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Measurement(s)Abundance center dot Nematoda center dot environmental factorTechnology Type(s)Elutriative Centrifugation center dot computational modeling techniqueFactor Type(s)geographic locationSample Characteristic - OrganismNematodaSample Characteristic - Environmentsoil environment center dot climateSample Characteristic - LocationEarth (planet) Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11925843 As the most abundant animals on earth, nematodes are a dominant component of the soil community. They play critical roles in regulating biogeochemical cycles and vegetation dynamics within and across landscapes and are an indicator of soil biological activity. Here, we present a comprehensive global dataset of soil nematode abundance and functional group composition. This dataset includes 6,825 georeferenced soil samples from all continents and biomes. For geospatial mapping purposes these samples are aggregated into 1,933 unique 1-km pixels, each of which is linked to 73 global environmental covariate data layers. Altogether, this dataset can help to gain insight into the spatial distribution patterns of soil nematode abundance and community composition, and the environmental drivers shaping these patterns.

In the aftermath of natural disasters such as earthquakes and hurricanes, the collection, evaluation, and dissemination of damage data is critical for a timely and accurate response. Information concerning the spatial extent and severity of urban damage is demanded by emergency response personnel, and also by reconnaissance teams conducting post-disaster impact assessments. Data requirements have traditionally been met through ground-based damage surveys during the days and weeks following the event. However, the integration of remote sensing data and geo-referenced damage information collected in the field can effectively streamline, accelerate, and increase the volume and diversity of data captured during post-disaster reconnaissance. Specifically, the integration of these two damage detection technologies offers advantages including: the fusion of multiple perspectives on a given structure from the ground and air, yielding a more complete and highly detailed picture of damage sustained; capturing a permanent visual record of the post-disaster situation for decision support in the immediate aftermath of the event, and for subsequent recall as ground truth for ongoing research activities. In terms of specific applications, geo-referenced ground damage information has recently been used to develop remote sensing-based damage scales, and to calibrate and validate the information obtained using semi-automated remote sensing-based damage detection algorithms that are applied across a wide geographic area to provide a ‘quick-look’ initial estimate of damage and potential loss. This paper describes mobile reconnaissance and mapping applications recently developed to support urban damage scale development, and to validate damage detection activities following major disasters including the 1999 Marmara earthquake, 2003 Bam earthquake, and 2004 Hurricane Katrina.

The documentation of the effects of natural and man made disaster is one of the most important applications for remote sensing. High performing satellite platforms, having acquisition capabilities in near real time, just few days after an event, are proposing new scenarios also for the use of image information in an early response phase. The aim of the work presented is to analyze some existing procedures of image analysis, particularly evaluating steps involved in information production, from a geographic database, passing trough proper image integration. Some case studies have been analyzed, concerning major earthquakes in last decade, where remote sensing technology played an important role. The approach proposed is useful both for information collection, for immediate on the field data gathering, and for the evaluation of occurred damage. The example presented, related to the Boumerdes earthquake, is the creation of an image-based building inventory database, evaluating object oriented image processing capabilities and simulating also a geo-database connection. A great importance will be given to the applicative part, evaluating the time consumed for the application.

After a severe disaster, information concerning the spatial extent and severity of urban damage is demanded by emergency response personnel, and also by reconnaissance teams conducting post-disaster impact assessments. Data requirements have traditionally been met through ground-based damage surveys during the days and weeks following the event. However, the integration of remote sensing data and geo-referenced damage information collected in the field can effectively streamline, accelerate, and increase the volume and diversity of data captured during post-disaster reconnaissance. The paper describes mobile reconnaissance and mapping applications recently developed to support urban damage scale development, and to validate damage detection activities following major disasters like earthquakes and hurricanes.

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Observations concerning the level and distribution of building damage after a destructive earthquake are of primary importance in the immediate aftermath for planning response efforts, and later on for better understanding the effect of shaking on buildings. After recent earthquakes (the 2003 Boumerdes and Bam events) and the 2004 Indian ocean tsunami, very high-resolution (VHR) satellite imagery (QuickBird and Ikonos II) proved to be a valuable source of spatial information. For damage detection, textural change indexes have proved to be a useful means of describing and detecting changes between a temporal sequence of ‘before’ and ‘after’ images (Adams, 2003). However, for conventional spectrally-based approaches (e.g. band difference/ratio, principal component analysis) there are several factors that can hinder the direct extraction of useful information from an image, such as illumination differences and variable geometry angles (elevation and azimuth of sun and satellite). This paper explores an object-oriented approach to the detection of buildings damage caused by the Bam earthquake, which minimizes these sources of error. It also introduces a novel “edge detection” approach to characterizing visible changes accompanying building collapse.

L'osservazione del livello e della distribuzione del danno dopo un evento catastrofico sono di primaria importanza nei momenti successivi ad un evento, per la pianificazione immediata per la risposta all’evento e, successivamente, per una migliore comprensione degli effetti del terremoto sull'ambiente costruito. Dopo il terremoto di Bam, che ha colpito l'Iran il 26 dicembre 2003, satelliti ad alta risoluzione (Quickbird e Ikonos) sono diventati delle valide fonti di informazioni, sia per le caratteristiche divulgative (diffusione di informazioni tramite Internet), sia con lo scopo vero e proprio di mappatura della distribuzione del danno e per la ricognizione delle aree maggiormente colpite. Saranno l’argomento della presente comunicazione l’analisi delle immagini satellitari, tramite tecnologia ad oggetti: lo specifico obiettivo della ricerca è la descrizione e la definizione di un inventario degli edifici estratto esclusivamente da immagini satellitari, attraverso specifici algoritmi di parametrizzazione della segmentazione. La possibilità di estrarre informazioni di tipo quantitativo dalle immagini satellitari in modo semi-automatico è, infatti, particolarmente utile per lo sviluppo di applicazioni in regioni non coperte da un sistema informativo geografico o da cartografia adeguati. Le immagini QuickBird saranno poi analizzate nell’ottica della definizione di un indice di cambiamento, che potrà essere utilizzato per definire la distribuzione del danneggiamento.

Performance Analysis of the Maclean Avenue Detention Tank City of Toronto Lijing Xu1 and Barry J. Adams2* 1 XCG Consultants, 2620 Bristol Circle, #300, Oakville, Ontario, Canada, L6H6Z7 2Department of Civil Engineering, University of Toronto, Toronto, Ontario, Canada,M5S1A4 e-mail: adams@ecf.utoront.ca ABSTRACT The Maclean Avenue detention tank was constructed by the City of Toronto to decrease the frequency of beach closing caused by directly discharged stormwater and CSOs in the Eastern Beaches area of the City. The purpose of this study is to evaluate the performance of the detention tank for wet weather flow pollution control through a 5-year field monitoring program and to improve the engineering understanding of such stormwater control facilities. Tank performance measures on runoff quantity control are obtained from the analysis of 220 runoff events recorded in those five years, and the tank performance for pollution control is subsequently calculated from the quantity control analysis results and influent and effluent EMCs. According to the specific operational features of the Maclean tank and the principles of analytical probabilistic models, models are derived for both runoff quantity control and quality control analysis of the tank. Field monitoring data are used to calibrate and validate the derived models. A comparison among the observed results, predicted results from QQS continuous simulations and the derived analytical probabilistic models is made and conclusions are drawn on the current performance of the tank and on the utility of monitoring and modeling results.

This paper presents two methodologies for estimating the impact of infiltration facilities on reducing stormwater runoff volumes, and pollutant loads, from urban drainage systems. The methodologies have previously been developed using derived probability distribution theory, often referred to in the literature as analytical probabilistic modelling, and differ in that they employ different hydrologic models for the transformation of rainfall to runoff. Moreover, the original model derivations employed herein were developed for the analysis of stormwater detention facilities (dry ponds), and are adapted herein for the analysis of infiltration facilities. The summary of model expressions presented in this paper permits the reader to perform the necessary calculations to design and analyze the performance of stormwater infiltration facilities with the use of a calculator or computer-based spreadsheet application. Results are generated using the models, and various sensitivity analyses presented, illustrating the power of the models. The models are then used as a basis for comparison with guidelines on the design of such facilities in the province of Ontario, Canada. The results of this comparison suggest that the current guidelines may yield infiltration facilities insufficient in size to meet the performance levels intended to be satisfied by such facilities.