WE2.P1.3
On the role of Interferometric SAR features in deriving forest parameters using Deep Learning
Daniel Carcereri, Paola Rizzoli, Luca Dell’Amore, German Aerospace Center, Germany; Stefano Tebaldini, Politecnico di Milano, Italy
Session:
WE2.P1: Forest Mapping with Radar and Lidar II Oral
Track:
Land Applications
Location:
Room P1
Presentation Time:
Wednesday, 6 August, 11:00 - 11:15
Presentation
Discussion
Resources
No resources available.
Session WE2.P1
WE2.P1.1: Relations Between P- to L-band Radar Tomography and Sub-Daily Tree Water Dynamics in a Boreal Forest
Patrik J. Bennet, Albert R. Monteith, Theresa Leistner, Lars M. H. Ulander, Chalmers University of Technology, Sweden
WE2.P1.2: UNDERSTANDING VEGETATION WATER CONTENT DRIVEN CLOSURE PHASES USING TOWER-BASED OBSERVATIONS
Yan Yuan, Delft University of Technology, Netherlands; Albert Monteith, Lars Ulander, Chalmers University of Technology, Sweden; Paco López-Dekker, Delft University of Technology, Netherlands
WE2.P1.3: On the role of Interferometric SAR features in deriving forest parameters using Deep Learning
Daniel Carcereri, Paola Rizzoli, Luca Dell’Amore, German Aerospace Center, Germany; Stefano Tebaldini, Politecnico di Milano, Italy
WE2.P1.4: Forest Canopy Height Mapping Using GEDI-SAWA and Sentinel-1/2 with Deep Learning Model in Northeast China
Chuan Wang, Wenjian Ni, Zhiyu Zhang, Zhifeng Guo, State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute, China
WE2.P1.5: An Open-Access Cross-Modal Forest Benchmark Training Dataset With Sentinel-1 and Lidar data
Hossein Aghababaei, University of Twente, Netherlands; Giampaolo Ferraioli, Gilda Schirinzi, Universita degli Studi di Napoli “Parthenope,”, Italy; Erkki Tomppo, University of Helsinki, Finland; Jaan Parks, Aalto University, Finland
Resources
No resources available.