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The consequences of recent floods and flash floods in many parts of the world have been devastating. One way to improving flood management practice is to invest in data collection and modelling activities which enable an understanding of the functioning of a system and the selection of optimal mitigation measures. A Digital Terrain Model (DTM) provides the most essential information for flood managers. Light Detection and Ranging (LiDAR) surveys which enable the capture of spot heights at a spacing of 0.5m to 5m with a horizontal accuracy of 0.3m and a vertical accuracy of 0.15m can be used to develop high accuracy DTM but needs careful processing before using it for any application.This book presents the augmentation of an existing Progressive Morphological filtering algorithm for processing raw LiDAR data to support a 1D/2D urban flood modelling framework. The key characteristics of this improved algorithm are: (1) the ability to deal with different kinds of buildings; (2) the ability to detect elevated road/rail lines and represent them in accordance to the reality; (3) the ability to deal with bridges and riverbanks; and (4) the ability to recover curbs and the use of appropriated roughness coefficient of Manning‘s value to represent close-to-earth vegetation (e.g. grass and small bush).
Modelling urban flood dynamics requires proper handling of a number of complex urban features. Although high-resolution topographic data can nowadays be obtained from aerial LiDAR surveys, such top-view LiDAR data still have difficulties to represent some key components of urban features. Incorrectly representing features like underpasses through buildings or apparent blockage of flow by sky trains may lead to misrepresentation of actual flood propagation, which could easily result in inadequate flood-protection measures. Hence proper handling of urban features plays an important role in enhancing urban flood modelling. This research explores present-day capabilities of using computer-based environments to merge side-view Structure-from-Motion data acquisition with top-view LiDAR data to create a novel multi-source views (MSV) topographic representation for enhancing 2D model schematizations. A new MSV topographic data environment was explored for the city of Delft and compared with the conventional top-view LiDAR approach. Based on the experience gained, the effects of different topographic descriptions were explored for 2D urban flood models of (i) Kuala Lumpur, Malaysia for the 2003 flood event; and (ii) Ayutthaya, Thailand for the 2011 flood event. It was observed that adopting the new MSV data as the basis for describing the urban topography, the numerical simulations provide a more realistic representation of complex urban flood dynamics, thus enhancing conventional approaches and revealing specific features like flood watermarks identification and helping to develop improved flood-protection measures.
This book presents the latest research developments in geoinformation science, which includes all the sub-disciplines of the subject, such as: geomatic engineering, GIS, remote sensing, digital photogrammetry, digital cartography, etc.
This book is a must-have for anyone interested in leveraging geospatial technology, as it covers a wide range of applications and offers valuable insights into the mapping, visualization, and analysis of natural resource planning using GIS, remote sensing, and GPS. Geospatial technology (GT) is a combination of geographic information systems (GIS), remote sensing (RS), and the global position system (GPS) for the mapping, visualization, and analysis of natural resource planning. Nowadays, GIS is widely used throughout the globe for a wide range of applications. GIS is a system that combines locations, geography, hardware, software, statistics, planning, and digital mapping. GIS is a system in which one can store, manipulate, analyze, and visualize or display spatial data. The basic components of GIS are hardware, software, data, input, and manpower. One can develop spatial, temporal, and dynamic models using GIS, which may help in effective decision-making tools. Geospatial information is a computer programme that collects, stores, verifies, and presents information on locations on the surface of the Earth. Geographical information systems play a key role in sustainable development. Geospatial technology combines traditional database operations like query and statistical analysis with the specific graphical and geographic analytical capabilities offered by maps.
Recent advances in Light Detection and Ranging (LIDAR) technology and integration have resulted in vehicle-borne platforms for urban LIDAR scanning, such as Terrapoint Inc.'s TITAN system. Such technology has lead to an explosion in ground LIDAR data. The large size of such mobile urban LIDAR data sets, and the ease at which they may now be collected, has shifted the bottleneck of creating abstract urban models for Geographical Information Systems (GIS) from data collection to data processing. While turning such data into useful models has traditionally relied on human analysis, this is no longer practical. This thesis outlines a methodology for automatically recovering the necessary information to create abstract urban models from mobile urban LIDAR data using computer vision methods. As an integral part of the methodology, a novel scale-based interest operator is introduced (Di erence of Normals) that is e cient enough to process large datasets, while accurately isolating objects of interest in the scene according to real-world parameters. Finally a novel localized object recognition algorithm is introduced (Local Potential Well Space Embedding), derived from a proven global method for object recognition (Potential Well Space Embedding). The object recognition phase of our methodology is discussed with these two algorithms as a focus.
Compared with traditional remote sensing technologies, airborne Lidar data can provide researchers with additional 3D positional information, which is a key factor for advanced urban research, and particularly that of urban landscape ecology. Therefore, the need for applying Lidar data to a variety of disciplines is rapidly growing. However, the lack of remote sensing background makes the wider use of Lidar data highly difficult for scholars from other disciplines. In contrast to the majority of Lidar-related books that focus on sophisticated principles and general applications of Lidar data, this book provides the reader with a feasible framework for applying airborne Lidar data to urban research. In addition to providing a general introduction to the subject, this book explains in detail a series of case studies to demonstrate how these theoretical models can be employed to address practical urban issues. As such, this book not only provides Lidar scholars with a series of specifically designed research methods, but will also serve to inspire scholars from other disciplines, such as geographers, urban planners, ecologists, and decision-makers, with a complete framework of potential application fields.
Floodplain maps serve as the basis for determining whether homes or buildings require flood insurance under the National Flood Insurance Program run by the Federal Emergency Management Agency (FEMA). Approximately $650 billion in insured assets are now covered under the program. FEMA is modernizing floodplain maps to better serve the program. However, concerns have been raised as to the adequacy of the base map information available to support floodplain map modernization. Elevation Data for Floodplain Mapping shows that there is sufficient two-dimensional base map imagery to meet FEMA's flood map modernization goals, but that the three-dimensional base elevation data that are needed to determine whether a building should have flood insurance are not adequate. This book makes recommendations for a new national digital elevation data collection program to redress the inadequacy. Policy makers; property insurance professionals; federal, local, and state governments; and others concerned with natural disaster prevention and preparedness will find this book of interest.
Global Flood Hazard Subject Category Winner, PROSE Awards 2019, Earth Science Selected from more than 500 entries, demonstrating exceptional scholarship and making a significant contribution to the field of study. Flooding is a costly natural disaster in terms of damage to land, property and infrastructure. This volume describes the latest tools and technologies for modeling, mapping, and predicting large-scale flood risk. It also presents readers with a range of remote sensing data sets successfully used for predicting and mapping floods at different scales. These resources can enable policymakers, public planners, and developers to plan for, and respond to, flooding with greater accuracy and effectiveness. Describes the latest large-scale modeling approaches, including hydrological models, 2-D flood inundation models, and global flood forecasting models Showcases new tools and technologies such as Aqueduct, a new web-based tool used for global assessment and projection of future flood risk under climate change scenarios Features case studies describing best-practice uses of modeling techniques, tools, and technologies Global Flood Hazard is an indispensable resource for researchers, consultants, practitioners, and policy makers dealing with flood risk, flood disaster response, flood management, and flood mitigation.
Nowadays, the degree and scale of flood hazards has been massively increasing as a result of the changing climate, and large-scale floods jeopardize lives and properties, causing great economic losses, in the inundation-prone areas of the world. Early flood warning systems are promising countermeasures against flood hazards and losses. A collaborative assessment according to multiple disciplines, comprising hydrology, remote sensing, and meteorology, of the magnitude and impacts of flood hazards on inundation areas significantly contributes to model the integrity and precision of flood forecasting. Methodologically oriented countermeasures against flood hazards may involve the forecasting of reservoir inflows, river flows, tropical cyclone tracks, and flooding at different lead times and/or scales. Analyses of impacts, risks, uncertainty, resilience, and scenarios coupled with policy-oriented suggestions will give information for flood hazard mitigation. Emerging advances in computing technologies coupled with big-data mining have boosted data-driven applications, among which Machine Learning technology, with its flexibility and scalability in pattern extraction, has modernized not only scientific thinking but also predictive applications. This book explores recent Machine Learning advances on flood forecast and management in a timely manner and presents interdisciplinary approaches to modelling the complexity of flood hazards-related issues, with contributions to integrative solutions from a local, regional or global perspective.