PNNL

Kyle’s research spans a wide range of environmental topics, most of which have a common thread of integrating techniques from the fields of data science, geoinformatics, and remote sensing. Recent examples include integrating machine learning and remote sensing techniques to map invasive grasses and assess grass-wildfire cycles, developing automated remote sensing algorithms to assess damage caused by natural disasters and track changes at high-risk waste sites, and integrating climate-informed modeling and geoinformatics to create decision support frameworks for water-energy management. Other investigative areas of Kyle’s research include landscape and wildfire ecology, conservation and management of sensitive species and habitats, spatial planning for conventional hydropower and offshore renewable energy, environmental effects of hydropower, and environmental contamination monitoring and remediation.

• Geoinformatics and remote sensing
• Multi-modal data fusion
• "Big Earth Data" analytics
• Landscape ecology
• Environmentally sustainable energy development

• M.S., Environmental Science, Washington State University, Pullman, 2009
• B.S., Wildlife Biology, University of Idaho, 2004