干旱区生态环境知识资源中心(Arid): Improved mapping and understanding of desert vegetation-habitat complexes from intraannual series of spectral endmember space using cross-wavelet transform and logistic regression

Arid

DOI	10.1016/j.rse.2019.111516
	Improved mapping and understanding of desert vegetation-habitat complexes from intraannual series of spectral endmember space using cross-wavelet transform and logistic regression
	Sun, Qiangqiang 1,2,3; Zhang, Ping 1,2; Wei, Hai 4; Liu, Aixia 4; You, Shucheng 4; Sun, Danfeng 1,2,3
通讯作者	Sun, Danfeng
来源期刊	REMOTE SENSING OF ENVIRONMENT
ISSN	0034-4257
EISSN	1879-0704
出版年	2020
卷号	236
英文摘要	Desert vegetation-habitat complexes in dryland systems are fragile ecosystems with complex vegetation-habitat feedback, and have significant implications for natural environment protection and global climate change mitigation. However, a spatial-detailed and high-precision remote sensing method for the identification of desert vegetation-habitat complexes and characterization of their biophysical processes remain scarce. Here, we developed an innovative cross-wavelet transform (XWT)-based approach coupled with logistic regression to extract critical vegetation-habitat interaction characteristics in order to identify, map, and understand their complex ecological processes. Fine intraannual profiles between the green vegetation (GV) fraction and habitat fractions including dark material (DA), saline land (SA), sand land (SL) were unmixed by Multiple Endmember Spectral Mixture Analysis (MESMA) from 16-period Gaofen-1 (GF-1) wide field of view (WFV) images in Minqin County, after which XWT was performed to extract feedback characteristics as feature parameters. Major principal components (PCs) were obtained from those feature parameters to reduce dimensions and solve multi-collinearity, logistic regression was applied for mapping. The results demonstrate that the proposed procedure efficiently reproduced desert vegetation-habitat complexes with high accuracy (overall accuracy: 87.33%; Kappa coefficient: 0.86) in the entire Minqin County, representing a 3.42% overall accuracy increase relative to a previously published decision tree (DT) method. The new method also had a lower quantity and allocation disagreement. Moreover, this procedure not only achieved comparable accuracy to that of an optimized Support Vector Machine (SVM) and superior to a Convolutional Neural Network (CNN)-based U-net model, but also explored biophysical processes and complex relationships with better interpretability. Therefore, the developed approach has the potential for accurately monitoring the highly heterogeneous dryland landscape and characterizing the land degradation processes in the spectral endmember space of fine spatial-temporal remote sensing data.
英文关键词	Desert vegetation-habitat complex Endmembers fraction series Cross-wavelet transform Feature parameters Logistic regression
类型	Article
语种	英语
国家	Peoples R China
收录类别	SCI-E
WOS记录号	WOS:000502894400008
WOS关键词	NDVI TIME-SERIES ; PATTERN-ANALYSIS ; MINQIN COUNTY ; LANDSAT ; EXTRACTION ; DYNAMICS
WOS类目	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
EI主题词	2020-01-01
来源机构	中国农业大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/312214
作者单位	1.China Agr Univ, Coll Land Sci & Technol, Beijing 100193, Peoples R China; 2.Minist Nat Resources, Key Lab Agr Land Qual, Beijing 100193, Peoples R China; 3.Minist Agr, Key Lab Remote Sensing Agrihazards, Beijing 100083, Peoples R China; 4.Minist Nat Resources, Land Satellite Remote Sensing Applicat Ctr, Beijing 100035, Peoples R China
推荐引用方式 GB/T 7714	Sun, Qiangqiang,Zhang, Ping,Wei, Hai,et al. Improved mapping and understanding of desert vegetation-habitat complexes from intraannual series of spectral endmember space using cross-wavelet transform and logistic regression[J]. 中国农业大学,2020,236.
APA	Sun, Qiangqiang,Zhang, Ping,Wei, Hai,Liu, Aixia,You, Shucheng,&Sun, Danfeng.(2020).Improved mapping and understanding of desert vegetation-habitat complexes from intraannual series of spectral endmember space using cross-wavelet transform and logistic regression.REMOTE SENSING OF ENVIRONMENT,236.
MLA	Sun, Qiangqiang,et al."Improved mapping and understanding of desert vegetation-habitat complexes from intraannual series of spectral endmember space using cross-wavelet transform and logistic regression".REMOTE SENSING OF ENVIRONMENT 236(2020).