Gis And Remote Sensing For Renewable Energy Assessment And Maps

This book aims at providing the state-of-the-art on all of the aforementioned tools in different energy applications and at different scales, i.e., urban, regional, national, and even continental for renewable scenarios planning and policy making.

This volume is a comprehensive guide to the use of geographic information systems (GIS) for the spatial analysis of supply and demand for energy in the global and local scale. It gathers the latest research and techniques in GIS for spatial and temporal analysis of energy systems, mapping of energy from fossil fuels, optimization of renewable energy sources, optimized deployment of existing power sources, and assessment of environmental impact of all of the above. Author Lubos Matejicek covers GIS for assessment a wide variety of energy sources, including fossil fuels, hydropower, wind power, solar energy, biomass energy, and nuclear power as well as the use of batteries and accumulators. The author also utilizes case studies to illustrate advanced techniques such as multicriteria analysis, environmental modeling for prediction of energy consumption, and the use of mobile computing and multimedia tools.

This book aims at providing the state-of-the-art on all of the aforementioned tools in different energy applications and at different scales, i.e., urban, regional, national, and even continental for renewable scenarios planning and policy making.

This book covers several themes related to forestry, agriculture, water, soil, urban, and atmospheric research. GIScience technology systems have increased in significance in recent decades and have the ability to acquire information at ground level with a higher spectral resolution using a field radio-spectrometer, which is a great improvement compared to other remote sensing systems. GIScience technology systems are widely used for solving and understanding the concept of forestry, crop, water resources, and related research themes. This book aims to advance the scientific understanding of GIScience technology and applications. The chapters present GIScience data integration with other sources such as LiDAR, Multi-spectral data and their applications in forestry, crop assessment, soil assessment, mineral mapping and related themes. The book will be of interest to geospatial experts, modellers, foresters, agricultural scientists, hyperspectral remote sensing and space community, ecologists and conservation communities, environmental consultants, big data compilers, and computing experts.

Urban Remote Sensing, Second Edition assembles a team of professional experts to provide a much-needed update on the applications of remote sensing technology to urban and suburban areas. This book reflects new developments in spaceborne and airborne sensors, image processing methods and techniques, and wider applications of urban remote sensing to meet societal and economic challenges. In various sections of the book the authors address methods for upscaling urban feature extraction to the global scale, new methods in mapping and detecting urban landscape features and structures, and mapping and monitoring urbanization in developing countries. Additionally, readers are provided with valuable case studies such as the HEAT (Heat Energy Assessment Technologies) project in Calgary, Canada and the use of VHR (very high resolution) satellite monitoring in Salzburg, Austria to tackle challenges of urban green planning. Features Explores the most up-to-date developments in the field of urban remote sensing Integrates both technical and practical aspects covering all different topics of global urban growth issues Provides new and updated contributions addressing data mining of remotely sensed big data, recent urban studies on a global scale, accuracy assessment and validation, and new technical challenges Examines various applications of urban remote sensing in support of urban planning, environmental management, and sustainable urban development Authors are renowned figures in the field of remote sensing

Based upon a special symposium sponsored by the U.S. Environmental Protection Agency (EPA), Remote Sensing and GIS Accuracy Assessment evaluates the important scientific elements related to the performance of accuracy assessments for remotely sensed data, GIS data analysis, and integration products. Scientists from federal, state, and local governments, academia, and nongovernmental organizations present technical papers which examine sampling issues, reference data collection, edge and boundary effects, error matrix and fuzzy assessments, error budget analysis, and change detection accuracy assessment. This compilation contains 20 chapters that represent important symposium outcomes.

In recent years, several projects and studies have been launched towards the development and use of new methodologies, in order to assess, monitor, and support clean forms of energy. Accurate estimation of the available energy potential is of primary importance, but is not always easy to achieve. The present Special Issue on ‘Renewable Energy Resource Assessment and Forecasting’ aims to provide a holistic approach to the above issues, by presenting multidisciplinary methodologies and tools that are able to support research projects and meet today’s technical, socio-economic, and decision-making needs. In particular, research papers, reviews, and case studies on the following subjects are presented: wind, wave and solar energy; biofuels; resource assessment of combined renewable energy forms; numerical models for renewable energy forecasting; integrated forecasted systems; energy for buildings; sustainable development; resource analysis tools and statistical models; extreme value analysis and forecasting for renewable energy resources.

Most government agencies and private companies are investing significant resources in the production and use of geographical data. The capabilities of Geographical Information Systems (GIS) for data analysis are also improving, to the extent that the potential performance of GIS software and the data available for analysis outstrip the abilities of