教育背景
2007.09-2011.06 南京林业大学 地理信息系统 工学学士
2011.09-2014.06 南京林业大学 森林经理学（遥感与GIS） 农学硕士
2014.09-2018.06 南京林业大学与马里兰大学联合培养 森林经理学（遥感与GIS） 农学博士
工作经历
2015.11-2016.11 马里兰大学（美国）地理科学系 访问学者
2018.08-2020.09 南京林业大学 助理研究员
2020.10-2022.03 南京林业大学 讲师
2022.04-至今 南京林业大学 副教授
2021-至今 硕士生导师（学术型） 090704森林经理学
沈文娟，女，副教授，硕士生导师，马里兰大学联培博士、访问学者，入选2024年江苏省科协青年人才托举工程。主要从事基于多源遥感的长时序森林资源监测与气候效应研究。中国自然资源学会自然资源信息系统研究专业委员会委员，中国林学会林业计算机分会理事，中国图形图象学学会遥感图像专业委员会委员,Frontiers in Remote Sensing期刊评审编辑，Forests期刊客座编辑。近年来先后主持国家级与省部级基金项目(面上、青年)与资助5项，参与国家级及省部级科研项目10余项。在国内外期刊发表论文40余篇，第一/通讯作者论文20余篇，国内外TOP期刊10篇，申请专利5项，授权专利4项，多次获得行业优秀论文与报告奖励。担任多个林业、遥感、地学领域（AFM、FEM、FE、IJDE、IJRS、JAG等）国内外主流杂志审稿人。参与编写国家林草局“十四五”规划教材-《林业专业综合实践教程》、“十三五”江苏省重点教材-《森林经营规划》，主讲专业导论、遥感数字图像处理、森林碳汇监测与计量、遥感与地理信息系统、Forestry Remote Sensing(全英授)课程与实践。
https://www.researchgate.net/profile/Wenjuan-Shen-2
研究兴趣：
森林变化及其生物物理机制的气候效应评价；
主被动遥感数据集成、融合与应用；
光学、雷达、激光雷达集成反演时序森林生物量；
精细森林类型变化的碳计量模型
长时间序列森林覆盖变化检测；森林干扰监测；
结合云平台与大数据处理的森林资源监测应用等。
主要学术贡献：
论证了长时序多源、主被动遥感用于监测森林变化、森林生物量的可行性，研建了精细森林类型变化驱动的生物量、碳计量模型,创新发展了森林类型变化的气候效应评价的多尺度时空变化分析方法与改进的地表能量平衡模型、气候模式。
1. 江苏省科协青年人才托举工程，优化森林变化产品评估森林变化的碳源汇与气候效应交互影响，2024-10至2026-09，在研，主持
2. 国家自然科学基金项目，长时序遥感监测的森林变化及其水热碳交互响应机制研究，2024-01至 2027-12，在研，主持
3. 国家自然科学基金项目，集成多源遥感与能量平衡模型的历史森林变化对区域温度的影响研究，2021-01至2023-12，在研，主持
4. 江苏省自然科学基金项目，多源遥感重建的造林历史与能量平衡模型耦合对区域气候的影响研究，2020-07至2023-06，在研，结题（优秀结题项目）
5. 中国博士后科学基金面上资助，联合多源遥感的人工林空间变化及其对区域气候的影响，2019-01至2020-12，结题，主持
6. 江苏省博士后基金，2019年江苏省资助经费招收博士后研究人员，2019.07-2020.12
7. 国家重点研发计划，“森林扰动全球变化效应的评估研究”，2019-2024年，参加
8. 国家自然科学基金面上项目，基于Landsat TM/ETM+/OLI长时间序列的森林变化类型、方向和强度监测方法研究，2020-01至2023-12，在研，参加
9. 国家自然科学基金面上项目，基于干扰和恢复历史的南方人工林碳核算改进方法研究，2017-01至2020-12，参加
10. 美国地质调查局USGS，G14AS00075，Carbon Consequences of Land Management: A Multi-Region Assessment，2015-12至2016-12，参加
11. 国家自然科学基金面上项目，基于模型-遥感整合的人工林应对干扰及气候变化的响应规律研究，2013-01至2016-12，参加
课题论文：
15. Chenfeng Gu, Wenjuan Shen*, et al. 2024. Impacts of forests cover change and compound drought and heat events on carbon dynamics in two areas of China. In preparation.
14. Tongyu Wang, Wenjuan Shen*, et al. 2024. Evaluating the seasonal and regional hydrothermal effects of forest disturbance and recovery based on the WRF model in Northern Guangdong Province, Southern China. In preparation.
13. Mei Ji, Wenjuan Shen*, et al. 2024. Spatiotemporal impacts of urban forest change on water-heat-carbon and their interaction mechanism based on two southern Chinese citie. In preparation.
12. Liu, Q., Shen, W*., Wang, T., He, J., Cao, P., Sun, T., ... & Huang, C. (2024). Impacts of forest cover change on local temperature in Yangtze River Delta and Pearl River Delta urban agglomerations of China. Agricultural and Forest Meteorology, 357, 110205.
11. Tai, Z., Su, X., Shen, W*., Wang, T., Gu, C., He, J., & Huang, C. (2024). Identification of Spatial Distribution of Afforestation, Reforestation, and Deforestation and Their Impacts on Local Land Surface Temperature in Yangtze River Delta and Pearl River Delta Urban Agglomerations of China. Remote Sensing, 16(18), 3528.
10. Shen, W*., Liu, Q., Ji, M., He, J., He, T., & Huang, C. (2023). Impacts of urban forests and landscape characteristics on land surface temperature in two urban agglomeration areas of China. Sustainable Cities and Society, 99, 104909.
9. Shen, W*., He, J., He, T., Hu, X., Tao, X., & Huang, C. (2022). Biophysical effects of afforestation on land surface temperature in Guangdong Province, southern China. Journal of Geophysical Research: Biogeosciences, 127(8), e2022JG006913.
8. Shen, W*., He, J., Huang, C., & Li, M. (2020). Quantifying the actual impacts of forest cover change on surface temperature in Guangdong, China. Remote Sensing, 12(15), 2354.
7. Shen, W., Mao, X., He, J., Dong, J., Huang, C., & Li, M. (2020). Understanding current and future fragmentation dynamics of urban forest cover in the Nanjing Laoshan Region of Jiangsu, China. Remote Sensing, 12(1), 155.
6. Shen, W., Li, M., Huang, C., He, T., Tao, X., & Wei, A. (2019). Local land surface temperature change induced by afforestation based on satellite observations in Guangdong plantation forests in China. Agricultural and Forest Meteorology, 276, 107641.
5. Shen, W., Li, M., Huang, C., Tao, X., Li, S., & Wei, A. (2019). Mapping annual forest change due to afforestation in Guangdong Province of China using active and passive remote sensing data. Remote Sensing, 11(5), 490.
4. Shen, W., Li, M., Huang, C., Tao, X., & Wei, A. (2018). Annual forest aboveground biomass changes mapped using ICESat/GLAS measurements, historical inventory data, and time-series optical and radar imagery for Guangdong province, China. Agricultural and Forest Meteorology, 259, 23-38.
3. Shen, W., Li, M., & Wei, A. (2017). Spatio-temporal variations in plantation forests’ disturbance and recovery of northern Guangdong Province using yearly Landsat time series observations (1986–2015). Chinese Geographical Science, 27, 600-613.
2. Shen, W., Li, M., Huang, C., & Wei, A. (2016). Quantifying live aboveground biomass and forest disturbance of mountainous natural and plantation forests in Northern Guangdong, China, based on multi-temporal Landsat, PALSAR and field plot data. Remote sensing, 8(7), 595.
1. Shen, W., Wu, T., & Li, M. (2012, October). Mapping urban impervious surfaces of Nanjing from the dense Landsat imagery. In 2012 5th International Congress on Image and Signal Processing (pp. 1068-1072). IEEE.
合作论文：
12. Zhang, Y., Liu, J., & Shen, W. (2022). A review of ensemble learning algorithms used in remote sensing applications. Applied Sciences, 12(17), 8654。ESI高被引用论文.
11. Qiu, J., Wang, H., Shen, W., Zhang, Y., Su, H., & Li, M. (2021). Quantifying forest fire and post-fire vegetation recovery in the daxin’anling area of northeastern China using landsat time-series data and machine learning. Remote sensing, 13(4), 792.
10. Su, H., Shen, W., Wang, J., Ali, A., & Li, M. (2020). Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests. Forest Ecosystems, 7, 1-20.
9. Zhang, Y., Shen, W., Li, M., & Lv, Y. (2020). Integrating landsat time series observations and corona images to characterize forest change patterns in a mining region of Nanjing, Eastern China from 1967 to 2019. Remote Sensing, 12(19), 3191.
8. Zhu, F., Wang, H., Li, M., Diao, J., Shen, W., Zhang, Y., & Wu, H. (2020). Characterizing the effects of climate change on short-term post-disturbance forest recovery in southern China from Landsat time-series observations (1988–2016). Frontiers of Earth Science, 14, 816-827.
7. Mao, L., Li, M., & Shen, W. (2020). Remote sensing applications for monitoring terrestrial protected areas: Progress in the last decade. Sustainability, 12(12), 5016.
6. Diao, J., Feng, T., Li, M., Zhu, Z., Liu, J., Biging, G., ... & Ji, B. (2020). Use of vegetation change tracker, spatial analysis, and random forest regression to assess the evolution of plantation stand age in Southeast China. Annals of Forest Science, 77(2), 1-16.
5. Zhang, Y., Shen, W., Li, M., & Lv, Y. (2020). Assessing spatio-temporal changes in forest cover and fragmentation under urban expansion in Nanjing, eastern China, from long-term Landsat observations (1987–2017). Applied Geography, 117, 102190.
4. Zhu, F., Shen, W., Diao, J., Li, M., & Zheng, G. (2020). Integrating cross-sensor high spatial resolution satellite images to detect subtle forest vegetation change in the Purple Mountains, a national scenic spot in Nanjing, China. Journal of Forestry Research, 31(5), 1743-1758.
3. Li, M., Huang, C., Shen, W., Ren, X., Lv, Y., Wang, J., & Zhu, Z. (2016). Characterizing long-term forest disturbance history and its drivers in the Ning-Zhen Mountains, Jiangsu Province of eastern China using yearly Landsat observations (1987–2011). Journal of forestry research, 27, 1329-1341.
2. Li, M. S., Mao, L. J., Shen, W. J., Liu, S. Q., & Wei, A. S. (2013). Change and fragmentation trends of Zhanjiang mangrove forests in southern China using multi-temporal Landsat imagery (1977–2010). Estuarine, Coastal and Shelf Science, 130, 111-120.
1. Li, M., Du, L., & Shen, W. (2012, October). Spatio-temporal variations in urban heat islands effects of Nanjing, China derived from the dense Landsat imagery (1992–2011). In 2012 5th International Congress on Image and Signal Processing (pp. 1104-1108). IEEE.
中文：
3. 沈文娟, 纪梅, 李明诗, 2022. 基于多源遥感的森林变化对区域温度影响的监测方法研究进展. 南京林业大学学报（自然科学版）, 46(3):1-11.
2. 沈文娟, 李明诗, 黄成全, 2018. 长时间序列多源遥感数据的森林干扰监测算法研究进展. 遥感学报, 22(6), 1005-1022. Shen, W., Li, M., & Huang, C. (2018). Review of remote sensing algorithms for monitoring forest disturbance from time series and multi-source data fusion. Journal of Remote Sensing, 22(6), 1005-1022.
1. Shen, W., & Li, M. (2017). Mapping disturbance and recovery of plantation forests in southern China using yearly Landsat time series observations. Acta Ecologica Sinica, 37(5), 1438-1449.
专利：
4. 沈文娟，王通宇，辜晨枫，邰志国，苏晓堃. 一种基于森林扰动与恢复数据的森林气温效应评价方法. ZL202410343718.4
3. 沈文娟, 纪梅, 王通宇, 辜晨枫, 刘晴. 基于卫星观测与空间换算量化城市森林对温度影响的方法. ZL202310811281.8
2. 沈文娟, 刘晴, 张影, 叶雯静. 一种用于造林空间监测的便携式自动气象站. ZL202122267714.7
1. 沈文娟. 基于多源遥感与能量平衡模型造林响应地表温度的方法. ZL202110392852.X
获奖与荣誉：
2018年、2020年梁希青年论文奖三等奖；
2019年中国林学会森林经理分会学术研讨会优秀论文一等奖；
2019年中国林业青年学术年会森林经理与信息技术分会场优秀报告；
2023年中国林草计算机应用大会优秀报告奖（研究生纪梅）
中国自然资源学会自然资源信息系统研究专业委员会委员
中国林学会林业计算机分会理事
中国图形图象学学会遥感图像专业委员会委员
AGU会员，中国地理学会会员，中国林学会会员，中国图形图象学学会会员
担任Agricultural and Forest Meteorology, Forest Ecology and Management, Forest Ecosystems,Urban Forestry&Urban Greening，International Journal of Digital Earth, International Journal of Remote Sensing,IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Remote Sensing，Journal of Cleaner Production，Scientific Data, Land Use Policy, Global Ecology and Conservation, Ecological Indicators, 遥感技术与应用,植物生态学报等国内外杂志审稿人。
担任Forests期刊特刊：Image Processing For Forest Characterization的客座编辑；
Review Editor on the Editorial Board of Terrestrial Carbon Cycle (specialty section of Frontiers in Remote Sensing);
Topic Editor (Remote Sensing and Forest Carbon Monitoring) of Frontiers in Remote Sensing;
Associate Editor of Frontiers in Forests and Global Change
