Peter Massam



Peter is a Geospatial Technologist who specialises in the acquisition, processing and analysis of Accurate LiDAR and Photogrammetry of Vegetation via Terrestrial or Aerial means. Peter has an extensive surveying and engineering history and spent several decades in the NZDF in a wide variety of roles. He is an experienced UAV  Pilot and is currently Scions Part 102 primary person responsibility for UAS operations. He has 30 years of Geospatial Software experience, in a multitude of environments.


  • Higher School Certificate, Twizel Area School, 1988
  • National Certificates in Leadership, Adult Education, Surveying and Computing

Research capabilities

  • Use of UAVs to acquire remote sensed data
  • Terrestrial and GNSS surveying
  • Geospatial data processing and analysis
  • BIG Data management
  • Climate and meteorology sensor use

Career highlights

  • Surveying in the Auckland Islands with the RNZN
  • Deploying to Afghanistan with the NZDF
  • Flying a UAV over a fire research experiment in Rakaia Gorge
  • Processing an incredibly dense colorised native forest LiDAR dataset
  • Night flying with UAVs and thermal vision

Selected papers

Hartley RJL, Leonardo EM, Massam P, Watt MS, Estarija HJ, Wright L, Melia N, Pearse GD. An Assessment of High-Density UAV Point Clouds for the Measurement of Young Forestry Trials. Remote Sensing. 2020; 12(24):4039.

Watt, M.S., Buddenbaum, H., Leonardo, E.M.C., Estarija, H.J.C., Bown, H.E., Gomez-Gallego, M., Hartley, R., Massam, P., Wright, L., Zarco-Tejada, P.J., 2020. Using hyperspectral plant traits linked to photosynthetic efficiency to assess N and P partition. ISPRS Journal of Photogrammetry and Remote Sensing 169, 406–420.. doi:10.1016/j.isprsjprs.2020.09.006.

Watt, M. S., Buddenbaum, H., Leonardo, E. M. C., Estarija, H. J., Bown, H. E., Gomez-Gallego, M., Hartley, R. J., Pearse, G. D., Massam, P., & Wright, L. (2020). Monitoring biochemical limitations to photosynthesis in N and P-limited radiata pine using plant functional traits quantified from hyperspectral imagery. Remote Sensing of Environment, 248, 112003.