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Scientia Silvae Sinicae ›› 2022, Vol. 58 ›› Issue (4): 152-164.doi: 10.11707/j.1001-7488.20220416

• Research papers • Previous Articles     Next Articles

Forest Canopy Height Retrieval Based on TanDEM-X Data and Improved Three-Stage Inversion Algorithm

Guofei Zhang,Wanqiu Zhang,Cairong Yue*   

  1. Forestry College, Southwest Forestry University Kunming 650224
  • Received:2020-12-28 Online:2022-04-25 Published:2022-07-20
  • Contact: Cairong Yue

Abstract:

Objective: The random-volume-over-ground(RVoG) model has been widely used to estimate forest height through polarimetric and interferometric synthetic aperture radar(PolInSAR) technology, and the three-stage inversion algorithm is a geometrical method to solve the RVoG model to estimate forest canopy height. However, the conventional three-stage algorithm, solving the ground phase and the volume coherence based on the polarization scattering features of L and P bands, could not accurately estimate the ground phase and the volume coherence with TanDEM-X data of X-band. In practical application, the establishment conditions of the model were difficult to strictly meet, and due to the influences of terrain, the accuracy of tree overestimation was not high. In order to improve the estimation accuracy, an improved method of forest height retrieval based on RVoG model and TanDEM-X SAR data was proposed in this paper. Method: The research object was Pinus kesiya var. langbianensis pure forest and P. kesiya var. langbianensis mixed forest in Simao district of Pu'er city. The experimental method included: (1) the classical three-stage inversion algorithm, (2) the improved three-stage inversion algorithm with the ground phase optimization, (3) the improved three-stage inversion algorithm with the volume coherence optimization and (4) the improved three-stage inversion algorithm with underestimation compensation. Result: The results showed that: the forest height of method (1) based on RVoG model was underestimated (r=0.11, bias=-26.20 m and RMSE=7.16 m); the estimation accuracy of method (2), (3) and (4) was better than that of method (1), in which method (4) was the best(r=0.79, bias=-1.69 m and RMSE=2.56 m); the estimation effect of P. kesiya var. langbianensis pure forest (r=0.81, bias=1.40 m and RMSE=2.27 m) was better than that of P. kesiya var. langbianensis mixed forest (r=0.72, bias=-3.09 m and RMSE=2.87 m). Conclusion: Compared with the classical three-stage inversion algorithm, the improved three-stage inversion algorithm based on TanDEM-X SAR data might have a better accuracy in this paper.

Key words: TanDEM-X, PolInSAR(polarimetric and interferometric synthetic aperture radar), forest canopy height, three-stage inversion algorithm, RVoG(random-volume-over-ground) model

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