June 11, 2019

Selected peer-review journal publications

Google Scholar Citations: https://scholar.google.com/citations?user=Bno1TuAAAAAJ&hl=en

**2020

33. Jiang, M., Lin, Y., Chan, T.O. Yao, Y., Zheng, G., Luo, S., Zhang, L., Liu, D. Geologic factors leadingly drawing the macroecological pattern of rocky desertification in southwest China. Sci Rep 10, 1440 (2020).

32. Diao, J., Feng, T., Li, M.,Zhu, Z., Liu, J., Biging, G., Zheng, G., Shen, W., Wang, H., Wang, J., Ji, B..  Use of vegetation change tracker, spatial analysis, and random forest regression to assess the evolution of plantation stand age in Southeast China. Annals of Forest Science77, 27 (2020).

31. Wang, X.; Zheng, G.; Yun, Z.; Xu, Z.S., Moskal, L.M., Tian, Q.J. Characterizing the spatial variations of forest sunlit and shaded components using discrete aerial lidar. Remote Sens. 2020,

30. Wu, B.; Zheng, G.; Chen, Y. An Improved Convolution Neural Network-Based Model for Classifying Foliage and Woody Components from Terrestrial Laser Scanning Data. Remote Sens. 2020, 12, 1010.

29. Wang, X.; Zheng, G.; Yun, Z.; Moskal, L.M. Characterizing Tree Spatial Distribution Patterns Using Discrete Aerial Lidar Data. Remote Sens. 2020, 12, 712.

**2019

28. Changqiao Hong, Xiaobin Jin, Yeting Fan, Xiaomin Xiang, Shuai Cao, Changchun Chen,Guang Zheng, Yinkang Zhou. Determining the effect of land consolidation on agricultural production using a novel assessment framework. Land Degradation & Development, 2020, 31(3).

27. Zeng, K.; Zheng, G.; Ma, L.; Ju, W.; Pang, Y. Modelling Three-Dimensional Spatiotemporal Distributions of Forest Photosynthetically Active Radiation Using UAV-Based Lidar Data. Remote Sens. 2019,11, 2806.

26. Zhu, F., Shen, W., Diao, J., Li, M., Zheng, G.. Integrating cross-sensor high spatial resolution satellite images to detect subtle forest vegetation change in the Purple Mountains, a national scenic spot in Nanjing, China. Journal of Forestry Research. Journal of Forestry Research, 2019(5).

**2018

25. Xinlian Liang, Juha Hyyppä, Harri Kaartinen, Matti Lehtomäki, Jiri Pyörälä, Xiaowei Yu, Norbert Pfeifer, Hopkinson Cristopher, Pirotti Francesco, Brolly Gábor, Jan Heckenberg, Huabing Huang, Hyun-Woo Jo, Masato Katoh, Luxia Liu, Martin Mokroš, Jules Morel, Kenneth Olofsson, Jose Poveda-Lopez, Jan Trochta, Di Wang, Jinbu Wang, Bisheng Yang, Guang Zheng, Yunsheng Wang. International benchmarking of terrestrial laser scanning approaches for forest inventories, ISPRS Journal of Photogrammetry and Remote Sensing, 2018 144（137 – 179）.

24.路璐，郑光，马利霞. 激光雷达和点云切片算法结合的森林有效叶面积指数估算. 2018. 遥感学报 22(3):432 – 449.

23. Ma, L.X., Zheng, G.Shiming Li, Yi, Lin, Weimin Ju. Retrieving forest canopy clumping index using terrestrial laser scanning data. 2018. Remote Sensing of Environment 210(2018) 452-472.

**2017

22. Lu, X.; Zheng, G; Miller, C.; Alvarado, E. Combining Multi-Source Remotely Sensed Data and a Process-Based Model for Forest Aboveground Biomass Updating. Sensors 2017, 17(9), 2062.

21.Ma, L.X., Zheng, G., Eitel, Jan U.H., Magney, T.S., Moskal, L.M. Retrieving forest canopy extinction coefficient from terrestrial and airborne lidar. Agricultural and Forest Meteorology, 236(2017)1-21.

20.Zheng, G. Ma, L.X., Eitel, J.U.H., He, W., Magney, T.S., Moskal, L.M., Li, M.S. Retrieving directional gap fraction, extinction coefficient, and effective leaf area index by incorporating scan angle information from discrete aerial laser scanning (ALS) data. IEEE Transactions on Geoscience and Remote Sensing. 2017. IEEE Transactions on Geoscience and Remote Sensing. 55(1) 577-590.

**2016

19.Geng, J., Chen, J.M., Tu, L.L., Tian, Q.J., Wang, L., Yang, R.R., Yang, Y.J., Huang, Y., Fan, W.L., Lv, C.G., Zheng, G., Influence of the exclusion distance among trees on gap fraction and foliage clumping index of forest plantations. Trees – Structures and Function. 2016 30(5): 1683 – 1693.

18.Eitel, J.U.H., Magney, T.S., Vierling, L.A., Greaves, H., Zheng, G. An automated method to quantify crop height and calibrate satellite-derived biomass using hypertemporal lidar. Remote Sensing of Environment. 187, 414-422, 15 December, 2016.

17. Li, Y.M., Guo, Q.H., Tao,S.L., Zheng, G., Zhao, K.G., Xue, B.L., Su, Y.J. Derivation, validation, and sensitivity analysis of terrestrial laser scanning-based leaf area index. Canadian Journal of Remote Sensing, 2016. 42(6) 719-729.

16.Ma, L. X. Zheng, G., Magney, T.S., Eitel, J. U. H.,Moskal, L. M. Determining woody-to-total area ratio using terrestrial laser scanning data. Agricultural and Forest Meteorology.228-229(2016), 217-228.

15.Magney, T.S., Eitel, J. U. H., Griffin K.L., Boelman N. T., Creaves, H., Prager, C. M., Logan, B. A. Zheng, G., Ma, L.X.,

Fortin, E.A., Oliver, R.Y., Vierling, L.A. LiDAR canopy radiation model reveals patterns of photosynthetic partitioning in an arctic shrub. Agricultural and Forest Meteorology. 221(2016), 76-93.

14. Kong, F.H. Yan, W.J. Zheng, G., Yin, H.W., Cavan, Gina, Zhan, W. F., Zhang, N., Cheng, L. Retrieval of three-dimensional tree canopy and shade using terrestrial laser scanning (TLS) data to analyze the cooling effect of vegetation. Agricultural and Forest Meteorology. 217(2016), 22-34.

13.Zheng, G., Ma, L.X., He, W., Eitel, J.U.H., Moskal, L.M., Zhang, Z.Y. Assessing the contribution of woody materials to forest angular gap fraction and effective leaf area index using terrestrial laser scanning (TLS) data. IEEE Transactions on Geoscience and Remote Sensing. 2016 54(3) 1475-1487.

12. Ma, L.X., Zheng, G., Eitel, J.U.H., Moskal, L.M., He, W., Huang, H. B. Improved salient feature-based approach for automatically separating photosynthetic and nonphotosynthetic components within terrestrial lidar point cloud data of forest canopies. IEEE Transactions on Geoscience and Remote Sensing.2016, 54(2) 679-696.

**2015

11. Zhang, C.H., Ju, W.M., Chen, J.M., Wang, X., Yang, L., Zheng, G., Disturbance-induced reduction of biomass carbon sinks of China’s forests in recent years. Environmental Research Letters 10 (11), 114021

 

**2013

10. Zheng, G., Moskal, L.M., Kim, S.H. Retrieval of effective leaf area index in heterogeneous forests with a terrestrial laser scanner. 2013. IEEE Transactions on Geoscience and Remote Sensing. 51(2) 777-786.

9. Lu, Y.C., Li, X., Tian, Q. J., Zheng, G., Sun, S. J., Liu, Y. X., Yang, Q. Progress in marine oil spill optical remote sensing: detected targets, spectral response characteristics, and theories. 2013. Marine Geodesy, 36 (3), 334-346

8. Lu, Y.C., Tian, Q.J., Wang X., Zheng, G., Li, X., Determining oil slick thickness using hyperspectral remote sensing in the Bohai Sea of China. 2013. International Journal of Digital Earth 6 (1), 76-93

7. Lu, Y.C., Zheng, G., Tian, Q.J., Lyu, C.G., Sun, S.J., Analyzing the effects of particle size on remotely sensed spectra: a study on optical properties and spectral similarity scale of suspended particulate matters in water. Applied optics 52 (4), 879-888

**2012

6. Zheng, G. and Moskal, L. M. Computational geometry based retrieval of effective leaf area index using terrestrial laser scanning. 2012. IEEE Transactions on Geoscience and Remote Sensing, 50(10), 3958-3969.

5. Zheng, G. and Moskal, L. M. Leaf orientation retrieval from terrestrial laser scanning. 2012, IEEE Transactions on Geoscience and Remote Sensing, 50(10), 3970-3979.

4. Zheng, G. and Moskal, L.M., Spatial variability of terrestrial laser scanner based leaf area index. International Journal of Applied Observation and Geoinformation. 2012(19), 26-237.

3. Moskal, L.M and Zheng, G. Retrieving forest inventory variables with terrestrial laser scanning in urban heterogeneous forest. 2012，Remote Sensing, 4, 1-20.

**before 2011

2. Zheng, G. and Moskal, L. M. Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors. 2009，Sensors，9 (4):2719-2745.

1. Zheng, G., Chen, J.M., Tian, Q.J., Ju, W.M., Xia, X.Q. Combining remote sensing imagery and forest age inventory for biomass mapping. 2007, Journal of Environmental Management, 85(3) pp: 616-623.