Digital Elevation Model Research Articles

Characteristics and Tectonic Implications of the Geomorphic Indices of the Watersheds around the Lijiang–Jinpingshan Fault

The Lijiang–Jinpingshan fault (LJF) is an important secondary boundary fault that obliquely cuts the Sichuan–Yunnan rhombic block. It is of great significance for understanding the tectonic evolution of the Sichuan–Yunnan rhombic block and even the southeastern margin of the Tibet Plateau. Based on a digital elevation model (DEM), this work combines ArcGIS with MATLAB script programs to extract geomorphic indices including slope, the relief degree of the land surface (RDLS), hypsometric integral (HI), and channel steepness index (ksn) of 593 sub–watersheds and strip terrain profiles around the LJF. By analyzing the spatial distribution characteristics of the geomorphic indices and combining the regional lithology and precipitation conditions, the spatial distribution of the geomorphic indices around the study area was analyzed to reveal the implications of the LJF’s activity. The results of this work indicate that (1) the distribution of geomorphic indices around the LJF may not be controlled by climate and lithological conditions, and the LJF is the dominant factor controlling the geomorphic evolution of the region. (2) The spatial distribution patterns of geomorphic indices and strip terrain profiles reveal that the vertical movement of the LJF resulted in a pronounced uplift on its northwest side, with tectonic activity gradually diminishing from northeast to southwest. Furthermore, based on the spatial distribution characteristics of these geomorphic indices, the activity intensity of the LJF can be categorized into four distinct segments: Jianchuan–Lijiang, Lijiang–Ninglang, Ninglang–Muli, and Muli–Shimian. (3) The activity of the LJF obtained from tectonic geomorphology is consistent with the conclusions obtained in previous geological and geodesic studies. This work provides evidence of the activity and segmentation of the LJF in tectonic geomorphology. The results provide insight for the discussion of tectonic deformation and earthquake disaster mechanisms in the southeastern margin of the Tibet Plateau.

Failed tableland: geomorphological map of the Sarmiento Basin (extra-Andean Patagonia, Argentina)

ABSTRACT We present a 1:120,000 landslide-focused geomorphological map of the Sarmiento Basin (Chubut Province, Argentina) and the adjacent landscape. The study area covers about 8000 km2 and can be schematically described as an endorheic basin enclosed by tableland. However, the landscape shows a high diversity of landforms and features. The mapping was mostly conducted manually, based on high-resolution satellite images and a digital elevation model, and supported by field observations. We divided the mapped landforms and features into six categories based on their prevailing origin: structural and volcanic, mass movement, hillslope erosional, fluvial, lacustrine and aeolian. We highlight the abundance of landslides (23% of the study area) in the context of volcanic plateaus and (paleo)lake level evolution. The presented map is unique due to the overall scarcity of detailed geomorphological maps of tablelands worldwide and the insufficient documentation of landslides in the extra-Andean Patagonia.

Estimating vegetation and litter biomass fractions in rangelands using structure-from-motion and LiDAR datasets from unmanned aerial vehicles

ContextThe invasion of annual grasses in western U.S. rangelands promotes high litter accumulation throughout the landscape that perpetuates a grass-fire cycle threatening biodiversity.ObjectivesTo provide novel evidence on the potential of fine spatial and structural resolution remote sensing data derived from Unmanned Aerial Vehicles (UAVs) to separately estimate the biomass of vegetation and litter fractions in sagebrush ecosystems.MethodsWe calculated several plot-level metrics with ecological relevance and representative of the biomass fraction distribution by strata from UAV Light Detection and Ranging (LiDAR) and Structure-from-Motion (SfM) datasets and regressed those predictors against vegetation, litter, and total biomass fractions harvested in the field. We also tested a hybrid approach in which we used digital terrain models (DTMs) computed from UAV LiDAR data to height-normalize SfM-derived point clouds (UAV SfM-LiDAR).ResultsThe metrics derived from UAV LiDAR data had the highest predictive ability in terms of total (R2 = 0.74) and litter (R2 = 0.59) biomass, while those from the UAV SfM-LiDAR provided the highest predictive performance for vegetation biomass (R2 = 0.77 versus R2 = 0.72 for UAV LiDAR). In turn, SfM and SfM-LiDAR point clouds indicated a pronounced decrease in the estimation performance of litter and total biomass.ConclusionsOur results demonstrate that high-density UAV LiDAR datasets are essential for consistently estimating all biomass fractions through more accurate characterization of (i) the vertical structure of the plant community beneath top-of-canopy surface and (ii) the terrain microtopography through thick and dense litter layers than achieved with SfM-derived products.

Archaeological cognition of the Eastern mausoleum of Qin state using integrated space-ground observation tools

The Eastern Mausoleum of Qin State is a significant component of the Qin Dynasty's royal tombs, reflecting the social development level during the Warring States period (475 BC ~ 221 BC) in China. At the onset of our investigation, we mapped the site's boundaries utilizing Corona satellite imagery and employed the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) to identify archaeological features. Additionally, this study is the first to propose the use of thermal infrared band data from the SDGSAT-1 satellite to explore thermal archaeological traces, demonstrating their viability for archaeological site analysis. Subsequently, electromagnetic (EM) prospection was utilized to validate the presence of an ancient burial chamber passage. Landscape monitoring and analysis of the No.1 Mausoleum were performed using Corona and Google Earth images, revealing the accuracy of the Geomancy Theory of Chinese mausoleums through dynamic remote sensing of surface changes. Furthermore, our employment of space-to-ground observational modalities and resultant Digital Elevation Models (DEM) have been used to provided new insights into the application of Remote Sensing (RS) and Geomancy in archaeology, thereby emphasizing the pivotal role of site selection in heritage preservation. This research underscores the promise of synergistic space-ground observations in both exploiting the archaeological riches of cultural heritage sites and ensuring the enduring conservation of these irreplaceable patrimonial assets.

The geomorphic work of the European mole (Talpa europaea): Long‐term monitoring of molehills using structure‐from‐motion photogrammetry

AbstractMoles—small insectivorous mammals of the family Talpidae—are widespread across the Northern Hemisphere. These subterranean mammals are easily detected through the mounds, or molehills, they construct as surface bioproducts of tunnel systems excavated underground. The dense aggregation of these bioconstructions in a range of environments (e.g., floodplains, woodland, coastal dunes and upland regions) indicates that moles may play an important role in sediment systems. However, compared with other fossorial mammals (e.g., gophers and rabbits), the impact of moles as direct and indirect biogeomorphic agents is poorly understood. Furthermore, little is known about how molehills develop and degrade over time or how long they persist as landscape features. By examining molehills created by the European mole (Talpa europaea) over 4 months on a floodplain in Oxfordshire, UK, we provide a quantitative assessment of how these landforms evolve over time and space. Through the creation of high‐resolution digital elevation models (DEMs) using structure‐from‐motion (SfM) photogrammetry, we derive a variety of metrics describing molehill morphology and produce a detailed record of how molehills change at weekly time intervals. In addition, measurements of molehill volume are used to estimate the excavation rate of moles over a month. Findings show that (i) molehills are dynamic landforms that change in size and shape in response to phases of construction, collapse, erosion and rebuilding; (ii) rates of degradation are influenced by soil characteristics and seasonal weather conditions; (iii) molehills can persist as landscape features for several months; and (iv) moles are capable of moving a substantial volume of sediment in highly active areas (3.89 m3 ha−1 month−1). Future work is now needed to determine the geomorphic impact of T. europaea over larger spatial scales (e.g., river catchments) and longer timescales (e.g., years–decades) to determine its importance in relation to other bioturbators and within the wider sediment system.

GeomorPM: a geomorphic pretrained model integrating convolution and Transformer architectures based on DEM data

As the domain of artificial intelligence has advanced, the integration of deep learning techniques into terrain and landform analysis has become more prevalent. Nevertheless, many existing methods are fully supervised and designed for specific tasks; thus, their transferability is limited and massive annotated samples are required. This study introduces a geomorphic pretrained model (GeomorPM) capable of performing multiple tasks. First, an architecture was designed that combined a convolution-based Vector Quantised-Variational Autoencoder (VQVAE) with a Transformer-based masked autoencoder (MAE) framework, allowing it to autonomously learn local details and global patterns from large-scale digital elevation model (DEM) data. Subsequently, GeomorPM, based on the VQMAE architecture, was pretrained on massive DEM data and fine-tuned for three specific tasks: DEM void filling, DEM superresolution, and landform classification. GeomorPM outperformed the traditional and other deep learning methods in all three tasks, demonstrating the superior learning ability and transferability of the model. This study provides a practical framework for developing pretrained models based on DEMs that can be expanded to other continuous geoscientific data.

Traffic noise prediction model using GIS and ensemble machine learning: a case study at Universiti Teknologi Malaysia (UTM) Campus.

This study represents a pioneering effort to integrate geographic information systems (GIS) and ensemble machine learning methods to predict noise levels on a university campus. Three ensemble models including random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGB) were developed to predict traffic noise based on data collected over a 4-week period at the Universiti Teknologi Malaysia (UTM) campus. Noise measurements were obtained during peak morning hours (7:30 to 9:30 a.m.) on weekdays within the UTM campus in Johor. Additional predictor variables, including data from the digital elevation model (DEM) and land use, were incorporated to capture the complex nonlinear relationships influencing noise levels. The models were optimized through hyperparameter tuning, resulting in high precision, as evidenced by performance metrics such as the coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE). The XGB model emerged as the most accurate, with R2 = 0.96, MAE = 0.9, and MSE = 0.3. Noise maps generated using the inverse distance weighting (IDW) interpolation technique highlighted the spatial distribution of noise levels, classified into five classes considering WHO standards. The findings identified distance from roads, the number of light vehicles, and proximity to green areas as the most significant predictors. However, challenges remain in accurately predicting noise levels associated with other predictors. The outcomes of the study indicate the superior performance of the XGB model compared to the GB and RF models. The study recommends several measures to manage and control noise pollution on the UTM campus, including raising awareness, regulating and enforcing vehicle speed limits, reevaluating land use, installing sound insulation systems, and planting trees and vegetation buffer zones around and within educational buildings.

Integration of very high-resolution stereo satellite images and airborne or satellite Lidar for Eucalyptus canopy height estimation

Eucalyptus plantations cover extensive areas in tropical regions and require accurate growth monitoring for efficient management. Traditional in-situ measurements, while necessary, are labor-intensive and impractical for large-scale assessments. Very high-resolution satellite stereo imagery is playing an increasingly important role in the estimation of fine Digital Surface Models (DSMs) across landscapes. However, its ability to estimate canopy height models (CHMs) has not been widely investigated. This study investigates the integration of high-resolution satellite stereo imagery from the Pleiades sensor with airborne or satellite Lidar data to estimate canopy height over eucalyptus plantations. Two study sites were selected in Brazil, representing flat and semi-mountainous topographies, Mato Grosso do Sul (MS) and Sao Paulo (SP), respectively. Digital Surface Models generated from Pleiades images (DSMP) were combined with Digital Terrain Models extracted from airborne Lidar data (DTMALS) to create Canopy Height Models (CHMALS). The evaluation of the CHMALS was based on two in situ canopy height measurements (Hmax and Hmean). For the SP site, the CHMALSmax, which is the average height of top 10% pixel values within each plot, correlated well with in situ Hmean, which is the average height of 10 central trees (r = 0.98), showing a bias of 1.4 m, RMSE of 3.1 m, and rRMSE of 18.5%. At the MS site, CHMALSmax demonstrated a bias of 1.9 m, RMSE of 2.3 m, rRMSE of 17.3%, and r correlation of 0.92. Despite a tendency to underestimate heights below 20 m in young tree plantations with open canopy, the results indicate reliable canopy height estimation. The study also investigates the potential of Global Ecosystem Dynamics Investigation (GEDI) elevation data as an alternative to DTMALS in absence of airborne Lidar data. The resulting CHMGedi is promising but slightly less accurate than Lidar-based CHMs. The best GEDI-based CHM (CHMGedimax) showed a bias and rRMSE of 1.3 m and 20.5% for the SP site, and 2.2 m and 24.9% for the MS site. These findings highlight the potential for integrating Pleiades and Lidar data for efficient and accurate canopy height monitoring in eucalyptus plantations.

Tectonic Activity Analysis of the Laji-Jishi Shan Fault Zone: Insights from Geomorphic Indices and Crustal Deformation Data

Fault segmentation plays a critical role in assessing seismic hazards, particularly in tectonically complex regions. The Laji-Jishi Shan Fault Zone (LJSFZ), located on the northeastern margin of the Tibetan Plateau, is a key structure that accommodates regional tectonic stress. This study integrates geomorphic indices, cross-fault deformation rate profiles, and 3D crustal electrical structure data to analyze the varying levels of tectonic activity across different segments of the LJSFZ. We extracted 160 drainage basins along the strike of the LJSFZ from a 30 m resolution digital elevation model and calculated geomorphic indices, including the hypsometric integral (HI), stream length-gradient index (SL), and channel steepness index (ksn), to assess the variations in tectonic activity intensity along the strike of the LJSFZ. The basins were categorized based on river flow directions to capture potential differences across the fault zone. Our results show that the eastern basins of the LJSFZ exhibit the strongest tectonic activity, demonstrated by significantly higher SL and ksn values compared to other regions. A detailed segmentation analysis along the northern Laji Shan Fault and eastern Jishi Shan Fault identified distinct fault segments characterized by variations in SL and ksn indices. Segments with high SL values (>500) correspond to higher crustal uplift rates (~3 mm/year), while segments with lower SL values exhibit lower uplift rates (~2 mm/year), as confirmed by cross-fault deformation profiles derived from GNSS and InSAR data. This correlation demonstrates that geomorphic indices effectively reflect fault activity intensity. Additionally, 3D crustal electrical structure data further indicate that highly conductive mid- to lower-crustal materials originating from the interior of the Tibetan Plateau are obstructed at segment L3 of the LJSFZ. This obstruction leads to localized intense uplift and enhanced fault activity. These findings suggest that while the regional stress–strain pattern of the northeastern Tibetan Plateau is the primary driver of the segmented activity along the Laji-Jishi Shan belt, the direction of localized crustal flow is a critical factor influencing fault activity segmentation.

Environmental risks associated with quarry activities and its anthropogenic changes in Weija Hills, Ga South Municipal, Ghana

Quarrying has become an important socio-economic activity in many societies as it provides several services for human well-being. Weija Hills at the Ga South Municipality in Ghana has been witnessing quarrying activities at various sections of the hills causing a series of environmental problems. The study examined the changes that have occurred in the geological structure of the Weija Hills and its environmental hazards. The study employed the mixed method approach following the exploratory sequential research design. The primary data were collected directly from field observation during which much data was gathered including photographs. The Digital Elevation Model (DEM) was used for elevation changes between 2007 and 2014. The Limit Equilibrium Method (LEM) was used for slope stability analysis. The study found that the Weija Hills's local geology is still stable. However, serious environmental problems such as erosion taking place and large quantities of weathered and eroded sediments being carried downslope any time it rains blocking the Mallam-Kasoa Highway making it risky for road users. We recommended that the Minerals Commission and other stakeholders should collaborate and reclaim the land.

Land Cover Mapping in East China for Enhancing High-Resolution Weather Simulation Models

This study was designed to develop a 30 m resolution land cover dataset to improve the performance of regional weather forecasting models in East China. A 10-class land cover mapping scheme was established, reflecting East China’s diverse landscape characteristics and incorporating a new category for plastic greenhouses. Plastic greenhouses are key to understanding surface heterogeneity in agricultural regions, as they can significantly impact local climate conditions, such as heat flux and evapotranspiration, yet they are often not represented in conventional land cover classifications. This is mainly due to the lack of high-resolution datasets capable of detecting these small yet impactful features. For the six-province study area, we selected and processed Landsat 8 imagery from 2015–2018, filtering for cloud cover. Complementary datasets, such as digital elevation models (DEM) and nighttime lighting data, were integrated to enrich the inputs for the Random Forest classification. A comprehensive training dataset was compiled to support Random Forest training and classification accuracy. We developed an automated workflow to manage the data processing, including satellite image selection, preprocessing, classification, and image mosaicking, thereby ensuring the system’s practicality and facilitating future updates. We included three Weather Research and Forecasting (WRF) model experiments in this study to highlight the impact of our land cover maps on daytime and nighttime temperature predictions. The resulting regional land cover dataset achieved an overall accuracy of 83.2% and a Kappa coefficient of 0.81. These accuracy statistics are higher than existing national and global datasets. The model results suggest that the newly developed land cover, combined with a mosaic option in the Unified Noah scheme in WRF, provided the best overall performance for both daytime and nighttime temperature predictions. In addition to supporting the WRF model, our land cover map products, with a planned 3–5-year update schedule, could serve as a valuable data source for ecological assessments in the East China region, informing environmental policy and promoting sustainability.

Integrating proximal geophysical sensing and machine learning for digital soil mapping: Spatial prediction and model evaluation using a small dataset

Geophysical methods support soil security by providing non-invasive tools to assess soil properties, monitor degradation, and guide sustainable management strategies. However, studies focusing the spatial prediction of geophysical data remain limited. In this research, we aimed to model and predict the spatial distribution of soil geophysical properties using parent material and terrain attributes with machine learning algorithms. In addition, we tested the nested leave-one-out cross validation (nested-LOOCV) method to deal with datasets with limited size. We performed a geophysical survey using three types of sensors (radiometric, magnetic and electric methods). The random forest (RF) and support vector machine (SVM) algorithms presented the best results, with RF showing higher performance for K40 and magnetic susceptibility, and SVM had higher performance for eU, eTh and apparent electrical conductivity. Parent materials and digital elevation model were the most significant variables for the modelling. The nested-LOOCV method proved to be adequate for small soil dataset. Machine learning techniques are potential tools for modelling soil geophysical variables. The combination with computational techniques shows the great relevance of geophysical measurements for the estimation of soil properties related to fertility and soil genesis.

Catalogue of coastal-based instances with bathymetric and topographic data

Abstract. We provide a catalogue of 17 700 unique coastal-based instances distributed throughout the globe and derived from bathymetric and topographic data made publicly available by the General Bathymetric Chart of the Oceans (GEBCO) as of 2022. These instances, or digital elevation models (DEMs), are delivered in the form of raster grids with a 15 arcsec resolution and are divided equally into three libraries, namely A, B, and C. In a given library, the dimensions range from a minimum of 10×10 cells to a maximum of 300×300 cells, with an incremental step of 5, i.e. 59 unique dimensions with 100 instances per dimension. In addition, for each dimension, these instances are ordered by increasing number of maritime cells and have in common the presence of a unique maritime-connected component with a ratio of maritime cells lying between 25 % and 95 % so as to cover a broad spectrum of different coastline geometries. In this paper, we will describe in detail the procedure used for their automated generation. The resulting catalogue can be downloaded from Zenodo, a general-purpose repository operated by CERN (European Organisation for Nuclear Research) and developed under the European OpenAIRE programme, at the following persistent address: https://doi.org/10.5281/zenodo.10530247 (Thuillier et al., 2024c). Additionally, a set of 18 colour palettes specifically designed for the visualisation of DEMs has been derived for this occasion and is available at the following address: https://doi.org/10.5281/zenodo.10530296 (Thuillier et al., 2024e). Both of these repositories come with comprehensive documentation.

Giant landslide, hidden caldera structure, magnetic anomalies and tectonics in southern Tyrrhenian Sea (Italy)

Phlegraean Fields and Ischia Island are large, densely populated, active volcanic structures located in the Campanian Tyrrhenian margin between the onshore and offshore sectors. While the emerged landforms provide significant insights into a series of intense volcano-tectonic events of the last 60 ka, our understanding of the sub-marine portion of these volcanoes remains limited due to its inaccessibility to direct exploration. In the last decades, the use of magnetic methods has proven to be useful in volcanic context, given the presence of large magnetization contrasts, whereas the implementation of a high-resolution Digital Elevation Model (DEM) has allowed the integration of detailed morphological analyses into the geophysical studies. In this work, we present two new high-resolution airborne and shipborne magnetic surveys, carried out by the National Institute of Geophysics and Volcanology (INGV) in the marine sector of the Phlegraean Fields caldera (Pozzuoli Bay) and surrounding sea sectors. Moreover, the new datasets have been integrated with older marine surveys carried out by the National Research Council (CNR) to extend the resulting magnetic anomaly map embracing the whole Campanian offshore volcanic structures, excluding Ischia Island. As a result, we present here three new magnetic anomaly maps used for an integrated interpretation of magnetic, morphological, and tectonic features. The study results provide valuable insights into the relationship between volcanic, magmatic, and tectonic activity, which may be useful for further investigations. Among the most notable findings are: the identification of the magnetic signatures of regional faults, previously unknown caldera structures, and a massive rock avalanche. Consequently, our research provides a significant contribution to seabed exploration aimed at identifying potential hazard factors in the study area.

Lake water storage changes and their cause analysis in Mongolia

Lake is an important water resources in Mongolia, which has undergone a large variation in past decades. However, it is still challenging to monitor long-term changes in lake water storage (LWS) due to the lack of lake level monitoring and long-term satellite altimetry data for Mongolian lakes. According to the Advanced Land Observing Satellite Digital Elevation Model (ALOS DEM) and the Joint Research Centre (JRC) dataset, we estimated the LWS changes of 55 Mongolian lakes (> 10 km2) from 1991 to 2020. The results showed that the LWS increased by 40.24 km3 from 1991 to 1997, especially for northwestern Mongolia with 31.47 km3. However, the LWS decreased by 32.44 km3 from 1998 to 2010, and the lakes in the northwestern and southern decreased by 20.24 km3 (62%) and 7.38 km3 (23%), respectively, and then the LWS continued to decrease by 10.22 km3 from 2011 to 2020. The precipitation was the primary cause of lake change, which could explain 45.13% of LWS change based on Generalized Linear Model, followed by temperature (26.33%) and irrigated area (16.72%). Analyzing the changing characteristic and driving mechanisms of LWS can provide a scientific basis for local water resources management and planning.

Reliability of Data obtained by ASTER Satellite for Digital Elevation Models

Reliability of Data obtained by ASTER Satellite for Digital Elevation Models

A landslide runout model for sediment transport, landscape evolution, and hazard assessment applications

Abstract. We developed a new rule-based, cellular-automaton algorithm for predicting the hazard extent, sediment transport, and topographic change associated with the runout of a landslide. This algorithm, which we call MassWastingRunout (MWR), is coded in Python and implemented as a component for the package Landlab. MWR combines the functionality of simple runout algorithms used in landscape evolution and watershed sediment yield models with the predictive detail typical of runout models used for landslide inundation hazard mapping. An initial digital elevation model (DEM), a regolith depth map, and the location polygon of the landslide source area are the only inputs required to run MWR to model the entire runout process. Runout relies on the principle of mass conservation and a set of topographic rules and empirical formulas that govern erosion and deposition. For the purpose of facilitating rapid calibration to a site, MWR includes a calibration utility that uses an adaptive Bayesian Markov chain Monte Carlo algorithm to automatically calibrate the model to match observed runout extent, deposition, and erosion. Additionally, the calibration utility produces empirical probability density functions of each calibration parameter that can be used to inform probabilistic implementation of MWR. Here we use a series of synthetic terrains to demonstrate basic model response to topographic convergence and slope, test calibrated model performance relative to several observed landslides, and briefly demonstrate how MWR can be used to develop a probabilistic runout hazard map. A calibrated runout model may allow for region-specific and more insightful predictions of landslide impact on landscape morphology and watershed-scale sediment dynamics and should be further investigated in future modeling studies.

LightGBM hybrid model based DEM correction for forested areas.

The accuracy of digital elevation models (DEMs) in forested areas plays a crucial role in canopy height monitoring and ecological sensitivity analysis. Despite extensive research on DEMs in recent years, significant errors still exist in forested areas due to factors such as canopy occlusion, terrain complexity, and limited penetration, posing challenges for subsequent analyses based on DEMs. Therefore, a CNN-LightGBM hybrid model is proposed in this paper, with four different types of forests (tropical rainforest, coniferous forest, mixed coniferous and broad-leaved forest, and broad-leaved forest) selected as study sites to validate the performance of the hybrid model in correcting COP30DEM in different forest area DEMs. In the hybrid model of this paper, the choice was made to use the Densenet architecture of CNN models with LightGBM as the primary model. This choice is based on LightGBM's leaf-growth strategy and histogram linking methods, which are effective in reducing the data's memory footprint and utilising more of the data without sacrificing speed. The study uses elevation values from ICESat-2 as ground truth, covering several parameters including COP30DEM, canopy height, forest coverage, slope, terrain roughness and relief amplitude. To validate the superiority of the CNN-LightGBM hybrid model in DEMs correction compared to other models, a test of LightGBM model, CNN-SVR model, and SVR model is conducted within the same sample space. To prevent issues such as overfitting or underfitting during model training, although common meta-heuristic optimisation algorithms can alleviate these problems to a certain extent, they still have some shortcomings. To overcome these shortcomings, this paper cites an improved SSA search algorithm that incorporates the ingestion strategy of the FA algorithm to increase the diversity of solutions and global search capability, the Firefly Algorithm-based Sparrow Search Optimization Algorithm (FA-SSA algorithm) is introduced. By comparing multiple models and validating the data with an airborne LiDAR reference dataset, the results show that the R2 (R-Square) of the CNN-LightGBM model improves by more than 0.05 compared to the other models, and performs better in the experiments. The FA-SSA-CNN-LightGBM model has the highest accuracy, with an RMSE of 1.09 meters, and a reduction of more than 30% of the RMSE when compared to the LightGBM and other hybrid models. Compared to other forested area DEMs (such as FABDEM and GEDI), its accuracy is improved by more than 50%, and the performance is significantly better than other commonly used DEMs in forested areas, indicating the feasibility of this method in correcting elevation errors in forested area DEMs and its significant importance in advancing global topographic mapping.

Clockwise extrusion of the Sichuan‒Yunnan block toward the Red River Fault in the southeastern Tibetan Plateau

The Xiaojiang Fault and the Red River Fault, which are located on the southeastern margin of the Tibetan Plateau, are the eastern and southern boundaries of the Sichuan–Yunnan Block, respectively. The relationship between these two faults is highly important for the study on the tectonic evolution and seismic risk of the southeastern margin of the Tibetan Plateau. Using the digital elevation model (DEM) data, we extracted and analyzed the maximum elevation, peak steepness index, maximum slope, and relatively flat surface of the Ailaoshan Shear Zone on the southwest side of the Red River Fault. The results revealed that the geomorphic indices result around Yuanjiang were significantly lower than those on the northern and southern sides of Yuanjiang. On the basis of lithology, climate and tectonics, it is inferred that tectonic activity is the main factor controlling landform development. On the basis of existing geophysical and geomorphic survey results in this area, a geodynamic model of this area was constructed: in the mid-Miocene, tectonic inversion of the Red River Fault occurred, and material from the Tibetan Plateau flowed into the Sichuan‒Yunnan Block around the East Himalayan Syntax. The resulting extrusion caused the Red River Fault to bend of ∼50 km; in the early Pliocene, the middle and lower crust broke through the barrier and entered the interior of the Ailaoshan Shear Zone. Because of the extrusion of the middle and lower crust, the role of the Red River Fault as the boundary has weakened.

Tracking the Filling, Outburst Flood and Resulting Subglacial Water Channel From a Large Canadian Arctic Subglacial Lake

AbstractWe use digital elevation models (DEMs) and ICESat‐2 data to study the filling and outflow from a large subglacial lake under Manson Icefield in the Canadian Arctic. When full, the lake is ∼17 × 3 km with an area of 52 km2. Early in 2021 the ice surface over the center of the lake sank by >140 m implying a subglacial outburst flood of ∼4 km3. Rapid outflow occurred over ∼30 days at an average rate of ∼1,500 m3s−1 resulting in the formation of a single ∼15 km subglacial outflow path detectable from post‐outflow surface depression. The shape of the surface depression, 600–800 m wide by 2–4 m deep, reflects the shape of the subglacial channel prior to closure. Downstream ice movement appears unaffected by the outflow. After outflow ends the surface depression persisted over weeks, apparently dependent on the difference between water and overburden pressures.