WE4.R18.5
Characterizing Coastal Forest Change Using Landsat Time Series and Aerial Images with Deep Learning
Yuwei Fu, Zijun Yang, University of North Carolina Wilmington, United States
Session:
WE4.R18: Forest and Vegetation VI Oral
Track:
Land Applications
Location:
Monroe
Presentation Time:
Wednesday, 12 August, 17:15 - 17:30
Session Chair:
Fuzhong Weng, Nanjing University of Information Science and Technology
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Session WE4.R18
WE4.R18.1: Leveraging GEDI waveform lidar for next-generation fire behavior modeling
Birgit Peterson, US Geological Survey, United States; Seth Price, KBR, United States; Ethan Ilse, C2G, United States; Katelyn Woolfrey, KBR, United States
WE4.R18.2: FROM MULTI-MODAL SENTINEL FEATURES TO FOUNDATION EMBEDDINGS: RETRIEVAL OF FOREST DIAMETER AT BREAST HEIGHT USING SWEDISH NATIONAL FOREST INVENTORY DATA AND MACHINE LEARNING
Samet Aksoy, Elif Sertel, Istanbul Technical University, Türkiye; Johan E.S. Fransson, Linnaeus University, Sweden
WE4.R18.3: A HYDRAULIC FOREST MODEL DRIVEN BY TOMOGRAPHIC RADAR AT P-BAND
Patrik J. Bennet, Albert R. Monteith, Chalmers University of Technology, Sweden; Jose Gutierres Lopez, Swedish University of Agricultural Sciences, Sweden; Lars M. H. Ulander, Chalmers University of Technology, Sweden
WE4.R18.4: EVALUATION OF MICROWAVE LAND EMISSIVITY MODEL USING GROUND-BASED MEASUREMENTS
YiDan Wang, Nanjing University of Information Science and Technology, China; WenYing He, Institute of Atmospheric Physics, Chinese Academy of Sciences, China; Fuzhong Weng, CMA Earth System Modeling and Prediction Centre, China; MinZheng Duan, Institute of Atmospheric Physics, Chinese Academy of Sciences, China
WE4.R18.5: Characterizing Coastal Forest Change Using Landsat Time Series and Aerial Images with Deep Learning
Yuwei Fu, Zijun Yang, University of North Carolina Wilmington, United States
Resources
No resources available.