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Qinghua Guo

D-Index & Metrics

Discipline name D-Index World Ranking Current World Ranking National Ranking Current National Ranking Publications Citations
Environmental Sciences 68 1890 1801 181 179 188 15164

Qinghua Guo publications per year

The chart shows the history of publications by Qinghua Guo between 1999 and 2025, highlighting the no. of papers published in each year and offering an overview of the publication velocity of this scholar. Qinghua Guo published across 27 years, from 1999 to 2025, averaging 10.6 papers a year. Output peaked at 40 publications in 2023. 49 of the 285 publications appeared in the last two years.

No. of publications
10 20 30 40
Bar chart. Horizontal axis: year, 1999 to 2025. Vertical axis: number of publications, 0 to 40. Peak 40 publications in 2023. 1999: 1 publication 2000: 0 publications 2001: 0 publications 2002: 0 publications 2003: 3 publications 2004: 4 publications 2005: 1 publication 2006: 2 publications 2007: 4 publications 2008: 3 publications 2009: 3 publications 2010: 5 publications 2011: 6 publications 2012: 9 publications 2013: 7 publications 2014: 10 publications 2015: 11 publications 2016: 15 publications 2017: 12 publications 2018: 23 publications 2019: 12 publications 2020: 25 publications 2021: 16 publications 2022: 24 publications 2023: 40 publications 2024: 21 publications 2025: 28 publications
1999 2025

285 publications in total across all disciplines

View publications per year as a table
Qinghua Guo: publications per year, 1999 to 2025
Year Publications
1999 1
2000 0
2001 0
2002 0
2003 3
2004 4
2005 1
2006 2
2007 4
2008 3
2009 3
2010 5
2011 6
2012 9
2013 7
2014 10
2015 11
2016 15
2017 12
2018 23
2019 12
2020 25
2021 16
2022 24
2023 40
2024 21
2025 28
Total 285
Download as CSV

Qinghua Guo publication distribution in Environmental Sciences in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Environmental Sciences in 2026. The highlighted bar marks where Qinghua Guo sits on this spectrum.

No. of scientists
200 400 600
Bar chart with 66 bars. Horizontal axis: publications, 41–50 to 690+. Vertical axis: number of scientists, 0 to 617. Most scientists, 617, have 121–130 publications. The last bar groups every scientist with 690 publications or more. The highlighted bar, 181–190 publications, is where this scientist sits. 41–50 publications: 21 scientists 51–60 publications: 62 scientists 61–70 publications: 133 scientists 71–80 publications: 257 scientists 81–90 publications: 361 scientists 91–100 publications: 440 scientists 101–110 publications: 492 scientists 111–120 publications: 541 scientists 121–130 publications: 617 scientists 131–140 publications: 544 scientists 141–150 publications: 541 scientists 151–160 publications: 539 scientists 161–170 publications: 444 scientists 171–180 publications: 444 scientists 181–190 publications: 400 scientists 191–200 publications: 377 scientists 201–210 publications: 318 scientists 211–220 publications: 282 scientists 221–230 publications: 263 scientists 231–240 publications: 220 scientists 241–250 publications: 217 scientists 251–260 publications: 180 scientists 261–270 publications: 181 scientists 271–280 publications: 155 scientists 281–290 publications: 130 scientists 291–300 publications: 127 scientists 301–310 publications: 130 scientists 311–320 publications: 85 scientists 321–330 publications: 106 scientists 331–340 publications: 80 scientists 341–350 publications: 83 scientists 351–360 publications: 75 scientists 361–370 publications: 69 scientists 371–380 publications: 52 scientists 381–390 publications: 54 scientists 391–400 publications: 56 scientists 401–410 publications: 44 scientists 411–420 publications: 40 scientists 421–430 publications: 36 scientists 431–440 publications: 25 scientists 441–450 publications: 25 scientists 451–460 publications: 32 scientists 461–470 publications: 29 scientists 471–480 publications: 21 scientists 481–490 publications: 26 scientists 491–500 publications: 26 scientists 501–510 publications: 17 scientists 511–520 publications: 19 scientists 521–530 publications: 15 scientists 531–540 publications: 22 scientists 541–550 publications: 12 scientists 551–560 publications: 15 scientists 561–570 publications: 11 scientists 571–580 publications: 19 scientists 581–590 publications: 9 scientists 591–600 publications: 9 scientists 601–610 publications: 7 scientists 611–620 publications: 11 scientists 621–630 publications: 5 scientists 631–640 publications: 5 scientists 641–650 publications: 6 scientists 651–660 publications: 3 scientists 661–670 publications: 3 scientists 671–680 publications: 4 scientists 681–689 publications: 4 scientists 690+ publications: 100 scientists
41–50 publications 690+

This scientist: 188 publications — 60th percentile

60% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 690 publications or more.

View publications distribution as a table
Number of Environmental Sciences scientists by publication count, Research.com 2026 ranking edition. Based on 9,676 ranked scientists.
Publications Scientists This scientist
41–50 21
51–60 62
61–70 133
71–80 257
81–90 361
91–100 440
101–110 492
111–120 541
121–130 617
131–140 544
141–150 541
151–160 539
161–170 444
171–180 444
181–190 400 188
191–200 377
201–210 318
211–220 282
221–230 263
231–240 220
241–250 217
251–260 180
261–270 181
271–280 155
281–290 130
291–300 127
301–310 130
311–320 85
321–330 106
331–340 80
341–350 83
351–360 75
361–370 69
371–380 52
381–390 54
391–400 56
401–410 44
411–420 40
421–430 36
431–440 25
441–450 25
451–460 32
461–470 29
471–480 21
481–490 26
491–500 26
501–510 17
511–520 19
521–530 15
531–540 22
541–550 12
551–560 15
561–570 11
571–580 19
581–590 9
591–600 9
601–610 7
611–620 11
621–630 5
631–640 5
641–650 6
651–660 3
661–670 3
671–680 4
681–689 4
690+ 100
Download as CSV

Qinghua Guo D-index placement in Environmental Sciences in 2026

The chart shows the D-index (discipline H-index) distribution of Environmental Sciences scientists ranked by Research.com in 2026. The highlighted bar marks where Qinghua Guo sits on this spectrum.

No. of scientists
100 200 300
Bar chart with 97 bars. Horizontal axis: D-Index, 30 to 126+. Vertical axis: number of scientists, 0 to 348. Most scientists, 348, have 45 D-Index. The last bar groups every scientist with 126 D-Index or more. The highlighted bar, 68 D-Index, is where this scientist sits. 30 D-Index: 12 scientists 31 D-Index: 26 scientists 32 D-Index: 51 scientists 33 D-Index: 88 scientists 34 D-Index: 123 scientists 35 D-Index: 163 scientists 36 D-Index: 206 scientists 37 D-Index: 267 scientists 38 D-Index: 265 scientists 39 D-Index: 275 scientists 40 D-Index: 321 scientists 41 D-Index: 343 scientists 42 D-Index: 305 scientists 43 D-Index: 336 scientists 44 D-Index: 330 scientists 45 D-Index: 348 scientists 46 D-Index: 291 scientists 47 D-Index: 275 scientists 48 D-Index: 272 scientists 49 D-Index: 273 scientists 50 D-Index: 263 scientists 51 D-Index: 232 scientists 52 D-Index: 266 scientists 53 D-Index: 217 scientists 54 D-Index: 198 scientists 55 D-Index: 177 scientists 56 D-Index: 202 scientists 57 D-Index: 204 scientists 58 D-Index: 166 scientists 59 D-Index: 177 scientists 60 D-Index: 166 scientists 61 D-Index: 152 scientists 62 D-Index: 143 scientists 63 D-Index: 150 scientists 64 D-Index: 124 scientists 65 D-Index: 119 scientists 66 D-Index: 120 scientists 67 D-Index: 118 scientists 68 D-Index: 82 scientists 69 D-Index: 98 scientists 70 D-Index: 94 scientists 71 D-Index: 105 scientists 72 D-Index: 74 scientists 73 D-Index: 84 scientists 74 D-Index: 70 scientists 75 D-Index: 67 scientists 76 D-Index: 78 scientists 77 D-Index: 60 scientists 78 D-Index: 59 scientists 79 D-Index: 52 scientists 80 D-Index: 47 scientists 81 D-Index: 38 scientists 82 D-Index: 48 scientists 83 D-Index: 42 scientists 84 D-Index: 42 scientists 85 D-Index: 43 scientists 86 D-Index: 29 scientists 87 D-Index: 37 scientists 88 D-Index: 29 scientists 89 D-Index: 30 scientists 90 D-Index: 34 scientists 91 D-Index: 20 scientists 92 D-Index: 22 scientists 93 D-Index: 17 scientists 94 D-Index: 19 scientists 95 D-Index: 24 scientists 96 D-Index: 21 scientists 97 D-Index: 20 scientists 98 D-Index: 24 scientists 99 D-Index: 17 scientists 100 D-Index: 17 scientists 101 D-Index: 21 scientists 102 D-Index: 25 scientists 103 D-Index: 18 scientists 104 D-Index: 26 scientists 105 D-Index: 20 scientists 106 D-Index: 15 scientists 107 D-Index: 10 scientists 108 D-Index: 13 scientists 109 D-Index: 15 scientists 110 D-Index: 12 scientists 111 D-Index: 8 scientists 112 D-Index: 7 scientists 113 D-Index: 9 scientists 114 D-Index: 6 scientists 115 D-Index: 12 scientists 116 D-Index: 7 scientists 117 D-Index: 8 scientists 118 D-Index: 3 scientists 119 D-Index: 5 scientists 120 D-Index: 7 scientists 121 D-Index: 2 scientists 122 D-Index: 4 scientists 123 D-Index: 8 scientists 124 D-Index: 7 scientists 125 D-Index: 9 scientists 126+ D-Index: 92 scientists
30 D-Index 126+

This scientist: 68 D-Index — 81st percentile

81% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 126 D-Index or more.

View D-Index distribution as a table
Number of Environmental Sciences scientists by D-index, Research.com 2026 ranking edition. Based on 9,676 ranked scientists.
D-Index Scientists This scientist
30 12
31 26
32 51
33 88
34 123
35 163
36 206
37 267
38 265
39 275
40 321
41 343
42 305
43 336
44 330
45 348
46 291
47 275
48 272
49 273
50 263
51 232
52 266
53 217
54 198
55 177
56 202
57 204
58 166
59 177
60 166
61 152
62 143
63 150
64 124
65 119
66 120
67 118
68 82 68
69 98
70 94
71 105
72 74
73 84
74 70
75 67
76 78
77 60
78 59
79 52
80 47
81 38
82 48
83 42
84 42
85 43
86 29
87 37
88 29
89 30
90 34
91 20
92 22
93 17
94 19
95 24
96 21
97 20
98 24
99 17
100 17
101 21
102 25
103 18
104 26
105 20
106 15
107 10
108 13
109 15
110 12
111 8
112 7
113 9
114 6
115 12
116 7
117 8
118 3
119 5
120 7
121 2
122 4
123 8
124 7
125 9
126+ 92
Download as CSV

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

External Links

Personal website of Qinghua Guo

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Related Online Degrees & Career Pathways

Pursuing a degree in Environmental Sciences opens a variety of career pathways, many of which can be enhanced by complementary online programs. For example, expertise in geographic data is crucial in environmental research and conservation efforts. Students interested in this can explore colleges with GIS programs to gain advanced skills in Geographic Information Systems, helping them map and analyze environmental data effectively.

Many leadership roles in environmental policy and public administration require strong management knowledge. Online options like online MPA degrees offer an accelerated path to develop competencies in public affairs, budgeting, and organizational leadership, which are vital in managing environmental projects and agencies.

Social factors play a key role in environmental issues, making a background in social sciences valuable. Students can consider best online sociology bachelor degree programs to deepen their understanding of human behavior and societal trends impacting the environment.

For professionals seeking to further their expertise, advanced education is available through flexible options like doctoral programs online no dissertation, which provide doctoral-level credentials without the traditional dissertation requirement. This format supports continued learning and career advancement while balancing other commitments.

Best Scientists Citing Qinghua Guo

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