Shallow slope instability poses a significant ecological threat, often leading to severe environmental degradation. While vegetation, particularly woody plants, is commonly employed in slope stabilization, herbaceous vegetation offers distinct and underexplored advantages. This paper reviews the role of herbaceous plants in enhancing slope stability, analyzing their mechanical and ecological mechanisms. Through an extensive review of the literature, this review challenges the prevailing view that woody vegetation is superior for slope stabilization, finding that herbaceous plants can be equally or more effective under certain conditions. The key findings include the identification of specific root parameters and species that contribute to soil reinforcement and erosion control. The review highlights the need for further research on optimizing plant species selection and management practices to maximize the slope stabilization effects. These insights have practical implications for ecological slope engineering, offering guidance on integrating herbaceous vegetation into sustainable land management strategies.

Spatially continuous canopy height is a vital input for modeling forest structures and functioning. The global ecosystem dynamics investigation (GEDI) waveform can penetrate a canopy to precisely find the ground and measure canopy height, but it is spatially discontinuous over the earth’s surface. A common method to achieve wall-to-wall canopy height mapping is to integrate a set of field-measured canopy heights and spectral bands from optical and/or microwave remote sensing data as ancillary information. However, due partly to the saturation of spectral reflectance to canopy height, the product of this method may misrepresent canopy height. As a result, neither GEDI footprints nor interpolated maps using the common method can accurately produce spatially continuous canopy height maps alone. To address this issue, this study proposes a framework of point-surface fusion for canopy height mapping (FPSF-CH) that uses GEDI data to calibrate the initial wall-to-wall canopy height map derived from a sub-model of FPSF-CH. The effectiveness of the proposed FPSF-CH was validated by comparison to canopy heights derived from (1) a high-resolution canopy height model derived from airborne discrete point cloud lidar across three test sites, (2) a global canopy height product (GDAL RH95), and (3) the results of the FPSF-CH sub-model without fusing with the GEDI canopy height. The results showed that the RMSE and rRMSE of FPSF-CH were 3.82, 4.05, and 3.48 m, and 18.77, 16.24, and 13.81% across the three test sites, respectively. The FPSF-CH achieved improvement over GDAL RH95, with reductions in RMSE values of 1.28, 2.25, and 2.23 m, and reductions in rRMSE values of 6.29, 9.01, and 8.90% across the three test sites, respectively. Additionally, the better performance of the FPSF-CH compared with its sub-model further confirmed the effectiveness of integrating GEDI data for calibrating wall-to-wall canopy height mapping. The proposed FPSF-CH integrates GEDI LiDAR data to provide a new avenue for accurate wall-to-wall canopy height mapping critical to applications, such as estimations of biomass, biodiversity, and carbon stocks.

The growing need to monitor changes in the surface of the Earth requires a high-quality, accessible Digital Elevation Model (DEM) dataset, whose development has become a challenge in the field of Earth-related research. The purpose of this paper is to improve the overall accuracy of public domain DEMs by data fusion. Multi-scale decomposition is an important analytical method in data fusion. Three multi-scale decomposition methods—the wavelet transform (WT), bidimensional empirical mode decomposition (BEMD), and nonlinear adaptive multi-scale decomposition (N-AMD)—are applied to the 1-arc-second Shuttle Radar Topography Mission Global digital elevation model (SRTM-1 DEM) and the Advanced Land Observing Satellite World 3D—30 m digital surface model (AW3D30 DSM) in China. Of these, the WT and BEMD are popular image fusion methods. A new approach for DEM fusion is developed using N-AMD (which is originally invented to remove the cycle from sunspots). Subsequently, a window-based rule is proposed for the fusion of corresponding frequency components obtained by these methods. Quantitative results show that N-AMD is more suitable for multi-scale fusion of multi-source DEMs, taking the Ice Cloud and Land Elevation Satellite (ICESat) global land surface altimetry data as a reference. The fused DEMs offer significant improvements of 29.6% and 19.3% in RMSE at a mountainous site, and 27.4% and 15.5% over a low-relief region, compared to the SRTM-1 and AW3D30, respectively. Furthermore, a slope position-based linear regression method is developed to calibrate the fused DEM for different slope position classes, by investigating the distribution of the fused DEM error with topography. The results indicate that the accuracy of the DEM calibrated by this method is improved by 16% and 13.6%, compared to the fused DEM in the mountainous region and low-relief region, respectively, proving that it is a practical and simple means of further increasing the accuracy of the fused DEM.

The problem of phase unwrapping (PhU) in the large-gradient deformation areas is the bottleneck problem of interferometric synthetic aperture radar (InSAR) data processing. However, the extraction of large-gradient deformation areas is one of the key issues in coal mining deformation monitoring. Here, we propose a novel multimodel fusion PhU Network, abbreviated as MMPhU-Net, and apply it to the extraction of large-gradient deformation areas. The major advantages of MMPhU-Net are as follows: First, MMPhU-Net combines the advantages of different basic network models, which can improve the model convergence speed and phase gradient estimation accuracy. MMPhU-Net can improve the lack of recognition effect of a single basic model. Second, different from existing deep learning PhU methods, MMPhU-Net directly estimates the gradient ambiguity numbers, k , so its phase gradient estimation completely breaks through the (-π, π) limitation. Therefore, MMPhU-Net can obtain ideal PhU results in large-gradient deformation areas. In addition, optimization algorithm models are used to optimize the estimation results of the multimodel fusion network. Subsequently, the obtained k and a novel two-step filtering method are combined to obtain the final PhU results. Through the verifications of simulated data sets and realistic GaoFen-3 SAR data sets, the proposed MMPhU-Net method can achieve superior excellent results than the commonly used PhU method.

The denoising of phase is a crucial process that impacts the accuracy of data processing in differential interferometric synthetic aperture radar (DInSAR). Especially in the area of large-gradient deformation, the phase filtering method is very easy to cause phase losses. This has a significant impact on the final deformation acquisition. To address this issue, here, a deep convolutional blind denoising network-based interferometric phase filtering method, named CBIPDNet, is proposed. Different from the previously proposed deep learning phase filtering methods, CBIPDNet does not add noise to the input before filtering, but adds noise to the input during the training process. Furthermore, CBIPDNet uses a CNN structure for adaptive noise estimation, and uses a residual module for non-blind filtering. Therefore, CBIPDNet can be considered as an adaptive phase filtering algorithm. More importantly, the added noise is composed of heteroscedastic Gaussian noise + simulated real noise of imaging process, which is closer to the real interferometric noise phase. Moreover, the denoising effect of targets of different scales through the asymmetric loss function has been significantly improved, which can improve the detail preservation ability of regions with substantial gradient deformations. The experimental results demonstrate that CBIPDNet is capable of enhancing phase quality and increasing phase unwrapping accuracy compared to the current interferometric filtering methods.

Airborne hyperspectral imaging plays an increasingly important role in environmental monitoring. However, due to the limitations of the acquisition conditions, there are uneven radiation and chromatic aberrations in the mosaic data. Accurate preprocessing of the original data is the premise of qualitative and quantitative remote sensing. In this study, we proposed a comprehensive radiation distortion correction method that integrates radiation attenuation difference correction, topographic correction, and multi-strip images consistency adjustment (RA-TOC-CA). First, the radiation attenuation equation was constructed by combining the viewing geometry, terrain, and the elevation difference between the UAV and the ground to eliminate the radiation attenuation difference of pixels acquired at the different instantaneous field of view (IFOV). Second, an improved kernel-driven BRDF model was built combining terrain information and illumination-viewing (flight attitude and sensor IFOV) geometry to eliminate the radiation unevenness and BRDF distortion caused by topography. Third, adjusting the reflectance of multi-strip images according to the homonymous points’ reflectance of adjacent strips should be equal, eliminating the radiation differences between multiple strips. Based on multi-strip airborne hyperspectral images collected in the Shaanxi province of China, the correction results of the RA-TOC-CA method were compared with those of the SCS+C and Minnaert+SCS methods regarding various evaluation criteria. The results showed that SCS+C and Minnaert+SCS can reduce the topographic effect but cannot eliminate the reflectance difference at the edges of adjacent images, and SCS+C overcorrects the reflectance. RA-TOC-CA weakened the topographic effects and brightness gradient, which was physically stable and generalizable. Compared with previous studies, RA-TOC-CA provided a complete radiation distortion correction method for airborne hyperspectral images and had a solid theoretical basis. This study introduces an effective method for radiation distortion correction of airborne hyperspectral images and provides technical support for large-scale applications of hyperspectral images.