Matthew Noonan

I am an Msc student researching the capacity for remote-sensing based approaches to effectively assess the success of ecological restoration efforts. A goal of this research is development of a standardized model that can be applied to a variety of different contexts to provide accurate results with affordability and access in mind. The development of Unmanned Aerial Systems presents a unique opportunity for remote sensing to accurately measure phenomena that previously suffered from technological limitations.

Publications

1. Zhang, C., Atkinson, P. M., George, C., Wen, Z., Diazgranados, M., & Gerard, F. (2020). Identifying and mapping individual plants in a highly diverse high-elevation ecosystem using UAV imagery and deep learning. ISPRS Journal of Photogrammetry and Remote Sensing, 169, 280-291.
2. Robinson, J. M., Harrison, P. A., Mavoa, S., & Breed, M. F. (2022). Existing and emerging uses of drones in restoration ecology. Methods in Ecology and Evolution, 13(9), 1899-1911.
3. Buters, T. M., Bateman, P. W., Robinson, T., Belton, D., Dixon, K. W., & Cross, A. T. (2019). Methodological ambiguity and inconsistency constrain unmanned aerial vehicles as a silver bullet for monitoring ecological restoration. Remote Sensing, 11(10), 1180.
4. Vali, A., Comai, S., & Matteucci, M. (2020). Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review. Remote Sensing, 12(15), 2495.