【摘 要】
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Capturing leaf color variances over space is important for diagnosing plant nutrient and health status,estimating water availability as well as improving orname
【机 构】
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College of Resources and Environment,Southwest University,Chongqing 400716,People’s Republic of Chin
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Capturing leaf color variances over space is important for diagnosing plant nutrient and health status,estimating water availability as well as improving ornamen-tal and tourism values of plants.In this study,leaf color vari-ances of the Eurasian smoke tree,Cotinus coggygria were estimated based on geographic and climate variables in a shrub community using generalized elastic net(GELnet)and support vector machine(SVM)algorithms.Results reveal that leaf color varied over space,and the variances were the result of geography due to its effect on solar radiation,temperature,illumination and moisture of the shrub envi-ronment,whereas the influence of climate were not obvi-ous.The SVM and GELnet algorithm models were similar estimating leaf color indices based on geographic variables,and demonstrates that both techniques have the potential to estimate leaf color variances of C.coggygria in a shrubbery with a complex geographical environment in the absence of human activity.
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