Research Assistant
Center for Global Change and Earth Observations
Department of Geography, Environment, and Spatial Sciences
Michigan State University
Email: qiuyuean@msu.edu
Links: ResearchGate; Google Scholar
CV: CV_Yuean_Qiu.pdf
Manly Miles Building, 1405 S. Harrison Rd, Room 101, East Lansing, MI 48824
I'm in my fourth year as a Ph.D. student in Prof. David Roy's lab at the Center for Global Change and Earth Observations at Michigan State University. I'm working on forest cover loss detection and future fire forecasting using multi-source geospatial datasets.
Geography
Land use and land cover change
Future Fire Forecasting
Deforestation and Forest Degradation
Time Series Change Detection
2022 - Present Ph.D. Student, Geography, Michigan State University, USA
2019 - 2022 M.Sc., Cartography and Geography Information System, Beijing Normal Univeristy, China
2015 - 2019 B.Sc., Geographical Information Science, Sun Yat-sen University, China
2025 Computational Modeling, Dept. of Computational Mathematics, Science and Engineering, Michigan State University, USA
Huang, H., Roy, D. P., De Lemos, H., Qiu, Y., & Zhang, H. K. (2025). A global Swin-Unet Sentinel-2 surface reflectance-based cloud and cloud shadow detection algorithm for the NASA Harmonized Landsat Sentinel-2 (HLS) dataset. Science of Remote Sensing, 100213.
Liu, H., Zhang, H. K., Huang, B., Yan, L., Tran, K. K., Qiu, Y., Zhang, X., & Roy, D. P. (2024). Reconstruction of seamless harmonized Landsat Sentinel-2 (HLS) time series via self-supervised learning. Remote Sensing of Environment, 308, 114191.
Gu, Z., Chen, J., Chen, Y., Qiu, Y., Zhu, X., & Chen, X. (2023). Agri-Fuse: A novel spatiotemporal fusion method designed for agricultural scenarios with diverse phenological changes. Remote Sensing of Environment, 299, 113874.
Liu, S., Zhou, J., Qiu, Y., Chen, J., Zhu, X. and Chen, H. (2022). The FIRST model: Spatiotemporal fusion incorporating spectral autocorrelation. Remote Sensing of Environment, 279, 113111.
Chen, H., Qiu, Y., Yin, D., Chen, J., Chen, X., Liu, S. and Liu, L. (2022). Stacked spectral feature space patch: An advanced spectral representation for precise crop classification based on Convolutional Neural Network. The Crop Journal, 10(5), 1460–69.
Zhou, J., Qiu, Y., Chen, J. and Chen, X. (2021). A geometric misregistration resistant data fusion approach for adding red-edge (RE) and short-wave infrared (SWIR) bands to high spatial resolution imagery. Science of Remote Sensing, 4, 100033.
Qiu, Y., Zhou, J., Chen, J. and Chen, X. (2021). Spatiotemporal fusion method to simultaneously generate full-length normalized difference vegetation index time series (SSFIT). International Journal of Applied Earth Observation and Geoinformation, 100, 102333.
Zhou, J., Chen, J., Chen, X., Zhu, X., Qiu, Y., Song, H., Rao, Y., Zhang, C., Cao, X. and Cui, X. (2021). Sensitivity of six typical spatiotemporal fusion methods to different influential factors: A comparative study for a normalized difference vegetation index time series reconstruction. Remote Sensing of Environment, 252, 112130.
Chen, G., Li, X., Liu, X., Chen, Y., Liang, X., Leng, J., Xu, X., Liao, W., Qiu, Y.A., Wu, Q. and Huang, K. (2020). Global projections of future urban land expansion under shared socioeconomic pathways. Nature Communications, 11(1), 1-12.
Chen, S., Zhang, X., Lin, J., Huang, J., Zhao, D., Yuan, T., Huang, K., Luo, Y., Jia, Z., Zang, Z. and Qiu, Y.A. (2019). Fugitive road dust PM2. 5 emissions and their potential health impacts. Environmental science & technology, 53(14), 8455-8465.
Qiu, Y., Roy, D.P., Zhang, H.K., Huang, H., Self-supervised explainable deep learning to forecast future fire at monthly, seasonal, and annual, landscape scale, Poster in Advancements in Wildland Fire Science, Management, and Engagement: Integrating Earth Observation Technologies and Collaborative Development, Fall Meeting, AGU, New Orleans LA, 14 - 19 December 2025.
Qiu, Y., Roy, D.P., Zhang, H.K., Huang, H., Self-supervised deep learning Forest Cover Loss detection over the Conterminous United States, Poster in Earth Observation–Based Methods to Monitor Forest Loss, Degradation, and Growth, Fall Meeting, AGU, Washington D.C., 9-13 December 2024.
Qiu, Y., Roy, D., Zhang, H., Huang, H., & Liu, H., Self-supervised Deep Learning Experiments to Detect Forest Cover Loss Using Landsat Time Series, Poster in Advances in Characterizing and Monitoring Land System Change Using Remote Sensing Data I eLightning, Fall Meeting, AGU, San Francisco CA, 11 - 15 December 2023.
