determination of mining interests and classification of ore grades. The utilization of this new terrestrial remote sensing technique has great potentials in resource exploration and exploitation.

Ensemble-Based Cascaded Constrained Energy Minimization for Hyperspectral Target Detection

Ensemble learning is an important group of machine learning techniques that aim to enhance the nonlinearity and generalization ability of a learning system by aggregating multiple learners. We found that ensemble techniques show great potential for improving the performance of traditional hyperspectral target detection algorithms, while at present, there are few previous works have been done on this topic. To this end, we propose an Ensemble based Constrained Energy Minimization (E-CEM) detector for hyperspectral image target detection. Classical hyperspectral image target detection algorithms like Constrained Energy Minimization (CEM), matched filter (MF) and adaptive coherence/cosine estimator (ACE) are usually designed based on constrained least square regression methods or hypothesis testing methods with Gaussian distribution assumption. However, remote sensing hyperspectral data captured in a real-world environment usually shows strong nonlinearity and non-Gaussianity, which will lead to performance degradation of these classical detection algorithms. Although some hierarchical detection models are able to learn strong nonlinear discrimination of spectral data, due to the spectrum changes, these models usually suffer from the instability in detection tasks. The proposed E-CEM is designed based on the classical CEM detection algorithm. To improve both of the detection nonlinearity and generalization ability, the strategies of “cascaded detection”, “random averaging” and “multi-scale scanning” are specifically designed. Experiments on one synthetic hyperspectral image and two real hyperspectral images demonstrate the effectiveness of our method. E-CEM outperforms the traditional CEM detector and other state-of-the-art detection algorithms. Our code will be made publicly available.

Entropy-Mediated Decision Fusion for Remotely Sensed Image Classification

To better classify remotely sensed hyperspectral imagery, we study hyperspectral signatures from a different view, in which the discriminatory information is divided as reflectance features and absorption features, respectively. Based on this categorization, we put forward an information fusion approach, where the reflectance features and the absorption features are processed by different algorithms. Their outputs are considered as initial decisions, and then fused by a decision-level algorithm, where the entropy of the classification output is used to balance between the two decisions. The final decision is reached by modifying the decision of the reflectance features via the results of the absorption features. Simulations are carried out to assess the classification performance based on two AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) hyperspectral datasets. The results show that the proposed method increases the classification accuracy against the state-of-the-art methods.

Nonconvex-sparsity and Nonlocal-smoothness Based Blind Hyperspectral Unmixing

Blind hyperspectral unmixing (HU), as a crucial technique for hyperspectral data exploitation, aims to decompose mixed pixels into a collection of constituent materials weighted by the corresponding fractional abundances. In recent years, nonnegative matrix factorization (NMF) based methods have become more and more popular for this task and achieved promising performance. Among these methods, two types of properties upon the abundances, namely the sparseness and the structural smoothness, have been explored and shown to be important for blind HU. However, all of previous methods ignores another important insightful property possessed by a natural hyperspectral images (HSI), non-local smoothness, which means that similar patches in a larger region of an HSI are sharing the similar smoothness structure. Based on previous attempts on other tasks, such a prior structure reflects intrinsic configurations underlying a HSI, and is thus expected to largely improve the performance of the investigated HU problem. In this paper, we firstly consider such prior in HSI by encoding it as the nonlocal total variation (NLTV) regularizer. Furthermore, by fully exploring the intrinsic structure of HSI, we generalize NLTV to non-local HSI TV (NLHTV) to make the model more suitable for the bind HU task. By incorporating these two regularizers, together with a non-convex log-sum form regularizer characterizing the sparseness of abundance maps, to the NMF model, we propose novel blind HU models named NLTV/NLHTV and log-sum regularized NMF (NLTV-LSRNMF/NLHTV-LSRNMF), respectively. To solve the proposed models, an efficient algorithm is designed based on alternative optimization strategy (AOS) and alternating direction method of multipliers (ADMM). Extensive experiments conducted on both simulated and real hyperspectral data sets substantiate the superiority of the proposed approach over other competing ones for blind HU task.

Hyperspectral Analysis of Leaf Pigments and Nutritional Elements in Tallgrass Prairie Vegetation

Understanding the spatial distribution of forage quality is important to address critical research questions in grassland science. Due to its efficiency and accuracy, there has been a widespread interest in mapping the canopy vegetation characteristics using remote sensing methods. In this study, foliar chlorophylls, carotenoids, and nutritional elements across multiple tallgrass prairie functional groups were quantified at the leaf level using hyperspectral analysis in the region of 470-800 nm, which was expected to be a precursor to further remote sensing of canopy vegetation quality. A method of spectral standardization was developed using a form of the normalized difference, which proved feasible to reduce the interference from background effects in the leaf reflectance measurements. Chlorophylls and carotenoids were retrieved through inverting the physical model PROSPECT 5. The foliar nutritional elements were modeled empirically. Partial least squares regression was used to build the linkages between the high-dimensional spectral predictor variables and the foliar biochemical contents. Results showed that the retrieval of leaf biochemistry through hyperspectral analysis can be accurate and robust across different tallgrass prairie functional groups. In addition, correlations were found between the leaf pigments and nutritional elements. Results provided insight into the use of pigment-related vegetation indices as the proxy of plant nutrition quality.

An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing

Hyperspectral imagery collected from airborne or satellite sources inevitably suffers from spectral variability, making it difficult for spectral unmixing to accurately estimate abundance maps. The classical unmixing model, the linear mixing model (LMM), generally fails to handle this sticky issue effectively. To this end, we propose a novel spectral mixture model, called the augmented linear mixing model (ALMM), to address spectral variability by applying a data-driven learning strategy in inverse problems of hyperspectral unmixing. The proposed approach models the main spectral variability (i.e., scaling factors) generated by variations in illumination or typography separately by means of the endmember dictionary. It then models other spectral variabilities caused by environmental conditions (e.g., local temperature and humidity, atmospheric effects) and instrumental configurations (e.g., sensor noise), as well as material nonlinear mixing effects, by introducing a spectral variability dictionary. To effectively run the data-driven learning strategy, we also propose a reasonable prior knowledge for the spectral variability dictionary, whose atoms are assumed to be low-coherent with spectral signatures of endmembers, which leads to a well-known low-coherence dictionary learning problem. Thus, a dictionary learning technique is embedded in the framework of spectral unmixing so that the algorithm can learn the spectral variability dictionary and estimate the abundance maps simultaneously. Extensive experiments on synthetic and real datasets are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods.

Thermal Infrared Hyperspectral Imaging for Mineralogy Mapping of a Mine Face

Remote sensing systems are largely used in geology for regional mapping of mineralogy and lithology mainly from airborne or spaceborne platforms. Earth observers such as Landsat, ASTER or SPOT are equipped with multispectral sensors, but suffer from relatively poor spectral resolution. By comparison, the existing airborne and spaceborne hyperspectral systems are capable of acquiring imagery from relatively narrow spectral bands, beneficial for detailed analysis of geological remote sensing data. However, for vertical exposures, those platforms are inadequate options since their poor spatial resolutions (metres to tens of metres) and NADIR viewing perspective are unsuitable for detailed field studies. Here, we have demonstrated that field-based approaches that incorporate thermal infrared hyperspectral technology with about a 40-nm bandwidth spectral resolution and tens of centimetres of spatial resolution allow for efficient mapping of the mineralogy and lithology of vertical cliff sections. We used the Telops lightweight and compact passive thermal infrared hyperspectral research instrument for field measurements in the Jura Cement carbonate quarry, Switzerland. The obtained hyperspectral data were analysed using temperature emissivity separation algorithms to isolate the different contributions of self-emission and reflection associated with different carbonate minerals. The mineralogical maps derived from measurements were found to be consistent with the expected carbonate results of the quarry mineralogy. Our proposed approach highlights the benefits of this type of field-based lightweight hyperspectral instruments for routine field applications such as in mining, engineering, forestry or archaeology.

Underwater hyperspectral imaging as an in situ taxonomic tool for deep-sea megafauna

Identification of benthic megafauna is commonly based on analysis of physical samples or imagery acquired by cameras mounted on underwater platforms. Physical collection of samples is difficult, particularly from the deep sea, and identification of taxonomic morphotypes from imagery depends on resolution and investigator experience. Here, we show how an Underwater Hyperspectral Imager (UHI) can be used as an alternative in situ taxonomic tool for benthic megafauna. A UHI provides a much higher spectral resolution than standard RGB imagery, allowing marine organisms to be identified based on specific optical fingerprints. A set of reference spectra from identified organisms is established and supervised classification performed to identify benthic megafauna semi-autonomously. The UHI data provide an increased detection rate for small megafauna difficult to resolve in standard RGB imagery. In addition, seafloor anomalies with distinct spectral signatures are also detectable. In the region investigated, sediment anomalies (spectral reflectance minimum at ~675 nm) unclear in RGB imagery were indicative of chlorophyll a on the seafloor. Underwater hyperspectral imaging therefore has a great potential in seafloor habitat mapping and monitoring, with areas of application ranging from shallow coastal areas to the deep sea.

Spatial Spectroscopic Models for Remote Exploration

Ancient hydrothermal systems are a high-priority target for a future Mars sample return mission because they contain energy sources for microbes and can preserve organic materials (Farmer, 2000 ; MEPAG Next Decade Science Analysis Group, 2008 ; McLennan et al., 2012 ; Michalski et al., 2017 ). Characterizing these large, heterogeneous systems with a remote explorer is difficult due to communications bandwidth and latency; such a mission will require significant advances in spacecraft autonomy. Science autonomy uses intelligent sensor platforms that analyze data in real-time, setting measurement and downlink priorities to provide the best information toward investigation goals. Such automation must relate abstract science hypotheses to the measurable quantities available to the robot. This study captures these relationships by formalizing traditional "science traceability matrices" into probabilistic models. This permits experimental design techniques to optimize future measurements and maximize information value toward the investigation objectives, directing remote explorers that respond appropriately to new data. Such models are a rich new language for commanding informed robotic decision making in physically grounded terms. We apply these models to quantify the information content of different rover traverses providing profiling spectroscopy of Cuprite Hills, Nevada. We also develop two methods of representing spatial correlations using human-defined maps and remote sensing data. Model unit classifications are broadly consistent with prior maps of the site's alteration mineralogy, indicating that the model has successfully represented critical spatial and mineralogical relationships at Cuprite. Key Words: Autonomous science-Imaging spectroscopy-Alteration mineralogy-Field geology-Cuprite-AVIRIS-NG-Robotic exploration. Astrobiology 18, 934-954.

Remote Detection of Clay Minerals

Spectral remote sensing in the visible/near-infrared (VNIR) and mid-IR (MIR) regions has enabled detection and characterisation of multiple clays and clay minerals on Earth and in the Solar System. Remote sensing on Earth poses the greatest challenge due to atmospheric absorptions that interfere with detection of surface minerals. Still, a greater variety of clay minerals have been observed on Earth than other bodies due to extensive aqueous alteration on our planet. Clay minerals have arguably been mapped in more detail on the planet Mars because they are not masked by vegetation on that planet and the atmosphere is less of a hindrance. Fe/Mg-smectite is the most abundant clay mineral on the surface of Mars and is also common in meteorites and comets where clay minerals are detected.

Detecting Unknown Artificial Urban Surface Materials Based on Spectral Dissimilarity Analysis

High resolution imaging spectroscopy data have been recognised as a valuable data resource for augmenting detailed material inventories that serve as input for various urban applications. Image-specific urban spectral libraries are successfully used in urban imaging spectroscopy studies. However, the regional- and sensor-specific transferability of such libraries is limited due to the wide range of different surface materials. With the developed methodology, incomplete urban spectral libraries can be utilised by assuming that unknown surface material spectra are dissimilar to the known spectra in a basic spectral library (BSL). The similarity measure SID-SCA (Spectral Information Divergence-Spectral Correlation Angle) is applied to detect image-specific unknown urban surfaces while avoiding spectral mixtures. These detected unknown materials are categorised into distinct and identifiable material classes based on their spectral and spatial metrics. Experimental results demonstrate a successful redetection of material classes that had been previously erased in order to simulate an incomplete BSL. Additionally, completely new materials e.g., solar panels were identified in the data. It is further shown that the level of incompleteness of the BSL and the defined dissimilarity threshold are decisive for the detection of unknown material classes and the degree of spectral intra-class variability. A detailed accuracy assessment of the pre-classification results, aiming to separate natural and artificial materials, demonstrates spectral confusions between spectrally similar materials utilizing SID-SCA. However, most spectral confusions occur between natural or artificial materials which are not affecting the overall aim. The dissimilarity analysis overcomes the limitations of working with incomplete urban spectral libraries and enables the generation of image-specific training databases.

Toward Reliable Off Road Autonomous Vehicles Operating in Challenging Environments

The DARPA PerceptOR program has implemented a rigorous evaluative test program which fosters the development of field relevant outdoor mobile robots. Autonomous ground vehicles were deployed on diverse test courses throughout the USA and quantitatively evaluated on such factors as autonomy level, waypoint acquisition, failure rate, speed, and communications bandwidth. Our efforts over the three year program have produced new approaches in planning, perception, localization, and control which have been driven by the quest for reliable operation in challenging environments. This paper focuses on some of the most unique aspects of the systems developed by the CMU PerceptOR team, the lessons learned during the effort, and the most immediate challenges that remain to be addressed.

Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember Estimation in Hyperspectral Images

Mixing phenomena in hyperspectral images depend on a variety of factors, such as the resolution of observation devices, the properties of materials, and how these materials interact with incident light in the scene. Different parametric and nonparametric models have been considered to address hyperspectral unmixing problems. The simplest one is the linear mixing model. Nevertheless, it has been recognized that the mixing phenomena can also be nonlinear. The corresponding nonlinear analysis techniques are necessarily more challenging and complex than those employed for linear unmixing. Within this context, it makes sense to detect the nonlinearly mixed pixels in an image prior to its analysis, and then employ the simplest possible unmixing technique to analyze each pixel. In this paper, we propose a technique for detecting nonlinearly mixed pixels. The detection approach is based on the comparison of the reconstruction errors using both a Gaussian process regression model and a linear regression model. The two errors are combined into a detection statistics for which a probability density function can be reasonably approximated. We also propose an iterative endmember extraction algorithm to be employed in combination with the detection algorithm. The proposed detect-then-unmix strategy, which consists of extracting endmembers, detecting nonlinearly mixed pixels and unmixing, is tested with synthetic and real images.

Oil slick morphology derived from AVIRIS measurements of the Deepwater Horizon oil spill: Implications for spatial resolution requirements of remote sensors

Using fine spatial resolution (~7.6m) hyperspectral AVIRIS data collected over the Deepwater Horizon oil spill in the Gulf of Mexico, we statistically estimated slick lengths, widths and length/width ratios to characterize oil slick morphology for different thickness classes. For all AVIRIS-detected oil slicks (N=52,100 continuous features) binned into four thickness classes (≤50 μm but thicker than sheen, 50-200 μm, 200-1000 μm, and >1000 μm), the median lengths, widths, and length/width ratios of these classes ranged between 22 and 38 m, 7-11 m, and 2.5-3.3, respectively. The AVIRIS data were further aggregated to 30-m (Landsat resolution) and 300-m (MERIS resolution) spatial bins to determine the fractional oil coverage in each bin. Overall, if 50% fractional pixel coverage were to be required to detect oil with thickness greater than sheen for most oil containing pixels, a 30-m resolution sensor would be needed.

Progressive forest canopy water loss during the 2012-2015 California drought

The 2012-2015 drought has left California with severely reduced snowpack, soil moisture, ground water, and reservoir stocks, but the impact of this estimated millennial-scale event on forest health is unknown. We used airborne laser-guided spectroscopy and satellite-based models to assess losses in canopy water content of California's forests between 2011 and 2015. Approximately 10.6 million ha of forest containing up to 888 million large trees experienced measurable loss in canopy water content during this drought period. Severe canopy water losses of greater than 30% occurred over 1 million ha, affecting up to 58 million large trees. Our measurements exclude forests affected by fire between 2011 and 2015. If drought conditions continue or reoccur, even with temporary reprieves such as El Niño, we predict substantial future forest change.