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Juan Carlos Laso Bayas (Ecuador) joined IIASA's Ecosystems Services and Management Program (ESM) in September 2015 as a research scholar. He currently works with the Earth Observation Systems group (EOS) and his current scientific interests include the use of GIS and spatial statistics, more specifically mixed models, to analyze remote sensing data, aiming to contribute to agricultural production, food security, disaster management and community resilience.
Dr. Laso Bayas obtained his BSc in Agricultural Sciences at Zamorano University, Honduras, specializing in Socio-Economic Development and Environment. He continued his studies in Germany where he obtained his MSc in Agricultural Sciences in the Tropics and Subtropics as well as his PhD in Agricultural Sciences at the University of Hohenheim, Stuttgart. For his dissertation, he studied the mega-tsunami event of December 2004 and combined remote sensing, GIS and generalized linear mixed models in order to relate land use, distance to the sea and topography to tsunami casualties and damages in West-Aceh, Indonesia and the Seychelles.
His experience includes: Advanced tropical agriculture and forestry field work at Zamorano; field technician at a joint USAID-Zamorano rehabilitation project after hurricane Mitch (1998) in south Honduras; student counselor at Zamorano; research assistant for the Illinois Natural History Survey at the University of Illinois Urbana-Champaign; technical assistant for the Illinois Crop Improvement Association, Champaign, USA and researcher in cooperation with the World Agroforestry Centre (ICRAF-SEA), at Bogor, Indonesia.
Before joining IIASA's ESM Program, Dr. Laso Bayas worked as a statistical consultant for the Biostatistics Institute, at the University of Hohenheim, Stuttgart under the supervision of Prof. Dr. Hans-Peter Piepho.
Last update: 11-SEP-2015
Danylo O, Moorthy I, Sturn T, See L, Laso Bayas J-C, Domian D, Fraisl D, Giovando C, et al. (2018). The Picture Pile Tool for Rapid Image Assessment: A Demonstration using Hurricane Matthew. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4: 27-32. DOI:10.5194/isprs-annals-IV-4-27-2018.
Lesiv M, Fritz S, Laso Bayas JC, Dürauer M, Domian D, See L, McCallum I, Danylo O, et al. (2018). Global Field Sizes Dataset for Ecosystems Modeling. In: European Geosciences Union General Assembly 2018, 9-13 April 2018, Vienna, Austria.
Moorthy I, Sturn T, Fraisl D, Karner M, Laso Bayas JC, See L, & McCallum I (2018). FotoQuest Go: A citizen science tool for in-situ land use and land cover monitoring. In: European Geosciences Union General Assembly 2018, 9-13 April 2018, Vienna, Austria.
Lesiv M, See L, Laso Bayas JC, Sturn T, Schepaschenko D, Karner M, Moorthy I, McCallum I, et al. (2018). Characterizing the Spatial and Temporal Availability of Very High Resolution Satellite Imagery for Monitoring Applications. Earth System Science Data Discussions: 1-24. DOI:10.5194/essd-2018-13. (In Press)
Schepaschenko D, Fritz S, See L, Laso Bayas JC, Lesiv M, Kraxner F, & Obersteiner M (2017). Comment on “The extent of forest in dryland biomes”. Science 358 (6362): eaao0166. DOI:10.1126/science.aao0166.
Laso Bayas JC, Lesiv M, Waldner F, Schucknecht A, Duerauer M, See L, Fritz S, Fraisl D, et al. (2017). A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform. Scientific Data 4: e170136. DOI:10.1038/sdata.2017.136.
Moorthy I, See L, Fritz S, McCallum I, Perger C, Dürauer M, Dresel C, Sturn T, et al. (2017). Crowd-driven tools for the calibration and validation of Earth Observation products. In: Earth Observation Open Science 2017 Conference, 25-28 September 2017, Frascati, Italy.
Laso Bayas JC, See L, Perger C, Justice C, Nakalembe C, Dempewolf J, & Fritz S (2017). Validation of Automatically Generated Global and Regional Cropland Data Sets: The Case of Tanzania. Remote Sensing 9 (8): e815. DOI:10.3390/rs9080815.
See L, Laso Bayas JC, Schepaschenko D, Perger C, Dresel C, Maus V, Salk C, Weichselgartner J, et al. (2017). LACO-Wiki: A New Online Land Cover Validation Tool Demonstrated Using GlobeLand30 for Kenya. Remote Sensing 9 (7): e754. DOI:10.3390/rs9070754.
See L, Dunwoody A, Laso Bayas J C, Moorthy I, McCallum I, Fritz S, Jacques D, & Waldner F (2016). Global Agricultural Monitoring Systems: Current Gaps and Possible Solutions. In: Global Land Project 3rd Open Science Meeting (GLPOSM16), 24-27 October 2016, China National Convention Center, Beijing, China.
McCallum I, Liu W, See L, Mechler R, Keating A, Hochrainer-Stigler S, Mochizuki J, Fritz S, et al. (2016). Technologies to Support Community Flood Disaster Risk Reduction. International Journal of Disaster Risk Science 7 (2): 198-204. DOI:10.1007/s13753-016-0086-5.
Moorthy I, Fritz S, See L, McCallum I, & Laso Bayas J C (2016). LandSense: A Citizen Observatory and Innovation Marketplace for Land Use and Land Cover Monitoring. In: The First International ECSA Citizen Science Conference 2016, May 19 – 21, 2016, Berlin, Germany.
Laso Bayas JC, See L, Fritz S, Sturn T, Perger C, Dürauer M, Karner M, Moorthy I, et al. (2016). Crowdsourcing In-Situ Data on Land Cover and Land Use Using Gamification and Mobile Technology. Remote Sensing 8 (11): e905. DOI:10.3390/rs8110905.
Laso-Bayas JC, See L, Fritz S, Sturn T, Karner M, Perger C, Dürauer M, Mondel T, et al. (2016). Assessing the quality of crowdsourced in-situ land-use and land cover data from FotoQuest Austria application. In: European Geosciences Union (EGU) General Assembly 2016, 17–22 April 2016, Vienna, Austria.
Liu W, McCallum I, See L, Dugar S, & Laso-Bayas JC (2016). Digital technologies in support of flood resilience: A case study for Nepal. In: European Geosciences Union (EGU) General Assembly 2016, 17–22 April 2016, Vienna, Austria.
See L, McCallum I, Liu W, Keating A, Hochrainer-Stigler S, Mochizuki J, Fritz S, Dugar S, et al. (2016). The potential of crowdsourcing and mobile technology to support flood disaster risk reduction. In: European Geosciences Union (EGU) General Assembly 2016, 17–22 April 2016, Vienna, Austria.
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