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[Objective] The lakes of the Tibetan Plateau are important indicators of climate change, and their expansion or contraction also have an important impact on the natural environment of the Tibetan Plateau. [Methods] Three typical lakes located in different climatic sub-regions of the Qinghai-Tibet Plateau (Qinghai Lake, Yangzhuo Yongcuo, and Ulan Ula Lake) were selected to study the temporal and spatial variation of the hydrological characteristics of the three typical lakes from 1985 to 2021 by remote sensing monitoring methods, to reveal the role of key climatic factors, and to further explore the influence of glaciers and permafrost on the typical lakes. [Results] The area and water level of Qinghai Lake showed a significant upward trend, with an area increase of 238.68 km2 and a water level increase of 1.32 m. In space, it showed a trend of expansion in the east-west direction. The area and water level of Yangzhuo Yongcuo Lake first fluctuated and then decreased, decreasing by 16.31 km2 and 3.25 m respectively. In space, the overall area showed a shrinking trend from the surrounding to the center. The area and water level of Ulan Ula Lake showed a significant upward trend, increasing by 125.57 km2 and 8.12 m, respectively, and the expansion area was mainly concentrated in the south. [Conclusion] The analysis of key climate factors shows that the lake expansion caused by the increase of precipitation and the accelerated melting of glacial permafrost in the warm and humid environment is the most prominent environmental change characteristics of the Qinghai-Tibet Plateau. The main reason for the rise in the water level of Ulan Ula Lake is the seasonal thaw of permafrost caused by rising temperatures. Exploring the changes in lake area on the Tibetan Plateau is of great guiding significance for in-depth research on global climate change and surface water resource assessment.

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The distribution range of first-level watershed in endorheic basin of China was gotten by combing digital elevation model and drainage basin partition plan of China. Besides, based on Landsat TM/ETM+/OLI remote sensing images in the period of 2000-2014, the vectorized boundary of 88 lakes with areas greater than 100 km2 were obtained by artificial visual interpretation technology and then the causes of lake variation were analyzed. The results show that: a) Among seven primary endorheic basins of China, there are 88 lakes that area greater than 100 km2 and the total area is 32,018.46 km2 except Ili River watershed. b) During the period of 2000-2014, the area of endorheic basin of China expends with the speed of 231.95 km2 per year, which totally increases to 2,347.33 km2. The lake area of Qaidam Basin and Qinghai-Tibet Plateau hinterland is continuously expending. Although the areas of Tarim Basin and Junggar Basin are shrinking after 2003, the lakes of the north of Yinshan and Qilian Mountains are increased smoothly. c) During researching, majority of lakes expend obviously, most of them concentrated on Hohxil and Qiangtang Plateau. The shrinking area scatters throughout the study area. d) The main reason why lakes expending are because of increasing precipitation, decreasing evaporation and melting of glaciers and frozen oil. Local climate, waterway of lakes and human activities also affect the reduction and fluctuation of some lakes. [ABSTRACT FROM AUTHOR]

As the elevation information is not considered in the traditional building change detection methods, this paper presents an algorithm of combining LiDAR data and aerial imagery for the 3D building change detection. With the proposed method, we can extract both the elevation change information and the area change information of the buildings at the same time. Firstly, two DSMs are generated using two periods of LiDAR data. Secondly, differencing, filtering and morphological operations are performed to get the changed DSM area, which is then projected onto the aerial images according to the collinearity equations. After that, the interference of the pseudo-changing areas such as trees is removed using spectrum and texture information of aerial image. Finally, the value of elevation changes and area changes are calculated. Experimental results show that the proposed algorithm can extract the change information of the elevation and area quantitatively, which can provide more comprehensive and accurate information for the building change detection. [ABSTRACT FROM AUTHOR]