Dr. Alba Viana Soto

• Remote sensing
• Artificial intelligence
• Spatial data analysis
• Disturbance and resilience
• Forest, fire and landscape ecology

Viana-Soto, A., Okujeni, A., Pflugmacher, D., García, M., Aguado, I., & Hostert, P. (2022). Quantifying post-fire shifts in woody-vegetation cover composition in Mediterranean pine forests using Landsat time series and regression-based unmixing. Remote Sensing of Environment, 281, 113239. https://doi.org/10.1016/j.rse.2022.113239

Viana-Soto, A., García, M., Aguado, I., & Salas, J. (2022). Assessing post-fire forest structure recovery by combining LiDAR data and Landsat time series in Mediterranean pine forests. International Journal of Applied Earth Observation and Geoinformation, 108, 102754. https://doi.org/10.1016/j.jag.2022.102754

Moreno-Fernandez, D., Viana-Soto, A., Camarero, J. J., Zavala, M. A., Tijerín, J., & Garcia, M. (2021). Using spectral indices as early warning signals of forest dieback: The case of drought-prone Pinus pinaster forests. Science of The Total Environment, 793, 148578. https://doi.org/10.1016/j.scitotenv.2021.148578

García, M., North, P., Viana-Soto, A., Stavros, N. E., Rosette, J., Martín, M. P., ... & Zhao, K. (2020). Evaluating the potential of LiDAR data for fire damage assessment: A radiative transfer model approach. Remote Sensing of Environment, 247, 111893. https://doi.org/10.1016/j.rse.2020.111893

Viana-Soto, A., Aguado, I., Salas, J., & García, M. (2020). Identifying post-fire recovery trajectories and driving factors using landsat time series in fire-prone mediterranean pine forests. Remote Sensing, 12(9), 1499. https://doi.org/10.3390/rs12091499