基于熵权-云模型的环洞庭湖森林健康评价

Assessment of forest health around Dongting lake based on entropy weight-cloud model

【目的】森林健康评价过程中迫切需要解决评价指标权重赋值的人为主观性,以及定量指标与定性评价结果之间的相互转换问题,从而提高森林健康评价的科学性。【方法】采用熵权法客观计算评价指标的权重,基于云模型方法综合评定森林健康等级。以湖南省森林资源二类调查结果与通过遥感数据获取的增强植被指数(EVI)和地表温度(LST)为数据来源,通过ArcMap均匀分布采样并进一步去除重复采样点以及其他不符合要求的采样点后,最终选取4627个小班进行森林健康评价。通过定性与定量分析确定了由11个指标组成森林健康评价指标体系;对各指标进行频数分析,建立健康等级与指标取值区间的对应关系;运用熵权-云模型法综合评定各森林小班的健康等级。【结果】在评价的4627个小班中,优质小班数量仅有2个;健康小班数量最多,占总数的46.92%;亚健康小班数量次之,占总数的20.83%;不健康小班与疾病小班数量分别占总数的19.88%与12.32%。环洞庭湖区域整体森林健康程度一般,从分布区域来看,亚健康及以上的小班主要分布在研究区的东部以及南部,该区域主要是以马尾松和杉木为优势树种的针叶林和以栎类为优势树种的阔叶林;不健康小班主要分布在研究区的中部和西北部,该区域主要是平原,以耕地居多,森林小班穿插在其中,小班面积相对较小;疾病小班零散分布在整个研究区。【结论】熵权-云模型法的应用减少了森林健康评价的主观性,实现了评价指标与评价等级之间的不确定性映射,为森林健康的科学评价提供了一种新的思路和方法。

【Objective】In order to improve the scientificity of forest health assessment,it is urgent to solve the problem of human dominated evaluation and the mutual conversion between quantitative indexes and qualitative evaluation results in the process of forest health assessment.【Method】Based on the results of the second class inventory of forest resources in Hunan province and the enhanced vegetation index(EVI)and surface temperature(LST)obtained from remote sensing data as data sources,4627 subcompartments were selected for forest health assessment after the uniform distribution sampling through ArcMap and further removal of repeated sampling points and other sampling points that did not meet the requirements.Through qualitative and quantitative analysis,the forest health evaluation index system is made up of 11 indexes;frequency analysis of each index is carried out to establish the corresponding relationship between health level and index value range;entropy weight cloud model method is used to comprehensively evaluate the health level of each forest subcompartment.【Result】Among all the evaluated 4627 subcompartments,there are only two high-quality subcompartments;the number of healthy subcompartments is the largest,accounting for 46.92%of the total;the number of sub-healthy subcompartments is the second,accounting for 20.83%of the total;the number of unhealthy subcompartments and disease subcompartments respectively accounts for 19.88%and 12.32%of the total.The overall forest health level is general around Dongting lake.From the perspective of distribution area,sub-healthy and healthy subcompartments are mainly distributed in the East and south of the study area.There are coniferous forest with Pinus massoniana and Cunninghamia lanceolata as dominant species and broad-leaved forest with Quercus as dominant species;unhealthy subcompartments are mainly distributed in the middle and northwest of the study area.There is mainly plain,most of them are cultivated land,with forest subcompartments interspersed,and the area of subcompartments is relatively small;disease subcompartments are scattered throughout the study area.【Conclusion】The subjectivity of forest health evaluation is reduced,and the uncertainty mapping between evaluation indexes and evaluation grades is realized by using entropy weight-cloud model method.It provides a new way of thinking and method for the scientific evaluation of forest health.