SPECTRAL SIGNATURE OF COUPLED FLOW IN THE REFREEZING ACTIVE LAYER,NORTHERN ALASKA

Soil temperature records obtained from the active layer above permafrost at a site in northern Alaska during autumn and winter have variance spectra inconsistent with a purely conductive heat-transfer system. Although conductive heat-transfer theory predicts that temperature fluctuations are attenuated with depth, sub-diurnal thermal variance at the 50-cm level, near the base of the active layer, exceeded that at the 10-cm level. Short segments of the temperature record were drawn from three distinct periods of soil-frost conditions: (1) at the maximum vertical development of the active layer in early autumn; (2) during frost penetration and the formation of a zero curtain in early winter; and (3) after freezeback of the active layer. The variance spectra of these time series show systematic seasonal transitions that reflect changing mechanisms of heat transfer. During the first and second periods, heat transfer by internal evaporation and condensation dominates at wavelengths in the diurnal range. The spectral traces are not strongly self-similar and the fractal dimensions indicate extreme space-filling, especially at deeper levels. Once the active layer is frozen, conductive heat transfer dominates, producing a trend toward self-similarity. Both the thermal variance and the fractal dimension decrease with depth in the frozen regime. [Key words: Alaska, active layer, coupled flow, fractal dimension, frozen ground, heat transfer, permafrost, soil freezing, spectral analysis, zero curtain.]     

A method for discovery and analysis of temporal patterns in complex event data

Pattern analysis techniques currently common within geography tend to focus either on characterizing patterns of spatial and/or temporal recurrence of a single event type (e.g., incidence of flu cases) or on comparing sequences of a limited number of event types where relationships between events are already represented in the data (e.g., movement patterns). The availability of large amounts of multivariate spatiotemporal data, however, requires new methods for pattern analysis. Here, we present a technique for finding associations among many different event types where the associations among these varying event types are not explicitly represented in the data or known in advance. This pattern discovery method, known as T-pattern analysis, was first developed within the field of psychology for the purpose of finding patterns in personal interactions. We have adapted and extended the T-pattern method to take the unique characteristics of geographic data into account and implemented it within a geovisualization toolkit for an integrated computational-geovisual environment we call STempo. To demonstrate how T-pattern analysis can be employed in geographic research for discovering patterns in complex spatiotemporal data, we describe a case study featuring events from news reports about Yemen during the Arab Spring of 2011–2012. Using supplementary data from the Global Database of Events, Language, and Tone, we briefly summarize and reference a separate validation study, then evaluate the scalability of the T-pattern approach. We conclude with ideas for further extensions of the T-pattern technique to increase its utility for spatiotemporal analysis.     

Shear and vorticity in inflationary Brans–Dicke cosmology with lambda-term

We find a solution for exponential inflation in Brans–Dicke cosmology endowed with a cosmological term, which includes time-varying shear and vorticity. We find that the scalar field and the scale factor increase exponentialy while shear, vorticity, energy density, cosmic pressure and the cosmological term decay exponentialy for negative beta, where beta is defined in the text.     

Resistance to relocation in flood-vulnerable coastal areas: a proposed composite index

The increasing number of coastal floods in recent years in France has resulted in the design of new adaptation principles for the most endangered coastal areas. The aim of the government is to reduce the vulnerability of these areas by relocating property and infrastructure. These measures have, however, come up against considerable opposition from the population concerned. Using a survey of 421 inhabitants of Hyères, a coastal town in the South of France, this article proposes the study of resistance to relocation through the creation of an index for resistance that incorporates attachment to place, residential mobility and risk perception. The results show a correlation for the index and distance from the sea that highlights the existence of conflicting interests with adaptation measures depending upon population categories.

Key policy insights

• In France, although coastal flooding risk is a key issue in numerous populated coastal areas, coastal dwellers show little willingness to relocate.

• Resistance to relocation can be assessed through a composite index integrating place attachment, residential mobility and risk perception.

• Application of such an index shows a correlation between willingness to relocate and distance from the sea.

• Conflicts of interest with adaptation measures also depend on the age of the dwellers, their standard of living and on home ownership.

     

Employing ground and satellite-based QuickBird data and random forest to discriminate five tree species in a Southern African Woodland

With the emergence of very high spatial and spectral resolution data set, the resolution gap that existed between remote-sensing data set and aerial photographs has decreased. The decrease in resolution gap has allowed accurate discrimination of different tree species. In this study, discrimination of indigenous tree species (n?=?5) was carried out using ground based hyperspectral data resampled to QuickBird bands and the actual QuickBird imagery for the area around Palapye, Botswana. The purpose of the study was to compare the accuracies of resampled hyperspectral data (resampled to QuickBird sensors) with the actual image (QuickBird image) in discriminating between the indigenous tree species. We performed Random Forest (RF) using canopy reflectance taking from ground-based hyperspectral sensor and the reflectance delineated regions of the tree species. The overall accuracies for classifying the five tree species was 79.86 and 88.78% for both the resampled and actual image, respectively. We observed that resampled data set can be upscale to actual image with the same or even greater level of accuracy. We therefore conclude that high spectral and spatial resolution data set has substantial potential for tree species discrimination in savannah environments.     

Testing the reliability and stability of the internal accuracy assessment of random forest for classifying tree defoliation levels using different validation methods

In this study, the strength and reliability of internal accuracy estimate built in random forest (RF) ensemble classifier was evaluated. Specifically, we compared the reliability of the internal validation methods of RF with independent data-sets of different splitting options for defoliation classification. Furthermore, we set out to statistically validate the best independent split option for image classification using RF and multispectral Rapideye imagery. Results show that the internal accuracy measure yields comparable results with those derived from an independent test data-set. More important, it was observed that the errors produced by the internal validation methods of RF were relatively stable as statistically shown by the lower confidence interval obtained as compared to the independent test data. Results also showed that the 70–30% split option had the lowest mean standard errors (0.2351) and hence highest accuracy when compared to the other split options. The study confirms the reliability and stability of the internal bootstrapping estimate of accuracy built within the random forest algorithm.