The Tibetan Plateau (TP) is one of the most sensitive regions to global climate warming, not only at the inter-annual time scale but also at the altitudinal scale. We aim to investigate the contrasting effects of temperature and precipitation on vegetation greenness at different altitudes across the TP. In this study, interannual and elevational characteristics of the Normalized Difference Vegetation Index (NDVI), temperature, and precipitation were examined during the growing season from 1982 to 2015. We compared the elevational movement rates of the isolines of NDVI, temperature, and precipitation, and the sensitivities of elevational NDVI changes to temperature and precipitation. The results show that from 1982 to 2015, the elevational variation rate of isolines for NDVI mismatched with that for temperature and precipitation. The elevational movements of NDVI isolines were mostly controlled by precipitation at elevations below 2400 m and by the temperature at elevations above 2400 m. Precipitation appears to plays a role similar to temperature, and even a more effective role than the temperature at low elevations, in controlling elevational vegetation greenness changes at both spatial and interannual scales in the TP. This study highlights the regulation of temperature and precipitation on vegetation ecosystems along elevation gradients over the whole TP under global warming conditions.

This paper used cloud computing platform of Google Earth Engine (GEE), to access and analyze Sentinel-2, MODIS and other multi-source remote sensing data in the last 5 years to monitor the growth of crops in China, especially in Hubei Province, during the period of the rapid spread of COVID-19 (i.e., from late January to mid-March 2020), and compared with the growth over the same period under similar climate conditions in the past 4 years. We further analyzed the indirect effects of COVID-19 on crop growth. The results showed that: The area of the crops with better growth (51%) was much more than which with worse growth (22%); the crops with better and worse growth were mainly distributed in the North China Plain (the main planting areas of winter wheat in China) and the South China regions (such as Guangxi, Guangdong province), respectively. The area of the crops with similar growth occupied 27%. In Hubei Province, the area of the crops with better growth (61%) was also more than which with worse growth (27%). It was found that there was no obvious effect from COVID-19 on the overall growth of crops in China during the period from late January to mid-March 2020, and the growth of crops was much better than which during the same period in previous years. The findings in this study are helpful in evaluating the impact of the COVID-19 on China's agriculture, which are conducive to serve the relevant agricultural policy formulation and to ensure food security.

This paper uses Data Envelopment Analysis (DEA) to empirically analysis the efficiency of Beijing-Tianjin-Hebei traffic integration and the results are as follows. Firstly, the railway network is running well while the difficulties to alleviate the contradiction between supply and demand of the highway transport has existed for a long time, and the homogenization of competition in the port construction is one of the burning issues of our times. Secondly, the scale ratio of transportation system within the region is not reasonably adapted, and increasing infrastructure investment is conducive to the rapid advance of the regional integration at present. Finally, according to the issues above, this paper proposes three suggestions.

In the case of frequent extreme weather, in order to improve the accuracy of winter wheat yield forecasts. Based on the data from the Internet of Things, this paper takes Henan Province as the research area and calculates the influence of extreme temperatures on the growth of winter wheat, and takes the sum of growth degree-day (SGDD) and Sum of Extreme Degrees (SEDD)as two major factors; Based on remote sensing technology, this paper calculates the growth area of winter wheat. And the normalized vegetation index (NDVI) of each period; finally use the above three factors, to establish two regression models for winter wheat yield forecast. The model constructed by the two influencing factors of SEDD shows that NDVI is a sensitive factor for yield prediction. The results show that both models in northeastern Henan Province have achieved good results. The model can provide methods for winter wheat production management and decisionmaking in Henan Province support.