Neo-Atlantis: The Netherlands under a 5-m sea level rise

What could happen to the Netherlands if, in 2030, the sea level starts to rise and eventually, after 100 years, a sea level of 5 m above current level would be reached? This question is addressed by studying literature, by interviewing experts in widely differing fields, and by holding an expert workshop on this question. Although most experts believe that geomorphology and current engineering skills would enable the country to largely maintain its territorial integrity, there are reasons to assume that this is not likely to happen. Social processes that precede important political decisions – such as the growth of the belief in the reality of sea level rise and the framing of such decisions in a proper political context (policy window) – evolve slowly. A flood disaster would speed up the decision-making process. The shared opinion of the experts surveyed is that eventually part of the Netherlands would be abandoned.     

Hyperspectral image classification by a variable interval spectral average and spectral curve matching combined algorithm

Classification of hyperspectral images has been receiving considerable attention with many new applications reported from commercial and military sectors. Hyperspectral images are composed of a large number of spectral channels, and have the potential to deliver a great deal of information about a remotely sensed scene. However, in addition to high dimensionality, hyperspectral image classification is compounded with a coarse ground pixel size of the sensor for want of adequate sensor signal to noise ratio within a fine spectral passband. This makes multiple ground features jointly occupying a single pixel. Spectral mixture analysis typically begins with pixel classification with spectral matching techniques, followed by the use of spectral unmixing algorithms for estimating endmembers abundance values in the pixel. The spectral matching techniques are analogous to supervised pattern recognition approaches, and try to estimate some similarity between spectral signatures of the pixel and reference target. In this paper, we propose a spectral matching approach by combining two schemes—variable interval spectral average (VISA) method and spectral curve matching (SCM) method. The VISA method helps to detect transient spectral features at different scales of spectral windows, while the SCM method finds a match between these features of the pixel and one of library spectra by least square fitting. Here we also compare the performance of the combined algorithm with other spectral matching techniques using a simulated and the AVIRIS hyperspectral data sets. Our results indicate that the proposed combination technique exhibits a stronger performance over the other methods in the classification of both the pure and mixed class pixels simultaneously.     

It is a moral issue: The relationship between environmental self-identity,obligation-based intrinsic motivation and pro-environmental behaviour

In order to effectively mitigate climate change, people need to adopt environmentally-friendly actions. We argue that some people act in an environmentally-friendly manner without external incentives to do so, but rather because they are intrinsically motivated to do so. There is some initial evidence to suggest that this is particularly likely for people with a strong environmental self-identity. However, not much is known about how environmental self-identity influences pro-environmental actions. In this research, we aimed to test whether, and if so, via which process environmental self-identity is related to environmentally-friendly behaviour. We conducted three studies to test our hypotheses. In the first study, our hypotheses were confirmed in a sample of the general population using a correlational design. In the second study, we replicated our findings with a different indicator of moral obligation and with a different dependent variable. In the third study, we tested our model in an experimental design. Again, we found support for our hypotheses that environmental self-identity is related to one's obligation-based intrinsic motivation (that is, feelings of moral obligation) to act pro-environmentally, which in turn affects pro-environmental actions. As expected, the obligation-based intrinsic motivation mediates the relationship between environmental self-identity and environmentally-friendly behaviour. Our findings suggest that strengthening environmental self-identity may be a cost-effective way to promote pro-environmental actions, as people with a strong environmental self-identity are likely to act in an environmentally-friendly manner without an external incentive to do so.     

Quantifying engineering parameters of expansive soils from their reflectance spectra

Occurrence 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 large volume changes in response to moisture content fluctuations following seasonal climatic variations. This can lead to deformation of structures built up on them. For this study, soil samples were collected from the eastern part of Addis Ababa. Specific expansive soil engineering parameters namely; Atterberg limits (LL, PL and PI), free swell and cation exchange capacity were measured in a soil mechanics laboratory. Reflectance spectra of each soil sample were acquired in a remote sensing laboratory using ASD fieldspec full range spectrometer. A multivariate calibration method, partial least squares regression (PLSR) analysis, through simple wavelength approach, was used to construct empirical prediction models for estimating engineering parameters of expansive soils from their respective reflectance spectra. Correlation coefficients of 0.90, 0.87, 0.71, 0.81 and 0.71 for CEC, LL, PL, PI and FS respectively were obtained. The correlation coefficients showed that large portion of variations in engineering parameters of expansive soils could be accounted for by spectral parameters. Apart from these 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. The results indicate potential of spectroscopy in deriving engineering parameters of expansive soils from their respective reflectance spectra and hence, its potential applicability in supporting geotechnical investigations of such soils.     

Remote sensing and petroleum seepage: a review and case study

When reservoirs leak, varying quantities of oil and gas migrate to the surface as macroseeps, which are visible, and microseeps, which are invisible. This review article describes the mechanisms of seepage and the resulting surface manifestations in relation to optical high-resolution remote sensing data. Oil pools and tar deposits (macroseeps) often can be detected directly by remote sensing. Microseeps are more difficult to study using remote sensing, but they give rise to vegetation stress, and cause geochemical alterations in soil and rocks, which can be studied indirectly using hyperspectral sensors. An integrated methodology is presented to combine various geoscience and remote sensing datasets for seepage detection. A combination of red-edge modelling algorithms and spectral matching tools is identified that provides a validated technique for onshore microseepage detection. These remote sensing tools are not only important for petroleum exploration, but also have environmental implications because seeps emit greenhouse gases. A statistical data integration approach was developed based on Bayesian assumptions, which can be used to integrate hyperspectral remote sensing data, other satellite remote sensing data, and ancillary field geological and geochemical datasets, for modelling microseepage. In a case study from the Ventura basin (Santa Barbara) in Southern California, Probe-1 data from the 1998 Geosat Group Shoot are integrated with field and subsurface geological and geochemical data to predict possible sites of hydrocarbon microseepage.     

Lithological mapping and fuzzy set theory: Automated extraction of lithological boundary from ASTER imagery by template matching and spatial accuracy assessment

Lithological boundaries provide information useful for activities such as mineral and hydrocarbon exploration, water resource surveys, and natural hazard evaluation. Automated detection of lithological boundaries reduces bias inherent in expert interpretation of boundaries and thus improves the reliability of lithological mapping. The Rotation Variant Template Matching (RTM) algorithm was applied to ASTER imagery to detect pre-defined lithological boundaries. Templates incorporating the mineral combinations gypsum–calcite and calcite–illite were designed to detect boundaries between evaporites, marly limestone, and sandstone. The RTM algorithm successfully detected lithological boundaries by rotating the templates over the ASTER imagery. The accuracy of the detected boundaries was spatially assessed using fuzzy set theory. Boundaries from a published geological map and boundaries interpreted from a stereo pair of aerial photos by five experts were used as references for assessing the accuracy. A confidence region unifying spatial errors was defined for the geological map and stereo-pair interpretation to provide boundary zones from these references. The correspondence between detected boundaries and the boundary zones of the aerial photo was better than between detected boundaries and boundary zones of the geological map.     

A spectral-geophysical approach for detecting pipeline leakage

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.     

Scenario-based seismic hazard analysis using spectral element method in northeastern Pakistan

Natural Hazards - 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...