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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...
Citations
... Forest structural parameters such as canopy cover, tree density, diameter at breast height, and tree height serve as important criteria characterizing the growth status of a forest, determining the rationality of the forest spatial structure, and evaluating the ecological functions of a forest. Tree height stands out as one of the most important forest structural parameters, and it is widely used to estimate terrestrial forest carbon reserves, as well as in biomass monitoring and forest health assessment [6][7][8]. ...
... In this formula, H A represents the height precision of the extraction tree, M H represents the measured tree height, and H represents the inversion tree height. Equation (8) was used to find the accuracy values of the two methods for the three plots ( Table 4). The overall deviation between the tree estimation and the measured value, extracted based on the VF-processed data used in the study, was relatively small and more accurate than the SOR-processed data. ...
Forests, as the main body of the terrestrial ecosystem, have long been focal points for accurate structural parameter extraction. Among these parameters, tree height is a fundamental measurement factor that plays an important role in monitoring forest structure and biomass. The emergence of unmanned aerial vehicle light detection and ranging (UAV-LiDAR) technology has provided a strong guarantee of the acquisition of forest tree height parameters. However, UAV-LiDAR point cloud data have problems such as a large volume and data redundancy, and different point cloud data processing methods have different effects. Based on voxel filtering (VF) and statistical outlier removal (SOR)point cloud data processing experimental analysis, this study explored the influence of different filtering methods on the forest tree height inversion efficiency and accuracy. First, the point cloud data processed by VF is significantly better than that of SOR in terms of point cloud number, file size, running time, etc. The number of point clouds for VF decreased by an average of 96.91% compared with the original point clouds. Second, the VF tree height inversion accuracy was better than the tree height inversion data using SOR. The average accuracy of VF was 96.24%, while that of SOR was 94.17%. In summary, VF can effectively reduce data redundancy and improve tree height inversion accuracy.
... Second, in this study, we utilized only one pair of SAR images for each test case to evaluate our approach. Therefore, beyond the constraints related to the scattering model, there could be a potential limitation associated with the InSAR observation geometry, particularly the vertical wavenumber (or height of ambiguity), which directly determines the sensitivity of the observed data to forest parameters [59]. Regarding this issue, [60] investigated the influence of an optimized range of vertical wavenumber on the performance of forest height estimation and demonstrated that the interferometric coherence of a single baseline has an associated interval of successful inversion which depends on the vertical wavenumber. ...
To address the challenge of retrieving sub-canopy topography using single-baseline single-polarization TanDEM-X InSAR data, we propose a novel InSAR processing framework. Our methodology begins by employing the SINC model to estimate the penetration depth (PD). Subsequently, we establish a linear relationship between PD and phase center height (PCH) to generate a wall-to-wall PCH product. To achieve this, space-borne LiDAR data are employed to capture the elevation bias between actual ground elevation and InSAR-derived elevation. Finally, the sub-canopy topography is derived by subtracting the PCH from the conventional InSAR-based DEM. Moreover, this approach enables the simultaneous estimation of forest height from single-baseline TanDEM-X data by combining the estimated PD and PCH components. The approach has been validated against Airborne Lidar Scanning data over four diverse sites encompassing different forest types, terrain conditions, and climates. The derived sub-canopy topography in the boreal and hemi-boreal forest sites (Krycklan and Remningstorp) demonstrated notable improvement in accuracy. Additionally, the winter acquisitions outperformed the summer ones in terms of inversion accuracy. The achieved RMSEs for the winter scenarios were 2.45 m and 3.83 m, respectively, representing a 50% improvement over the InSAR-based DEMs. And the forest heights are also close to the ALS measurements, with RMSEs of 2.70 m and 3.33 m, respectively. For the Yanguas site in Spain, characterized by rugged terrain, sub-canopy topography in forest areas was estimated with an accuracy of 4.27m, a 35% improvement over the original DEM. For the denser tropical forest site, only an average elevation bias could be corrected.
