TY - GEN
T1 - Exploring Canopy Temperature and Height Dynamics in Forest Ecosystems
AU - Shaik, Riyaaz Uddien
AU - Thangavel, Kathiravan
AU - Jallu, Sriram Babu
AU - Spiller, Dario
AU - Sabatini, Roberto
AU - Zeng, Weiping
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Within this study, we examined the correlation between tree canopy temperature, canopy height, and vegetation types. Furthermore, we conducted a similar analysis in the southern region of the island of Sardinia, renowned for its dense forests and frequent wildfires. We successfully mapped the vegetation types in the region using PRISMA hyperspectral data and the SVM classifier with an accuracy of over 80% for all classes. We utilized Random Forest Regression on Sentinel-1 SAR data, Sentinel-2 multispectral data, and the SRTM DEM to determine the canopy heights of various plant classes. Our estimation had an RMSE of 2.9176 meters and an R2 of 0.791. In addition, we used the MODIS LST and emissivity product regardless of Land Use and Land Cover (LULC) type to calculate the ground surface temperature. Using LST measurements over tree canopies, we identified a correlation between canopy temperature and corresponding canopy heights as well as vegetation types for five vegetation types, including evergreen oak, olive, juniper, silicicole, and riparian trees. For various vegetation types, the results and graph demonstrate that lower tree canopy temperatures corresponded to higher tree canopies, with a range of -0.4 to -0.5.
AB - Within this study, we examined the correlation between tree canopy temperature, canopy height, and vegetation types. Furthermore, we conducted a similar analysis in the southern region of the island of Sardinia, renowned for its dense forests and frequent wildfires. We successfully mapped the vegetation types in the region using PRISMA hyperspectral data and the SVM classifier with an accuracy of over 80% for all classes. We utilized Random Forest Regression on Sentinel-1 SAR data, Sentinel-2 multispectral data, and the SRTM DEM to determine the canopy heights of various plant classes. Our estimation had an RMSE of 2.9176 meters and an R2 of 0.791. In addition, we used the MODIS LST and emissivity product regardless of Land Use and Land Cover (LULC) type to calculate the ground surface temperature. Using LST measurements over tree canopies, we identified a correlation between canopy temperature and corresponding canopy heights as well as vegetation types for five vegetation types, including evergreen oak, olive, juniper, silicicole, and riparian trees. For various vegetation types, the results and graph demonstrate that lower tree canopy temperatures corresponded to higher tree canopies, with a range of -0.4 to -0.5.
KW - canopy heights
KW - climate change
KW - Earth observation
KW - estimation
KW - forestry
KW - GEDI
KW - machine learning
KW - random forest regression
KW - SAR
UR - http://www.scopus.com/inward/record.url?scp=85185817841&partnerID=8YFLogxK
U2 - 10.1109/MetroXRAINE58569.2023.10405767
DO - 10.1109/MetroXRAINE58569.2023.10405767
M3 - Conference contribution
AN - SCOPUS:85185817841
T3 - 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 - Proceedings
SP - 706
EP - 710
BT - 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023
Y2 - 25 October 2023 through 27 October 2023
ER -