MO4.P1.4
MITIGATING UNDERESTIMATION OF FIRE EMISSIONS FROM THE ADVANCED HIMAWARI IMAGER: A MACHINE LEARNING AND MULTI-SATELLITE ENSEMBLE APPROACH
Yoojin Kang, Jungho Im, Ulsan National Institute of Science and Technology, Korea (South)
Session:
MO4.P1: Improved Characterization and Understanding of Wildfires and their Environmental Impacts using Satellite Data and Artificial Intelligence II Oral
Track:
Community-Contributed Sessions
Location:
Room P1
Presentation Time:
Monday, 4 August, 16:30 - 16:45
Presentation
Discussion
Resources
No resources available.
Session MO4.P1
MO4.P1.1: A TWO-LAYER CANOPY REFLECTANCE MODEL FOR ESTIMATING LIVE FUEL MOISTURE CONTENT
Jingyu Zhang, Rui Jiang, Xingwen Quan, University of Electronic Science and Technology of China, China
MO4.P1.2: FOREST WILDFIRE DETECTION BASED ON GEO-KOMPSAT-2A REMOTE SENSING IMAGERY DATA AND FUEL MOISTURE CONTENT
Jingyu Zhang, Rui Jiang, University of Electronic Science and Technology of China, China
MO4.P1.3: PRE- AND POST-FIRE AIRBORNE LIDAR TO QUANTIFY CANOPY AND GROUND BIOMASS COMBUSTION FOLLOWING FIRE IN A PERMAFROST-PEATLAND COMPLEX
Laura Chasmer, Linda Flade, Emily Jones, Kailyn Nelson, University of Lethbridge, Canada; William Quinton, Wilfrid Laurier University, Canada; Chris Hopkinson, University of Lethbridge, Canada
MO4.P1.4: MITIGATING UNDERESTIMATION OF FIRE EMISSIONS FROM THE ADVANCED HIMAWARI IMAGER: A MACHINE LEARNING AND MULTI-SATELLITE ENSEMBLE APPROACH
Yoojin Kang, Jungho Im, Ulsan National Institute of Science and Technology, Korea (South)
MO4.P1.5: LIVE FUEL MOISTURE CONTENT FORECASTING BY INTEGRATING REMOTE SENSING AND WEATHER DATA
Qinglong Jia, Xingwen Quan, University of Electronic Science and Technology of China, China
Resources
No resources available.