陆灯盛教授

发布者：孙杰发布时间：2018-10-08浏览次数：23555


基本信息 Basic information

姓    名：陆灯盛

职    称：教授

硕导/博导：博导

最高学位：博士

行政职务：无

其它兼职：

单    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：福建师范大学旗山校区16号楼

邮政编码：350117

电子邮箱：ludengsheng@fjnu.edu.cn

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研究方向 Research Interests

遥感技术与应用，林业遥感（森林分类，干扰监测，生物量/碳储量估测建模等），城市遥感。

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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：

1989.7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  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2013.04 美国密歇根州立大学全球变化与对地观测研究中心 教授

2013.04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

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个人简介  Brief

陆灯盛，福建师范大学地理科学学院教授、博士生导师。2001年博士毕业于美国印第安纳州立大学，曾在印第安纳大学（2001-2012, 博士后、助理/副/高级研究员）、密歇根州立大学（2012-2018，教授）等高校工作。GEO和联合国人居署共同组织实施“可持续城市和社区的对地观测工具箱”科学指导委员会委员、浙江省千人计划入选者、钱江学者、福建省高层次人才。中国地理学会理事、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、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”第一和第二届技术委员会委员。长期从事森林与城市生态遥感研究。最近三年，研究项目主要来自国家重点研发计划、国家自然科学基金等。已发表140余篇SCI论文，在Google Scholar Citations中显示被引用2万4千多次。爱思唯尔2019，2020和2021年中国高被引学者。全球前2%顶尖科学家榜单中，入选2020，2021和2022“终身科学影响力排行榜”和2019，2020和2021年度“科学影响力排行榜”，以及入选2021和2022全球学者学术影响力排行榜。担任International Journal of Digital Earth、Geo-spatial Information Science、International Journal of Image and Data Fusion、Remote Sensing、Frontiers in Remote Sensing期刊编委。

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近三年的代表性论文 Selected Publications

  1. Liao, K., Li, Y., Zou, B., Li, D., *Lu, D., 2022.  Examining the Role of UAV Lidar Data in Improving Tree Volume Calculation Accuracy. Remote Sensing. 14, 4410. https://doi.org/10.3390/rs14174410.

  2. Li, D., *Lu, D., Wu, Y., Luo, K., 2022. Retrieval of eucalyptus planting history and stand age using random localization segmentation and continuous land-cover classification based on Landsat time-series data. GIScience & Remote Sensing. 59:1, 1426-1445, DOI: 10.1080/15481603.2022.2118440

  3. Jiang, X., Zhao, S., Chen, Y. and *Lu D. 2022. Exploring tree species classification in subtropical regions with a modified hierarchy-based classifier using high spatial resolution multisensor data. Journal of Remote Sensing. vol. 2022, pp. 16. https://doi.org/10.34133/2022/9847835

  4. Pang, S.; *Li, G.; Jiang, X.; Chen, Y.; Lu, Y.; and Lu, D. 2022. Retrieval of forest canopy height in a mountainous region with ICESat-2 ATLAS. Forest Ecosystems. 9: 100046. https://doi.org/10.1016/j.fecs.2022.100046.

  5. Fan, M.; Liao, K.; Lu, D.; *Li, D. 2022. Examining Vegetation Change and Associated Spatial Patterns in Wuyishan National Park at Different Protection Levels. Remote Sensing, 14, 1712. https://doi.org/10.3390/rs14071712.

  6. Lin, W.; Lu, Y.; *Li, G.; Jiang, X.; Lu, D. 2022. A comparative analysis of modeling approaches and canopy height-based data sources for mapping forest growing stock volume in a northern subtropical ecosystem of China. GIScience & Remote Sensing. 59(1), 568-589. https://doi.org/10.1080/15481603.2022.2044139.

  7. 林文科，陆亚刚, 蒋先蝶, 李桂英, 李登秋, *陆灯盛， 2022. 协同多源遥感数据的北亚热带森林蓄积量贝叶斯分层估测研究。遥感学报，26(3)，468-479，DOI：10.11834/jrs.20221545

  8. 赵帅，曹美芹，蒋先蝶，陈耀亮，*陆灯盛，2022. 安徽省利辛县平原区人工林树种分类研究. 遥感技术与应用，37（3），589-598. DOI：10.11873/j.issn.1004⁃0323.2022.3.0589。

  9. Kuang, W.; Hou, Y.; Dou, Y.; Lu, D.; Yang, S. 2021. Mapping Global Urban Impervious Surface and Green Space Fractions Using Google Earth Engine. Remote Sensing, 13, 4187. https://doi.org/10.3390/rs13204187

  10. Zhao, S., Jiang, X., Li, G., Chen, Y., and *Lu, D., 2021. Integration of ZiYuan-3 multispectral and stereo data for mapping urban vegetation using the hierarchical-based classifier. International Journal of Applied Earth Observation and Geoinformation, 105, 102594. https://doi.org/10.1016/j.jag.2021.102594.

  11. Chen, Y., Peng, Z., Ye, Y., Jiang, X., *Lu, D., and Chen, E. 2021. Exploring a uniform procedure to map Eucalyptus plantations based on fused medium–high spatial resolution satellite image. International Journal of Applied Earth Observations and Geoinformation, 103, 102462. https://doi.org/10.1016/j.jag.2021.102462

  12. Kuang, W., †Liu, J., †Tian, H., Shi, H., Dong, J., Li, X., Du, G., Hou, Y., Lu, D., Chi, W., Pan, T., Zhang, S., Hamdi, R. Yin, Z., Yan, H., Yan, C., Wu, S., Li, R., Yang, J., Dou, Y., Wu, W., Liang, L., and Xiang, B. 2021. Cropland redistribution to marginal lands undermines environmental sustainability. National Science Review. https://doi.org/10.1093/nsr/nwab091.

  13. Li, D., *Lu, D., Zhao, Y., Zhou, M., Chen, G. 2021. Spatial patterns of vegetation change in giant panda habitat Based on MODIS time-series observations and local indicators of spatial association. Ecological Indicators. 124(107418). https://doi.org/10.1016/j.ecolind.2021.107418.

  14. Chen, Y., Huang, X., Huang, J., Liu, S., *Lu, D., Zhao, S. 2021. Fractional monitoring of desert vegetation degradation, recovery, and greening using optimized multi-endmembers spectral mixture analysis in a dryland basin of Northwest China. GIScience & Remote Sensing. 58(2), 300–321. https://doi.org/10.1080/15481603.2021.1883940

  15. Li, L., Li, N., Zang, Z., *Lu, D., Wang, G. & Wang, N. 2021. Examining phenological variation of on-year and off-year bamboo forests based on the vegetation and environment monitoring on a New Micro-Satellite (VENµS) time-series data, International Journal of Remote Sensing, 42:6, 2203-2219: https://doi.org/10.1080/01431161.2020.1851802

  16. Kuang, W., Du, G., Lu, D., Don, Y., Li, X., Zhang, S., Chi, W., Dong, J., Chen, G., Yin, Z., Pan, T., Hamdi, R., Hou, Y., Chen, C., Li, H., and Miao, C. 2021. Global observation of urban expansion and land-cover dynamics using satellite big-data. Science Bulletin. 66, 297-300. https://doi.org/10.1016/j.scib.2020.10.022.

  17. Kuang, W., Zhang, S., Li, X., Lu, D. 2021. A 30-meter resolution dataset of China’s urban impervious surface area and green space, 2000–2018. Earth Syst. Sci. Data, 13, 63–82, https://doi.org/10.5194/essd-13-63-2021.

  18. Jiang, X., Li, G., *Lu, D., Moran, E., and Batistella, M., 2020. Modeling forest aboveground carbon density in the Brazilian Amazon with integration of MODIS and airborne Lidar data. Remote Sens. 12, 3330; doi:10.3390/rs12203330.

  19. Yu, X., *Lu, D., Jiang, X., Li, G., Chen, Y., Li, D., and Chen, E., 2020. Examining the Roles of Spectral, Spatial, and Topographic Features in Improving Land-Cover and Forest Classifications in a Subtropical Region. Remote Sens. 12, 2907; doi:10.3390/rs12182907.

  20. Li, D., *Lu, D., Moran, E., and Da Silva, R.F., 2020. Examining Water Area Changes Accompanying Dam Construction in the Madeira River in the Brazilian Amazon. Water, 12, 1921; doi:10.3390/w12071921

  21. Chen, Y., Zhao, S., Xie, Z., *Lu, D., and Chen, E., 2020. Mapping multiple tree species classes using a hierarchical procedure with optimized node variables and thresholds based on high spatial resolution satellite data. GIScience & Remote Sensing. 57:4, 526-542, DOI: 10.1080/15481603.2020.1742459.

  22. Li, G., Li. L., *Lu, D., Guo, W., Kuang, W. 2020. Mapping impervious surface distribution in China using multi-source remotely sensed data. GIScience & Remote Sensing, 57:4, 543-552, DOI:10.1080/15481603.2020.1744240.

  23. Jiang, X., Li, G., *Lu, D., Chen, E., and Wei, X., 2020. Stratification-based forest aboveground biomass estimation in a subtropical region using airborne Lidar data. Remote Sensing. 12, 1101; doi:10.3390/rs12071101

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近三年的科研项目 Research projects

  1. 国家自然科学基金，协同无人机激光雷达和时间序列遥感数据的杉木蓄积量估测研究。1/2023-12/2026.项目批准号：32271870

  2. 国家重点研发计划重点专项 ，基于遥感的土地利用变化和森林恢复对森林植被碳源汇格局的影响研究（碳中和背景下森林碳汇形成及经营响应机理)。12/2021 – 11/2026。2021YFD2200401

  3. 福建省科学技术厅2021公益类科研院所专项，面向碳中和目标的福建省碳达峰和林业碳汇研究， 11/2021-11/2024。2021R1002008。

  4. 福建省海峡气象科学研究所，卫星遥感和生态气象服务系统建设–无人机野外观测试验项目。8/2021-7/2022。

  5. 浙江省森林资源监测中心，基于激光雷达的森林资源监测技术与应用研究，7/2021-12/2022。