学科专业：摄影测量与遥感 研究领域：激光雷达遥感、森林生态遥感、北极遥感、地球交互

[1] 基于背包式MLS与UAV遥感协同的单木-林分尺度森林地上生物量精细反演、国家自然科学基金面上项目、项目负责人、2015/01-2018/12

[2] 针对形态特征的激光扫描植物表型技术研究、北京市自然科学基金青年项目、项目负责人、2015/01-2016/12

[3] 基于背包式移动地基激光扫描的林下灌木层形态分布模式测量、博士学科点专项科研基金、项目负责人、2014/01-2016/12

[4] 面向多领域遥感应用的轻小型激光扫描技术研发、热博rb88体育官网985学科建设、项目负责人、2013/01-2015/12

[5] 基于地基激光扫描反演森林冠层结构参数、教育部留学回国人员科研启动基金、项目负责人、2014/01-2016/12

[1] Lin Y*, Jiang M, Pellikka P, Heiskanen J, 2018. Recruiting conventional tree architecture models into state-of-the-art LiDAR mapping for investigating tree growth habits in structure. Frontiers in Plant Science, 9, 220.

[2] Jiang M, Lin Y*, 2018. Desertification in the south Junggar Basin, 2000-2009: Part I. Spatial analysis and indicator retrieval. Advances in Space Research, doi: 10.1016/j.asr.2017.11.038.

[3] Zhang L, Chen Y, Zhao Y, Henze D, Zhu L, Song Y, Paulot F, Liu X, Pan Y, Lin Y, Huang B, 2018. Agricultural ammonia emissions in China: reconciling bottom-up and top-down estimates, Atmospheric Chemistry and Physics, 18, 339–355.

[4] Luo S, Chen J, Wang C, Gonsamo A, Xi X, Lin Y, Qian M, Peng D, Nie S, Qin H, 2018. Comparative performance of airborne LiDAR height and intensity data for leaf area index estimation. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 11(1), 300-310.

[5] Zeng W, Fang X, Lin Y, Huang X, Yao Y, 2018. On the errors-in-variables model with inequality constraints of dependent variables for geodetic transformation. Survey Review, doi: 10.1080/00396265.2017.1396407.

[6] Zeng W, Fang X, Lin Y, Huang X, Zhou Y, 2018. On the total least-squares estimation for autoregressive model. Survey Review, doi: 10.1080/00396265.2017.1281096.

[7] Lin Y*, Jiang M, 2017. Maximum temperature drove snow cover expansion from the Arctic, 2000-2008. Scientific Reports, 10(7), 701-718.

[8] Lin Y*, Wei T, Yang B, Knyazikhin Y, Zhang Y, Sato H, Fang X, Liang X, Yan L, Sun S, 2017. TLS-bridged co-imputation of tree-level multifarious stem structure variables from WorldView-2 panchromatic imagery: A case study of boreal forest. International Journal of Digital Earth, 10(7), 701-718.

[9] Lin Y*, Jiang M, 2017. Towards extending terrestrial laser scanning applications in forestry: A case study of broad- and needle-leaf tree classification. Journal of Applied Remote Sensing, 11(1), 016037.

[10] Wang H, Lin Y*, Wang Z, Yao Y, Zhang Y, Wu L, 2017. Development of a low-cost 2D laser scanner based mobile terrestrial proximal sensing system for 3D plant structure phenotyping in indoor environments. Computers and Electronics in Agriculture, 140, 180-189.

[11] Jing X, Yan L, Hu X, He L, Zhao S, Hu X, Xu H, Lin Y*, Ma A, 2017. NPP/VIIRS solar reflectance bands radiation validation based on mid-infrared reference standard on sea surface sun glint sites. Journal of Infrared and Millimeter Waves, 36(6), 694-700.

[12] Yao Y, Liang S, Yu J, Chen J, Liu S, Lin Y, Fisher J, McVicar T, Cheng J, Jia K, Zhang X, Xie X, Jiang B, Sun L, 2017. A simple temperature domain two-source model for estimating agricultural field surface energy fluxes from Landsat imagery. Journal of Geophysical Research: Atmospheres, 122(10), 5211-5236.

[13] Qin H, Wang C, Pan F, Lin Y, Xi X, Luo S, 2017. Estimation of FPAR and FPAR profile for maize canopies using airborne LiDAR. Ecological Indicators, 83, 53-61.

[14] Yao Y, Liang S, Yu J, Zhao S, Lin Y, Jia K, Zhang X, Cheng J, Xie X, Sun L, Wang X, Zhang L, 2017. Difference in estimating terrestrial water flux from three satellite-based Priestley-Taylor algorithms. International Journal of Applied Earth Observation and Geoinformation, 56, 1-12.

[15] Fan A, Chen W, Liang L, Sun W, Lin Y, Che H, Zhao X, 2017. Evaluation and comparison of long-term MODIS C5.1 and C6 products against AERONET observations over China. Remote Sensing, 9(12), 1269.

[16] Luo S, Wang C, Xi X, Pan F, Qian M, Peng D, Nie S, Qin H, Lin Y, 2017. Retrieving aboveground biomass of wetland Phragmites australis (common reed) using a combination of airborne discrete-return LiDAR and hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 58, 107-117.

[17] Rimal B, Zhang L, Keshtkar H, Wang N, Lin Y, 2017. Monitoring and modeling of spatiotemporal urban expansion and land-use/land-cover change using integrated Markov Chain cellular automata model. ISPRS International Journal of Geo-Information, 6(9), 288.

[18] Lin Y*, Herold M, 2016. Tree species classification based on explicit tree structure feature parameters derived from static terrestrial laser scanning data. Agriculture and Forest Meteorology, 216, 105-114.

[19] Lin Y*, Hyyppä J, 2016. A comprehensive but efficient framework of proposing and validating feature parameters from airborne LiDAR data for tree species classification. International Journal of Applied Earth Observation and Geoinformation, 46, 45-55.

[20] Lin Y*, West G, 2016. Reflecting conifer phenology using mobile terrestrial LiDAR: A case study of Pinus sylvestris growing under the Mediterranean climate in Perth, Australia. Ecological Indictors, 70, 1-9.

[21] Lin Y*, Zhang L, Wang C, 2016. Airborne LiDAR laser return intensity-based investigation into crown-inside? - a case study on Quercus robur trees. Journal of Applied Remote Sensing, 10(2), 026024.

[22] Lin Y*, West G, 2016. Retrieval of effective leaf area index (LAIe) and leaf area density (LAD) profile at individual tree level using high density multi-return airborne LiDAR. International Journal of Applied Earth Observation and Geoinformation, 50, 150-158.

[23] Wei T, Lin Y*, Yan L, Zhang L, 2016. Tree species classification based on stem-related feature parameters derived from static terrestrial laser scanning data. International Journal of Remote Sensing, 37(18), 4420-4440.

[24] Yang B, Knyazikhin Y, Lin Y, Yan K, Chen C, Park T, Choi S, Mottus M, Rautainen M, Myneni R, Yan L, 2016. Analysis of impact of needle surface properties on estimation of needle absorption spectrum: Case study with coniferous needle and shoot samples. Remote Sensing, 8(7), 563.

[25] Zhang Y, Yao Y, Lin Y, Xiang L, 2016. Satellite characterization of terrestrial drought over Xinjiang Uygur Autonomous Region of China over past three decades. Environmental Earth Sciences, 75, 6.

[26] Wang S, Huang C, Zhang L, Lin Y, Cen Y, Wu T, 2016. Monitoring and assessing the 2012 drought in the Great Plains: Analyzing satellite retrieved solar-induced chlorophyll fluorescence, drought indices, and flux measured gross primary production. Remote Sensing, 8, 61.

[27] Lin Y*, 2015. LiDAR: An important tool for next-generation phenotyping technology of high potential for plant phenomics? Computers and Electronics in Agriculture, 119, 61-73.

[28] Lin Y*, Jiang M, Yao Y, Zhang L, Lin J, 2015. Automatic detection of individual trees in UAV oblique images of residential environments. Urban Forestry and Urban Greening, 14(2), 404-412.

[29] Lin Y*, West G, Belton D, Helmholz P, 2015. MLS-assisted validation of VHR WorldView panchromatic imagery for estimating Pinus sylvestris crown height. Remote Sensing Letters, 6(2), 125-134.

[30] Lin Y*, Holopainen M, Kankare V, Hyyppä J, 2014. Validation of mobile laser scanning for understory tree mapping in urban forest. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 7(7), 167-3173.

[31] Lin Y*, Hyyppä J, 2014. Geometrically modeling of 2D scattered points: A review potential for methodically improving mobile laser scanning in data processing. International Journal of Digital Earth, 7(6), 432-449.

[32] Yao Y, Liang S, Cheng J, Lin Y, Jia K, Liu M, 2014. Impacts of deforestation and climate variability on terrestrial evapotranspiration in subarctic China. Forests, 5(10), 2542-2560.

[33] Lin Y*, Puttonen E, Hyyppä J, 2013. Investigation of tree spectral reflectance characteristics using mobile terrestrial line spectrometer and laser scanner. Sensors – Special Issue: Sensor-based Technologies and Processes in Agriculture and Forestry, 13(7), 9305-9320.

[34] Lin Y*, Hyyppä J, Rosnell T, Jaakkola A, Honkavaara E, 2013. Development of a UAV-MMS-collaborative aerial-to-ground remote sensing system – A preparatory field validation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(4), 1893-1898.

[35] Holopainen M, Kankare V, Vastaranta M, Liang X, Lin Y, Vaaja M, Yu X, Hyyppä J, Hyyppä H, Kaartinen H, Kukko A, Tanhuanpää T, Alho P, 2013. Tree mapping using airborne, terrestrial and mobile laser scanning – A case study in a heterogeneous urban forest. Urban Forest and Urban Greening, 12(4), 546-553.

[36] Lin Y*, Hyyppä J, Kukko A, 2013. Stop-and-go mode: Sensor manipulation as essential as sensor development in terrestrial laser scanning. Sensors, 13(7), 8140-8154.

[37] Lin Y*, Hyyppä J, Kaartinen H, Kukko A, 2013. Performance analysis of mobile laser scanning systems in target representation. Remote Sensing – Special Issue: Advances in Mobile Laser Scanning and Mobile Mapping, 5(7), 3140-3155.

[38] Jiang M, Lin Y*, 2013. Individual deciduous tree recognition in leaf-off aerial ultra high spatial resolution remotely sensed imagery. IEEE Geoscience and Remote Sensing Letters, 10(1), 38-42.

[39] Lin Y*, Hyyppä J, 2012. Multiecho-recording mobile laser scanning for enhancing individual tree crown reconstruction. IEEE Transactions on Geoscience and Remote Sensing, 50(11), 4323-4332.

[40] Lin Y*, Hyyppä J, Antero K, Anttoni J, Kaartinen H, 2012. Tree-level height growth investigation by integrating airborne, static terrestrial, and mobile LiDAR techniques. Sensors – Special Issue: Laser sensing and Imaging, 12(9), 12798-12813.

[41] Lin Y*, Hyyppä J, Jaakkola A, Yu X, 2012. Three-level frame and RD-schematic algorithm for automatic recognition of individual trees from MLS point clouds. International Journal of Remote Sensing, 33(6), 1701-1716.

[42] Lin Y*, Hyyppä J, Jaakkola A, Holopainen M, 2012. Characterization of mobile LiDAR data collected with multiple echoes per pulse from crowns during foliation. Scandinavian Journal of Forest Research, 8(3), 298-311.

[43] Lin Y*, Hyyppä J, 2012. Automatic extraction of parallel edges based on eigenvalue analysis and collateral expansion. International Journal of Remote Sensing, 33(2), 382-395.

[44] Lin Y*, Hyyppä J, Jaakkola A, 2011. Mini-UAV-borne Lidar for fine-scale mapping. IEEE Geoscience and Remote Sensing Letters, 8(3), 426-430.

[45] Lin Y*, Hyyppä J, Jaakkola A, 2011. Combining mobile and static terrestrial laser scanners for investigation of individual crown attributes during foliation. Canadian Journal of Remote Sensing, 37(4), 359-375.

[46] Lin Y*, Hyyppä J, 2011. k-segments-based geometric modeling of VLS scan lines. IEEE Geoscience and Remote Sensing Letters, 8(1), 93-97.

[47] Lin Y*, 2010. Hausdorff-based RC and IESIL combined positioning algorithm for underwater geomagnetic navigation. EURASIP Journal on Advances in Signal Processing, Article Number: 593238.

[48] Jaakkola A, Hyyppä J, Kukko A, Yu X, Kaartinen H, Lehtomäki M, Lin Y, 2010. A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements. ISPRS Journal of Photogrammetry and Remote Sensing, 65(6), 514-522.

[49] Lin Y*, Hyyppä J, 2010. Geometry and intensity based culvert detection in mobile laser scanning point clouds. Journal of Applied Remote Sensing, 4, Article No. 043553.

[50] Lin Y*, Jaakkola A, Hyyppä J, and Kaartinen H, 2010. From TLS to VLS: Biomass estimation at individual tree level. Remote Sensing, 2(8), 1864-1879.

2017 How did temperature extremes chase each other around the circumpolar Arctic during 1900-2013? DBAR2017

2017 偏振激光雷达对地观测遥感，偏振遥感新方法应用探索高端论坛

2016 太平洋海温异常对中高纬欧亚大陆高温气候因子的影响，高校GIS论坛

2016 高光谱激光雷达：三维生物物理化学生态测量学，测绘地理信息青年学者论坛

2016 LiDAR-based investigation of boreal island ecosystems，Digital Earth Summit 2016

2015 激光雷达遥感：从技术基础的角度助推对森林生态的认知, 激光雷达森林生态应用培训班

2015 森林生态：激光雷达与无人机的遥感切入, 无人机遥感遥测学术研讨会

[1] 2017 航海学会科学技术奖一等奖

[2] 2016 高校GIS创新人物奖

[3] 2015 科技部国家遥感中心遥感创新青年科技人才计划

[4] 2014 德国DAAD Research Scholarship

[5] 2014 澳大利亚Endeavour Research Fellowship

[6] 2013 北京市科学技术奖三等奖

2017- 至今 北京市空间信息集成重点实验室, 副主任

2016- 至今 中-芬北极遥感联合研究中心，中方Coordinator

2015- 至今 桂林航天工业学院, 客座教授