A New Approach for Forest Height Inversion Using X-Band Single-Pass InSAR Coherence Data

Zhao, Lei; Chen, Erxue; Li, Zengyuan; Zhang, Wangfei; Yang, Fan

doi:10.1109/tgrs.2021.3072125

Cited by 12 publications

(14 citation statements)

References 30 publications

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“…In the model, forest is assumed as multilayers of negligible thickness without dielectric penetration (Figure 4c). Originally, the MLM was proposed for the inversion of forest height [28]. In this case, it was assumed that the radar signal of X-band InSAR penetrates the forest and reaches the ground surface, that is, it was assumed that D max in Figure 4c is equal to the forest height.…”

Section: Estimation Methods Of Bias Based On the Mlm General Estimati...mentioning

confidence: 99%

“…The original MLM model is derived from the maximum likelihood estimator of coherence based on the following three assumptions [28]: Assumption 1. The backscattering coefficients of the primary and secondary images are equal.…”

Section: Estimation Methods Of Bias Based On the Mlm General Estimati...mentioning

confidence: 99%

“…Soja et al considered this characteristic of the X-band and proposed a two-level model (TLM) [27]. Moreover, Zhao et al proposed a more generalized multi-level model (MLM) [28]. The TLM and MLM models have been used for forest height retrieval or forest biomass estimation based on Xband InSAR [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, Zhao et al proposed a more generalized multi-level model (MLM) [28]. The TLM and MLM models have been used for forest height retrieval or forest biomass estimation based on Xband InSAR [27][28][29][30]. The related research results indicated that the gap penetration model represented by the TLM and MLM conforms to the scattering mechanism characteristics of X-band InSAR.…”

Section: Introductionmentioning

confidence: 99%

“…However, gap penetration is actually a fundamental capability of SAR that cannot be ignored. In recent years, the inversion of forest structure parameters based on the gap penetration scattering mechanism has gradually increased [18,26,28,29,31].…”

Section: Introductionmentioning

confidence: 99%

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Forest Height Estimation Approach Combining P-Band and X-Band Interferometric SAR Data

Zhao

Chen

et al. 2022

Remote Sensing

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Forest height is an essential parameter used to derive important information about forest ecosystems, such as forest above-ground biomass. In this article, a forest height estimation approach combining P-band and X-band interferometric synthetic aperture radar (InSAR) was introduced. The forest height was estimated using the difference in the penetration of long- and short-wavelength radars to the forest. That is, the P-band and X-band InSAR data were used to extract the digital terrain model (DTM) and digital surface model (DSM), respectively. For the DTM, an improved time-frequency (TF) analysis method was used to reduce the effect of forest scatterers on the extraction of a pure understory terrain phase based on P-band InSAR. For the DSM, a novel compensation algorithm based on a multi-layer model (MLM) was proposed to remove the penetration bias of the X-band. Compared to the existing method based on the infinitely deep uniform volumes (IDUV) model, the MLM-based method is more in line with the characteristics of forest structure and the scattering mechanism for X-band InSAR. The airborne P-band repeat-pass InSAR and spaceborne X-band (TanDEM-X) single-pass InSAR data were used to verify the proposed method over the study area in the Saihanba Forest Farm in Hebei, China. The results demonstrated that the improved TF method can achieve high-precision DTM extraction based on P-band InSAR data, and the root mean square error (RMSE) was 0.94 m. The proposed MLM-based compensation method of the DSM achieved a smaller error (RMSE: 1.67 m) compared to the IDUV-based method (RMSE: 3.01 m). Under the same DTM extracted by P-band InSAR, the estimation accuracy of forest height based on the MLM method was 86.58% (RMSE: 1.81 m), which was 8.49% higher than that of the IDUV-based method (RMSE: 2.98 m).

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Section: Estimation Methods Of Bias Based On the Mlm General Estimati...mentioning

confidence: 99%

Section: Estimation Methods Of Bias Based On the Mlm General Estimati...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Forest Height Estimation Approach Combining P-Band and X-Band Interferometric SAR Data

Zhao

Chen

et al. 2022

Remote Sensing

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Error Analysis and Compensation for SINC Simplified Model in Forest Height Inversion

Zhao

Chen

2022

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

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Polarimetric SAR Interferometry Forest Height Inversion Error Model: The Impact of the Nonideal System Parameters

Huang,

Lv,

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The model-based polarimetric interferometric synthetic aperture radar (PolInSAR) forest height inversion is inherently affected by non-ideal system parameters, including channel imbalance, crosstalk, and noise. To investigate the impact of non-ideal system parameters on the estimated forest height, this paper introduces an analytical forest height inversion error model based on the two-layer randomly oriented volume over ground model. Firstly, the error transfer function is derived to present the forest height error caused by the disturbance of the volume coherence. Then, the coupled effects of the non-ideal system parameters on the volume coherence are established and demonstrated to reduce the amplitude of the volume coherence. The effects of channel imbalance and crosstalk are explicitly derived and found to be coupled together to amplify the noise. Finally, the error model is established by combining the error transfer function and the disturbance of the volume coherence caused by the non-ideal system parameters. The proposed error model is verified by simulation analyses on real airborne repeatpass BioSAR 2008 datasets. The results demonstrate that the proposed model can accurately capture the relationship between the height estimation errors and the system parameters.

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A New Approach for Forest Height Inversion Using X-Band Single-Pass InSAR Coherence Data

Cited by 12 publications

References 30 publications

Forest Height Estimation Approach Combining P-Band and X-Band Interferometric SAR Data

Forest Height Estimation Approach Combining P-Band and X-Band Interferometric SAR Data

Error Analysis and Compensation for SINC Simplified Model in Forest Height Inversion

Polarimetric SAR Interferometry Forest Height Inversion Error Model: The Impact of the Nonideal System Parameters

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