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掌握森林病虫害的发生范围和危害程度,对于森林管理部门制定及时、有效的防治决策至关重要。本研究以2014年湖北省神农架林区华山松大小蠹(Dendroctonus armandi)灾害为背景,以野外调查数据、多光谱陆地资源卫星影像(Landsat)和数字高程模型(DEM)为基础数据源,结合最大熵(Max Ent)模型和迭代阈值分割算法,提出了适用于复杂林区的森林病虫害遥感监测方法(Max Ent-Segmentation),实现了神农架林区华山松大小蠹灾害空间分布范围和灾害程度的专题制图与精度评价。同时,为衡量所提出方法对于灾害程度评估的可靠性与准确度,本文还与传统光谱指数分析法进行了对比研究。结果表明:结合遥感光谱指数、海拔、坡度及有效太阳辐射等环境因子构建的Max Ent模型能够较为准确地监测华山松大小蠹灾害发生范围,受试者工作特征曲线下面积(AUC)值为0.938;当分类类型包括健康、轻度和重度时,Max Ent-Segmentation法分类精度最高达73.68%,明显高于传统光谱指数分析法(64.47%),表明该算法能够提高森林虫灾监测精度,适合用于植被类型多样、地形复杂林区的病虫害遥感监测。
Grasping the scope and degree of damage caused by forest pests and diseases is of crucial importance to forest management departments in formulating timely and effective prevention and control decisions. In this study, based on the disasters of Dendroctonus armandi (Dendroctonus armandi) in Shennongjia forest region of Hubei Province in 2014, based on field survey data, Landsat and DEM data, Entropy (Max Ent) model and iterative thresholding algorithm, this paper proposes a Max Ent-Segmentation method for forest pests and diseases which is suitable for complex forest areas, and realizes the spatial distribution range and disaster degree of the giant-bellied Pinus armandii in Shennongjia forest area Cartography and Precision Evaluation. At the same time, in order to measure the reliability and accuracy of the proposed method for the assessment of disaster degree, this paper also compared with the traditional spectral index analysis method. The results showed that the Max Ent model constructed by environmental factors such as remote sensing spectral index, elevation, slope and effective solar radiation could accurately monitor the occurrence of the giant-tree beetle disaster, and the area under the working characteristic curve (AUC) was 0.938 The classification accuracy of Max Ent-Segmentation method was 73.68%, which was significantly higher than that of the traditional spectral index method (64.47%) when the classification types included health, mildness and severeness, indicating that this algorithm can improve the monitoring precision of forest pests and diseases, Remote sensing monitoring of pests and diseases in diverse and topographical complex forest areas.