黄文丽，博士，副教授，主要研究兴趣是将遥感及地理信息等技术应用在生态环境领域，包括植被生态参数遥感估测、森林碳监测与模型模拟、森林碳核查等方面。于2015年获马里兰大学地理学博士学位，并继续开展博后研究至2018年。2019年入职武汉大学资源与环境科学学院。

• 作为项目负责人，先后承担国家自然科学基金项目、国家重点研发计划/自然科学基金地区联合重点项目子课题及专题任务，国家人力资源部赤子计划重点资助课题、遥感科学国家重点实验室开放基金重点资助项目、自然资源部城市土地资源监测与仿真重点实验室开放课题及横向技术开发等多项科研课题。

• 作为重要研究骨干，参与了美国航空航天局的陆地生态系统专项（2009-2013）、碳监测与预测专项（2012-2018）、土地利用/覆盖变化专项（2015-2018）等科研课题。
  

• 在国内外刊物发表学术论文40余篇，参编学术专著2部；担任Remote Sensing期刊学术编委，Forestry Research期刊青年编委，Forest专刊客座编辑，以及Remote Sensing of Environment、IEEE Transactions Geoscience and Remote Sensing、Environmental Research Letters、Remote Sensing、人民长江等20余个国内外期刊审稿人。

• 指导学生参与校省及国家级大学生创新创业赛事活动，获2023年度武汉大学优秀指导教师称号。

  
  

指导研究生专业如下，欢迎有意向的同学联系（Email: wenli.huang@whu.edu.cn；QQ：179578478）

•  学术型学位硕士：地图学与地理信息系统

•  学术型学位硕士：地图制图学与地理信息工程

•  专业学位博士：资源与环境

• 专业学位硕士：资源与环境（测绘工程）

主要研究论文（*为通讯作者，^#为共同第一/贡献作者，⁺为指导本科/研究生

• Min W⁺, Huang W*, Chen Y*, Xu R, Bao L. (2025). Improving the Spatial Continuity of GEDI Aboveground Biomass Density Products Using Multisource Remote Sensing Data with Consideration of Spatial Correlation and Heterogeneity. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 18: 24783-24800 SCI. JCR-Q1.中科院2区
  

        DOI: 10.1109/JSTARS.2025.3611427 (https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=11172296)

• Liu X⁺, Pan J*, Hu R, Huang W*, Lin J, Hu J. (2025). DSMF-Net: A One-stage SAR Ship Detection Network Based on Deformable Strip Convolution and Multi-scale Feature Refinement and Fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. JCR-Q1 中科院2区
  DOI: 10.1109/JSTARS.2025.3559414 (https://ieeexplore.ieee.org/document/10960366)

• Wang Z⁺, Huang W*, Guan X. (2024). Ecosystem Resilience Trends and Its Influencing Factors in China’s Three-River Headwater Region: A Comprehensive Analysis Using CSD Indicators (1982–2023). Land.  13(8):1224. https://doi.org/10.3390/land13081224 SSCI. 

• Min W⁺, Chen Y*, Huang W*, Wilson JP, Tang H, Guo M, Xu R. (2024). Incorporating of spatial effects in forest canopy height mapping using airborne, spaceborne lidar and spatial continuous remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 133; JCR-Q1 中科院1区Top. DOI: https://doi.org/10.1016/j.jag.2024.104123

• Huang W*, Tao Y⁺, Gan W, Shen H*. (2023). An Evaluation of Radiometric Normalization Methods for GaoFen-2 Vegetation Index Mapping. IEEE Geoscience and Remote Sensing Letters. DOI: 10.1109/LGRS.2023.3332610. SCI&EI.

• 刘宇承⁺，罗芳，杜清运，黄文丽*，石仪伟.(2023). 深圳市宝安区土地利用遥感分类方法对比分析. 时空信息学报. v.30(4), 491-499.

• Zheng M⁺, Huang W^#, Xu G*, Li X, & Jiao L. (2023). Spatial gradients of urban land density and nighttime light intensity in 30 global megacities. Humanities and Social Sciences Communications, 10, 404. DOI: https://www.nature.com/articles/s41599-023-01884-8 SSCI. 中科院1区

• Luan Q, Yang C, Feng T⁺, & Huang W*. (2023). Analysis of the urban turbidity island effect: a case study in Beijing City, China. Environmental Research Letters, 18, 024010. SCI, IF=6.096; JCR-Q1; 中科院2区Top.

• Yang R⁺, Luo F, Ren F, Huang W*, Li Q, Du K, Yuan D. (2022). Identifying Urban Wetlands through Remote Sensing Scene Classification Using Deep Learning: A Case Study of Shenzhen, China. ISPRS International Journal of Geo-Information. 2022; 11(2):131. DOI: https://doi.org/10.3390/ijgi11020131. 

• Huang W*, Min W^#+, Ding J⁺, Liu Y, Hu Y, Ni W, Shen H*. (2022). Forest height mapping using inventory and multi-source satellite data over Hunan Province in southern China. Forest Ecosystems (9): 100006. DOI: https://doi.org/10.1016/j.fecs.2022.100006 SCI. IF=6.096; JCR-Q1, 中科院1区Top.

• Chen S⁺, Huang W*, Chen Y, Feng M. (2021). An adaptive thresholding approach toward rapid flood coverage extraction from Sentinel-1 SAR imagery. Remote Sensing, 13(23): 4899. DOI: 10.3390/rs13234899. SCI. JCR-Q1, 中科院2区Top. 

• 丁家祺⁺，黄文丽*，胡杨，刘迎春 (2021).基于机器学习及多源数据的湘西北森林地上生物量估测.
  林业科学. 2021, 57 (10): 36-48. https://doi.org/10.11707/j.1001-7488.20211004 EI. 

• Song XP*, Huang W*, Hansen MC, Potapov P. (2021). An evaluation of Landsat, Sentinel-2, Sentinel-1 and MODIS data for crop type mapping. Science of Remote Sensing, .DOI: https://doi.org/10.1016/j.srs.2021.100018

• Huang W*, Dolan K, Swatantran A, Johnson K, O'Neil Dunne J, Tang H, Dubayah R, Hurtt G. (2019). High-Resolution Mapping of Aboveground Biomass for Forest Carbon Monitoring System in the Tri-State Region of Maryland, Pennsylvania and Delaware, USA. Environment Research Letters. 14(6): 095002. DOI: 10.1088/1748-9326/ab2917  SCI&EI, IF=6.096; 中科院1区Top.

• Huang W*, DeVries B, Huang C, Jones J, Lang, M, Creed I, & Carroll M. (2018). Automated Extraction of Surface Water Extent from Sentinel-1 data. SCI. JCR-Q1, 中科院2区Top. Remote Sensing. 10 (5): 797-816. DOI: 10.3390/rs10050797. SCI&EI, IF=4.118; JCR-Q1; 中科院2区Top.

• Huang W*, Swatantran A, Duncanson LI, Johnson K, Watkinson D, Dolan K, O-Neil Dune J, Hurtt G, & Dubayah R. (2017). County-Scale Biomass Map Comparison: a Case Study for Sonoma, California. Carbon Management. 8: 417-434. DOI: 10.1080/17583004.2017.1396840. SCI&SSCI&EI.

• Huang W*, Swatantran A^#, Johnson K, Duncanson L, Tang H, O'Neil Dunne J, Hurtt G, Dubayah R. (2015). Local Discrepancies in Continental Scale Biomass Maps: A Case Study over Forested and Non-Forested Landscapes in Maryland, USA. Carbon Balance and Management. 10: 19. SCI, IF=1.48; JCR-Q2.
  DOI: 10.1186/s13021-015-0030-9. 

• Huang W*, Sun G, Ni W, Zhang Z, Dubayah R. (2015). Sensitivity of multi-source SAR backscatter to changes in forest aboveground biomass. Remote Sensing. 8 (7): 9587-9609. DOI: 10.3390/rs70809587. SCI&EI, IF=3.036; JCR-Q1; 中科院2区Top.

• Huang W*, Sun G, Montesano P, Ni W, Zhang Z. (2013). Mapping biomass change after forest disturbance: Applying LiDAR footprint-derived models at key map scales. Remote Sensing of Environment. 134: 319-332. DOI: 10.1016/j.rse.2013.03.017. SCI&EI, IF=7.65; JCR-Q1; 中科院1区Top.