Linking canopy images to forest structural parameters: potential of a modeling framework
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Remote sensing methods, and in particular very high (metric) resolution optical imagery, are essential assets to obtain forest structure data that cannot be measured from the ground because they are too difficult to measure or because the areas to sample are too large or inaccessible.
To understand what kind of, and how precisely and accurately, information on forest structure can be inverted from RS data, we propose a modeling framework allowing to produce forest canopy images for any type of forest based on basic inventory data.
This framework combines a simple 3D forest model named “Allostand,” based on empirically or theoretically derived diameter at breast height distributions and allometry rules, with a well-established radiative transfer model, discrete anisotropic radiative transfer.
Resulting simulated images appear of good realism for textural analysis. The potential of the approach for the development of quantitative methods to assess forest structure, dynamics, matter and energy budgets, and degradation, including in tropical contexts, is illustrated emphasizing broad-leaved natural forests.
Consequently, this theoretical framework appears as a valuable component for developing inversion methods from canopy images and studying their sensitivity to structural and instrumental effects.
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- Linking canopy images to forest structural parameters: potential of a modeling framework
Annals of Forest Science
Volume 69, Issue 2 , pp 305-311
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- Texture analysis
- Remote sensing
- 3D forest model
- Radiative transfer model
- Broad-leaved tropical forests
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