$56.94
1st Edition
by Guangxing Wang (Editor), Qihao Weng (Editor)
Highlighting new technologies, Remote Sensing of Natural Resources explores advanced remote sensing systems and algorithms for image processing, enhancement, feature extraction, data fusion, image classification, image-based modeling, image-based sampling design, map accuracy assessment and quality control. It also discusses their applications for evaluation of natural resources, including sampling design, land use and land cover classification, natural landscape and ecosystem assessment, forestry, agriculture, biomass and carbon-cycle modeling, wetland classification and dynamics monitoring, and soils and minerals mapping.
The book combines review articles with case studies that demonstrate recent advances and developments of methods, techniques, and applications of remote sensing, with each chapter on a specific area of natural resources. Through a comprehensive examination of the wide range of applications of remote sensing technologies to natural resources, the book provides insight into advanced remote sensing systems, technologies, and algorithms for researchers, scientists, engineers, and decision makers.
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(Lecture Notes in Geoinformation and Cartography) 2010th Edition
by Milan Konecny (Editor), Sisi Zlatanova (Editor), Temenoujka L. Bandrova (Editor)
Paperback – July 20, 2001
by F.J. Ahern (Author), J.G. Goldammer (Author), C.O. Justice (Author)
Paperback – June 10, 2010
by Genanaw Alemu (Author)
Forest of the world is under serious threat owing to deforestation and fire. It is vital to understand the extent and pattern of deforestation to devise strategies before the problem results in massive environmental impact. On top of this identifying the degree of susceptibility of the forest to forest fire is also plays the same role. However, determining the extent and pattern of deforestation and identifying the level of susceptibility of the forest to fire is challenging by traditional approach. Thus, this book provides a technique to determine the extent, pattern and rate of deforestation. Moreover, it provides a method to analyze the degree susceptibility of the forest to forest fire. Although fire is one of the devastating factors for most vegetation,surprisingly it creates favorable condition for ericaceous vegetation of Ethiopian highland. The analysis in this book provides help to utilize GIS and remote sensing for forest fire susceptibility analysis, cover change detection and land cover pattern analysis. Particularly it offers useful information to professionals in GIS and remote sensing ,forestry, and agriculture fields.
Hardcover – August 13, 1999
by Emilio Chuvieco (Editor)
The book gives a systematic review of the different applications of remote sensing and geographical information system techniques in research and management of forest fires. The authors have been involved in this topic of research for several years, resulting in more than 100 papers in highly rated journals and proceedings. The book also benefits from data generated within the Megafires project, founded under the DG-XII of the European Union. Most of the contributors to this book have been involved in the Megafires project, and therefore a clear integration of research and experience is provided. New data from fires affecting European countries between 1991 and 1997 are included as well as satellite images and auxiliary cartographic information.
Paperback – December 6, 2013
by Kamal R. AL-Rawi (Author)
Forests represent the lung of our planet. Wildfires constitute one of the major problems facing the forest ecosystem in the world. In the last decades number of forest fires has been increased due to the increasing in the human activities and climate change. Fast alarm for forest fires is very crucial issue to prevent their spread. In this book we employed satellite images together with artificial neural networks to build a very fast alarm system for forest fire detection. The satellite NOAA-AVHRR (National Oceanic and Atmospheric Administration / Advanced Very High Resolution Radiometer) images are used here. The TAGged Adaptive Resonance Theory ART-TAG Artificial Neural Network ANN is employed. ART-TAG is a supervised form for Compact Fuzzy ART. Burned Area Mapping System (BAMS) and Fire Detection System (FDS) have been built. Integrated Fire Evolution Monitoring System (IFEMS) has been constructed by integrating BAMS and FDS. IFEMS has the ability to distinguish burned area, area in active fire, and area beneath flames. Moreover, it has the ability to detect Fires that occur between two consecutive images and tracing sub pixel fires as well.
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