High Spatial Resolution Remotely Sensed Data for Ecosystem Characterization
نویسندگان
چکیده
منابع مشابه
High Spatial Resolution Remotely Sensed Data for Ecosystem Characterization
R sensed data have been employed for the characterization of ecologically important variables from local through global contexts. These data may be used to generate a wide range of estimates that are valuable to ecologists, including information on land cover, vegetation cover, habitat, forest structure, and forest function (Kerr and Ostrovsky 2003), and to track changes in these variables. Rec...
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EMPs Extended morphological profiles EMPs Extended morphological profiles LDA Linear discriminant analysis LogDA Logarithmic discriminant analysis MLR Multinomial logistic regression MLRsubMRF Subspace-based multinomial logistic regression followed by Markov random fields MPs Morphological profiles MRFs Markov random fields PCA Principal component analysis QDA Quadratic discriminant analysis RH...
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Exploitation of imaging spectroscopy (hyperspectral) data using classification and spectral unmixing algorithms is a major research area in remote sensing, with reference data required to assess algorithm performance. However, we are limited by our inability to generate rapid, accurate, and consistent reference data, thus making quantitative algorithm analysis difficult. As a result, many inves...
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Cloud cover can influence numerous important ecological processes, including reproduction, growth, survival, and behavior, yet our assessment of its importance at the appropriate spatial scales has remained remarkably limited. If captured over a large extent yet at sufficiently fine spatial grain, cloud cover dynamics may provide key information for delineating a variety of habitat types and pr...
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ژورنال
عنوان ژورنال: BioScience
سال: 2004
ISSN: 0006-3568
DOI: 10.1641/0006-3568(2004)054[0511:hsrrsd]2.0.co;2