中文摘要：

【目的】利用混合像元分解技术研究一种快速、准确提取山核桃空间分布信息的新方法,为亚热带经济林资源及其动态变化的快速检测提供新手段。【方法】以浙江省临安市西部为研究区,首先,采用线性混合像元分解技术获取植被（GV）、阴影（shade）和土壤（soil）3个分量图,据实地考察,基于山核桃的GV,shade和soil分量与其他植被的区分性较大的特征,构建植被-土壤指数、植被-阴影指数和归一化多分量指数3种新的指数;然后,基于归一化植被指数和新的指数建立决策树模型提取山核桃;最后,将研究区的土地覆盖类型分为山核桃和其他地类,并通过地面调查收集的样地数据和Google Earth高分辨率影像对分类结果进行验证。【结果】归一化多分量指数可最大限度地扩大山核桃与其他在光谱上易混淆的植被之间的差距,与其他植被的可分离性最好,因此,将归一化多分量指数作为提取山核桃的最优指数。基于该指数提取山核桃的总体精度达88.67%,Kappa系数为0.76,成功实现临安西部区域的山核桃信息提取,证明使用线性混合像元分解模型提取山核桃的潜力。【结论】针对山核桃经济林提取而提出的归一化多分量指数,物理意义明确,实现简单,易于理解和分析,尽可能地降低由于步骤复杂、样本类数多而造成的系统误差和人为误差,其结果还可为今后其他地区山核桃的提取或具有相似生长条件的经济林空间分布信息的提取提供参考,具有较高的应用潜力和推广价值。

英文摘要：

【Objective】Hickory（ Carya cathayensis）,one of the most important cash forests in Zhejiang province,plays an important role in improving economic conditions for local people and government. Currently,the hickory plantation area is mainly calculated from the estimation of hickory owners,but this area amount is often inaccurate and lack of spatial distribution information. Remote sensing with its unique characteristics in data collection and presentation has become the primary data source for mapping land cover distribution in a large area. However,mapping hickory plantation using remote sensing data remains a challenge because of the fact that hickory is a broadleaf tree and its plantation is often confused with other broadleaf forests in spectral signatures. Therefore,this research selected region of western Lin＇an county,Zhejiang province,as a study area to explore the approach to map hickory distribution. Two Landsat 8 OLI images with leaf-on and leaf-off seasons in 2013 were used. 【Method 】 Firstly,spectral mixture model（ LSMM） was used to unmix Landsat multispectral imagery into three fraction images-green vegetation,shade and soil. Secondly,because hickory plantation has slightly different forest stand structure comparing with other broadleaf forest,their compositions of green vegetation,shade,and soil will be various. Based on this feature,three new indices,those are,vegetation-soil index,vegetationshade index,and normalized multi-fraction index were proposed. Field survey data covering hickory plantations and other broadleaf forests were used to conduct a comparative analysis of these fraction images and newly proposed indices for theseparation between hickory and other broadleaf forests. Thirdly,a decision-tree classifier was constructed by taking into account of Normalized Difference Vegetation Index（ NDVI） and new index for mapping hickory distribution. Finally,the land-cover types of the research area were divided into two categories： hickory-others. The accuracy assessment of cl