JRSRP Publications 2019
Journal Articles
Fisher, A., Armston, J., Goodwin, N., Scarth, P. (2020) Modelling canopy gap probability, foliage projective cover and crown projective cover from airborne lidar metrics in Australian forests and woodlands. Remote Sensing of Environment. doi: 10.1016/j.rse.2019.111520
Fisher, A., Hesse, P.P., (2020) The response of vegetation cover and dune activity to rainfall, drought and fire observed by multitemporal satellite imagery. Earth Surface Processes and Landforms. doi: 10.1002/esp.4721
Flood, N., Watson, F., and Collett, L. (2019) Using a U-net convolutional neural network to map woody vegetation extent from high resolution satellite imagery across Queensland, Australia. International Journal of Applied Earth Observation and Geoinformation 82:101897. doi: 10.1016/j.jag.2019.101897.
Heim, R.H.; Wright, I.J.; Scarth, P.; Carnegie, A.J.; Taylor, D.; Oldeland, J. (2019) Multispectral, Aerial Disease Detection for Myrtle Rust (Austropuccinia psidii) on a Lemon Myrtle Plantation. Drones 2019, 3, 25. doi: https://doi.org/10.3390/drones3010025
Lymburner, L., Bunting, P., Lucas, R., Scarth, P., Alam, I., Phillips, C., Ticehurst, C., and Held, A. (2019) Mapping the multi-decadal mangrove dynamics of the Australian coastline. Remote Sensing of Environment: 111185. doi: https://doi.org/10.1016/j.rse.2019.05.004.
Melville, Bethany, Adrian Fisher, and Arko Lucieer. (2019) Ultra-high spatial resolution fractional vegetation cover from unmanned aerial multispectral imagery. International Journal of Applied Earth Observation and Geoinformation 78:14-24. doi: https://doi.org/10.1016/j.jag.2019.01.013.
Scarth, Peter, John Armston, Richard Lucas, and Peter Bunting. (2019) A Structural Classification of Australian Vegetation Using ICESat/GLAS, ALOS PALSAR, and Landsat Sensor Data. Remote Sensing 11 (2):147. doi: https://doi.org/10.3390/rs11020147.
Tu, Y. H., K. Johansen, S. Phinn, and A. Robson. (2019) Measuring canopy structure and condition using multi-spectral UAS imagery in a horticultural environment. Remote Sensing 11 (3). doi: 10.3390/rs11030269.