The negative effects of PM2.5 concentration in urban development are becoming more and more prominent. Bernaola-Galvan Segmentation Algorithm (BGSA) and wavelet analysis are powerful tools for processing non-linear and non-stationary signals. First, we use BGSA that reveals there are 41 mutation points in the PM2.5 concentration in Guiyang. Then, we reveal the multi-scale evolution of PM2.5 concentration in Guiyang by wavelet analysis. In the first part, we performed one-dimensional continuous wavelet transform (CWT) on the eight monitoring points in the study area, and the results showed that they have obviously similar multi-scale evolution characteristics, with a high-energy and significant oscillation period of 190-512 days. Next, the wavelet transform coherence (WTC) reveals the mutual relationship between the PM2.5 concentration and the atmospheric pollutants and meteorological factors. PM2.5 concentration variation is closely linked to that of PM10 concentration. But, it is not to be ignored that the increase in the SO2 and NO2 concentrations will cause the PM2.5 concentration to rise on different scales. Lastly, the variation of the PM2.5 concentration can be better explained by the combination of multiple factors (2-4) using the multiple-wavelet coherence (MWC). Under the combination of the two factors, the average temperature (Avgtem) and relative humidity (ReH) have the highest AWC and PASC. In the case of the combination of four factors, CO-Avgtem-Wind-ReH plays the largest role in determining PM2.5 concentration.

The Qinghai-Tibet Railway (QTR) is the highest plateau artificial facility, connecting Lhasa and Golmud over Qinghai-Tibet Plateau. Climate change and anthropogenic activities are changing the condition of plateau, with potential influences on the stabilities of QTR. Synthetic aperture radar interferometry (InSAR) technique could retrieve ground millimeter scale deformation utilizing phase information from SAR images. In this study, the structure and deformation features of QTR are retrieved and analyzed using time-series interferometry with Sentinel-1A and TerraSAR-X images. The backscattering and coherence features of QTR are analyzed in medium and very high-resolution SAR images. Then, the deformation results from different SAR datasets are estimated and analyzed. Experimental results show that some of the QTR sections undergo serious deformation, with the maximum deformation rate of -20 mm/year. Moreover, the detailed deformation feature in the Beiluhe has been analyzed as well as the effects of different cooling measurements underline QTR embankment. It is also found that embankment-bridge transition along QTR is prone to undergo deformation. Our study demonstrates the application potential of high-resolution InSAR in deformation monitoring of QTR.

The Qinghai-Tibet Plateau (QTP) is heavily affected by climate change and has been undergoing serious permafrost degradation due to global warming. Synthetic aperture radar interferometry (InSAR) has been a significant tool for mapping surface features or measuring physical parameters, such as soil moisture, active layer thickness, that can be used for permafrost modelling. This study analyzed variations of coherence in the QTP area for the first time with high-resolution SAR images acquired from June 2014 to August 2016. The coherence variation of typical ground targets was obtained and analyzed. Because of the effects of active-layer (AL) freezing and thawing, coherence maps generated in the Beiluhe permafrost area exhibits seasonal variation. Furthermore, a temporal decorrelation model determined by a linear temporal-decorrelation component plus a seasonal periodic-decorrelation component and a constant component have been proposed. Most of the typical ground targets fit this temporal model. The results clearly indicate that railways and highways can hold high coherence properties over the long term in X-band images. By contrast, mountain slopes and barren areas cannot hold high coherence after one cycle of freezing and thawing. The possible factors (vegetation, soil moisture, soil freezing and thawing, and human activity) affecting InSAR coherence are discussed. This study shows that high-resolution time series of TerraSAR-X coherence can be useful for understanding QTP environments and for other applications.