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| 1 | MSCANet: multiscale context information aggregation network for Tibetan Plateau lake extraction from remote sensing images显示文摘Qinghai-Tibet Plateau lakes are important carriers of water resources in the‘Asian’s Water Tower’,and it is of great significance to grasp the spatial distribution of plateau lakes for the climate,ecological environment,and regional water cycle.However,the differences in spatial-spectral characteristics of various types of plateau lakes,and the complex background information of plateau both influence the extraction effect of lakes.Therefore,it is a great challenge to completely and effectively extract plateau lakes.In this study,we proposed a multiscale contextual information aggregation network,termed MSCANet,to automatically extract Plateau lake regions.It consists of three main components:a multiscale lake feature encoder,a feature decoder,and a Multicore Pyramid Pooling Module(MPPM).The multiscale lake feature encoder suppressed noise interference to capture multiscale spatial-spectral information from heterogeneous scenes.The MPPM module aggregated the contextual information of various lakes globally.We applied the MSCANet to the lake extraction of the Qinghai-Tibet Plateau based on Google data;additionally,comparative experiments showed that the MSCANet proposed had obvious improvement in lake detection accuracy and morphological integrity.Finally,we transferred the pre-trained optimal model to the Landsat-8 and Sentinel-2A dataset to verify the generalization of the MSCANet. | Zhihui Tian Xiaoyu Guo Xiaohui He Panle Li Xijjie Cheng Guangsheng Zhou | 2023 | International Journal of Digital Earth2023，16，1： | 0 |
| 2 | Analysis of the future trends of typical mountain glacier movements along the Sichuan-Tibet Railway based on ConvGRU network显示文摘The anomalous movements of glaciers cause disasters,such as debrisflows and landslides.It is very important to assess the glacier movements and their future trends.Glacier velocity refers to movement process.The current research aims to analyse past and current spatiotemporal changes in glacier velocity.No study has used neural network model to conduct a spatiotemporal prediction for glacier velocity.Therefore,this paper selected typical mountain glaciers G2 and G5 along the Sichuan-Tibet Railway as research objects and constructed the Convolutional Gate Recurrent Unit(ConvGRU)spatiotemporal prediction model based on 1988–2018 Landsat data to predict velocities in 2019–2028,and analysed the future trends of G2 and G5.The evaluation indexes met the model requirements to a large extent,quantitatively showing that the model has high accuracy and can successfully capture thefluctuation changes in time series data of glacier velocity.The mean deviations of G2 and G5 were 0.09 and-0.47 m/yr,respectively,reflecting the high reliability of the model applied to extraction of glacier velocity.The velocities of G2 and G5 showed a slow downtrend withfluctuations;that is,they will not cause damage to the construction and operation of the Sichuan-Tibet Railway in the short term. | Yali Zhang Lifeng Zhang Yi He Sheng Yao Wang Yang Shengpeng Cao Qiang Sun | 2023 | International Journal of Digital Earth2023，16，1： | 0 |
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