High-precision Digital Elevation Model Research Articles

Qinghai-Tibet Plateau Glacier-DEM product derived from the Chinese stereo mapping satellites

Abstract. The Digital Elevation Model (DEM) is an important basic data for glacier research on the Qinghai-Tibet Plateau. With the rapid development of China 's stereo mapping satellite, it is possible to obtain high-precision DEM in the glacier area of the Qinghai-Tibet Plateau. In this study, the stereo images and laser altimetry data of ZY-3 and GF-7 satellites were comprehensively used, and the matching and joint adjustment methods of laser elevation control points and stereo images were proposed. A high-precision DEM product in glacier area was established, and the elevation accuracy of DEM was verified by using UAV data and ICESat-2 data. The results show that the accuracy of GF-7 DEM is better than 1.5 m when the slope is less than 6 degrees, and the overall accuracy is better than 2.8 m. The accuracy of ZY-3 DEM is better than 3.5 m when the slope is less than 6 degrees, and the overall accuracy is better than 5.0 m. Compared with the open source DEM with medium spatial resolution, the DEM elevation accuracy based on domestic stereo mapping satellite images is better, and the grid is finer and more detailed to describe the texture characteristics of the end of the glaciers. Compared with the high spatial resolution data set HMA DEM, the ZY-3 DEM is slightly worse and the GF-7 DEM is better, and both the ZY-3 DEM and GF-7 DEM are better than the HMA DEM in terms of coverage integrity in the Qinghai- Tibet Plateau. Chinese stereo mapping satellites can generate high-precision DEM in the glacier area, and can provide more accurate and reliable terrain reference data than the existing open free data for the study of glaciers in the Qinghai-Tibet Plateau.

Evaluating the Accuracy of Contour Ridgeline Positioning for Soil Conservation in the Northeast Black Soil Region of China

The Northeast black soil region is China’s vital commercial grain base. However, severe soil erosion on slope farmland poses a significant threat to this region’s sustainable agricultural productivity. The transition from traditional downslope ridging to contour ridging (briefly referred to as “contour ridging”) is one of the primary sustainable measures for preventing soil erosion on slope farmland. By integrating high-precision Digital Elevation Models (DEMs) and design standards, ArcMap can plan the orientation and position of contour ridgelines and estimate the reduction in soil erosion on slope farmland after implementing contour ridging. Therefore, the degree of discrepancy between the designed and implemented positions of the contour ridgelines directly affects the effectiveness of contour ridging and the precision in evaluating its impacts and benefits. This study aims to assess the position accuracy of contour ridgelines designed by ArcMap 10.5 (here after ArcMap) using high-precision DEMs obtained from unmanned aerial vehicles (UAV). For this purpose, three fields where contour ridging had already been implemented were selected in the Sanjiang Plain of Heilongjiang Province, China. CORS RTK was used to measure the XYZ coordinates of verification points along implemented ridges precisely. Those measured coordinates were compared with the designed ridgeline positions to analyze the positional discrepancies between the designed and implemented ridgelines. The results indicated the following: (1) The average slope gradient along contour ridgelines measured in the field (0.5~0.6°) was relatively close to that along the ridgelines designed using ArcMap (0.6°), and the elevation changes along most of the contour ridgelines showed a pattern of being higher in the middle and lower on both sides. (2) The positional offset between most of the measured and designed ridgelines was less than the width of a single ridge (1.3 m), and the median offset was one-third of the width of a single ridge. (3) The positional offset caused by the movement of ridging machinery could be larger than the offset resulting from the baseline setup, and verification points with larger positional offsets were often located at the edges of the plots as well as turns of the ridgelines. Therefore, during the designing ridgelines process, the turns should be made as smooth as possible. During ridging, reducing the speed at these turns to minimize errors and maintain the accuracy of the ridgeline was recommended. The findings of this study can provide a scientific basis to improve contour ridging design and effect prediction in slope farmland to control soil erosion and enhance agricultural sustainability.

A photographic method to identify reservoir geohazards induced by rock mass deterioration of hydro-fluctuation belt

Potential geohazards triggered by the rock mass deterioration (RMD) of the hydro-fluctuation belt (HFB) in the Three Gorges Reservoir area (TGRA) severely threaten the lives and property of people, it is necessary and urgent to timely identify this kind of potential geohazard. Aiming at this issue, several typical evolution modes of potential RMD-induced geohazards were generalized, and an unmanned aerial vehicle (UAV) photographic method integrating the orthophotography, three-dimensional multi-angle oblique photography and high-precision digital elevation model (DEM) was proposed to early recognize the newly formed potential RMD-induced geohazards. By resolving the problem of poor precision in the orthophotographic images of high steep bank slopes, the proposed method improved the recognition reliability of potential reservoir geohazards in the HFBs. Taking the bank slopes from Zigui County to Badong County as the study area, 116 new potential RMD-induced geohazard sites were identified by the presented approach, including 64 and 52 sites on the left and right bank, respectively. Through field verifications, 17 and 19 sites on the left and right bank were found to be consistent with existing hazards, indicating the effectiveness of the proposed approach in avoiding the false and missing recognitions of potential RMD-induced reservoir geohazards at current levels of knowledge and technology. The study can provide a valuable reference for the early identification of RMD-induced geohazards in reservoir areas.

The High-Resolution Calibration of the Topographic Wetness Index Using PAZ Satellite Radar Data to Determine the Optimal Positions for the Placement of Smart Sustainable Drainage Systems (SuDS) in Urban Environments

This study addresses the growing interest in utilizing remote sensing tools for locating sustainable drainage systems (SuDS) in urban environments. SuDS, recognized as Nature-based Solutions (NbS), play a crucial role in enhancing urban resilience against climate change. This study focuses on the calibration process required to establish a correlation between the Topographic Wetness Index (TWI), derived from high-precision digital elevation models (DEMs), and soil moisture (SM) data obtained from satellite imaging. This calibration serves as a method to optimize the placement of sustainable urban drainage system vegetated techniques in urban areas. This study leveraged the exceptional resolution of PAZ satellite radar data to effectively detect variations in SM, particularly in grass-type vegetated land. The sensitivity of the X-band radar signal to moisture levels and changes in ground roughness proved valuable in tracking SM dynamics. The core of the study involved deriving the TWI from a high-resolution digital terrain model (DTM). The correlation between the TWI and SM values demonstrates robustness, with an R2 value of 0.77. These findings significantly advance the calibration of TWI values with SM measurements, enhancing their practicality in identifying areas prone to water accumulation. The study’s outcomes provide valuable insights for guiding the strategic placement of SuDS in urban environments, contributing to the effective management of water-related challenges in the face of urbanization and climate change.

Performance Analysis of a Drone Development Kit-derived Digital Elevation Model

Surface modeling constitutes is a crucial aspect in numerous engineering inquiries and earth observation endeavors. In contemporary times, the acquisition of geospatial data essential for the digital representation of local regions is increasingly facilitated through drone-based methodologies, supplanting conventional terrestrial data gathering techniques. The market presently hosts a plethora of cost-effective, "ready-to-fly" unmanned aerial vehicles (UAVs), offering users the capability to generate photogrammetric outputs, including high geometric precision Digital Elevation Models (DEMs). Moreover, modularly structured drone development kits, designed for multifarious applications, are readily accessible for purchase. These drone kits offer an economically advantageous platform that users can customize to suit their specific needs. Nevertheless, the geometric precision of DEMs created using these kits hinges upon the capabilities of the imaging and navigation systems, in addition to the stabilization of the platform during autonomous flight. In this study, using a drone development kit and a commercial drone, simultaneous image acquisition was performed for the same study area and two different DEMs were produced. The efficacy of the DEM generated using the drone development kit was assessed through a comparative analysis with the DEM obtained from a commercial drone. In addition, geometric accuracy assessment was conducted for both DEMs using ground control points. The findings reveal the usability of drone development kits in precision DEM production, as well as their limitations.

A High-Precision Baseline Calibration Method Based on Estimation of Azimuth Fringe Frequency with THz Interferometry SAR

In this study, repeat-pass synthetic aperture radar interferometry (repeat-pass THz InSAR) is first extended to the terahertz band, and it has tremendous potential in the application of high-resolution three-dimensional (3D) imaging due to its shorter wavelength, larger bandwidth, and greater sensitivity to elevation variation. The super-resolution and high sensitivity of THz InSAR pose greater demands on the baseline calibration for high-precision digital elevation model (DEM) generation. To meet the elevation accuracy requirement of THz InSAR, we propose a baseline calibration method relying on the estimation of the azimuth fringe frequency (EAFF) of the interferometric phase. Initially, a model for non-parallel sampling path errors within the squint SAR repeat-pass interferometry was established, and then, we conducted the theoretical analysis of the phase errors induced by the non-parallel errors. Following this, using a reference DEM, the relationship between the fringe frequency of the error phase and the bias in the repeat-path positioning was established. This allowed the estimation of the position errors to be transformed into the frequency spectrum estimation based on the FFT, which would mitigate the impact of unknown SAR sampling positions. Ultimately, we investigated the accuracy of the proposed EAFF calibration method, and the simulation showed that it can achieve the theoretical accuracy when the correlation coefficient exceeds 0.3. Furthermore, we configured the repeat-pass THz InSAR system with the 0.3 THz stepped-frequency radar. Compared to the conventional calibration based on ground control points (GCPs), the 3D reconstruction of both a knife and a terrain model, calibrated using the proposed EAFF algorithm, demonstrated that the elevation accuracy can achieve millimeter-level precision across the entire image swath. The above results also proved the great potential of THz InSAR in high-precision 3D imaging and remote sensing.

First Assessment of Bistatic Geometric Calibration and Geolocation Accuracy of Innovative Spaceborne Synthetic Aperture Radar LuTan-1

LuTan-1 (LT-1) is a bistatic synthetic aperture radar (BiSAR) system consisting of two identical L-band SAR satellites. The bistatic mode of LT-1 plays a critical role in generating high-precision digital elevation models (DEMs), which requires precise geometric calibration of initial range and azimuth times for both SARs to ensure the reliability and quality of geolocation. However, existing geometric calibration methods predominantly focus on monostatic SAR systems, with limited literature on slave SAR calibration in bistatic systems. This research addresses this gap by establishing geometric calibration models for both SARs based on signal echo history and the range–Doppler model. The geometric errors are effectively resolved using corner reflector data from Xinjiang, China. Through statistical analysis of LT-1 SAR images acquired between July and November in bistatic mode, this paper has demonstrated range delay accuracy of better than 5 ns and azimuth time accuracy of better than 0.1 ms. This level of precision translates into a positional accuracy better than 0.8 m. The proposed models have been successfully applied to geometric calibration, providing precise geolocation for LT-1, thus enhancing its utility for a wide range of Earth observation applications. This paper is the first endeavor to present the assessment of the geometric calibration and geolocation accuracy of LT-1 and discuss the results of the bistatic geometric calibration of the master and slave SARs in a BiSAR formation.

Interferometric Calibration Based on a Constrained Evolutionary Algorithm without Ground Control Points for a Tiangong-2 Interferometric Imaging Radar Altimeter

The interferometric imaging radar altimeter (InIRA), mounted on the Tiangong-2 space laboratory, utilizes a small incidence and a short interferometric baseline to achieve altimetry for wide swathes of ocean surface topography and inland water surface elevation. To obtain a high-precision digital elevation model (DEM), calibration of the interferometric system parameters is necessary. Because InIRA utilizes the small-incidence interference system design, serious coupling occurs between the interferometric parameters. Commonly used interferometric calibration methods tend to fall into the local optimal solution for InIRA. Because evolutionary algorithms have a stronger robustness and global search ability, they are better suited to handling the solution space structure under the coupling of complex interferometric parameters. This article establishes an interferometric calibration optimization model for InIRA by utilizing the relative flatness of the lake surface as an inequality constraint. Furthermore, an adaptive penalty coefficient constraint evolutionary algorithm is designed to solve the model. The proposed method was tested on actual InIRA data, and the results indicate that it efficiently adjusts interferometric parameters, enhancing the precision of measurements for Qinghai Lake elevation.

Research on Landslide Trace Recognition by Fusing UAV-Based LiDAR DEM Multi-Feature Information

Landslide traces are crucial geomorphological features of landslides. Through the recognition of landslide traces, a better grasp of the topographical features of landslides can be achieved, thereby aiding in the enhancement of capabilities for the prevention, response, and management of landslides. Aiming at the complex topographic features of landslide traces, only using a single DEM product could provide a complete and comprehensive recognition of landslide traces. A method of landslide tracing recognition based on the fusion of multi-feature information from the Unmanned Aerial Vehicle-based Light Detection and Ranging (UAV-based LiDAR) Digital Elevation Model (DEM) is proposed. First, a high-precision DEM is constructed by using the LiDAR point cloud data. Based on the DEM, four multi-feature images that can enhance the landslide geomorphology are generated: hillshading, slope, positive openness, and sky-view factor. Furtherore, the DEM multi-feature images were fused using the Visualization for Archaeological Topography (VAT) method to obtain the DEM Multi-Feature Fusion Image (DEM-DFFI). Finally, the landslide traces were extracted from the DEM-DFFI based on fractal theory. The method presented in this paper makes full use of DEM multi-feature images and fuses them, which can accurately and clearly show the topographic and geomorphological features of landslides. Based on this, it helps improve landslide trace recognition accuracy.

Precision Terrain Modeling Approach in Complex Mountainous Areas Based on Compact UAV Ka-InSAR Data

A High-Precision digital elevation model (DEM) is useful for disaster investigation and evaluation in cloudy, rainy, and complex mountainous areas. However, clouds and rain make the optical images and laser point cloud data acquisition difficult, while noise prohibits obtaining accurate surface information. Additionally, the complex elevation difference in mountainous areas increases the data processing difficulty, such as phase unwrapping and filtering. To overcome these problems, first, we introduce a new airborne multi-baseline Ka-Interferometric Synthetic Aperture Radar (InSAR) system developed by the Beijing Institute of Radio Measurement. The system affords high resolution, small volume, is lightweight, has a good top view angle, and is flexible. Thus, it reduces the flight platform’s dependence and improves the aircrafts adaptability and universality. Moreover, a multi-baseline phase unwrapping method of a two-stage programming approach (TSPA) is selected to overcome the influence of severe noise and the phase continuity assumption limitation. Additionally, an adaptive filtering method for InSAR point clouds considering coherence and optimal bending energy is proposed. This methods validity is verified using stereo satellite images, ground observation point precision checks, and geomorphic texture analysis against existing DEM results. The experimental results demonstrate that the proposed scheme has a good filtering effect on noise, vegetation, residential building areas, and bridges, significantly reducing manual intervention. Moreover, the results highlight that our method is well integrated with stereo images and has more texture details than conventional stereo mapping results, with a mean square error of elevation of 1.938m.

A novel multi-strategy hydrological feature extraction (MHFE) method to improve urban waterlogging risk prediction, a case study of Fuzhou City in China

Reliable hydrological data ensure the precision of the urban waterlogging simulation. To reduce the simulation error caused by insufficient basic data, a multi-strategy method (MHFE) for extracting hydrological features is proposed, which includes land use/land cover (LULC) extraction (LE) and digital elevation model (DEM) reconstruction (DR). First, the high-resolution remote image, satellite DEM, precipitation, flood points and depth, and planned LULC were collected. Second, the buildings, roads, and other areas of the satellite image were segmented using the U-Net model, and the LULC data with drainage features were extracted by combining the segmentation result with the planned LULC and drainage data. Then, the terrain features of the road were enhanced to construct high-precision DEM based on the fusion of multi-source data, such as elevation points, LULC, and satellite DEM. Finally, the waterlogging model was implemented under different return periods of rainfalls and typhoon rainfall to obtain the waterlogging distribution and water depth. The simulation results were compared with historical waterlogging event data and water depth observations. The results indicated that the proposed method significantly improved the accuracy of the simulation. In terms of identifying the waterlogging points, the average F1 score increased by 0.36, 0.20, and 0.07 compared to the raw model and the single LE and DR methods, respectively. In terms of water depth simulation, the average Nash-Sutcliffe efficiency (NSE) was increased from −0.24 to 0.86, with DR and LE contributing to 79.1 % and 20.9 %, respectively. The principal contribution and novelty of this paper is to explore the generic method that enhance the hydrological data, and the findings of this study improved the performance of urban waterlogging simulation.

Assessing loess landslide volume using high-precision UAV-derived DEM: A case study of the 15 March 2019 landslide in Zaoling Township, Xiangning County in North China

Landslides pose significant hazards on China's Loess Plateau, potentially resulting in catastrophic consequences for human life and property. A prompt assessment of landslide disasters is crucial in providing scientific decision support for post-disaster relief efforts and serves as a fundamental basis for risk assessment of secondary disasters such as debris flow. On March 15, 2019, a loess landslide occurred in Zaoling Town, Xiangning County, Shanxi Province. This event led to the collapse of three buildings, resulting in 20 fatalities and 13 injuries. Subsequently, high-resolution orthophotos and an unmanned aerial vehicle-derived digital elevation model (UAV-DEM) of the landslide area were obtained through photogrammetry. By analyzing the shape of the exposed main scarp of the landslide in the 0.2 ​m UAV-DEM and the softened layer, this study successfully identified an unexposed underground sliding surface. Through further analysis of the shape of the exposed main scarp of the landslide in the 0.2 ​m UAV-DEM and the softened layer, this study accurately reconstructed the unexposed underground sliding surface. To reconstruct the pre-landslide digital elevation model (DEM), Google Earth images depicting the pre-landslide terrain features were utilized. The volume of each component of the landslide was then calculated using the pre-, post- DEM, and the sliding surface. The calculation results reveal that volume of the landslide is about 54,000 ​m3, the expansion rate was determined to be 13.9%.

Applicability Assessment of Multi-Source DEM-Assisted InSAR Deformation Monitoring Considering Two Topographical Features

The high-precision digital elevation model (DEM) is of great significance for improving the accuracy of InSAR deformation monitoring. In today’s free opening of multi-source DEM, there is no consensus on how to select suitable DEMs to assist InSAR in deformation monitoring for different landforms. This article introduces five types of DEMs: ALOS12.5, SRTM-1, ASTER V3, AW3D30, and Copernicus 30, and uses SBAS-InSAR technology to analyze the applicability of deformation monitoring in the Qinghai Tibet Plateau and Central China Plain regions. The coverage, average value, standard deviation, and unwrapping efficiency of the phase unwrapping results, the temporal deformation rate curves of six random deformation points in the key deformation area, as well as the consistency with the second-level data and the comparative analysis of RMSE of all deformation points, show that in the Qinghai Tibet Plateau region, Copernicus 30 is the best, followed by ASTER V3, AW3D30, and SRTM-1 having low accuracy, and ALOS12.5 is the worst. In the Central China Plain region, AW3D30 is the best, followed by Copernicus 30, SRTM-1, and ASTER V3 having low accuracy, and ALOS12.5 is still the worst. Although ALOS12.5 has the highest resolution, it is not recommended for deformation monitoring based on its worst performance in plateau and plain areas. It is recommended to use Copernicus 30 in plateau areas and AW3D30 for deformation monitoring in plain areas.

The 21 July 2020 Shaziba landslide in China: Results from multi-source satellite remote sensing

A catastrophic landslide occurred on 21 July 2020, 30 km from Enshi city, in Mazhe County of Hubei province, China. In this paper, we aimed to investigate the kinematic evolution and volumetric change related to this landslide using multi-source remote sensing measurements from synthetic aperture radar (SAR) and optical data. C-band Sentinel-1 and X-band TerraSAR-X SAR data are analyzed using several multi-temporal interferometry (MTI) time-series techniques including Persistent Scatterer Interferometry (PSI), Small Baseline subset (SBAS), and Combined eigenvalue maximum likelihood Phase Linking (CPL). The spatial pattern of surface deformation resulting from the interferometric analysis is then statistically analyzed to retrieve the pre-failure and post-failure displacements. Co-failure motions are analyzed using an image correlation technique applied to both the Planetscope and Sentinel-2 images. Moreover, 4 pairs of bistatic TerraSAR-X/TanDEM-X (TDX) data are utilized to generate high-precision digital elevation models (DEMs) and estimate the volumetric change related to the main slope failure.The pre-failure ground deformation analysis results suggest that the landslide was already active before the July 2020 failure, with the seasonality and hydraulic diffusivity being characteristics of a slow-moving landslide. Among the three different MTI methods applied, the CPL method results in a greater measurement points (MPs) density than the PSI and SBAS method when estimating the pre-failure movement of the Shaziba landslide. The July 2020 Shaziba disaster is divided into three main parts: (1) slightly horizontal deformation of 0.5–1.5 m within the northern part with ground and house cracks, (2) less collapse in the eastern part with horizontal motions reaching 30 m and (3) a highly eroded western part where vegetation was wholly lost in the main event, resulting in an collapse volume of approximately 4.98 million m3, out of which approximately around 3.4 million m3 was deposited and approximately 1.58 million m3 was washed away into the Qing River. After the failure, the marginal scrap of the main failure body, above crown of landslide and eastern part showed instability with rates of 20–30 mm/yr, suggesting that the failure zone may continue to expand.

Identification of Ground Deformation Patterns in Coal Mining Areas via Rapid Topographical Analysis

Coal mining inevitably brings some negative impacts, such as surface subsidence, aquifer breakage, and land degradation, to the eco-geological environment in the mining area. Among these impacts, coal mining-induced ground deformation is the most serious and has threatened the geological, ecological, and human settlement securities of mining areas. Efforts existing in the literature apply to ground deformation identification in mined-out areas at the meso-/micro and short-time scales. However, when looking back at coal mining history, there are few ways to quickly and accurately quantify ground deformation at the regional and long-time scales. In this context, we propose a method for identifying ground deformation patterns in coal mining areas using historical high-precision digital elevation models (DEMs), including data preprocessing, DEM subtraction operations, interpretation, and fitting correction. This method was applied to the Yulin National Energy and Chemical Base and successfully identified the ground deformation characteristics of the Yulin coal mining area from 2015 to 2019. By determining surface subsidence displacement, excavation depth, stacking height, and the position of the goaf suspended roof area, the objective situation of ground deformation in Yulin mining area was obtained, and the mining methods and distribution characteristics of different surface deformations were analyzed and determined. The research results are of great significance for the development of mineral resources in mining areas, reducing geological disaster risks, protecting the ecological environment, and achieving the goal of coordinated development in mining areas.

Baseline Calibration of L-Band Spaceborne Bistatic SAR TwinSAR-L for DEM Generation

The Terrain Wide-swath Interferometric L-band Synthetic Aperture Radar (TwinSAR-L) mission is a spaceborne bistatic synthetic aperture radar (SAR) mission to derive a high-quality global digital elevation model (DEM). The prerequisite of the high-accuracy DEM is knowing the interferometric baseline with high precision. The challenging problem is that the baseline of the bistatic system is highly dynamic due to the fast relative motion between the two satellites. In this paper, a pixel-related baseline model based on the geometrical shift is proposed to accurately reflect the position change of satellites. The baseline error is then calibrated using height gradient information and a small number of point targets with a slight incidence angle difference, eliminating the need for low-frequency corner reflectors and avoiding the difficulty of selecting a calibration site. The proposed method has been successfully exploited during the initial Commissioning Phase of TwinSAR-L, demonstrating its effectiveness in evaluating the precise baseline and supporting the generation of high-precision DEM.

Predicting the regime shift of coastal wetlands based on the bistability features in the intertidal zone: A case study in the Liaohe estuary

Influenced by human activities and natural interference, the worldwide distribution of coastal wetlands is now undergoing rapid evolution. The prediction on the locations of vegetation conversion is greatly important for the management of these coastal ecosystems in terms of early warning. In this paper, a series of waterlines extracted from multiple satellite images were used to generate a high-precision digital elevation model (DEM) in the intertidal zone of the Liaohe estuary. Based on the characteristics of the alternative stable states in elevation and normalized difference vegetation index (NDVI), the Logistic Regression Model was applied to predict the potential locations of vegetation expansion with geomorphic factors, such as elevation, slope, and annual changing rate of elevation. Before the prediction, the existence of two stable states in the landscape was confirmed in the study area, i.e., low-lying tidal flats and high-lying saltmarshes. When the geomorphic parameters exceeded the thresholds, the stable state transition would occur. By using the Logistic Regression Model, the elevation was the best explainer for determining the vegetation conversion in the single-factor simulation, while the slope was the worst. When multiple factors were integrated in simulations, the prediction with the elevation, slope, and annual elevation change rate was the best, with R2 = 0.739, and the overall accuracy of prediction reached 88.6%. The prediction result indicated that the area of saltmarshes in the Liaohe estuary increased by 16.7 km2 at a rate of 0.8% per year between 2011 and 2015. When considering the popularization in restoration practice, it is necessary to evaluate the reliability and flexibility of the Logistic Regression Model in predicting vegetation conversion in more types of estuarine wetlands.

Construction of high-precision DEMs for urban plots

ABSTRACT High-precision digital elevation models (DEMs) are the basic data for constructing digital cities. With the acceleration of urbanization, the topography of urban plots is constantly transformed by human activities, so that surface morphology shows the characteristics of diversification and discontinuity. Existing modelling methods focus on the expression of continuous terrain. Constructing high-precision DEM in urban plots is still challenging. A block-based modelling method that considers the morphological characteristics of urban plot elements was proposed. The Jinzhai County urban area was selected as the research area. The elements of urban plots were classified into six types, in which the boundaries and elevation information were extracted with real sense 3D models, Digital Orthophoto Maps (DOMs) and dense matching point cloud data. The DEMs of the 6 types of elements were generated separately with different methods, that are fused to complete the DEM of urban plots. The DEM obtained using the method in this manuscript was consistent with the reality in terms of topographic relief within each element and clarified the boundary of each element. The accuracy assessment showed that the RMSE results of roads, slopes, other terrains and natural terrains are approximately 0.05 m, which meets the elevation accuracy requirements of the 1:1000 large-scale mapping.

Effects of digital elevation model data source on HSPF-based watershed-scale flow and water quality simulations.

Digital elevation models (DEMs) from different sources have been widely utilized in watershed modeling and environment management. Yet, little is known about how DEMs from different data sources affect modeling results and management decisions. This paper presents new insights into how the DEMs from three different sources affect model-simulated flow, nitrate (NO3), phosphorus (P), and sediment by using the BASINS/HSPF watershed modeling system. It was found that DEM source-induced uncertainties in simulation results are higher than the DEM resolution-induced uncertainties regardless of watershed slope or delineation method. Moreover, DEM source introduces higher uncertainties in simulation results for automatically delineated low-gradient watersheds than high gradient watersheds. Sediment and NO3 concentrations were the most and the least sensitive water quality parameters, respectively, to DEM sources. The uncertainties in simulation results may be reduced by using the manual method for watershed delineation but they cannot be completely eliminated. It is recommended that high precision (such as NED) DEMs be employed especially for flat watersheds. The findings provide guidelines for selection of DEM source based on available resources.

High Precision Lava Flow Simulation Using 8K Drone Digital Elevation Data

This study applied the numerical simulations of lava flows to evaluate the damage caused by volcanic hazards. The accuracy of the numerical simulation was governed by the reliability of the input parameters in the numerical models. One of the most important parameters is the information pertaining to terrain data, which is generally provided as a digital elevation model (DEM). The recently-developed 8K technology, mainly applied in the broadcasting field, provides a precise photographic survey of the terrain and can produce a detailed digital topographic map. The Japan Broadcasting Cooperation (NHK) deployed an 8K drone to generate a digital elevation model of Sakurajima Volcano, Japan. These data were used to formulate the lava flow under certain prescribed conditions. This study analyzes the influence of DEM mesh size on lava flow simulations, showing that high-precision DEM outlines the distribution of inundated areas in detail. Moreover, a smaller mesh resulted in a faster arrival time of the lava flow. The study’s results are useful for both risk and crisis management.