气候变化领域集成服务门户(CCPortal): Improved mapping and understanding of desert vegetation-habitat complexes from intraannual series of spectral endmember space using cross-wavelet transform and logistic regression

CCPortal

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 Q.; Zhang P.; Wei H.; Liu A.; You S.; Sun D.
发表日期	2020
ISSN	00344257
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 multicollinearity, 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. © 2019 Elsevier Inc.
英文关键词	Cross-wavelet transform; Desert vegetation-habitat complex; Endmembers fraction series; Feature parameters; Logistic regression
语种	英语
scopus关键词	Climate change; Complex networks; Decision trees; Ecosystems; Landforms; Neural networks; Photomapping; Regression analysis; Remote sensing; Space optics; Support vector machines; Vegetation; Cross-wavelet transform; Desert vegetation-habitat complex; Endmembers; Feature parameters; Logistic regressions; Wavelet transforms; accuracy assessment; climate change; dryland farming; environmental protection; field of view; global climate; land degradation; mapping method; parameter estimation; principal component analysis; regression analysis; remote sensing; satellite data; support vector machine; transform; vegetation dynamics; wavelet analysis; China; Gansu; Minqin
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179575
作者单位	College of Land Science and Technology, China Agricultural University, Beijing, 100193, China; Key Laboratory of Agricultural Land Quality, Ministry of Natural Resources, Beijing, 100193, China; Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing, 100083, China; Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing, 100035, China
推荐引用方式 GB/T 7714	Sun Q.,Zhang P.,Wei H.,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],2020,236.
APA	Sun Q.,Zhang P.,Wei H.,Liu A.,You S.,&Sun D..(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 Q.,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).