Dr Luz Angelica Suarez
Adjunct Senior Research Fellow - Applied Agricultural Remote Sensing Centre
Phone: +61 6773 1832
Email: lsuarezc@une.edu.au
Biography
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 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.
Qualifications
BEng Land Surveying, Master in Advanced Multipurpose Cadastre, PhD in Agriculture Remote Sensing
Research Interests
Agricultural systems, time series analysis, remote sensing, statistics
Publications
Suarez, L.A., Robson, A., McPhee, J. et al. Accuracy of carrot yield forecasting using proximal hyperspectral and satellite multispectral data. Precision Agric (2020).
Suarez, L. A., McPhee, J., O'Halloran. J., Van Sprang. C., Robson, A., (2018). Literature review of adoption of precision system technologies in vegetable production Hort Innovation, Adoption of precision systems technology in vegetable production (VG16009).
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
Consultancy Interests
Agriculture, Remote sensing, land administration, GIS, cartography
External Profiles
Related Links
In October 2020 Angelica, on behalf of AARSC was invited speak about remote sensing technologies support monitoring activities during COVID-19. The event was organised by OEFA (Peru) in representation of the Regional Environmental Compliance and Enforcement Network (Red Lafica) . Participant countries included: Argentina, Brasil, Bolivia, Colombia, Costa Rica, Chile, Ecuador, El Salvador, Guatemala, Honduras, México, Panamá, Paraguay, Perú and Dominican Republic
Angelica presented experiences AARSC has had mapping crops at the National level, the relevance of that information when it is linked to official warning systems and the distribution channel to industries during natural disasters or biosecurity threats.
Watch webinar (please note this webinar was delivered in Spanish)