森林叶面积指数遥感反演模型构建及区域估算  

Application of Remote Sensing to Inverse the Forest Leaf Area Index and Regional Estimation

作　　者：刘婧怡[1,2] 汤旭光[2,3] 常守志[2,4] 贾明明[2] 董张玉[2] 邵田田[2] 丁智[2] 

机构地区：[1]中国地质大学信息工程学院,湖北武汉430074 [2]中国科学院东北地理与农业生态研究所,吉林长春130102 [3]黄河水利委员会信息中心,河南郑州450004 [4]长春市城乡规划设计研究院,吉林长春130012

出　　处：《遥感技术与应用》2014年第1期18-25,共8页Remote Sensing Technology and Application

基　　金：中国科学院战略性先导科技专项(XDA05050101);国家973计划项目(2009CB421103)

摘　　要：基于eCognition面向对象分类算法及校正后的TM遥感影像,获取研究区2010年土地利用/覆被数据。同时在ArcGIS平台下,提取遥感影像6个波段反射率及RVI、NDVI、SLAVI、EVI、VII、MSR、NDVIc、BI、GVI和WI等10个植被指数,并辅助于DEM、ASPECT、SLOPE等地形信息,在与植物冠层分析仪(TRAC)实测各森林类型叶面积指数相关性分析的基础上,研究表明:相对多元线性回归方法,偏最小二乘法能够更好地把握各森林类型LAI动态变化,而后结合研究区森林覆被信息进行区域估算。Based on the object-oriented land cover classification technique from eCognition and the corrected Landsat TM data,this paper acquired the land use/cover data of the study area in 2010. Then the data was further divided into coniferous forest, broad-leaved forest, mixed broadleaf-conifer forest and non-forestry land. Meanwhile under the ArcGIS platform, six bands of reflectance values, ten kinds of vegetation index including RVI, NDVI, SLAVI, EVI, VII, MSR, NDVIc, BI, GVI and WI, and the topographic factors as DEM,ASPECT and SLOPE, were calculated to analyze the correlations between the corresponding forest LAI and measured using TRAC with each factor. Then compared the model performance of multiple linear regression with partial least squares method, this paper established the optimal model to retrieve the forest LAI of each forest type. At last, the distribution map of forest LAI in this study area was made by integra- ting remote sensing inversion model with the forest classification data acquired beforehand.

关 键 词：森林 叶面积指数 遥感反演 区域估算 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]