Guo Q., Su Y., Hu T., 2023. LiDAR Principles, Processing and Applications in Forest Ecology. Academic Press.

100. Wang, J., Liu, X., Qi, X., Wu, X., Long, Y., Feng, Y., Dong, Q., Yan, J., Huang, L., Luo, Y., Cao, M., Xu, K., Zhao, C., Wang, Y., Hu, T., Wu, J., Liu, L., & Su, Y. (2025). Boosting leaf trait estimation from reflectance spectra by elucidating the transferability of PLSR models. Plant Phenomics, 100054, doi: https://doi.org/10.1016/j.plaphe.2025.100054.

99. Wu, X., Niu, C., Liu, X., Hu, T., Feng, Y., Zhao, Y., Liu, S., Liu, Z., Dai, G., Zhang, Y., Van Meerbeek, K., Wu, J., Liu, L., Guo, Q., & Su, Y. (2024). Canopy structure regulates autumn phenology by mediating the microclimate in temperate forests. Nature Climate Change，doi: https://doi.org/10.1038/s41558-024-02164-2.

98. Hu T.#, Cao M.#, Zhao X., Liu X., Liu Z., Liu L., Huang Z., Tao S., Tang Z., Guo Y., Ji C., Zheng C., Wang G., Hu X., Zhou L., Cheng Y., Ma W., Wang Y., Zhang P., Fan Y., Yu F., Wang Z., Qiao X., Cheng X., Yin C., Ma H., Li L., Yang Y., Luo W., Gong Y., Wang L., Chen L., Liu G., Song C., Yang X., Ye X., Zhang S., Zhu X., Wang Q., Li S., Yang Y., Liu L., Kelly M., Fang J., Su Y.*, 2024. High-resolution mapping of grassland canopy cover in China through the integration of extensive drone imagery and satellite data. ISPRS Journal of Photogrammetry and Remote Sensing, 218, 69-83, doi: https://doi.org/10.1016/j.isprsjprs.2024.09.004.

97. Feng Y., Su Y.*, Wang J., Yan J., Qi X., Maeda E.E., Nunes M.H., Zhao X., Liu X., Wu X., Yang C., Pan J., Dong K., Zhang D., Hu T., Fang J.*, 2024. L1-Tree: A novel algorithm for constructing 3D tree models and estimating branch architectural traits using terrestrial laser scanning data. Remote Sensing of Environment, 314, 114390, doi: https://doi.org/10.1016/j.rse.2024.114390.

96. Zhao Y., Wang Z., Yan Z., Moon M., Yang D., Meng L., Bucher S.F., Wang J., Song G., Guo Z. Su Y., Wu J., 2024. Exploring the role of biotic factors in regulating the spatial variability in land surface phenology across four temperate forest sites. New Phytologist, 242, 1965-1980, doi: https://doi.org/10.1111/nph.19684.

95. Liu X., Feng Y., Hu T., Luo Y., Zhao X., Wu J., Maeda E.E., Ju W., Liu L., Guo Q., Su Y.*, 2024. Enhancing ecosystem productivity and stability with increasing canopy structural complexity in global forests. Science Advances, 10, eadl1947, doi: https://doi.org/10.1126/sciadv.adl1947.

94. Cheng K., Yang H., Tao S., Su Y., Guan H., Ren Y., Hu T., Li W., Xu G., Chen M., Lu X., Yang Z., Tang Y., Ma K., Fang J., Guo Q., 2024. Carbon storage through China’s planted forest expansion. Nature Communications, 15, 4106, doi: https://doi.org/10.1038/s41467-024-48546-0.

93. Ao Z., Hu X., Tao S., Hu X., Wang G., Li M., Wang F., Hu L., Liang X., Xiao J., Yusup A., Qi W., Fang J., Chang J., Zeng Z., Fu Y., Xue B., Wang P., Zhao K., Li L., Li W., Li. Y., Jiang M., Yang Y., Shen H., Zhao X., Shi Y., Wu B., Yan Z., Wang M., Su Y., Hu T., Ma Q., Bai H., Wang L., Yang Z., Feng Y., Zhang D., Huang E., Pan J., Ye H., Yang C., Qin Y., He C., Guo Y., Cheng K., Ren Y., Yang H., Zheng C., Zhu J., Wang S., Ji C., Zhu B., Liu H., Tang Z., Zhao S., Tang Y., Xing H., Guo Q., Fang J., 2024. A national-scale assessment of land subsidence in China’s major cities. Science, 384, 301-306, doi: https://doi.org/10.1126/science.adl4366.

92. Tan S., Zhang Y., Qi J., Su Y., Ma Q., Qiu J., 2024. Exploring the potential of GEDI in characterizing tree height composition based on advanced radiative transfer model simulations. Journal of Remote Sensing, 4, 0132, doi: https://doi.org/10.34133/remotesensing.0132.

91. Ren Y., Li C., Chau K., Fan G., Xu G., Yang H., Cheng K., Hua F., Hu R., Shi X., Guan H., Chen M., Yang Z., Cheng Z., Mao K., Su Y., Guo Q., Lv Z., 2024. Conserving the primary forests in the Yarlung Tsangpo Grand Canyon for people and nature. Nature Ecology & Evolution, 8, 837-839, doi: https://doi.org/10.1038/s41559-024-02383-y.

90. Li W., Hu X., Su Y., Tao S., Ma Q., Guo Q., 2024. A new method for voxel‐based modelling of three‐dimensional forest scenes with integration of terrestrial and airborne LiDAR data. Methods in Ecology and Evolution, 15, 569-582, doi: https://doi.org/10.1111/2041-210X.14290.

89. Zhang R., Jin S., Zhang Y., Zang J., Wang Y., Li Q., Sun Z., Wang X., Zhou Q., Cai J., Xu S., Su Y., Wu J., Jiang D., 2024. PhenoNet: A two-stage lightweight deep learning framework for real-time wheat phenophase classification. ISPRS Journal of Photogrammetry and Remote Sensing, 208, 136-157, doi: https://doi.org/10.1016/j.isprsjprs.2024.01.006.

88. Ma Q., Su Y.*, Hu T., Jiang L., Mi X., Lin L., Cao M., Wang X., Lin F., Wang B., Sun Z., Wu J., Ma K., Guo Q., 2024. The coordinated impact of forest internal structural complexity and tree species diversity on forest productivity across forest biomes. Fundamental Research, 4, 1185-1195, doi: https://doi.org/10.1016/j.fmre.2022.10.005.

87. Yang Z., Su Y., Li W., Cheng K., Guan H., Ren Y., Hu T., Xu G., Guo, Q., 2023. Segmenting individual trees from terrestrial LiDAR data using tree branch directivity. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 956-969, doi: http://dx.doi.org/10.1109/JSTARS.2023.3334014.

86. Ma Q., Su Y.*, Niu C., Ma Q., Hu T., Luo X., Tai X., Qiu T., Zhang Y., Bales R.C., Liu L., Kelly M., Guo Q., 2023. Tree mortality during long-term droughts is lower in structurally complex forest stands. Nature Communications, 14, 7467, doi: https://doi.org/10.1038/s41467-023-43083-8.

85. Liu Z., Jin S.*, Liu X., Yang Q., Li Q., Zang J., Li Z., Hu T., Guo Z., Wu J., Jiang D., Su Y.*, 2023. Extraction of wheat spike phenotypes from field-collected lidar data and exploration of their relationships with wheat yield. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-13, doi: https://doi.org/10.1016/j.rse.2023.113612.

84. Liu S., Yan Z., Wang Z., Serbin S., Visser M., Zeng Y., Ryu Y., Su Y., Guo Z., Song G., Wu Q., Zhang H., Cheng K.H., Dong J., Hau B.C.H, Zhao P., Yang X., Liu L., Rogers A., Wu J., 2023. Mapping foliar photosynthetic capacity in sub-tropical and tropical forests with UAS-based imaging spectroscopy: Scaling from leaf to canopy. Remote Sensing of Environment, 293, 113612, doi: https://doi.org/10.1016/j.rse.2023.113612.

83. Peng Z., Wu Y., Guo L., Yang L., Wang B., Wang X., Liu W., Su Y., Wu J., Liu L., 2023. Foliar nutrient resorption stoichiometry and microbial phosphatase catalytic efficiency together alleviate the relative phosphorus limitation in forest ecosystems. New Phytologist, 238, 1033-1044, doi: https://doi.org/10.1111/nph.18797.

82. Zang J., Jin S., Zhang S., Li Q., Mu Y., Li Z., Li S., Wang X., Su Y., Jiang D., 2023. Field-measured canopy height may not be as accurate and heritable as believed: evidence from advanced 3D sensing. Plant Methods, 19, 39, doi: https://doi.org/10.1186/s13007-023-01012-2.

81. Zhao X., Feng Y., Xu K., Cao M., Hu S., Yang Q., Liu X., Ma Q., Hu T., Kelly M., Guo, Q., Su Y.*, 2023. Canopy structure: An intermediate factor regulating grassland diversity-function relationships under human disturbances. Fundamental Research, 3, 179-187, doi: https://doi.org/10.1016/j.fmre.2022.10.007.

80. Cheng K., Su Y., Guan H., Tao S., Ren Y., Hu T., Ma K., Tang Y., Guo Q., 2023. Mapping China’s planted forests using high resolution imagery and massive amounts of crowdsourced samples. ISPRS Journal of Photogrammetry and Remote Sensing, 196, 356-371, doi: https://doi.org/10.1016/j.isprsjprs.2023.01.005.

79. Tao S., Ao Z., Wigneron J.P., Saatchi S., Ciais P., Chave J., Le Toan T., Frison P.L., Hu X., Chen C., Fan L., Wang M., Zhu J., Zhao X., Li X., Liu X., Su Y., Hu T., Guo Q., Wang Z., Tang Z., Liu Y.Y., Fang J., 2023. C-band Scatterometer (CScat): the first global long-term satellite radar backscatter data set with a C-band signal dynamic. Earth System Science Data Discussions, 2022, 15, 1577-1596, doi: https://doi.org/10.5194/essd-2022-264.

78. Liu L., Sayer E.J., Deng M., Li P., Liu W., Wang X., Yang S., Huang J., Luo J., Su Y., Grünzweig, J.M., 2022. The grassland carbon cycle: mechanisms, responses to global changes, and potential contribution to carbon neutrality. Fundamental Research, 3, 209-218, doi: https://doi.org/10.1016/j.fmre.2022.09.028.

77. Liu X., Ma Q., Wu X., Hu T., Dai G., Wu J., Tao S., Wang S., Liu L., Guo Q., Su, Y.*, 2022. Nonscalability of Fractal Dimension to Quantify Canopy Structural Complexity from Individual Trees to Forest Stands. Journal of Remote Sensing, 2022, 0001, doi: https://doi.org/10.34133/remotesensing.0001.

76. Liu X., Ma Q., Wu X., Hu T., Liu Z., Liu L., Guo Q., Su Y.*, 2022. A novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds. Remote Sensing of Environment, 282, 113280, doi: https://doi.org/10.1016/j.rse.2022.113280.

75. Li Q., Jin S., Zang J., Wang X., Sun Z., Li Z., Xu S., Ma Q., Su Y., Guo Q., Jiang, D., 2022. Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing. The Crop Journal, 10, 1334-1345, doi: https://doi.org/10.1016/j.cj.2022.06.005.

74. Zhu J., Qiu L., Su Y., Guo Q., Hu T., Bao H., Luo J., Wu S., Xu Q., Wang Z., Pan Y., 2022. Disentangling the effects of the surrounding environment on street-side greenery: Evidence from Hangzhou. Ecological Indicators, 143, 109153, doi: https://doi.org/10.1016/j.ecolind.2022.109153.

73. Su Y., Guo Q., Guan H., Hu T., Jin S., Wang Z., Liu L., Jiang L., Guo K., Xie Z., An S., Chen X., Hao Z., Hu Y., Huang Y., Jiang M., Li J., Li Z., Li X., Li X., Liang C., Liu R., Liu Q., Ni H., Peng S., Shen Z., Tang Z., Tian X., Wang X.Wang R., Xie Y., Xu X., Yang X., Yang Y., Yu L., Yue M., Zhang F., Chen J., Ma K., 2022. Human-climate coupled changes in vegetation community complexity of China since the 1980s. Earth’s Future, 10, e2021EF002553, doi: https://doi.org/10.1029/2021EF002553.

72. Ao Z., Wu F., Hu S., Sun Y., Su Y., Guo Q., Xin Q., 2022. Automatic segmentation of stem and leaf components and individual maize plants in field terrestrial LiDAR data using convolutional neural networks. The Crop Journal, 10, 1239-1250, doi: https://doi.org/10.1016/j.cj.2021.10.010.

71. Niu C., Woodgate W., Phinn S.R., Roelfsema C.M., Su Y.*, 2022. Extending a canopy reflectance model for mangroves: A case study in southeast Queensland, Australia. Agricultural and Forest Meteorology, 316, 108875, doi: https://doi.org/10.1016/j.agrformet.2022.108875.

70. Ju Y., Xu Q., Jin S., Li W., Su Y., Dong X., Guo Q., 2022. Loess landslide detection using object detection algorithms in Northwest China. Remote Sensing, 14, 1182, doi: https://doi.org/10.3390/rs14051182.

69. Hu T., Wei D., Su Y., Wang X., Zhang J., Sun X., Liu Y., Guo Q., 2022. Quantifying the shape of urban street trees and evaluating its influence on their aesthetic functions based on mobile lidar data. ISPRS Journal of Photogrammetry and Remote Sensing, 184, 203-214, doi: https://doi.org/10.1016/j.isprsjprs.2022.01.002.

68. Liu X., Su Y.*, Hu T., Yang Q., Liu B., Deng Y., Tang H., Tang Z., Fang J., Guo, Q., 2022. Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data. Remote Sensing of Environment, 269, 112844, doi: https://doi.org/10.1016/j.rse.2021.112844.

67. Zhao X., Su Y.*, Hu T., Cao M., Liu X., Yang Q., Guan H., Liu L., Guo Q., 2022. Analysis of UAV lidar information loss and its influence on the estimation accuracy of structural and functional traits in a meadow steppe. Ecological Indicators, 135, 108515, doi: https://doi.org/10.1016/j.ecolind.2021.108515.

66. Yi X., Wang N., Ren H., Yu J., Hu T., Su Y., Mi X., Guo Q., Ma K., 2022. From canopy complementarity to asymmetric competition: The negative relationship between structural diversity and productivity during succession. Journal of Ecology, 110, 457-465, doi:https://doi.org/10.1111/1365-2745.13813.

65. Yan Z., Guo Z., Serbin S.P., Song G., Zhao Y., Chen Y., Wu S., Wang J., Wang X., Li J., Wang B., Wu Y., Su Y., Wang H., Rogers A., Liu L., Wu J., 2021. Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types. New Phytologist, 232, 134-147, doi: https://doi.org/10.1111/nph.17579.

64. Jin S., Su Y., Zhang Y., Song S., Li Q., Liu Z., Ma Q., Ge Y., Liu L., Ding Y., Baret F., 2021. Exploring Seasonal and Circadian Rhythms in Structural Traits of Field Maize from LiDAR Time Series. Plant Phenomics, 9895241, doi: https://doi.org/10.34133/2021/9895241.

63. Guan H., Sun X., Su Y., Hu T., Wang H., Wang H., Peng C., Guo, Q., 2021. UAV-lidar aids automatic intelligent powerline inspection. International Journal of Electrical Power & Energy Systems, 130, 106987, doi: https://doi.org/10.1016/j.ijepes.2021.106987.

62. Jin S., Sun X., Wu F., Su Y., Li Y., Song S., Xu K., Ma Q., Baret F., Jiang D., Ding Y., 2021. Lidar sheds new light on plant phenomics for plant breeding and management: Recent advances and future prospects. ISPRS Journal of Photogrammetry and Remote Sensing, 171, 202-223, doi: https://doi.org/10.1016/j.isprsjprs.2020.11.006.

61. Wu S., Wang J., Yan Z., Song G., Chen Y., Ma Q., Deng M., Wu Y., Zhao Y., Guo Z., Yuan Z., Dai G., Xu X., Yang X., Su Y., Liu L., Wu J., 2021. Monitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations. ISPRS Journal of Photogrammetry and Remote Sensing, 171, 36-48, doi: https://doi.org/10.1016/j.isprsjprs.2020.10.017

60. Su Y., Guo Q., Jin S., Guan H., Sun X., Ma Q., Hu T., Wang R., Li Y., 2021. The development and evaluation of backpack LiDAR system for accurate and efficient forest inventory. IEEE Geoscience and Remote Sensing Letters, 18, 1660-1664, doi: https://doi.org/10.1109/LGRS.2020.3005166.

59. Hu T., Sun X., Su Y., Guan H., Sun Q., Sun Kelly M., Guo Q., 2021. Development and performance evaluation of a very low-cost UAV-lidar system for forestry applications. Remote Sensing, 13, 77, doi: https://doi.org/10.3390/rs13010077.

58. Guo Q.#, Su Y.#, Hu T., Guan H., Jin S., Zhang J., Zhao X., Xu K., Wei D., Kelly M., Coops N.C., 2020. Lidar boosts three-dimensional ecological observations and modelling: A review and perspective. IEEE Geosceicne and Remote Sensing Magazine, 9, 232-257, doi: https://doi.org/10.1109/MGRS.2020.3032713.

57. Jin S., Su Y., Zhao X., Hu T., Guo Q., 2020. Parameter-free point-based fully convolutional neural network for airborne Lidar ground point filtering in forested environments. IEEE Selected Topics in Applied Earth Observations and Remote Sensing, 13, 3958 – 3974, doi: https://doi.org/10.1109/JSTARS.2020.3008477.

56. Jin S., Su Y., Song S., Xu K., Hu T., Yang Q., Wu F., Xu G., Ma Q., Guan H., Pang S., Li Y., Guo Q., 2020. Non-destructive estimation of field maize biomass using terrestrial lidar: An evaluation from plot level to individual leaf level. Plant Methods, 16, 69, doi: https://doi.org/10.1186/s13007-020-00613-5.

55. Hu T., Zhang Y., Su Y., Zheng Y., Lin G., Guo Q., 2020. Mapping the global mangrove forest aboveground biomass using multisource remote sensing data. Remote Sensing, 12, 1690, doi: https://doi.org/10.3390/rs12101690.

54. Guan H., Su Y.*, Sun X., Xu G., Li W., Ma Q., Wu X., Wu J., Liu L., Guo Q., 2020. A marker-free method for registering multi-scan terrestrial laser scanning data in forest environments. ISPRS Journal of Photogrammetry and Remote Sensing, 166, 82-94, doi: https://doi.org/10.1016/j.isprsjprs.2020.06.002.

53. Yu Y., Zhu J., Gao T., Guo Q., Su Y., Li Y., Deng S., Li M., 2020. Terrestrial laser scanning-derived canopy interception index as a descriptor of canopy water storage capacity. Hydrological Processes, 13, e2212, doi: https://doi.org/10.1002/eco.2212.

52. Guo Q., Hu T., Ma Q., Xu K., Yang Q., Sun Q., Li Y., Su Y., 2020. The advances for the new remote sensing technology in ecosystem ecology research. Chinese Journal of Plant Ecology, 44, (In Chinese), doi: https://dx.doi.org/10.17521/cjpe.2019.0206.

51. Guo Q., Jin S., Li M., Yang Q., Xu K., Ju Y., Zhang J., Xuan J., Liu J., Su Y., Xu Q., Liu Y., 2020. Application of deep learning in ecological resource research: theories, methods, and challenges. Science China Earth Sciences, 63, 1457-1474, doi: https://doi.org/10.1007/s11430-019-9584-9.

50. Su Y., Guo Q., Hu T., Guan H., Jin S., An S., Chen X., Guo K., Hao Z., Hu Y., Huang Y., Jiang M., Li Z., Li X., Liang C., Liu R., Liu Q., Ni H., Peng S., Shen Z., Tang Z., Tian X., Wang X., Wang R., Xie Z., Xie Y., Xu X., Yang X., Yang Y., Yu L., Yue M., Zhang F., Ma K., 2020. An Updated Vegetation Map of China (1: 1000000). Science Bulletin, 65, 1125-1136, doi: https://doi.org/10.1029/2019JG005306.

49. Su Y., Hu T., Wang Y., Li Y., Dai J., Liu H., Jin S., Ma Q., Wu J., Liu L., Fang J., Guo Q., 2020. Large-scale geographical variations and climatic controls on crown architecture traits. Journal of Geophysical Research: Biogeosciences, 125, e2019JG005306, doi: https://doi.org/10.1029/2019JG005306.

48. Jin S., Su Y.*, Hu T., Gao S., Wu F., Ma Q., Xu K., Ma Q., Hu T., Liu J., Pang S., Guan H., Zhang J., Guo Q.*, 2020. Separating the structural components of maize for field phenotyping using terrestrial Lidar data and deep convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 58, 2644 – 2658, (Front cover paper), doi: https://doi.org/10.1109/TGRS.2019.2953092.

47. Guan H., Su Y.*, Hu T., Ma Q., Yang Q., Sun X., Li Y., Jin S., Zhang J., Ma Q., Liu M., Wu F., Guo Q., 2020. A novel framework to automatically fuse multi-platform lidar data in forest environments based on tree locations. IEEE Transactions on Geoscience and Remote Sensing, 58, 2165-2177, doi: https://doi.org/10.1109/TGRS.2019.2953654.

46. Li Y., Su Y.*, Zhao X., Yang M., Hu T., Zhang J., Liu M., Liu J., Guo Q., 2020. Retrieval of tree branch architecture attributes from terrestrial laser scan data using a Laplacian algorithm. Agricultural and Forest Meteorology, 284, 107874, doi: https://doi.org/10.1016/j.agrformet.2019.107874.

45. Wang D., Wan B., Liu J., Su Y., Guo Q., Qiu P., Wu X., 2020. Estimating aboveground biomass of the most diverse mangrove forests in China using an upscaling method from field plots, UAV-LiDAR data and Sentinel-2 imagery. International Journal of Applied Earth Observations and Geoinformation, 85, 101986, doi: https://doi.org/10.1016/j.jag.2019.101986.

44. Xu K., Su Y., Liu J., Hu T., Jin S., Ma Q., Zhai Q., Wang R., Zhang J., Li Y., Liu H., Guo Q., 2020. Estimation of degraded grassland aboveground biomass using machine learning methods from terrestrial laser scanning data. Ecological Indicators, 108, 105747, doi: https://doi.org/10.1016/j.ecolind.2019.105747.

43. Guan H., Su Y., Hu T., Chen J., Guo Q., 2019. An object-based strategy for improving the accuracy of spatiotemporal satellite imagery fusion for vegetation mapping applications. Remote Sensing, 11, 2917, doi: https://doi.org/10.3390/rs11242927.

42. Yang Q., Su Y., Jin S., Kelly M., Ma Q., Hu T., Li Y., Zhang J., Xu G., Guo Q., 2019. The influences of vegetation characteristics on individual tree segmentation methods with airborne LiDAR data. Remote Sensing, 11, 2880, doi: https://doi.org/10.3390/rs11232880.

41. Zheng Z., Ma Q., Jin S., Su Y., Guo Q., Bales R., 2019. Canopy and terrain interactions on spatial distributions of snowpack in the Sierra Nevada. Water Resource Research, 108, 105747, doi: https://doi.org/10.1029/2018WR023758.​

40. Su Y.#, Wu F.#, Ao Z., Jin S., Qin F., Liu B., Pang S., Liu L., Guo Q., 2019. Evaluating maize phenotype dynamics under drought stress using terrestrial lidar. Plant Methods, 15, 11, doi: https://doi.org/10.1186/s13007-019-0396-x.​

39. Hu T., Ma Q., Su Y.*, Battles J.J., Collins B.M., Stephens S.L., Kelly M., Guo Q., 2019. A simple and integrated approach for fire severity assessment using bi-temporal airborne LiDAR data. International Journal of Applied Earth Observation and Geoinformation, 78, 25-38, doi: https://doi.org/10.1016/j.jag.2019.01.007.

38. Jin S., Su Y., Wu F., Pang S., Gao S., Hu T., Liu J., Guo Q., 2019. Stem–leaf segmentation and phenotypic trait extraction of individual maize using terrestrial lidar data. IEEE Transactions on Geoscience and Remote Sensing, 57, 1336-1346, doi: https://doi.org/10.1109/TGRS.2018.2866056.

37. Zhou Z., Liu R., Shi S., Su Y., Li W., Guo Q., 2018. Ecological niche modeling with LiDAR data: A case study of modeling the distribution of fisher in the southern Sierra Nevada Mountains, California. Biodiversity Science, 26, 878-891, (In Chinese), doi: https://doi.org/10.17520/biods.2018051.

36. Guo Q., Hu T., Jiang Y., Jin S., Wang R., Guan H., Yang Q., Li Y., Wu F., Zhai Q., Liu J., Su Y., 2018. Advances in remote sensing application for biodiversity research. Biodiversity Science, 26, 789-806, (In Chinese), doi: https://doi.org/10.17520/biods.2018054.

35. Zhao X., Su, Y., Li W., Hu, T., Liu J., Guo Q. 2018. A comparison of LiDAR filtering algorithms in vegetated mountain areas. Canadian Journal of Remote Sensing, 11, 287-298, doi: https://doi.org/10.1080/07038992.2018.1481738.

34. Wang D., Wan B., Qiu P., Su Y., Guo Q., Wang R., Sun F., and Wu X., 2018. Evaluating the performance of Sentinel-2, Landsat 8 and Pléiades-1 in mapping mangrove extent and species. Remote Sensing, 10, 1468, doi: https://doi.org/10.3390/rs10091468.

33. Li Y., Su Y.*, Hu T., Xu G, Guo Q*. 2018. Retrieving 2-D leaf angle distributions for deciduous trees from terrestrial laser scanner data. IEEE Transactions on Geoscience and Remote Sensing, 56, 4945-4955, doi: https://doi.org/10.1109/TGRS.2018.2843382.

32. Ma, Q., Su, Y.*, Luo, L., Li, L., Kelly, M. and Guo, Q., 2018. Evaluating the uncertainty of Landsat-derived vegetation indices in quantifying forest fuel treatments using bi-temporal LiDAR data. Ecological Indicators, 95, 298-310, doi: https://doi.org/10.1016/j.ecolind.2018.07.050.

31. Jin, S.#, Su, Y.#, Gao, S., Hu, T., Liu, J. and Guo, Q., 2018. The Transferability of Random Forest in Canopy Height Estimation from Multi-Source Remote Sensing Data. Remote Sensing, 10, 1183, doi: https://doi.org/10.3390/rs10081183.

30. Jin S., Su Y.*, Gao S., Wu F., Hu T., Liu J., Li W., Wang D., Chen S., Jiang Y., Pang S., Guo Q.*, 2018. Deep Learning: Individual maize segmentation from terrestrial Lidar data using Faster R-CNN and regional growth algorithms. Frontiers in Plant Science, 9, 866, doi: https://doi.org/10.3389/fpls.2018.00866.

29. Luo L., Zhai, Q., Su Y.*, Ma Q., Kelly M., Guo Q.*, 2018. Simple method for direct crown base height estimation of individual conifer trees using airborne LiDAR data. Optics Express, 26, A562-A578, doi: https://doi.org/10.1364/OE.26.00A562.

28. Zhao X.#, Su Y.#, Hu T., Chen L., Gao S., Wang R., Jin S., Guo Q., 2018. A global corrected SRTM DEM product for vegetated areas. Remote Sensing Letters, 9, 393-402, doi: https://doi.org/10.1080/2150704X.2018.1425560.

27. Li W., Guo Q., Tao S. and Su Y., 2018. VBRT: A novel voxel-based radiative transfer model for heterogeneous three-dimensional forest scenes. Remote Sensing of Environment, 206, 318-335, doi: https://doi.org/10.1016/j.rse.2017.12.043.

26. Ma Q., Su Y., Guo Q., 2018. Quantifying individual tree growth and tree competition using bi-temporal airborne laser scanning data: a case study in the Sierra Nevada Mountains, California. International Journal of Digital Earth, 11, 485-503, doi: https://doi.org/10.1080/17538947.2017.1336578.

25. Wang D., Wan B., Qiu P., Su Y., Guo Q., Wu X., 2018. Artificial mangrove species mapping using Pléiades-1: an evaluation of pixel-based and object-based classifications with selected machine learning algorithms. Remote Sensing, 10, p.294, doi: https://doi.org/10.3390/rs10020294.

24. Kelly M., Su Y., Di Tommaso S., Fry D.L., Collins B.M., Stephens,S.L., Guo Q., 2017. 1. Remote Sensing, 10, p.10, doi: https://doi.org/10.3390/rs10010010.

23. Su Y., Bales R.C., Ma Q., Nydick K., Ray R.L., Li W., Guo Q., 2017. Emerging stress and relative resiliency of giant sequoia groves experiencing multiyear dry periods in a warming climate. Journal of Geophysical Research: Biogeosciences, 122, 3063-3075, doi: https://doi.org/10.1002/2017JG004005.

22. Ao Z., Su Y., Li, W., Guo Q., Zhang J., 2017. One-class classification of airborne LiDAR Data in urban areas using a presence and background learning algorithm. Remote Sensing, 9, 1001, doi: https://doi.org/10.3390/rs9101001.

21. Xue B., Guo Q., Hu T., Xiao J., Yang Y., Tao S., Su Y., Liu J., Zhao X., 2017. Global patterns of woody residence time and its influence on model simulation of aboveground biomass. Global Biogeochemical Cycles, 31, 821-835, doi: https://doi.org/10.1002/2016GB005557.

20. Li Y., Su Y., Tao S., Zhao K., Xu G., 2017. Retrieving the gap fraction, element clumping index, and leaf area index of individual trees using single-scan data from a terrestrial laser scanner. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 308-316, doi: https://doi.org/10.1016/j.isprsjprs.2017.06.006.

19. Xue B., Guo Q., Hu T., Wang Y., Tao S., Su Y., Liu J., Zhao X., 2017. Evaluation of modeled global carbon dynamics: analysis based on global carbon flux and above-ground biomass data. Ecological Modelling, 355, 84-96, doi: https://doi.org/10.1016/j.ecolmodel.2017.04.012.

18. Ma Q., Su Y., Guo Q., 2017. Comparison of canopy cover estimations from airborne LiDAR, aerial imagery, and satellite imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 4225-4236, doi: https://doi.org/10.1109/JSTARS.2017.2711482.

17. Guo Q., Su Y., Hu T., Zhao X., Wu F., Li, Y., Liu J., Chen L., Xu G., Lin G., Zheng Y., Lin Y., Mi X., Fei L., Wang X., 2017. An integrated UAV-borne LiDAR system for 3D habitat mapping in three forest ecosystems across China. International Journal of Remote Sensing, 38, 2954-2972, doi: https://doi.org/10.1080/01431161.2017.1285083.

16. Zhu J., Su Y., Guo Q., Harmon T.C., 2017. Unsupervised object-based differencing procedure for land-use change detection using very high spatial resolution data. Photogrammetric Engineering & Remote Sensing, 83, 225-236, doi: https://doi.org/10.14358/PERS.83.3.225.

15. Su Y., Ma Q., Guo Q., 2017. Fine-resolution forest tree height estimation across the Sierra Nevada through the integration of spaceborne LiDAR, airborne LiDAR and optical imagery. International Journal of Digital Earth, 10, 307-323, doi: https://doi.org/10.1080/17538947.2016.1227380.

14. Li Y., Guo Q., Tao S., Zheng G., Zhao K., Su Y., 2016. Derivation, validation, and sensitivity analysis of terrestrial laser scanning-based leaf area index. Canadian Journal of Remote Sensing, 42, 719-729, doi: https://doi.org/10.1080/07038992.2016.1220829.

13. Hu T.#, Su Y.#, Xue B., Liu J., Zhao X., Fang J., Guo Q., 2016. Mapping global forest aboveground biomass with spaceborne LiDAR, optical imagery, and forest inventory data. Remote Sensing, 8, 565, doi: https://doi.org/10.3390/rs8070565.

12. Su Y., Guo Q., Collins B., Fry D., Kelly M., 2016. Forest fuel treatment detection using multi-temporal airborne LiDAR data and high-resolution aerial imagery: A case study at Sierra Nevada Mountains, California. International Journal of Remote Sensing, 37, 3322-3345, (Front cover paper), doi: https://doi.org/10.1080/01431161.2016.1196842.

11. Zhao X., Guo Q., Su Y., Xue B., 2016. Improved progressive TIN densification filtering algorithm for airborne LiDAR in forested areas. ISPRS Journal of Photogrammetry and Remote Sensing, 117, 79-91, doi: https://doi.org/10.1016/j.isprsjprs.2016.03.016.

10. Su Y., Guo Q., Xue B., Hu T., Alvarez O., Tao S., Fang J., 2016. Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne LiDAR, optical imagery, and forest inventory data. Remote Sensing of Environment, 173, 187-199, doi: https://doi.org/10.1016/j.rse.2015.12.002.

9. Su Y., Guo Q., Fry D., Collins B., Kelly M., Flanagan J., Battles J., 2016. A vegetation mapping strategy for conifer forests by combining airborne LiDAR data and aerial imagery. Canadian Journal of Remote Sensing, 42, 1-15, doi: https://doi.org/10.1080/07038992.2016.1131114.

8. Su Y., Guo Q., Ma Q., Li W., 2015. SRTM DEM correction in vegetated mountain areas through the integration of spaceborne LiDAR, airborne LiDAR, and optical imagery. Remote Sensing, 7, 11202-11225, doi: https://doi.org/10.3390/rs70911202.

7. Tempel D.J., Gutiérrez R.J., Battles J.J., Fry D.L., Su Y., Guo Q., Reetz M.J., Whitmore S.A., Jones G.M., Collins B.M., Stephens L.L., Kelly M., Berigan W.J., Peery M.Z., 2015. Evaluating short- and long-term impacts of fuels treatments and wildfire on an old-forest species. Ecosphere, 12, art261, doi: https://doi.org/10.1890/ES15-00234.1.

6. Wan B., Guo Q., Fang F., Su Y., Wang R., 2015. Mapping US urban extents from time-series MODIS data using one-class. Remote Sensing, 7, 10143-10163, doi: https://doi.org/10.3390/rs70810143.

5. Tao S., Guo Q., Xu S., Su Y., Li Y., Wu F., 2015. A geometric method for wood–leaf separation using terrestrial LiDAR data and simulated point cloud. Photogrammetric Engineering & Remote Sensing, 81, 767-776, doi: https://doi.org/10.14358/PERS.81.10.767.

4. Tao S., Guo Q., Li L., Xue B., Kelly M., Li W., Su Y., 2014. Airborne LiDAR-derived volume metrics for aboveground biomass estimation: A comparative assessment for conifer stands. Agricultural and Forest Meteorology, 198. 24-32, doi: https://doi.org/10.1016/j.agrformet.2014.07.008.

3. Su Y., Guo Q., 2014. A practical method for SRTM DEM correction over vegetated mountain areas. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 216-228, doi: https://doi.org/10.1016/j.isprsjprs.2013.11.009.

2. Wang Y., Su Y.*, 2013. Influence of solar activity on breaching, overflowing and course shifting events of the lower yellow river in the late Holocene. Holocene, 23, 656-666, doi: https://doi.org/10.1177/0959683612467481.

1. Wang Y., Su Y.*, 2011. The geo-pattern of course shifts of the Lower Yellow River. Journal of Geographical Sciences, 21, 1019-1036, doi: https://doi.org/10.1007/s11442-011-0897-7.