Publications

UAV spray articles

Chyrva, I., Jermy, M., Strand, T., Richardson, B. 2022. Evaluation of the pattern of spray released from a moving multicopter. Pest Management Science. https://doi.org/10.1002/ps.7320

Richardson, B., Rolando, C.A., Kimberley, M.O. 2020. Quantifying spray deposition from a UAV configured for ‘spot’ spray applications to individual plants. Trans ASABE 63(4): 1049-1058

Richardson, B., Rolando, C. A., Kimberley, M. O., & Strand, T. M. (2019a). Spray Application Efficiency from a Multi-Rotor Unmanned Aerial Vehicle Configured for Aerial Pesticide Application. Transactions of the ASABE, 62(6), 1447-1453.

Richardson, B., Rolando, C. A., Somchit, C., Dunker, C., Strand, T. M., & Kimberley, M. O. (2019b). Swath pattern analysis from a multi‐rotor unmanned aerial vehicle configured for pesticide application. Pest Management Science. https://doi.org/10.1002/ps.5638

UAV articles

Hartley, R.J.L., Henderson, I.L. and Jackson, C.L., 2022. BVLOS Unmanned Aircraft Operations in Forest Environments. Drones, 6(7), p.167.
https://doi.org/10.3390/drones6070167

Hartley, R.J., Jayathunga, S., Massam, P.D., De Silva, D., Estarija, H.J., Davidson, S.J., Wuraola, A. and Pearse, G.D., 2022. Assessing the Potential of Backpack-Mounted Mobile Laser Scanning Systems for Tree Phenotyping. Remote Sensing, 14(14), p.3344.
https://doi.org/10.3390/rs14143344

Lipwoni, V., Watt, M.S., Hartley, R.J., Leonardo, E.M.C. and Morgenroth, J., 2022. A comparison of photogrammetric software for deriving structure-from-motion 3D point clouds and estimating tree heights. NZ Journal of Forestry, 66(4), p.19.

Christensen, B., Herries, D., Hartley, R. J., & Parker, R. (2021). UAS and smartphone integration at wildfire management in Aotearoa New Zealand. New Zealand Journal of Forestry Science, 51.
https://doi.org/10.33494/nzjfs512021x127x
Fire, UAVs
Peer-reviewed article

Dash, J. P., Pearse, G. D., & Watt, M. S. (2018). UAV multispectral imagery can complement satellite data for monitoring forest health. Remote Sensing, 10(8), 1216.
https://doi.org/10.3390/rs10081216
Multispectral, UAVs
Peer-reviewed article

Dash, J. P., Watt, M. S., & Hartley, R. J. L. (2019). Testing UAV-borne Riegl Mini VUX-1 scanner for phenotyping a mature genetics trial.
https://gcff.nz/publications/technical-notes
UAVs, lidar
Industry Report

Dash, J. P., Watt, M. S., Paul, T. S., Morgenroth, J., & Hartley, R. (2019). Taking a closer look at invasive alien plant research: A review of the current state, opportunities, and future directions for UAVs. Methods in Ecology and Evolution, 10(12), 2020-2033.
https://doi.org/10.1111/2041-210x.13296
UAVs, invasive species
Peer-reviewed article

Dash, J. P., Watt, M. S., Paul, T. S., Morgenroth, J., & Pearse, G. D. (2019). Early detection of invasive exotic trees using UAV and manned aircraft multispectral and LiDAR Data. Remote Sensing, 11(15), 1812.
https://doi.org/10.3390/rs11151812
Multispectral, UAVs, invasive species, lidar
Peer-reviewed article

Dash, J. P., Watt, M. S., Pearse, G. D., Heaphy, M., & Dungey, H. S. (2017). Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak. ISPRS Journal of Photogrammetry and Remote Sensing, 131, 1-14.
https://doi.org/10.1016/j.isprsjprs.2017.07.007
Multispectral, UAVs
Peer-reviewed article

Hartley, R. (2017). Unmanned aerial vehicles in forestry-reaching for a new perspective. NZ Journal of Forestry, 62(1), 31-39.
http://nzjf.org.nz/abstract.php?volume_issue=j62_1&first_page=31
https://nzjf.org.nz/free_issues/NZJF62_1_2017/F831A30F-9517-423e-AAD1-E9146F9206C9.pdf
UAVs, forestry
Peer-reviewed article

Hartley, R., Melia, N., Estarija, H. J., Watt, M. S., Pearse, G., Massam, P., Wright, L. A. H., & Stovold, G. T. (2019). An assessment of UAV laser scanning and photogrammetric point clouds for the measurement of young forestry trials. In Proceedings of the Growing Confidence in Forestry’s Future Research Programme, Wellington, New Zealand, 15–16 October 2019.
https://scionforestryfuture.files.wordpress.com/2019/08/gcff-tn028.pdf
UAVs, lidar, SfM
Industry Report

Hartley, R. J., Leonardo, E. M., Massam, P., Watt, M. S., Estarija, H. J., Wright, L., Melia, N., & Pearse, G. D. (2020). An Assessment of High-Density UAV Point Clouds for the Measurement of Young Forestry Trials. Remote Sensing, 12(24), 4039.
https://doi.org/10.3390/rs12244039
UAVs, lidar, SfM
Peer-reviewed article

Heaphy, M., Watt, M. S., Dash, J. P., & Pearse, G. D. (2017). UAVs for data collection-plugging the gap. New Zealand Journal of Forestry, 62(1), 23-30.
http://www.nzjf.org.nz/free_issues/NZJF62_1_2017/606FE0D3-CF7F-4f82-A080-42ED7B9B3423.pdf
UAVs, forestry
Peer-reviewed article

Morley, C. G., Broadley, J., Hartley, R., Herries, D., MacMorran, D., & McLean, I. G. (2017). The potential of using Unmanned Aerial Vehicles (UAVs) for precision pest control of possums (Trichosurusvulpecula). Rethinking Ecology, 2, 27.
https://doi.org/10.3897/rethinkingecology.2.14821
UAVs, invasive species
Peer-reviewed article

Pearse, G. D., Tan, A. Y., Watt, M. S., Franz, M. O., & Dash, J. P. (2020). Detecting and mapping tree seedlings in UAV imagery using convolutional neural networks and field-verified data. ISPRS Journal of Photogrammetry and Remote Sensing, 168, 156-169.
https://ui.adsabs.harvard.edu/link_gateway/2020JPRS..168..156P/doi:10.1016/j.isprsjprs.2020.08.005
UAVs, deep learning
Peer-reviewed article

Pearse, G. D., Watt, M. S., Dash, J. P., Stone, C., & Caccamo, G. (2019). Comparison of models describing forest inventory attributes using standard and voxel-based lidar predictors across a range of pulse densities. International Journal of Applied Earth Observation and Geoinformation, 78, 341-351.  doi:10.1016/j.jag.2018.10.008
https://doi.org/10.1016/j.jag.2018.10.008
Lidar, voxels
Peer-reviewed article

Puliti, S., Dash, J. P., Watt, M. S., Breidenbach, J., & Pearse, G. D. (2020). A comparison of UAV laser scanning, photogrammetry and airborne laser scanning for precision inventory of small-forest properties. Forestry: An International Journal of Forest Research, 93(1), 150-162.
https://doi.org/10.1093/forestry/cpz057
UAVs, lidar, SfM
Peer-reviewed article