Back to Search Start Over

End-to-End Simulation for a Forest-Dedicated Full-Waveform Lidar Onboard a Satellite Initialized from Airborne Ultraviolet Lidar Experiments

Authors :
Xiaoxia Shang
Patrick Chazette
Source :
Remote Sensing, Vol 7, Iss 5, Pp 5222-5255 (2015)
Publication Year :
2015
Publisher :
MDPI AG, 2015.

Abstract

In order to study forests at the global scale, a detailed link budget for a lidar system onboard satellite is presented. It is based on an original approach coupling airborne lidar observations and an end-to-end simulator. The simulator is initialized by airborne lidar measurements performed over temperate and tropical forests on the French territory, representing a wide range of forests ecosystems. Considering two complementary wavelengths of 355 and 1064 nm, the end-to-end simulator computes the performance of spaceborne lidar systems for different orbits. The analysis is based on forest structural (tree top height, quadratic mean canopy height) and optical (forest optical thickness) parameters. Although an ultraviolet lidar appears to be a good candidate for airborne measurements, our results show that the limited energy is not favorable for spaceborne missions with such a wavelength. A near infrared wavelength at 1064 nm is preferable, requiring ~100 mJ laser emitted energy, which is in agreement with current and future spaceborne missions involving a lidar. We find that the signal-to-noise ratio at the ground level to extract both the structural and optical parameters of forests must be larger than 10. Hence, considering the presence of clouds and aerosols in the atmosphere and assuming a stationary forest, a good detection probability of 99% can be reached when 4 or 5 satellite revisits are considered for a lidar system onboard the ISS or ICESat, respectively. This concerns ~90% of forest covers observed from the lidar, which have an optical thickness less than 3.

Details

Language :
English
ISSN :
20724292
Volume :
7
Issue :
5
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.b50c9fb0e6694ed6823429183619c913
Document Type :
article
Full Text :
https://doi.org/10.3390/rs70505222