Optical remote sensing-based forest volume precision optimization evaluation system and method

Abstract

The application discloses a forest stock volume precision optimization evaluation system and method based on optical remote sensing, and belongs to the technical field of big data processing, and comprises the following steps: obtaining multi-source optical remote sensing images of a target forest area under different satellite scanning angle conditions, performing quality screening and angle conversion processing on the images to obtain to-be-stitched images, performing image stitching and generating a target complete image, and extracting a remote sensing feature set of the image. The target complete image is divided into regions, and a stock volume region type is determined according to the remote sensing features; a conventional inversion method is used to estimate the forest stock volume in a medium-low stock volume region, and the remote sensing features are selectively optimized in a high stock volume region, inversion parameters are constructed, and a forest stock volume estimation result is output, so that the problem that the sensitivity and the distinguishing ability of the remote sensing features to high stock volume change obviously decrease in the prior art, and then the inversion precision of the forest stock volume in the high stock volume region is low and the feature effectiveness is insufficient is solved.

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