Spatial differentiation of the NPP and NDVI and its influencing factors vary with grassland type on the Qinghai-Tibet Plateau

2021 ◽  
Vol 193 (1) ◽  
Author(s):  
Yixuan Liu ◽  
Shiliang Liu ◽  
Yongxiu Sun ◽  
Mingqi Li ◽  
Yi An ◽  
...  
2021 ◽  
Author(s):  
Shibao Wang ◽  
Jianqi Zhuang ◽  
Jiaqi Mu ◽  
Jia Zheng ◽  
Jiewei Zhan ◽  
...  

Abstract The Qinghai-Tibet Plateau is one area with the most frequent landslide hazards due to its unique geology, topography, and climate conditions, posing severe threats to engineering construction and human settlements. The Sichuan-Tibet Railway that is currently under construction crosses the Qinghai-Tibet Plateau; there are frequent landslide disasters along the line, which seriously threaten the construction of the railway. This paper applied two deep learning (DL) algorithms, the convolutional neural network (CNN) and deep neural network (DNN), to landslide susceptibility mapping of the Ya’an-Linzhi section of the Sichuan-Tibet Railway. A geospatial database was generated based on 587 landslide hazards determined by Interferometric Synthetic Aperture Radar (InSAR) Stacking technology, field geological hazard surveys, and 18 landslide influencing factors were selected. The landslides were randomly divided into training data (70%) and validation data (30%) for the modeling training and testing. The Pearson correlation coefficient and information gain method were used to perform the correlation analysis and feature selection of 18 influencing factors. Both models were evaluated and compared using the receiver operating characteristic (ROC) curve and confusion matrix. The results show that better performance in both the training and testing phases was provided by the CNN algorithm (AUC = 0.88) compared to the DNN algorithm (AUC = 0.84). Slope, elevation, and rainfall are the main factors affecting the occurrence of landslides, and the high and very high landslide susceptibilities were primarily distributed in the Jinsha, Lancang, and Nujiang River Basins along the railway. The research results provide a scientific basis for the construction of the Ya'an-Linzhi section of the Sichuan-Tibet Railway within the region, as well as the disaster prevention and mitigation work during future safe operations.


2018 ◽  
Vol 10 (6) ◽  
pp. 1864 ◽  
Author(s):  
Zhonghe Zhao ◽  
Gaohuan Liu ◽  
Naixia Mou ◽  
Yichun Xie ◽  
Zengrang Xu ◽  
...  

2020 ◽  
Author(s):  
Yanzhen Hou ◽  
Wenwu Zhao ◽  
Yanxu Liu ◽  
Siqi Yang ◽  
Xiangping Hu ◽  
...  

2007 ◽  
Vol 29 (2) ◽  
pp. 161 ◽  
Author(s):  
Zheng Gang Guo ◽  
Rui Jun Long ◽  
Fu Jun Niu ◽  
Qing Bo Wu ◽  
Yu Kun Hu

During 2002–2004, a broad-scale survey on the plant diversity of grassland communities along a natural elevation gradient in the permafrost regions of the Qinghai–Tibet plateau, China, was conducted to investigate the effect of highway construction nearly 30 years ago. Richness index was not significantly different among undisturbed communities (Kobresia pygmaea meadow, K. humilis meadow, Stipa purpurea steppe, Carex moorcroftii steppe), but significant differences (P < 0.05) were observed for evenness and diversity indices among four undisturbed communities. Three indices significantly decreased from communities 100 m (lightly disturbed communities), 200 m (undisturbed communities), and 50 m (severely disturbed communities) from the Qinghai–Xicang Highway, and three indices of severely disturbed communities were similar to that of 30 m communities (extremely-severely disturbed communities). Diversity and richness indices peaked at intermediate elevations of 4720 m in undisturbed communities and lightly disturbed communities, but were uniform in the severely disturbed communities and extremely-severely disturbed communities along with the increase of elevation. β-Diversity decreased in communities at 30, 50, and 100–200 m distance from the highway. This indicated that β-diversity of communities was enhanced with the increase of disturbance for each grassland type in the study region. Both undisturbed and disturbed communities showed the same changeable bell-shaped trend with elevation increase, increasing from 4320–4620 m, decreasing from 4720 to 4920 m, and peaking at 4620 to ~4720 m, indicating that elevation from 4620–4720 m was a transition zone in permafrost region.


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