Yong Wu

512 total citations
45 papers, 361 citations indexed

About

Yong Wu is a scholar working on Environmental Engineering, Ecology and Nature and Landscape Conservation. According to data from OpenAlex, Yong Wu has authored 45 papers receiving a total of 361 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Environmental Engineering, 17 papers in Ecology and 16 papers in Nature and Landscape Conservation. Recurrent topics in Yong Wu's work include Remote Sensing and LiDAR Applications (18 papers), Forest ecology and management (15 papers) and Archaeology and ancient environmental studies (15 papers). Yong Wu is often cited by papers focused on Remote Sensing and LiDAR Applications (18 papers), Forest ecology and management (15 papers) and Archaeology and ancient environmental studies (15 papers). Yong Wu collaborates with scholars based in China, Germany and United Kingdom. Yong Wu's co-authors include Guanglong Ou, Xiaohong Tian, Dong Wei, Xiaoli Zhang, Tian‐Bao Huang, Bing Xu, Guijin Mu, Jing Tang, Xiaoguang Qin and Leiguang Wang and has published in prestigious journals such as Aquaculture, Remote Sensing and Ecological Indicators.

In The Last Decade

Yong Wu

40 papers receiving 353 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Yong Wu China 12 117 117 110 89 72 45 361
M. Kathryn Brown United States 10 212 1.8× 79 0.7× 60 0.5× 39 0.4× 64 0.9× 26 451
Judicaël Lebamba France 9 44 0.4× 52 0.4× 68 0.6× 158 1.8× 171 2.4× 14 384
Boun Suy Tan Australia 7 108 0.9× 82 0.7× 126 1.1× 14 0.2× 35 0.5× 7 379
Michael L. Brennan United States 13 73 0.6× 189 1.6× 7 0.1× 40 0.4× 39 0.5× 62 426
Jörgen Olofsson Sweden 8 33 0.3× 97 0.8× 13 0.1× 126 1.4× 38 0.5× 10 393
Stéphane De Greef Australia 5 114 1.0× 16 0.1× 67 0.6× 7 0.1× 13 0.2× 5 342
Shelley Danzer United States 8 53 0.5× 95 0.8× 14 0.1× 361 4.1× 80 1.1× 10 519
Juris Zariņš Latvia 11 268 2.3× 47 0.4× 8 0.1× 49 0.6× 22 0.3× 25 469
Chiara Compostella Italy 13 25 0.2× 123 1.1× 9 0.1× 278 3.1× 67 0.9× 27 468
B.R. Ramesh India 8 21 0.2× 138 1.2× 24 0.2× 84 0.9× 145 2.0× 15 409

Countries citing papers authored by Yong Wu

Since Specialization
Citations

This map shows the geographic impact of Yong Wu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yong Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yong Wu more than expected).

Fields of papers citing papers by Yong Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yong Wu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yong Wu. The network helps show where Yong Wu may publish in the future.

Co-authorship network of co-authors of Yong Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Yong Wu. A scholar is included among the top collaborators of Yong Wu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yong Wu. Yong Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Hu, Xin, et al.. (2025). Water biofloc microorganism enhance shrimp resistance to microplastics. Journal of Environmental Sciences. 164. 559–571. 1 indexed citations
2.
Chen, Dapeng, Zhi Liu, Jianjun Pan, et al.. (2025). A Dual-Variable Selection Framework for Enhancing Forest Aboveground Biomass Estimation via Multi-Source Remote Sensing. Remote Sensing. 17(14). 2493–2493. 3 indexed citations
3.
Ou, Guanglong, et al.. (2025). A framework for montane forest canopy height estimation via integrating deep learning and multi-source remote sensing data. International Journal of Applied Earth Observation and Geoinformation. 138. 104474–104474. 1 indexed citations
4.
Wu, Yong, et al.. (2024). An efficient method for estimating tropical forest canopy height from airborne PolInSAR data. Ecological Indicators. 166. 112566–112566.
5.
Wu, Yong, Guanglong Ou, Tian‐Bao Huang, et al.. (2024). Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna. Remote Sensing. 16(7). 1276–1276. 10 indexed citations
7.
Huang, Tian‐Bao, Xiaoli Zhang, Yong Wu, et al.. (2024). Interacting Sentinel-2A, Sentinel 1A, and GF-2 Imagery to Improve the Accuracy of Forest Aboveground Biomass Estimation in a Dry-Hot Valley. Forests. 15(4). 731–731. 1 indexed citations
8.
Qin, Xiaoguang, Bing Xu, Guijin Mu, et al.. (2024). Environmental and human history in the hyper-arid eastern Tarim Basin (Lop Nur), northwest China: A critical review for sustaining the natural and cultural landscapes. Quaternary International. 694. 51–69. 4 indexed citations
9.
Li, Wentao, Tairui Zhang, Xiaochao Liu, et al.. (2024). Lap joining of Ti6Al4V titanium alloy by vortex flow-based friction stir welding. Materials Characterization. 218. 114462–114462. 3 indexed citations
10.
Liu, Zhi, et al.. (2024). LiDAR-based individual tree AGB modeling of Pinus kesiya var. langbianensis by incorporating spatial structure. Ecological Indicators. 169. 112973–112973. 3 indexed citations
11.
Qin, Xiaoguang, Gill Plunkett, Samantha Presslee, et al.. (2024). Multi-proxy investigations of Bronze Age diet and environment in the hyper-arid eastern Tarim Basin (Lop Nur), northwest China. Journal of Archaeological Science. 171. 106089–106089.
13.
Li, Lu, Yanfeng Liu, Yong Wu, et al.. (2023). Reduction in Uncertainty in Forest Aboveground Biomass Estimation Using Sentinel-2 Images: A Case Study of Pinus densata Forests in Shangri-La City, China. Remote Sensing. 15(3). 559–559. 17 indexed citations
15.
Zhang, Xiaoli, Lu Li, Yanfeng Liu, et al.. (2023). Improving the accuracy of forest aboveground biomass using Landsat 8 OLI images by quantile regression neural network for Pinus densata forests in southwestern China. Frontiers in Forests and Global Change. 6. 7 indexed citations
16.
Huang, Tian‐Bao, Guanglong Ou, Yong Wu, et al.. (2023). Estimating the Aboveground Biomass of Various Forest Types with High Heterogeneity at the Provincial Scale Based on Multi-Source Data. Remote Sensing. 15(14). 3550–3550. 25 indexed citations
17.
Huang, Tian‐Bao, Guanglong Ou, Hui Xu, et al.. (2023). Comparing Algorithms for Estimation of Aboveground Biomass in Pinus yunnanensis. Forests. 14(9). 1742–1742. 9 indexed citations
18.
Tian, Xiaohong, et al.. (2023). Wood-Use Strategies at a Han Dynasty Military Outpost: New Archaeological Evidence from Shichengzi, Xinjiang. Environmental Archaeology. 31(1). 1–7. 2 indexed citations
19.
Storozum, Michael, et al.. (2020). Foodways on the Han dynasty’s western frontier: Archeobotanical and isotopic investigations at Shichengzi, Xinjiang, China. The Holocene. 30(8). 1174–1185. 17 indexed citations
20.
Zhang, Xinyu, Dong Wei, Yong Wu, Ying Nie, & Yaowu Hu. (2016). Carbon and nitrogen stable isotope ratio analysis of Bronze Age humans from the Xiabandi cemetery, Xinjiang, China: Implications for cultural interactions between the East and West. Chinese Science Bulletin (Chinese Version). 61(32). 3509–3519. 19 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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