Ms. Karine Adeline Profile

Karine Adeline

at ONERA

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Proceedings Article | 19 October 2023 Presentation + Paper

Sensitivity analysis of Sentinel-2 data for urban tree characterization using DART model

Théo Le Saint, Sidonie Lefebvre, Laurence Hubert-Moy, Jean Nabucet, Karine Adeline

Proceedings Volume 12735, 127350H (2023) https://doi.org/10.1117/12.2680259

KEYWORDS: Vegetation, 3D modeling, Simulations, Chlorophyll, Buildings, Sensors

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The estimation of vegetation traits, which is essential to characterize the health of trees from remote sensing data, presents several challenges in urban environments, due to the topography of 3D buildings and associated shading, the spectral diversity of materials, or the variety of urban morphology. Moreover, the difficulty to estimate the vegetation traits increases with the decrease of spatial resolution, mixed pixels including information on trees and their environment. The objective of this study is to estimate the influence of tree-endogenous (chlorophyll, LAI...) and tree-exogenous (urban form, tree distance to buildings, street orientation, solar angles, material types...) factors on the reflectance of Sentinel-2 pixels (10/20 m resolution). For this, a sensitivity analysis was carried out with the DART 3D radiative transfer model. First, a design of experiments was built using 15 variables describing the trees and their environment. Four urban 3D scenes that were elaborated based on the Local Climate Zone (LCZ) typology. For each of these urban 3D scenes, 3000 simulations were generated. Then, Sobol indices were computed to estimate the influence of each factor on the Sentinel-2 reflectance, more specifically on the ten spectral bands and eight vegetation indices correlated to vegetation traits. These experiments were conducted on isolated and aligned trees. In addition, the influence of the geo-registration uncertainty of the Sentinel-2 products was assessed in comparing the results obtained using a single tree-centered pixel with those using pixels offset from the tree. Results showed that Sentinel-2 data at 10 m resolution, NDVI et ARVI indices are the most relevant for the estimation of vegetation traits both for isolated and aligned trees, especially in LCZ five and eight, and in using a single tree-centered pixel approach.

Proceedings Article | 17 October 2023 Presentation + Paper

Adequacy of Mediterranean forest simulations from DART radiative transfer model and UAV laser scanning data to hyperspectral images

Marianne Debue, Grégoire Vincent, Samuel Alleaume, Florian de Boissieu, Xavier Briottet, Jean-Baptiste Féret, Jean-Philippe Gastellu-Etchegorry, Jean-Marc Limousin, Damien Longepierre, Karine Adeline

Proceedings Volume 12727, 127270U (2023) https://doi.org/10.1117/12.2678531

KEYWORDS: Simulations, Unmanned aerial vehicles, Reflectivity, Radiative transfer, Data modeling, Optical properties, Hyperspectral imaging, Laser scanners, LIDAR, Remote sensing

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Mediterranean-type ecosystems are among the most affected by global climate change due to an increase in droughts and fires. Sentinel-2 satellites are currently among the best alternative for operational vegetation properties monitoring because of their temporal revisit and global coverage. The increasing availability of spaceborne imaging spectrometer (e.g. DESIS, PRISMA, EnMAP) and the preparation of missions ensuring global accessibility (e.g. CHIME, BIODIVERSITY) will enable the estimation of vegetation traits with better accuracies. The SENTHYMED project aims to study the complementarity between multi- and hyperspectral images to evaluate Mediterranean forest functional traits. The objective is to estimate canopy pigment, leaf water and dry matter contents from physical model inversion using DART radiative transfer model. A preliminary step is to study the influence of DART optical properties parametrization on remote sensing image simulation in order to simulate scenes as accurately as possible. Two forests in the South of France, mainly composed of evergreen oaks and pubescent oaks, with heterogeneous canopy structure, were studied. UAV LiDAR data were first acquired and converted into voxel matrices of plant area density values with AMAPVox. Pytools4dart was then used to build the mock-ups, handle DART parameterization and generate images in spectral reflectance unit at canopy level. Several simulations were implemented, assigning different optical properties to the underground and to the canopy. These images were compared to airborne AVIRIS-Next generation acquisitions, acquired close to the field campaign that took place in June 2021 and where in-situ measurements were collected for calibration and validation of DART simulations.

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