7 results match your criteria: "Shaanxi Meteorological Service Center of Agricultural Remote Sensing and Economic Crops[Affiliation]"

In January 2020, the novel coronavirus (COVID-19) outbreak emerged in China, prompting the enforcement of stringent lockdown measures nationwide to contain its spread. Multiple studies have demonstrated that these measures successfully reduced the levels of air pollutants except for ozone (O). However, the potential risks of nationwide O changes during this period remain uncertain.

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Pistacia chinensis Bunge. is one of the main woody oil crops with a large artificial planting area in China and has important economic and ecological value. Here, based on 237 occurrence data and 22 environmental variables, we explored the potential planting area of P.

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In order to solve the problem of low efficiency of image feature matching in traditional remote sensing image database, this paper proposes the feature matching optimization of multimedia remote sensing images based on multiscale edge extraction, expounds the basic theory of multiscale edge, and then registers multimedia remote sensing images based on the selection of optimal control points. In this paper, 100 remote sensing images with a size of 3619825 with a resolution of 30 m are selected as experimental data. The computer is configured with 2.

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Changes in extreme events have received increasing attention in the context of climate change. Extreme changes in wet and dry events due to changes in meteorological elements, such as the spatial and temporal redistribution of precipitation and temperature increases, are extreme weather events that have attracted much attention in recent years. In contrast, there is a relative lack of research on extreme compound events that focuses on a transition between wet and dry spells in adjacent months.

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Quantifying ecological and health risks of ground-level O across China during the implementation of the "Three-year Action Plan for Cleaner Air".

Sci Total Environ

April 2022

Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China; Institute of Eco-Chongming (IEC), Shanghai 200062, China. Electronic address:

After China implemented the Air Pollution Prevention and Control Action Plan (APPCAP), PM concentrations decreased but were still higher than national standards in major areas and ozone (O) concentration increased unintentionally. To further decrease PM concentrations and reduce days with severe air pollution, the government promulgated the "Three-year (2018-2020) Action Plan for Cleaner Air" (the Three-year Action Plan) in 2018. During the three-year Action Plan, a few studies reported a continuous decline in PM, but it is unclear whether O and its effects also increase with the decrease of PM like during APPCAP.

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Studies on variations in chilling and heat accumulation in apple trees and their effects on first flowering date under climate change are important for guiding apple planting and productions. In this study, we carried out experiments in representative stations of apple planting areas in the northern China, including Fushan of Shandong, Wanrong of Shanxi, Xifeng of Gansu and Akesu of Xinjiang. The first flowering data and hourly temperature data during 1996-2018 were used to calculate the daily chilling and heat accumulation units by applying the dynamic model and growing degree hour model.

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To reveal the spatio-temporal variation characteristics of apple's phenology and their critical response time period and intensity to the temperature change in the main production areas of northern China, we chose Fushan, Wanrong and Akesu to respresent the Bohai Gulf, the Loess Plateau and Xinjiang apple production areas, respectively. Apple's phenology data of buds opening (BO), first leaf unfolding (LU), first flowering (FF), fruit maturing (FM), end of leaf coloring (LC) and the end of leaf fall (LF) at the three stations during 1996-2018 were used to analyze the changes of phenological occurrence dates and different growth stage lengths. Partial least squares (PLS) regression was applied to identify the impacts of climate warming on different phenology events at daily resolution.

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