基于Landsat TM数据的大兴安岭盘古林场森林健康评价

  目的  健康评价是实施森林资源健康经营的前提和基础，但现有研究多从单一尺度开展，未充分考虑森林生态系统的层级结构。为此，该文以林木冠层特征为基础，结合Landsat TM数据和统计学方法实现森林健康评价的多尺度转换，为我国森林健康经营提供理论依据和技术支撑。  方法  以大兴安岭盘古林场50块固定样地单木健康调查数据为基础，采用熵值?AHP综合指数法构建单木健康评价模型，并汇总得到样地尺度健康得分的平均值（H_m）、标准差（H_std）、变异系数（H_cv）、偏度（H_pd）和峰度（H_fd）共5项统计指标，结合Landsat TM和地形数据并采用非线性度量误差联立方程组模型构造区域尺度森林健康评价综合反演模型，实现了森林健康从单木?林分?区域的多尺度综合评估。  结果  样地调查结果表明:盘古林场单木健康得分均值为0.663 8 ± 0.091 2，整体处于亚健康水平，其中亚健康树木所占比例最高（79.43%）；区域内不同树种健康等级间差异显著，主要表现为云杉 > 白桦 > 兴安落叶松 > 山杨 > 樟子松；林分尺度健康得分的H_m、H_std、H_cv、H_pd和H_fd值分别为0.663 3、0.084 1、12.84%、?0.607 6和0.846 0，表明该地区约78.43%的林分中单木健康得分呈明显左偏尖削状正态分布；遥感反演结果表明:区域内森林健康得分H_m均值约为0.619 4 ± 0.054 3，其主要受地形（DEM）、植被指数（RVI、DVI、EVI和Green）和原始波段（B1、B3）等多种因素的综合影响，所建模型的预估精度可达到75%以上，能够满足森林健康评价的需求；林场范围内林木健康得分整体呈现南低北高的格局，且H_m较高区域主要集中在居民点、道路沿线等交通便利地区。  结论  盘古林场森林整体以亚健康为主，亟待开展科学的健康经营；同时，该文提出的以林木冠层特征为基础并充分结合遥感数据和统计学方法，能够有效实现森林健康的多尺度评价。 

Forest health assessment of Pangu Forest Farm based on Landsat TM in Great Xing'an Mountains of northeastern China

Objective]Health assessment is one of the important prerequisites for implementing sustainable forest management,however most of the previous studies were carried out only on a single scale,without considering the hierarchical structures of forest ecosystems.Therefore,the present study focused on the canopy characteristics,and studied the method of scale transformation for the forest health assessment by the remote sensing and statistical method,which can provide theoretical support and guidance for the forest health management in China.Method]Based on the datasets of individual-tree health survey from 50 sample plots in Pangu Forest Farm,the health assessment model of individual-tree was constructed using the entropy-AHP comprehensive index method.Five commonly used statistical indicators,namely mean value(H_m),standard deviation(H_std),coefficient of variation(H_cv),skewness(H_pd)and kurtosis(Hfd),were summarized for each sample plot based on the health assessment results from tree-level.Then,a comprehensive forest health assessment model of regional-level was developed by combining the Landsat TM and topographic data using the nonlinear error-in-variable simultaneous equations model.Finally,the forest health status and their spatial distribution characteristics of Pangu Forest Farm were quantitatively analyzed.Result]The sample plot survey datasets indicated that the average health score of individual-tree in Pangu Forest Farm was 0.6638±0.0912,belonging to the sub-health level,among which the proportion of sub-healthy trees was the highest(79.43%);the differences of the health grades among different tree species were significant,namely Picea asperata>Betula platyphylla>Larix gmelinii>Populus davidiana>Pinus sylvestris;the statistical values of H_m,H_std,H_cv,H_pd and Hfd,for the health scores at stand-level were 0.6633,0.0841,12.84,-0.6076 and 0.8460,respectively,indicating that approximately 78.43%of the total forests had a significant left-pointed normal distribution;the remote sensing inversion results showed that the regional-level health score H_m was about 0.6194±0.0543,in which topographic(DEM),vegetation index(RVI,DVI,EVI and Green)and original bands(B1,B3)were the key driving factors.The estimated accuracy of the constructed NESEM model was all larger than 75%,which could meet the needs of forest health assessment;in addition,a significant pattern that gradually decreased from north to south was observed for the mean forest health scores,in which the higher scores of H_m were usually concentrated in the convenient transportation areas,such as the areas of residential and forest roads.Conclusion]The forests in study area were mainly sub-health,which may be urgent to carry out scientific health management.Meanwhile,the multi-scale transformation method presented in the study,namely combining the canopy characteristics with the results of forest health assessments by remote sensing and statistical methods,could achieve the scale conversions of forest health assessments among different levels very well.