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依据系统科学原理,从影响森林健康包括的系统活力、系统组成、系统恢复力和林地环境等方面筛选13个指标构建森林健康评价指标体系;针对健康评价取值未知(无决策属性)的状况,利用粗糙集理论的属性重要度分析各指标因子对森林健康的影响程度,提出将基于知识粒度和属性重要度的森林健康评价指标赋权法。根据二类调查和样地调查数据,以湖南省大围山自然保护区8个次生林群落和2个人工林样地为应用实例,结果表明:森林健康评价指标体系的林分郁闭度、林分蓄积量、叶面积指数等13个指标在大围山自然保护区森林健康评价中的权重与专家打分法得到的结果基本相符,并且对样地进行了评价,其中只有1个处于健康,7个处于亚健康,2个处于不健康。与层次分析法、主成分分析法等赋权方法相比,基于粗糙集的森林健康评价指标赋权法不需要提供任何先验信息,以森林健康系统相关信息为分析依据,直接从反映森林健康的指标数据中挖掘各因子之间的相关性及其重要度,不依赖专家经验,信息量越大,所得权重越客观。该评价指标赋权法真实有效,评价结果更符合客观实际,为森林健康评价和健康经营提供理论依据。
According to the principles of Systematic Science, we selected 13 indicators from the aspect of system health, system composition, system resilience and forestland environment which affect the forest health to construct the index system of forest health assessment. In view of the situation that the health evaluation value is unknown (no decision attribute) Based on the degree of attribute importance of rough set theory, the paper analyzes the degree of influence of each index on forest health and proposes a method of weighting forest health evaluation index based on knowledge granularity and attribute importance. According to the data of the second type survey and the sample survey, taking 8 secondary forest communities and 2 plantation plots in Dawei Mountain Nature Reserve of Hunan Province as examples, the results showed that: the forest health assessment index system canopy stand density, forest The weights of 13 indicators such as the stock volume and leaf area index in the forest health assessment of Daweishan Nature Reserve are basically in line with the results obtained by the expert scoring method and the samples were evaluated, of which only 1 was healthy and 7 A sub-health, two in unhealthy. Compared with AHP and PCA, weighting method of forest health evaluation index based on rough set does not need to provide any a priori information. Based on the information of forest health system, Of the index data to explore the correlation between the various factors and their importance, independent of expert experience, the greater the amount of information, the more objective the weight. The evaluation indicator weight method is true and effective, the evaluation results are more in line with the objective reality, and provide a theoretical basis for forest health assessment and health management.