| 陆灯盛 |
| 发布时间:2019-09-26 | 浏览次数: 5119 |
姓 名：陆灯盛
性 别：男
出生年月：1965.2
职 称：教授
研究方向：遥感技术及应用
学科专长：遥感，土地利用/覆盖变化，森林生物量，不透水地表信息提取，水土流失等
E– MAIL：ludengsheng@fjnu.edu.cn
通信地址：福州市仓山区上三路8号
邮 编：350007
个人简介：陆灯盛，男，1965年2月生，教授，博士生导师。2001年毕业于美国印第安纳州立大学，获自然地理学博士学位，后在美国印第安纳大学从事遥感博士后研究，2002年开始先后在印第安纳大学全球环境变化研究中心、奥本大学林业与野生动物学院、密歇根州立大学全球变化与对地观测中心工作。于2012年入选浙江省“千人计划”、浙江省 “钱江学者”。于2018年11月在福建师范大学地理科学学院任教授一职。主持和参与了23个科研项目，包括美国NASA/NIH/NSF、巴西CNPq、国家重点研发项目、国家自然科学基金面上项目以及浙江省自然基金重点项目等。自2001年以来在《Remote Sensing of Environment》等国际刊物发表105篇SCI论文，其中以第一作者或通讯作者发表近80篇SCI论文。引用次数达15760, H-指数为51，i10 指数为95。担任《Remote Sensing of Environment》,《ISPRS Journal of Photogrammetry and Remote Sensing》等30多种遥感/地理信息系统期刊的审稿专家。
教 育：
1998.01-2001.05 美国 印第安纳州立大学 自然地理专业 博士
1986.09-1989.06 北京林业大学 森林经理学专业 硕士
1982.09-1986.07 浙江林学院（现浙江农林大学）林学专业 学士
工 作：
1989.7至1997.12 林业部华东规划设计院
2001.01至2002.05 美国印第安纳大学， 制度、人口和环境变化研究中心，博士后
2002.05至2006.12 美国印第安纳大学， 制度、人口和环境变化研究中心，助理研究员 2007.01至2008.07 美国奥本大学林业与野生动物学院 研究员
2008.07至2011.06 美国印第安纳大学全球环境变化研究中心 副研究员
2011.07至2012.08 美国印第安纳大学全球环境变化研究中心 研究员
2012.08至2018.05 美国密歇根州立大学全球变化与对地观测研究中心 教授
2013.04至2018.10 浙江农林大学环境与资源学院 教授
2018.11至今 福建师范大学地理科学学院 教授
学术兼职：
（1）期刊编委（Editorial Board Member）
International Journal of Image and Data Fusion. 8/2013 – Remote Sensing. 2018-
（2）中国地理学会第十二届理事会理事：2018-2023
（3）3S技术与资源优化利用福建省高校重点实验室第二届学术委员会主任：2019-2023
（4）中国林业科学研究院资源信息研究所“国家林业和草原局遥感工程技术研究中心”第一届技术委员会委员
成果奖励：
2015年: “城市高精度时空信息获取关键技术及应用示范”项目 荣获“环境保护科学技术奖” 二等奖；
2016年: “城市生态环境监测及管控关键技术研发与示范”项目 荣获“环境保护科学技术奖” 二等奖。
近5年主要科研项目：
1.单木-林分尺度人工林资源遥感精细监测技术 (人工林资源监测关键技术研究), 国家重点研发计划重点专项, 7/2017—12/2020, 子课题主持.
2.基于多源数据的亚热带森林地上生物量遥感信息模型的构建及其应用研究. 国家自然科学基金. No# 41571411. 1/2016–12/2019. 主持
3.浙江省特色经济林水土流失形成机理及适宜性研究, 浙江省自然基金重点项目. LZ15C160001. 1/2015–12/2018. 主持
4.INFEWS/T3: Rethinking Dams: Innovative Hydropower Solutions to Achieve Sustainable Food and Energy Production and Sustainable Communities. US NSF, #1639115. 1/2017–12/2020.
5.Integration of Multi-sensor and Multi-scale Remote Sensing Data for Examining Land Use/Cover Disturbance at a Regional Scale in the Brazilian Amazon. Brazilian Science without Borders Program, Brazil CNPq (401528/2012-0), 10/2012–9/2016.
教学情况：
硕士研究生：遥感技术与应用（Seminar）
博士研究生：GIS原理与方法（Seminar）
论文著作：近5年发表的代表性学术论文
Li, L., Li, N., *Lu, D., Chen, Y., 2019. Mapping Moso bamboo forest and its on-year and off-year distribution in a subtropical region using time-series Sentinel-2 and Landsat 8 data. Remote Sensing of Environment. 231(111265). https://doi.org/10.1016/j.rse.2019.111265.
Li, G., Cheng, Z., *Lu, D., Lu, W., Huang, J., Zhi, J., and Li, S., 2019. Examining hickory plantation expansion and evaluating suitability for it using multitemporal satellite imagery and ancillary data. Applied Geography 109 (2019) 102035 https://doi.org/10.1016/j.apgeog.2019.102035.
Li, D., *Lu, D., Li, N., Wu, M., and Shao, X., 2019. Quantifying annual land-cover change and vegetation greenness variation in a coastal ecosystem using dense time-series Landsat data. GIScience & Remote Sensing. 56(5), 769-793. https://doi.org/10.1080/15481603.2019.1565104.
Xie, Z., Chen, Y., *Lu, D., Li, G., and Chen, E., 2019. Classification of Land Cover, Forest, and Tree Species Classes with ZiYuan-3 Multispectral and Stereo Data. Remote Sens. 11, 164; doi:10.3390/rs11020164.
Li, G., *Lu, D., Moran, E., Calvi, M.F., Dutra, L.V., and Batistella, M., 2019. Examining deforestation and agropasture dynamics along the Brazilian TransAmazon highway using multitemporal Landsat imagery. GIScience & Remote Sensing. 56(2), 161-183. https://doi.org/10.1080/15481603.2018.1497438.
Chen, Y., Li, L., *Lu, D. and Li, D., 2019. Exploring Bamboo Forest Aboveground Biomass Estimation Using Sentinel-2 Data. Remote Sensing. 11, 7; doi:10.3390/rs11010007.
Li, N., *Lu, D., Wu, M., Zhang, Y., and Lu, L., 2018. Coastal wetland classification with multi-seasonal high-spatial resolution satellite imagery. International Journal of Remote Sensing. 39:23, 8963-8983, https://doi.org/10.1080/01431161.2018.1500731.
Cheng, Z., *Lu, D., Li, G., Huang, J., Sinha, N., Zhi, J., and Li, S., 2018. A Random Forest–Based Approach to Map Soil Erosion Risk Distribution in Hickory Plantations in Western Zhejiang Province, China. Remote Sensing. 10, 1899; doi:10.3390/rs10121899.
9. *Lu, D., Li, L., Li, G., Fan, P., Ouyang, Z., and Moran, E., 2018. Examining spatial patterns of urban distribution and impacts of physical conditions on urbanization in coastal and inland metropoles. Remote Sensing. 10, 1101; doi:10.3390/rs10071101.
10. Jiang, X., *Lu, D., Moran, E., Calvi, M.F., and Dutra, L.V., 2018. Examining impacts of the Belo Monte hydroelectric dam construction on land-cover changes using multitemporal Landsat imagery. Applied Geography. 97, 35-47. https://doi.org/10.1016/j.apgeog.2018.05.019.
11. Gao, Y., *Lu, D., Li, G., Wang, G., Chen, Q., Liu, L., and Li, D., 2018. Comparative analysis of modeling algorithms for forest aboveground biomass estimation in a subtropical region. Remote Sensing, 10, 627; doi:10.3390/rs10040627.
12. Chen, Y., *Lu, D., Moran, E., Batistella, M., Dutra, L.V., Sanches, I.D., da Silva, R. F. B., Huang, J., Luiz, A.J.B., de Oliveira, M.A.F. 2018. Mapping croplands, cropping patterns, and crop types using MODIS time-series data. International Journal of Applied Earth Observation and Geoinformation. 69, 133–147. https://doi.org/10.1016/j.jag.2018.03.005.
13. Lu, W., *Lu, D., Wang, G., Wu, J., Huang, J., and Li, G., 2018. Examining soil organic carbon distribution and dynamic change in a hickory plantation region with Landsat and ancillary data. Catena. 165, 576-589. https://doi.org/10.1016/j.catena.2018.03.007
14. Guo, W., *Li, G., Ni, W., Zhang, Y., and Lu, D., 2018. Exploring improvement of impervious surface estimation at national scale through integration of nighttime light and Proba-V data. GIScience & Remote Sensing. 55(05), 699–717, https://doi.org/10.1080/15481603.2018.1436425.
15. Li, D., *Lu, D., Wu, M., Shao, X., and Wei, J., 2018. Examining land cover and greenness dynamics in Hangzhou Bay in 1985-2016 using Landsat time series data. Remote Sensing. 10, 32; doi:10.3390/rs10010032.
16. Chen, Y., Lu, D., Luo, L., Pokhrel, Y., Deb, K., Huang, J., Ran, Y. 2018. Detecting irrigation extent, frequency, and timing in a heterogeneous arid agricultural region using MODIS time series, Landsat imagery, and ancillary data. Remote Sensing of Environment. 204, 197-211. https://doi.org/10.1016/j.rse.2017.10.030.
17. Pan, T.; Lu, D.; Zhang, C.; Chen, X.; Shao, H.; Kuang, W.; Chi, W.; Liu, Z.; Du, G.; Cao, L. 2017. Urban land-cover dynamics in arid China based on high-resolution urban land mapping products. Remote Sensing. 9(7), 730; doi:10.3390/rs9070730.
18. Wang, Y., *Lu, D., 2017. Mapping Torreya Grandis spatial distribution using high spatial resolution satellite imagery with the expert rules based approach. Remote Sensing. 9, 564; doi:10.3390/rs9060564.
19. Liu, S., Wei, X., Li, D., *Lu, D., 2017. Examining forest disturbance and recovery in the subtropical forest region of Zhejiang Province using Landsat time-series data. Remote Sensing. 9, 479. doi:10.3390/rs9050479.
20. Oda, T., Lauvaux, T., Lu, D., Rao, P., Miles, N.L., Richardson, SJ, and Gurney, K.R. 2017. On the impact of granularity of space-based urban CO2 emissions in urban atmospheric inversions: A case study for Indianapolis, IN. Elem Sci Anth. 5: 28. DOI: http://doi.org/10.1525/elementa.146.
21. Feng, Y., *Lu, D., Moran, E., Dutra, L.V., Calvi, M. F., and de Oliveira, M. A. F. 2017. Examining spatial distribution and dynamic change of urban land covers in the Brazilian Amazon using multitemporal multisensor high spatial resolution satellite imagery. Remote Sensing. 9, 381. doi:10.3390/rs9040381.
22. Guo, W., *Lu, D., Kuang, W., 2017. Improving fractional impervious surface mapping performance through combination of DMSP-OLS and MODIS NDVI data. Remote Sensing. 9, 371. doi: 10.3390/rs9040371.
23. Feng, Y., *Lu, D., Chen, Q., Keller, M., Moran, E., dos-Santos, M.N., Bolfe, E.L., and Batistella, M. 2017. Examining effective use of data sources and modeling algorithms for improving biomass estimation in a moist tropical forest of the Brazilian Amazon. International Journal of Digital Earth. 10(10), 996–1016. http://dx.doi.org/10.1080/17538947.2017.1301581.
24. Zhao, P., *Lu, D., Wang, G., Liu, L., Li, D., Zhu, J., and Yu, S. 2016. Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data. International Journal of Applied Earth Observation and Geoinformation. 53: 1-15. http://dx.doi.org/10.1016/j.jag.2016.08.007.
25. Zhao, P., *Lu, D., Wang, G., Wu, C., Huang, Y., and Yu, S. 2016, Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation. Remote Sensing. 8, 469; doi:10.3390/rs8060469.
26. Xi, Z., *Lu, D., Liu, L., and Ge, H., 2016. Detection of drought-induced hickory disturbances in western Lin An County, China, using multitemporal Landsat imagery. Remote Sensing. 8, 345; doi:10.3390/rs8040345.
27. Li, L., and *Lu, D., 2016. Mapping population density distribution at multiple scales in Zhejiang Province using Landsat Thematic Mapper and census data. International Journal of Remote Sensing. 37(18), 4243-4260. Doi: 10.1080/01431161.2016.1212422.
28. Li, L., *Lu, D., and Kuang, W., 2016. Examining Urban Impervious Surface Distribution and Its Dynamic Change in Hangzhou Metropolitan. Remote Sensing. 8(3), 265; doi:10.3390/rs8030265.
29. Zhang, C., Lu, D., Chen, X., Zhang, Y., Maisupova, B., and *Tao, Y., 2016. The spatiotemporal patterns of vegetation coverage and biomass of the temperate deserts in Central Asia and their relationships with climate controls. Remote Sensing of Environment, 175, 271–281. http://dx.doi.org/10.1016/j.rse.2016.01.002.
30. Zhu, C., *Lu, D., Victoria, D., and Dutra, L., 2016. Mapping Fractional Cropland Distribution in Mato Grosso, Brazil Using Time Series MODIS Enhanced Vegetation Index and Landsat Thematic Mapper Data. Remote Sensing. 8, 22; doi:10.3390/rs8010022. Pp.14.
31. Chen, Q., *Lu, D., Keller, M., dos-Santos, M.N., Bolfe, E.L., Feng, Y., and Wang, C., 2016. Modeling and Mapping Agroforestry Aboveground Biomass in the Brazilian Amazon Using Airborne Lidar Data. Remote Sensing. 8, 21; doi:10.3390/rs8010021. Pp.17.
32. *Lu, D., Chen, Q., Wang, G., Liu, L., Li, G., and Moran, E., 2016. A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. International Journal of Digital Earth. 9(1), 63-105. http://dx.doi.org/10.1080/17538947.2014.990526.
33. Li, D., Ju, W., and Lu. D., 2015. Impact of estimated solar radiation on GPP simulation in subtropical plantation in southeast China. Solar Energy. 120:175-186. DOI: 10.1016/j.solener.2015.07.033.
34. Guo, W., *Lu, D., Wu, Y., and Zhang, J., 2015. Mapping impervious surface distribution with integration of SNNP VIIRS-DNB and MODIS NDVI data. Remote Sensing. 7: 12459-12477; doi:10.3390/rs70912459.
35. Chen, Y., *Lu, D., Luo, G., and Huang, J., 2015. Detection of vegetation abundance change in the alpine tree line using multitemporal Landsat Thematic Mapper imagery. International Journal of Remote Sensing. 36(18), 4683-4701. http://dx.doi.org/10.1080/01431161.2015.1088675.
36. Zhang, C., Chen, Y., and *Lu, D., 2015. Detecting fractional land-cover change in arid and semiarid urban landscapes with multitemporal Landsat Thematic Mapper imagery. GIScience & Remote Sensing. 52(6), 700-722. http://dx.doi.org/10.1080/15481603.2015.1071965.
37. Zhang, C., Chen, Y., and *Lu, D., 2015. Mapping the land-cover distribution in arid and semiarid urban landscapes with Landsat Thematic Mapper imagery. International Journal of Remote Sensing. 36(17), 4483-4500. http://dx.doi.org/10.1080/01431161.2015.1084552.
38. Sheng, L., Lu, D., and Huang, J., 2015. Impacts of land-cover types on an urban heat island in Hangzhou, China. International Journal of Remote Sensing. 36(6), 1584-1603. http://dx.doi.org/10.1080/01431161.2015.1019016.
39. Yin, K., Lu, D., Tian, Y., Qianjun Zhao, Q., and Yuan, C., 2015. Evaluation of carbon and oxygen balance in urban ecosystems using land use/land cover and statistical data. Sustainability, 7, 195-221; doi:10.3390/su7010195.
指导研究生：
博士研究生：8
硕士研究生：18
