1. 首页 > 研究队伍
姓　　名： 苏艳军
职务/职称： 研究员、研究组组长
联系电话： (86)-010-62836159
电子邮件： ysu@ibcas.ac.cn
个人网页： http://lvec.ibcas.ac.cn/rcdw/yjy/202103/t20210305_627874.html
课 题 组： 数字生态系统研究组
苏艳军，男，博士，研究员，博士生导师。

2009年于中国地质大学（北京）获学士学位，2012年于中国科学院地理科学与资源研究获硕士学位，2017年于加州大学默塞德分校获博士学位，并于同年进入中国科学院植物研究所数字生态研究组工作。曾获得美国摄影测量学会“William A. Fischer Memorial Scholarship”、李小文遥感科学青年奖、中国自然资源协会青年科技奖、国家优秀自费留学生等奖项。

主要研究工作：

利用以激光雷达（LiDAR）为主的遥感技术探讨人类活动以及全球气候变化对陆地生态系统的影响

主持和参加的科研项目：

[1]  全球变化下的典型森林生态系统观测和预警，国家重点研发计划青年科学家项目，2022.12-2027.11，主持

[2]  基于众源采集和人工智能的信息化植被调查与绘制技术研发，中科院网信专项项目，2022.01-2023.12，主持

[3]  我国北方草地图集编制，中科院先导专项子任务，2020.11-2025.10，主持

[4]  基于激光雷达与光学影像的作物表型采集与翻译，中科院先导子任务，2019.01-2024.12，主持

[5]  基于激光雷达技术的树木三维构型空间格局及驱动力分析：以蒙古栎为例，国家自然科学基金面上项目，2019.01.01-2021.12.31，主持

[6]  基于激光雷达与深度学习技术的作物三维表型特征提取研究，国家自然科学基金应急项目，2018.01.01-2018.12.31，主持

发表论文（注*为通讯作者,§为共同第一作者）：

2024

  1. Xiaoqiang Liu, Yuhao Feng, Tianyu Hu, Yue Luo, Xiaoxia Zhao, Jin Wu, Eduardo E. Maeda, Weimin Ju, Lingli Liu, Qinghua Guo, Yanjun Su*. Enhancing ecosystem productivity and stability with increasing canopy structural complexity in global forests. Science Advances, in press.

2023

  1. Ma Q, Su YJ*, Niu CY, Ma Q, Hu TY, Luo XZ, Tai XN, Qiu T, Zhang Y, Bales RC, Liu LL, Kelly M, Guo QH. 2023. Tree mortality during long-term droughts is lower in structurally complex forest stands. Nature Communications, 14(1): 7467.
  2. Liu ZH, Jin SC*, Liu XQ, Yang QL, Li Q, Zang JR, Li ZF, Hu TY, Guo ZF, Wu J, Jiang D, Su YJ*. 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.
  3. Cheng K, Su YJ, Guan HC, Tao SL, Ren Yu, Hu TY, Ma KP, Tang YH,Guo QH. 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.
  4. Zhao XX, Feng YH, Xu KX, Cao MQ, Hu SY, Yang QL, Liu XQ, Ma Q, Hu TY, Kelly M, Guo QH, Su YJ*.2023. Canopy structure: An intermediate factor regulating grassland diversity-function relationships under human disturbances. Fundamental Research. 3(2): 179-187.
  5. Ma Q, Su YJ*, Hu TY, Jiang L, Mi XC, Lin LX, Cao M, Wang XG, Lin F, Wang BJ, Sun ZH, Wu J, Ma KP, Guo QH. The coordinated impact of forest internal structural complexity and tree species diversity on forest productivity across forest biomes. Fundamental Research. (Online). https://doi.org/10.1016/j.fmre.2022.10.005

2022

  1. Liu XQ, Ma Q, Wu XY, Hu TY, Dai GH, Wu J, Tao SL, Wang SP, Liu LL, Guo QH,Su YJ*. 2022. Non-scalability of fractal dimension to quantify canopy structural complexity from individual trees to forest stands. Journal of Remote Sensing, 2022, 0001.
  2. Liu XQ, Ma Q, Wu XY, Hu TY, Liu ZH Liu LL, Guo, QH, Su YJ*. 2022. A novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds. Remote Sensing of Environment. 282: 113280.
  3. Zhu JX, Qiu LF, Su YJ, Guo QH, Hu TY, Bao HJ, Luo JH, Wu SH, Xu Q, Wang ZL, Pan Y. 2022. Disentangling the effects of the surrounding environment on street-side greenery: Evidence from Hangzhou. Ecological Indicators.143: 109153.
  4. Li Q, Jin SC, Zang JR, Wang X, Sun ZZ, Li ZY, Xu S, Ma Q, Su YJ, Guo QH, Jiang D. 2022. Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing. The Crop Journal.10(5): 1334-1345.
  5. Yang QL, Su YJ, Hu TY, Jin SC, Liu XQ, Niu CY, Liu ZH, Kelly M, Wei JX,Guo QH. 2022. Allometry-based estimation of forest aboveground biomass combining LiDAR canopy height attributes and optical spectral indexes. Forest Ecosystems. 9: 100059.
  6. Su YJ, Guo QH, Guan HC, Hu TY, Jin SC, Wang ZH, Liu LL, Jiang L, Guo K, Xie ZQ, An SZ, Chen XL, Hao ZQ, Hu YM, Huang YM, Jiang MX, Li JX, Li ZJ, Li XK, Li XW, Liang CZ, Liu RL, Liu Q, Ni HW, Peng SL, Shen ZH, Tang ZY, Tian XJ, Wang XH, Wang RQ, Xie YZ, Xu XN, Yang XB, Yang YC, Yu LF, Yue M, Zhang F, Chen J, Ma KP. 2022. Human-climate coupled changes in vegetation community complexity of China since the 1980s. Earth's Future. 10(7): e2021EF002553.
  7. Ju YZ, Xu Q, Jin SC, Li WL, Su YJ, Dong XJ, Guo QH. 2022. Loess Landslide Detection Using Object Detection Algorithms in Northwest China. Remote Sensing. 14:1182.
  8. Hu TY, Wei DJ, Su YJ, Wang XD, Zhang J, Sun XL, Liu Y, Guo QH. 2022. Quantifying the shape of urban street trees and evaluating its influence on their aesthetic functions based mobile lidar data. ISPRS Journal of Photogrammetry and Remote Sensing. 184:203-214.
  9. Ao ZR, Wu FF, Hu SH, Sun Y, Su YJ, Guo QH, Xin QC. 2022. Automatic Segmentation of Stem and Leaf Components and Individual Maize Plants from Field Terrestrial LiDAR Data Using Convolutional Neural Networks. The Crop Journal. 10(5): 1239-1250.
  10. Zhao XX, Su YJ*, Hu TY, Cao MQ, Liu XQ, Yang QL, Guan HC, Liu LL,Guo QH. 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.
  11. Niu CY, Woodgate W, Phinn R. S., Roelfsema M. C., Su YJ*. 2022. Extending a canopy reflectance model for mangroves: A case study in south east queensland, Australia. Agricultural and Forest Meteorology. 316: 108875.
  12. Liu XQ, Su YJ*, Hu TY, Liu BB, Deng YF, Tang H, Tang ZY, Fang JY, Guo QH. 2022. Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data. Remote Sensing of Environment. 112844.
  13. 任淯; 陶胜利; 胡天宇; 杨海涛; 关宏灿; 苏艳军; 程凯; 陈梦玺; 万华伟; 郭庆华. 2022. 中国生物多样性核心监测指标遥感产品体系构建与思考. 生物多样性. 30(10): 22530.
  14. 苏艳军*; 严正兵; 吴锦; 刘玲莉. 2022. 生态遥感新方法及其在自然保护地天空地一体化监测中的应用. 植物生态学报. 46: 1125-1128.
  15. 王嘉童; 牛春跃; 胡天宇; 李文楷; 刘玲莉; 郭庆华; 苏艳军*. 2022. 三维辐射传输模型在森林生态系统研究中的应用与展望. 植物生态学报. 46: 1200-1218.
  16. 刘兵兵; 魏建新; 胡天宇; 杨秋丽; 刘小强; 吴发云; 苏艳军; 郭庆华. 2022. 卫星遥感监测产品在中国森林生态系统的验证和不确定性分析——基于海量无人机激光雷达数据. 植物生态学报. 46: 1305-1316.

2021

  1. Yi XX, Wang NN, Ren HB, Yu JP, Hu TY, Su YJ, Mi XC, Guo QH, Ma KP. 2021. From Canopy complementarity to asymmetric competition: the negative relationaship between structural diversity and productivity duting succession. Journal of Ecology. 110: 457-465.
  2. Jin SC, Su YJ, Zhang YG, Song SL, Li Q, Liu ZH, Ma Q, Ge Y, Liu LL, Ding YF, Frédéric B, Guo QH. 2021. Exploring seasonal and circadian rhythms in structural traits of field maize from LiDAR time-series. Plant Phenomics. 2021: 1-15.
  3. Guan HC, Sun XL, Su YJ, Hu TY, Wang HT, Wang HP, Peng CG, Guo QH. 2021. UAV-lidar aids automatic intelligent powerline inspection. International Journal of Electrical Power and Energy Systems. 130: 106987.
  4. Guo QH§, Su YJ§, Hu TY, Guan HC, Jin SC, Zhang J, Zhao XX, Xu KX, Wei DJ, Kelly M, Coops C. N. 2021. Lidar Boosts Three-Dimensional Ecological Observations and Modelling: A Review and Perspective. IEEE Geoscience and Remote Sensing Magazine. 9(1): 232-257.
  5. Su YJ,Guo QH, Jin SC, Guan HC, Sun XL, Ma Q, Hu TY, Wang R, Li YM. 2021. The Development and Evaluation of Backpack Lidar System for Accurate and Efficient Forest Inventory. IEEE Geoscience and Remote Sensing Letters. 18(9): 1660-1664.
  6. Jin SC, Sun XL, Wu FF, Su YJ, Li YM, Song SL, Xu KX, Ma Q, Baret F, Jiang D, Ding YF, Guo QH. 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.
  7. 孙喜亮;关宏灿;苏艳军;徐光彩;郭庆华.2021. 面向高精度城市测绘的激光紧耦合SLAM方法研究. 测绘学报. 50（11）： 1585-1593.

2020

  1. Hu TY, Sun XL, Su YJ, Guan HC, Sun QH, Kelly M, Guo QH. 2020. Development and Performance Evaluation of A Very Low-Cost UAV-Lidar System for Forestry Applications. Remote Sensing. 13(1):77
  2. Wu SB, Wang J, Yan ZB, Ssong GQ, Chen Y, Ma Q, Deng MF, Wu YT, Zhao YY, Guo ZF, Yuan ZQ, Dai GH, Xu XT, Yang X, Su YJ, Liu LL, Wu J. 2020.  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.
  3. Jin SC, Su YJ, Zhao XQ, Hu TY, Guo QH. 2020. A Point-Based Fully Convolutional Neural Network for Airborne LiDAR Ground Point Filtering in Forested Environments. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 13: 3958-3974.
  4. Guan HC, Su YJ*, Sun XL, Xu GC, Ma Q, Wu XY Wu J, Liu LL, Guo QH. 2020. A Marker-Free Method for Registering Multi-Scan Terrestrial Laser Scanning Data in Forest Environments. ISPRS Journal of Photogrammetry and Remote Sensing. 166(8): 82-94.
  5. Hu TY, Zhang YY, Su YJ, Zheng Y, Lin GH, Guo QH. 2020. Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data. Remote Sensing. 12: 1690.
  6. Jin SC, Su YJ, Song SL, Xu KX, Hu TY, Yang QL, Wu FF, Xu GC, Ma Q, Guan HC, Pang SX, Li YM, Guo QH. 2020. Non-Destructive Estimation of Field Maize Biomass Using Terrestrial Lidar: An Evaluation from Plot Level to Individual Leaf Level. Plant Methods. 16(69): 1-19.
  7. Su YJ, Guo QH, Hu TY, Guan HC, Jin SC, An SZ, Chen XL, Guo K, Hao ZQ, Hu YM, Huang YM, Jiang MX, Li JX, Li ZJ, Li XK, Li XW, Liang CZ, Liu RL, Liu Q, Ni HW, Peng SL, Shen ZH, Tang ZY, Tian XJ, Wang XH, Wang RQ, Xie ZQ, Xie YZ, Xu XN, Yang XB, Yang YC, Yu LF, Yue M, Zhang F, Ma KP. 2020. An Updated Vegetation Map of China (1: 1000000). Science Bulletin. 65(13): 1125-1136.
  8. Su YJ, Hu TY, Wang YC, Li YM, Dai JY, Liu HY, Jin SC, Ma Q, Wu J, Liu LL, Fang JY, Guo QH. 2020. Large-Scale Geographical Variations and Climatic Controls on Crown Architecture Traits. Journal of Geophysical Research – Biogeosciences. 125(2): e2019JG005306.
  9. Guo QH, Jin SC, Li M, Yang QL, Xu KX, Ju YZ, Zhang J, Xuan J, Liu J, Su YJ, Xu Q, Liu Y. 2020. Application of Deep Learning In Ecological Resource Research: Theories, Methods, And Challenges. Science China Earth Science. 63: 1457–1474.
  10. Li YM, Su YJ*, Zhao XX, Yang MH, Hu TY, Zhang J, Liu J, Liu M, Guo QH. 2020. Retrieval of Tree Branch Architecture Attributes from Terrestria Laser Scan Data Using A Laplacian Algorithm. Agricultural and Forest Meteorology. 284: 107874.
  11. Wang DZ, Wan B, Liu J, Su YJ, Guo QH, Qiu PH, Wu XC. 2020. Estimating Aboveground Biomass of The Mangrove Forests on Northeast Hainan Island in China Using An Upscaling Method From Field Plots, UAV-LiDAR Data and Sentinel-2 Imagery. International Journal of Applied Earth Observation and Geoinformation. 85: 101986.
  12. Xu KX, Su YJ, Liu J, Hu TY, Jin SC, Ma Q, Zhai QP, Wang R, Zhang J, Li YM, Liu HY, Guo QH. 2020. Estimation of Degraded Grassland Aboveground Biomass Using Machine Learning Methods from Terrestrial Laser Scanning Data. Ecological Indicators. 108: 105747.
  13. Guan HC, Su YJ*, Hu TY, Wang R, Ma Q, Yang QL, Sun XL, Li YM, Jin SC, Zhang J, Ma Q, Liu M, Wu FY, Guo QH. 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(3): 2165-2177.
  14. Yu Yue, Gao Tian, Zhu Jiaojun, Guo QH, Su YJ, Li Yumei, Deng Songqiu, Li Mingcai. 2020. Terrestrial Laser Scanning-derived Canopy Interception Index as A Descriptor of Canopy Water Storage Capacity. Ecohydrology. 13(5): e2212.
  15. Jin SC, Su YJ*, Gao S, Wu FF, Ma Q, Xu KX, Ma Q, Hu TY, Liu J, Pang SX, Guan HC, Zhang J, Guo QH*. 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(4): 2644 - 2658. (Cover Paper) (ESI Highly cited paper).
  16. 郭庆华，胡天宇，马勤，徐可心，杨秋丽，孙千惠，李玉美，苏艳军, 2020. 新一代遥感技术助力生态系统生态学研究. 植物生态学报. 44(4): 418-435.

2019

  1. Guan HC, Su YJ, Hu TY, Chen J, Guo QH. 2019. An Object-Based Strategy for Improving The Accuracy of Spatiotemporal Satellite Imagery Fusion for Vegetation Mapping Applications.Remote Sensing. 11(24): 2927.
  2. Yang QL, Su YJ, Kelly M, Hu TY, Ma Q, Li YM, Song SL, Zhang J, Xu GC, Wei JX, Guo QH. 2019. The Influences of Vegetation Characteristics on Individual Tree Segmentation Methods with Airborne Lidar Data. Remote Sensing. 11: 2880.
  3. Hu TY, Ma Q, Su YJ*, Battles JJ, Collins BM., Stephens SL, Kelly M, Guo QH. 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.
  4. Su YJ§, Wu FF§, Ao Zr, Jin SC, Qin F, Liu BX, Pang SX, Liu LL, Guo QH. 2019. Evaluating Maize Phenotype Dynamics Under Drought Stress Using Terrestrial Lidar. Plant Methods. 15: 11.
  5. Sun F, Wang R, Wan B, Su YJ, Guo QH, Huang YX, Wu XC. 2019. Efficiency of Extreme Gradient Boosting for Imbalanced Land Cover Classification Using an Extended Margin and Disagreement Performance. ISPRS International Journal of Geo-Information. 8(7): 315.
  6. Zheng ZS, Ma Q, Jin SC, Su YJ, Guo QH, Bales RC. 2019. Canopy and Terrain Interactions Affecting Snowpack Spatial Patterns in the Sierra Nevada of California. Water Resources Research. 55: 8721–8739.
  7. Jin SC, Su YJ, Wu FF, Pang SX, Gao S, Hu TY, Liu J, Guo QH. 2019. Stem-Leaf Segmentation and Phenotypic Trait Extraction of Individual Maize Using Terrestrial LiDAR Data.IEEE Transactions on Geoscience and Remote Sensing. 57(3): 1336-1346.

2018

  1. Jin SC, Su YJ*, Gao S, Wu FF, Hu TY, Liu J, Li WK, Wang DC, Chen SJ, Jiang YX, Pang SX, Guo QH*. 2018. Deep Learning: Individual Maize Segmentation From Terrestrial Lidar Data Using Faster R-CNN and Regional Growth Algorithms.Front Plant Sci. 9: 866-875.
  2. Jin SC§,Su YJ§, Gao S, Hu TY, Liu J, Guo QH. 2018. The Transferability of Random Forest in Canopy Height Estimation from Multi-Source Remote Sensing Data. Remote Sensing. 10(8): 1183.
  3. Li WK, Guo QH, Tao SL, Su YJ. 2018. VBRT: A Novel Voxel-Based Radiative Transfer Model for Heterogeneous Three-Dimensional Forest Scenes. Remote Sensing of Environment. 206: 318-335.
  4. Li YM, Su YJ*, Hu TY, Xu GC, Guo QH*. 2018. Retrieving 2-D Leaf Angle Distributions for Deciduous Trees From Terrestrial Laser Scanner Data.IEEE Transactions on Geoscience and Remote Sensing. 56(8): 4945-4955.
  5. Luo LP, Zhai QP, Su YJ*, Ma Q, Kelly M, Guo QH*. 2018. Simple Method for Direct Crown Base Height Estimation of Individual Conifer Trees Using Airborne Lidar Data. Opt Express. 26(10): A562-A578.
  6. Ma Q, Su YJ*, Luo LP, Li Le, Kelly M, Guo QH. 2018. Evaluating The Uncertainty of Landsat-Derived Vegetation Indices in Quantifying Forest Fuel Treatments Using Bi-Temporal Lidar Data. Ecological Indicators. 95: 298-310.
  7. Wang DZ, Wan B, Qiu PH, Su YJ, Guo QH, Wang R, Sun F, Wu XC. 2018. Evaluating the Performance of Sentinel-2, Landsat 8 and Pleiades-1 in Mapping Mangrove Extent and Species. Remote Sensing. 10(9): 27.
  8. Wang DZ, Wan B, Qiu PH, Su YJ, Guo QH, Wu XC. 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(2): 294.
  9. Zhao XQ, Su YJ, Li WK, Hu TY, Liu J, Guo QH. 2018. A Comparison of LiDAR Filtering Algorithms in Vegetated Mountain Areas. Canadian Journal of Remote Sensing. 1-12.
  10. Zhao XQ§, Su YJ§, Hu TY, Chen LH, Gao S, Wang R, Jin SC, Guo QH. 2018. A Global Corrected SRTM DEM Product for Vegetated Areas. Remote Sensing Letters. 9(4): 393-402.
  11. 周中一，刘冉，时书纳，苏艳军，李文楷，郭庆华, 2018. 基于激光雷达数据的物种分布模拟: 以美国加州内华达山脉南部区域食鱼貂分布模拟为例. 生物多样性, 26, 878-891.
  12. 郭庆华, 胡天宇, 姜媛茜, 金时超, 王瑞, 关宏灿, 杨秋丽, 李玉美, 吴芳芳, 翟秋萍, 刘瑾, 苏艳军. 2018. 遥感在生物多样性研究中的应用进展. 生物多样性, 26, 789-806.

2017

  1. Ao ZR, Su YJ, Li WK, Guo QH, Zhang J. 2017. One-Class Classification of Airborne LiDAR Data in Urban Areas Using a Presence and Background Learning Algorithm. Remote Sensing. 9(10): 1001.
  2. Guo QH, Su YJ, Hu TY, Zhao XQ, Wu FF, Li YM, Liu J, Chen LH, Xu GC, Lin GH, Zheng Y, Lin YQ, Mi XC, Fei L, Wang XG. 2017. An Integrated UAV-Borne Lidar System for 3D Habitat Mapping in Three Forest Ecosystems Across China. International Journal of Remote Sensing.38(8-10): 2954-2972.
  3. Kelly M, Su YJ, Di T S, Fry D, Collins B, Stephens S, Guo QH. 2017. Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA.Remote Sensing. 10(2): 10.
  4. Li YM, Guo QH, Su YJ, Tao SL, Zhao KG, Xu GC. 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.
  5. Ma Q, Su YJ, Guo QH. 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(9): 4225-4236.
  6. Ma Q, Su YJ, Tao SL, Guo QH. 2017. 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(5): 485-503.
  7. Su YJ, Bales RC., Ma Q, Nydick K, Ray RL., Li WK, Guo QH. 2017. Emerging Stress and Relative Resiliency of Giant Sequoia Groves Experiencing Multiyear Dry Periods in a Warming Climate. Journal of Geophysical Research: Biogeosciences. 122(11): 3063-3075.
  8. Su YJ, Ma Q, Guo QH. 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(3): 307-323.
  9. Xue BL, Guo QH, Hu TY, Wang GQ, Wang YC, Tao SL, Su YJ, Liu J, Zhao XQ. 2017. Evaluation of Modeled Global Vegetation Carbon Dynamics: Analysis Based on Global Carbon Flux and Above-Ground Biomass Data. Ecological Modelling. 355: 84-96.
  10. Xue BL, Guo QH, Hu TY, Xiao JF, Yang YH, Wang GQ, Tao SL, Su YJ, Liu J, Zhao XQ. 2017. Global Patterns of Woody Residence Time and Its Influence on Model Simulation of Aboveground Biomass. Global Biogeochemical Cycles. 31(5): 821-835.
  11. Zhu JX, Su YJ, Guo QH, Harmon TC. 2017. Unsupervised Object-Based Differencing for Land-Cover Change Detection. Photogrammetric Engineering & Remote Sensing. 83(3): 225-236.

2016

  1. Hu TY§, Su YJ§, Xue BL, Liu J, Zhao XQ, Fang JY, Guo QH. 2016. Mapping Global Forest Aboveground Biomass with Spaceborne LiDAR, Optical Imagery, and Forest Inventory Data.Remote Sensing. 8(7): 1-27.
  2. Li YM, Guo QH, Tao SL, Zheng G, Zhao KG, Xue BL, Su YJ. 2016. Derivation, Validation, and Sensitivity Analysis of Terrestrial Laser Scanning-Based Leaf Area Index. Canadian Journal of Remote Sensing. 42(6): 719-729.
  3. Su YJ, Guo QH, Collins BM., Fry DL., Hu TY, Kelly M. 2016. Forest Fuel Treatment Detection Using Multi-Temporal Airborne Lidar Data and High-Resolution Aerial Imagery: A Case Study in the Sierra Nevada Mountains, California. International Journal of Remote Sensing. 37(14): 3322-3345. (Cover Paper)
  4. Su YJ, Guo QH, Fry DL., Collins BM., Kelly M, Flanagan JP., Battles JJ. 2016. A Vegetation Mapping Strategy for Conifer Forests by Combining Airborne LiDAR Data and Aerial Imagery. Canadian Journal of Remote Sensing. 42(1): 1-15.
  5. Su YJ, Guo QH, Xue BL, Hu TY, Alvarez O, Tao SL, Fang JY. 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.
  6. Zhao XQ, Guo QH, Su YJ, Xue BL. 2016. Improved Progressive Tin Densification Filtering Algorithm for Airborne Lidar Data in Forested Areas. ISPRS Journal of Photogrammetry and Remote Sensing. 117: 79-91.

2015及以前

  1. Su YJ, Guo QH, Ma Q, Li WK. 2015. SRTM DEM Correction in Vegetated Mountain Areas through the Integration of Spaceborne LiDAR, Airborne LiDAR, and Optical Imagery.Remote Sensing. 7(9): 11202-11225.
  2. Tao SL, Guo QH, Xu SW, Su YJ, Li YM, Wu FF. 2015. A Geometric Method for Wood-Leaf Separation Using Terrestrial and Simulated Lidar Data. Photogrammetric Engineering & Remote Sensing. 81(10): 767-776.
  3. Tempel DJ., Gutiérrez RJ., Battles JJ., Fry DL., Su YJ, Guo QH, Reetz MJ., Whitmore SA., Jones GM., Collins BM., Stephens SL., Kelly M, Berigan WJ., Peery MZ. 2015. Evaluating Short- and Long-Term Impacts of Fuels Treatments and Simulated Wildfire on An Old-Forest Species. Ecosphere. 6(12): 1-19.
  4. Wan B, Guo QH, Fang F, Su YJ, Wang R. 2015. Mapping US Urban Extents from MODIS Data Using One-Class Classification Method.Remote Sensing. 7(8): 10143-10163.
  5. Su YJ, Guo QH. 2014. A Practical Method for SRTM DEM Correction Over Vegetated Mountain Areas. ISPRS Journal of Photogrammetry and Remote Sensing. 87: 216-228.
  6. Tao SL, Guo QH, Li L, Xue BL, Kelly M, Li WK, Xu GC, Su YJ. 2014. Airborne Lidar-Derived Volume Metrics for Aboveground Biomass Estimation: A Comparative Assessment for Conifer Stands.Agricultural and Forest Meteorology. 198-199: 24-32.
  7. Wang YJ, Su YJ*, 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.
  8. Wang YJ, Su YJ*, 2011. The geo-pattern of course shifts of the Lower Yellow River. Journal of Geographical Sciences, 21, 1019-1036.
  9. 苏艳军, 王英杰, 罗斌, & 余卓渊, 2009. 新型网络地图符号概念模型及其描述体系. 地球信息科学, 11, 839-844.
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