Urban agglomeration is an advanced spatial form of integrating cities, resulting from the global urbanization of recent decades. Understanding spatiotemporal patterns and evolution is of great importance for improving urban agglomeration management. This study used continuous time-series NTL data from 2000 to 2018 combined with land-use images to investigate the spatiotemporal patterns of urbanization in the three most developed urban agglomerations in China over the past two decades: the Beijing–Tianjin–Hebei urban agglomeration (BTH), the Yangtze River Delta urban agglomeration (YRD), and the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). The NTL intensity indexes, dynamic thresholds, extracted urban areas, and landscape metrics were synthetically used to facilitate the analysis. This study found that the urbanization process in the study areas could be divided into three stages: rapid urbanization in core cities from 2000 to 2010, a fluctuating urbanization process in both core cities and surrounding cities from 2010 to 2015, and stable urbanization, mainly in surrounding cities with a medium size after 2015. Meanwhile, the urbanization level of GBA was higher than that of YRD and BTH. However, with the acceleration of urban development in YRD, the gap in the urbanization level between GBA and YRD narrowed significantly in the third stage. In addition, this study confirmed that the scattered, medium-sized cities in YRD and GBA were more developed than those in BTH. This study showed that continuous NTL data could be effectively applied to monitor the urbanization patterns of urban agglomerations.

The development of artificial intelligence makes it possible to rapidly segment landslides. However, there are still some challenges in landslide segmentation based on remote sensing images, such as low segmentation accuracy, caused by similar features, inhomogeneous features, and blurred boundaries. To address these issues, we propose a novel deep learning model called AST-UNet in this paper. This model is based on structure of SwinUNet, attaching a channel Attention and spatial intersection (CASI) module as a parallel branch of the encoder, and a spatial detail enhancement (SDE) module in the skip connection. Specifically, (1) the spatial intersection module expands the spatial attention range, alleviating noise in the image and enhances the continuity of landslides in segmentation results; (2) the channel attention module refines the spatial attention weights by feature modeling in the channel dimension, improving the model’s ability to differentiate targets that closely resemble landslides; and (3) the spatial detail enhancement module increases the accuracy for landslide boundaries by strengthening the attention of the decoder to detailed features. We use the landslide data from the area of Luding, Sichuan to conduct experiments. The comparative analyses with state-of-the-art (SOTA) models, including FCN, UNet, DeepLab V3+, TransFuse, TranUNet, and SwinUNet, prove the superiority of our AST-UNet for landslide segmentation. The generalization of our model is also verified in the experiments. The proposed AST-UNet obtains an F1-score of 90.14%, mIoU of 83.45%, foreground IoU of 70.81%, and Hausdorff distance of 3.73, respectively, on the experimental datasets.

The contents of major and trace elements were analyzed in 204 different types of water samples in 138 villages across 51 counties and cities of Tibet. The average concentrations of arsenic (As), selenium, and fluorine for each water category decreased in the following order: arsenic (in μg/L: hot spring 241.37 > lake 27.46 > stream 22.11 > shallow well 11.57 > deep well 6.22), selenium (in μg/L: deep well 0.85 > shallow well 0.68 > stream 0.62 > hot spring 0.39 > lake 0.36), and fluorine (in mg/L: hot spring 2.10 > lake 1.06 > deep well 0.45 > stream 0.20 > shallow well 0.15). The distribution of arsenic in Tibetan waters ranged between 77.35 μg/L in Ali prefecture and 1.17 μg/L in Chamdo prefecture, with intermediate values of 4.39, 2.52, 2.10, 1.68, and 1.51 μg/L in the prefectures of Shigatse, Nagchu, Lhasa, Lhoka, and Nyingchi, respectively. Carbonatite is a major source of elements in these waters. The non-carcinogenic risk in Tibet caused by heavy metals in drinking water is low overall, except in Ali prefecture’s surface and shallow ground waters, which contain high levels of As. Thus, deep well water in Tibet is safe to drink.

Landslides can be triggered by natural and human activities, threatening the safety of buildings and infrastructures. Mapping potential landslide runout zones are critical for regional risk evaluation. Although remote sensing technology has been widely used to discover unstable areas, an entire landslide runout zone is difficult to identify using these techniques alone. Some simplified methods based on empirical models are used to simulate full-scale movements, but these methods do not consider the kinematic uncertainties caused by random particle collisions in practice. In this paper, we develop a semi-empirical landslide dynamics method considering kinematic uncertainties to solve this problem. The uncertainties caused by the microtopography and anisotropy of the material are expressed by the diffusion angle. Monte Carlo (MC) simulations are adopted to calculate the probability of each cell. Compared with the existing Flow-R model, this method can more accurately and effectively estimate runout zones of the Yigong landslide where random particle collisions are intense. Combining the D-InSAR technique, we evaluate the runout zones in the Jinsha River from June 2019 to December 2020. This result shows that the method is of great significance in early warning and risk mitigation, especially in remote areas. The source area of the landslide and DEM resolution together affect the number of MC simulations required. A landslide with a larger volume requires a larger diffusion angle and more MC simulations.

High-resolution environmental records from the Tibetan Plateau are essential to understand past global climatic and environmental changes. Magnetic minerals in lake sediments are important proxies to reconstruct environmental and climatic changes. Nam Co (lake) is a typical great lake in the transitional region of southwest monsoon in the Tibetan Plateau. Previous studies have extensively focused on geochemistry, microfossils, sedimentology and biochemistry analysis of Nam Co, which provides sound interpretation of paleoclimatic and paleoenvironmental changes. However, up to now, no systematic environmental magnetic studies have been carried out. Therefore, high-resolution and systematic magnetic studies combined with geochemical parameters were carried on lake sediments of core NC 08/01 from Nam Co for the Holocene period (11.3 cal ka BP) in order to explore how magnetic properties of the sediments respond to climatic changes. Based on variations of magnetic proxies, the sequence can be separated into 3 units. Unit 1 (236–199 cm, 11.3–7.8 cal ka BP) contains dominantly coarse-grained magnetite with homogeneous grain size. A positive correlation between magnetite and Ti strongly suggests that these coarse-grained detrital magnetites reflect detrital input signals due to insignificant effects of postdepositional dissolution processes on these coarse-grained magnetite particles. For Unit 2 (198–102 cm, 7.8–2.1 cal ka BP), magnetic grain size is finer and the corresponding concentration of magnetite is also reduced. This is mainly due to significant dissolution of these fine-grained detrital magnetite particles, which were transported under reduced water flow conditions during this period. For Unit 3 (101-0 cm, 2.1-0 cal ka BP), the bulk magnetic properties are dominated by a mixture of single domain biogenic magnetite and detrital magnetite. The concentration of magnetic minerals is not correlated with the Ti content. In conclusion, the preservation of magnetic minerals in the lake sediment and thus the corresponding magnetic properties are related to the initial grain size. Combination of magnetic properties (including variation of grain size and concentration) and other proxies of detrital inputs (e.g. Ti) can be used to infer the variation of redox conditions in Nam Co. These results provide a viable framework for reconstructing the paleoenvironmental changes of this lake.