陆灯盛教授

发布者:孙杰 发布时间:2018-10-08 浏览次数:28

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基本信息 Basic information

    名:陆灯盛

    称:教授

硕导/博导:博导

最高学位:博士

行政职务:无

其它兼职:

    位:福建师范大学地理科学学院

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联系方式 Contact

通讯地址:福建师范大学仓山校区艺术基地5206

邮政编码:350007

办公电话:0591-83465214    

电子邮箱:ludengsh@msu.edu

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研究方向 Research Interests

土地利用/覆盖的分类和变化监测,森林生物量遥感定量估算,不透水地表信息提取,水土流失及土地退化评估等。

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个人履历 Resume

教  育

博士  1998.01-2001.05  美国 印第安纳州立大学 自然地理专业

硕士  1986.09-1989.06  北京林业大学 森林经理学专业

学士  1982.09-1986.07  浙江林学院  林学专业

工  作

1989.71997.12  林业部华东规划设计院

2001.012002.05 美国印第安纳大学  博士后

2002.052006.12 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

2007.012008.07 美国奥本大学林业与野生动物学院  研究员

2008.072011.06 美国印第安纳大学全球环境变化研究中心 副研究员

2011.072012.08 美国印第安纳大学全球环境变化研究中心 研究员

2012.082013.04 美国密歇根州立大学全球变化与对地观测研究中心 教授

2013.042018.10 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

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个人简介  Brief

陆灯盛,男,19652月生,教授,博士生导师,福建师范大学地理科学学院教授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201811在福建师范大学地理科学学院任教授一职

陆灯盛教授主持和参与了23个科研项目,包括美国NASA项目, NIHNSF项目、巴西CNPq项目、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。自2001年以来在《Remote Sensing of Environment》等国际刊物发表近100SCI论文,其中以第一作者或通讯作者发表80SCI论文。担任《Remote Sensing of Environment,ISPRS Journal of Photogrammetry and Remote Sensing》等近30种遥感/地理信息系统期刊的审稿专家。2004年发表的遥感动态变化监测的文章《Change Detection Techniques》被引用2387次,2007年发表的遥感图像分类方法的文章《A Survey of Image Classification Methods and Techniques for Improving Classification Performance》 被引用1760次。还有近30篇文章被引次数均在百次以上。

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近三年的代表性论文 Selected Publications

  1. Kuang, W., Liu, A., Dong, Y., Li, G., and *Lu, D., 2018. Examining the impacts of urbanization on surface radiation using Landsat imagery. GIScience & Remote Sensing. https://doi.org/10.1080/15481603.2018.1508931.

  2. *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.

  3. 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. https://doi.org/10.1080/01431161.2018.1500731.

  4. Li, G., *Lu, D., Moran, E., Calvi, M.F., Dutra, L.V., and Batistella, M., 2018. Examining deforestation and agropasture dynamics along the Brazilian TransAmazon highway using multitemporal Landsat imagery. GIScience & Remote Sensing. https://doi.org/10.1080/15481603.2018.1497438.

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  21. 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.

  22. 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.

  23. 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.

  24. 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.

  25. 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.

  26. *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.

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主要获奖成果 The Main Achievements

201512: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;

201611: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。

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近三年的科研项目 Research projects

  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. 珠三角菜地镉砷和氮磷面源污染控制适用技术集成与模式构建(珠三角镉砷和面源污染农田综合防治与修复技术示范项目), 国家重点研发计划. 7/2017—12/2020, 子课题主持.

  3. 北京市地表类型空间分布特征及其对海绵城市建设的适应度研究. 北京市自然科学基金重点项目. No. 8171004.  2017—2019. 子课题主持.

  4. 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

  5. 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

  6. 人与自然引起的干扰对森林生物量动态变化的影响机制。浙江农林大学科研发展基金(人才启动项目)2013FR0524/2013–3/2018。主持

  7. INFEWS/T3: Rethinking Dams: Innovative Hydropower Solutions to Achieve Sustainable Food and Energy Production and Sustainable Communities. US NSF, #1639115. 1/2017–12/2020.

  8. 浙江省滨海湿地生态服务功能及其恢复技术研究, 省院合作林业科技项目. 2015SY011/2015–12/2017. 子课题主持.

  9. Urbanization and sustainability under global change and traditional economies: synthesis from Southeast, East, and North Asia (SENA). US NASA LULC program, Grant # NNX15AD51G. 2/2015 – 1/2018.

  10. Land use changes and their interactions with forest degradation processes in Amazonia. Brazil CNPq – LBA, 1/ 2014-12/2017.

  11. 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.