民族：汉

    政治面貌：中共党员

    学历学位：博士研究生

    毕业院校：中国林业科学研究院

    办公电话：010-62889179

    E-Mail：fuly@ifrit.ac.cn


    硕士生导师，中国林业科学院资源信息研究所森林经理与林业统计研究室副主任，中国林业工程建设协会林草高新技术成果推广应用专业委员会常务副主任，国际林联（IUFRO）第四学部森林连续清查工作组副组长，中国林学会林业计算机应用分会常务理事，中国林学会青年工作委员会委员，中国林科院杰出青年，中国科协首批“青年人才托举工程”被托举对象（全国林业行业共3位），第十四届中国林业青年科技奖获得者，首届国家林业和草原科技创新青年拔尖人才，第四批国家“万人计划”青年拔尖人才。美国宾夕法尼亚州立大学生物统计专业博士后。
 

主持包括国家自然基金在内的项目22项，其中省部级以上8项。作为项目骨干参加省部级以上课题14项，参与其他项目9项。

[1] 中组部“万人计划”青年拔尖人才项目、2019/01-2021/12，在研、主持

[2] 中国科协首届“青年人才托举工程”项目、2016/01-2018/12、在研、主持。

[3] 国家自然科学基金面上项目，基于森林生物量的天然林立地质量评价和生产力估计、2020/01-2023/12、在研、主持。

[4] 国家自然科学基金面上项目，含随机效应和度量误差的生物量相容性方程系统研究、2016/01-2019/12、在研、主持。

[5] 国家自然科学基金面上项目子课题，三维树干曲面的模拟与构建、2015/01-2018/12、在研、主持。

[6] 国家自然科学基金青年项目，林业中含度量误差的非线性混合效应模型研究、2014/01-2016/12、已结题、主持。

[7] “十三五”国家重点研发计划“陆地生态系统碳源汇监测技术及指标体系”子课题，新增林地区域的确定及其碳汇潜力评估、2017/01-2020/12、在研、主持

[8] “十三五”国家重点研发计划“天然次生林生长收获预估及树种更新模型构建”子课题，新增林地区域的确定及其碳汇潜力评估、2017/01-2020/12、在研、主持。

工作至今，共发表学术论文80余篇，其中以第一作者或通讯作者发表的SCI收录论文34篇，单篇最高影响因子11.67，累积影响因子113.23，中科院JCR分区一区11篇，二区15篇。包括1篇IEEE T Neur Net Lear，1篇Brief Bioinform，4篇Neural Networks，1篇IEEE T Image Process，1篇IEEE T Geosci Remote。

副主编专著1部，登记软件著作权14项。

2012年、2014年、2016年曾3次获第四届和第五届梁希青年论文奖二等奖、第六届梁希青年论文奖一等奖。2018年获梁希林业科学技术奖三等奖（排名第一）。作为主要骨干所开发的生物统计和数据分析软件（ForStat）已推广到国内外80余所高等院校和科研院所使用。国际林业期刊Forestry（二区，影响因子2.43）编委和林业遥感期刊Remote Sensing（二区，影响因子3.75）特约编辑。


发表论文

    2019年

[1] Ye Q., Li D., Fu L* (Corresponding author)., Zhang Z., Yang, W. 2019. Non-Peaked Discriminant Analysis for Data Representation. IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2019.2944869. (IF=11.68)

[2] Liu Q., Fu L* (Corresponding author)., Wang G., Li S., Li Z., Chen E., Pang Y., Hu K. 2019. Improving Estimation of Forest Canopy Cover by Introducing Loss Ratio of Laser Pulses Using Airborne LiDAR. IEEE Transactions on Geoscience and Remote Sensing, DOI: 10.1109/TGRS.2019.2938017. (IF=5.63)

[3] Wang L., Wang B., Zhang Z* (Corresponding author)., Ye Q., Fu L* (Corresponding author)., Liu G., Wang M., 2019. Robust auto-weighted projective low-rank and sparse recovery for visual representation. Neural Networks, 117: 201-215. (IF=5.79)

[4] Zhao H., Fu L* (Corresponding author)., Gao Z., Ye Q., Yang Z., Yang X. 2019. Flexible non-greedy discriminant subspace feature extraction. Neural Networks, 116: 166-177. (IF=5.79)

[5] Wang C., Ye Q., Luo P., Ye N., Fu L* (Corresponding author). 2019. Robust capped L1-norm twin support vector machine. Neural Networks, 114: 47-59. (IF=5.79)

[6] Yang X., Yang H., Zhang F., Zhang L., Fan X., Ye Q., Fu L* (Corresponding author). 2019. Piecewise Linear Regression Based on Plane Clustering. IEEE Access, 7: 29845 – 29855. (IF=4.10)

[7] Zhao H., Ye Q., Naiem M A., Fu L. 2019. Robust L2,1 -Norm Distance Enhanced Multi-Weight Vector Projection Support Vector Machine. IEEE Access, 7: 3275 – 3286. (IF=4.10)

[8] Zhang X., Chhin S., Fu L., Lu L., Duan A., Zhang J. 2019. Climate-sensitive tree height-diameter allometry for Chinese fir in southern China. Forestry, 92(2):167-176. (IF=2.88)

[9] Wang M., Liu Q., Fu L., Wang G., Zhang X. 2019. Airborne LIDAR-Derived Aboveground Biomass Estimates Using a Hierarchical Bayesian Approach. Remote Sensing, 11(9):1050. (IF=4.12)

[10] Wang Q., Gao Z., Hu Z., Luo P., Duan G., Sharma R P., Song X., Fu L* (Corresponding author). 2019. Comparing independent climate-sensitive models of aboveground biomass and diameter growth with their compatible simultaneous model system for three larch species in China. International Journal of Biomathematics, DOI: 10.1142/S1793524519500530. (IF=0.89)

[11] Fu L., Wang M., Wang Z., Song X., Tang S. 2019. Maximum likelihood estimation of nonlinear mixed-effects models with crossed random effects by combining first order conditional linearization and sequential quadratic programming. International Journal of Biomathematics, DOI: 10.1142/ S1793524519500402. (IF=0.89)

2018年

[12] Fu L., Jiang L., Ye M., Sun L., Tang S., Wu R. 2018. How trees allocate stem carbon for optimal growth: Insight from a game-theoretic model. Briefings in Bioinformatics, 19(4): 593-602. (IF=9.10)

[13] Ye Q* (Corresponding author)., IEEE Member., Zhao H., Fu L* (corresponding author)., Gao S. 2018. Underlying Connections Between Algorithms For Nongreedy LDA-L1. IEEE Transactions on Image Processing, 27(5): 2557-2559. (IF(5 years)=6.79)

[14] Ye Q., Zhao H., Gao S., Naiem M., Fu L* (Corresponding author). 2018. Lp- and Ls-Norm Distance Based Robust Linear Discriminant Analysis. Neural Networks, 105: 393-404. (IF=5.79)

[15] Li T., Liu X., Li Z., Ma H* (Corresponding author)., Wan Y., Liu X., Fu L* (corresponding author). 2018. Study on reproductive biology of rhododendron longipedicellatum: A newly discovered and special threatened plant surviving in Limestone Habitat in southeast Yunnan, China. Frontiers in plant science, doi: 10.3389/fpls.2018.00033. (IF=4.11)

[16] Yan He., Ye Q., Zhang T., Yu D., Yuan X., Xu Y., Fu L. 2018. Least squares twin bounded support vector machines based on L1-norm distance metric for classification. Pattern Recognition, 74, 434-447. (IF=5.90)

[17] Fu L., Liu Q., Wang G., Li Z., Chen E., Pang Y., Tang S., Song X., Wang G. 2018. Developing a system of compatible individual tree diameter and aboveground biomass prediction models using error-in-variable regression and airborne LiDAR data. Remote Sensing, 10(2), 325, doi:10.3390/ rs10020325. (IF=4.12)

[18] Ya L*., Fu L*., Affleck D L R., Nelson AS., Shen C., Wag M., Zheng J., Ye Q., Yang G. 2018. Additivity of nonlinear tree crown width models: Aggregated and disaggregated model structures using nonlinear simultaneous equations. Forest Ecology and Management, 427, 372-382. (IF=3.13)

[19] Zhu G., Fu L (Corresponding author). 2018. k-step adaptive cluster sampling with 6 Horvitz–Thompson estimator. International Journal of Biomathematics, 2. 11,doi: 10.1142/S1793524518500298. (IF=0.89)

[20] Duan G., Gao Z., Wang Q., Fu L (Corresponding author). 2018. Comparison of Different Height–Diameter Modelling Techniques for Prediction of Site Productivity in Natural Uneven-Aged Pure Stands. Forests, 9, 63; doi:10.3390/f9020063. (IF=2.12)

[21] Liu X., Ma H (Corresponding author)., Li T., Li Z., Wan Y., Liu X., Fu L (Corresponding author). 2018. Development of novel EST-SSR markers for Phyllanthus emblica (Phyllanthaceae) and cross-amplification in two related species. Applications in Plant Sciences, 6(7): e1169. (IF=1.23)

[22]Fu L., Ram P. S., Zhu G., Li H., Hong L., Guo H., Duan G., Shen C., Lei Y., Li Y., Lei X., Tang S. 2018. Comparing height–age and height–diameter modelling approaches for estimating site productivity of natural uneven-aged forests. Forestry, 91(4):419-433 (IF=2.88).

[23] Zeng W., Fu L., Xu Ming., Wang X., Chen Z., Yao, S. 2018. Developing individual-tree-based models for estimating aboveground biomass of five key coniferous species in China. Journal of Forestry Research, 29(5):1251-1261. (IF=1.16)

2017年

[24] Fu L., Sharma R. P., Wang G., Tang S. 2017. Modelling a system of nonlinear additive crown width models applying seemingly unrelated regression for Prince Rupprecht larch in northern China. Forest Ecology and Management, 386:71-80. (IF=3.13)

[25] Fu L., Sharma R. P., Hao K., Tang S. 2017. A generalized interregional nonlinear mixed-effects crown width model for Prince Rupprecht larch in northern China. Forest Ecology and Management, 389, 364-373. (IF=3.13)

[26] Fu L., Zhang H., Sharma R. P., Pang L., Wang G. 2017. A generalized nonlinear mixed-effects height to crown base model for Mongolian oak in northeast China. Forest Ecology and Management, 384, 34-43. (IF=3.13)

[27] Fu L., Xiang W., Wang G., Hao K., Tang S. 2017. Additive crown width models comprising nonlinear simultaneous equations for Prince Rupprecht larch (Larix principis-rupprechtii) in northern China. Trees, 31(6):1959–1971 (IF=1.80).

[28]Fu L., Ram P. S., Zhu G., Li H., Hong L., Guo H., Duan G., Shen C., Lei Y., Li Y., Lei X., Tang S. 2017. A Basal Area Increment-Based Approach of Site Productivity Evaluation for Multi-Aged and Mixed Forests. Forests, 8, 119; doi:10.3390/f8040119. (IF=2.12)

[29] Fu L., Lei X., Hu Z., Zeng W., Tang S., Marshall P., Cao L., Song X., Yu L., Liang J. 2017. Integrating regional climate change into allometric equations for estimating tree aboveground biomass of Masson pine in China. Annals of forest science, 74:42,1-15. (IF(5 years)=2.63)

[30] Fu L., Sun W., Wang G. 2017. A climate-sensitive aboveground biomass model for three larch species in northeastern and northern China. Trees, 31(2): 557-573. (IF=1.80)

[31] Fu L., Zeng W., Tang S. 2017. Individual tree biomass models to estimate forest biomass for large spatial regions developed using four pine species in China. Forest Science, 63(1): 42-50. (IF=1.06)

2016年

[32] Hu Z., Liu S., Liu X., Fu L., Wang J., Liu K., Huang X., Zhang Y., He F. 2016. Soil respiration and its environmental response varies by day/night and by growing /dormant season in a subalpine forest. Scientific report, 6:37864. (IF=4.01)

[33] Cao L., Coops N. C., Innes J. L., Sheppard S.R.J., Fu L., Ruan H., She G. 2016. Estimation of forest biomass dynamics in subtropical forests usingmulti-temporal airborne LiDAR data. Remote Sensing of Environment, 178: 158-171. (IF=8.22)

[34] Fu L., Lei Y., Wang G., Bi H., Tang S., Song X. 2016. Comparison of seemingly unrelated regressions with error-invariable models for developing a system of nonlinear additive biomass equations. Trees, 30(3): 839-857. (IF=1.80)

[35] Pang L., Ma Y., Sharma R.P., Shawn R., Song X., Fu L (Corresponding author). 2016. Developing an improved parameter estimation method for the segmented taper equation through combination of constrained two-dimensional optimum seeking and least square regression. Forests, 7: 194. (IF=2.12)

2015年

[36] Fu L., Zhang H., Lu J., Zang H., Lou M., Wang G. 2015. Multilevel Nonlinear Mixed-Effect Crown Ratio Models for Individual Trees of Mongolian Oak (Quercus mongolica) in Northeast China. PLoS ONE, 10(8): e0133294. (IF=2.78)

2014年

[37] Diao J., Lei X., Wang J., Lu J., Guo H., Fu, L., Shen C., Ma W., Shen J. 2014. Quantifying the variability of internode allometry within and between trees for Pinus tabulaeformis Carr. using a multilevel nonlinear mixed-effect model. Forests, 5, 2825-2845. (IF=2.12)

[38] Fu L., Lei Y., Sharma R. P., Tang S. 2014. Parameter estimation of nonlinear mixed- effects models using first-order conditional linearization and the EM algorithm. Journal of applied statistics, 40(2): 252-265. (IF=0.77)

[39] Fu L., Tang S., Sharma R. P., Zhang H., Liu Y., Lei Y., Wang H. 2014. Developing, testing and application of rodent population dynamics and capture models based on an adjusted leslie matrix-based population. International Journal of Biomathematics, 7(2): 1-15. (IF=0.89)

[40] Fu L., Wang M., Lei Y., Tang S. 2014. Parameter estimation of two-level nonlinear mixed effects models using first order conditional linearization and the EM algorithm. Computational Statistics & Data Analysis, 69: 173-183. (IF=1.32)

[41] Fu L., Zeng W., Zhang H., Wang G., Lei Y., Tang S. 2014. Generic linear mixed-effects individual-tree biomass models for Pinus massoniana Lamb. in southern China. Southern Forests, 76(1): 47-56. (IF=0.90)

2013年

[42] Fu L., Sun H., Sharma R. P., Lei Y., Zhang H., Tang S. 2013. Nonlinear mixed-effects crown width models for individual trees of Chinese fir (Cunninghamia lanceolata) in south-central China. Forest Ecology and Management, 302: 210-220. (IF=3.13)

著作

[1] 唐守正，李勇，符利勇，生物数学模型的统计学基础，高等教育出版社，310页，2015

奖项荣誉

[1]第四批国家“万人计划”青年拔尖人才(2019)，中组部

[2] 首届国家林业和草原科技创新青年拔尖人才(2019)，国家林业局

[3]第十四届中国林业青年科技奖(2017)，国家林业局.(排名：1/1)

[4]中国科协首届“青年人才托举工程”被托举对象(2016)，中国科协

[5] 第四届“中国林科院杰出青年”(2014)，中国林业科学研究院

[6] 第九届梁希林业科学技术奖三等奖(2018)，国家林业局. (排名：1/10)

[7] 第六届梁希青年论文奖一等奖(2016)， 国家林业局科学技术委员会、中国林学会. (排名：1/1)

[8] 第五届梁希青年论文奖二等奖(2014)， 国家林业局科学技术委员会、中国林学会. (排名：1/1)

[9] 第四届梁希青年论文奖二等奖(2012)， 国家林业局科学技术委员会、中国林学会. (排名：1/1)