陆灯盛，浙江农林大学环境与资源学院教授, 浙江省“千人计划”入选者、浙江省高等学校“钱江学者”特聘教授，博士生导师。2001年毕业于美国印第安那州立大学，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。曾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。主要研究领域包括土地利用/覆盖变化、森林生物量/碳储量遥感定量估算、土地退化评估、城市不透水地表提取等。曾主持或参与美国NASA、NSF、NIH 及巴西CNPq 等资助的科研项目。目前承担的主要项目包括人才启动项目<人与自然引起的干扰对森林生物量动态变化的影响机制>, 浙江省自然基金重点项目<浙江省特色经济林水土流失形成机理及适宜性研究>, 国家自然科学基金项目<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>等。在《Remote Sensing of Environment》，《ISPRS Journal of Photogrammetry and Remote Sensing》等刊物发表SCI论文近80篇。担任30多种遥感/地理信息系统等相关期刊的审稿专家。承担研究生的《遥感技术及应用》，本科生的《森林经理学》课程的教学工作。

一、近五年主持和参与项目

· 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. 2016/01-2019/12. No# 41571411, 72万. 主持。

· 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. 2015/1/-2018/12/. 30万. 主持.

· 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. 2015/1-2017/12. 120万 (浙江农林大学: 20万) 。参与.

· 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）2013FR052。2013/4-2018/3. 200 万。主持.

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

· Land use changes and their interactions with forest degradation processes in Amazonia. CNPq – LBA, 1/ 2014-12/2016, R$ 926,656.00.

· 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, R$414,855.80. 

二、近五年发表论文

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.

Kuang, W., Chen, L., Liu, J., Xiang, W., Chi, W., Lu, D. Yang, T., Pan, T., and Liu, A. 2016. Remote sensing-based artificial surface cover classification in Asia and spatial pattern analysis. Science China: Earth Sciences, 59 (1), 1-18. doi: 10.1007/s11430-016-5295-7.

Negri, R.G., Dutra, L.V., Sant’Anna, S.J.S., and Lu, D., 2016. Examining region-based methods for land cover classification using stochastic distances. International Journal of Remote Sensing. 37(8), 1902–1921. http://dx.doi.org/10.1080/01431161.2016.1165883

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.

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.

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.

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.

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.

Cak, A. D., Moran, E.F., Figueiredo, R., Lu, D., Li, G., and Hetrick, S. 2016. Urbanization and Small Household Agricultural Land Use Choices in the Brazilian Amazon and the Role for the Water Chemistry of Small Streams. Journal of Land Use Science. 11(2), 203-221. http://dx.doi.org/10.1080/1747423X.2015.1047909.

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.

奚祯苑, 刘丽娟, 陆灯盛, 葛宏立, 陈耀亮. 2015. 基于线性混合像元分解技术提取山核桃空间分布. 林业科学. 51(10), 43-52. Doi:10.11707/j.1001-7488.20150000.

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.

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.

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.

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.

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.

Zhou, G., Tang, S., Lu, D., Liu, L., Han, J., and Dong, W., Wireless Sensor Networks for Agriculture and Forestry, International Journal of Distributed Sensor Networks, vol. 2015, Article ID 845364, 2 pages, 2015. doi:10.1155/2015/845364.

Li, P., Zhou, G., Du, H., Lu, D., Mo, L., Xu, X., Shi, Y., and Zhou, Y., 2015. Current and potential carbon stocks in Moso bamboo forests in China. Journal of Environmental Management, 156, 89-96. doi:10.1016/j.jenvman.2015.03.030.

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.

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.

Du, G., Kuang, W., Meng, F., Chi, W., and Lu, D., 2015. Spatiotemporal pattern and drivers of national land-cover change across Brazil (In Chinese). Progress in Geography, 34 (1): 73-82; doi: 10.11820/dlkxjz.2015.01.009

Lu, D., Li, G., Moran, E., Dutra, L., and Batistella, M., 2014. The roles of textural images in improving land-cover classification in the Brazilian Amazon. International Journal of Remote Sensing. 35(24), 8818-8207. http://dx.doi.org/10.1080/01431161.2014.980920.

Lu, D., Li, G., Moran, E., and Kuang, W., 2014. A comparative analysis of approaches for successional vegetation classification in the Brazilian Amazon. GIScience and Remote Sensing. 51(6), 695-709. http://dx.doi.org/10.1080/15481603.2014.983338.

Lu, D., Li. G., and Moran, E., 2014. Current situation and needs of change detection techniques. International Journal of Image and Data Fusion, 5(1), 13-38. doi.org/10.1080/19479832.2013.868372.

Lu, D., Li. G., Kuang, W., and Moran, E., 2014. Methods to extract impervious surface areas from satellite images. International Journal of Digital Earth, 7(2), 93-112. doi.org/10.1080/17538947.2013.866173.

Kuang, W., Chi, W.,Lu, D.,* and Dou, Y., 2014. A comparative analysis of megacity expansions in China and the U.S.: Patterns, rates and driving forces. Landscape and Urban Planning, 132, 121-135. http://dx.doi.org/10.1016/j.landurbplan.2014.08.015.

Lu, D., Li, G., Moran, E., and Hetrick, S., 2013. Spatiotemporal analysis of land-use and land-cover change in the Brazilian Amazon.International Journal of Remote Sensing. 34:16, 5953-5978.

Li, G.,Lu, D*., Moran, E., and Hetrick, S., 2013. Mapping impervious surface area in the Brazilian Amazon using Landsat imagery. GIScience & Remote Sensing. 50(2), 172-183. http://dx.doi.org/10.1080/15481603.2013.780452.

Li, G.,Lu, D*., Moran, E., and Sant’Anna, S.J.S., 2012. A comparative analysis of classification algorithms and multiple sensor data for land use/land cover classification in the Brazilian Amazon.Journal of Applied Remote Sensing,6(1), 061706 (Dec 14, 2012). doi:10.1117/1.JRS.6.061706.

Lu, D., Batistella, M., Li, G., Moran, E., Hetrick, S., Freitas, C., Dutra, L., and Sant’Anna, S.J.S., 2012. Land use/cover classification in the Brazilian Amazon using satellite images.Brazilian Journal of Agricultural Research, 47(9), 1185-1208. 

Lu, D., Hetrick, S., Moran, E., and Li, G., 2012. Application of time series Landsat images to examining land use/cover dynamic change.Photogrammetric Engineering & Remote Sensing. 78(7), 747-755.

Lu, D., Chen, Q., Wang, G., Moran, E., Batistella, M., Zhang, M., Laurin, G.V., and Saah, D., 2012. Aboveground forest biomass estimation with Landsat and LiDAR data and uncertainty analysis of the estimates.International Journal of Forestry Research. Volume 2012, doi:10.1155/2012/436537. Pp. 16.

Li, G.,Lu, D*., Moran, E., Dutra, L., and Batistella, M., 2012. A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region.ISPRS Journal of Photogrammetry and Remote Sensing,70, 26-38.

Huang, J.,Lu, D*., Li, J., Wu, J., Chen, S., Zhao, W., Ge, H., Huang, X., and Yan, X., 2012. Integration of Remote Sensing and GIS for Evaluating Soil Erosion Risk in Northwestern Zhejiang, China.Photogrammetric Engineering & Remote Sensing, 78(9), 935-946.

Lu, D., Li, G., Moran, E., Batistella, M., and Freitas, C., 2011. Mapping impervious surfaces with the integrated use of Landsat Thematic Mapper and radar data: a case study in an urban-rural landscape in the Brazilian Amazon.ISPRS Journal of Photogrammetry and Remote Sensing.66(6), 798–808, DOI: 10.1016/j.isprsjprs.2011.08.004.

Lu, D., Li, G., Moran, E., Dutra, L., and Batistella, M., 2011. A comparison of multisensor integration methods for land-cover classification in the Brazilian Amazon. GIScience & Remote Sensing. 48(3), 345-370. DOI: 10.2747/1548-1603.48.3.345.

Lu, D., Batistella, M., Moran, E., Hetrick, S., Alves, D., and Brondizio, E. 2011. Fractional forest cover mapping in the Brazilian Amazon with a combination of MODIS and TM images. International Journal of Remote Sensing. 32(22), 7131-7149. DOI: 10.1080/01431161.2010.519004. 

       Lu, D., Hetrick, S., and Moran, E. 2011. Impervious surface mapping with QuickBird imagery.International Journal of Remote Sensing. 32(9), 2519-2533, DOI: 10.1080/01431161003698393.

Lu, D., Moran, E., and Hetrick, S., 2011. Detection of impervious surface change with multitemporal Landsat images in an urban-rural frontier.ISPRS Journal of Photogrammetry and Remote Sensing. 66(3), 298-306. doi:10.1016/j.isprsjprs.2010.10.010.

Li, G.,Lu, D*., Moran, E., and Hetrick, S., 2011. Land-cover classification in a moist tropical region of Brazil with Landsat TM imagery.International Journal of Remote Sensing. 32(23), 8207-8230, DOI:10.1080/01431161.2010.532831.

Zhang, Y.,Lu, D*., Yang, B., Sun, C., and Sun, M. 2011. Coastal wetland vegetation classification with a Landsat Thematic Mapper image.International Journal of Remote Sensing. 32(2), 545–561, DOI: 10.1080/01431160903475241.

Kuang, W., Liu, J., andLu, D. 2011. Pattern of impervious surface change and its effect on water environment in the Beijing-Tianjin-Tangshan metropolitan area. ACTA GEOGRAPHICA SINICA, 66(11), 11p. (in Chinese).

三 、 获奖

2015年12月,  环境保护科学技术奖，二等奖。获奖项目： 城市生态环境监测及管控关键技术研发与示范。 证书号： KJ2015-2-08-G05.

四、联系方式

luds@zafu.edu.cn