Forest stock prediction method and system

Abstract

The application discloses a forest stock prediction method and system, relates to the technical field of image data processing, and comprises the following steps: acquiring a target area SAR image through an L-band synthetic aperture radar to generate a polarization interference coherence matrix; combining a digital elevation model to extract a terrain slope and a local incident angle, and calculating a polarization azimuth offset; performing model polarization decomposition based on the coherence matrix and the offset to estimate a pure body de-coherence coefficient and a body scattering intensity; combining an interference fringe diagram to introduce a three-layer S-RVoG model to invert a forest canopy height and an extinction coefficient, and constructing a multi-dimensional feature vector; inputting the multi-dimensional feature vector into a pre-training model to output a forest stock prediction result, so that the technical effect of reducing the influence of terrain and improving prediction accuracy is achieved.

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