TY - GEN
T1 - Forest Height Mapping Combining GEDI, ALOS-2, Sentinel-1/2, and Ancillary Data
AU - Pereira-Pires, João E.
AU - Guerra-Hernández, Juan
AU - Silva, João Manuel das Neves
AU - Fonseca, José M.
AU - Guida, Raffaella
AU - Mora, André
N1 - info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00239%2F2020/PT#
info:eu-repo/grantAgreement/FCT/OE/2020.05015.BD/PT#
Funding information:
Research Units, Centre of Technology and Systems – Uninova (UIDB/00066/2020), Forest Research Centre (UIDB/00239/2020), and the Surrey Space Centre at University of Surrey. João Eduardo Pereira-Pires acknowledges the Fundação para a Ciência e Tecnologia for the Ph.D. Grant 2020.05015.BD
Publisher Copyright:
© 2024 IEEE.
PY - 2024/9/5
Y1 - 2024/9/5
N2 - The impacts of the climate change in the society make forest monitoring increasingly important. Consequently, there is a growing interest in mapping variables as the Forest Height (FH). The direct measurement of the FH through field campaigns is expensive and difficult to scale. Alternatively, Airborne Laser Scanning (ALS) campaigns can be used to map it, however they share the same disadvantages of the previous approach. Therefore, Remote Sensing (RS) data have been used for local and large-scale mapping of the FH. In this paper a Regression Methodology (RM) that combines GEDI, ALOS-2, Sentinel-1/2, and ancillary data is proposed for mapping the FH in Mediterranean forests. The proposed RM, tested for the 15 regions of interest, achieves a RMSE/rRMSE of 4.95m/33.93%, when evaluated with GEDI data, and 5.11m/41.70%, when evaluated with ALS data.
AB - The impacts of the climate change in the society make forest monitoring increasingly important. Consequently, there is a growing interest in mapping variables as the Forest Height (FH). The direct measurement of the FH through field campaigns is expensive and difficult to scale. Alternatively, Airborne Laser Scanning (ALS) campaigns can be used to map it, however they share the same disadvantages of the previous approach. Therefore, Remote Sensing (RS) data have been used for local and large-scale mapping of the FH. In this paper a Regression Methodology (RM) that combines GEDI, ALOS-2, Sentinel-1/2, and ancillary data is proposed for mapping the FH in Mediterranean forests. The proposed RM, tested for the 15 regions of interest, achieves a RMSE/rRMSE of 4.95m/33.93%, when evaluated with GEDI data, and 5.11m/41.70%, when evaluated with ALS data.
KW - ALOS-2
KW - Forest Height
KW - GEDI
KW - LiDAR
KW - Mediterranean Forests
KW - Multispectral
KW - Sentinel-1/2
KW - Synthetic Aperture Radar
UR - http://www.scopus.com/inward/record.url?scp=85204880820&partnerID=8YFLogxK
U2 - 10.1109/IGARSS53475.2024.10640684
DO - 10.1109/IGARSS53475.2024.10640684
M3 - Conference contribution
AN - SCOPUS:85204880820
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 5337
EP - 5340
BT - IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024
Y2 - 7 July 2024 through 12 July 2024
ER -
