CAJ | 학술논문

【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。
【목적】이용혼합상원분해기술연구일충쾌속、준학제취산핵도공간분포신식적신방법，위아열대경제림자원급기동태변화적쾌속검측제공신수단。【방법】이절강성림안시서부위연구구，수선，채용선성혼합상원분해기술획취식피(GV)、음영(shade)화토양(soil)3개분량도，거실지고찰，기우산핵도적 GV，shade 화 soil분량여기타식피적구분성교대적특정，구건식피－토양지수、식피－음영지수화귀일화다분량지수3충신적지수；연후，기우귀일화식피지수화신적지수건립결책수모형제취산핵도；최후，장연구구적토지복개류형분위산핵도화기타지류，병통과지면조사수집적양지수거화 Google Earth고분변솔영상대분류결과진행험증。【결과】귀일화다분량지수가최대한도지확대산핵도여기타재광보상역혼효적식피지간적차거，여기타식피적가분리성최호，인차，장귀일화다분량지수작위제취산핵도적최우지수。기우해지수제취산핵도적총체정도체88.67%，Kappa계수위0.76，성공실현림안서부구역적산핵도신식제취，증명사용선성혼합상원분해모형제취산핵도적잠력。【결론】침대산핵도경제림제취이제출적귀일화다분량지수，물리의의명학，실현간단，역우리해화분석，진가능지강저유우보취복잡、양본류수다이조성적계통오차화인위오차，기결과환가위금후기타지구산핵도적제취혹구유상사생장조건적경제림공간분포신식적제취제공삼고，구유교고적응용잠력화추엄개치。
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,vegetation-shade 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 the separation 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 classification map was obtained by using field inventory data and high-resolution image of Google Earth.[Result]This study indicated that the normalized multi-fraction index could enlarge the difference of hickory from other broadleaf forests and could be successfully used to extract hickory plantation in this study area. The accuracy assessment result indicated that an overall accuracy of 88. 67% with kappa coefficient of 0. 76 was obtained in this study and implied that the LSMM based approach was promising in mapping hickory plantation.[Conclusion] Comparing with commonly used classification methods,the proposed normalized multi-fraction index has advantages in physical meaning,easy use and understanding, and the requirement in sample plots,thus,this new approach has the potential to provide a better classification accuracy than traditional classification algorithms. Furthermore,this approach may be used to map other plantations such as bamboo forest spatial distributions.