Dust particles from the Taklimakan Desert can be lofted vertically up to 10 km due to the unique topography and northeasterly winds associated with certain synoptic conditions. Then they can be transported horizontally to regions far downwind by westerlies. We combined data from the Multiangle Imaging Spectroradiometer (MISR) and the Cloud-Aerosol Lidar with Orthogonal Polarization to investigate the three-dimensional distribution of dust over the Taklimakan Desert and surrounding areas. During spring and summer, a dust belt with high aerosol optical depths (AOD) extends eastward from the Taklimakan Desert to the Loess Plateau along the Hexi Corridor and southward to the Tibetan Plateau. However, the dust extinction coefficients decrease rapidly from 0.340 km À1 near surface to 0.015 km À1 at 5 km in spring, while the extinction values vary within 0.100 ± 0.020 between the altitudes of 1.6 and 3.5 km and decrease to 0.023 km À1 at 5 km in summer, indicating that dust aerosol is relatively well mixed vertically. We further used MISR daily AOD to identify high-and low-dust days and then analyzed composite difference patterns of temperature, geopotential height, and wind between high-and low-dust days. It was found that although the synoptic situations of spring and summer are quite different, there are two common features: a strong anticyclonic wind anomaly over the Taklimakan at 500 hPa and an enhanced easterly wind over the Tarim Basin at 850 hPa for the two seasons. These conditions are favorable for dust entrainment from the dry desert surface, vertical lofting, and horizontal transport.

Abstract. Street trees are common features and important assets in urban scenes. They are huge in numbers and are constantly changing, thus are difficult to monitor on a regular basis. A method of automatic extraction and dynamic analysis of street trees based on mobile LiDAR data is proposed. First, ground and low objects are filtered from the point clouds. Then, based on a geometric tree model and semantic information, each tree point cloud is extracted, and geometrical parameters such as location, trunk diameter, trunk structure line, tree height, crown width, and crown volume of each tree is obtained. A dynamic analysis combined with the growing characteristics of trees is conducted to compare and analyse the street trees from different epochs, in order to understand whether the trees have grown or been pruned, replanted, or displaced. The proposed algorithm was tested on three epochs of mobile LiDAR data, obtained in 2010, 2016 and 2018, respectively. Experimental results showed that the proposed method was able to accurately detect trees and extract tree parameters for detailed dynamics analysis.