سابِق|تال

Retrieval of forest aboveground biomass via compact polarimetric SAR data

  • role： First author第一作者
  • Affiliation：

    College of Forestry, Southwest Forestry University, Kunming 650224, China

  • Email：zhan98gisrs@swfu.edu.cn
  • Introduction：赵含，研究方向为林业遥感。E-mail：zhan98gisrs@swfu.edu.cn
ZHAO Han1,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Forestry, Southwest Forestry University, Kunming 650224, China

  • Email：mewhff@163.com
  • Introduction：张王菲，研究方向为农林业微波遥感机理及应用研究。E-mail：mewhff@163.com
ZHANG Wangfei1*,  
  • Affiliation：

    College of Forestry, Southwest Forestry University, Kunming 650224, China

JI Yongjie1,  
  • Affiliation：

    Weihai Wuzhou Navi-Tech, Weihai 264400, China

HAN Zongtao2

ملخص

Compact Polarimetric Synthetic Aperture Radar (CP-SAR) is a new type SAR that has attracted most researchers, especially the application of CP-SAR data. However, only a few studies have explored the application of forest aboveground biomass (AGB) retrieval using CP-SAR information. In consideration of the global climate change and the goals of achieving peak carbon emissions and carbon neutrality, the accurate inversion of forest AGB has become urgent in recent years. This study aims to explore the feasibility of CP-SAR data applied in forest AGB inversion. In this study, we took Xiaoshao Forest Farm in Yiliang County as the test site, using simulated CP-SAR data from quad polarimetric GF-3 data with four modes, i.e., Stokes1 mode (Stokes-related parameters were extracted from horizontal transmission and dual-orthogonal linear receipt), Stokes2 mode (Stokes-related parameters were extracted from vertical transmission and dual-orthogonal linear receipt), π/4 linear mode (π/4 transmission and orthogonal linear receipt), and CTLR mode (circular transmission and dual-orthogonal linear receipt), to explore the potential of CP-SAR data in forest AGB estimation. First, several SAR parameters of various modes were extracted on the basis of wave dichotomy theory, then the k-nearest neighbor algorithms with fast iterative feature selection (KNN-FIFS) method were applied to estimate the forest AGB in the study area. Finally, the accuracy of the KNN-FIFS inversion results were verified using the leave-one-out cross-validation methods. An R2 of 0.28 and an RMSE of 16.36 t/hm2 were acquired for the forest AGB estimation using Stokes1 mode, and the corresponding optimal feature combination was γ, μl, δ; for Stokes2 mode, an R2 of 0.35 and an RMSE of 14.96 t/hm2 were obtained, and the corresponding optimal feature combination was P2, γ, m1, P1. Compared with Stokes1 and Stokes2 modes, the similar performance was shown in π/4 mode for forest AGB estimation; the R2 value was 0.34, while the RMSE was 15.21 t/hm2, and the corresponding optimal feature combination was ms, ml, vs1, μc, g0. Among four CP-SAR modes, CTLR mode exhibited the best performance in forest AGB inversion with an R2 of 0.52 and an RMSE of 13.02 t/hm2, and the corresponding optimal feature combination is ml, σRL0. The forest AGB inversion result combining four sets of CP-SAR parameters showed remarkable improvement with an R2 of 0.58 and an RMSE of 12.16 t/hm2. The CTLR CP-SAR mode outperformed the other modes in terms of forest AGB estimation when the parameters extracted from four CP SAR modes were combined and applied for forest AGB estimation; the improvement of inversion result was remarkable. KNN-FIFS is suitable for forest AGB estimation via CP-SAR parameters, and no considerable difference was found between the estimation results estimated using CTLR CP-SAR data and quad polarimetric SAR data. Among all the extracted CP-SAR parameters, the degree of linear polarization (ml) and the power of the linear polarization component at a tilt angle of 45 degrees or 135 degrees (g2) showed the best performance in the forest AGB estimation because both of them are selected in all the four modes as the optimized features. It revealed that they can better characterize the forest AGB changes. Meanwhile, the parameters that can reflect the forest density to a certain extent (vs1), the parameters that reflect the characteristics of the forest scattering direction (δ), and the parameters that represent the degree of forest depolarization all have good performance in the forest AGB inversion.

مفهوم

remote sening;Forest AGB;GF-3;Stokes;CP SAR;KNN-FIFS

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