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Titlebook: Spatial Uncertainty in Ecology; Implications for Rem Carolyn T. Hunsaker,Michael F. Goodchild,Ted J. Ca Book 2001 Springer Science+Business

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发表于 2025-3-21 16:13:15 | 显示全部楼层 |阅读模式
书目名称Spatial Uncertainty in Ecology
副标题Implications for Rem
编辑Carolyn T. Hunsaker,Michael F. Goodchild,Ted J. Ca
视频video
图书封面Titlebook: Spatial Uncertainty in Ecology; Implications for Rem Carolyn T. Hunsaker,Michael F. Goodchild,Ted J. Ca Book 2001 Springer Science+Business
描述The huge growth in the use of geographic information systems, remote sensing platforms and spatial databases have made accurate spatial data more available for ecological and environmental models. Unfortunately, there has been too little analysis of the appropriate use of this data and the role of uncertainty in resulting ecological models. This is the first book to take an ecological perspective on uncertainty in spatial data. It applies principles and techniques from geography and other disciplines to ecological research. It brings the tools of cartography, cognition, spatial statistics, remote sensing and computer sciences to the ecologist using spatial data. After describing the uses of spatial data in ecological research, the authors discuss how to account for the effects of uncertainty in various methods of analysis. Carolyn T. Hunsaker is a research ecologist in the USDA Forest Service in Fresno, California. Michael F. Goodchild is Professor of Geography at the University of California, Santa Barbara. Mark A. Friedl is Assistant Professor in the Department of Geography and the Center for Remote Sensing at Boston University. Ted J. Case is Professor of Biology at the Universi
出版日期Book 2001
关键词biology; cartography; classification; databases; digital elevation model; ecology; environment; forest; geog
版次1
doihttps://doi.org/10.1007/978-1-4613-0209-4
isbn_softcover978-0-387-98889-4
isbn_ebook978-1-4613-0209-4
copyrightSpringer Science+Business Media New York 2001
The information of publication is updating

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Mark A. Friedl,Kenneth C. McGwire,Douglas K. McIver
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The Use and Uncertainties of Spatial Data for Landscape Models: An Overview with Examples from the Fstribution of weather stations, kriging techniques, data collection errors, the influence of scale, or knowledge of meteorological models, yet these things and more are all factors that contribute to the spatial uncertainty of this .. These uncertainties become extremely important when one tries to
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An Overview of Uncertainty in Optical Remotely Sensed Data for Ecological Applicationsechnologies (e.g., radar, laser altimeter, and lidar systems, which provide detailed information regarding topography and vegetation structure in three dimensions) suggest that the use of remote sensing by ecologists is likely to increase in the future.
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Modeling Forest Net Primary Productivity with Reduced Uncertainty by Remote Sensing of Cover Type an forest growth assessment, is increasingly clear. For example, the models can be used to estimate stand or site net primary production (NPP) when the necessary information on species, soils, topography, and climate are available. Improved ecosystem process models in the future may replace empirical
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Spatially Variable Thematic Accuracy: Beyond the Confusion Matrix1988a; Steele et al. 1998), and standard methods have not been adopted for presenting the spatial distribution of error in thematic maps. The confusion matrix is the most commonly accepted method for assessing the accuracy of thematic maps, but it is entirely devoid of spatial context. This chapter
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