Guanglong Ou

708 total citations
56 papers, 485 citations indexed

About

Guanglong Ou is a scholar working on Environmental Engineering, Nature and Landscape Conservation and Ecology. According to data from OpenAlex, Guanglong Ou has authored 56 papers receiving a total of 485 indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Environmental Engineering, 33 papers in Nature and Landscape Conservation and 32 papers in Ecology. Recurrent topics in Guanglong Ou's work include Remote Sensing and LiDAR Applications (39 papers), Forest ecology and management (31 papers) and Remote Sensing in Agriculture (28 papers). Guanglong Ou is often cited by papers focused on Remote Sensing and LiDAR Applications (39 papers), Forest ecology and management (31 papers) and Remote Sensing in Agriculture (28 papers). Guanglong Ou collaborates with scholars based in China and United States. Guanglong Ou's co-authors include Hui Xu, Weiheng Xu, Leiguang Wang, Yong Wu, Shuaifeng Li, Xuedong Lang, Wande Liu, Guangxing Wang, Xiaoli Zhang and Tian‐Bao Huang and has published in prestigious journals such as PLoS ONE, Scientific Reports and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Guanglong Ou

49 papers receiving 467 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Guanglong Ou China 13 287 256 216 212 55 56 485
Alkan Günlü Türkiye 12 219 0.8× 194 0.8× 188 0.9× 181 0.9× 21 0.4× 69 420
Dennis M. Jacobs United States 5 326 1.1× 275 1.1× 226 1.0× 229 1.1× 31 0.6× 16 487
M. N. dos-Santos Brazil 11 289 1.0× 273 1.1× 275 1.3× 372 1.8× 32 0.6× 14 589
Christopher R. Hakkenberg United States 13 156 0.5× 201 0.8× 148 0.7× 187 0.9× 38 0.7× 29 393
Fugen Jiang China 11 306 1.1× 281 1.1× 163 0.8× 138 0.7× 44 0.8× 20 419
Zhaoju Zheng China 12 181 0.6× 295 1.2× 114 0.5× 202 1.0× 68 1.2× 24 451
Samuel Hislop Australia 14 253 0.9× 412 1.6× 183 0.8× 471 2.2× 46 0.8× 27 645
Emmanuel Da Ponte Germany 10 123 0.4× 174 0.7× 103 0.5× 250 1.2× 38 0.7× 16 410
Patricio Corvalán Chile 6 401 1.4× 358 1.4× 273 1.3× 144 0.7× 29 0.5× 10 514
Sassan Saatchi United States 7 213 0.7× 132 0.5× 153 0.7× 123 0.6× 16 0.3× 15 321

Countries citing papers authored by Guanglong Ou

Since Specialization
Citations

This map shows the geographic impact of Guanglong Ou'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 Guanglong Ou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guanglong Ou more than expected).

Fields of papers citing papers by Guanglong Ou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Guanglong Ou. 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 Guanglong Ou. The network helps show where Guanglong Ou may publish in the future.

Co-authorship network of co-authors of Guanglong Ou

This figure shows the co-authorship network connecting the top 25 collaborators of Guanglong Ou. A scholar is included among the top collaborators of Guanglong Ou 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 Guanglong Ou. Guanglong Ou 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.
Fu, Haoyu, Ruiqi Yang, Weiheng Xu, et al.. (2025). OMRF-HS: Object Markov Random Field With Hierarchical Semantic Regularization for High-Resolution Image Semantic Segmentation. IEEE Transactions on Geoscience and Remote Sensing. 63. 1–18.
2.
Wu, Yonghong, Hong Wang, Zhi Liu, et al.. (2025). A precise estimation framework for individual tree AGB of Pinus kesiya var. Langbianensis utilizing point cloud registration Optimization. International Journal of Applied Earth Observation and Geoinformation. 140. 104612–104612. 1 indexed citations
3.
Liu, Yun, Weiheng Xu, Leiguang Wang, et al.. (2025). A new method of three-dimensional green volume retrieval and its applications in urban greenery evaluation. Ecological Indicators. 176. 113629–113629.
4.
Liu, Yun, Huimei Liu, Weiheng Xu, et al.. (2024). Advances and challenges of carbon storage estimation in tea plantation. Ecological Informatics. 81. 102616–102616. 5 indexed citations
5.
Wu, Yong, et al.. (2024). An efficient method for estimating tropical forest canopy height from airborne PolInSAR data. Ecological Indicators. 166. 112566–112566.
6.
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.
8.
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
9.
Ou, Guanglong, et al.. (2024). Remote Sensing Estimation of Forest Carbon Stock Based on Machine Learning Algorithms. Forests. 15(4). 681–681. 25 indexed citations
10.
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
11.
12.
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
13.
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
14.
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16.
Ou, Guanglong, MA Huan-cheng, Xu Hui, & Junrong Tang. (2015). UNDERSTORY PLANT DIVERSITY IN MIXED AND PURE PLANTATIONS OF JATROPHA CURCAS VS. NATIVE VEGETATION IN THE LOWER-MIDDLE REACHES OF THE LANCANG-MEIKONG RIVER WATERSHED, CHINA. Pakistan Journal of Botany. 47(4). 1391–1398. 1 indexed citations
17.
18.
Wang, Junfeng, et al.. (2012). Biomass estimation model of shrub community at Jatropha curcas growing area in Lincang of Yunnan.. 41(6). 53–57. 1 indexed citations
19.
Ou, Guanglong. (1992). ON THE MORPHOLOGY OF THE GENUS CHEYLETIELLA AND DESCRIPTION OF A NEW SPECIES FROM XINJIANG, CHINA (ACARI: CHEYLETIELLIDAE). Acta Zootaxonomica Sinica. 1 indexed citations
20.
Ou, Guanglong, et al.. (1981). ENKEPHALIN ANALOGS WITH EXTREMELY HIGH AFFINITY FOR δ-RECEPTOR. 科学通报(英文版). 1 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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