摘要
以青岛黄岛区为研究区,利用资源三号卫星立体像对提取精细的DEM (Digital Elevation Model),使用5种地形校正模型(Teillet-回归,VECA,Cosine-C,C和SCS+C)对Quick Bird多光谱影像进行地形校正,并结合面向对象方法提取得到山区黑松的空间分布信息。结果表明:5种模型中,Quick Bird影像经VECA,SCS+C,C校正模型校正后山区阴影有较好的减弱效果,且山区黑松分布提取的精度均有所提高,其中以VECA模型的提取精度最佳,提取精度从70. 25%提高到84. 30%,提高了14. 05%; Kappa系数从0. 53提高到0. 72,提高了0. 19。本研究可为光学高分遥感影像在山区松树的分布提取上提供参考。
Five topographic correction models(Teillet-regression,VECA,Cosine-C,C,and SCS+C)were employed in the present study in Huangdao,Qingdao to calibrate the Quick-Bird multispectral images combining with fine Digital Elevation Model(DEM)generated by stereo images of domestic ZY-3 satellite.Then,we examined and compared the results of 5 models to evaluate the effects of different topographic correction models on extracting the distribution of pine.The results show that Quick-Bird images corrected by a combination of 2m DEM and 3 models(VECA,C and SCS+C)can better maintain imagery’s spectral characteristic and weaken the effect of mountain shadows than the other 2.Quick-Bird images calibrated by these 3 models have significantly improved the extraction accuracy of Pinus thunbergii.Among these 3 models,VECA is the best one for its elevation of the overall accuracy by 14.05%(from 70.25% to 84.30%)and the Kappa coefficient by 0.19(from 0.53 to 0.72).This research can provide a reference for the extraction of Pinus thunbergii distribution by using remote sensing imagery.
作者
邓世晴
陶欢
李存军
刘荣
胡海棠
DENG Shiqing;TAO Huan;LI Cunjun;LIU Rong;HU Haitang(Faculty of Geomatics,East China University of Technology,Nanchang 330013,China;Beijing Research Center for Information Technology in Agriculture,Beijing 100097,China;National Engineering Research Center for Information Technology in Agriculture,Beijing 100097,China)
出处
《林业资源管理》
北大核心
2018年第6期138-145,共8页
Forest Resources Management
基金
国家重点研发计划(2016YFC0501601)
