Forest height estimation based on the RVoG inversion model and the PolInSAR decomposition technique

Aghabalaei, Amir; Ebadi, Hamid; Maghsoudi, Yasser

doi:10.1080/01431161.2019.1694726

Cited by 12 publications

(4 citation statements)

References 26 publications

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“…Nowadays, polarimetric SAR data acquisition in interferometric mode shows a great potential in modelling to estimate forest parameters [74][75][76]. Several studies have been carried out for SAR image-based forest height estimation and most successfully demonstrated airborne systems [42,77,78].…”

Section: Discussionmentioning

confidence: 99%

Spaceborne Multifrequency PolInSAR-Based Inversion Modelling for Forest Height Retrieval

et al. 2020

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Spaceborne and airborne polarimetric synthetic-aperture radar interferometry (PolInSAR) data have been extensively used for forest parameter retrieval. The PolInSAR models have proven their potential in the accurate measurement of forest vegetation height. Spaceborne monostatic multifrequency data of different SAR missions and the Global Ecosystem Dynamics Investigation (GEDI)-derived forest canopy height map were used in this study for vegetation height retrieval. This study tested the performance of PolInSAR complex coherence-based inversion models for estimating the vegetation height of the forest ranges of Doon Valley, Uttarakhand, India. The inversion-based forest height obtained from the three-stage inversion (TSI) model had higher accuracy than the coherence amplitude inversion (CAI) model-based estimates. The vegetation height values of GEDI-derived canopy height map did not show good relation with field-measured forest height values. It was found that, at several locations, GEDI-derived forest height values underestimated the vegetation height. The statistical analysis of the GEDI-derived estimates with field-measured height showed a high root mean square error (RMSE; 5.82 m) and standard error (SE; 5.33 m) with a very low coefficient of determination (R2; 0.0022). An analysis of the spaceborne-mission-based forest height values suggested that the L-band SAR has great potential in forest height retrieval. TSI-model-based forest height values showed lower p-values, which indicates the significant relation between modelled and field-measured forest height values. A comparison of the results obtained from different SAR systems is discussed, and it is observed that the L-band-based PolInSAR inversion gives the most reliable result with low RMSE (2.87 m) and relatively higher R2 (0.53) for the linear regression analysis between the modelled tree height and the field data. These results indicate that higher wavelength PolInSAR datasets are more suitable for tree canopy height estimation using the PolInSAR inversion technique.

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Section: Discussionmentioning

confidence: 99%

Spaceborne Multifrequency PolInSAR-Based Inversion Modelling for Forest Height Retrieval

et al. 2020

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“…The third-order Pauli basis coherence matrix T can be obtained by multiplying the three-dimensional Pauli basis vector k p and the target scattering vector of the lexicographic target basis vector with their own conjugate transpose in an outer product, as shown in Equation ( 5) [51,52], where (.) * is the conjugate complex number.…”

Section: Polarimetric Synthetic Aperture Radar Observationsmentioning

confidence: 99%

“…Based on the above polarimetric observation matrix, it is possible to obtain multiple types of polarimetric parameters for forest canopy height estimation [51][52][53][54]. Eight polarimetric observation variables were selected in this study (see Table 2).…”

Section: Polarimetric Synthetic Aperture Radar Observationsmentioning

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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“…The polarimetric decomposition technique of the physical model was introduced to consider the surface scattering as the ground contribution, as well as the double and volume scattering as the contribution from the vegetation layer. The ground and volume contributions were modeled separately, avoiding the model estimation error caused by excessively large ground scattering in many cases and obtaining a better separation of the ground and volume phases, which makes it more suitable for the inversion of forest heights for the complex forest structure (Equation ( 9)) [61,62].…”

Section: Vertical Structure Index (H-vsi)mentioning

confidence: 99%

Forest Structure Mapping of Boreal Coniferous Forests Using Multi-Source Remote Sensing Data

Sa,

Fan

2024

Remote Sensing

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Modeling forest structure using multi-source satellite data is beneficial to understanding the relationship between vertical and horizontal structure and image features to provide more comprehensive and abundant information for the study of forest structural complexity. This study investigates and models forest structure as a multivariate structure based on sample data and active-passive remote sensing data (Landsat8, Sentinel-2A, and ALOS-2 PALSAR) from the Saihanba Forest in Hebei Province, Northern China, to measure forest structural complexity, relying on a relationship-driven model between field and satellite data. In this study, we considered the effects of the role of satellite variables in different vertical structure types and horizontal structure ranges, used two methods to stepwise select significant variables (stepwise forward selection and Pearson correlation coefficient), and employed a multivariate modeling technique (redundancy analysis) to derive a forest composite structure index (FSI), combining both horizontal and vertical structure attributes. The results show that optical texture can better represent forest structure characteristics, polarization interferometric radar information can represent the vertical structure information of forests, and combining the two can represent 77% of the variance of multiple forest structural attributes. The new FSI can explain 93% of the relationship between stand structure and satellite variables, and the linear fit R2 to the measured data reaches 0.91, which largely shows the situation of the measured data. The generated forest structure map more accurately reflects the complexity of the forest structure in the Saihanba Forest, achieving a supplementary explanation of the measured data.

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Forest height estimation based on the RVoG inversion model and the PolInSAR decomposition technique

Cited by 12 publications

References 26 publications

Spaceborne Multifrequency PolInSAR-Based Inversion Modelling for Forest Height Retrieval

Spaceborne Multifrequency PolInSAR-Based Inversion Modelling for Forest Height Retrieval

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

Forest Structure Mapping of Boreal Coniferous Forests Using Multi-Source Remote Sensing Data

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