30-m Digital Elevation Model Research Articles

Unsupervised machine learning with different sampling strategies and topographic factors for distinguishing between landslide source and runout areas to improve landslide inventory production

This study derived 12 topographical and hydrological factors related to landslides from a 10-m digital elevation model. Three unsupervised machine learning algorithms were employed to distinguish between the features of landslide sources and runout areas for Typhoon Morakot. Two sampling strategies were designed in this study. The first strategy involved creating a data set by pairing landslide sources and runout areas on the basis of the size of polygonal areas recorded in the inventory and then exploring the effectiveness of feature separation with k-means++ as the primary method and EM (expectation maximization) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) as supplementary methods. Because of practical challenges associated with the inability to determine whether remote sensing results correspond to landslide sources or runout areas, the first sampling strategy was used to test the feasibility of the adopted algorithms. In the second sampling strategy, the effectiveness of feature separation was tested by dividing polygon samples into several intervals on the basis of their sizes, thereby verifying the practicability of the adopted algorithms in real-world operations. This study verified that combining unsupervised machine learning algorithms with topographic factors facilitates the effective separation of landslide sources and runout areas, thereby enhancing the quality of landslide inventories and reducing the cost of landslide inventory creation. Moreover, it indicated that meter-scale topographic data yield classification results similar to those obtained from manually created landslide inventories based on centimeter-scale stereo aerial photos. The proposed method effectively balances the cost and quality of landslide inventories.

Identification of hydrologically homogenous watersheds and climate-vegetation dynamics in the Blue Nile Basin of Ethiopia

Abstract Identification of hydrologically homogenous watersheds in the Upper Blue Nile Basin of Ethiopia is challenging due to the large number of watersheds and the lack of consistent and reliable data. Traditional methods, such as expert-based classification, are time-consuming, subjective, and often not reproducible. Therefore, this study aims to identify homogenous gauged watersheds using hydrometeorological and remote sensing data. In this study 76 watersheds were delineated from a 30-m digital elevation model (SRTM-DEM). Twelve watershed characteristics were selected to aid the classification process. Three homogenous climate regions were identified using rainfall data from 42 stations, and for each homogeneous climate region, gauged watersheds were identified. Principal component analysis (PCA) and K-means clustering were used for classification. The PCA reduced 12 watershed characteristics into three principal components using a threshold of 80% accounted variance and eigenvalues greater than one. K-means clustering classified the 76 watersheds into nine homogenous clusters. In the classified regions, vegetation dynamics within three decades have also been analyzed. This helped identify trends in vegetation cover and its spatial and temporal dynamics. The results of the investigation will potentially be used for runoff prediction of ungauged watersheds and for water resource management models in the future.

Watershed Delineation from DEM by Model Builder in ArcGIS

The water level fluctuates continuously at local, continental, and global levels. Consequently, remote sensing offers extensive data for assessing the location, extent, and variability of change, including the causes and changes that occur and the reactions and repercussions of change. Considering the significance of the water level, this research focuses on describing the methodology and process to determine the water level in the Al-Zab River. This study offers a straightforward and cost-effective approach and procedure for precisely delineating flood danger regions in the Al-Zab River basin utilizing a geographic information system and the existing database. The proposed strategy for regionally dispersing flood risks combines the size of prior flood events and the geographical distribution of their causes. This approach allows for the calculation of a weighted score for each participant's causative component. The regional distribution of the flood hazard intensity level is obtained by categorizing the spatial patterns of the flood hazard index. The 30-m digital elevation model with a high resolution was utilized to evaluate the water level in this river. Furthermore, this work is considered the first step towards enhancing information management in the studied area. This study mainly aims to explain how to outline and define watersheds using ArcGIS for flood mapping. Doi: 10.28991/CEJ-2023-09-11-011 Full Text: PDF

Using remote sensing technology in estimating flood discharge

Floods are the most natural disasters occurring on earth. Recently, Egypt has been subjected to more frequent floods due to climate change. The main objective of this paper is creating hydrological analysis and estimating water discharge due to a flood hitting a hazardous zone near Safaga city. The research is divided into four main stages; 1) Morphological study, 2) Meteorological study, 3) Losses estimation, and 4) Flood discharge estimation. First, morphological study in which watershed boundaries, main streams and morphological data are delineated using geographic information system (GIS) with the aid of Landsat-8 satellite images and 30-m resolution digital elevation model (DEM). Second, meteorological study in which conventional rainfall frequency analysis is generated to estimate rainfall depth at different return periods using 15-years rainfall historical data from weather stations. Third, losses estimation is determined by using soil conservation service-curve number method (SCS-CN). Fourth, water discharge volume is carried out through hydrological modelling using a hydrologic engineering centre model. The estimated water volume is an appropriate information for decision makers to propose the adequate protection works to mitigate flood impacts.

Aerial characterization of surface depressions in urban watersheds

Digital elevation models (DEM) are one of the most fundamental inputs for hydrological modeling. It has been a common practice to remove all surface depressions in a DEM as they are assumed to be data errors. The emerging technology of unmanned aircraft systems (UAS) provides an opportunity to re-examine this assumption at the hyperspatial resolution. This study was the first attempt to characterize small surface depressions in urban environments using UAS imagery. Using an urban area in south Texas as the study site, UAS flights were conducted to yield hybrid DEMs at the resolution of 8–14 cm, coupled with comprehensive ground truth collection. Surface depressions identified from the UAS DEMs were first corrected based on the vertical accuracy of DEMs and then validated through field surveys, with comparisons to two existing LiDAR DEMs (1-m and 10-m). The hydrological impacts of different DEM-derived estimates of catchment depression storage were examined using the Curve Number method across different design storms. Results show that the UAS DEMs outperformed the LiDAR DEMs in describing the microtopographic control of urban overland flow and associated hydrological connectivity across built and natural features. The 8-cm UAS DEM revealed 926% more depression storage than the 10-m LiDAR DEM. This demonstrates a compelling correlation between increasing DEM resolution and enhanced quantification of depression volume. Consequently, the increased depression storage reduced surface runoff by 41% under a two-year design storm and 13% under a 200-year design storm. The results suggest a strong relationship between the DEM resolution and the derived depression estimates, aligning with the fractal nature of watershed systems. Also, the results indicate that the centimeter-level UAS DEMs were not immune from problems. They could yield fake depressions caused by factors such as vegetation, temporary street objects, and underground sewer pipes. The findings of this study suggest the need to quantify the relationships between DEM resolution and associated hydrological attributes and develop new digital drainage analysis algorithms that could effectively incorporate UAS data into urban hydrological modeling.

The influence of roads on depressional storage capacity estimates from high-resolution LiDAR DEMs in a Canadian Prairie agricultural basin

The Canadian Prairies are a post-glacial agricultural landscape, where millions of small depressions store surface water, form wetlands and control runoff contributing area. Their management is key to flood and drought hydrology, groundwater recharge, ecological integrity, migratory bird habitat and agricultural productivity. Depression drainage and infilling is common in the region, where it is often used to increase cropped area. The regularly spaced, rural ‘grid-road‘ network also impedes drainage, but associated culvert drainage can mitigate those effects. Management of depressions can be informed by hydrological modelling, but accurate surface water storage capacity estimates are needed to ensure accurate model results. Simple representation of road embankments in digital elevation models (DEMs) neglects the effects of culvert drainage. Here, a raster-based depression-filling algorithm was used to delineate depressions from three LiDAR-derived DEMs: a 10-m DEM with roads intact, a 2-m DEM with roads intact, and a 2-m DEM with roads breached at culvert locations. Road breaching was conducted manually in the 2-m DEM to remove artifact depressions that form alongside roads where culverts exist. Results indicated that increasing DEM resolution from 10-m to 2-m in a 393.5 km2 basin did not significantly change depression area or storage capacity estimates; however, breaching roads in the 2-m DEM decreased depression area by 29% (from 98.5 km2 to 69.8 km2) and estimated storage capacity by 48% (from 47.4 × 106 m3 to 23.8 × 106 m3), compared to leaving roads intact in the 2-m DEM. Depressions delineated from the 2-m roads-breached DEM also covered 48% more area and offered 53% more storage capacity than Canadian Wetland Inventory (CWI) aerial-photograph delineated wetlands, which occupied 47.1 km2 with an estimated storage capacity of 15.5 × 106 m3. The implications of these results for the ability of hydrological models to calculate runoff contributing areas and streamflow are discussed.

A performance comparison of SRTM v. 3.0, AW3D30, ASTER GDEM3, Copernicus and TanDEM-X for tectonogeomorphic analysis in the South American Andes

Digital Elevation Models (DEMs) are widely used to assess the degree of tectonic activity in mountainous landscapes. But hardly ever have quality assessments of DEMs been carried out to assess their suitability for the calculation of the most widely used tectonogeomorphic indices. For that reason, we have analysed the five most commonly used DEMs for two tectonic basins in the Peruvian Andes. Those are the 30-m SRTM v.3.0, AW3D30, ASTER GDEM3, Copernicus and the 12-m TanDEM-X. The analysed indices are related to the characteristics of 22 drainage networks and we included a vertical accuracy assessment based on available GNSS control points. Copernicus produced the smoothest river profiles followed by AW3D30 and TanDEM-X. River profiles from the rainforest-covered Moyobamba tectonic basin were noisier than those from the more arid Huancayo tectonic basin. All DEMs performed statistically similar in the calculation of drainage basin area, θ, m/n, Ksn and the Hypsometric Integral. Copernicus and TanDEM-X generated the longest drainage networks. TanDEM-X showed the highest vertical accuracy with a RMSE of 3.174 m in the rugged Huancayo basin, and 2.172 in the Moyobamba basin, followed by AW3D30. Copernicus showed very uneven results between both tectonic basins. TanDEM-X allowed the most detailed mapping of fluvial and tectonic landforms, with the identification of six out of seven fluvial terraces, while Copernicus performed best of all 30-m DEMs. The overall best performing DEMs were Copernicus and TanDEM-X, closely followed by AW3D30. ASTER GDEM3 generally performed worst. In general, there was more statistical variability between DEMs in the more rugged Huancayo basin, suggesting that steeper slopes had a significant impact on the calculated indices. Our results provide a clear guideline for the scientific community of which DEMs to pick for the calculation of the various tectonogeomorphic indices.

The Suitability of UAV-Derived DSMs and the Impact of DEM Resolutions on Rockfall Numerical Simulations: A Case Study of the Bouanane Active Scarp, Tétouan, Northern Morocco

Rockfall simulations constitute the first step toward hazard assessments and can guide future rockfall prevention efforts. In this work, we assess the impact of digital elevation model (DEM) resolution on the accuracy of numerical rockfall simulation outputs. For this purpose, we compared the simulation output obtained using 1 m, 2 m and 3 m resolution UAV-derived DEMs, to two other models based on coarser topographic data (a 5 m resolution DEM obtained through interpolating elevation contours and the Shuttle Radar Topographic Mission 30m DEM). To generate the validation data, we conducted field surveys in order to map the real trajectories of three boulders that were detached during a rockfall event that occurred on 1 December 2018. Our findings suggest that the use of low to medium-resolution DEMs translated into large errors in the shape of the simulated trajectories as well as the computed runout distances, which appeared to be exaggerated by such models. The geometry of the runout area and the targets of the potential rockfall events also appeared to be different from those mapped on the field. This hindered the efficiency of any prevention or correction measures. On the other hand, the 1m UAV-derived model produced more accurate results relative to the field data. Therefore, it is accurate enough for rockfall simulations and hazard research applications. Although such remote sensing techniques may require additional expenses, our results suggest that the enhanced accuracy of the models is worth the investment.

An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar inundation areas at volcán Copahue (Argentina & Chile)

Lahars are debris flows originating at volcanoes with very erosive and destructive capabilities. Lahars constitute a serious hazard for the communities close to the Volcán Copahue, lying at the Argentina-Chile border. This study presents a comprehensive analysis of the sensitivity of the geographic information system (GIS) assisted model LAHARZ to the spatial resolution and quality of the digital elevation models (DEMs) for mapping lahar inundation areas within the context of Volcán Copahue. In addition, critical lahar volumes required to reach the nearby settlements are investigated, following a literature review to characterize plausible triggering mechanisms. We compared lahar inundation areas at the four main catchments of Volcán Copahue resulting from five DEMs: three 30-m spatial resolution datasets (SRTM, ASTER and AW3D30), the 12-m WorldDEM™, and a 5-m DEM derived from SPOT-7 data. Our sensitivity analysis shows that DEM spatial resolution and quality precipitate into the LAHARZ results. The interaction between the spatial resolution and the channel confinement constitutes the main control of the output geometries. Higher-spatial resolution DEMs (i.e. smaller pixel size) better depict channels and better define confined channels zones resulting in narrower output areas and longer runouts. For the same volume, the inundation areas obtained using the 5- and 12-m pixel DEMs are narrower and 2–3 km longer than when derived from any of the 30-m DEMs. At unconfined channels, LAHARZ is less sensitive to the spatial resolution and inundation areas tend to show ragged edges regardless of the input DEM. The quality of the DEM controls the inundation trajectories. These results highlight the importance of evaluating topographic models for LAHARZ simulations before the implementation of the model for hazard mapping. Our results suggest that lahars of volumes of 105–106 m3 would reach the nearest villages within a radius of 15 km from the volcano. More work is required to improve the knowledge on lahar deposits and in turn hazard assessment.

Soil erosion risk assessment and treatment priority classification: A case study on guder watersheds, Abay river basin, Oromia, Ethiopia

Soil erosion is the most persistent environmental problem in the Upper Blue Nile River (UBNR) basin of Ethiopia. Guder River is one of thetributaries of UBNR basin which critically required soil conservation practices. The main objective of this particular research article was to appraise soil erosion hazard priority classification with an easy and uncomplicated erosion modelling tool, the universal soil loss equation (USLE) using GIS software and RS data. Remote Sensing data such as annual mean precipitation, land-use land-cover, and soil map, digital elevation model map were used to determine the USLE factor values. The average annual rainfall data was derived from the widely used climate dataset CRU TS (Climatic Research Unit gridded Time Series) and converted to rainfall erosivity factor. Soil Erodibility Factor Soil (K) was calculated from FAO soil data “Digital Soil Map of the World - ESRI shapefile format”. Topographic Factor (LS) was delineated from a 30m digital elevation model. Cover Factor (C) and Support Practice Factor (P) were estimated from a 20m Ethiopia Sentinel2 Land-use Land-cover year, 2016. The study classified the Guder watersheds into different kinds of severity classes for prioritization of soil and water management options and conservation strategy. The mean annual soil eroded for the whole sub-basin was estimated at 25.23 tha−1y−1. The study output outcomes demonstrated that about 0.1% (426ha) 6.9% (46764 ha), 8.9% (60055 ha), and 19.8 % (134320ha) have been under Catastrophic, very severe, severe, high erosion severity class respectively. About half of the Guder sub-basin has been underneath a very slight erosion. Nevertheless, the area covered by very severe erosion was 6.9%, and the annual percent of sum-total soil erosion accounted for was 46.86%. The second and third in magnitude soil lost annually from the sub-basin with regards to per cent of total soil loss were severe (26.53%), and high (21.53%) respectively. In only 7% of the area under investigation, soil erosion estimated was to go beyond 100 t/ha/yr. erosion rate. District wise erosion affected and hotspot areas were identified: Middle of Steep slopes Mountainous parts of Ginde Beret, Jeldu, Ifata, Ambo, parts Ababo and Horo Guduru located in the study area borderline, Toke Kutaye, along the boundary of Midakegn and Cheliya were found in severe to very severe erosion. Finally, the study proposed that the government, policymakers, and soil and water management agents plan and implement the conservation measures and give awareness to stakeholders for optimum use of limited precious resources.

Positional accuracy assessment of historical Google Earth imagery in Lagos State, Nigeria

The horizontal accuracy of historical Google Earth (GE) images at four epochs between the years 2000 and 2018, and the vertical accuracy of its elevation data within Lagos State, in Nigeria, are respectively evaluated by comparison with a very high–resolution digital orthomosaic and comparison with 558 ground control points. Two readily available 30-m digital elevation models (DEMs) — the Shuttle Radar Topography Mission (SRTM) v3.0 and the Advanced Land Observing Satellite World 3D (AW3D) DEM v2.1. — were also compared with GE elevations. A novel approach for assessing the space–time variations in the magnitude and direction of errors in GE imagery is presented. For horizontal accuracy, the root mean square errors (RMSEs) are as follows — year 2000 (16.9 m), year 2008 (16.4 m), year 2012 (6.1 m) and year 2018 (6.1 m). The most recent GE imagery (year 2018) had the least horizontal error while year 2000 had the largest horizontal error. The horizontal shift was skewed towards the western and north-western directions, indicative of systematic error. In terms of the vertical accuracy, GE elevation data had the lowest accuracy and highest RMSE of 6.21 m followed by AW3D with an RMSE of 4.39 m and SRTM with an RMSE of 3.68 m.

Developing a DEM and Elucidating through SWAT to Conserve Soil in Kulfo Watershed of Rift Valley Basin, Ethiopia

Due to uninterrupted erosion and transportation, huge volume of sediments carried away by streams and rivers are finally deposited on the meanders, lakes, and reservoirs when the velocity of the surface water flow decreases. Kulfo River in the southern part of Ethiopia faces a challenge due to massive deposit of sediments. Hydrometeorological and spatial data of Kulfo watershed from the observed stream flow data series near Kulfo bridge and four meteorological station data were used to assess the depositional environment of Kulfo watershed. The data length covers the period from 2000 to 2019. Geomorphic parameters of the watershed were developed by using a 30m × 30m digital elevation model (DEM). The spatial distribution of sediment yield of the study area was estimated using SWAT, the soil and water assessment tool. Scenarios were developed to assess the effectiveness of watershed management interventions provided at the watershed and critical subwatershed level. The model genuinely replicated the observed discharge and sediment with an overall performance of 0.75 as measured by NSE. Twenty-one subbasins were created, and the observed average sediment yield was calculated as 11.9 ton/ha/y. The observed average sediment yield reduction at the hotspot subwatershed level postapplication of contouring, filter strip, terracing, and strip cropping were 40.79%, 57.94%, 66.02%, and 62.93%, respectively. By intricately analyzing, it can be referred that terracing is the best conservation measure to be incorporated into the affected subbasins.

Mapping soil nutrients via different covariates combinations: theory and an example from Morocco

BackgroundMapping of soil nutrients using different covariates was carried out in northern Morocco. This study was undertaken in response to the region's urgent requirement for an updated soil map. It aimed to test various covariates combinations for predicting the variability in soil properties using ordinary kriging and kriging with external drift.MethodsA total of 1819 soil samples were collected at a depth of 0–40 cm using the 1-km grid sampling method. Samples were screened for their pH, soil organic matter (SOM), potassium (K2O), and phosphorus (P2O5) using standard laboratory protocols. Terrain attributes (T) computed using a 30-m resolution digital elevation model, bioclimatic data (C), and vegetation indices (V) were used as covariates in the study. Each targeted soil property was modeled using covariates separately and then combined (e.g., pH ~ T, pH ~ C, pH ~ V, and pH ~ T + C + V). k = tenfold cross-validation was applied to examine the performance of each employed model. The statistical parameter RMSE was used to determine the accuracy of different models.ResultsThe pH of the area is slightly above the neutral level with a corresponding 7.82% of SOM, 290.34 ppm of K2O, and 100.86 ppm of P2O5. This was used for all the selected targeted soil properties. As a result, the studied soil properties showed a linear relationship with the selected covariates. pH, SOM, and K2O presented a moderate spatial autocorrelation, while P2O5 revealed a strong autocorrelation. The cross-validation result revealed that soil pH (RMSE = 0.281) and SOM (RMSE = 9.505%) were best predicted by climatic variables. P2O5 (RMSE = 106.511 ppm) produced the best maps with climate, while K2O (RMSE = 209.764 ppm) yielded the best map with terrain attributes.ConclusionsThe findings suggest that a combination of too many environmental covariates might not provide the actual variability of a targeted soil property. This demonstrates that specific covariates with close relationships with certain soil properties might perform better than the compilation of different environmental covariates, introducing errors due to randomness. In brief, the approach of the present study is new and can be inspiring to decision-makers in the region and other world areas as well.

Integrative Approach for Designing the Collection System Network for Malappuram City, Kerala using MIKE+ Model

MIKE+ model is used to design the collection system of Malappuram city situated at 54 km south west of Calicut and 90 km North West of Palakkad of Kerala state. The shapefile of the study area, SRTM 30m Digital Elevation Model, and rainfall data for the years 2017-2020 were collected and added as layers in MIKE+. Through manhole digitization followed by link creation, catchment delineation, catchment connection, and various boundary conditions, the rainfall-runoff model and catchment discharge by rational formula were determined. In Malappuram city, there are nine outlets, majority of sewers are delivering water to outlets at nodes 1156, 2344, and 2955 to reduce the burden of waste. According to the diameter of the manholes, the collection system was observed to be overdesigned, but it is capable of collecting all wastewater from each residence and catchment runoff to outlets without the need of pumps by gravity flow. In future perspective, if the water level exceeds the set critical level, mainly for the trunks sewer A and G, it will be a point of concern and requires attention.

The impact of (DEM) Accuracy on the Watersheds areas as a function of spatial data

Digital Elevation Model (DEM) is main input for watershed modelling. Recently, (DEM) is available online for free in different accuracies, and spatial resolutions as a product of several remote sensing satellites. Hence, it is necessary to find out which one is the best for watershed modeling in the study area. In this study, the different accuracies 30m spatial resolution DEMs of (Copernicus, SRTM, and ASTER) can be examined by using Remote sensing (RS) and Geographic Information systems (GIS) techniques to delineate and calculate the topographic characteristics for five different size and topography watersheds (Swaidy, Garlond, Khuwayr Hirah, Naqab, and Kalak) located on both sides of the Mosul reservoir in the northeastern part of Iraq. The analysis results can be led to find that the Copernicus (GLO-30) 30 m resolution DEM is the optimum and most accurate DEM in the selected study area at the vertical accuracy (1.3521 m) and with 95% confidence level is (2.6502 m) represented by the minimum Root Mean Square Error (RMSE) of the elevations differences between check points (MOWR elevations and LIDAR DEM 1 m resolution) and the used DEMs. The watershed delineation and calculated topographic characteristics (watersheds boundary, elevations, area, perimeter and slope areas) are affected by DEM accuracy. Where the considerable accuracy of the differences is with the (Copernicus-SRTM) DEMS at the minimum RMSE of watersheds characteristics.

Late Quaternary activity of the Qingchuan Fault, eastern Tibetan Plateau margin: Insights from stream channel offsets and catchment erosion

The Qingchuan Fault (QCF) is the main fault of the northeastern segment of the Longmenshan Fault zone (LMSFZ) on the eastern Tibetan Plateau boundary. The QCF accommodates ongoing deformation in the eastern Tibetan Plateau; however, its fault motion and potential seismic risk are still debated. This study combines a systematic analysis of deflected stream channels along the fault using 10-m digital elevation models with field investigations and measurements of the cosmogenic 10Be-derived erosion rates. The degree of channel deflection due to the interaction between strike-slip motion and channel growth processes is analyzed, followed by a quantitative extraction of the actual tectonic offsets and a calculation of the erosion rates. The long-term average strike-slip rates calculated from channel offsets range from 0.4 ± 0.1 to 1.7 ± 0.1 mm/a along the fault, with those in the southwestern section lower than those in the central section. Such differences may be attributed to slip partitioning of the northeastern extension of the Yingxiu–Beichuan Fault. In addition, analysis of stream longitudinal profiles and along-fault topographic variations illustrate that the small thrust component of the QCF decreases to the northeast, and a minor normal component exists at its northeastern end. The distributions of strike-slip and vertical-slip motions of the QCF indicate that fault slip of the central LMSFZ propagates to the northeast across the Minshan uplift and continues to slip along the QCF. Moreover, thrusting of the southern-central segment of the LMSFZ and dextral strike-slip faulting of the QCF may both accommodate complex deformation related to eastward expansion of the Tibetan Plateau. These findings have implications for constraining potential seismic hazards and understanding characteristics of the regional tectonic deformation.

A Geomorphic Approach for Identifying Flash Flood Potential Areas in the East Rapti River Basin of Nepal

Basin geomorphology is a complete system of landforms and topographic features that play a crucial role in the basin-scale flood risk evaluation. Nepal is a country characterized by several rivers and under the influence of frequent floods. Therefore, identifying flood risk areas is of paramount importance. The East Rapti River, a tributary of the Ganga River, is one of the flood-affected basins, where two major cities are located, making it crucial to assess and mitigate flood risk in this river basin. A morphometric calculation was made based on the Shuttle Radar Topographic Mission (SRTM) 30-m Digital Elevation Model (DEM) in the Geographic Information System (GIS) environment. The watershed, covering 3037.29 km2 of the area has 14 sub-basins (named as basin A up to N), where twenty morphometric parameters were used to identify flash flood potential sub-basins. The resulting flash flood potential maps were categorized into five classes ranging from very low to very high-risk. The result shows that the drainage density, topographic relief, and rainfall intensity have mainly contributed to flash floods in the study area. Hence, flood risk was analyzed pixel-wise based on slope, drainage density, and precipitation. Existing landcover types extracted from the potential risk area indicated that flash flood is more frequent along the major Tribhuvan Rajpath highway. The landcover data shows that human activities are highly concentrated along the west (Eastern part of Bharatpur) and the east (Hetauda) sections. The study concludes that the high human concentrated sub-basin “B” has been categorized as a high flood risk sub-basin; hence, a flood-resilient city planning should be prioritized in the basin.

Quality Assessment of TanDEM-X DEMs, SRTM and ASTER GDEM on Selected Chinese Sites

Digital elevation models (DEMs) are the basic data of science and engineering technology research. SRTM and ASTER GDEM are currently widely used global DEMs, and TanDEM-X DEM, released in 2016, has attracted users’ attention due to its unprecedented accuracy. These global datasets are often used for local applications and the quality of DEMs affects the results of applications. Many researchers have assessed and compared the quality of global DEMs on a local scale. To provide some additional insights on quality assessment of 12- and 30-m resolution TanDEM-X DEMs, 30-m resolution ASTER GDEM and 30-m resolution SRTM, this study assessed differences’ performance in relation to not only geographical features but also the ways in which DEMs have been created on selected Chinese sites, taking ICESat/GLAS points with 14-cm absolute vertical accuracy but size of 70-m diameter and 12-m resolution TanDEM-X DEM with less than 10-m absolute vertical accuracy as the reference data for comprehensive quality evaluation. When comparing the three 30-m DEMs with the reference DEM, an improved Least Z-Difference (LZD) method was applied for co-registration between models, and Quantile–Quantile (Q-Q) plot was used to identify if the DEM errors follow a normal distribution to help choose proper statistical indicators accordingly. The results show that: (1) TanDEM-X DEMs have the best overall quality, followed by SRTM. ASTER GDEM has the worst quality. The 12-m TanDEM-X DEM has significant advantages in describing terrain details. (2) The quality of DEM has a strong relationship with slope, aspect and land cover. However, the relationship between aspect and vertical quality weakens after data co-registration. The quality of DEMs gets higher with the increasing number of images used in the fusion process. The quality in where slopes opposite to the radar beam is the worst for SRTM, which could provide a new perspective for quality assessment of SRTM and other DEMs whose incidence angle files are available. (3) Systematic deviations can reduce the vertical quality of DEM. The differences have non-normal distribution even after co-registration. For researchers who want to know the quality of a DEM in order to use it in further applications, they should pay more attention to the terrain factors and land cover in their study areas and the ways in which the DEM has been created.

Real-World DEM Super-Resolution Based on Generative Adversarial Networks for Improving InSAR Topographic Phase Simulation

Topographic phase simulation is important for deformation estimation in differential synthetic aperture radar (SAR) interferometry (DInSAR). The most commonly used 30-m resolution SRTM digital elevation model (DEM) is usually required to be resampled due to its relatively low resolution (LR) comparing to the high resolution (HR) SAR images. Although the WorldDEM^TM with a 12-m resolution achieves global coverage, it is not available freely. Consequently, it is useful to evaluate the practicability of the super-resolution (SR) from LR SRTM DEMs to HR WorldDEM^TM ones, which has not been investigated. Most existing DEM SR models are trained with synthetic datasets in which the LR DEMs are downsampled from their HR counterparts. However, these models become less effective when applied to real-world scenarios due to the domain gap between the synthetic and real LR DEMs. In this paper, we constructed a real-world DEM SR dataset where the LR and HR DEMs were collected from SRTM and WorldDEM^TM, respectively. An ESRGAN model was adapted to train on the dataset. Considering that the real LR-HR pairs may suffer from misalignment, we introduced the perceptual loss for better optimizing the model. Moreover, a logarithmic normalization was proposed to compress the wide elevation range and adjust the uneven distribution. We also pretrained the model using natural images since collecting sufficient HR DEMs is costly. Experiments demonstrate that the proposed method achieves near 0.69dB improvement of peak signal-to-noise ratio (PSNR). In addition, our method is also validated to improve the topographic phase simulation by 23.42% of MSE.

Evaluation of the Vertical Accuracy of Open Global DEMs over Steep Terrain Regions Using ICESat Data: A Case Study over Hunan Province, China.

The global digital elevation model (DEM) is important for various scientific applications. With the recently released TanDEM-X 90-m DEM and AW3D30 version 2.2, the open global or near-global coverage DEM datasets have been further expanded. However, the quality of these DEMs has not yet been fully characterized, especially in the application for regional scale studies. In this study, we assess the quality of five freely available global DEM datasets (SRTM-1 DEM, SRTM-3 DEM, ASTER GDEM2, AW3D30 DEM and TanDEM-X 90-m DEM) and one 30-m resampled TanDEM-X DEM (hereafter called TDX30) over the south-central Chinese province of Hunan. Then, the newly-released high precision ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) altimetry points are introduced to evaluate the accuracy of these DEMs. Results show that the SRTM1 DEM offers the best quality with a Root Mean Square Error (RMSE) of 8.0 m, and ASTER GDEM2 has the worst quality with the RMSE of 10.1 m. We also compared the vertical accuracies of these DEMs with respect to different terrain morphological characteristics (e.g., elevation, slope and aspect) and land cover types. It reveals that the DEM accuracy decreases when the terrain elevation and slope value increase, whereas no relationship was found between DEM error and terrain aspect. Furthermore, the results show that the accuracy increases as the land cover type changes from vegetated to non-vegetated. Overall, the SRTM1 DEM, with high spatial resolution and high vertical accuracy, is currently the most promising dataset among these DEMs and it could, therefore, be utilized for the studies and applications requiring accurate DEMs.