Self-organised clustering for road extraction in classified imagery

The extraction of road networks from digital imagery is a fundamental image analysis operation. Common problems encountered in automated road extraction include high sensitivity to typical scene clutter in high-resolution imagery, and inefficiency to meaningfully exploit multispectral imagery (MSI). With a ground sample distance (GSD) of less than 2 m per pixel, roads can be broadly described as elongated regions. We propose an approach of elongated region-based analysis for 2D road extraction from high-resolution imagery, which is suitable for MSI, and is insensitive to conventional edge definition. A self-organising road map (SORM) algorithm is presented, inspired from a specialised variation of Kohonen's self-organising map (SOM) neural network algorithm. A spectrally classified high-resolution image is assumed to be the input for our analysis. Our approach proceeds by performing spatial cluster analysis as a mid-level processing technique. This allows us to improve tolerance to road clutter in high-resolution images, and to minimise the effect on road extraction of common classification errors. This approach is designed in consideration of the emerging trend towards high-resolution multispectral sensors. Preliminary results demonstrate robust road extraction ability due to the non-local approach, when presented with noisy input.     

Non-stationary Approaches for Mapping Terrain and Assessing Prediction Uncertainty

It is well known that terrain may vary markedly over small areas and that statistics used to characterise spatial variation in terrain may be valid only over small areas. In geostatistical terminology, a non-stationary approach may be considered more appropriate than a stationary approach. In many applications, local variation is not accounted for sufficiently. This paper assesses potential benefits in using non-stationary geostatistical approaches for interpolation and for the assessment of uncertainty in predictions with implications for sampling design. Two main non-stationary approaches are employed in this paper dealing with (1) change in the mean and (2) change in the variogram across the region of interest. The relevant approaches are (1) kriging with a trend model (KT) using the variogram of residuals from local drift and (2) locally-adaptive variogram KT, both applied to a sampled photogrammetrically derived digital terrain model (DTM). The fractal dimension estimated locally from the double-log variogram is also mapped to illustrate how spatial variation changes across the data set. It is demonstrated that estimation of the variogram of residuals from local drift is worthwhile in this case for the characterisation of spatial variation. In addition, KT is shown to be useful for the assessment of uncertainty in predictions. This is shown to be true even when the sample grid is dense as is usually the case for remotely-sensed data. In addition, both ordinary kriging (OK) and KT are shown to provide more accurate predictions than inverse distance weighted (IDW) interpolation, used for comparative purposes.     

Atmospheric, oceanic and hydrological contributions to seasonal variations in length of day

 The annual and semiannual residuals derived in the axial angular momentum budget of the solid Earth–atmosphere system reflect significant signals. They must be caused by further excitation sources. Since, in particular, the contribution for the wind term from the atmospheric layer between the 10 and 0.3 hPa levels to the seasonal variations in length of day (LOD) is still missing, it is necessary to extend the top level into the upper stratosphere up to 0.3 hPa. Under the conservation of the total angular momentum of the entire Earth, variations in the oceanic angular momentum (OAM) and the hydrological angular momentum (HAM) are further significant excitation sources at seasonal time scales. Focusing on other contributions to the Earth's axial angular momentum budget, the following data are used in this study: axial atmospheric angular momentum (AAM) data derived for the 10–0.3 hPa layer from 1991 to 1997 for computing the missing wind effects; axial OAM functions as generated by oceanic general circulation models (GCMs), namely for the ECHAM3 and the MICOM models, available from 1975 to 1994 and from 1992 to 1994, respectively, for computing the oceanic contributions to LOD changes, and, concerning the HAM variations, the seasonal estimates of the hydrological contribution as derived by Chao and O'Connor [(1988) Geophys J 94: 263–270]. Using vector representation, it is shown that the vectors achieve a close balance in the global axial angular momentum budget within the estimated uncertainties of the momentum quantities on seasonal time scales. Received: 6 April 2000 / Accepted: 13 December 2000     

Evaluation of significant sources influencing the variation of physico-chemical parameters in Port Blair Bay,South Andaman,India by using multivariate statistics

Port Blair is the capital city of Andaman & Nicobar Islands, the union territory of India. More than 50% of the population of these islands lives around Port Blair Bay. Therefore the anthropogenic effects in the bay water were studied for monitoring purpose from seven stations. Physico-chemical parameters of seawater were analyzed in samples collected once in every 3 months for 2 years from seven sampling stations located in Port Blair Bay, South Andaman Island to evaluate the spatial and tidal variation. Cluster analysis and factor analysis were applied to the experimental data in an attempt to understand the sources of variation of physico-chemical parameters. In cluster analysis, the stations Junglighat Bay and Phoenix Bay having high anthropogenic influence formed a separate group. The factors obtained from factor analysis indicated that the parameters responsible for physico-chemical variations are mainly related to land run-off, sewage outfall and tidal flow.     

Origin of chaos in the Prometheus-Pandora system

We demonstrate that the chaotic orbits of Prometheus and Pandora are due to interactions associated with the 121:118 mean motion resonance. Differential precession splits this resonance into a quartet of components equally spaced in frequency. Libration widths of the individual components exceed the splitting, resulting in resonance overlap which causes the chaos. Mean motions of Prometheus and Pandora wander chaotically in zones of width 1.8 and 3.1 deg yr⁻¹, respectively. A model with 1.5 degrees of freedom captures the essential features of the chaotic dynamics. We use it to show that the Lyapunov exponent of 0.3 yr⁻¹ arises because the critical argument of the dominant member of the resonant quartet makes approximately two separatrix crossings every 6.2 year precessional cycle.     

Anthropogenic relief changes in a long-lasting lignite mining area (‘Ville’, Germany) derived from historic maps and digital elevation models

Humans constitute one of the main geomorphological agents in modern times. As an example, post-mining regions represent a typical landscape of the Anthropocene. Strong relief modifications are particularly obvious with open pit mining. However, many existing mining areas are lacking detailed pre-mining information for the quantification of anthropogenic relief changes, which is a considerable challenge in regions with historic mining activities. Here, the Ville (Rhenish lignite district, Germany) is used to quantify surface mining induced relief changes in one of the oldest and currently largest lignite districts in Europe. Historical maps from first geodetic mapping in 1893 enabled construction of a historic digital elevation model to quantify the relief changes in comparison to elevation data from 2000 and 2015. The vertical accuracy of the historic data is remarkably high, with relief differences < 2 m in areas not affected by mining. In total, 49.2% of the investigated area (184 km²) shows a relief deficit and 14.5% has positive relief differences. Absolute changes account for more than 80 m heightening (dumpsites of overburden) and lowering of the natural relief (pits). Besides these altitudinal changes, overall steeper slopes are significant for the new topography, but levelling exists likewise. The spatial variabilities are discussed in the context of the regional geology and the mining techniques. Undoubtedly, such large-scale anthropogenic relief changes persist for a very long time and will last as a human legacy far into the future. Only the detailed reconstruction of the pre-mining relief offers the ability to clarify the dimension of humans as geomorphological agents and to understand landscape perception. Due to the fact that the impact of open pit mining has such a large vertical and horizontal extension, their consideration as part of anthropogeomorphology can significantly contribute to support future Critical Zone research in the Anthropocene.     

Estimating coseismic deformations from near source strong motion records: methods and case studies

Digital strong-motion accelerographs have opened up the possibility of extracting ground motion characteristics at much lower frequencies than was offered by analogue instruments. High-quality digital data obtained close to the faults have tempted several efforts to retrieve permanent ground displacements after an earthquake. Such attempts have been partly successful, and somewhat subjective, the main reason being the presence of baseline offsets in the accelerometric data. We review existing methods for such applications, discuss their limitations and propose a more objective and improved scheme to make baseline adjustments and obtain permanent displacements. The proposed technique is applied to 26 digital recordings from the 29 May 2008 Ölfus Earthquake in South Iceland and 9 recordings from the 1999 Chi-Chi, Taiwan, Earthquake, and the permanent displacements obtained are compared with published results and GPS measurements from nearby stations. Our case studies show that the proposed technique, in addition to being simple and objective, is effective in making adjustments for baseline errors in accelerometric data.