Publications

2024

Bryson, M., Ravendran, A., Mercier, C., Frickey, T., Jayathunga, S., Pearse, G. and Hartley, R.J., 2024. Domain adaptation of deep neural networks for tree part segmentation using synthetic forest trees. ISPRS Open Journal of Photogrammetry and Remote Sensing, p.100078. https://doi.org/10.1016/j.ophoto.2024.100078

Hartley, J. L., Jayathunga, S, Morgenroth, J, Pearse, G. D. (2024).  Tree Branch Characterisation from Point Clouds: a Comprehensive Review. Remote Sensing. Current Forestry Reports 2024.  https://doi.org/10.1007/s40725-024-00225-5

Watt, M. S., Jayathunga, S., Hartley, R.J.L., Pearse, G.D., Massam, P.D., Cajes, D., Steer, B.S.C. and Estarija, H.J.C. (2024).  Use of a Consumer-Grade UAV Laser Scanner to Identify Trees and Estimate Key Tree Attributes across a Point Density Range. Forests 15 (6), 899  https://doi.org/10.3390/f15060899

Watt, M. S., Estarija, H. J. C., Bartlett, M., Main, R., Pasquini, D., Yorston, W., McLay, E., Zhulanov, M., Dobbie, K., Wardhaugh, K., Hossain, Z., Fraser, S. & Buddenbaum, H., (2024). Early Detection of Myrtle Rust on Pōhutukawa Using Indices Derived from Hyperspectral and Thermal Imagery. In: Remote Sensing. 16, 6, 1050. https://doi.org/10.3390/rs16061050

Watt, M.S.; Holdaway, A.; Watt, P.; Pearse, G.D.; Palmer, M.E.; Steer, B.S.C.; Camarretta, N.; McLay, E.; Fraser, S. (2024). Early Prediction of Regional Red Needle Cast Outbreaks Using Climatic Data Trends and Satellite-Derived Observations. Remote Sens. 2024, 16, 1401. https://doi.org/10.3390/rs16081401

Watt, M. S., de Silva, D., Estarija, H. J. C., Yorston, W., & Massam, P. (2024). Detecting the short-term effects of water stress on radiata pine physiology using thermal imagery. Forests, 15(1), Article 28. https://doi.org/10.3390/f15010028

2023

Watt, M. S., Poblete, T., de Silva, D., Estarija, H. J. C., Hartley, R. J. L., Leonardo, E. M. C., Massam, P., Buddenbaum, H., & Zarco-Tejada, P. J. (2023). Prediction of the severity of Dothistroma needle blight in radiata pine using plant based traits and narrow band indices derived from UAV hyperspectral imagery. Agricultural and Forest Meteorology, 330, Article 109294. https://doi.org/10.1016/j.agrformet.2022.109294

Ewane, E. B., Mohan, M., Bajaj, S., Galgamuwa, G. A. P., Watt, M. S., Arachchige, P. P., Hudak, A. T., Richardson, G., Ajithkumar, N., Srinivasan, S., Corte, A. P. D., Johnson, D. J., Broadbent, E. N., de-Miguel, S., Bruscolini, M., Young, D. J. N., Shafai, S., & Abdullah, M. M. (2023). Climate-change-driven droughts and tree mortality: Assessing the potential of UAV-derived early warning metrics. Remote Sensing, 15(10), Article 2627. https://doi.org/10.3390/rs15102627

Jayathunga, S., Pearse, G. D., & Watt, M. S. (2023). Unsupervised methodology for large-scale tree seedling mapping in diverse forestry settings using UAV-based RGB imagery. Remote Sensing, 15(22), Article 5276. https://doi.org/10.3390/rs15225276

Lawrence, J., Wreford, A., Blackett, P., Hall, D., Woodward, A., Awatere, S., Livingston, M. E., Macinnis-Ng, C., Walker, S., Fountain, J., Costello, M. J., Ausseil, A. G. E., Watt, M. S., Dean, S. M., Cradock-Henry, N. A., Zammit, C., & Milfont, T. L. (2023). Climate change adaptation through an integrative lens in Aotearoa New Zealand. Journal of the Royal Society of New Zealand. https://doi.org/10.1080/03036758.2023.2236033

Watt, M. S., & Moore, J. R. (2023). Modeling spatial variation in radiata pine slenderness (height/diameter ratio) and vulnerability to wind damage under current and future climate in New Zealand. Frontiers in Forests and Global Change, 6, Article 1188094. https://doi.org/10.3389/ffgc.2023.1188094

2022

Hartley, R., Jayathunga, S., Massam, P., De Silva, D., Estarija, H. J., Davidson, S., Wuraola, A., & Pearse, G. (2022). Assessing the potential of backpack-mounted mobile laser scanning systems for tree phenotyping. Remote Sensing, 14(14), Article 3344. https://doi.org/10.3390/rs14143344

Conference presentations

Remote sensing cluster group 2024

  1. Introduction by Michael Watt (Scion)
  2. Early prediction of regional red needle cast outbreaks using climatic data trends and satellite-derived observations by Andrew Holdaway (Indufor)
  3. AI solutions in forestry: Monitoring growth and dynamics throughout a full rotation by Chaplin Chan (Indufor)
  4. Aerial surveying services: A supplier's perspective by Steve Smith (Aerial Surveys)
  5. ForestInsights: Mapping New Zealand's forests through deep learning and data-centric AI by Melanie Palmer (Scion)
  6. The challenges of quantifying woody debris using machine learning by David Herries (Interpine)
  7. Assessing forestry plantations with UAVs: A comparative study of laser scanning and photogrammetry by Robin Hartley and Sadeepa Jayathunga (Scion)
  8. Enhancing seedling detection in New Zealand forestry: A multi-datastream approach by Blake Singleton (University of Canterbury)
  9. Virtual reality in thinning training. Interactive forest system design by Elizaveta Graevskaya (Scion) and Claire Stewart (Forest Growers Research)
  10. Spatial comparisons of carbon and volume for radiata pine and 10 alternative exotic species using recently developed tools by Michael Watt (Scion) and Jamie Heather (Carbon Critical)
  11. Ferntech: New Zealand’s Drone Experts by Cody Stevens (Ferntech)
  12. Applications of spectral and thermal data by Russell Main (Scion)

For other publications and resources, see the Tools for Foresters website.