Capturing leaf color variances over space is important for diagnosing plant nutrient and health status, estimating water availability as well as improving ornamental and tourism values of plants. In this study, leaf color variances 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 environment, whereas the influence of climate were not obvious. 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.

捕获整个空间的叶色变化对于诊断植物营养和健康状况，估计水的利用率以及提高植物的观赏和旅游价值非常重要。在这项研究中，欧亚烟树Cotinus coggygria的叶子颜色变化利用广义弹性网（GELnet）和支持向量机（SVM）算法，基于灌木群落中的地理和气候变量对植被进行了估算。结果表明，叶片颜色随空间变化，其变化是由于地理因素对灌丛环境的太阳辐射，温度，光照和湿度的影响，而气候的影响并不明显。SVM和GELnet算法模型是基于地理变量的相似估计叶色指数，并且证明了这两种技术都有可能在没有人类活动的情况下，在具有复杂地理环境的灌木丛中估计C. coggygria的叶色变异。