Effects of Permafrost and Seasonally Frozen Ground on the Seismic Response of Transportation Infrastructure Sites

INE/AUTC 11.0

Meme Maps: A Tool for Configuring Memes in Time and Space

This paper presents an elegant method for visually representing the life and times of a meme, an element of cultural transmission. The elements of a Meme Map are described, and the steps for constructing a generic Meme Map are listed. A worked example of a Meme Map (using the "organic" meme) is provided. A Meme Map is a powerful and flexible tool for capturing, displaying and sharing memetic information - illustrating the time and space propinquity of Meme Events - yet it is easy to learn and simple to apply. Meme Maps can readily be drawn on “the back on an envelope”, or they can be professionally produced using any standard drawing package on a computer

Applications of remote sensing to stream discharge predictions

A feasibility study has been initiated on the use of remote earth observations for augmenting stream discharge prediction for the design and/or operation of major reservoir systems, pumping systems and irrigation systems. The near-term objectives are the interpolation of sparsely instrumented precipitation surveillance networks and the direct measurement of water loss by evaporation. The first steps of the study covered a survey of existing reservoir systems, stream discharge prediction methods, gage networks and the development of a self-adaptive variation of the Kentucky Watershed model, SNOPSET, that includes snowmelt. As a result of these studies, a special three channel scanner is being built for a small aircraft, which should provide snow, temperature and water vapor maps for the spatial and temporal interpolation of stream gages

Seismic vulnerability assessment of clustered historical centers: fragility curves based on local collapse mechanisms analyses

open5noProtection and preservation of minor historical centres against earthquakes effects are not trivial tasks, particularly in the cases, quite common, of complex aggregate buildings. The paper aims at evaluating the seismic vulnerability of historical clustered buildings on a urban scale, through the analysis of local collapse mechanisms, carried out with a parametric approach. This procedure is applied to four historical centres of L’Aquila province struck by the 2009 earthquake. The identification of the significant parameters is related to the definition of representative typologies within the city centres. Each typology is characterized by recurring features, mainly recognizable with a survey performed outside the buildings. After the identification of the relevant aspects, local mechanisms of collapse analyses are carried out, by varying the parameters between defined range of values, in order to take into account possible uncertainties in the data collection. A set of fragility curves for each identified typology is defined and the obtained results are compared to the damage data gathered in the post-earthquake phase for the buildings. This approach allows for possible extensions to buildings that may be included in the typology categories here defined.openTaffarel, S.; Caliman, M.; Valluzzi, M.R; da Porto, F.; Modena, C.Taffarel, Sabrina; Caliman, M.; Valluzzi, MARIA ROSA; DA PORTO, Francesca; Modena, Claudi

Complexity in action: Untangling latent relationships between land quality, economic structures and socio-spatial patterns in Italy

Land quality, a key economic capital supporting local development, is affected by biophysical and anthropogenic factors. Taken as a relevant attribute of economic systems, land quality has shaped the territorial organization of any given region influencing localization of agriculture, industry and settlements. In regions with long-established human-landscape interactions, such as the Mediterranean basin, land quality has determined social disparities and polarization in the use of land, reflecting the action of geographical gradients based on elevation and population density. The present study investigates latent relationships within a large set of indicators profiling local communities and land quality on a fine-grained resolution scale in Italy with the aim to assess the potential impact of land quality on the regional socioeconomic structure. The importance of land quality gradients in the socioeconomic configuration of urban and rural regions was verified analyzing the distribution of 149 socioeconomic and environmental indicators organized in 5 themes and 17 research dimensions. Agriculture, income, education and labour market variables discriminate areas with high land quality from areas with low land quality. While differential land quality in peri-urban areas may reflect conflicts between competing actors, moderate (or low) quality of land in rural districts is associated with depopulation, land abandonment, subsidence agriculture, unemployment and low educational levels. We conclude that the socioeconomic profile of local communities has been influenced by land quality in a different way along urban-rural gradients. Policies integrating environmental and socioeconomic measures are required to consider land quality as a pivotal target for sustainable development. Regional planning will benefit from an in-depth understanding of place-specific relationships between local communities and the environment

Remote Sensing for Site-Specific Crop Management: Evaluating the Potential of Digital Multi-Spectral Imagery for Monitoring Crop Variability and Weeds within Paddocks

This paper analyses the potential and limitations of airborne remote sensing systems for detecting crop growth variability and weed infestation within paddocks at specified capture times. The detection of areas of crop growth variability can help farmers become aware of regions within their paddock where they may be experiencing above and below average yields due to changes in soil or management conditions. For instance, the early detection of weed infestation within cereal crops is crucial for lessening their impact on the final yield. Transect sampling within a canola paddock of a broad acre agricultural property in the South West of Western Australia was conducted synchronous with the capture of 1m spatial resolution DMSI. The four individual bands (blue, green, red and near- infrared) of the DMSI were correlated with LAI and weed density counts collected in the paddock. Statistical analyses show the LAI of canola had strong negative correlations with the blue (-0.93) and red (-0.89) bands and a strong positive correlation was found with the near-infrared band (0.82). The strong correlations between the canola LAI and selected bands of the DMSI indicate that this may be a suitable technique for monitoring canola variability to derive information layers that can be used in creating meaningful "within-field" management units. Likewise, DMSI could be used as a non-invasive tool for in season crop monitoring. The correlation analysis with the weed density (e.g. self sown wheat, ryegrass and clover) attributed to only one negative weak correlation with the red band (-0.38). The less successful detection of weeds is attributed to the minimal weeddensity within the paddock (e.g. mean 34 plants m-2) and indistinct spectral difference from canola at the early time of imagery capture required by farmers for effective variable rate applications of herbicides.LAI, remote sensing, crop density, vegetation indices, weed mapping., Crop Production/Industries,

Global soil moisture bimodality in satellite observations and climate models

A new diagnostic metric based on soil moisture bimodality is developed in order to examine and compare soil moisture from satellite observations and Earth System Models. The methodology to derive this diagnostic is based on maximum likelihood estimator encoded into an iterative algorithm, which is applied to the soil moisture probability density function. This metric is applied to satellite data from the Advanced Microwave Scanning Radiometer for the Earth Observing System and global climate models data from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Results show high soil moisture bimodality in transitional climate areas and high latitudes, potentially associated with land-atmosphere feedback processes. When comparing satellite versus climate models, a clear difference in their soil moisture bimodality is observed, with systematically higher values in the case of CMIP5 models. These differences appear related to areas where land-atmospheric feedback may be overestimated in current climate models

Variation in Spatial Predictions Among Species Distribution Modeling Methods

<p>Prediction maps produced by species distribution models (SDMs) influence decision-making in resource management or designation of land in conservation planning. Many studies have compared the prediction accuracy of different SDM modeling methods, but few have quantified the similarity among prediction maps. There has also been little systematic exploration of how the relative importance of different predictor variables varies among model types. Our objective was to expand the evaluation of SDM performance for 45 plant species in southern California to better understand how map predictions vary among model types, and to explain what factors may affect spatial correspondence, including the selection and relative importance of different environmental variables. Four types of models were tested. Correlation among maps was highest between generalized linear models (GLMs) and generalized additive models (GAMs) and lowest between classification trees and GAMs or GLMs. Correlation between Random Forests (RFs) and GAMs was the same as between RFs and classification trees. Spatial correspondence among maps was influenced the most by model prediction accuracy (AUC) and species prevalence; map correspondence was highest when accuracy was high and prevalence was intermediate. Species functional type and the selection of climate variables also influenced map correspondence. For most (but not all) species, climate variables were more important than terrain or soil in predicting their distributions. Environmental variable selection varied according to modeling method, but the largest differences were between RFs and GLMs or GAMs. Although prediction accuracy was equal for GLMs, GAMs, and RFs, the differences in spatial predictions suggest that it may be important to evaluate the results of more than one model to estimate a range of spatial uncertainty before making planning decisions based on map outputs. This may be particularly important if models have low accuracy or if species prevalence is not intermediate.</p>

Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery

Remote sensing technologies have been commonly used to perform greenhouse detection and mapping. In this research, stereo pairs acquired by very high-resolution optical satellites GeoEye-1 (GE1) and WorldView-2 (WV2) have been utilized to carry out the land cover classification of an agricultural area through an object-based image analysis approach, paying special attention to greenhouses extraction. The main novelty of this work lies in the joint use of single-source stereo-photogrammetrically derived heights and multispectral information from both panchromatic and pan-sharpened orthoimages. The main features tested in this research can be grouped into different categories, such as basic spectral information, elevation data (normalized digital surface model; nDSM), band indexes and ratios, texture and shape geometry. Furthermore, spectral information was based on both single orthoimages and multiangle orthoimages. The overall accuracy attained by applying nearest neighbor and support vector machine classifiers to the four multispectral bands of GE1 were very similar to those computed from WV2, for either four or eight multispectral bands. Height data, in the form of nDSM, were the most important feature for greenhouse classification. The best overall accuracy values were close to 90%, and they were not improved by using multiangle orthoimages