摘要
以内蒙古自治区根河市根河生态站为研究区,探讨在大面积复杂林区、具有红边波段卫星数据支持下,高空间分辨率遥感影像林地类型精细分类方法。以2016年7月的RapidEye遥感影像和2017年的GF-1PMS遥感影像为主要数据源,综合利用影像的光谱特征、纹理特征与根河森林资源小班数据等辅助信息,以及2016年林地类型外业调查样本数据,分别对2种数据源采用传统的监督分类方法[最大似然法(MLC)和支持向量机法(SVM)]和基于IDL语言的ImageSVM和ImageRF分类方法进行林地类型精细识别。最后以外业调查数据和根河森林资源小班数据作为检验样本对分类结果进行精度验证,通过建立混淆矩阵对分类结果进行评价。结果表明:①ImageRF和ImageSVM等2种分类方法对林地类型信息提取精度较高。在RapidEye影像中,针叶林、阔叶林、灌木林等8种地物类型总体分类精度分别为90.26%和90.02%,Kappa系数均大于0.88。ImageSVM和ImageRF分类结果中,灌木林、针叶林和阔叶林制图精度和用户精度均高于支持向量机法和最大似然法;相对于支持向量机法和最大似然法,ImageSVM法总体分类精度分别提高了6.18%和7.06%,Kappa系数分别提高了0.07和0.08;ImageRF法总体分类精度分别提高了5.93%和6.82%,Kappa系数分别提高了0.07和0.08,能确保森林资源调查成果的精细化、准确性、高效性。②在林地类型精细识别中,携带红边波段信息的RapidEye影像比无红边波段信息的GF-1影像具有更好的识别精度和可分性。研究证明,ImageSVM和ImageRF分类方法是有效的林地类型信息精细识别方法,具有精度高和可信度高的优势,是进行复杂山区林地类型精细分类的有效手段,可满足森林资源调查、变化监测、数字更新等林业应用需求。
To compare classification methods of forest land types based on high spatial resolution and remote sensing image data in forested areas,Genhe Ecological Station,Genhe City,Inner Mongolia Autonomous Region was selected and red-edge band satellite data was used.With the RapidEye and GF-1 remote sensing images as the main data source,a comprehensive use of imaged spectral features,textural features,forest resources data,and other auxiliary information,as well as the forest land type field survey sample data in 2016,were compared.The ImageSVM,an Interactive Data Language(IDL)based tool for the support vector machine(SVM)classification,ImageRF,an IDL based tool for the random forest(RF)classification,and traditional classifications,like Maximum Likelihood Classification(MLC)and SVM,were used to precisely classify forest land types.Finally,results of a field survey and forest resources data were used as test samples to verify the classification results with precision verification.Results showed that ImageRF and ImageSVM had high precision for forest type information extraction.With RapidEye image,the overall classification accuracy of eight species types such as coniferous forest,broad-leaved forest and shrubbery were 90.26%and 90.02%respectively with a Kappa coefficient greater than 0.88.At the same time,the protraction accuracy and user precision of shrubbery,coniferous forest,and broadleaf forest in ImageSVM and ImageRF were higher than SVM and MLC.The overall classification accuracy with the ImageSVM method was higher than SVM and MLC classification increased 6.18%and 7.06%respectively,and the Kappa coefficient increased 0.07 with SVM and 0.08 with MLC.The ImageRF method improved 5.93%on SVM and 6.82%on MLC,and the Kappa coefficient increased by 0.07 for SVM and 0.08 for MLC.Also,for fine identification of forest land types,the RapidEye image carrying red-edge band information had better recognition precision and separation ability than Landsat 8 OLI images with no red edge band information.Thus,the ImageSVM and th
作者
张兆鹏
李增元
田昕
ZHANG Zhaopeng;LI Zengyuan;TIAN Xin(The First Geodetic Surveying Brigade of MNR,Xi’an 710054,Shaanxi,China;Forest Institute of Forest Resources Information Technique,Chinese Academy of Forestry,Beijing 100091,China)
出处
《浙江农林大学学报》
CAS
CSCD
北大核心
2019年第5期857-867,共11页
Journal of Zhejiang A&F University
基金
高分辨率对地观测系统重大专项（民用部分）科研项目（21-Y20A06-9001-17/18）
中央级公益性科研院所中国林业科学院“杰出青年”基金项目(CAFYBB2017MA005)
