Forest Height Estimation Based on P-Band Pol-InSAR Modeling and Multi-Baseline Inversion

Sun, Xiaofan; Wang, Bingnan; Xiang, Maosheng; Zhou, Liangjiang; Jiang, Shuai

doi:10.3390/rs12081319

Cited by 9 publications

(4 citation statements)

References 33 publications

(88 reference statements)

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“…The polarization mode and polarimetric decomposition parameters described in Sections 2.1 and 2.2 of this paper can be directly obtained and characterized, so the structural equation model used in this study uses only the observed variables as model inputs to explore the relationships between the eight parameters and forest canopy height. In forest canopy height estimation, since the different variables contribute to the estimation results to different degrees, applying different weights to them can highlight the data characteristics and improve the estimation accuracy, to some extent [64]. Based on this, we propose to determine the weights between the different polarimetric observation variables and forest canopy height based on SEM, while taking into account both the direct and indirect contributions of the different variables to the estimation of forest canopy height, and combining the path coefficients of SEM.…”

Section: Weight Calculation Based On Structural Equation Modeling (Sem)mentioning

confidence: 99%

Forest Canopy Height Estimation by Integrating Structural Equation Modeling and Multiple Weighted Regression

Zhu,

Zhang,

Song

et al. 2024

Forests

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As an important component of forest parameters, forest canopy height is of great significance to the study of forest carbon stocks and carbon cycle status. There is an increasing interest in obtaining large-scale forest canopy height quickly and accurately. Therefore, many studies have aimed to address this issue by proposing machine learning models that accurately invert forest canopy height. However, most of the these approaches feature PolSAR observations from a data-driven viewpoint in the feature selection part of the machine learning model, without taking into account the intrinsic mechanisms of PolSAR polarization observation variables. In this work, we evaluated the correlations between eight polarization observation variables, namely, T11, T22, T33, total backscattered power (SPAN), radar vegetation index (RVI), the surface scattering component (Ps), dihedral angle scattering component (Pd), and body scattering component (Pv) of Freeman-Durden three-component decomposition, and the height of the forest canopy. On this basis, a weighted inversion method for determining forest canopy height under the view of structural equation modeling was proposed. In this study, the direct and indirect contributions of the above eight polarization observation variables to the forest canopy height inversion task were estimated based on structural equation modeling. Among them, the indirect contributions were generated by the interactions between the variables and ultimately had an impact on the forest canopy height inversion. In this study, the covariance matrix between polarization variables and forest canopy height was calculated based on structural equation modeling, the weights of the variables were calculated by combining with the Mahalanobis distance, and the weighted inversion of forest canopy height was carried out using PSO-SVR. In this study, some experiments were carried out using three Gaofen-3 satellite (GF-3) images and ICESat-2 forest canopy height data for some forest areas of Gaofeng Ridge, Baisha Lizu Autonomous County, Hainan Province, China. The results showed that T11, T33, and total backscattered power (SPAN) are highly correlated with forest canopy height. In addition, this study showed that determining the weights of different polarization observation variables contributes positively to the accurate estimation of forest canopy height. The forest canopy height-weighted inversion method proposed in this paper was shown to be superior to the multiple regression model, with a 26% improvement in r and a 0.88 m reduction in the root-mean-square error (RMSE).

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Section: Weight Calculation Based On Structural Equation Modeling (Sem)mentioning

confidence: 99%

Forest Canopy Height Estimation by Integrating Structural Equation Modeling and Multiple Weighted Regression

Zhu,

Zhang,

Song

et al. 2024

Forests

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“…Subsequently, some researchers focus on modifying the expression of f (z) to update the scattering model, such as [99]. Han et al use an isotropic plate, isotropic dihedral, and dipole particles to model vegetation scatterers [100].…”

Section: Vs-rvog Modelmentioning

confidence: 99%

A Review of Forest Height Inversion by PolInSAR: Theory, Advances, and Perspectives

et al. 2023

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Forests cover approximately one-third of the Earth’s land surface and constitute the core region of the carbon cycle on Earth. The paramount importance and multi-purpose applications of forest monitoring have gained widespread recognition over recent decades. Polarimetric synthetic aperture radar interferometry (PolInSAR) has been demonstrated as a promising technique to retrieve the forest height over large areas with a limited cost. This paper presents an overview of forest height inversion (FHI) techniques based on PolInSAR data. Firstly, we introduce the basic theories of PolInSAR and FHI procedures. Next, we review the established data-based algorithms for single-baseline data and describe innovative techniques related to multi-baseline data. Then, the model-based algorithms are also introduced with their corresponding forest scattering models under multiple data acquisition modes. Subsequently, a case study is presented to demonstrate the applicable scenarios and advantages of different algorithms. Model-based algorithms can provide accurate results when the scene and forest properties are well understood and the model assumptions are valid. Data-based algorithms, on the other hand, can handle complex scattering scenarios and are generally more robust to uncertainties in the input parameters. Finally, the prospect of forest height inversion was analyzed. It is our hope that this review will provide guidelines to future researchers to enhance further FHI algorithmic developments.

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“…Interferogram images contain information associated with differences in the phase of the SAR signal returned to the satellite. Interferometric coherence observations produced from InSAR analysis have been of great interest recently, with studies demonstrating the efficacy of time-series InSAR products over a variety of environments, such as urban [26][27][28], wetland [29][30][31][32][33], permafrost [34][35][36], forested [37][38][39], and ice-covered sea [40][41][42] areas.…”

Section: Introductionmentioning

confidence: 99%

Applying Machine Learning and Time-Series Analysis on Sentinel-1A SAR/InSAR for Characterizing Arctic Tundra Hydro-Ecological Conditions

Merchant

Obadia

Brisco³

et al. 2022

Remote Sensing

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Synthetic aperture radar (SAR) is a widely used tool for Earth observation activities. It is particularly effective during times of persistent cloud cover, low light conditions, or where in situ measurements are challenging. The intensity measured by a polarimetric SAR has proven effective for characterizing Arctic tundra landscapes due to the unique backscattering signatures associated with different cover types. However, recently, there has been increased interest in exploiting novel interferometric SAR (InSAR) techniques that rely on both the amplitude and absolute phase of a pair of acquisitions to produce coherence measurements, although the simultaneous use of both intensity and interferometric coherence in Arctic tundra image classification has not been widely tested. In this study, a time series of dual-polarimetric (VV, VH) Sentinel-1 SAR/InSAR data collected over one growing season, in addition to a digital elevation model (DEM), was used to characterize an Arctic tundra study site spanning a hydrologically dynamic coastal delta, open tundra, and high topographic relief from mountainous terrain. SAR intensity and coherence patterns based on repeat-pass interferometry were analyzed in terms of ecological structure (i.e., graminoid, or woody) and hydrology (i.e., wet, or dry) using machine learning methods. Six hydro-ecological cover types were delineated using time-series statistical descriptors (i.e., mean, standard deviation, etc.) as model inputs. Model evaluations indicated SAR intensity to have better predictive power than coherence, especially for wet landcover classes due to temporal decorrelation. However, accuracies improved when both intensity and coherence were used, highlighting the complementarity of these two measures. Combining time-series SAR/InSAR data with terrain derivatives resulted in the highest per-class F1 score values, ranging from 0.682 to 0.955. The developed methodology is independent of atmospheric conditions (i.e., cloud cover or sunlight) as it does not rely on optical information, and thus can be regularly updated over forthcoming seasons or annually to support ecosystem monitoring.

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Forest Height Estimation Based on P-Band Pol-InSAR Modeling and Multi-Baseline Inversion

Cited by 9 publications

References 33 publications

Forest Canopy Height Estimation by Integrating Structural Equation Modeling and Multiple Weighted Regression

Forest Canopy Height Estimation by Integrating Structural Equation Modeling and Multiple Weighted Regression

A Review of Forest Height Inversion by PolInSAR: Theory, Advances, and Perspectives

Applying Machine Learning and Time-Series Analysis on Sentinel-1A SAR/InSAR for Characterizing Arctic Tundra Hydro-Ecological Conditions

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