DocumentCode :
3626962
Title :
Physically-based retrievals of Norway spruce canopy variables from very high spatial resolution hyperspectral data
Author :
Zbynek Malenovsky;Lucie Homolova;Pavel Cudlin;Raul Zurita-Milla;Michael E. Schaepman;G.P.W. Jan;Emmanuel Martin;Jean-Philippe Gastellu-SEtchegorry
Author_Institution :
Institute of Systems Biology and Ecology Academy of Sciences of the Czech Republic Brno, Czech Republic
fYear :
2007
fDate :
7/1/2007 12:00:00 AM
Firstpage :
4057
Lastpage :
4060
Abstract :
This study was conducted to answer two research questions: (1) what is the spatial variability of the leaf optical properties between 400-1600 nm (hemispherical-directional reflectance, transmittance, absorption) within young Norway spruce crowns, and (2) how to design a suitable physically-based approach retrieving the total chlorophyll content of a complex coniferous canopy from very high spatial resolution (0.4 m) hyperspectral data? It was proved that sun-exposed needles of current age-class statistically differ (alpha-level = 0.01) from rest of the needles in reflectance between 510-760 nm. Last four age-classes of sun-exposed needles were also found to be significantly different from almost all age-classes of sun-shaded needles in transmittance from 760-1350 nm. An operational estimation of chlorophyll a+b content (Cab) from an airborne AISA Eagle hyperspectral image was proposed by means of a PROSPECT-DART inversion employing an artificial neural network (ANN). A spatial pattern of estimated Cab was successfully validated against the Cab map produced by a vegetation index ANCB650-720. Coefficients of determination (R2) between ground measured and retrieved Cab were 0.81 and 0.83, respectively, with root mean square errors (RMSE) of 2.72 mug cm-2 for ANN and 3.27 mug cm-2 for ANCB650-720.
Keywords :
"Information retrieval","Spatial resolution","Hyperspectral imaging","Needles","Artificial neural networks","Reflectivity","Absorption","Optical design","Content based retrieval","Vegetation mapping"
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International
ISSN :
2153-6996
Print_ISBN :
978-1-4244-1211-2
Electronic_ISBN :
2153-7003
Type :
conf
DOI :
10.1109/IGARSS.2007.4423740
Filename :
4423740
Link To Document :
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