Forest Types Classification Based on Multi-Source Data Fusion

doi:10.3390/rs9111153

	Forest Types Classification Based on Multi-Source Data Fusion
	Lu, Ming 1,2; Chen, Bin 3; Liao, Xiaohan 1; Yue, Tianxiang 1,2; Yue, Huanyin 1; Ren, Shengming 4; Li, Xiaowen 3; Nie, Zhen 5; Xu, Bing 3,5
刊名	REMOTE SENSING
	2017-11-01
卷号	9 期号:11 页码:22
关键词	data fusion forest types classification
ISSN号	2072-4292
DOI	10.3390/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.
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关键词	REFLECTANCE FUSION ; RESOLUTION IMAGES ; LANDSAT DATA ; COVER ; PROFILES ; BIOMASS
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
内容类型	期刊论文
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.