Dr Luz Angelica Suarez
Postdoctoral Research Fellow - Precision Agriculture Research Group (PARG)
Phone: +61 6773 1832
Dr. Luz Angelica Suarez is a postdoctoral research fellow with the Applied Agricultural Remote Sensing Centre (AARSC) and the Precision Agriculture Research Group - PARG at UNE. She has been involved in spatial science and agricultural research since 2005 in diverse industries such as coffee, cotton, grains and vegetable. Her research interest is in developing methodologies that facilitate and increase better management practices by:
·the proper evaluation and adoption of remote sensing technologies
·analysis of environmental variables and crop production factors in yield performance
·crop variability and health assessments
·mapping and generation of spatial data
Her expertise includes hemispherical, LiDAR, hyperspectral and multispectral sensors in proximal and remote platforms applied to land administration and agriculture and a broad range of sophisticated statistical approaches.
Angelica did her bachelor and honorous in Topographic Engineering (BEng Land Surveying) in Colombia and started her career working on coffee at the International Centre for Tropical Agriculture (Cali, Colombia). She did her Masters at the Jaen University (Spain), focusing on developing semi-automatic methodologies for detection of new infrastructure to support land administration decisions and cadastral surveying data updates. She did her PhD in Agriculture Remote Sensing with the University of Southern Queensland (Toowoomba, Australia). Her thesis, funded by the Cotton Research and Development Corporation (CRDC), assessed proximal and remote sensing technologies for predicting yield loss as a result of herbicide drift.
Angelica is leading the establishment of international relationships with highly-ranked research institutions and industries in Latin America due to her understandings of the present needs and challenges of the agricultural systems in this region.
BEng Land Surveying, Master in Advanced Multipurpose Cadastre, Phd in Agriculture Remote Sensing
Agricultural systems, time series analysis, remote sensing, statistics
§Potgieter, A. B., George-Jaeggli, B., Chapman, S. C., Laws, K., Suárez Cadavid, L. A., Wixted, J., . . . Hammer, G. L. (2017). Multi-spectral imaging from an unmanned aerial vehicle enables the assessment of seasonal leaf area dynamics of sorghum breeding lines. Frontiers in Plant Science, 8(1532). doi: 10.3389/fpls.2017.01532. Retrieved from https://www.frontiersin.org/article/10.3389/fpls.2017.01532
§Suarez, L. A., Apan, A., & Werth, J. (2017). Detection of phenoxy herbicide dosage in cotton crops through the analysis of hyperspectral data. International Journal of Remote Sensing, 38(23), 6528-6553. doi: 10.1080/01431161.2017.1362128. Retrieved from http://dx.doi.org/10.1080/01431161.2017.1362128
§Apan, A., Suarez, L. A., Maraseni, T., & Castillo, J. A. (2017). The rate, extent and spatial predictors of forest loss (2000 -2012) in the terrestrial protected areas of the Philippines. Applied Geography, 81, 32-42. doi:10.1016/j.apgeog.2017.02.007
§Suarez, L. A., Apan, A., & Werth, J. (2016). Hyperspectral sensing to detect the impact of herbicide drift on cotton growth and yield. ISPRS Journal of Photogrammetry and Remote Sensing, 120, 65-76. doi: 10.1016/j.isprsjprs.2016.08.004. Retrieved from http://www.sciencedirect.com/science/article/pii/S0924271616302635
Agriculture, Remote sensing, land administration, GIS, cartography