Natural Hazards and Earth Systems Science, 12, 3229–3240, 2012
D. Ceresetti, E. Ursu, J. Carreau, S. Anquetin, J. D. Creutin, L. Gardes, S. Girard, and G. Molini´e
“Extreme rainfall is classically estimated using raingauge data at raingauge locations. An important related issue is to assess return levels of extreme rainfall at ungauged sites. Classical methods consist in interpolating extreme value models. In this paper, such methods are referred to as regionalization schemes. Our goal is to evaluate three classical regionalization schemes. Each scheme consists of an extreme-value model (block maxima, peaks over threshold) taken from extreme-value theory plus a method to interpolate the parameters of the statistical model throughout the C´evennes-Vivarais region. From the interpolated parameters, the 100-yr quantile level can be estimated over this whole region. A reference regionalization scheme is made of the couple block maxima/kriging, where kriging is an optimal interpolation method. The two other schemes differ from the reference by replacing either the extreme-value model block maxima by peaks over threshold or kriging by a neural network interpolation procedure. Hyper-parameters are selected by cross-validation and the three regionalization schemes are compared by double cross-validation.
Kriging error for RS3 evaluated as the kriging standard deviation normalized by the 100-yr return levels.
“Our evaluation criteria are based on the ability to interpolate the 100-yr return level both in terms of precision and spatial distribution. It turns out that the best results are obtained by the regionalization scheme combining the peaks-over-threshold method with kriging.”
Remote Sensing of Environment, Volume 127, December 2012, Pages 237–246
Alexis Comber, Peter Fisher, Chris Brunsdon, and Abdulhakim Khmag
- The confusion matrix provides no information on the spatial distribution of errors.
- The spatial distribution of correspondence provides richer accuracy information.
- Geographically weighted models were used to map Boolean and Fuzzy accuracy.
- This is a methodological advance in accuracy assessment in remote sensing.
“The error matrix is the most common way of expressing the accuracy of remote sensing image classifications, such as land cover. However, it and the measures that can be calculated from it have been criticised for not providing any indication of the spatial distribution of errors. Other research has identified the need for methods to analyse the spatial non-stationarity of error and to visualise the spatial variation in classification uncertainty. This research uses geographically weighted approaches to model the spatial variations in the accuracy of both (crisp) Boolean and (soft) fuzzy land cover classes. Remotely sensed data were classified using a maximum likelihood classifier and a fuzzy classifier to predict Boolean and fuzzy land cover classes respectively. Field data were collected at sub-pixel locations and used to generate soft and crisp validation data. A Geographically Weighted Regression was used to analyse spatial variations in the relationships between observations of Boolean land cover in the field and land cover classified from remote sensing imagery. A geographically weighted difference measure was used to analyse spatial variations in fuzzy land cover accuracy. Maps of the spatial distribution of accuracy were created for fuzzy and Boolean classes. This research demonstrates that data collected as part of a standard remote sensing validation exercise can be used to estimate mapped, spatial distributions of accuracy that would augment standard accuracy measures reported in the error matrix. It suggests that geographically weighted approaches, and the spatially explicit representations of accuracy they support, offer the opportunity to report land cover accuracy in a more informative way.”
SSRN, 06 October 2012
Omer Cem Ozturk and Sriram Venkataraman
“This study presents a semi-parametric spatio-temporal regression model to understand how theater level differentiation affects the performance of independent and affiliate movie theaters. Independent movie theaters are privately owned and managed retail establishments, while affiliate theaters are managed by national-level chains such as AMC and Regal. Theater-level performance is measured using two standard industry metrics – theater-level daily revenue and theater-level daily revenue per screen. In our proposed model, we analyze how theater-level differences such as ticket price, movie assortment, geographic location, and theater format (independent vs. affiliate) affect a theater’s competitiveness. Our semi-parametric specification allows us to flexibly estimate how both time- and location-varying competitive effects (due to theater-level differentiation) influence the performance of independent and affiliate movie theaters.
“Our empirical analysis yields several interesting and novel findings for movie theaters. Some own differentiation strategies affect independent and affiliate theaters differently. For example, contrary to industry belief, none of the independent theaters benefit from the strategy of decreasing ticket price or increasing assortment breadth. However, the majority of affiliate theaters gain from decreasing ticket price and increasing assortment breadth. There also exist some own differentiation strategies that affect independent and affiliate theaters similarly. For example, most independent and affiliate theaters are better off increasing their number of screens. These findings also suggest that the performance impact of theater-level differentiation varies even within theaters of the same format. Furthermore, the effect of competitors’ differentiation strategies on theaters’ performance varies by theater format. For example, when a competing affiliate theater increases its assortment breadth, independent theaters face losses whereas other affiliates stand to gain. Another interesting result suggests that the relationship between the individual dimensions of differentiation and the intensity of competition varies substantially across differentiation dimensions. For instance, while competitive intensity decreases almost linearly with assortment breadth, contrary to previous research it changes in a nonlinear and non-monotonic fashion with geographic distance.
“Armed with our proposed model, independent and affiliate movie theater managers can better understand the impact of theater-level differentiation on their own and competitors’ revenues, and they can use this to make more informed marketing investments. ”
Remote Sensing, 2012, 4(4), 810-829
“Accurate estimation of aboveground biomass and carbon stock has gained importance in the context of the United Nations Framework Convention on Climate Change (UNFCCC) and the Kyoto Protocol. In order to develop improved forest stratum–specific aboveground biomass and carbon estimation models for humid rainforest in northeast Madagascar, this study analyzed texture measures derived from WorldView-2 satellite data. A forest inventory was conducted to develop stratum-specific allometric equations for dry biomass. On this basis, carbon was calculated by applying a conversion factor. After satellite data preprocessing, vegetation indices, principal components, and texture measures were calculated. The strength of their relationships with the stratum-specific plot data was analyzed using Pearson’s correlation. Biomass and carbon estimation models were developed by performing stepwise multiple linear regression.
Overview and zoom map of the study area.
“Pearson’s correlation coefficients revealed that (a) texture measures correlated more with biomass and carbon than spectral parameters, and (b) correlations were stronger for degraded forest than for non-degraded forest. For degraded forest, the texture measures of Correlation, Angular Second Moment, and Contrast, derived from the red band, contributed to the best estimation model, which explained 84% of the variability in the field data (relative RMSE = 6.8%). For non-degraded forest, the vegetation index EVI and the texture measures of Variance, Mean, and Correlation, derived from the newly introduced coastal blue band, both NIR bands, and the red band, contributed to the best model, which explained 81% of the variability in the field data (relative RMSE = 11.8%). These results indicate that estimation of tropical rainforest biomass/carbon, based on very high resolution satellite data, can be improved by (a) developing and applying forest stratum–specific models, and (b) including textural information in addition to spectral information.”
Remote Sensing, 2012, 4(3), 598-621
Jose Raul Romo Leon, Willem J.D. van Leeuwen, and Grant M. Casady
“Post-fire vegetation response is influenced by the interaction of natural and anthropogenic factors such as topography, climate, vegetation type and restoration practices. Previous research has analyzed the relationship of some of these factors to vegetation response, but few have taken into account the effects of pre-fire restoration practices. We selected three wildfires that occurred in Bandelier National Monument (New Mexico, USA) between 1999 and 2007 and three adjacent unburned control areas. We used interannual trends in the Normalized Difference Vegetation Index (NDVI) time series data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess vegetation response, which we define as the average potential photosynthetic activity through the summer monsoon. Topography, fire severity and restoration treatment were obtained and used to explain post-fire vegetation response.
Monsoon trend for each pixel within the fire and the reference areas were computed. For Frijoles Canyon (A) the trend is calculated from 2008 to 2010, for Mid Elevation Mesas (B) from 2000 to 2010, and for Capulin (C) from 2006 to 2010.
“We applied parametric (Multiple Linear Regressions-MLR) and non-parametric tests (Classification and Regression Trees-CART) to analyze effects of fire severity, terrain and pre-fire restoration treatments (variable used in CART) on post-fire vegetation response. MLR results showed strong relationships between vegetation response and environmental factors (p < 0.1), however the explanatory factors changed among treatments. CART results showed that beside fire severity and topography, pre-fire treatments strongly impact post-fire vegetation response. Results for these three fires show that pre-fire restoration conditions along with local environmental factors constitute key processes that modify post-fire vegetation response.”
Journal of Hydrometeorology, Volume 13 Issue 4, August 2012
Rebekka Erdin, Christoph Frei, and Hans R. Künsch
“Geostatistics provides a popular framework for deriving high-resolution quantitative precipitation estimates (QPE) by combining radar and rain gauge data. However, the skewed and heteroscedastic nature of precipitation is in contradiction to assumptions of classical geostatistics. This study examines the potential of trans-Gaussian kriging to overcome this contradiction. Combination experiments are undertaken with kriging with external drift (KED) using several settings of the Box–Cox transformation. Hourly precipitation data in Switzerland for the year 2008 serve as test bed to compare KED with and without transformation. The impact of transformation is examined with regard to compliance with model assumptions, accuracy of the point estimate, and reliability of the probabilistic estimate. Data transformation improves the compliance with model assumptions, but some level of contradiction remains in situations with many dry gauges. Very similar point estimates are found for KED with untransformed and appropriately transformed data. However, care is needed to avoid excessive transformation (close to log) because this can introduce a positive bias. Strong benefits from transformation are found for the probabilistic estimate, which is rendered positively skewed, sensitive to precipitation amount, and quantitatively more reliable. Without transformation, 44% of all precipitation observations larger than 5 mm h−1 are considered as extremely unlikely by the probabilistic estimate in the test application. Transformation reduces this rate to 4%. Although transformation cannot fully remedy the complications for geostatistics in radar–gauge combination, it seems a useful procedure if realistic and reliable estimation uncertainties are desired, such as for the stochastic simulation of QPE ensembles.”
International Journal of Geographical Information Science, Volume 26, Issue 4, 2012
Haixiang Zou, Yang Yue, Qingquan Li & Anthony G.O. Yeh
“The interpolation of link-based traffic data is an important topic for transportation researchers and engineers. In recent years the kriging method has been used in traffic data interpolation from the viewpoint of spatial analysis. This method has shown promising results, especially for a large-scale road network. However, existing studies using the Euclidean distance metric, which is widely used in traditional kriging, fail to accurately describe the spatial distance in a road network. In this article we introduce road network distance to describe spatial distance between road links, and we propose an improved distance metric called approximate road network distance (ARND), based on the isometric embedding theory, for solving the problem of the invalid spatial covariance function in kriging caused by the non-Euclidean distance metric. An improved Isomap algorithm is also proposed for obtaining the ARND metric. This study is tested on a large-scale urban road network with sparse road-link travel speeds derived from approximately 1200 ‘floating cars’ (GPS-enabled taxis). Comparison was conducted on both the Euclidean distance metric and the ARND metric. The validation results show that the use of the ARND metric can obtain better interpolation accuracy in different time periods and urban regions with different road network structures. Therefore, we conclude that the improved distance metric has the ability for improving kriging interpolation accuracy for link-based traffic data within real situations, providing more reliable basic traffic data for various traffic applications.”
University of South Florida, Doctor of Philosophy dissertation, 04 April 2012
“Achieving an understanding of the nature of monogenetic volcanic fields depends on identification of the spatial and temporal patterns of volcanism in these fields, and their relationships to structures mapped in the shallow crust and inferred in the deep crust and mantle through interpretation of geochemical, radiometric and geophysical data.
Spatial density maps of volcanic events based on the (a) SAMSE algorithm and (b) LSCV algorithm.
“We investigate the spatial and temporal distributions of volcanism in the Abu Monogenetic Volcano Group, Southwest Japan. E-W elongated volcano distribution, which is identified by a nonparametric kernel method, is found to be consistent with the spatial extent of P-wave velocity anomalies in the lower crust and upper mantle, supporting the idea that the spatial density map of volcanic vents reflects the geometry of a mantle diapir. Estimated basalt supply to the lower crust is constant. This observation and the spatial distribution of volcanic vents suggest stability of magma productivity and essentially constant two-dimensional size of the source mantle diapir.
“We mapped conduits, dike segments, and sills in the San Rafael sub-volcanic field, Utah, where the shallowest part of a Pliocene magmatic system is exceptionally well exposed. The distribution of conduits matches the major features of dike distribution, including development of clusters and distribution of outliers. The comparison of San Rafael conduit distribution and the distributions of volcanoes in several recently active volcanic fields supports the use of statistical models, such as nonparametric kernel methods, in probabilistic hazard assessment for distributed volcanism.
“We developed a new recurrence rate calculation method that uses a Monte Carlo procedure to better reflect and understand the impact of uncertainties of radiometric age determinations on uncertainty of recurrence rate estimates for volcanic activity in the Abu, Yucca Mountain Region, and Izu-Tobu volcanic fields. Results suggest that the recurrence rates of volcanic fields can change by more than one order of magnitude on time scales of several hundred thousand to several million years. This suggests that magma generation rate beneath volcanic fields may change over these time scales. Also, recurrence rate varies more than one order of magnitude between these volcanic fields, consistent with the idea that distributed volcanism may be influenced by both the rate of magma generation and the potential for dike interaction during ascent.”
Archaeological Prospection, published online 21 June 2012
“A ground-penetrating radar survey was conducted inside the Cathedral of Tarragona in order to explore the first 2 m beneath the ground surface. Initial processing and interpretation revealed a square perimeter related to a massive structure between 1 and 1.4 m below the ground surface. The results of the survey were used to test geostatistical tools by achieving a quantified data analysis and by filtering the data. The data analysis provided a description of the resolution and of the spatial distribution of the data. It included the characterization of the acquisition noise that was then filtered out by factorial kriging. This filtering technique allowed removing the acquisition noise, before the time-slice computation or any interpolation of the data, using weights of interpolation that are based on the spatial variations of the data. Having performed the filtering on the data at acquisition sampling allowed the use of optimized parameters for the visualization of the results, without having to remove remaining acquisition noise with the visualization operators. ”
Arabian Journal of Geosciences, Published Online 27 February 2012
D. Gamvroula, D. Alexakis and G. Stamatis
“In this study, hydrochemical analysis, statistical analysis and GIS database have been successfully used to explain the main factors and mechanisms controlling the distribution of major and trace elements in groundwater. The groundwater of Megara basin is subject to intense exploitation to accommodate all the water demands of this agricultural area. Water quality data obtained from 58 sampling sites of the Megara basin, aims to describe groundwater quality in relation to geology and anthropogenic activities. Factor analysis revealed that four factors accounted for 79.96% of the total data variability. The contribution of each factor at sampling sites was calculated. Evaluation of water samples by comparing quality standards and levels recorded in the literature for both drinking and irrigation uses is discussed. ”