【目的】森林生态系统具有明显的层级结构,如何将样本点或样本区健康评价结果推广到区域尺度上是一个亟待解决的关键问题。为此,本研究尝试采用统计学方法实现森林健康评价结果在单木、林分和区域尺度间的转换,为森林健康经营提供理论依据和技术支撑。【方法】以大兴安岭地区盘古林场51块样地和森林资源二类调查数据为基础,采用熵值-AHP法构造单木尺度健康评价模型,并汇总得到林分尺度健康评价结果,即平均值(H_m)、标准差(H_sd)、变异系数(H_cv)、偏度(H_pd)和峰度(H_fd),进而采用度量误差模型建立林分尺度健康综合评价模型。最后,借助所建模型和森林资源二类调查数据以及DEM数据进行区域尺度森林健康得分空间分布制图,并分析其空间分布规律。【结果】所建林分尺度健康综合评价模型的预估精度相对较高,H_m、H_sd、H_cv、H_pd和H_fd预测模型的确定系数R²分别达到0.464 3、0.305 6、0.909 6、0.298 1和0.448 5,能够基本满足森林健康评价的需求;单木、林分和区域尺度评价结果均表明该地区森林整体处于亚健康水平,其中林分因子(树高、优势高、树种数量、株树密度、断面积和单位蓄积)与地形因子(海拔、坡度、坡位)共同影响林分健康得分及其分布情况;盘古林场森林健康得分平均值(H_m)为0.623 4,整体处于亚健康水平;各评价指标均具有明显的空间分布趋势,其中H_m、H_cv、H_pd和H_fd较大的区域主要集中在林场北部且靠近居民点和交通便利的地区,而在南部偏远地区则普遍较低,但H_sd的空间分布格局则完全相反,表明适当的森林经营有助于提升森林的健康水平。【结论】统计学方法能够实现森林健康评价结果从单木、林分到区域尺度的有效转换。

【Objective】 Forest ecosystems have an obvious hierarchical structure; thus, the concept of health assessment results from the sample points or sample areas at the regional scale is currently an important issue in sustainable forest management. Therefore, the goal of this study was to achieve the efficient conversion of forest health assessment results among trees, stands and regional levels using statistical methods. It may provide a theoretical basis and technical support for forest health management in northeastern China. 【Method】 Based on the datasets of 51 sample plots and forest resource inventories in the Pangu Forest Farm in the Greater Khingan Mountains, a tree-level health assessment model was constructed using the entropy-AHP method. Then, five statistical indicators, namely, mean value (H_m), standard deviation (H_sd), coefficient of variation (H_cv), skewness (H_pd) and kurtosis (H_fd), were generated for each plot, which were treated as the results of a stand-level health assessment. The error-in-variable regression model was applied to develop a comprehensive evaluation model of stand-level health status. Finally, the spatial distributions of regional-level forest health scores were mapped using the estimated regression model and forest management inventory datasets. The basic characteristics and patterns of the five statistical indicators were analyzed. 【Result】 The prediction accuracy of the estimated forest health comprehensive evaluation model was relatively high, in which the values of determination coefficients (R²) of the H_m, H_sd, H_cv, H_pd and H_fd models were as high as 0.464 3, 0.305 6, 0.909 6, 0.298 1 and 0.448 5, respectively, meeting the potential demands of forest health assessment. The results of tree-, stand-, and regional level health assessments indicated that the forests within the study area mainly belonged to the category of sub-health, in which the stand (height of individual tree, height of dominant tree, number of tree species, number of trees per hectare, stand basal area and stand volume) and topographic factors (elevation, slope and slope position) affected the forest health scores and their distributions. The average values of forest health were 0.623 4, which belonged to the sub-health level. With respect to the five statistical indicators, they all exhibited obvious spatial patterns, in which the stands with larger H_m, H_cv, H_pd and H_fd values were mainly concentrated in the northern part of the forest farm, in particular being close to settlements and roads. However, the values were significantly lower in the southern portion where transportation was usually not convenient. Furthermore, the spatial distribution pattern of H_sd was completely opposite to that of the other indicators, revealing that the adaptive management is meaningful in the improvement of forest health levels. 【Conclusion】 The statistical methods used in this study could efficiently achieve scale conversions of forest health assessments at the trees, stands and regional levels.