IGSNRR OpenIR
Forest Types Classification Based on Multi-Source Data Fusion
Lu, Ming1,2; Chen, Bin3; Liao, Xiaohan1; Yue, Tianxiang1,2; Yue, Huanyin1; Ren, Shengming4; Li, Xiaowen3; Nie, Zhen5; Xu, Bing3,5
2017-11-01
Source PublicationREMOTE SENSING
ISSN2072-4292
Volume9Issue:11Pages:22
Corresponding AuthorYue, Tianxiang(yue@lreis.a.cn) ; Xu, Bing(bingxu@tsinghua.edu.cn)
AbstractForest 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.
Keyworddata fusion forest types classification
DOI10.3390/rs9111153
WOS KeywordREFLECTANCE FUSION ; RESOLUTION IMAGES ; LANDSAT DATA ; COVER ; PROFILES ; BIOMASS
Indexed BySCI
Language英语
Funding ProjectNational 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
Funding OrganizationNational 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
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000416554100068
PublisherMDPI AG
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/56765
Collection中国科学院地理科学与资源研究所
Corresponding AuthorYue, Tianxiang; Xu, Bing
Affiliation1.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
Recommended Citation
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.
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