Spatio-Temporal Changes of Snow Cover and Its Response to Climate Change over Tibetan Plateau

Snow cover, as an important part of land cover, is one of the most active natural elements on the earth surface. This program used the MODIS/Terra-Aqua daily snow products (MOD10A1 and MYD10A1) and AMSR-E/Aqua daily snow water equivalent product (AE_DySno) from 2003 to 2010 of Tibetan Plateau (TP), together with systematic study on MODIS daily snow cover product composite and a merging of multi-sensor and snow line approaches (Liang et al. 2008) to put forward a new snow cover mapping algorithm. Daily cloud-free snow cover images were calculated based on the new algorithm and the response of climate change on snow cover dynamics was analysed

Monitoring Snow-Caused Disasters Using Remote Sensing and GIS Technologies in Pastoral Areas

To date, the emphasis in snow-caused disasters that occur in pastoral areas in China has been in monitoring the change in the distribution of snow and in assessing livestock loss in post-disaster. The lack of an operational model and information system for real-time warning of snow dis-asters has made it difficult to make risk assessments and provide early snow disaster warnings (Liu et al. 2008). The aim of this study is to establish an indicator system for the early warning of snow-caused livestock disasters based on data collected from 2001 to 2010 on the Tibetan Plateau, China

Spatio-Temporal Dynamics of Global Potential Vegetation Distributions Simulated by CSCS Approach

The study of Potential Natural Vegetation (PNV) has been proposed as a way to examine the impact of changes in climate on the distribution of vegetation. This study analyzes the influence of climate change in the potential vegetation distribution at global scale, using the Comprehensive Sequential Classification System (CSCS) approach to explore the changes of area, shift distance and direction for each broad vegetation category

A Comparative Study of Systolic and Diastolic Mechanical Synchrony in Canine, Primate, and Healthy and Failing Human Hearts.

Aim: Mechanical dyssynchrony (MD) is associated with heart failure (HF) and may be prognostically important in cardiac resynchronization therapy (CRT). Yet, little is known about its patterns in healthy or diseased hearts. We here investigate and compare systolic and diastolic MD in both right (RV) and left ventricles (LV) of canine, primate and healthy and failing human hearts. Methods and Results: RV and LV mechanical function were examined by pulse-wave Doppler in 15 beagle dogs, 59 rhesus monkeys, 100 healthy human subjects and 39 heart failure (HF) patients. This measured RV and LV pre-ejection periods (RVPEP and LVPEP) and diastolic opening times (Q-TVE and Q-MVE). The occurrence of right (RVMDs) and left ventricular systolic mechanical delay (LVMDs) was assessed by comparing RVPEP and LVPEP values. That of right (RVMDd) and left ventricular diastolic mechanical delay (LVMDd) was assessed from the corresponding diastolic opening times (Q-TVE and Q-MVE). These situations were quantified by values of interventricular systolic (IVMDs) and diastolic mechanical delays (IVMDd), represented as positive if the relevant RV mechanical events preceded those in the LV. Healthy hearts in all species examined showed greater LV than RV delay times and therefore positive IVMDs and IVMDd. In contrast a greater proportion of the HF patients showed both markedly increased IVMDs and negative IVMDd, with diastolic mechanical asynchrony negatively correlated with LVEF. Conclusion: The present IVMDs and IVMDd findings have potential clinical implications particularly for personalized setting of parameter values in CRT in individual patients to achieve effective treatment of HF

Fractional Snow-Cover Mapping Based on MODIS and UAV Data over the Tibetan Plateau

Moderate-resolution imaging spectroradiometer (MODIS) snow-cover products have relatively low accuracy over the Tibetan Plateau because of its complex terrain and shallow, fragmented snow cover. In this study, fractional snow-cover (FSC) mapping algorithms were developed using a linear regression model (LR), a linear spectral mixture analysis model (LSMA) and a back-propagation artificial neural network model (BP-ANN) based on MODIS data (version 006) and unmanned aerial vehicle (UAV) data. The accuracies of the three models were validated against Landsat 8 Operational Land Imager (OLI) snow-cover maps (Landsat 8 FSC) and compared with the MODIS global FSC product (MOD10A1 FSC, version 005) for the purpose of finding the optimal algorithm for FSC extraction for the Tibetan Plateau. The results showed that (1) the overall retrieval results of the LR and BP-ANN models based on MODIS and UAV data were relatively similar to the OLI snow-cover maps; the accuracy and stability were greatly improved, with even some reduction in errors; compared to the Landsat 8 FSC, the correlation coefficients (r) were 0.8222 and 0.8445 respectively and the root-mean-square errors (RMSEs) were 0.2304 and 0.2201, respectively. (2) The accuracy and stability of the fully constrained LSMA model using the pixel purity index (PPI) endmember extraction method based only on MODIS data suffered the worst performance of the three models; r was only 0.7921 and the RMSE was as large as 0.3485. There were some serious omission phenomena in the study area, specifically for the largest mean absolute error (MAE = 0.2755) and positive mean error (PME = 0.3411). (3) The accuracy of the MOD10A1 FSC product was much lower than that of the LR and BP-ANN models, although its accuracy slightly better that of the LSMA based on comprehensive evaluation of six accuracy indices. (4) The optimal model was the BP-ANN model with combined inputs of surface reflectivity data (R1–R7), elevation (DEM) and temperature (LST), which can easily incorporate auxiliary information (DEM and LST) on the basis of (R1–R7) during the relationship training period and can effectively improve the accuracy of snow area monitoring—it is the ideal algorithm for retrieving FSC for the Tibetan Plateau

The Restoration Potential of the Grasslands on the Tibetan Plateau

While the alpine grassland ecosystems on the Tibetan Plateau (TP) have generally improved in recent years, some grasslands still suffer from varying degrees of degradation. Studying the restoration potential (R) of the grasslands on the TP is crucial to the conservation and restoration of its alpine grassland ecosystems. Few studies have assessed the restoration value of the alpine grasslands on the TP. We attempt to estimate the actual (ANPP) and potential net primary productivity (PNPP) of the grasslands on the TP. On this basis, we defined R as the “gap” between the current and highest achievable levels of restoration of a grassland. Then, R estimates were yielded for the alpine grasslands on the TP, which we used to analyze the restoration value of these grasslands. Specifically, based on the meteorological data for the period 2001–2019, in conjunction with remote-sensing imagery acquired by a moderate-resolution imaging spectroradiometer for the same period, the Carnegie–Ames–Stanford approach model was selected to produce ANPP estimates for the grasslands on the TP. Then, the Thornthwaite memorial model, the principle of similar habitats, and the Chikugo model, were employed to generate PNPP estimates for these grasslands. In addition, the R of these grasslands was then assessed based on the difference between their PNPP and ANPP. The main results are summarized as follows. (1) A multiyear mean R of 332.33 g C·m–2 (81.59% of the ANPP) was determined for the grasslands on the TP over the period 2001–2019. A notable spatial distribution pattern of high Rs in the southwestern, eastern and middle parts of the TP, and low Rs in the northwestern part of the TP were also identified. Most of the grasslands in areas such as the southern part of Nagqu, the southwestern part of Ngari, Xigaze, Garze Tibetan Autonomous Prefecture, Aba Tibetan and Qiang Autonomous Prefecture, Gannan Tibetan Autonomous Prefecture, Huangnan Tibetan Autonomous Prefecture, Haibei Tibetan Autonomous Prefecture, Guoluo Tibetan Autonomous Prefecture and Yushu Tibetan Autonomous Prefecture were found to have high restoration value. (2) Grasslands with a stable R account were the highest proportion (76.13%) of all the grasslands on the TP, followed by those with a decreasing R (19.62%) and those with an increasing R (4.24%). Grasslands with an increasing R were mainly concentrated in the southern part of Xigaze, and parts of Yushu Tibetan Autonomous Prefecture, Guoluo Tibetan Autonomous Prefecture and Garze Tibetan Autonomous Prefecture. (3) Analysis based on the local conditions of the TP revealed a high restoration value for three types of grassland (i.e., alpine meadows, mountain meadows, and temperate meadow steppes), the grasslands distributed at altitudes of 3000–4000 m, and the grasslands located in the warm temperate zone. The results of this study are expected to provide scientific and theoretical support for the formulation of policies and measures aimed at conserving grasslands, as well as restoring ecosystems and degraded grasslands on the TP

Reliability Modeling and Evaluation of Electric Vehicle Motor by Using Fault Tree and Extended Stochastic Petri Nets

Performing reliability analysis of electric vehicle motor has an important impact on its safety. To do so, this paper proposes its reliability modeling and evaluation issues of electric vehicle motor by using fault tree (FT) and extended stochastic Petri nets (ESPN). Based on the concepts of FT and ESPN, an FT based ESPN model for reliability analysis is obtained. In addition, the reliability calculation method is introduced and this work designs a hybrid intelligent algorithm integrating stochastic simulation and NN, namely, NN based simulation algorithm, to solve it. Finally, taking an electric vehicle motor as an example, its reliability modeling and evaluation issues are analyzed. The results illustrate the proposed models and the effectiveness of proposed algorithms. Moreover, the results reported in this work could be useful for the designers of electric vehicle motor, particularly, in the process of redesigning the electric vehicle motor and scheduling its reliability growth plan