Retrieval of Forest Stand Age From SAR Image Texture for Varying Distance and Orientation Values of the Gray Level Co-Occurrence Matrix - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Geoscience and Remote Sensing Letters Année : 2014

Retrieval of Forest Stand Age From SAR Image Texture for Varying Distance and Orientation Values of the Gray Level Co-Occurrence Matrix

Résumé

Data on forest variables (e.g., biomass, trunk height, density) are necessary for environmental and forest management applications. It has been shown that texture can be used instead of the usual σ/age relationships at P-band to retrieve plantation forest parameters, but the analysis of σ spatial characteristics has not been fully explored. The aim of this letter is to investigate the relationships between stand age (which is correlated to forest variables) and texture descriptors calculated from statistics generated by the gray-level co-occurrence matrix for varying distance d, and orientation α, values used to calculate the matrix. Synthetic aperture radar images are P-band airborne data acquired by the ONERA RAMSES instrument over a controlled homogeneous test site located in the Landes region, France. It is found that texture descriptors contrast, inverse difference moment, homogeneity, and correlation are strongly influenced by the parameters (d, α) related to forest stand structure (forest rows, stand density) and image resolution. In contrast, energy and entropy are observed to be highly correlated to stand age and displayed a stable performance whatever the distance and orientation parameters (d, α), thus rendering them a good contender as an alternative to the usual σ based relationships applied to this type of forest.
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Dates et versions

hal-00903994 , version 1 (13-11-2013)

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Isabelle Champion, Christian Germain, Jean-Pierre da Costa, Arnaud Alborini, Pascale Dubois-Fernandez. Retrieval of Forest Stand Age From SAR Image Texture for Varying Distance and Orientation Values of the Gray Level Co-Occurrence Matrix. IEEE Geoscience and Remote Sensing Letters, 2014, 11 (1), pp.5-9. ⟨10.1109/LGRS.2013.2244060⟩. ⟨hal-00903994⟩
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