符利勇，男，湖南省耒陽市人，1984年9月出生，博士生導師，研究員，中國林業科學研究院資源信息研究所森林經理與林業統計研究室副主任，中國林業工程建設協會林草高新技術成果推廣套用專業委員會常務副主任，國際林聯（IUFRO）第四學部森林連續清查工作組副組長，中國林學會林業計算機套用分會常務理事，中國林學會青年工作委員會委員，中國林科院傑出青年，中國科協首批“青年人才托舉工程”被托舉對象（全國林業行業共3位），第十四屆中國林業青年科技獎獲得者，首屆國家林業和草原科技創新青年拔尖人才，第四批國家“萬人計畫”青年拔尖人才。美國賓夕法尼亞州立大學生物統計專業博士後。
主要從事近代統計模型和森林生長模型模擬方面的工作。
基本介紹
個人經歷,教育經歷,工作經歷,研究成果,科研項目,發表論文,2019年,2018年,2017年,2016年,2015年,2014年,2013年,2012年,2011年,著作,獎項榮譽,
個人經歷
教育經歷
2007.09–2009.07， 南京林業大學， 森林經理學， 碩士，研究方向為套用數理統計
2003.09–2007.07， 山西農業大學， 林學， 學士
工作經歷
2018.07-至今，中國林業科學研究院資源信息研究所研究員
2017.09-至今，中國林業科學研究院資源信息研究所研究室副主任
2014.11–2018.06，中國林業科學研究院資源信息研究所副研究員
2016.03–2017.03，美國賓夕法尼亞州立大學歐柏麗自然科學學院博士後
2012.07–2014.10，中國林業科學研究院資源信息研究所助理研究員
研究成果
工作至今，圍繞該領域主持包括國家自然基金在內的項目22項，其中省部級以上8項。作為項目骨幹參加省部級以上課題14項，參與其他項目9項。共發表學術論文80餘篇，其中以第一作者或通訊作者發表的SCI收錄論文34篇，單篇最高影響因子11.67，累積影響因子113.23，中科院JCR分區一區11篇，二區15篇。包括1篇IEEE T NeurNet Lear，1篇Brief Bioinform，4篇Neural Networks，1篇IEEE T Image Process，1篇IEEE T Geosci Remote。副主編專著1部，登記軟體著作權14項。2012年、2014年、2016年曾3次獲第四屆和第五屆梁希青年論文獎二等獎、第六屆梁希青年論文獎一等獎。2018年獲梁希林業科學技術獎三等獎（排名第一）。作為主要骨幹所開發的生物統計和數據分析軟體（ForStat）已推廣到國內外80餘所高等院校和科研院所使用。國際林業期刊Forestry（二區，影響因子2.88）編委和林業遙感期刊Remote Sensing（二區，影響因子4.12）特約編輯。
科研項目
[1]、中組部“萬人計畫”青年拔尖人才項目、2019/01-2021/12，在研、主持
[2]、中國科協首屆“青年人才托舉工程”項目、2016/01-2018/12、已結題、主持。
[4]、國家自然科學基金面上項目，含隨機效應和度量誤差的生物量相容性方程系統研究、2016/01-2019/12、在研、主持。
[5]、國家自然科學基金面上項目子課題，三維樹幹曲面的模擬與構建、2015/01-2018/12、已結題、主持。
[6]、國家自然科學基金青年項目，林業中含度量誤差的非線性混合效應模型研究、2014/01-2016/12、已結題、主持。
[7]、“十三五”國家重點研發計畫“陸地生態系統碳源匯監測技術及指標體系”子課題，新增林地區域的確定及其碳匯潛力評估、2017/01-2020/12、在研、主持
[8]、“十三五”國家重點研發計畫“天然次生林生長收穫預估及樹種更新模型構建”子課題，新增林地區域的確定及其碳匯潛力評估、2017/01-2020/12、在研、主持。
發表論文
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. RobustL2,1-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=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. Developingindividual-tree-based models for estimating aboveground biomassof 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 Mongolianoak 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)
[32]、Fu, L.,Lei, X., Zhu, G., Li, H., Hong, L., Guo, H., Duan, G., Shen, C., Lei, Y., Li, Y., Tang, S. 2017. Dominant height–diameter models for estimating forest site productivity in natural uneven-aged pure stands. Forest Science, accepted. (SCI, IF=1.78, 三區).
2016年
[33]、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)
[34]、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)
[35]、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)
[36]、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
[37]、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
[38]、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)
[39]、FuL., Lei Y.,SharmaR. P., TangS. 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)
[40]、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)
[41]、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)
[42]、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)
[43]、符利勇，雷淵才，曾偉生，幾種相容性生物量模型及估計方法的比較，林業科學，2014，（06）：42-54.
[44]、符利勇，雷淵才，孫偉，唐守正，曾偉生，不同林分起源的相容性生物量模型構建，生態學報，2014，（06）：1461-1470.
2013年
[45]、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)
[46]、符利勇，孫華，基於混合效應模型的杉木單木冠幅預測模型，林業科學，2013，（08）：65-74.
[47]、符利勇，張會儒，李春明，唐守正，非線性混合效應模型參數估計方法分析，林業科學，2013，（01）：114-119.
2012年
[48]、Fu,L.Y.,Zeng,W.S.,Tang,S.Z.,Sharma,RP.,andLi,HK.2012.Using Linear Mixed Model and Dummy Variable Model Approaches to Construct Compatible Single-Tree Biomass Equations at Different Scales—A Case Study for Masson Pine in Southern China. Journal of Forest Science ,58(3):101-115.
[49]、符利勇，李永慈，李春明，唐守正，利用2種非線性混合效應模型(2水平)對杉木林胸徑生長量的分析，林業科學，2012，（05）：36-43 .
[50]、符利勇, 張會儒, 唐守正. 基於非線性混合模型的杉木林優勢木平均高，林業科學，2012，48(7)： 66-71.
2011年
[51]、符利勇, 唐守正, 劉應安. 關帝山天然次生針葉林林隙徑高比， 生態學報，2011，31(5)：1260-1268.
[52]、符利勇, 曾偉生, 唐守正. 利用混合模型分析地域對國內馬尾松生物量的影響，生態學報，2011，31(19)：5797-5808.
著作
[1]、唐守正，李勇，符利勇，生物數學模型的統計學基礎，高等教育出版社，310頁，2015
獎項榮譽
[1]、第四批國家“萬人計畫”青年拔尖人才(2019)，中組部
[2]、首屆國家林業和草原科技創新青年拔尖人才(2019)，國家林業和草原局
[3]、第十四屆中國林業青年科技獎(2017)，國家林業局(排名：1/1)
[4]、中國科協首屆“青年人才托舉工程”入選者(2016)，中國科協
[5]、第四屆“中國林科院傑出青年” ，中國林業科學研究院(2014)
[6]、第九屆梁希林業科學技術獎三等獎(2018)，國家林業局. (排名：1/10)
[7]、第六屆梁希青年論文獎一等獎(2016)，國家林業局科學技術委員會、中國林學會. (排名：1/1)
[8] 、第五屆梁希青年論文獎二等獎(2014)，國家林業局科學技術委員會、中國林學會. (排名：1/1)
[9] 、第四屆梁希青年論文獎二等獎(2012)，國家林業局科學技術委員會、中國林學會. (排名：1/1)
[10]、中國林學會青年科技優秀論文獎(2012)，中國林學會(排名：1/1)
[11]、2017年第十四屆林業青年科技獎
