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Seismic hazard analysis is carried out in this study by estimating ground motion for hypothetical earthquakes in the area of Muzaffarabad, Pakistan, with the MT solution of the 2005 Kashmir earthquake. The earth’s topography influences seismic waves by scattering and reflecting it, thereby causing spatial variation in seismic response. Using the moment tensor solution of the 2005 Kashmir earthquake, we perform 25 spectral element method (SEM)-based 3D simulations along major faults in the study area. The SEM model incorporates the topography and homogeneous half-space characteristics. Our results show that, beside topography, the relative location of the source with respect to slopes also has an influence on the observed variation in ground shaking amplitudes. By integrating the mean and standard deviation of estimated ground shaking from 25 simulations, we present a seismic hazard map for the study area. The map summarizes the topographic and potential source location effect on seismic-induced ground shaking in the study area. It provides a classification from hazardous to safe in relative terms and can be used as a guide in earthquake preparedness.

A laboratory study on rare earth element bearing mine tailings, collected from Bangka Island, Indonesia, reported a new spectral absorption feature at 674 nm associated with Erbium. The present study aims to evaluate the capability of the European Space Agency’s Sentinel-2 MSI sensors to detect this absorption feature from space. An arithmetic band operation is performed on selected visible and near-infrared spectral bands of a Sentinel-2 image. The results show that Sentinel-2 MSI is capable of detecting the 674 nm Erbium-related absorption feature within the particular environmental setting of the study area.

A comprehensive landslide inventory and susceptibility maps are prerequisite for developing and implementing landslide mitigation strategies. Landslide susceptibility maps for the landslides prone regions in northern Pakistan are rarely available. The Hunza-Nagar valley in northern Pakistan is known for its frequent and devastating landslides. In this paper, we have developed a landslide inventory map for Hunza-Nagar valley by using the visual interpretation of the SPOT-5 satellite imagery and mapped a total of 172 landslides. The landslide inventory was subsequently divided into modelling and validation data sets. For the development of landslide susceptibility map seven discrete landslide causative factors were correlated with the landslide inventory map using weight of evidence and frequency ratio statistical models. Four different models of conditional independence were used for the selection of landslide causative factors. The produced landslides susceptibility maps were validated by the success rate and area under curves criteria. The prediction power of the models was also validated with the prediction rate curve. The validation results shows that the success rate curves of the weight of evidence and the frequency models are 82% and 79%, respectively. The prediction accuracy results obtained from this study are 84% for weight of evidence model and 80% for the frequency ratio model. Finally, the landslide susceptibility index maps were classified into five different varying susceptibility zones. The validation and prediction result indicates that the weight of evidence and frequency ratio model are reliable to produce an accurate landslide susceptibility map, which may be helpful for landslides management strategies.

A novel method is presented to measure rock microstructure in hyperspectral mineral maps of rock specimens. Shape parameters were calculated from rock objects in segmented mineral maps. Object area, object perimeter, object hull perimeter and fitted ellipses were used to calculate shape parameters such as compactness, convexity and a cookie-cutter parameter. Shape parameters were used to describe a variety of microstructures and microstructural elements. The parameters were tested on microstructures in artificial imagery and subsequently applied to hyperspectral mineral maps of rocks. Analyses of parameters calculated on artificial imagery showed that object shapes could be measured by the flattening of fitted ellipses as a measure of sphericity and elongation, together with the cookie-cutter parameters that measured angularity. Compactness and convexity could differentiate between euhedral, subhedral and anhedral crystal shapes. Aphanitic, phaneritic and porphyritic igneous microstructures could be identified and differentiated by homogeneity and relative object size parameters. The degree of sorting of sedimentary rocks was measured by the distribution of object sizes and statistical parameters describing the distribution. Orientation of single objects was measured by the angle between the major axis of a fitted ellipse and the vertical of the image. Preferred orientations in the rock microstructure were determined by calculation of a standardized resultant of orientation vectors and a mean angle. Layering and banding of the rock was identified by the length of major axes of fitted ellipses relative to the image dimension. The shape parameters calculated on objects in segmented hyperspectral mineral maps of rock specimens were able to discriminate between sedimentary and volcanic microstructures using the size distribution of mineral objects, the presence of a preferred orientation of the rock and a layered microstructure. The volcanic microstructures could be differentiated by the size distribution of amygdales, phenocrysts and xenocrysts in the rock. Shape parameters could be used to differentiate between xenocrysts and phenocrysts, the latter being more elongated in the studied samples. The study shows that object shape parameters can be used to measure microstructure and microstructural elements in mineral maps, and subsequently discriminate between different rock types and microstructures. The expression of microstructure into numeric parameters is a first step towards quantification of microstructures in mineral maps of rocks. Further development of the methodology could contribute to the creation of unbiased classification scheme of rocks, improved statistical modeling of compositional rock parameters such as mineral ore grades, and the automated recognition of microstructures in large image databases of rocks and drill-core.

We introduce a normalization algorithm which highlights short-term, localized, non-periodic fluctuations in hyper-temporal satellite data by dividing each pixel by the mean value of its spatial neighbourhood set. In this way we suppress signal patterns that are common in the central and surrounding pixels, utilizing both spatial and temporal information at different scales. We test the method on two subsets of a hyper-temporal thermal infra-red (TIR) dataset. Both subsets are acquired from the SEVIRI instrument onboard the Meteosat-9 geostationary satellite; they cover areas with different spatiotemporal TIR variability. We impose artificial fluctuations on the original data and apply a window-based technique to retrieve them from the normalized time series. We show that localized short-term fluctuations as low as 2K, which were obscured by large-scale variable patterns, can be retrieved in the normalized time series. Sensitivity of retrieval is determined by the intrinsic variability of the normalized TIR signal and by the amount of missing values in the dataset. Finally, we compare our approach with widely used techniques of statistical and spectral analysis and we discuss the improvements introduced by our method. HighlightsWe introduce a normalization approach for detection of extremes.We consider both the spatial and temporal dimensions of geophysical data.We apply the method and test its sensitivity on hyper-temporal satellite data.

Sentinel-2 is ESA's medium spatial resolution (10–60 m) super-spectral instrument aimed at ensuring data continuity for global land surface monitoring of Landsat and SPOT. Several simulation studies have been conducted that show the potential of Sentinel-2 for estimating biophysical and biochemical parameters such as leaf area index, chlorophyll and nitrogen, and spectral products such as the red edge position and NDVI time series, providing data continuity for a number of other operational sensors. This paper aims at simulating Sentinel-2 products that are relevant to the geology and soil science community and we compare these to well established band ratio products from ASTER. As the basis for the simulation we use airborne hyperspectral imagery from the HyMAP sensor which were spectrally and spatially resampled to the resolutions of ASTER and Sentinel-2 using actual spectral response functions and pixel aggregate, respectively. The simulated image products demonstrate a good correspondence between ASTER and Sentinel-2 VNIR and SWIR bands. A number of band ratios are proposed for Sentinel-2 to derive the following products: ferric iron, ferrous iron, laterite, gossan, ferrous silicate and ferric oxides. These are compared to established band ratios of real ASTER data as well as simulated (from HyMAP) ASTER data and they correlate favourably. Investigating the spatial patterns reveals that there is a good match between the proposed Sentinel-2 band ratio products and those of ASTER. The resulting band ratio products are compared to the local geologic map of the imaged hydrothermal area (the Rodalquilar mining area, Cabo de Gata volcanic field, SE Spain). It is shown that they support the existing conceptual geologic model of the epithermal deposit.

A graphics processing unit (GPU) can perform massively parallel computations at relatively low cost. Software interfaces like NVIDIA CUDA allow for General Purpose computing on a GPU (GPGPU). Wrappers of the CUDA libraries for higher-level programming languages such as MATLAB and IDL allow its use in image processing. In this paper, we implement GPGPU in IDL with two distance measures frequently used in image classification, Euclidean distance and spectral angle, and apply these to hyperspectral imagery. First we vary the data volume of a synthetic dataset by changing the number of image pixels, spectral bands and classification endmembers to determine speed-up and to find the smallest data volume that would still benefit from using graphics hardware. Then we process real datasets that are too large to fit in the GPU memory, and study the effect of resulting extra data transfers on computing performance. We show that our GPU algorithms outperform the same algorithms for a central processor unit (CPU), that a significant speed-up can already be obtained on relatively small datasets, and that data transfers in large datasets do not significantly influence performance. Given that no specific knowledge on parallel computing is required for this implementation, remote sensing scientists should now be able to implement and use GPGPU for their data analysis.

Spectral matching algorithms, such as the Spectral Angle Mapper (SAM), utilize the spectral similarity between individual image pixel spectra and a spectral reference library with known components. Here, we illustrate and quantify the effects that different sources of reference libraries have on SAM classification results. Synthetic images of three mineral endmembers were classified by using reference libraries derived from airborne hyperspectral imagery, ground spectra (Portable Infrared Mineral Analyser), and from a standard library (United States Geologic Survey). Results show that the source of the reference library strongly influences the classification results if all available wavelengths are used. This effect can be partially neutralized by using appropriate preprocessing methods. Two different types of spectral subsetting of the data, two types of continuum removal, and a combination thereof were tested. Best results were achieved by using a feature subset (i.e., limiting the input wavelengths to the diagnostic absorption features). This increased the average classification accuracy from 74% to 95% (ground spectral library) and from 68% to 94% (standard library).

A common challenge in remote sensing is the classification of objects that are spectrally similar but represent physically different types of ground cover. In this paper, we describe and apply three complementary shape measures to classify morphologically different waterbodies in a Landsat image. Image segmentation was used to create objects of image pixels containing water, and shape measures were calculated for all obtained objects. A shape-based, a spectra-based and a combined spatial-spectral classification were carried out on a subset of the image using endmembers acquired outside the subset. The spectral classification was based on Euclidean distance. The shape-based and combined spectral-shape classification were based on vector angle, as the chosen shape measures are influenced by the image lattice and could only be used as a relative measure. The results of this approach are discussed and compared to an expert interpretation of the same dataset. Results show that shape measures are affected by image resolution and should be used as a relative measure when objects consist of 500 pixels or less. Although the combined spectral-shape classification was not satisfactory and needs more research, the classification that is solely based on shape measures can distinguish spectrally identical waterbodies and had a score of 94% compared to the expert classification.

Edge operators are widely used on gray-level images and are recently improved to work with multispectral and even hyperspectral imagery. The high spectral information content in hyperspectral images allows a detailed description of boundaries and thus a supervised boundary detection. In this letter, we describe a template matching algorithm for the detection of fuzzy and crisp boundaries. For this purpose, the template has a one-dimensional design consisting of two different spectra. This template is matched to a remote sensing image by moving and rotating the template over the image. A statistical spatial and spectral fit of the template is calculated for every position and orientation. Important steps in this approach are the design of a template according to our knowledge of a boundary, and, mainly depending on the template design, the interpretation of the algorithm output. The algorithm has been used for the detection of boundaries between selected mineral assemblages in a hyperspectral image that covers a hydrothermal alteration system. Results show that the algorithm successfully detects the boundaries that had been defined in the templates. In addition, it is shown that rotation of the template in the algorithm reveals information on the type of boundary (crisp or fuzzy) and identifies pixels where only one of the template endmembers is present

Reflectance spectra of carbonate minerals in the shortwave infrared (SWIR) and thermal infrared (TIR) wavelength regions contain a number of diagnostic absorption features. The shape of these features depends on various physical and chemical parameters. To accurately identify carbonate minerals or rocks in pure and mixed form, it is necessary to analyze the effects of the parameters on spectral characteristics. In this study, we analyzed spectral absorption feature characteristics of calcite and dolomite in the SWIR (features at 2.3 and 2.5 μm) and TIR (features at 11.5 and 14 μm) wavelength regions, as a function of grain size and carbonate mineral mixtures. Results showed that varying grain sizes and mineral contents in the sample, influence reflectance values and absorption feature characteristics. Absorption band positions of pure and mixed calcite and dolomite in the SWIR and TIR regions for both features were displaced slightly as observed in previous studies. The band positions of calcite and dolomite varied relative to grain size only in the TIR region. These positions shifted to longer wavelengths for the feature at 11.5 μm and to shorter wavelengths for the feature at 14 μm from fine to coarse grain size. The band positions of calcite-dolomite mixtures in the SWIR and TIR regions were determined by the quantity of calcite and dolomite in the sample. These results can be applied for the identification of pure and mixed calcite and dolomite, as well as estimating the relative abundance of both minerals with different grain size and mineral mixtures in a synthetic sample or rock. They can also be used as a preliminary proxy for assessing dolomitization patterns in carbonate rocks.

Most endmember extraction algorithms are based on the spectral properties of the dataset only to discriminate between the pixels. Endmembers with distinct spectral profiles or high spectral contrast are easier to detect, whereas the endmembers having low spectral contrast with respect to the whole image are difficult to determine. The spatial-spectral integration approach searches for endmembers by analyzing the image in subsets such that it increases the local spectral contrast of the low contrast endmembers and increases their odds of selection. Spatial spectral integration process utilizes Hyperspectral subspace identification by minimum error (HySime) to determine a set of locally defined eigenvectors explaining the maximum variability of the subsets of the image. The image data is then projected onto these locally defined eigenvectors which produces a set of candidate endmember pixels. The candidate endmember pixels, that are spectrally similar and having similar spatial coordinates, are averaged together and grouped into different endmember classes. The method is applied to spaceborne hyperspectral dataset to illustrate the effects of using spatial measures in the process of endmember extraction. The spatial-spectral integration results show that the endmember pixels obtained by imposing spatial constraints are cleaner and more representative of the land use land cover classes.

Leakage of hydrocarbon has a large economic and environmental impact. Traditional methods for investigating leakage and resulting pollution, such as drilling, are destructive, time consuming and expensive. Remote sensing is an alternative that is non-destructive and has been been tested extensively for exploration of onshore hydrocarbon reservoirs and detection of hydrocarbons at the Earth’s surface. In this research, a leaking pipeline is investigated through field reflectance spectrometry and the findings are validated with traditional drilling and geophysical measurements. The measurements show a significant increase of vegetation anomalies on the pipeline with respect to areas further away. The observed anomalies are positively related to hydrocarbon pollution through chemical analysis of drillings. Subsurface geophysical measurements show a large correlation with observed surface vegetation stress, enhancing the identification of hydrocarbon-related vegetation stress through spectroscopy.

Presence of expansive soils in construction sites has serious implications on planning, design, construction, maintenance, and overall performance especially of lightweight engineering infrastructures. Such soils are particularly susceptible to considerable volume changes in response to moisture content fluctuations following seasonal climatic variations. This property can cause severe damages to infrastructures unless proper measures are taken in their design. Identification of expansive soils and characterization of their anticipated behavior is thus important for site selection, design, and construction. In this study, specific expansive soil engineering parameters; consistency limits (liquid limits (LL), plastic limits (PL) and plasticity indices (PI)), free swell (FS), cation exchange capacity (CEC) and California bearing strength (CBR) were measured in a soil mechanics laboratory. Reflectance spectra of each soils sample were acquired in a remote sensing laboratory using ASD fieldspec full range spectrometer. A multivariate calibration method, partial least squares regression (PLSR) analysis, was used to relate engineering parameters and spectral parameters extracted from the reflectance spectra of expansive soils. Correlation coefficients obtained showed that a large portion of the variation in the engineering parameters (e.g. r=0.85, 0.86 for CEC and LL respectively) could be accounted for by the spectral parameters. The results indicate potential of spectroscopy in providing estimates of engineering parameters of expansive soils (e.g. subgrade characteristics), which can be useful in site selection, route planning and search for construction materials (borrow, subbase etc).

The OMEGA instrument onboard of ESA's Mars Express mission is the first hyperspectral sensor that has collected data from Mars. The OMEGA team has shown that Mars has considerable surface compositional variation. Spectral interpretation and mineral mapping, however, is difficult on a pixel-by-pixel basis due to sensor noise, an atmosphere dominated by carbon dioxide and especially an unknown surface cover. An object- based segmentation approach is for datasets that are acquired in areas from which we do not have a-priori knowledge useful to ignore the scene-wide effects of the unknown atmosphere and to enhance the spectral contrast of the planet's surface, without any human bias. Unlike common segmentation procedures where distances in feature space are used for pixel similarity criteria, the OMEGA data is segmented using similarity criteria based on spectral absorption feature parameters such as position, depth and area. This paper shows the first results of an object-based processing of OMEGA data and discusses possibilities of future development.

This study presents a novel approach in combining textural information from the panchromatic band with spectral information from the multispectral bands for improved image classification. Firstly, we develop a texture measure based on wavelet coefficients of the panchromatic band that can be aggregated to the resolution of the multispectral bands. Secondly, we combine the texture measures with the spectral information in the multispectral bands in a fuzzy classification framework. Thirdly, we illustrate our approach with a case study of vegetation and land cover classification based on a Quickbird image of subantarctic Macquarie Island.

Expansive soils are major geotechnical hazards that pose several problems on engineering structures causing billions of dollars of damage in many parts of the world especially at places where there are significant climatic differences between dry and wet periods (Gourley et al., 1993; Nelson and Miller, 1992). Such soils expand or swell when moistened and shrink and crack when dried which involves tremendous volume changes. Thus, they are of significant concern in the construction sector. Identification of expansive soils and characterization of their anticipated behavior should be done exhaustively for proper site selection, design, and construction of civil engineering infrastructures. Particular attention is required especially when dealing with lightweight structures like road infrastructures, airfields, and small buildings, etc. since the uplift pressure from the soil swell exceeds the downward pressure exerted from such structures. However, the conventional standard methods of assessing the geotechnical properties of expansive soils are expensive, labor intensive and time consuming. In addition it is not possible to get continuous representation of soil masses in space. Thus, the presence and spatial distribution of these soils can be overlooked and their types might not be precisely determined. This can lead to under-sampling of sites and subsequent inadequate design specifications. Hence, there is a need to identify and characterize expansive soils in a cheaper, rapid and continuous method as compared to the conventional methods of assessing the geotechnical properties of these soils. For this study a total of 80 disturbed soil samples were collected from the eastern part of Addis Ababa city. Much construction activities are taking place in the study area and problems due to expansive soils are frequently reported. Stratified random sampling was used to obtain the required samples, and disturbed soil samples were taken from each sampling location trough hand augering and digging. We measured specific expansive soil engineering parameters namely; Atterberg limits (liquid limits, plastic limits and plasticity indices), free swell and cation exchange capacities in a soil mechanics laboratory. We also acquired the reflectance spectra of each soil sample using the ASD fieldspec spectrometer that covers the 350 nm to 2500 nm wavelength region of the electromagnetic spectrum to establish a relationship between the engineering parameters and the soil reflectance spectra. Analyzing the engineering parameters as well as the spectral characteristics of the soil samples has revealed that the soil samples have a large variation in their expansion potential. Statistical links were established between engineering parameters of expansive soils and absorption feature parameters at specific wavelength regions (~1400 nm, ~1900 nm and ~2200 nm). A Multivariate calibration method, partial least squares regression (PLSR) analysis was used to construct empirical prediction models to enable the estimation of engineering parameters of expansive soils from absorption feature parameters calculated from those specific wavelength regions. Correlation coefficients (r) obtained showed that large portions of the variation in the engineering parameters could be accounted for by the spectral parameters (r = 0.85, 0.86, 0.68, 0.83 and 0.64) for CEC, LL, PL, PI and FS respectively). Apart from the high correlation coefficients, small root mean square errors of calibration (RMSEC) and prediction (RMSEP), standard error of calibration (SEC) and prediction (SEP) and minimum bias were obtained indicating the potential of spectroscopy in deriving engineering parameters of expansive soils from their respective reflectance spectra, and hence its potential applicability in supporting the geotechnical investigations of such soils. Results indicate that spectroscopy can be used to identify and subsequently characterize expansive soils and their engineering behaviors that can be explained by the measured engineering parameters. This contribution of spectroscopic methods (cheaper, fast and yet capable of covering large area) at the reconnaissance stage of site investigation will assist to tailor the detailed field investigation into site-specific needs in an effort to assess the feasibility of intended projects.

Object detection in remote sensing studies can be improved by incorporating spatial knowledge of an object in an image processing algorithm. This paper presents an algorithm based on sequential Hough transforms, which aims to detect botanical and mineralogical alterations that result from natural seepage of carbon dioxide and light hydrocarbons. As the observed alterations are not unique for gas seepages, these halos can only be distinguished from the background by their specific spatial pattern: the alterations are present as halos that line up along geological lineaments in the shallow subsurface. The algorithm is deployed in three phases: a prior spectral classification followed by two serialized Hough transforms. The first Hough transform fits circles through spectrally optimal matching pixels. Next, the centers of the detected circles are piped into the second Hough transform that detects points that are located on a line. Results show that our algorithm is successful in detecting the alteration halos. The number of false anomalies is sufficiently reduced to allow an objective detection based on field observations and spectral measurements.

Wetlands are particularly sensitive environments receiving attention from the natural sciences community due to their wealth of both flora and fauna, and often considered as natural parks. In the Tablas de Daimiel (La Mancha, Central Spain), Digital Airborne Imaging Spectrometer data (DAIS 7915) have been analyzed to map geological processes on areas around the receding wetland which have never been flooded by water in the past. Sediments permanently exposed to the atmosphere dehydrate and oxide, developing different mineralogical associations arranged on planation surfaces. Such planation surfaces are key in the geological knowledge of recent climate change and landscape evolution. Progressive iron oxide/hydroxide rate and decarbonation can be spectrally followed on the Holocene sands framing the current marshy area. Such mineralogical changes are geologically registered on flat surfaces at different heights over the receding shore of the paleolake. Interacting erosion and sedimentation processes are responsible for the development of the flat morphological surfaces with increasing dryness. Maps are built for four different morphological units consisting of planation surfaces following chronologically the receding marsh during the last 2000 years before the present. Interactive spectral responses of mineralogical associations are described on the imagery, field and laboratory spectra.