Forest biomass estimation method combining polarization radar and satellite-borne laser radar

By combining the random forest interpolation algorithm with ICESat-2 satellite-borne lidar and polarimetric SAR data, continuous satellite-borne lidar features were generated, which solved the data discontinuity problem, constructed a high-precision forest biomass prediction model, and achieved accurate estimation of forest biomass over a large area.

CN120630232APending Publication Date: 2025-09-12NAT UNIV OF DEFENSE TECH
0 Cites 1 Cited by

Patent Information

Application Number
CN202510763491.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-09
Publication Date
2025-09-12

AI Technical Summary

Technical Problem

Existing technologies make it difficult to effectively couple the discrete data of the ICESat-2 satellite-borne lidar with polarimetric SAR data, resulting in spatial discontinuity in forest biomass distribution mapping and limiting the realization of high-precision estimates.

Method used

By combining geographic location information and polarimetric SAR backscatter coefficients with the random forest interpolation algorithm, a nonlinear mapping relationship between spaceborne lidar and polarimetric SAR features is established, continuous spaceborne lidar features are generated, and the relative importance of features is calculated to select key features to construct a forest biomass prediction model.

Benefits of technology

It achieves large-scale, high-precision continuous spatial inversion of forest biomass, solves the problem of insufficient adaptability of traditional methods in complex forest environments, and improves estimation accuracy.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention relates to a forest biomass estimation method combining a polarization radar and a satellite-borne laser radar. The method comprises the following steps: preprocessing discrete footprint point data and polarimetric SAR data to obtain geographic position information and a polarimetric SAR backscattering coefficient; according to a random forest interpolation algorithm in combination with geographic position information and a polarized SAR backscattering coefficient, a nonlinear mapping relation among space coordinates, polarized SAR features and spaceborne laser radar parameters is automatically established through machine learning, and continuous spaceborne laser radar features are generated; and calculating the relative importance of all the satellite-borne laser radar features and the polarimetric SAR features with the forest biomass, sequentially selecting the features ranked in the top according to a relative importance ranking result to construct a forest biomass prediction model, and realizing forest biomass prediction by using the forest biomass prediction model. By adopting the method, high-precision forest biomass estimation can be realized.
Need to check novelty before this filing date? Find Prior Art