Alumnus (Guest Researcher)
Santos D. Chicas’ project will use machine learning algorithms and social science methods to identify the land use practices, biophysical and socioeconomic factors that increase wildfire vulnerability in the Maya Mountain Massif (MMM) thus, providing information to build more resilience to climate change and in so doing improving wildfire management.
The Maya Mountain Massif (MMM) provides critical ecosystems function (hydrological, soil protection, carbon sequestration, among others) at both national, regional and global levels. However, the MMM is being threatened by the increased frequency of wildfires which are expected to intensify as a result of climate change. In order to develop effective and efficient wildfire management strategies and safeguard the livelihoods of communities surrounding the Maya Mountain Massif there is the need to have a more in-depth understanding of the interplay between the biophysical and socioeconomic factors that influence wildfire vulnerability, which his research intends to address.
Santos was working at the University of Belize in the Faculty of Science and Technology, Natural Resource Management Program as an Assistant Professor from 2017 to 2020. He completed his PhD in Nagasaki University Japan in 2017, for his doctoral work he used Remote Sensing and Geographic Information Systems to study deforestation, forest degradation and erosion in the southern part of Belize.
- Climate Change
- Deforestation and Forest Fires
- Application of Remote Sensing in Natural Resource Management
Modeling wildfire vulnerability and assessing communities’ perspectives on the drivers and impacts of wildfires in the Maya Mountain Massif Belize
Chicas, S. D., Omine, K., Ford, J. B., Sugimura, K., & Yoshida, K. (2017). Using spatial metrics and surveys for the assessment of trans-boundary deforestation in protected areas of the Maya Mountain Massif: Belize-Guatemala border. Journal of Environmental Management, 187, 320-329.
Chicas, S. D., Omine, K., & Saqui, P. (2016). CLASlite algorithms and social surveys to asses and identify deforestation and forest degradation in Toledo’s protected areas and forest ecosystems, Belize. Applied Geography, 75, 144-155.
Chicas, S. D., Omine, K., & Ford, J. B. (2016). Identifying erosion hotspots and assessing communities’ perspectives on the drivers, underlying causes and impacts of soil erosion in Toledo’s Rio Grande Watershed: Belize. Applied Geography, 68, 57-67. doi: 10.1016/j.apgeog.2015.11.010