基于叶片反射光谱特征的银杏健康量化评价技术

准确地诊断树木健康状况是城市森林树木管理工作的基础，也是目前生产中急需的技术。通过土壤和植物养分分析诊断树木健康可靠性差，通过形态特征调查诊断树木健康费时、费力，如何快速、准确、无损地诊断树木健康已经成为城市树木健康管理的重要技术瓶颈。以北京市银杏为研究对象，对基于叶片反射光谱特征的树木健康诊断技术进行了研究。通过13个外貌形态特征聚类将树木健康划分成健康木、亚健康木、一般健康木和不健康木4个等级，不同健康等级树木叶片色素含量差异极其显著(p<0.001) ，因叶绿素含量与光谱反射率之间存在相关关系，所以采用叶片反射光谱特征判断树木健康状况是可行的。采用因子分析法，通过15个叶片反射光谱指标构建了能够综合反映叶片反射光谱特征的绿度指数、色素指数、三边指数。不同健康等级间叶片反射光谱指标以及三个反射光谱指数均有极显著差异(p<0.001)。所以，采用三个反射光谱指数构建了银杏健康评价的多元二次模型，经检验模型预测精度达到79%，可以作为银杏树木健康快速诊断。选取的光谱指标较为全面，方法简洁，并通过综合分析，确定了不同健康等级树木核心形态指标以及叶片的绿度指数、色素指数、三边指数等综合得分以及得分范围，为生产中直接使用该方法诊断银杏健康状况提供了标准。

Study on Quantitative Assessment of Ginkgo biloba Tree Health Based on Characteristics of Leaf Spectral Reflectance

Accurate diagnosis of tree health is the foundation of urban forest management as well as a technology urgently needed in the production. Tree health diagnosis through the analysis of the soil and plant nutrient health shows poor reliability while morphological diagnosis appears time-consuming and laborious, so how to diagnose the tree health fast, accurately and nondestructively has become an important technical bottlenecks of urban trees health management. This paper, took ginkgo trees in Beijing as the research object, has studied the trees health diagnosis technology based on the leaf spectral reflectance characteristics. Based on the clustering analysis of 13 exterior shape features, trees were divided to four healthy levels, excellent, good, fair, and poor. Leaf pigment content between different healthy levels of trees is extremely significant different (p<0.001). Because there is relationship between the spectral reflectance and chlorophyll content, the tree leaf spectral reflectance characteristics used for health diagnosis is feasible. Adopted the factor analysis, we constructed green degree index, index of pigment, trilateral index, reflecting leaf spectral reflectance characteristics, according to 15 leaf spectral reflectance indices. Leaf spectral reflectance indexes and three reflection spectrum indexes between different healthy levels of trees had extremely significant difference (p<0.001). Three reflection spectrum indexes were used to construct the multiple quadratic model of the ginkgo trees health evaluation, with prediction accuracy of up to 79%. Therefore, this method can be used as a rapid diagnosis method to evaluate ginkgo trees health. This study selected relatively comprehensive and concise spectral indexes, determined the comprehensive score and score range for the tree core morphological index, green degree index, index of pigment, trilateral index of leaves of different healthy levels of trees. It provides a standard for the health diagnosis of ginkgo trees in practical forest management.