Detecting anomalous CO2 flux using space borne spectroscopy

Over the time-scale, earth's atmospheric CO₂ concentration has varied and that is mostly determined by balance among the geochemical processes including burial of organic carbon in sediments, silicate rock weathering and volcanic activity. The best recorded atmospheric CO₂ variability is derived from Vostok ice core that records last four glacial/interglacial cycles. The present CO₂ concentration of earth's atmosphere has exceeded far that it was predicted from the ice core data. Other than rapid industrialization and urbanization since last century, geo-natural hazards such as volcanic activity, leakage from hydrocarbon reservoirs and spontaneous combustion of coal contribute a considerable amount of CO₂ to the atmosphere. Spontaneous combustion of coal is common occurrence in most coal producing countries and sometimes it could be in an enormous scale. Remote sensing has already proved to be a significant tool in coalfire identification and monitoring studies. However, coalfire related CO₂ quantification from remote sensing data has not endeavoured yet by scientific communities because of low spectral resolution of commercially available remote sensing data and relatively sparse CO₂ plume than other geological hazards like volcanic activity. The present research has attempted two methods to identify the CO₂ flux emitted from coalfires in a coalmining region in north China. Firstly, a band rationing method was used for column atmospheric retrieval of CO₂ and secondly atmospheric models were simulated in fast atmospheric signature code (FASCOD) to understand the local radiation transport and then the model was implemented with the inputs from hyperspectral remote sensing data. It was observed that retrieval of columnar abundance of CO₂ with the band rationing method is faster as less simulation required in FASCOD. Alternatively, the inversion model could retrieve CO₂ concentration from a (certain) source because it excludes the uncertainties in the higher altitude.     

Shape analysis and multispectral classification in geological remote sensing

This paper discusses the usage of mathematical morphology in image processing of remotely-sensed data for geologic interpretation. Particular attention is given to noise-reducing transformations of spectral bands before and after different methods of classification, and to the usage of textural context. The development of a viable processing strategy requires a multidisciplinary approach and expert knowledge in different areas: (a) geology, geomorphology, and vegetation in a study area, (b) properties of the sensor for imagery photointerpretation, (c) spectral/spatial properties of the digital data within an integrated dataset (remote sensing and ancillary data), and (d) data-processing tools including mathematical morphology theory. Examples of geometric characterization of Canadian LANDSAT scenes are described in which shape measurements are obtained using a PC-based hybrid image-processing and geographic information system, termed ILWIS, which was developed at ITC, in the Netherlands. Classes from supervised and unsupervised classification are compared to guide in geological mapping. Classes over individual occurrences of broad vegetation-landform units are studied to aid in environmental mapping. Field knowledge is the context necessary to construct expert procedures to drive sequences of data-processing steps toward a target result such as optimal classification, enhancement, or feature extraction. The interaction between expert rules and the image-processing steps can be based on synthetic measurements of shape to quantize the information either spatially or spectrally. Many useful geometrical transformations of spatially-distributed data are extensions or generalizations of spatial analysis functions typical of geographic information systems.     

Deriving Optimal Exploration Target Zones on Mineral Prospectivity Maps

This paper describes a quantitative methodology for deriving optimal exploration target zones based on a probabilistic mineral prospectivity map. The methodology is demonstrated in the Rodalquilar mineral district in Spain. A subset of known occurrences of mineral deposits of the type sought was considered discovered and then used as training data, and a map of distances to faults/fractures and three band ratio images of hyperspectral data were used as layers of spatial evidence in weights-of-evidence (WofE) modeling of mineral prospectivity in the study area. A derived posterior probability map of mineral deposit occurrence showing non-violation of the conditional independence assumption and having the highest prediction rate was then put into an objective function in simulated annealing in order to derive a set of optimal exploration focal points. Each optimal exploration focal point represents a pixel or location within a circular neighborhood of pixels with high posterior probability of mineral deposit occurrence. Buffering of each optimal exploration focal point, based on proximity analysis, resulted in optimal exploration target zones. Many of these target zones coincided spatially with at least one occurrence of mineral deposit of the type sought in the subset of cross-validation (i.e., presumed undiscovered) mineral deposits of the type sought. The results of the study showed the usefulness of the proposed methodology for objective delineation of optimal exploration target zones based on a probabilistic mineral prospectivity map.     

MERIS and the red-edge position

The Medium Resolution Imaging Spectrometer (MERIS) is a payload component of Envisat-1. MERIS will be operated over land with a standard 15 band setting acquiring images with a 300 m spatial resolution. The red-edge position (REP) is a promising variable for deriving foliar chlorophyll concentration, which plays an important role in ecosystem processes. The objectives of this paper are: (1) to study which factors effect the REP of vegetation, (2) to study whether this REP can be derived from the MERIS standard band setting and (3) to show what REP represents at the scale of MERIS data. Two different data sets were explored for simulating the REP using MERIS bands: (1) simulated data using reflectance models and (2) airborne reflectance spectra of an agricultural area obtained by the airborne visible-infrared imaging spectrometer (AVIRIS). A “linear method”, assuming a straight slope of the reflectance spectrum around the midpoint of the slope, was a robust method for determining the REP and the MERIS bands at 665, 708.75, 753.75 and 778.75 nm could be used for applying the “linear method” for REP estimation. Results of the translation to the scale of MERIS data were very promising for applying MERIS at, for instance, the ecosystem level.     

Pleistocene Rhine–Thames landscapes: geological background for hominin occupation of the southern North Sea region

This paper links research questions in Quaternary geology with those in Palaeolithic archaeology. A detailed geological reconstruction of The Netherlands' south‐west offshore area provides a stratigraphical context for archaeological and palaeontological finds. Progressive environmental developments have left a strong imprint on the area's Palaeolithic record. We highlight aspects of landscape evolution and related taphonomical changes, visualized in maps for critical periods of the Pleistocene in the wider southern North Sea region. The Middle Pleistocene record is divided into two palaeogeographical stages: the pre‐Anglian/Elsterian stage, during which a wide land bridge existed between England and Belgium even during marine highstands; and the Anglian/Elsterian to Saalian interglacial, with a narrower land bridge, lowered by proglacial erosion but not yet fully eroded. The Late Pleistocene landscape was very different, with the land bridge fully dissected by an axial Rhine–Thames valley, eroded deep enough to fully connect the English Channel and the North Sea during periods of highstand. This tripartite staging implies great differences in (i) possible migration routes of herds of herbivores as well as hominins preying upon them, (ii) the erosion base of axial and tributary rivers causing an increase in the availability of flint raw materials and (iii) conditions for loess accumulation in northern France and Belgium and the resulting preservation of Middle Palaeolithic sites. Copyright © 2011 John Wiley & Sons, Ltd.     

Imaging spectrometry for geological remote sensing

Without use of imaging spectrometry, imaging of the Earth's surface from aircraft and from spacecraft is hampered by the low spectral resolution and limited number of spectral bands, typically less than 10 bands of 100 to 200 nm width. Imaging spectrometry in remote sensing concerns the acquisition of image data in many narrow (< 40 nm wide) contiguous spectral bands with the ultimate goal of producing detailed spectral reflectance curves for each pixel in the image. Many minerals and rocks have unique spectral signatures with characteristic absorption features that are 20 to 40 nm wide. Imaging spectrometers allow to depict these narrow features and thus map surface mineralogy based on spectral image characterization. This paper gives a review of imaging spectrometry and addresses the following topics: airborne and spaceborne systems available, spectral and geometric data pre-processing, atmospheric correction, techniques for thematic data analysis, and applications in the field of geological remote sensing. In the final section a case study is described where imaging spectrometer data is used for mapping surface mineralogy in a hydrothermal alteration system, thus guiding gold exploration.