The mapping of tropical rainforest forest structure parameters plays an important role in biodiversity and carbon stock estimation. The current mechanism models based on PolInSAR for forest height inversion (e.g., the RVoG model) are physical process models, and realistic conditions for model parameterization are often difficult to establish for practical applications, resulting in large forest height estimation errors. As an alternative, machine learning approaches offer the benefit of model simplicity, but these tools provide limited capabilities for interpretation and generalization. To explore the forest height estimation method combining the mechanism model and the empirical model, we utilized UAVSAR multi-baseline PolInSAR L-band data from the AfriSAR project and propose a solution of a mechanism model combined with machine learning. In this paper, two mechanism models were used as controls, the RVoG three-phase method and the RVoG phase-coherence amplitude method. The vertical structure parameters of the forest obtained from the mechanism model were used as the independent variables of the machine learning model. Random forest (RF) and partial least squares (PLS) regression models were used to invert the forest canopy height. Results show that the inversion accuracy of the machine learning method, combined with the mechanism model, is significantly better than that of the single-mechanism model method. The most influential independent variables were penetration depth, volume coherence phase center height, coherence separation, and baseline selection. With the precondition that the cumulative contribution of the independent variables was greater than 90%, the number of independent variables in the two study areas was reduced from 19 to 4, and the accuracy of the RF-RVoG-DEP model was higher than that of the PLS-RVoG-DEP model. For the Lope test area, the R2 of the RVoG phase coherence amplitude method is 0.723, the RMSE is 8.583 m, and the model bias is −2.431 m; the R2 of the RVoG three-stage method is 0.775, the RMSE is 7.748, and the bias is 1.120 m, the R2 of the PLS-RVoG-DEP model is 0.850, the RMSE is 6.320 m, and the bias is 0.002 m; and the R2 of the RF-RVoG-DEP model is 0.900, the RMSE is 5.154 m, and the bias is −0.061 m. The results for the Pongara test area are consistent with the pattern for the Lope test area. The combined “fusion model” offers a substantial improvement in forest height estimation from the traditional mechanism modeling method.
PolInSAR is an active remote sensing technique that is widely used for forest canopy height estimation, with the random volume over ground (RVoG) model being the most classic and effective forest canopy height inversion approach. However, penetration of microwave energy into the forest often leads to a downward shift of the canopy phase center, which leads to model underestimation of the forest canopy height. In addition, in the case of sparse and low forests, the canopy height is overestimated, owing to the large ground-to-volume amplitude ratio in the RVoG model and severe temporal decorrelation effects. To solve this problem, in this study, we conducted an experiment on forest canopy height estimation with the RVoG model using L-band multi-baseline fully polarized PolInSAR data obtained from the Lope and Pongara test areas of the AfriSAR project. We also propose various RVoG model error correction methods based on penetration depth by analyzing the model’s causes of underestimation and overestimation. The results show that: (1) In tall forest areas, there is a general underestimation of canopy height, and the value of this underestimation correlates strongly with the penetration depth, whereas in low forest areas, there is an overestimation of canopy height owing to severe temporal decorrelation; in this instance, overestimation can also be corrected by the penetration depth. (2) Based on the reference height RH100, we used training sample iterations to determine the correction thresholds to correct low canopy overestimation and tall canopy underestimation; by applying these thresholds, the inversion error of the RVoG model can be improved to some extent. The corrected R2 increased from 0.775 to 0.856, and the RMSE decreased from 7.748 m to 6.240 m in the Lope test area. (3) The results obtained using the infinite-depth volume condition p-value as the correction threshold were significantly better than the correction results for the reference height, with the corrected R2 value increasing from 0.775 to 0.914 and the RMSE decreasing from 7.748 m to 4.796 m. (4) Because p-values require a true height input, we extended the application scale of the method by predicting p-values as correction thresholds via machine learning methods and polarized interference features; accordingly, the corrected R2 increased from 0.775 to 0.845, and the RMSE decreased from 7.748 m to 6.422 m. The same pattern was obtained for the Pongara test area. Overall, the findings of this study strongly suggest that it is effective and feasible to use penetration depth to correct for RVoG model errors.
Forest canopy height is an important forest indicator parameter. Synthetic aperture radar tomography (TomoSAR) is an effective method to characterize forest canopy height and describe forest 3D structure; however, the residual phase error of TomoSAR affects the focus of the relative reflectance and can lead to errors in forest canopy height estimation. Therefore, this paper proposes a semi-empirical method to overcome the residual phase effects on forest canopy height estimation. In this study, we used airborne multi-baseline UAVSAR data to estimate forest canopy height via TomoSAR techniques and applied a semi-empirical method to improve forest canopy height estimation without phase calibration to mitigate the effects of phase error. The process is divided into three stages: the first step uses a semi-empirical method to initially determine the optimal relative reflectance loss threshold (K) by excluding the inverse extremes; in the second and third steps, the percentile height was used to gradually reduce the height interval between the upper and lower envelopes to minimize overestimation of extreme values and the lower vegetation. When the root mean square error (RMSE) was minimized, the percentile combinations were determined between the inversion results and a LiDAR dataset of the area. The results show that the canopy height estimation results are not satisfactory when relying solely on the K value to estimate the height difference between the envelope at the top of the forest and the ground; the best result was obtained when K = 0.4, but the corresponding R2 value was only 0.13, and the RMSE was 15.23 m. In our proposed method, the K value is determined as 0.3 by excluding the extreme values of the inversion result in the initial step—the corresponding R2 and RMSE values were 0.59 and 10.73 m, respectively, representing an RMSE decrease of 29.54% relative to the initial K value. After two steps of correction overestimation, the inversion accuracy was significantly improved with an R2 value of 0.65 and an RMSE of 9.69 m, corresponding to an RMSE decrease of 36.38%. Overall, the findings of the study represent an important reference for optimizing future spaceborne TomoSAR forest canopy height estimates.
Mapping forest canopy height at large regional scales is of great importance for the global carbon cycle. Polarized interferometric synthetic aperture radar is an efficient and irreplaceable remote sensing tool. Developing an efficient and accurate method for forest canopy height estimation is an important issue that needs to be addressed urgently. In this paper, we propose a novel four-stage forest height inversion method based on a Fourier–Legendre polynomial (FLP) with reference to the RVoG three-stage method, using the multi-baseline UAVSAR data from the AfriSAR project as the data source. The third-order FLP is used as the vertical structure function, and a small amount of ground phase and LiDAR canopy height is used as the input to solve and fix the FLP coefficients to replace the exponential function in the RVoG three-stage method. The performance of this method was tested in different forest types (mangrove and inland tropical forests). The results show that: (1) in mangroves with homogeneous forest structure, the accuracy based on the four-stage FLP method is better than that of the RVoG three-stage method. For the four-stage FLP method, R2 is 0.82, RMSE is 6.42 m and BIAS is 0.92 m, while the R2 of the RVoG three-stage method is 0.77, RMSE is 7.33 m, and bias is −3.49 m. In inland tropical forests with complex forest structure, the inversion accuracy based on the four-stage FLP method is lower than that of the RVoG three-stage method. The R2 is 0.50, RMSE is 11.54 m, and BIAS is 6.53 m for the four-stage FLP method; the R2 of the RVoG three-stage method is 0.72, RMSE is 8.68 m, and BIAS is 1.67 m. (2) Compared to the RVoG three-stage method, the efficiency of the four-stage FLP method is improved by about tenfold, with the reduction of model parameters. The inversion time of the FLP method in a mangrove forest is 3 min, and that of the RVoG three-stage method is 33 min. In an inland tropical forest, the inversion time of the FLP method is 2.25 min, and that of the RVoG three-stage method is 21 min. With the application of large regional scale data in the future, the method proposed in this study is more efficient when conditions allow.
The purpose of this article is to explore forest height estimation strategies using topographic data (DEM) combined with TanDEM-X while comparing the effect of volume scattering complex coherence selection on forest height estimation in the traditional random volume over ground (RVoG) three-stage algorithm. In this study, four experimental strategies were designed for comparison based on TanDEM-X polarized interferometric synthetic aperture radar (PolInSAR) data, TanDEM-DEM, and 42 field-measured data. Our results show that in the RVoG model, (1) a reference ground phase to select the volume scattering complex coherence provides greater accuracy in determining forest height, (2) forest height estimation can be achieved by directly using DEM as ground phase information without relying on model solving and obtaining a more accurate forest height than TanDEM-X alone, and (3) the highest estimation accuracy is obtained by using DEM as coherence information among all schemes. Although the difference in forest height estimation results is not significant in this study, it still proves that the forest height estimation strategy of high-precision DEM combined with short-wavelength PolInSAR can not only improve the forest height estimation accuracy but also simplify the solving process of the RVoG model, which is an important reference for global forest parameter estimation and ecosystem detection based on spaceborne PolInSAR.
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