On the creation of high spatial resolution imaging spectroscopy data from multi-temporal low spatial resolution imagery

2017 ◽  
Author(s):  
Wei Yao ◽  
Jan van Aardt ◽  
David Messinger
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Imaging Spectroscopy ◽  
Spectroscopy Data ◽  
Multi Temporal ◽  
Resolution Imaging ◽  
The Creation ◽  
High Spatial Resolution Imaging

scholarly journals Impact of Tree Crown Transmittance on Surface Reflectance Retrieval in the Shade for High Spatial Resolution Imaging Spectroscopy: A Simulation Analysis Based on Tree Modeling Scenarios

2021 ◽  
Vol 13 (5) ◽  
pp. 931
Author(s):  
Karine R. M. Adeline ◽  
Xavier Briottet ◽  
Sidonie Lefebvre ◽  
Nicolas Rivière ◽  
Jean-Philippe Gastellu-Etchegorry ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Atmospheric Correction ◽  
Imaging Spectroscopy ◽  
Surface Reflectance ◽  
Tree Crown ◽  
Area Index ◽  
Resolution Imaging ◽  
Voxel Grid ◽  
High Spatial Resolution Imaging

With the advancement of high spatial resolution imaging spectroscopy, an accurate surface reflectance retrieval is needed to derive relevant physical variables for land cover mapping, soil, and vegetation monitoring. One challenge is to deal with tree shadows using atmospheric correction models if the tree crown transmittance Tc is not properly taken into account. This requires knowledge of the complex radiation mechanisms that occur in tree crowns, which can be provided by coupling the physical modeling of canopy radiative transfer codes (here DART) and the 3D representations of trees. First in this study, a sensitivity analysis carried out on DART simulations with an empirical 3D tree model led to a statistical regression predicting Tc from the tree leaf area index (LAI) and the solar zenith angle with good performances (RMSE ≤ 4.3% and R2 ≥ 0.91 for LAI ≤ 4 m2.m−2). Secondly, more realistic 3D voxel-grid tree models derived from terrestrial LiDAR measurements over two trees were considered. The comparison of DART-simulated Tc from these models with the previous predicted Tc over 0.4–2.5 µm showed three main sources of inaccuracy quoted in order of importance: (1) the global tree geometry shape (mean bias up to 21.5%), (2) the transmittance fraction associated to multiple scattering, Tscat (maximum bias up to 13%), and (3) the degree of realism of the tree representation (mean bias up to 7.5%). Results showed that neglecting Tc leads to very inaccurate reflectance retrieval (mean bias > 0.04), particularly if the background reflectance is high, and in the near and shortwave infrared – NIR and SWIR – due to Tscat. The transmittance fraction associated to the non-intercepted transmitted light, Tdir, can reach up to 95% in the SWIR, and Tscat up to 20% in the NIR. Their spatial contributions computed in the tree shadow have a maximum dispersion of 27% and 8% respectively. Investigating how to approximate Tdir and Tscat spectral and spatial variability along with the most appropriate tree 3D modeling is crucial to improve reflectance retrieval in tree shadows when using atmospheric correction models.

Lung Adenocarcinoma at CT with 0.25-mm Section Thickness and a 2048 Matrix: High-Spatial-Resolution Imaging for Predicting Invasiveness

Radiology ◽  
2020 ◽  
Vol 297 (2) ◽  
pp. 462-471 ◽  
Author(s):  
Masahiro Yanagawa ◽  
Mitsuko Tsubamoto ◽  
Yukihisa Satoh ◽  
Akinori Hata ◽  
Tomo Miyata ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Section Thickness ◽  
Resolution Imaging ◽  
High Spatial Resolution Imaging
Sign in / Sign up

Export Citation Format

Share Document