Yanjun Su

Dr. Yanjun Su

State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Science, Beijing 100093, China
orcid

Research Focus & Metrics

Academic Career Length

18Years (2009-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 July 2016 in ISPRS Journal of Photogrammetry and Remote Sensing
Verified
Journal Article
Closed access
XZXiaoqian Zhao
Xiaoqian Zhao
QGQinghua Guo
Qinghua Guo
Yanjun SuYanjun Su
Yanjun Su
BXBaolin Xue
Baolin Xue

1 February 2022 in Remote Sensing of Environment
Verified
Journal Article
Closed access
XLXiaoqiang Liu
Xiaoqiang Liu
Yanjun SuYanjun Su
Yanjun Su
THTianyu Hu
Tianyu Hu
QYQiuli Yang
Qiuli Yang
BLBingbing Liu
Bingbing Liu
QGQinghua Guo
Qinghua Guo

1 February 2016 in Remote Sensing of Environment
Verified
Journal Article
Closed access
Yanjun SuYanjun Su
Yanjun Su
QGQinghua Guo
Qinghua Guo
BXBaolin Xue
Baolin Xue
THTianyu Hu
Tianyu Hu
OAOtto Alvarez
Otto Alvarez
JFJingyun Fang
Jingyun Fang

1 January 2021 in ISPRS Journal of Photogrammetry and Remote Sensing
Verified
Journal Article
Closed access
SJShichao Jin
Shichao Jin
XSXiliang Sun
Xiliang Sun
FWFangfang Wu
Fangfang Wu
Yanjun SuYanjun Su
Yanjun Su
YLYumei Li
Yumei Li
QGQinghua Guo
Qinghua Guo

19 April 2024 in Science
Verified
Journal Article
Closed access
ZAZurui Ao
Zurui Ao
XHXiaomei Hu
Xiaomei Hu
Shengli TaoShengli Tao
Shengli Tao
XHXie Hu
Xie Hu
GWGuoquan Wang
Guoquan Wang
JFJingyun Fang
Jingyun Fang
China’s massive wave of urbanization may be threatened by land subsidence. Using a spaceborne synthetic aperture radar interferometry technique, we provided a systematic assessment of land subsidence in all of China’s major cities from 2015 to 2022. Of the examined urban lands, 45% are subsiding faster than 3 millimeters per year, and 16% are subsiding faster than 10 millimeters per year, affecting 29 and 7% of the urban population, respectively. The subsidence appears to be associated with a range of factors such as groundwater withdrawal and the weight of buildings. By 2120, 22 to 26% of China’s coastal lands will have a relative elevation lower than sea level, hosting 9 to 11% of the coastal population, because of the combined effect of city subsidence and sea-level rise. Our results underscore the necessity of enhancing protective measures to mitigate potential damages from subsidence.

4 July 2016 in Remote Sensing
Verified
Journal Article
Open access
PDF file
THTianyu Hu
Tianyu Hu
Yanjun SuYanjun Su
Yanjun Su
BXBao-Lin Xue
Bao-Lin Xue
JLJin Liu
Jin Liu
XZXiaoqian Zhao
Xiaoqian Zhao
QGQinghua Guo
Qinghua Guo
As a large carbon pool, global forest ecosystems are a critical component of the global carbon cycle. Accurate estimations of global forest aboveground biomass (AGB) can improve the understanding of global carbon dynamics and help to quantify anthropogenic carbon emissions. Light detection and ranging (LiDAR) techniques have been proven that can accurately capture both horizontal and vertical forest structures and increase the accuracy of forest AGB estimation. In this study, we mapped the global forest AGB density at a 1-km resolution through the integration of ground inventory data, optical imagery, Geoscience Laser Altimeter System/Ice, Cloud, and Land Elevation Satellite data, climate surfaces, and topographic data. Over 4000 ground inventory records were collected from published literatures to train the forest AGB estimation model and validate the resulting global forest AGB product. Our wall-to-wall global forest AGB map showed that the global forest AGB density was 210.09 Mg/ha on average, with a standard deviation of 109.31 Mg/ha. At the continental level, Africa (333.34 ± 63.80 Mg/ha) and South America (301.68 ± 67.43 Mg/ha) had higher AGB density. The AGB density in Asia, North America and Europe were 172.28 ± 94.75, 166.48 ± 84.97, and 132.97 ± 50.70 Mg/ha, respectively. The wall-to-wall forest AGB map was evaluated at plot level using independent plot measurements. The adjusted coefficient of determination (R2) and root-mean-square error (RMSE) between our predicted results and the validation plots were 0.56 and 87.53 Mg/ha, respectively. At the ecological zone level, the R2 and RMSE between our map and Intergovernmental Panel on Climate Change suggested values were 0.56 and 101.21 Mg/ha, respectively. Moreover, a comprehensive comparison was also conducted between our forest AGB map and other published regional AGB products. Overall, our forest AGB map showed good agreements with these regional AGB products, but some of the regional AGB products tended to underestimate forest AGB density.