Improved mapping and understanding of desert vegetation-habitat complexes from intraannual series of spectral endmember space using cross-wavelet transform and logistic regression

Abstract

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.