Remote sensing-based ground fissure extraction techniques (e.g., image classification, image segmentation, feature extraction) are widely used to monitor geological hazards and large-scale artificial engineering projects such as bridges, dams, highways, and tunnels. However, conventional technologies cannot be applied in loess areas due to their complex terrain, diverse textural information, and diffuse ground target boundaries, leading to the extraction of many false ground fissure targets. To rapidly and accurately acquire ground fissures in the loess areas, this study proposes a data processing scheme to detect loess ground fissure spatial distributions using unmanned aerial vehicle (UAV) images. Firstly, the matched filter (MF) algorithm and the first-order derivative of the Gaussian (FDOG) algorithm were used for image convolution. A new method was then developed to generate the response matrices of the convolution with normalization, instead of the sensitivity correction parameter, which can effectively extract initial ground fissure candidates. Directions, the number of MF/FDOG templates, and the efficiency of the algorithm are comprehensively considerate to conclude the suitable scheme of parameters. The random forest (RF) algorithm was employed for the step of the image classification to create mask files for removing non-ground-fissure features. In the next step, the hit-or-miss transform algorithm and filtering algorithm in mathematical morphology is used to connect discontinuous ground fissures and remove pixel sets with areas much smaller than those of the ground fissures, resulting in a final binary ground fissure image. The experimental results demonstrate that the proposed scheme can adequately address the inability of conventional methods to accurately extract ground fissures due to plentiful edge information and diverse textures, thereby obtaining precise results of small ground fissures from high-resolution images of loess areas.

The use of the conventional interferometric synthetic aperture radar (InSAR) to generate digital elevation models (DEMs) always encounters phase unwrapping (PU) errors in areas with a sizeable topographic gradient. Range split-spectrum interferometry (RSSI) can overcome this issue; however, it loses the spatial resolution of the SAR image. We propose the use of the RSSI-assisted In-SAR-derived DEM (RID) method to address this challenge. The proposed approach first applies the RSSI method to generate a prior DEM, used for simulating terrain phases. Then, the simulated terrain phases are subtracted from the wrapped InSAR phases to obtain wrapped residual phases. Finally, the residual phases are unwrapped by the minimum cost flow (MCF) method, and the unwrapped residual phases are added to the simulated phases. Both the simulated and TerraSAR-X data sets are used to verify the proposed method. Compared with the InSAR and RSSI methods, the proposed approach can effectively decrease the PU errors of large gradients, ensure data resolution, and guarantee the DEM’s accuracy. The root mean square error between the topographic phase simulated from the real DEM and the topographic phase generated from the proposed method is 2.22 rad, which is significantly lower than 6.60 rad for InSAR, and the improvement rate is about 66.36%.

SUMMARY: The Greenland Ice Sheet (GrIS) is the largest ice body in the Northern Hemisphere. Quantifying the spatiotemporal characteristics of its mass change is crucial for understanding global climate change. Many studies have been focused on the long-term ice mass change and acceleration, but a more detailed analysis of multitemporal signals, including annual, interannual and transient variations, is still imperative to study the periodic ice mass change. Here, we comprehensively characterize the multitemporal mass changes of the entire GrIS and subregions using a variational mode decomposition method, applied to Gravity Recovery and Climate Experiment satellite data, surface mass balance model output, climate parameters and GPS observations. We found that both the interannual and transient mass variations of the southern subregions have larger amplitudes associated with pronounced precipitations, indicating that the southern mass change patterns are more vulnerable to short-term climate variability. We also found that the reported rapid mass loss in 2010, 2012, 2016 and 2019 should be attributed to the interannual signals that result from low precipitations, significant runoff and evapotranspiration. The largest interannual mass variation was discovered in 2019 (−235 Gt), but due to the combined effect of interannual loss (−147 Gt) and transient attenuation (−380 Gt), the greatest mass loss (−527 Gt) was observed at the end of 2012. Our study emphasizes the importance of multitemporal signals in characterizing the spatiotemporal GrIS mass change and exploring the coupling effect between ice mass change and climate conditions.