VTechWorks staff will be away for the Thanksgiving holiday beginning at noon on Wednesday, November 27, through Friday, November 29. We will resume normal operations on Monday, December 2. Thank you for your patience.

Browsing by Author "Yan, Chuang"

Now showing 1 - 2 of 2
  • Thumbnail Image
    A meta-analysis of 1,119 manipulative experiments on terrestrial carbon-cycling responses to global change
    Song, Jian; Wan, Shiqiang; Piao, Shilong; Knapp, Alan K.; Classen, Aimee T.; Vicca, Sara; Ciais, Philippe; Hovenden, Mark J.; Leuzinger, Sebastian; Beier, Claus; Kardol, Paul; Xia, Jianyang; Liu, Qiang; Ru, Jingyi; Zhou, Zhenxing; Luo, Yiqi; Guo, Dali; Langley, J. Adam; Zscheischler, Jakob; Dukes, Jeffrey S.; Tang, Jianwu; Chen, Jiquan; Hofmockel, Kirsten S.; Kueppers, Lara M.; Rustad, Lindsey E.; Liu, Lingli; Smith, Melinda D.; Templer, Pamela H.; Thomas, R. Quinn; Norby, Richard J.; Phillips, Richard P.; Niu, Shuli; Fatichi, Simone; Wang, Yingping; Shao, Pengshuai; Han, Hongyan; Wang, Dandan; Lei, Lingjie; Wang, Jiali; Li, Xiaona; Zhang, Qian; Li, Xiaoming; Su, Fanglong; Liu, Bin; Yang, Fan; Ma, Gaigai; Li, Guoyong; Liu, Yanchun; Liu, Yinzhan; Yang, Zhongling; Zhang, Kesheng; Miao, Yuan; Hu, Mengjun; Yan, Chuang; Zhang, Ang; Zhong, Mingxing; Hui, Yan; Li, Ying; Zheng, Mengmei (2019-09)
    Direct quantification of terrestrial biosphere responses to global change is crucial for projections of future climate change in Earth system models. Here, we synthesized ecosystem carbon-cycling data from 1,119 experiments performed over the past four decades concerning changes in temperature, precipitation, CO2 and nitrogen across major terrestrial vegetation types of the world. Most experiments manipulated single rather than multiple global change drivers in temperate ecosystems of the USA, Europe and China. The magnitudes of warming and elevated CO2 treatments were consistent with the ranges of future projections, whereas those of precipitation changes and nitrogen inputs often exceeded the projected ranges. Increases in global change drivers consistently accelerated, but decreased precipitation slowed down carbon-cycle processes. Nonlinear (including synergistic and antagonistic) effects among global change drivers were rare. Belowground carbon allocation responded negatively to increased precipitation and nitrogen addition and positively to decreased precipitation and elevated CO2. The sensitivities of carbon variables to multiple global change drivers depended on the background climate and ecosystem condition, suggesting that Earth system models should be evaluated using site-specific conditions for best uses of this large dataset. Together, this synthesis underscores an urgent need to explore the interactions among multiple global change drivers in under-represented regions such as semi-arid ecosystems, forests in the tropics and subtropics, and Arctic tundra when forecasting future terrestrial carbon-climate feedback.
  • Thumbnail Image
    Pavement 3D Data Denoising Algorithm Based on Cell Meshing Ellipsoid Detection
    Yan, Chuang; Wei, Ya; Xiao, Yong; Wang, Linbing (MDPI, 2021-03-25)
    As a new measuring technique, laser 3D scanning technique has advantages of rapidity, safety, and accuracy. However, the measured result of laser scanning always contains some noise points due to the measuring principle and the scanning environment. These noise points will result in the precision loss during the 3D reconstruction. The commonly used denoising algorithms ignore the strong planarity feature of the pavement, and thus might mistakenly eliminate ground points. This study proposes an ellipsoid detection algorithm to emphasize the planarity feature of the pavement during the 3D scanned data denoising process. By counting neighbors within the ellipsoid neighborhood of each point, the threshold of each point can be calculated to distinguish if it is the ground point or the noise point. Meanwhile, to narrow down the detection space and to reduce the processing time, the proposed algorithm divides the cloud point into cells. The result proves that this denoising algorithm can identify and eliminate the scattered noise points and the foreign body noise points very well, providing precise data for later 3D reconstruction of the scanned pavement.