F.D. van der Meer

Imaging Spectrometry: basic principles and prospective applications

Acknowledgements About the Editors Contributors Introduction Part I: Basic principles of imaging spectrometry 1. Basic physics of spectrometry 2. Imaging spectrometry: Basic analytical techniques Part II: Prospective applications of imaging spectrometry 3. Imaging spectrometry for surveying and modelling land degradation 4. Field and imaging spectrometry for identification and mapping of expansive soils 5. Imaging spectrometry and vegetation science 6. Imaging spectrometry for agricultural applications 7. Imaging spectrometry and geological applications 8. Imaging spectrometry and petroleum geology 9. Imaging spectrometry for urban applications 10. Imaging spectrometry in the Thermal Infrared 11. Imaging spectrometry of water Acronyms Index References

Assessing the Influence of Reference Spectra on Synthetic SAM Classification Results

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).

Combining spectral signals and spatial patterns using multiple Hough transforms: An application for detection of natural gas seepages

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.

Rotation-Variant Template Matching for Supervised Hyperspectral Boundary Detection

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

Preliminary ASTER and INSAR imagery combination for mud volcano dynamics, Azerbaijan

In Azerbaijan oil mud volcanoes form on the surface as expressions of the vertical migration of oil and gas as a result of gravitationa l loading of largely unconsolidated sediments in combination with structure control and stress regime. In general it is believed that mud volcano eruptions are triggered by earthquake activity since this can cause hydrate instability and explosive dissociation of the hydrocarbons can occur. Through typical geomorphologic mud volcano vents called gryphons and salses, mud volcanoes eject argillaceous material (breccia) and build up their topography. Optical satellite images (Advanced Spaceborne Thermal Emission and Reflection - ASTER) and ground truth data from 2000 to 2002, centred on onshore Azerbaijan, are analysed using Variable Multiple Endmember Spectral Mixture Analysis (VMESMA) in combination with Interferometric Synthetic Aperture Radar (InSAR) from six ERS-2 scenes from 1996 to 1999. ASTER and InSAR imagery are used to look for evidence of mineral alterations and precursory surface deformation related to mud volcanism. Preliminary field spectral data of 5 onshore mud volcano vents show typical mineral zonations present in the mud breccia fields. ASTER image analyses on Aktharma -Pashaly mud volcano shows Al-OH mineral zonation patterns over various mud volcano vents. Initial InSAR processing for Aktharma-Pashaly shows little correlation over this particular mud volcano, which makes it hard to assess data combinations of ASTER and InSAR. Fair correlation was found for Touragai, Great - and Lesser Kjanizadag mud volcanoes showing high to moderate correlation over a time period of 3 years.

Basics of Remote Sensing

In July  the fi rst earth observation satellite was launched by the United States. In  this satellite was called Earth Resources Technology Satellite- (ERTS-), a name that held until January  when it was renamed into Landsat-. Th is fi rst earth observation satellite held a four waveband multi-spectral scanning system (MSS) aboard in two visible and two near-infrared spectral bands and three return beam vidicon (RBV) television cameras. Th is sensor wrote history as it proved to be of great importance to give remote sensing worldwide recognition as an important environmental technique (Harper, ).

Remote sensing of onshore hydrocarbon seepage: problems and solutions

Abstract Optical remote sensing has in the last two decades been extensively tested for the detection of hydrocarbons at the Earths surface. The spectral absorption features of seepage-related hydrocarbons can easily be confused with those of man-made bituminous surfaces such as tarred roads. The characteristic low albedo of bituminous surfaces can, at the same time, easily be confused with other dark surfaces such as shade. This paper presents the results of two pixel-based classifications that have been carried out on hyperspectral imagery acquired over seepage areas. The first classification algorithm is a ‘minimum distance to class means’ (MDC), which is sensitive to spectral absorption features as well as albedo differences. The second algorithm is a ‘spectral angle mapper’ (SAM), which is not sensitive to albedo differences. Both algorithms are applied for the detection of crude oil resulting from macroseepage and an anomalous halo of bare soil resulting from microseepage. The results show that, at best, only 48% and 29% of the pixels that respectively contain crude oil and seepage-related bare soil could be detected, with the inclusion of many false anomalies. Confusion mainly results from the physical characteristics of the anomalies, as these are not unique to seepages. It is concluded that remote sensing of natural hydrocarbon seepages can be improved by image processing algorithms that make use of spatial information.

Hydrocarbon micro-seepage detection from airborne hyper-spectral images by plant stress spectra based on the PROSPECT model

Abstract Hydrocarbon micro-seepage can result in vegetation spectral anomalies. Early detection of spectral anomalies in plants stressed by hydrocarbon micro-seepage could help reveal oil and gas resources. In this study, the origin of plant spectral anomalies affected by hydrocarbon micro-seepage was measured using indoor simulation experiments. We analyzed wheat samples grown in a simulated hydrocarbon micro-seepage environment in a laboratory setting. The leaf mesophyll structure (N) values of plants in oil and gas micro-seepage regions were measured according to the content of measured biochemical parameters and spectra simulated by PROSPECT, a model for extracting hydrocarbon micro-seepage information from hyper-spectral images based on plant stress spectra. Spectral reflectance was simulated with N, chlorophyll content (Cab), water content (Cw) and dry matter content (Cm). Multivariate regression equations were established using varying gasoline volume as the dependent variable and spectral feature parameters exhibiting a high rate of change as the independent variables. We derived a regression equation with the highest correlation coefficient and applied it to airborne hyper-spectral data (CASI/SASI) in Qingyang Oilfield, where extracted information regarding hydrocarbon micro-seepage was matched with known oil-producing wells.

Basic overview towards the assessment of landslide and subsidence risks along a geothermal pipeline network

An operating geothermal power plant consists of installation units that work systematically in a network. The pipeline network connects various engineering structures, e.g. well pads, separator, scrubber, and power station, in the process of transferring geothermal fluids to generate electricity. Besides, a pipeline infrastructure also delivers the brine back to earth, through the injection well-pads. Despite of its important functions, a geothermal pipeline may bear a threat to its vicinity through a pipeline failure. The pipeline can be impacted by perilous events like landslides, earthquakes, and subsidence. The pipeline failure itself may relate to physical deterioration over time, e.g. due to corrosion and fatigue. The geothermal reservoirs are usually located in mountainous areas that are associated with steep slopes, complex geology, and weathered soil. Geothermal areas record a noteworthy number of disasters, especially due to landslide and subsidence. Therefore, a proper multi-risk assessment along the geothermal pipeline is required, particularly for these two types of hazard. This is also to mention that the impact on human fatality and injury is not presently discussed here. This paper aims to give a basic overview on the existing approaches for the assessment of multi-risk assessment along geothermal pipelines. It delivers basic principles on the analysis of risks and its contributing variables, in order to model the loss consequences. By considering the loss consequences, as well as the alternatives for mitigation measures, the environmental safety in geothermal working area could be enforced.

Assessing Expansive Soil Engineering Parameters Using 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).