Dr. Mkaouar is interested in radiative transfer modeling for 3D vegetation structure and topography mapping, with a focus on simulating airborne and spaceborne imagery and LiDAR data (e.g. DART model https://dart.omp.eu/#/). Her research covers various areas related to vegetation structure estimation, such as leaf area index (LAI) and leaf inclination estimation from LiDAR data. Currently, she is working on the fusion of stereo photogrammetry and LiDAR data to refine vegetation structure estimation, aiming to improve accuracy in forest and landscape analysis.
Ameni Mkaouar
(Post-Doctoral Research Associate)
| Email: | ameni.mkaouar@nasa.gov |
| Org Code: | 618 |
| Address: |
NASA/GSFC Mail Code 618 Greenbelt, MD 20771 |
| Employer: | UNIVERSITY OF MARYLAND BALTIMORE CO |
Brief Bio
Current Projects
Surface, Topography and Vegetation
Remote Sensing
https://science.nasa.gov/earth-science/decadal-surveys/decadal-stv/
Positions/Employment
Post-Doctoral Research Associate
GESTAR II UMBC/ NASA GSFC - MD, USA
March 2023 - Present
Education
- PhD, computer system engineering, Image and signal processing, ENIS, University of Sfax, Tunisia, 2022
- Engineering degree, Telecommunication, ENET'COM, University of Sfax, Tunisia, 2017
- Bachelor's degree, Mathematics, 2012
Selected Publications
Refereed
2024. "Spaceborne Lidar and Stereogrammetry Data Fusion to Predict Aboveground Biomass in Tropical Forests." IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium 3592-3595 [10.1109/igarss53475.2024.10641914] [Proceedings]
2024. "Vegetation Height Stereo Reconstruction With BlackSky Commercial Frame Camera Imagery." IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium 116 2446-2450 [10.1109/igarss53475.2024.10642757] [Proceedings]
2024. "Leaf properties estimation enhancement over heterogeneous vegetation by correcting for terrestrial laser scanning beam divergence effect." Remote Sensing of Environment 302 113959 [10.1016/j.rse.2023.113959] [Journal Article/Letter]
2023. "Modeling forest canopy surface retrievals using very high-resolution spaceborne stereogrammetry: (II) optimizing acquisition configurations." Remote Sensing of Environment 298 113824 [10.1016/j.rse.2023.113824] [Journal Article/Letter]
2023. "Modeling forest canopy surface retrievals using very high-resolution spaceborne stereogrammetry: (I) methods and comparisons with actual data." Remote Sensing of Environment 298 113825 [10.1016/j.rse.2023.113825] [Journal Article/Letter]
2021. "Joint Estimation of Leaf Area Density and Leaf Angle Distribution Using TLS Point Cloud for Forest Stands." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 11095-11115 [10.1109/jstars.2021.3120521] [Journal Article/Letter]
Talks, Presentations and Posters
Other
2023
Accurate geolocation of spaceborne LiDAR data is essential for precise surface modeling and ecological assessments, particularly in complex forested regions. Geolocation errors of the Global Ecosystem Dynamics Investigation (GEDI) caused by sensor position and pointing error, present challenges for science applications in forested areas. Despite several efforts employing global-scale corrections and exploring the relationship between these error and canopy height metrics, persistent residual errors of~ 10 m remain. These inaccuracies impact data analysis and interpretation, emphasizing the necessity for continuous research to enhance measurement reliability in such complex sites.