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Aiming at the limited degree of revealing vegetation growth pattern by simple topographic factors, it is essential to find an index that can explain the vegetation cover mechanism to a greater extent. By introducing a certainty factor into vegetation cover evaluation, LSW, LS, topographic wetness index, and aspect were also chosen to carry out control group experiments. The grid digital elevation model of 10 disaster-stricken counties (cities) in Sichuan Province was used as the basis to obtain LSW, while vegetation susceptibility levels were categorized using the natural breaks classification. The results of the multi-factor analysis demonstrated that the certainty factor corresponding to LSW climbed linearly from negative to positive values as the susceptibility level rose, indicating that it was highly correlated with vegetation cover and had an obvious advantage in revealing the vegetation growth potential. Statistically, the vegetation occurrence rate increased regularly with the improvement of the susceptibility level, in which the high and very high susceptibility zones corresponded to 83.66% and 89.95% of vegetation coverage respectively, suggesting a good consistency between the delineated high susceptibility areas and the actual vegetation cover. The findings proved that LSW has a substantial advantage in evaluating vegetation cover, with an evaluation model accuracy of 72.9%. It has been established that LSW will serve as a promising new model for assessing vegetation restoration and planning. [ABSTRACT FROM AUTHOR]

Aerosol vertical profiling is crucial to understand the formation mechanism and evolution processes of haze, which have not yet been comprehensively clarified. In this study, we investigated a severe, persistent haze event in Wuhan (30.5° N, 114.4° E), China during 5–18 January 2013 by the use of a polarization lidar, a Cimel sun photometer, meteorological datasets, and the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model, focusing on the time–height evolution of aerosols in both the atmospheric boundary layer (ABL) and elevated layers. During the haze period, the integrated particle depolarization ratio was 0.05 ± 0.02, and the fine mode fraction reached 0.91 ± 0.03, indicating haze particles were rather spherical and predominately submicron, that is, of anthropogenic nature. Compared with the clear period, columnar aerosol optical depth at 500 nm tripled to 1.32 ± 0.31, and the strongest enhancement in aerosol concentration occurred from near the ground to an altitude of 1.2 km during the haze period. The daytime evolution of aerosol vertical distribution in the ABL exhibited a distinct pattern under haze weather. Abundant particles accumulated below 0.5 km in the morning hours due to stable meteorological conditions, including a strong surface-based inversion (4.4–8.1 °C), late development (from 1000–1100 LT) of the convective boundary layer, and weak wind (<4 m∙s−1) in the lowermost troposphere. In the afternoon, improved ventilation delivered an overall reduction in boundary layer aerosols but was insufficient to eliminate haze. Particularly, the morning residual layer had an optical depth of 0.29–0.56. It influenced air quality indirectly by weakening convective activities in the morning and directly through the fumigation process around noon, suggesting it may be an important element in aerosol–ABL interactions during consecutive days with haze. Our lidar also captured the presence of the elevated aerosol layers (EALs) embodying regional/long-range transport. Most of the EALs were observed to subside to <1.2 km and exacerbate the pollution level. Backward trajectory analysis and lidar data revealed the EALs originated from the transport of anthropogenic pollutants from the Sichuan Basin, China, and of dust from the deserts in the northwest. They were estimated to contribute ~19% of columnar aerosol-loading, pointing to a non-negligible role of transport during the intense pollution episode. The results could benefit the complete understanding of aerosol–ABL interactions under haze weather and air quality forecasting and control in Wuhan. [ABSTRACT FROM AUTHOR]