Stuart Green
Uid: 2930
http://www.agresearch.teagasc.ie/rerc/staff/green_s.asp
https://ie.linkedin.com/pub/stuart-green/3b/419/96a
https://earthobservation.wordpress.com/
Remote Sensing (RS) Specialist. Since 1998 Stuart has been the RS Specialist in Teagasc. As part of the National Indicative Soil Map project he was jointly responsible for the production of the Teagasc Landcover and Habitat maps. Stuart is the EOINET National Contact Point for Land use, and he currently supervises 4 PhD students in conjunction with UCD and UCC.
Current Externally Funded Projects:
Co-investigator for RS work packages in ILMO: Remote sensed driven project to characterise Irish agricultural parcels using OSI Prime 2 objects
Lead Investigator for the RS work package in the IdealHNV project: Using object based machine learning techniques for the “whole farm” characterisation of high nature value farmland from high resolution optical data
Lead Investigator for the RS packages in the CforRep project: Using signal processing techniques to detect small scale, sub-pixel forest change/disturbance in Ireland
Lead Investigator in Biomass Retieval in Ireland using Active Remote Sensing (BRIAR): Using new TerrsarX staring spotlight mode data to estiamte above ground biomass in Hedgerows
Lead Investigator- SatGrass, Using MODIS time series data to estimate grass growth rates and management regimes in Irish Intensive Grasslands
Last 5 Publications:
Raab, C., Barrett, B., Cawkwell, F., & Green, S. (2015). Evaluation of multi-temporal and multi-sensor atmospheric correction strategies for land-cover accounting and monitoring in Ireland. Remote Sensing Letters, 6(10), 784-793. doi: 10.1080/2150704x.2015.1076950
Ali, I., Cawkwell, F., Green, S., & Dwyer, N. (2014, 13-18 July 2014). Application of statistical and machine learning models for grassland yield estimation based on a hypertemporal satellite remote sensing time series. Paper presented at the Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International.
Barrett, B., Nitze, I., Green, S., & Cawkwell, F. (2014). Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches. Remote sensing of environment, 152(September 2014), 109-124. doi: http://dx.doi.org/10.1016/j.rse.2014.05.018
Black, K., Green, S., Mullooly, G., & Povida, A. (2014). Towards a national hedgerow biomass inventory for the LULUCF sector using LiDAR remote sensing (2010-CCRP-DS-1.1): Final report. Dublin: EPA.
Zimmermann, J., O'Brien, P., Green, S., Gonzales Del Campo, A., Jones, M., & Stout, J. (2014). Assessing land-use change in Ireland using the Land-Parcel Identification System. Paper presented at the EGU General Assembly Conference Abstracts.
Current Externally Funded Projects:
Co-investigator for RS work packages in ILMO: Remote sensed driven project to characterise Irish agricultural parcels using OSI Prime 2 objects
Lead Investigator for the RS work package in the IdealHNV project: Using object based machine learning techniques for the “whole farm” characterisation of high nature value farmland from high resolution optical data
Lead Investigator for the RS packages in the CforRep project: Using signal processing techniques to detect small scale, sub-pixel forest change/disturbance in Ireland
Lead Investigator in Biomass Retieval in Ireland using Active Remote Sensing (BRIAR): Using new TerrsarX staring spotlight mode data to estiamte above ground biomass in Hedgerows
Lead Investigator- SatGrass, Using MODIS time series data to estimate grass growth rates and management regimes in Irish Intensive Grasslands
Last 5 Publications:
Raab, C., Barrett, B., Cawkwell, F., & Green, S. (2015). Evaluation of multi-temporal and multi-sensor atmospheric correction strategies for land-cover accounting and monitoring in Ireland. Remote Sensing Letters, 6(10), 784-793. doi: 10.1080/2150704x.2015.1076950
Ali, I., Cawkwell, F., Green, S., & Dwyer, N. (2014, 13-18 July 2014). Application of statistical and machine learning models for grassland yield estimation based on a hypertemporal satellite remote sensing time series. Paper presented at the Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International.
Barrett, B., Nitze, I., Green, S., & Cawkwell, F. (2014). Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches. Remote sensing of environment, 152(September 2014), 109-124. doi: http://dx.doi.org/10.1016/j.rse.2014.05.018
Black, K., Green, S., Mullooly, G., & Povida, A. (2014). Towards a national hedgerow biomass inventory for the LULUCF sector using LiDAR remote sensing (2010-CCRP-DS-1.1): Final report. Dublin: EPA.
Zimmermann, J., O'Brien, P., Green, S., Gonzales Del Campo, A., Jones, M., & Stout, J. (2014). Assessing land-use change in Ireland using the Land-Parcel Identification System. Paper presented at the EGU General Assembly Conference Abstracts.