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陸燈盛教授

發布者:孫杰 發布時間:2018-10-08 瀏覽次數:4626

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基本信息 Basic information

    名:陸燈盛

    稱:教授

碩導/博導:博導

最高學位:博士

行政職務:無

其它兼職:

    位:福建師范大學地理科學學院

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聯系方式 Contact

通訊地址:福建師范大學倉山校區藝術基地5206

郵政編碼:350007

辦公電話:0591-83465214    

電子郵箱:ludengsh@msu.edu

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研究方向 Research Interests

土地利用/覆蓋的分類和變化監測,森林生物量遙感定量估算,不透水地表信息提取ag真人龙虎,水土流失及土地退化評估等。

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個人履歷 Resume

教  育

博士  1998.01-2001.05  美國 印第安納州立大學 自然地理專業

碩士  1986.09-1989.06  北京林業大學 森林經理學專業

學士  1982.09-1986.07  浙江林學院  林學專業

工  作

1989.71997.12  林業部華東規劃設計院

2001.012002.05 美國印第安納大學  博士后

2002.052006.12 美國印第安納大學 制度、人口和環境變化研究中心  助理研究員

2007.012008.07 美國奧本大學林業與野生動物學院  研究員

2008.072011.06 美國印第安納大學全球環境變化研究中心 副研究員

2011.072012.08 美國印第安納大學全球環境變化研究中心 研究員

2012.082013.04 美國密歇根州立大學全球變化與對地觀測研究中心 教授

2013.042018.10 浙江農林大學環境與資源學院 教授

2018.11至今       福建師范大學地理科學學院  教授

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個人簡介  Brief

陸燈盛ag真人龙虎,男,19652月生,教授,博士生導師,福建師范大學地理科學學院教授。2001年畢業于美國印第安納州立大學,獲自然地理學博士學位ag真人龙虎,后在美國印第安納大學從事遙感博士后研究,2002年開始先后在印第安納大學全球環境變化研究中心、奧本大學林業與野生動物學院、密歇根州立大學全球變化與對地觀測中心工作。于2012年入選浙江省“千人計劃”、浙江省 “錢江學者”,于201811在福建師范大學地理科學學院任教授一職。

陸燈盛教授主持和參與了23個科研項目,包括美國NASA項目, NIHNSF項目、巴西CNPq項目、國家重點研發項目、國家自然科學基金面上項目以及浙江省自然基金重點項目ag真人龙虎。自2001年以來在《Remote Sensing of Environment》等國際刊物發表近100SCI論文,其中以第一作者或通訊作者發表80SCI論文。擔任《Remote Sensing of Environment,ISPRS Journal of Photogrammetry and Remote Sensing》等近30種遙感/地理信息系統期刊的審稿專家。2004年發表的遙感動態變化監測的文章《Change Detection Techniques》被引用2387次,2007年發表的遙感圖像分類方法的文章《A Survey of Image Classification Methods and Techniques for Improving Classification Performance》 被引用1760次。還有近30篇文章被引次數均在百次以上ag真人龙虎。

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近三年的代表性論文 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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主要獲獎成果 The Main Achievements

201512:城市高精度時空信息獲取關鍵技術及應用示范項目榮獲環境保護科學技術獎二等獎;

201611:城市生態環境監測及管控關鍵技術研發與示范”項目 榮獲“環境保護科學技術獎” 二等獎。

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近三年的科研項目 Research projects

  1. 單木-林分尺度人工林資源遙感精細監測技術(人工林資源監測關鍵技術研究), 國家重點研發計劃重點專項, 7/2017—12/2020, 子課題主持.

  2. 珠三角菜地鎘砷和氮磷面源污染控制適用技術集成與模式構建(珠三角鎘砷和面源污染農田綜合防治與修復技術示范項目), 國家重點研發計劃. 7/2017—12/2020, 子課題主持.

  3. 北京市地表類型空間分布特征及其對海綿城市建設的適應度研究. 北京市自然科學基金重點項目. No. 8171004.  2017—2019. 子課題主持.

  4. 基于多源數據的亞熱帶森林地上生物量遙感信息模型的構建及其應用研究. 國家自然科學基金. No# 41571411. 1/2016–12/2019. 主持

  5. 浙江省特色經濟林水土流失形成機理及適宜性研究, 浙江省自然基金重點項目. LZ15C160001. 1/2015–12/2018.  主持

  6. 人與自然引起的干擾對森林生物量動態變化的影響機制。浙江農林大學科研發展基金(人才啟動項目)2013FR052。4/2013–3/2018ag真人龙虎。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. 浙江省濱海濕地生態服務功能及其恢復技術研究, 省院合作林業科技項目. 2015SY01ag真人龙虎。1/2015–12/2017. 子課題主持.

  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.


ag真人龙虎