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|Title:||Estimation of Leaf Area Index and Chlorophyll for a Mediterranean Grassland Using Hyperspectral Data|
|Authors:||DARVISHZADEH Roshanak; SKIDMORE Andrew; SCHLERF Martin; ATZBERGER Clement; CORSI Fabio; CHO Moses|
|Citation:||Proceedings of the ISPRS Conference 2008 Beijing - The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences vol. 37 p. 471-477|
|Type:||Articles in periodicals and books|
|Abstract:||The study shows that leaf area index (LAI) and canopy chlorophyll content can be mapped in a heterogeneous Mediterranean grassland from canopy spectral reflectance measurements. Canopy spectral measurements were made in the field using a GER 3700 spectroradiometer, along with concomitant in situ measurements of LAI and chlorophyll content. We tested the utility of univariate techniques, involving narrow band vegetation indices and the red edge inflection point, as well as multivariate calibration techniques, such as partial least squares regression. Among the various investigated models, canopy chlorophyll content was estimated with the highest accuracy (R2cv = 0.74, relative RMSEcv = 0.35) and LAI was estimated with intermediate accuracy (R2cv = 0.67). Compared with narrow band indices and red edge inflection point, partial least squares regression generally improved the estimation accuracies. The results of the study highlight the significance of using multivariate techniques such as partial least squares regression rather than univariate methods such as vegetation indices for providing enhanced estimates of heterogeneous grass canopy characteristics. To date, partial least squares regression has seldom been applied for studying heterogeneous grassland canopies. However, it can provide a useful exploratory and predictive tool for mapping and monitoring heterogeneous grasslands.|
|JRC Institute:||Space, Security and Migration|
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