Gang Shao

695 total citations
23 papers, 433 citations indexed

About

Gang Shao is a scholar working on Environmental Engineering, Nature and Landscape Conservation and Global and Planetary Change. According to data from OpenAlex, Gang Shao has authored 23 papers receiving a total of 433 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Environmental Engineering, 8 papers in Nature and Landscape Conservation and 8 papers in Global and Planetary Change. Recurrent topics in Gang Shao's work include Remote Sensing and LiDAR Applications (10 papers), Forest ecology and management (8 papers) and Remote Sensing in Agriculture (5 papers). Gang Shao is often cited by papers focused on Remote Sensing and LiDAR Applications (10 papers), Forest ecology and management (8 papers) and Remote Sensing in Agriculture (5 papers). Gang Shao collaborates with scholars based in United States, China and Germany. Gang Shao's co-authors include Tian Guo, Scott C. Stark, Danilo Roberti Alves de Almeida, Songlin Fei, Bruce Nelson, Eric Bastos Görgens, Pedro H. S. Brancalion, Jeffrey G. Arnold, Bernard A. Engel and Juliana Schietti and has published in prestigious journals such as Environmental Science & Technology, The Science of The Total Environment and Remote Sensing of Environment.

In The Last Decade

Gang Shao

21 papers receiving 427 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gang Shao United States 11 225 174 145 134 85 23 433
Marta Szostak Poland 14 288 1.3× 281 1.6× 165 1.1× 90 0.7× 42 0.5× 50 570
Jenet Austin Australia 12 205 0.9× 216 1.2× 160 1.1× 112 0.8× 64 0.8× 37 497
Margarida Silva Portugal 7 181 0.8× 141 0.8× 81 0.6× 58 0.4× 53 0.6× 11 350
Hormoz Sohrabi Iran 15 267 1.2× 326 1.9× 164 1.1× 222 1.7× 66 0.8× 52 612
František Zemek Czechia 12 143 0.6× 255 1.5× 132 0.9× 109 0.8× 36 0.4× 28 497
Jinliang Huang China 11 174 0.8× 238 1.4× 237 1.6× 109 0.8× 97 1.1× 20 479
Mingxia Yang China 11 129 0.6× 106 0.6× 148 1.0× 63 0.5× 32 0.4× 23 349
Kazukiyo Yamamoto Japan 11 223 1.0× 138 0.8× 132 0.9× 189 1.4× 64 0.8× 35 453
Martin Jansen Germany 11 178 0.8× 54 0.3× 102 0.7× 65 0.5× 45 0.5× 23 455
Meifang Zhao China 12 130 0.6× 147 0.8× 321 2.2× 184 1.4× 56 0.7× 28 558

Countries citing papers authored by Gang Shao

Since Specialization
Citations

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

Fields of papers citing papers by Gang Shao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gang Shao

This figure shows the co-authorship network connecting the top 25 collaborators of Gang Shao. A scholar is included among the top collaborators of Gang Shao 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 Gang Shao. Gang Shao 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.
2.
Shao, Gang, Songlin Fei, & Guofan Shao. (2023). A Robust Stepwise Clustering Approach to Detect Individual Trees in Temperate Hardwood Plantations using Airborne LiDAR Data. Remote Sensing. 15(5). 1241–1241. 5 indexed citations
3.
Oh, Sungchan, et al.. (2022). High-Resolution Canopy Height Model Generation and Validation Using USGS 3DEP LiDAR Data in Indiana, USA. Remote Sensing. 14(4). 935–935. 22 indexed citations
4.
Guo, Tian, Yaoze Liu, Gang Shao, et al.. (2022). Improving probabilistic monthly water quantity and quality predictions using a simplified residual-based modeling approach. Environmental Modelling & Software. 156. 105499–105499. 7 indexed citations
5.
Li, Siyu, Yaoze Liu, Younggu Her, et al.. (2021). Improvement of simulating sub-daily hydrological impacts of rainwater harvesting for landscape irrigation with rain barrels/cisterns in the SWAT model. The Science of The Total Environment. 798. 149336–149336. 17 indexed citations
6.
Hu, Tongxi, Elizabeth Myers Toman, Gang Chen, et al.. (2021). Mapping fine-scale human disturbances in a working landscape with Landsat time series on Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing. 176. 250–261. 55 indexed citations
7.
Guo, Tian, Laura T. Johnson, Chad J. Penn, et al.. (2020). Less Agricultural Phosphorus Applied in 2019 Led to Less Dissolved Phosphorus Transported to Lake Erie. Environmental Science & Technology. 55(1). 283–291. 47 indexed citations
8.
Almeida, Danilo Roberti Alves de, Scott C. Stark, Gang Shao, et al.. (2019). Optimizing the Remote Detection of Tropical Rainforest Structure with Airborne Lidar: Leaf Area Profile Sensitivity to Pulse Density and Spatial Sampling. Remote Sensing. 11(1). 92–92. 88 indexed citations
9.
Shao, Gang, Scott C. Stark, Danilo Roberti Alves de Almeida, & Marielle N. Smith. (2018). Towards high throughput assessment of canopy dynamics: The estimation of leaf area structure in Amazonian forests with multitemporal multi-sensor airborne lidar. Remote Sensing of Environment. 221. 1–13. 30 indexed citations
10.
Shao, Gang, Guofan Shao, & Songlin Fei. (2018). Delineation of individual deciduous trees in plantations with low-density LiDAR data. International Journal of Remote Sensing. 40(1). 346–363. 9 indexed citations
11.
Shao, Gang, Basil V. Iannone, & Songlin Fei. (2018). Enhanced forest interior estimations utilizing lidar-assisted 3D forest cover map. Ecological Indicators. 93. 1236–1243. 6 indexed citations
12.
Guo, Tian, Bernard A. Engel, Gang Shao, et al.. (2018). Development and improvement of the simulation of woody bioenergy crops in the Soil and Water Assessment Tool (SWAT). Environmental Modelling & Software. 122. 104295–104295. 28 indexed citations
13.
Shao, Gang, et al.. (2017). Improving Lidar-based aboveground biomass estimation of temperate hardwood forests with varying site productivity. Remote Sensing of Environment. 204. 872–882. 41 indexed citations
14.
Pauli, Benjamin P., et al.. (2015). The simulated effects of timber harvest on suitable habitat for Indiana and northern long‐eared bats. Ecosphere. 6(4). 1–24. 10 indexed citations
15.
Guo, Tian, Bernard A. Engel, Gang Shao, et al.. (2015). Functional Approach to Simulating Short-Rotation Woody Crops in Process-Based Models. BioEnergy Research. 8(4). 1598–1613. 23 indexed citations
16.
Shao, Gang, et al.. (2014). Mapping hardwood forests through a two-stage unsupervised classification by integrating Landsat Thematic Mapper and forest inventory data. Journal of Applied Remote Sensing. 8(1). 83546–83546. 13 indexed citations
17.
Shao, Gang, et al.. (2010). Combination multiclassifier for object-oriented classification of forest cover.. Nanjing Linye Daxue xuebao. 34(1). 73–79. 2 indexed citations
18.
Shao, Gang & Zhimin Yang. (2010). Design and research of SNS users network structure model based on pervasive computing. 2. 1475–1479. 1 indexed citations
19.
Shao, Gang, et al.. (2010). Object-oriented classification of forest cover using SPOT5 imagery.. 46(8). 130–139. 1 indexed citations
20.
Zhao, Ying, Gang Shao, & Guangwen Yang. (2008). A Survey of Methods and Applications for Trace Analysis in Grid Systems. 264–271. 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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