{"id":968,"date":"2026-04-09T20:15:56","date_gmt":"2026-04-09T20:15:56","guid":{"rendered":"https:\/\/dev.www.purdue.edu\/fnr\/nrsa-lab\/?page_id=968"},"modified":"2026-04-10T00:49:49","modified_gmt":"2026-04-10T00:49:49","slug":"uav-and-multimodal-remote-sensing","status":"publish","type":"page","link":"https:\/\/www.purdue.edu\/fnr\/nrsa-lab\/uav-and-multimodal-remote-sensing\/","title":{"rendered":"UAV and Multimodal Remote Sensing"},"content":{"rendered":"
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Area 3 \u2014 UAV and Multimodal Remote Sensing<\/strong><\/strong><\/h1>\n\n\n
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Our remote sensing work applies UAV-based platforms and multimodal AI frameworks to monitor forest ecosystems from the air. Key capabilities include: flower color indices for detecting phenological events from remote sensing imagery; vision-language learning models for species classification; and deep learning models for segmenting and mapping forest boundaries at regional scales across diverse landscapes.<\/p>\n\n\n\n

We also work on super-resolution methods that transform low-resolution inputs \u2014 whether satellite imagery or sparse LiDAR point clouds \u2014 into high-fidelity outputs suitable for individual-tree detection and measurement.<\/p>\n<\/div>\n\n\n\n

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Publications<\/h2>\n\n\n\n
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  1. Huang, Y., Ou, B., Meng, K., Yang, B., Carpenter, J., Jung, J., & Fei, S. (2024).<\/strong> Tree species classification from UAV canopy images with deep learning models. Remote Sensing<\/em>, 16(20), 3836. https:\/\/doi.org\/10.3390\/rs16203836<\/a><\/li>\n\n\n\n
  2. Thapa, B., Darling, L., Choi, D., Ardohain, C.M., Firoze, A., Aliaga, D.G., Hardiman, B.S., & Fei, S. (2024).<\/strong> Application of multi-temporal satellite imagery for urban tree species identification. Urban Forestry & Urban Greening<\/em>, 98, 128409. https:\/\/doi.org\/10.1016\/j.ufug.2024.128409<\/a><\/li>\n\n\n\n
  3. Thapa, B., Hardiman, B., & Fei, S. (2025).<\/strong> Flower color index for detecting and monitoring warm-colored flowering across scales. International Journal of Applied Earth Observation and Geoinformation<\/em>, 145, 104978. https:\/\/doi.org\/10.1016\/j.jag.2025.104978<\/a><\/li>\n\n\n\n
  4. Ou, B., Shao, G., Yang, B., & Fei, S. (2025).<\/strong> FocalSR: Revisiting image super-resolution transformers with Fourier-transform cross attention layers for remote sensing image enhancement. Geomatica<\/em>, 77, 100042. https:\/\/doi.org\/10.1016\/j.geomat.2024.100042<\/a><\/li>\n\n\n\n
  5. Ardohain, C., & Fei, S. (2025).<\/strong> The impacts of training data spatial resolution on deep learning in remote sensing. Science of Remote Sensing<\/em>, 11, 100185. https:\/\/doi.org\/10.1016\/j.srs.2024.100185<\/a><\/li>\n<\/ol>\n<\/div>\n\n\n\n
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