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Herein, the morphological characteristics of submarine archipelagic aprons were presented for five guyots, Suda, Arnold, Lamont, Niulang, and Zhinyv, which are over 80 Ma years old and are located in the Marcus–Wake seamount group, northwestern Pacific Ocean. Nearly 28 landslide deposits were recognized using the bathymetry and backscatter intensity data collected from the studied guyots. Landslides and their deposits that surround seamounts are mostly related to the morphology of debris avalanches, scarps, gullies/channels, and bedforms. The morphology of the archipelagic aprons of the studied guyots indicates mutual landslide processes, including slump and distinct debris avalanches arising from a cohesive or cohesionless landslide material flow. The superimposition of debris flows and sedimentation dominates the recent stages of the studied guyots. The archipelagic aprons corresponding to convex-arc-shaped scarps exhibit larger domains compared to the invagination-arc-shaped scarps with similar lateral lengths. The scarp morphologies of the studied guyots are predominantly of the complex-arc shape, indicating multiple landslide events. Parallel and convergent gullies and channels are mostly found on the elongated landslide deposits, whereas divergent and radial gullies and channels are mostly distributed on the fan-shaped aprons. Ubiquitous sediment waves occurred on the bedforms of the distal archipelagic apron across the studied guyots because of sediment creep. Small-scale sediment waves were only observed in the channels on the aprons of the Suda guyot. [ABSTRACT FROM AUTHOR]

Numerous analytical radar-scattering laws have been published through the past decades to interpret planetary radar observations, such as Hagfors' law, which has been commonly used for the Moon, and the cosine law, which is commonly used in the shape modeling of asteroids. Many of the laws have not been numerically validated in terms of their interpretation and limitations. This paper evaluates radar-scattering laws for self-affine fractal surfaces using a numerical approach. Traditionally, the autocorrelation function and, more recently, the Hurst exponent, which describes the self-affinity, have been used to quantify the height correlation. Here, hundreds of three-dimensional synthetic surfaces parameterized using a root-mean-square (rms) height and a Hurst exponent were generated, and their backscattering coefficient functions were computed to evaluate their consistency with selected analytical models. The numerical results were also compared to empirical models for roughness and radar-scattering measurements of Hawaii lava flows and found consistent. The Gaussian law performed best at predicting the rms slope regardless of the Hurst exponent. Consistent with the literature, it was found to be the most reliable radar-scattering law for the inverse modeling of the rms slopes and the Fresnel reflection coefficient from the quasi-specular backscattering peak, when homogeneous statistical properties and a ray-optics approach can be assumed. The contribution of multiple scattering in the backscattered power increases as a function of rms slope up to about 20% of the backscattered power at normal incidence when the rms slope angle is 46°. [ABSTRACT FROM AUTHOR]

In the field of open-pit geological risk management, landslide failure time prediction is one of the important topics. Based on the analysis of displacement monitoring data, the inverse velocity method (INV) has become an effective method to solve this issue. To improve the reliability of landslide prediction, four filters were used to test the velocity time series, and the effect of landslide failure time prediction was compared and analyzed. The results show that the sliding process of landslide can be divided into three stages based on the INV: the initial attenuation stage (regressive stage), the second attenuation stage (progressive stage), and the linear reduction stage (autoregressive stage). The accuracy of the INV is closely related to the measured noise of the monitoring equipment and the natural noise of the environment, which will affect the identification of different deformation stages. Compared with the raw data and the exponential smoothing filter (ESF) models, the fitting effect of the short-term smoothing filter (SSF) and long-term smoothing filter (LSF) in the linear autoregressive stage is better. A stratified prediction method combining SSF and LSF is proposed. The prediction method is divided into two levels, and the application of this method is given. [ABSTRACT FROM AUTHOR]

Deep, unstable slopes are highly developed in mountainous areas, especially in the Minjiang River Basin, Sichuan Province, China. In this study, to reveal their deformation evolution characteristics for stability evaluation and disaster prevention, multi-period optical remote sensing images (2010–2019), SBAS-InSAR data (January 2018–December 2019), and on-site real-time monitoring (December 2017–September 2020) were utilized to monitor the deformation of a large deep-seated toppling, named the Tizicao (TZC) Toppling. The obtained results by different techniques were cross-validated and synthesized in order to introduce the spatial and temporal characteristics of the toppling. It was found that the displacements on the north side of the toppling are much larger than those on the south side, and the leading edge exhibits a composite damage pattern of "collapse failure" and "bulging cracking". The development process of the toppling from the formation of a tensile crack at the northern leading edge to the gradual pulling of the rear edge was revealed for a time span of up to ten years. In addition, the correlation between rainfall, earthquakes, and GNSS time series showed that the deformation of the toppling is sensitive to rainfall but does not change under the effect of earthquakes. The surface-displacement-monitoring method in this study can provide a reference for the evolution analysis of unstable slopes with a large span of deformation. [ABSTRACT FROM AUTHOR]

In geological mapping, maps of K, Th, and U concentrations provided by airborne radiometric surveys are widely used to delineate geological units in tropical regions from the few rare outcrops. Indeed, thanks to their specific geochemical properties and behaviors, K, Th, and U allow us to trace geological processes. However, the combination of the concentrations of these radioelements does not allow us to determine the lithology in a unique way. We examined the potential of delineating the statistical parameters of K, Th, and U concentrations for geological mapping using the purpose airborne radiometric data in eastern Senegal. The mean, standard deviation, skewness, and kurtosis were calculated and mapped at a baseline of 3000 m. We noted the narrow dispersion of skewness and kurtosis values away from the expected curve for the log-normal distribution, implying that log-normal distributions dominate at the scale of analysis. The higher moments (kurtosis and skewness) varied more over shorter distances than lower order moments (mean and standard deviation). Mixtures of log-normal distributions across some lithological contacts with large differences in statistical parameters may account for this behavior. The area covered by the airborne radiometric data was classified into eight units according to the statistical parameters. The eight clusters do not show obvious correlations with geological units, but they may be interpreted in terms of the superposition of lithology and recent superficial processes (erosion and weathering). [ABSTRACT FROM AUTHOR]

Mapping volcanic flow deposits can be achieved by considering backscattering characteristics as a metric of surface roughness. In this study, we developed an approach to extract a measure of surface roughness from dual-band airborne Synthetic Aperture Radar (ASAR) backscattering data to characterize and map various volcanic flow deposits—namely, debris avalanches, lahars, lava flows, and pyroclastic density currents. We employed ASAR and Indian Space Research Organization (ISRO) airborne SAR datasets, from a joint project (ASAR-ISRO), acquired in December 2019 at 2 m spatial resolution, to assess the role and importance of incorporating dual-band data, i.e., L-band and S-band, into surface roughness models. Additionally, we derived and analyzed surface roughness from a digital surface model (DSM) generated from unoccupied aircraft systems (UAS) acquisitions using Structure from Motion (SfM) photogrammetry techniques. These UAS-derived surface roughness outputs served as meter-scale calibration products to validate the radar roughness data over targeted areas. Herein, we applied our method to a region in the United States over the Mount St. Helens volcano in the Cascade Range of Washington state. Our results showed that dual-band systems can be utilized to characterize different types of volcanic deposits and range of terrain roughness. Importantly, we found that a combination of radar wavelengths (i.e., 9 and 24 cm), in tandem with high-spatial-resolution backscatter measurements, yields improved surface roughness maps, compared to single-band, satellite-based approaches at coarser resolution. The L-band (24 cm) can effectively differentiate small, medium, and large-scale structures, namely, blocks/boulders from fine-grained lahar deposits and hummocks from debris avalanche deposits. Additionally, variation in the roughness estimates of lahar and debris avalanche deposits can be identified and quantified individually. In contrast, the S-band (9 cm) can distinguish different soil moisture conditions across variable terrain; for example, identify wet active channels. In principle, this dual-band approach can also be employed with time series of various other SAR data of higher coherence (such as satellite SAR), using different wavelengths and polarizations, encompassing a wider range of surface roughness, and ultimately enabling additional applications at other volcanoes worldwide and even beyond volcanology. [ABSTRACT FROM AUTHOR]

Disasters caused by landslides pose a considerable threat to people's lives and property, resulting in substantial losses each year. Landslide displacement rate prediction (LDRP) provides a useful fundamental tool for mitigating landslide disasters. However, more accurately predicting LDRP remains a challenge in the study of landslides. Lately, ensemble deep learning algorithms have shown promise in delivering a more precise and effective spatial modeling solution. The core aims of this research are to explore and evaluate the prediction capability of three progressive evolutionary deep learning (DL) techniques, i.e., a recurrent neural network (RNN), long short-term memory (LSTM), and a gated recurrent unit (GRU) ensemble AdaBoost algorithm for modeling rainfall-induced and reservoir-induced landslides in the Baihetan reservoir area in China. The outcomes show that the ensemble DL model could predict the Wangjiashan landslide in the Baihetan reservoir area with improved accuracy. The highest accuracy was achieved in the testing set when the window length equaled 30. However, assembling two predictors outperformed the accuracy of assembling three predictors, with the mean absolute error and root mean square error reaching 1.019 and 1.300, respectively. These findings suggest that the combination of strong learners and DL can yield satisfactory prediction results. [ABSTRACT FROM AUTHOR]

Impact craters with layered ejecta deposits are widespread on Mars. Prevailing views suggest that such ejecta were formed due to the involvement of target water and/or water ice in the impact excavation and/or the post-deposition movement of the impact ejecta. The long-runout landslides and lobate debris aprons that are likely formed due to the involvement of water ice are used as analogs to compare roughness at multiple scales, considering that these three landforms share some similarities in their geomorphology. Analog studies of the morphological similarities and differences of layered ejecta deposits with different emplacement mechanisms are an important approach to untangling how layered ejecta deposits might form on Mars and beyond. Earlier morphological comparisons were usually based on qualitative descriptions or one-dimensional topographic roughness characteristics at given azimuths; however, the emplacement processes of layered deposits are recorded in two-dimensional topography and at multiple scales. In this study, we designed a multiwavelet algorithm to characterize the multi-scale topographic roughness of different forms of Martian layered deposits. Our comparisons show that the inner facies of the layered ejecta deposits and long-runout landslides exhibited similar roughness characteristics, and the outer facies of the layered ejecta deposits were more similar in roughness to lobate debris aprons. This study highlights the importance of the spatial resolution of digital terrain models in characterizing fine topographic fluctuations on layered ejecta deposits, providing additional insights into the possible emplacement mechanisms of different parts of layered ejecta deposits. [ABSTRACT FROM AUTHOR]

The deformation characteristics and instability patterns of rotational landslides are complicated. Such landslides are large and occur continuously, seriously threatening people's lives. We used interferometry synthetic aperture radar (InSAR), digital elevation models of difference (DODs), numerical simulations, and other techniques for analyzing the topographic changes, surface deformation and movement process before, during and after a landslide. Based on the high-resolution terrain data before and after the landslide, the topographic changes were analyzed, and the active zone of the landslide was identified. The areas of the topographic changes were mainly located on the main scarp, toe and secondary landslides. The topographic changes were influenced by rainfall and rill erosion. The geomorphologically-guided InSAR interpretation method was applied to explore the displacement pattern. The deformation area in the middle of the landslide coincided with the secondary landslides. A time-series InSAR analysis revealed the dynamic evolution of the deformation before and after the landslide. Based on its evolution, the simulated landslide process included the main landslide and three secondary landslides. Based on the displacement of the longitudinal ground surface profiles, the displacement characteristics and kinematic behavior were summarized and compared with those of a single rotational landslide and multiple rotational landslides. The single rotational landslide had obvious secondary and progressive characteristics, developing into multiple rotational landslides triggered by conditions such as rainfall. [ABSTRACT FROM AUTHOR]

Landslide disasters cause serious property losses and casualties every year. Landslide displacement prediction is fundamental for mitigating landslide disasters. Several approaches have been used to predict landslide displacement, yet a more accurate and reliable displacement prediction still has a poor understanding of landslide early warning systems for landslide mitigation, due to limited data and mutational displacements. To boost the robustness and accuracy of landslide displacement prediction, this paper assembled a new hybrid model containing the local mean decomposition (LMD), innovations state space models for exponential smoothing (ETS), and the temporal convolutional network (TCN). The proposed model, which is based on over 10 years of long-term time series monitoring GPS data, was tested on the selected case—stepwise Baijiabao landslide in the Three Gorges Reservoir area of China (TGRA) was tested by the proposed model. The results presented that the LMD–ETS–TCN model has the best performance in comparison with other benchmark models. Compared with autoregressive integrated moving average (ARIMA), support vector regression (SVR), and long short-term memory neural network (LSTM), the accuracy was noticeably improved by an average of 40.9%, 46.2%, and 22.1%, respectively. The robustness and effectiveness of the presented approach are attested, and it has discernible improvements for landslide displacement prediction. [ABSTRACT FROM AUTHOR]

Flow‐like landslides in clay slopes pose major threats to people and infrastructure, which has led to numerous studies in recent decades. However, the mechanisms leading to the solid–fluid transition in clay are still poorly understood, despite numerous studies on its rheological evolution. The aim of this study is to contribute to quantify the degradation of clay at the surface of the Harmalière landslide (French Alps) from the analysis of a series of three unmanned aerial vehicle (UAV) acquisitions. Two approaches were combined to process the acquired optical images. First, image classification was performed applying object‐based image analysis (OBIA) to the red, green and blue (RGB) and surface roughness layers. Second, deeper analysis of the surface roughness allows to describe the morphology evolution and to interpret the degradation scheme from undegraded clay to degraded clay. The study shows that the applied methodology is appropriate to perform a thorough analysis of the material degradation pattern on the surface of a landslide. The temporal analysis shows an average degradation rate leading to the complete degradation of a block in about 2 years. Meanwhile, a spatial analysis shows that non‐degraded clays degrade faster in the lower part of the study area, reactivated 30 years ago, than in the upper part, reactivated only a few years ago. In addition, roughness analyses enabled to highlight the evolution of the morphology during the degradation process of the clay blocks, from angular blocks to mounds. [ABSTRACT FROM AUTHOR]

Understanding englacial and subglacial structures is a fundamental method of inferring ice sheets' historical evolution and surface mass balance. The internal layer continuity index and the basal roughness are key parameters and indicators for the speculation of the relationship between the ice sheet's internal structure or bottom and ice flow. Several methods have been proposed in the past two decades to quantitatively calculate the continuity index of ice layer geometry and the roughness of the ice–bedrock interface based on radar echo signals. These methods are mainly based on the average of the absolute value of the vertical gradient of the echo signal amplitude and the standard deviation of the horizontal fluctuation of the bedrock interface. However, these methods are limited by the amount and quality of unprocessed radar datasets and have not been widely used, which also hinders further research, such as the analysis of the englacial reflectivity, the subglacial conditions, and the history of the ice sheets. In this paper, based on geophysical processing methods for radar image denoising and deep learning for ice layer and bedrock interface extraction, we propose a new method for calculating the layer continuity index and basal roughness. Using this method, we demonstrate the ice-penetrating radar data processing and compare the imaging and calculation of the radar profiles from Dome A to Zhongshan Station, East Antarctica. We removed the noise from the processed radar data, extracted ice layer continuity features, and used other techniques to verify the calculation. The potential application of this method in the future is illustrated by several examples. We believe that this method can become an effective approach for future Antarctic geophysical and glaciological research and for obtaining more information about the history and dynamics of ice sheets from their radar-extracted internal structure. [ABSTRACT FROM AUTHOR]

Landslide displacement prediction is an essential base of landslide hazard prevention, which often needs to establish an accurate prediction model. To achieve accuracy prediction of landslide displacement, a displacement prediction model based on a salp-swarm-algorithm-optimized temporal convolutional network (SSA-TCN) is proposed. The TCN model, consisting of a causal dilation convolution layer residual block, can flexibly increase the receptive fields and capture the global information in a deeper layer. SSA can solve the hyperparameter problem well for TCN model. The Muyubao landslide displacement collected from a professional GPS monitoring system implemented in 2006 is used to analyze the displacement features of the slope and evaluate the performance of the SSA-TCN model. The cumulative displacement time series is decomposed into trend displacement (linear part) and periodic displacement (nonlinear part) by the variational modal decomposition (VMD) method. Then, a polynomial function is used to predict the trend displacement, and the SSA-TCN model is used to predict the periodic displacement of the landslide based on considering the response relationship between periodic displacement, rainfall, and reservoir water. This research also compares the proposed approach results with the other popular machine learning and deep learning models. The results demonstrate that the proposed hybrid model is superior to and more effective and accurate than the others at predicting the landslide displacement. [ABSTRACT FROM AUTHOR]

The landscape of Antarctica, hidden beneath kilometre‐thick ice in most places, has been shaped by the interactions between tectonic and erosional processes. The flow dynamics of the thick ice cover deepened pre‐formed topographic depressions by glacial erosion, but also preserved the subglacial landscapes in regions with moderate to slow ice flow. Mapping the spatial variability of these structures provides the basis for reconstruction of the evolution of subglacial morphology. This study focuses on the Jutulstraumen Glacier drainage system in Dronning Maud Land, East Antarctica. The Jutulstraumen Glacier reaches the ocean via the Jutulstraumen Graben, which is the only significant passage for draining the East Antarctic Ice Sheet through the western part of the Dronning Maud Land mountain chain. We acquired new bed topography data during an airborne radar campaign in the region upstream of the Jutulstraumen Graben to characterise the source area of the glacier. The new data show a deep relief to be generally under‐represented in available bed topography compilations. Our analysis of the bed topography, valley characteristics and bed roughness leads to the conclusion that much more of the alpine landscape that would have formed prior to the Antarctic Ice Sheet is preserved than previously anticipated. We identify an active and deeply eroded U‐shaped valley network next to largely preserved passive fluvial and glacial modified landscapes. Based on the landscape classification, we reconstruct the temporal sequence by which ice flow modified the topography since the beginning of the glaciation of Antarctica. [ABSTRACT FROM AUTHOR]

Mass wasting, as one of the most significant geomorphological processes, contributes immensely to planetary landscape evolution. The frequency and diversity of mass wasting features on any planetary body also put engineering constraints on its robotic exploration. Mass wasting on other Solar System bodies shares similar, although not identical, morphological characteristics with its terrestrial counterpart, indicating a possible common nature for their formation. Thus, planetary bodies with contrasting environmental conditions might help reveal the effects of the atmosphere, subsurface fluids, mass accumulation/precipitation, and seismicity on mass wasting, and vice versa. Their relative positions within our Solar System and the environmental and geophysical conditions on the Moon and the dwarf planet Ceres are not only extremely different from Earth's but from each other too. Their smaller sizes coupled with the availability of global-scale remote sensing datasets make them ideal candidates to understand mass wasting processes in widely contrasting planetary environments. Through this concept article, we highlight several recent advances in and prospects of using remote sensing datasets to reveal unprecedented details on lunar and Cerean mass wasting processes. We start with briefly discussing several recent studies on mass wasting using Lunar Reconnaissance Orbiter Camera (LROC) data for the Moon and Dawn spacecraft data for Ceres. We further identify the prospects of available remote sensing data in advancing our understanding of mass wasting processes under reduced gravity and in a scant (or absent) atmosphere, and we conclude the article by suggesting future research directions. [ABSTRACT FROM AUTHOR]

Jupiter's moon Europa harbors one of the most likely environments for extant extraterrestrial life. Determining whether Europa is truly habitable requires understanding the structure and thickness of its ice shell, including the existence of perched water or brines. Stereo-derived topography from images acquired by NASA Galileo's Solid State Imager (SSI) of Europa are often used as a constraint on ice shell structure and heat flow, but the uncertainty in such topography has, to date, not been rigorously assessed. To evaluate the current uncertainty in Europa's topography we generated and compared digital terrain models (DTMs) of Europa from SSI images using both the open-source Ames Stereo Pipeline (ASP) software and the commercial SOCET SET® software. After first describing the criteria for assessing stereo quality in detail, we qualitatively and quantitatively describe both the horizontal resolution and vertical precision of the DTMs. We find that the horizontal resolution of the SOCET SET® DTMs is typically 8–11× the root mean square (RMS) pixel scale of the images, whereas the resolution of the ASP DTMs is 9–13× the maximum pixel scale of the images. We calculate the RMS difference between the ASP and SOCET SET® DTMs as a proxy for the expected vertical precision (EP), which is a function of the matching accuracy and stereo geometry. We consistently find that the matching accuracy is ~0.5 pixels, which is larger than well-established "rules of thumb" that state that the matching accuracy is 0.2–0.3 pixels. The true EP is therefore ~1.7× larger than might otherwise be assumed. In most cases, DTM errors are approximately normally distributed, and errors that are several times the derived EP occur as expected. However, in two DTMs, larger errors (differences) occur and correlate with real topography. These differences primarily result from manual editing of the SOCET SET® DTMs. The product of the DTM error and the resolution is typically 4–8 pixel2 if calculated using the RMS image scale for SOCET SET® DTMs and the maximum images scale for the ASP DTMs, which is consistent with recent work using martian data sets and suggests that the relationship applies more broadly. We evaluate how ASP parameters affect DTM quality and find that using a smaller subpixel refinement kernel results in DTMs with smaller (better) resolution but, in some cases, larger gaps, which are sometimes reduced by increasing the size of the correlation kernel. We conclude that users of ASP should always systematically evaluate the choice of parameters for a given dataset. [ABSTRACT FROM AUTHOR]

A slow-moving landslide on the Huafan University campus, which is located on a dip slope in northern Taiwan, has been observed since 1990. However, reliable monitoring data are difficult to acquire after 2018 due to the lack of continuous maintenance of the field measurement equipment. In this study, the multitemporal interferometry (MTI) technique is applied with Sentinel-1 SAR images to monitor the slow-moving landslide from 2014–2019. The slow-moving areas detected by persistent scatterer (PS) pixels are consistent with the range of previous studies, which are based on in situ monitoring data and field surveys. According to the time series of the PS pixels, a long period gravity-induced deformation of the slow-moving landslide can be clearly observed. Moreover, a short period seasonal surface fluctuation of the slow-moving landslide, which has seldom been discussed before, can also be detected in this study. The seasonal surface fluctuation is in-phase with precipitation, which is inferred to be related to the geological and hydrological conditions of the study area. The MTI technique can compensate for the lack of surface displacement data, in this case, the Huafan University campus, and provide information for evaluating and monitoring slow-moving landslides for possible landslide early warning in the future. [ABSTRACT FROM AUTHOR]

With the development of deformation measuring technology at slope surfaces, prediction methods for rainfall-induced landslides based on the surface movements and tilting of slopes in the pre-failure stage have been recognized as a promising technique for risk reduction of slope failure triggered by rainfall. However, the correlation and possible mechanism for these prediction methods were rarely discussed. In this study, the comparison between the prediction methods of slope failure based on the time history of surface displacements and tilting in the acceleration stage was carried out by conducting a series of laboratory tests and field tests under rainfall, in which the movements and tilting behaviors at the slope surface were measured. The results show that the predicted failure time of tested slopes obtained by different prediction methods is consistent, and the correlation between these landslide prediction methods were also detected. A proportional relationship between the velocity of surface displacements and tilting rate was observed, and a possible mechanism for the sliding behavior was discussed based on this linear relationship as well. In addition, an expression for the linear relationship between the rate of the surface tilting and displacement was also established in this study, and the results could have significance for the understanding of the sliding behavior in the failure process in rainfall-induced landslides. [ABSTRACT FROM AUTHOR]

The paper explores the potential of the satellite advanced differential synthetic aperture radar interferometry (A-DInSAR) technique for the identification of impending slope failure. The advantages and limitations of satellite InSAR in monitoring pre-failure landslide behaviour are addressed in five different case histories back-analysed using data acquired by different satellite missions: Montescaglioso landslide (2013, Italy), Scillato landslide (2015, Italy), Bingham Canyon Mine landslide (2013, UT, USA), Big Sur landslide (2017, CA, USA) and Xinmo landslide (2017, China). This paper aimed at providing a contribution to improve the knowledge within the subject area of landslide forecasting using monitoring data, in particular exploring the suitability of satellite InSAR for spatial and temporal prediction of large landslides. The study confirmed that satellite InSAR can be successful in the early detection of slopes prone to collapse; its limitations due to phase aliasing and low sampling frequency are also underlined. According to the results, we propose a novel landslide predictability classification discerning five different levels of predictability by satellite InSAR. Finally, the big step forward made for landslide forecasting applications since the beginning of the first SAR systems (ERS and Envisat) is shown, highlighting that future perspectives are encouraging thanks to the expected improvement of upcoming satellite missions that could highly increase the capability to monitor landslides' pre-failure behaviour. [ABSTRACT FROM AUTHOR]

A digital elevation model (DEM) is a quantitative representation of terrain and an important tool for Earth science and hydrological applications. A high-resolution DEM provides accurate basic Geodata and plays a crucial role in related scientific research and practical applications. However, in reality, high-resolution DEMs are often difficult to obtain. Due to the self-similarity present within terrains, we proposed a method using the original DEM itself as a sample to expand the DEM using sliding windows method (SWM) and generate a higher resolution DEM. The main processes of SWM include downsampling the original DEM and constructing mapping sets, searching for the optimal matching, window replacement. Then, we repeat these processes with the small-scale expansion factor. In this paper, the grid resolution of the Taitou Basin was expanded from 30 to 10 m. Overall, the superresolution reconstruction results showed that the method could achieve better outcomes than other commonly used techniques and exhibited a slight deviation (root mean square error (RMSE) = 3.38) from the realistic DEM. The generated high-resolution DEM prove to be significant in the application of flood simulation modeling. [ABSTRACT FROM AUTHOR]