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Environmental Sciences

D-Index
68
Citations
15164
World Ranking
1887
National Ranking
177

Overview

Qinghua Guo is affiliated with the Chinese Academy of Sciences in China. Their main field of study is Environmental Science, with a specialized focus on several subfields including Environmental Engineering, Ecology, Global and Planetary Change, Nature and Landscape Conservation, and Geology.

The scope of Qinghua Guo's research covers diverse topics within environmental science and remote sensing. Key areas of work include:

  • Remote Sensing and LiDAR Applications
  • Remote Sensing in Agriculture
  • Forest Ecology and Management
  • Land Use and Ecosystem Services
  • 3D Surveying and Cultural Heritage
  • Ecology and Vegetation Dynamics Studies
  • Species Distribution and Climate Change

The scientist has contributed to numerous publications in a range of frequent venues such as:

  • ISPRS Journal of Photogrammetry and Remote Sensing
  • Remote Sensing
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • SSRN Electronic Journal
  • Nature Communications

Qinghua Guo has collaborated extensively with several co-authors throughout their career. Frequent collaborators include:

  • Yanjun Su
  • Tianyu Hu
  • Hongcan Guan
  • Shichao Jin
  • Qin Ma

Among notable recent papers authored or co-authored by Qinghua Guo are:

  • "Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data," 2021, Remote Sensing of Environment
  • "Lidar sheds new light on plant phenomics for plant breeding and management: Recent advances and future prospects," 2020, ISPRS Journal of Photogrammetry and Remote Sensing
  • "Development and Performance Evaluation of a Very Low-Cost UAV-Lidar System for Forestry Applications," 2020, Remote Sensing
  • "Lidar Boosts 3D Ecological Observations and Modelings: A Review and Perspective," 2020, IEEE Geoscience and Remote Sensing Magazine
  • "UAV-lidar aids automatic intelligent powerline inspection," 2021, International Journal of Electrical Power & Energy Systems

The research conducted by Qinghua Guo primarily focuses on advancing the applications of LiDAR and remote sensing for environmental and ecological monitoring, especially regarding forest and agricultural ecosystems. Their work includes integrating satellite and UAV-based data sources and employing neural network methodologies for spatial analysis.

Best Publications

  • A New Method for Segmenting Individual Trees from the Lidar Point Cloud

    Wenkai Li;Qinghua Guo;Marek K. Jakubowski;Maggi Kelly

  • Interannual variations of monthly and seasonal normalized difference vegetation index (NDVI) in China from 1982 to 1999

    Shilong Piao;Jingyun Fang;Liming Zhou;Qinghua Guo;Qinghua Guo

  • Rapid loss of lakes on the Mongolian Plateau.

    Shengli Tao;Jingyun Fang;Jingyun Fang;Xia Zhao;Shuqing Zhao

  • The point-radius method for georeferencing locality descriptions and calculating associated uncertainty

    John Wieczorek;Qinghua Guo;Robert J. Hijmans

  • Support vector machines for predicting distribution of Sudden Oak Death in California

    Qinghua Guo;Maggi Kelly;Catherine H. Graham;Catherine H. Graham

  • Effects of Topographic Variability and Lidar Sampling Density on Several DEM Interpolation Methods

    Qinghua Guo;Wenkai Li;Hong Yu;Otto Alvarez

  • Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas

    Xiaoqian Zhao;Qinghua Guo;Qinghua Guo;Yanjun Su;Baolin Xue

  • Increasing net primary production in China from 1982 to 1999

    Jingyun Fang;Shilong Piao;Christopher B. Field;Yude Pan

  • Tradeoffs between lidar pulse density and forest measurement accuracy

    Marek K. Jakubowski;Qinghua Guo;Maggi Kelly

  • Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data

    Unknown

  • Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data

    Yanjun Su;Yanjun Su;Qinghua Guo;Qinghua Guo;Baolin Xue;Tianyu Hu

  • Variation in a satellite-based vegetation index in relation to climate in China

    Shilong Piao;Jingyun Fang;Wei Ji;Qinghua Guo;Qinghua Guo

  • Delineating Individual Trees from Lidar Data: A Comparison of Vector- and Raster-based Segmentation Approaches

    Marek K. Jakubowski;Wenkai Li;Qinghua Guo;Maggi Kelly

  • Segmenting tree crowns from terrestrial and mobile LiDAR data by exploring ecological theories

    Shengli Tao;Shengli Tao;Fangfang Wu;Qinghua Guo;Qinghua Guo;Yongcai Wang

  • A Positive and Unlabeled Learning Algorithm for One-Class Classification of Remote-Sensing Data

    Wenkai Li;Qinghua Guo;Charles Elkan

  • A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data

    Xingcheng Lu;Xingcheng Lu;Qinghua Guo;Qinghua Guo;Wenkai Li;Jacob Flanagan

  • Mapping Global Forest Aboveground Biomass with Spaceborne LiDAR, Optical Imagery, and Forest Inventory Data

    Tianyu Hu;Yanjun Su;Baolin Xue;Jin Liu

  • Lidar sheds new light on plant phenomics for plant breeding and management: Recent advances and future prospects

    Shichao Jin;Xiliang Sun;Fangfang Wu;Yanjun Su

  • Evaluating the performance of Sentinel-2, Landsat 8 and Pléiades-1 in mapping mangrove extent and species

    Dezhi Wang;Bo Wan;Penghua Qiu;Yanjun Su

  • Global patterns, trends, and drivers of water use efficiency from 2000 to 2013

    Bao-Lin Xue;Qinghua Guo;Qinghua Guo;Alvarez Otto;Jingfeng Xiao

  • An integrated UAV-borne lidar system for 3D habitat mapping in three forest ecosystems across China

    Qinghua Guo;Yanjun Su;Tianyu Hu;Xiaoqian Zhao

  • Annual accumulation for Greenland updated using ice core data developed during 2000--2006 and analysis of daily coastal meteorological data

    Roger C. Bales;Qinghua Guo;Dayong Shen;Joseph R. McConnell

  • Spatial distribution of forest aboveground biomass in China: estimation through combination of spaceborne lidar, optical imagery, and forest inventory data

    B. L. Xue;Y. Su;Q. Guo;T. Hu

Frequent Co-Authors

Maggi Kelly
Maggi Kelly University of California, Berkeley
Baolin Xue
Baolin Xue Beijing Normal University
Jingyun Fang
Jingyun Fang Peking University
Yu Liu
Yu Liu Peking University
Brandon M. Collins
Brandon M. Collins University of California, Berkeley
Roger C. Bales
Roger C. Bales University of California, Merced
Scott L. Stephens
Scott L. Stephens University of California, Berkeley
Zhiyao Tang
Zhiyao Tang Peking University
Jin Chen
Jin Chen Beijing Normal University
Noah P. Molotch
Noah P. Molotch University of Colorado Boulder

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Related Online Degrees & Career Pathways

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