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Forest Types Classification Based on Multi-Source Data Fusion

文献类型：期刊论文

作者Lu, Ming1,2; Chen, Bin3; Liao, Xiaohan1; Yue, Tianxiang1,2; Yue, Huanyin1; Ren, Shengming4; Li, Xiaowen3; Nie, Zhen5; Xu, Bing3,5
刊名REMOTE SENSING
出版日期2017-11-01
卷号9期号:11页码:22
ISSN号2072-4292
关键词data fusion forest types classification
DOI10.3390/rs9111153
通讯作者Yue, Tianxiang(yue@lreis.a.cn) ; Xu, Bing(bingxu@tsinghua.edu.cn)
英文摘要Forest plays an important role in global carbon, hydrological and atmospheric cycles and provides a wide range of valuable ecosystem services. Timely and accurate forest-type mapping is an essential topic for forest resource inventory supporting forest management, conservation biology and ecological restoration. Despite efforts and progress having been made in forest cover mapping using multi-source remotely sensed data, fine spatial, temporal and spectral resolution modeling for forest type distinction is still limited. In this paper, we proposed a novel spatial-temporal-spectral fusion framework through spatial-spectral fusion and spatial-temporal fusion. Addressing the shortcomings of the commonly-used spatial-spectral fusion model, we proposed a novel spatial-spectral fusion model called the Segmented Difference Value method (SEGDV) to generate fine spatial-spectra-resolution images by blending the China environment 1A series satellite (HJ-1A) multispectral image (Charge Coupled Device (CCD)) and Hyperspectral Imager (HSI). A Hierarchical Spatiotemporal Adaptive Fusion Model (HSTAFM) was used to conduct spatial-temporal fusion to generate the fine spatial-temporal-resolution image by blending the HJ-1A CCD and Moderate Resolution Imaging Spectroradiometer (MODIS) data. The spatial-spectral-temporal information was utilized simultaneously to distinguish various forest types. Experimental results of the classification comparison conducted in the Gan River source nature reserves showed that the proposed method could enhance spatial, temporal and spectral information effectively, and the fused dataset yielded the highest classification accuracy of 83.6% compared with the classification results derived from single Landsat-8 (69.95%), single spatial-spectral fusion (70.95%) and single spatial-temporal fusion (78.94%) images, thereby indicating that the proposed method could be valid and applicable in forest type classification.
WOS关键词REFLECTANCE FUSION ; RESOLUTION IMAGES ; LANDSAT DATA ; COVER ; PROFILES ; BIOMASS
资助项目National Key Research and Development Program of China[2017YFB0503005] ; National Key Research and Development Program of China[2016YFA0600104] ; National Natural Science Foundation of China[41771388] ; National Natural Science Foundation of China[91325204] ; National Natural Science Foundation of China[41421001] ; Science and Technology Innovation project of Jiangxi Surveying and Mapping Geographical Information Bureau
WOS研究方向Remote Sensing
语种英语
出版者MDPI AG
WOS记录号WOS:000416554100068
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Science and Technology Innovation project of Jiangxi Surveying and Mapping Geographical Information Bureau
源URL[http://ir.igsnrr.ac.cn/handle/311030/56764]  
专题中国科学院地理科学与资源研究所
通讯作者Yue, Tianxiang; Xu, Bing
作者单位1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
3.Tsinghua Univ, Dept Earth Syst Sci, Beijing 100084, Peoples R China
4.Natl Adm Surveying Mapping & Geoinformat, Key Lab Watershed Ecol & Geog Environm Monitori, Nanchang 330209, Jiangxi, Peoples R China
5.Beijing Normal Univ, State Key Lab Remote Sensing Sci, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China
推荐引用方式
GB/T 7714		 Lu, Ming,Chen, Bin,Liao, Xiaohan,et al. Forest Types Classification Based on Multi-Source Data Fusion[J]. REMOTE SENSING,2017,9(11):22.
APA Lu, Ming.,Chen, Bin.,Liao, Xiaohan.,Yue, Tianxiang.,Yue, Huanyin.,...&Xu, Bing.(2017).Forest Types Classification Based on Multi-Source Data Fusion.REMOTE SENSING,9(11),22.
MLA Lu, Ming,et al."Forest Types Classification Based on Multi-Source Data Fusion".REMOTE SENSING 9.11(2017):22.

入库方式： OAI收割

来源：地理科学与资源研究所

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