Alexander Brenning

A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management

In recent years, social media emerged as a potential resource to improve the management of crisis situations such as disasters triggered by natural hazards. Although there is a growing research body concerned with the analysis of the usage of social media during disasters, most previous work has concentrated on using social media as a stand-alone information source, whereas its combination with other information sources holds a still underexplored potential. This article presents an approach to enhance the identification of relevant messages from social media that relies upon the relations between georeferenced social media messages as Volunteered Geographic Information and geographic features of flood phenomena as derived from authoritative data (sensor data, hydrological data and digital elevation models). We apply this approach to examine the micro-blogging text messages of the Twitter platform (tweets) produced during the River Elbe Flood of June 2013 in Germany. This is performed by means of a statistical analysis aimed at identifying general spatial patterns in the occurrence of flood-related tweets that may be associated with proximity to and severity of flood events. The results show that messages near (up to 10 km) to severely flooded areas have a much higher probability of being related to floods. In this manner, we conclude that the geographic approach proposed here provides a reliable quantitative indicator of the usefulness of messages from social media by leveraging the existing knowledge about natural hazards such as floods, thus being valuable for disaster management in both crisis response and preventive monitoring.

The influence of systematically incomplete shallow landslide inventories on statistical susceptibility models and suggestions for improvements

Complete landslide inventories are rarely available. The objectives of this study were to (i) elaborate the influence of incomplete landslide inventories on statistical landslide susceptibility models and to (ii) propose suitable modelling strategies that can reduce the effects of inventory-based incompleteness. In this context, we examined whether the application of a novel statistical approach, namely mixed-effects models, enables predictions that are less influenced by such inventory-based errors.The study was conducted for (i) an area located in eastern Austria and (ii) a synthetically generated data set. The applied methodology consisted of a simulation of two different inventory-based biases and an in-depth evaluation of subsequent modelling results. Inventory-based errors were simulated by gradually removing landslide data within forests and selected municipalities. The resulting differently biased inventories were introduced into logistic regression models while we considered the effects of including or excluding predictors that are directly related to the respective inventory-based bias. Mixed-effects logistic regression was used to account for variation that was due to an inventory-based incompleteness.The results show that most erroneous predictions, but highest predictive performances, were obtained from models generated with highly incomplete inventories and predictors that were able to directly describe the respective incompleteness. An exclusion of such bias-describing predictors led to systematically confounded relationships. The application of mixed-effects models proved valuable to produce predictions that were least affected by inventory-based errors.This paper highlights that the degree of inventory-based incompleteness is only one of several aspects that determine how an inventory-based bias may propagate into the final results. We propose a four-step procedure to deal with incomplete inventories in the context of statistical landslide susceptibility modelling.

Spatial‐temporal variation of near‐surface temperature lapse rates over the Tianshan Mountains, central Asia

Adequate estimates of near-surface temperature lapse rate (γlocal) are needed to represent air temperature in remote mountain regions with sparse instrumental records such as the mountains of Central Asia. To identify the spatial and temporal variation of γlocal in the Tianshan Mountains, long term (1961-2011) daily maximum, mean and minimum temperature (Tmax, Tmean and Tmin) data from 17 weather stations and one year of temperature logger data were analyzed considering three subregions: Northern Slopes, Kaidu Basin and Southern Slopes. Simple linear regression was performed to identify relationships between elevation and temperature, revealing spatial and seasonal variation in γlocal. γlocal are higher on the Southern slopes than the Northern slopes due to topography and regional climate conditions. Seasonally, γlocal are more pronounced higher in the summer than in the winter months. γlocal are generally higher for Tmax than Tmean and Tmin. The Kaidu Basin shows similar seasonal variability, but with the highest γlocal for Tmean and Tmin occurring in the spring. Formation of γlocal patterns is associated with the interactions of climate factors in different subregions. Overall, annual mean γlocal for Tmax, Tmean and Tmin in the studys subregions are lower than the standard atmospheric lapse rates (6.5u2009°Cu2009km-1), which would therefore be an inadequate choice for representing the near-surface temperature conditions in this area. Our findings highlight the importance of spatial and temporal variation of γlocal in hydro-meteorological research in the data-sparse Tianshan Mountains.

Permafrost Favorability Index: Spatial Modeling in the French Alps Using a Rock Glacier Inventory

In the present study we used the first rock glacier inventory for the entire French Alps to model spatial permafrost distribution in the region. The inventory, which does not originally belong to this study, was revised by the authors in order to obtain a database suitable for statistical modelling. Climatic and topographic data evaluated at the rock glacier locations were used as predictor variables in a Generalized Linear Model. Model performances are strong, suggesting that, in agreement with several previous studies, this methodology is able to model accurately rock glacier distribution. A methodology to estimate model uncertainties is proposed, revealing that the subjectivity in the interpretation of rock glacier activity and contours may substantially bias the model. The model highlights a North-South trend in the regional pattern of permafrost distribution which is attributed to the climatic influences of the Atlantic and Mediterranean climates. Further analysis suggest that lower amounts of precipitation in the early winter and a thinner snow cover, as typically found in the Mediterranean area, could contribute to the existence of permafrost at higher temperatures compared to the Northern Alps. A comparison with the Alpine Permafrost Index Map (APIM) shows no major differences with our model, highlighting the very good predictive power of the APIM despite its tendency to slightly overestimate permafrost extension with respect to our database. The use of rock glaciers as indicators of permafrost existence despite their time response to climate change is discussed and an interpretation key is proposed in order to ensure the proper use of the model for research as well as for operational purposes.

On the Effect of Spatially Non-Disjoint Training and Test Samples on Estimated Model Generalization Capabilities in Supervised Classification With Spatial Features

In this letter, we establish two sampling schemes to select training and test sets for supervised classification. We do this in order to investigate whether estimated generalization capabilities of learned models can be positively biased from the use of spatial features. Numerous spatial features impose homogeneity constraints on the image data, whereby a spatially connected set of image elements is attributed identical feature values. In addition to a frequent occurrence of intrinsic spatial autocorrelation, this leads to extrinsic spatial autocorrelation with respect to the image data. The first sampling scheme follows a spatially random partitioning into training and test sets. In contrast to that, the second strategy implements a spatially disjoint partitioning, which considers in particular topological constraints that arise from the deployment of spatial features. Experimental results are obtained from multi- and hyperspectral acquisitions over urban environments. They underline that a large share of the differences between estimated generalization capabilities obtained with the spatially disjoint and non-disjoint sampling strategies can be attributed to the use of spatial features, whereby differences increase with an increasing size of the spatial neighborhood considered for computing a spatial feature. This stresses the necessity of a proper spatial sampling scheme for model evaluation to avoid overoptimistic model assessments.

Analyzing Hydro-Climatic Data to Improve Hydrological Understanding in Rural Rio de Janeiro, Southeast Brazil

The rural area of Rio de Janeiro (RJ) state has experienced increased pressure on water resources, due to an increasing population linked with the growth of the industrial and agricultural sectors. High interannual variability of rainfall causes frequent extreme events leading to droughts, floods, and landslides. Therefore, it is crucial to understand how climate affects the interaction between the timing of extreme rainfall events, hydrological processes, vegetation growth, soil cover, and soil erosion. Ecohydrological modeling can contribute to a better understanding of spatial–temporal process dynamics to develop adaptation strategies. However, prior to modeling, it is crucial to evaluate the reliability of the climate and hydrological data. This study aims to homogenize the climatic data and to analyze the hydro-climatic time series needed for further hydrological studies (e.g., ecohydrological modeling) and to contribute to a better understanding of long-term hydro-climatic patterns in a mesoscale watershed, the Muriae River Basin. The analyses include homogeneity assessment, statistical analyses, and trend detection for a time period of over 50 years. The assessment provides important insights into long-term hydro-climatic patterns, such as an increase of the annual mean temperature, a decrease of the annual relative humidity, and an increase of the frequency of intense rainfall events.

Classifying fruit-tree crops by Landsat-8 time series

Landsat-8 time series were used to classify major crops types in Maipo and Aconcagua valleys, central Chile. In the former valley four fruit-tree crops were classified applying different machine learning techniques on feature sets comprising typical index-based temporal profiles, like those using the normalized difference vegetation index, and the complete spectral resolution of the time series. In the latter valley six fruit-tree crops were classified only by LDA (linear discriminant analysis), found the best performing classifier for the Maipo Valley. LDA was applied on the complete spectral resolution of the time series and on a feature set adding all possible NDIs (normalized difference indices) that can be constructed from the time series. Regardless of the feature set used good MERs (misclassification error rates) were found (≤ 0.21) for the Maipos crops, but they were reduced by 4 and 13 percentage points, depending on the classifier and the training sample size used, when using the complete spectral resolution of the time series. We further explored these findings in the Aconcagua Valley, where MERs were reduced from 0.13 to 0.1 when the NDI-based feature set was used. In both study cases, the most predictive bands belonged to the first image dates of the time series, corresponding to the crops greenup stage, and they were placed not only on the typical greenness spectral region but also on the shortwave infrared region.