Projects per year
Abstract
In this study, we evaluate the potential of deep learning models in predicting forest tree height in boreal forest zone using ESA Sentinel-1 and Sentinel-2 images. The performance of studied deep learning models is compared to several popular conventional machine learning approaches. The study area is located near Hyytiala forestry station in Finland, and represents a conifer-dominated mixed boreal forestland. Improved predictions were obtained when using combined optical and SAR data for all studied models. Our results indicate that UNet based models can achieve better accuracy in predicting forest tree heights (RMSE of 1.90m, mathrm{R}{2} of 0.69), compared to traditional parametric and machine learning models with RMSE range of 2.27-2.41m and mathrm{R}{2} range of 0.50-0.56 when satellite optical and radar data are combined.
Original language | English |
---|---|
Title of host publication | IGARSS 2022 |
Subtitle of host publication | 2022 IEEE International Geoscience and Remote Sensing Symposium |
Publisher | IEEE Institute of Electrical and Electronic Engineers |
Pages | 5688-5691 |
ISBN (Electronic) | 978-1-6654-2792-0, 978-1-6654-2791-3 |
ISBN (Print) | 978-1-6654-2793-7 |
DOIs | |
Publication status | Published - 28 Sept 2022 |
MoE publication type | A4 Article in a conference publication |
Event | 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, Malaysia Duration: 17 Jul 2022 → 22 Jul 2022 |
Publication series
Series | IEEE International Geoscience and Remote Sensing Symposium Proceedings |
---|---|
Volume | 2022-July |
ISSN | 2153-6996 |
Conference
Conference | 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 |
---|---|
Country/Territory | Malaysia |
City | Kuala Lumpur |
Period | 17/07/22 → 22/07/22 |
Keywords
- boreal
- deep learning
- forest height
- forest management
- Sentinel-1
- Sentinel-2
- synthetic aperture radar
Fingerprint
Dive into the research topics of 'Deep Learning Models in Forest Mapping Using Multitemporal SAR and Optical Satellite Data'. Together they form a unique fingerprint.Projects
- 1 Finished
-
MULTICO: Autonomous Sensing using Satellites, Multicopters, Sensors and Actuators
Lönnqvist, A. (Manager), Rantakari, P. (Participant), Näsilä, A. (Participant) & Korkalo, O. (Participant)
1/04/20 → 30/10/22
Project: Business Finland project