Resolution Digital Elevation Model Research Articles

Detection, Morphometric Analysis and Digital Surveying of Archaeological Mounds in Southern Iraq with CartoSat-1 and COSMO-SkyMed DEMs

In Near and Middle Eastern archaeology, satellite-derived digital elevation models (DEM) of medium spatial resolution (≥30 m) are mostly used to locate and map archaeological mounds (namely ‘tells’), whereas high resolution DEMs (≤10 m) are still poorly exploited. To fill this gap, the 5 m resolution CartoSat-1 Euro-Maps 3D Digital Surface Model (DSM) is assessed vs. the 30 m Shuttle Radar Topography Mission (SRTM) global DEM, the Advanced Land Observing Satellite (ALOS) World 3D–30 m (AW3D30) and a 10 m COSMO-SkyMed DEM, on a test area in Wasit, southern Iraq, where the high density of tells is yet to be exhaustively documented. A total of 344 sites was mapped, with one order of magnitude improvement compared to previous mapping exercises, existing databases and historical maps. The morphometric analysis not only highlights the reliability of CartoSat-1 DSM height and volume estimates, but also suggests that, in the test area, the volume of a tell can robustly be calculated based on the simple knowledge of its basal area, following a quadratic function. Morphology and elevation of at least 53% irregularly shaped tells were impacted by anthropogenic disturbances. Morphometric indices (e.g., Topographic Position Index, DEViation from mean elevation) are a viable automated method to ease tells detection. When integrated with other satellite datasets (e.g., CORONA, Google Earth, Sentinel-2 imagery), the CartoSat-1 DSM can unveil morphological changes and support condition assessment. In Wasit, agriculture and modern development are among the major threats for tells preservation, alongside looting.

Employment of Continuous Slope Cumulative Frequency Spectrum in geomorphology quantitative analysis – a case study on Loess Plateau

Slope spectrum is an effective methodology of digital terrain analysis in geomorphology. However, some limitations are still encountered in the construction of the slope spectrum. E.g., the uncertainties caused by slope classification and resolution of digital elevation model (DEM) may restrict the development of slope spectrum and cause a certain spatial information loss. In this paper, a new spectrum method, defined as continuous slope cumulative frequency (CSCF) spectrum, is proposed. Compared with the slope spectrum, the CSCF spectrum does not require slope classification and combines the spatial information of all grids. Hence, it possesses richer terrain feature information and minimal spatial information loss. Meanwhile, it is more convenient to be used in different study areas without requiring heavy work of slope spectrum in determining the suitable slope classification. We also found that the CSCF spectrum can be generally extracted from DEM with different resolutions, whereas the slope spectrum cannot. Experiment results show that the CSCF spectrum is an efficient geomorphology quantitative analysis method that can quantitatively depict the spatial distribution and evolution process of the typical loess landforms from different angles. Besides, the CSCF spectrum can accurately deduce the stable slope spectrum, whereas the slope spectrum cannot deduce the CSCF spectrum, which further suggests that the previous studies in slope spectrum can be conducted on the basis of the CSCF spectrum. Furthermore, the CSCF spectrum shows well application potentials in landform classification with comparative performance to previous methods. Overall, the CSCF spectrum is an effective method in geomorphology quantitative analysis. Considering that the CSCF spectrum solves two key deficiencies in the present model of slope spectrum (slope classification and DEM resolution) and extends the application scope and concept of spectrum method, it is promising to be the research paradigm of the spectrum method in digital terrain analysis.

Sensitivity assessment of morphometric parameters of monogenetic volcanic landforms with global free DEMs

Improvements in the resolution of Digital Elevation Model (DEM) have greatly facilitated the research of small to moderate landforms such as dunes, landslides/rockfalls, moraines/drumlins and monogenetic volcanoes. Global free 30 m DEMs including data derived from Shuttle Radar Topography Mission (SRTM), Advanced Land Observing Satellite (ALOS) World 3D 30 m (AW3D30), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER GDEM) are widely used. However, little is known of whether they can be used effectively in monogenetic volcanology. The focus of this study is to explore the accuracy and potential application of global free SRTM 30 m and AW3D30, as well as ASTER GDEM (previously studied) in monogenetic volcanic fields. We compared SRTM and AW3D30 with four reference higher-resolution DEM dataset, including National Elevation Dataset (NED) 10 m in Uinkaret Volcanic Field (UVF) (United States), WorldDEM 12 m in Longgang Volcanic Field (LVF) (China), Contour Line Based DEM (CLBD) 1 m in Jeju Island Volcanic Field (JIVF) (South Korea), and Light Detection And Ranging (LiDAR) 1 m in Lunar Crater Volcanic Field (LCVF) (United States). The results indicate that AW3D30 has higher accuracy than SRTM in all comparisons including elevation, slope angle and morphological parameters of scoria cones and associated lava flows. However, the accuracy of AW3D30 is almost the same as that of SRTM in that it is affected by slope angle, volume of volcanic edifices, and morphological features of scoria cones. In general, slope angle has a significant effect on the elevation and slope angle errors, which is consistent with previous studies. When the volume of the scoria cones is >5 × 106 m3 and the volume of the lava flows is above 10 × 106 m3, the average errors of their morphological parameters generated by SRTM and AW3D30 do not exceed 16.3 %. The morphological features are classified as crater (the central depression within a scoria cone), crater rim (the top 15 % of the highest elevations), lower flank (the lowest 15 % of the elevations), and middle flank (the remainder of a scoria cone). The area near the crater rim is often the area where larger errors occur, especially on the crater side. The middle and lower flank areas have milder errors. Additionally, the simulation of pre-eruptive surfaces should take into account the effects of certain factors such as roughness and flatness when calculating the volume. Overall, AW3D30 has the best potential in monogenetic volcano studies, followed by SRTM and ASTER GDEM.

LCNS Positioning of a Lunar Surface Rover Using a DEM-Based Altitude Constraint

With the renewed interest in lunar surface exploration, the European Space Agency envisions to stimulate the creation of lunar communications and navigation services (LCNS) to enable, among others, autonomous navigation capabilities for lunar rovers. As the number of satellites foreseen in such a service is much smaller compared to Earth-based global navigation satellite systems, different complementary technologies are pursued to improve the attainable navigation accuracy for lunar rovers. One way to improve the position accuracy provided by the LCNS satellites is to constrain their vertical position using a high resolution digital elevation model (DEM). This article presents the results of a variance covariance analysis of an extended Kalman filter implementation in which the LCNS ranging measurements are used together with the altitude provided by a DEM from the Lunar Orbiter Laser Altimeter instrument of the Lunar Reconnaissance Orbiter. Assuming a realistic orbit determination and time synchronization accuracy of the LCNS satellites, the usage of a navigation-grade inertial measurement unit and an oven-controlled crystal oscillator, a 3-sigma position accuracy of less than 10 m can be obtained. Furthermore, the availability is substantially improved as the DEM-aided solution enables a position solution in case of only 3 visible satellites.

An artificial intelligence approach to remotely assess pale lichen biomass

Although generally given little attention in vegetation studies, ground-dwelling (terricolous) lichens are major contributors to overall carbon and nitrogen cycling, albedo, biodiversity and biomass in many high-latitude ecosystems. Changes in biomass of mat-forming pale lichens have the potential to affect vegetation, fauna, climate and human activities including reindeer husbandry. Lichens have a complex spectral signature and terricolous lichens have limited growth height, often growing in mixtures with taller vegetation. This has, so far, prevented the development of remote sensing techniques to accurately assess lichen biomass, which would be a powerful tool in ecosystem and ecological research and rangeland management. We present a Landsat based remote sensing model developed using deep neural networks, trained with 8914 field records of lichen volume collected for >20 years. In contrast to earlier proposed machine learning and regression methods for lichens, our model exploited the ability of neural networks to handle mixed spatial resolution input. We trained candidate models using input of 1 × 1 (30 × 30 m) and 3 × 3 Landsat pixels based on 7 reflective bands and 3 indices, combined with a 10 m spatial resolution digital elevation model. We normalised elevation data locally for each plot to remove the region-specific variation, while maintaining informative local variation in topography. The final model predicted lichen volume in an evaluation set (n = 159) reaching an R2 of 0.57. NDVI and elevation were the most important predictors, followed by the green band. Even with moderate tree cover density, the model was efficient, offering a considerable improvement compared to earlier methods based on specific reflectance. The model was in principle trained on data from Scandinavia, but when applied to sites in North America and Russia, the predictions of the model corresponded well with our visual interpretations of lichen abundance. We also accurately quantified a recent historic (35 years) change in lichen abundance in northern Norway. This new method enables further spatial and temporal studies of variation and changes in lichen biomass related to multiple research questions as well as rangeland management and economic and cultural ecosystem services. Combined with information on changes in drivers such as climate, land use and management, and air pollution, our model can be used to provide accurate estimates of ecosystem changes and to improve vegetation-climate models by including pale lichens.

Assessment of the geomorphic indices in relation to tectonics along selected sectors of Borpani River Basin, Assam using Cartosat DEM data

• A total of eleven geomorphic indices were studied in detail for the assessment of tectonic activity along Borpani River. • Results indicates satellite imageries based Geomorphic indices can witnesses tectonic influences. • Overall tectonic activity indicates classes - active, moderately active and less active zones in the study region. • On average, 23.86%, 29.54%, and 46.58% of the geomorphic indices falls in active, moderately active and low active classes. We studied geomorphic indices related to tectonics along the Borpani River using Cartosat 10 m spatial resolution Digital Elevation Model (DEM) data. A total of 11 geomorphic indices such as transverse topographic symmetry factor (Tt), drainage basin asymmetry (AF), valley floor width to valley height ratio (Vf), bifurcation ratio (Rb), hypsometric integral (HI), basin elongation ratio (Re), basin shape index (Bs), circularity ratio (Rc), drainage density (Dd), relief ratio (Rh), hypsometric curves (HC) were studied to access the tectonics along the river in selected locations. Five locations along the river were selected for detailed analysis (sub-basin I, II a, II b, III, and IV). Horton's (1945) scheme was adopted for watershed delineation, while stream ordering was performed using Strahler's (1964) method. The results infer that the sub-basins along the Borpani River are tectonically active. In the case of sub-basin I, 3 geomorphic indices out of 11 falls in the category of highly active, and 2, 1, 2, and 4 indices out of 11 falls in the category of highly active in the case of sub-basins II a, II b, III, and IV. Similarly, 4, 1, 3, 6, 1 and 4, 8, 7, 3, 6 indices fall under moderately active and low active classes in sub-basins I, II a, II b, III, and IV. On average, 23.86 %, 29.54%, and 46.58% of the geomorphic indices fall in the active, moderately active, and low active classes.

A River Channel Extraction Method Based on a Digital Elevation Model Retrieved from Satellite Imagery

The river border positioning is an important part of river surveys, which is crucial for water conservation project development, water resource use, water disasters, river regime monitoring, and many other applications related to water resources. Currently, satellite images or field measurements are used to extract river channels. However, satellite images are insufficiently precise, and field measurement requires significant manpower and cost. In this paper, a new method for river channel extraction is proposed, which is based on the combination of Jenks natural breaks classification method and digital elevation model (DEM), and then the river channel range is complemented by using the water range monitored by GF-1(Gaofen-1 satellite) in flood season. The overall precision is greater than 85%, and the Kappa values achieve moderate stability (0.41–0.60). Using this method, the extraction of river range is practicable and achievable, and the higher the DEM resolution, the better the extraction result.

Geomorphological and Structural Assessment of the Coastal Area of Capo Faro Promontory, NE Salina (Aeolian Islands, Italy)

Capo Faro Promontory, located in Salina (Aeolian Islands, southern Italy), is a popular summer destination due to its volcanic morphologies, seaside, and enogastronomy. A flat area, right behind the scarp edge of a coastal cliff, hosts the Capo Faro Estate, one of the most renowned vineyards and residences on Salina Island. The promontory has been characterised in terms of geomorphological features. Remote sensing analysis, after nadir and off–nadir UAV flights, supports the field activities to explore the hazard to which the area is subjected. In particular, the coastal cliff turns out to be affected by a rapid retreat inducing landslides. Therefore, the cliff area has been investigated through a detailed stratigraphic and structural field survey. Using the generated high–resolution Digital Elevation Model, bathymetric–topographic profiles were extracted along the coastline facing the cliff. The thickness of volcanic deposits was evaluated to obtain a geological model of it. The main rock mass discontinuities have been characterised to define the structural features affecting the stability of the rock wall. The obtained results prove the contribution of such research fundamental in planning risk mitigation measures.

The microfacies distribution pattern of Cozumel Island in southeastern Mexico: An atoll-like model led by quaternary glacioeustatic sea-level changes

Isolated carbonate banks in stable tectonic settings are ideal for understanding the architecture of sedimentary systems and their response to flooding events. Within these environments, sea-level changes play a primary role in depositing sedimentary units during high sea levels and unconformities during sea-level drops. Microfacies analysis and geologic interpretation, combined with a High Resolution Digital Elevation Model from LiDAR data were used to reevaluate the sediment distribution pattern of Cozumel Island, proposing a new depositional model. The chronostratigraphic framework was established by correlating the exposure surfaces (caliches) of Cozumel with others in adjacent regions. The study of several sections around the island led to the identification of two lithostratigraphic units. Unit 1 comprises two coral reef facies associations (FAs 0 and 1), separated by the Caliche-0 layer. The upper limit of Unit 1 is represented by the Caliche 1 layer and the karst features developed during Marine Isotopic Stage (MIS) 6. Unit 2 was deposited over the ancient karst topography as the lower limit, with Caliche 2 as the upper limit. Unit 2 comprises three facies associations (FAs-2A, B, C), consistent with the protected, open lagoon and the facies of the platform margin, establishing atoll-like depositional conditions. Unit 2 developed during the last interglacial MIS 5e, whereas the Caliche 2 layer started to develop in a post-MIS 5e event during the sea-level drop. This exposed the previous depositional environments to meteoric diagenesis with the formation of the caliche layer. The Caliche 2 layer stabilized the landscape until the present day. Unit 2 correlates to certain morphostratigraphic and geomorphological structures (Terraces I, II, III) on the island. The emplacement of sand islands (FAs-2A), preexisting topography, relative sea-level variations, and sediment supply lead to the pattern and configuration of the depositional model. In addition, the evolution of Cozumel Island during the MIS 5e is similar to other Holocene atoll-like islands in the Caribbean region.

Influence of DEM resolution on landslide simulation performance based on the Scoops3D model

Using the physical deterministic model to analyze landslide stability has become a hotspot of landslide disasters research all over the world. The Digital Elevation Model (DEM) resolution has a great influence on the simulation effect of 3 D physical models. However, few researchers have studied the prediction performance of the 3 D models under different DEM resolutions. Therefore, based on the 3 D model Scoops3D, the spatial distribution of landslides was simulated and predicted under five different DEMs resolutions (2.5 m, 5 m, 10 m, 20 m, and 30 m). The optimal parameters of the model were obtained through field investigations and laboratory experiments, and then, the simulation results were compared with the actual landslides distribution. Receiver operating characteristic (ROC) analysis and %LRclass index were used to quantitatively evaluate the prediction performance of the 3 D model under five different DEMs resolutions. The results show that Scoops3D has good performance in landslide spatial distribution prediction. In addition, we also found that the simulation results of high-resolution DEM were not ideal, while the prediction results of medium resolution DEMs (i.e., 5 m and 10 m) were more accurate. Therefore, this study provide a reference to select the most suitable DEM resolution for landslide stability analysis.

Effects of Terrain Parameters and Spatial Resolution of a Digital Elevation Model on the Calculation of Potential Solar Radiation in the Mountain Environment: A Case Study of the Tatra Mountains

Solar radiation significantly affects many processes on Earth. In situ measurements are demanding and require a dense network of sensors. A suitable alternative solution could be the modelling of potential solar radiation based on a digital elevation model (DEM) in geographic information systems. The key issue of this study is to determine the influence of the terrain parameters and the spatial resolution of a DEM on the calculation of potential solar radiation. The area of study is the Tatra Mountains (the highest mountains of the Carpathians). The DEM determined from light detection and ranging (LiDAR) was used. To determine the influence of the terrain, the following terrain parameters were applied: slope; aspect, represented by northness and eastness; elevation; and topographical position index using six different circular neighbourhoods (10 m, 30 m, 50 m, 100 m, 500 m, and 1000 m). The results indicate a moderate correlation (0.32–0.46) between the solar radiation calculation errors and the absolute values of the topographic position indices with small neighbourhoods (10 m–100 m). To show the impact of the spatial resolution, the calculation was performed based on four different DEM resolutions, namely 5 m, 10 m, 30 m, and 90 m. Mutual differences in potential solar radiation were quantified concerning the topographic position index. The result is also a model of potential annual solar radiation in the Tatra Mountains, calculated at a resolution of 5 m or 2 m.

A fully automated and generic spatial discretization procedure for cultivated landscapes with human-made landscape elements

Abstract Landscape discretization is an essential hydrological modeling pre-processing step that comprises the numerical representation of different geographical objects considered in the modeling process and the connections between these objects within a graph structure. GROOV'Scape is a new landscape discretization procedure that (i) produces an oriented tree of a wide range of human-made landscape elements, such as plots, hedges, benches, grass stripes, and ditches, (ii) is fully automated to avoid local drainage anomalies that require user corrections, and (iii) is fast enough to enable sensitivity analysis and the interactive production of mediated modeling landscape management scenarios. GROOV'Scape was tested on a small agricultural catchment in south-west France (Doazit catchment, 9.05 km2). The results show good agreement between the connections of areal units computed by GROOV'Scape and those observed in the field. The system exhibited substantial sensitivity to the user's choice of minimum area unit size and of digital elevation model (DEM) resolution, suggesting trial-and-error approaches are needed to reach the best landscape discretization for any given modeling purpose. Finally, we demonstrated the feasibility of using GROOV'Scape to find an optimum configuration of an infrastructure network with respect to a given ecosystem function.

Generating Landslide Susceptibility Maps Using Mathematical Models and UAV data: The Case of Çankırı Region in Türkiye

Landslides are natural disasters that affect not only residential areas but alos forest ecosystems. In order to determine the areas with high landslide risk and take necessary measures in risky areas, landslides susceptible should analyzed and susceptible map (LSM) should be developed in advance. In this study, a LSM was produced for two study areas with different sizes including Çankırı province and in the Ilısılık Village of Çankırı in Türkiye. Analytical Hierarchy Process (AHP) and Logistic Regression Modeling (LRM) methods were used to generate LSM based on the main factors including elevation, slope, lithology, distance to faults - streams and roads. For Çankırı province, 30 m resolution Digital Elevation Model (DEM) was used to produce the map while one-meter resolution Digital Terrain Model (DTM), generated by using Unmanned Aerial Vehicle (UAV), was used for Ilısılık Village. As a result of the study, AHP model success was calculated as 73.9% and 91.7% for Çankırı and Ilısılık, respectively, considering the previous landslides occurred in the region. On the other hand, LRM model success was 75.2% and 93.1%, respectively. It was also indicated that DTM data is advantageous to DEM data by offering a more precise and detailed usage opportunity. The sensitivity is revealed more clearly and effectively in precision planning studies such as risk mapping of natural disasters that requires special measurement in small areas.

Understanding the effects of digital elevation model resolution and building treatment for urban flood modelling

Study regionA mountain city in the coastal region of Southeast China. Study focusThis study elucidated the effect of digital elevation model (DEM) resolution on flood simulation errors and inundation depth distributions through building treatment methods and river channel definition. The optimal DEM resolution was evaluated to determine whether satisfactory inundation simulation could be achieved for urban flash flood simulation (UFFS) and fluvial overbank flood simulation (FOFS).New hydrological insights for the region: UFFS and FOFS presented opposite effects regarding inundation extent and the spatial distribution of inundation depth with coarsening DEM resolution. In UFFS, the inundation extent and inundation depth in low-lying areas of the flood propagation pathway were underestimated. This effect was attributed to the change of local flood pathway caused by different treatment of buildings at coarser DEM resolution. The simulation error was magnified considerably when the DEM resolution was greater than the building width and gaps. In FOFS, owing to the changed channel geometry at coarser resolution, the inundation extent and inundation depth were overestimated. Using a DEM resolution and computational cell size that are lower than the river width is recommended. This study illustrated that a regional-scale effect, dependent on regional flood propagation behaviour and the impact of land use and topography on the flood propagation pathway, could be observed by coarsening the DEM resolution of inundation simulations.

Global polygons for terrain classification divided into uniform slopes and basins

Global terrain classification data have been used for various issues related to topography such as the estimation of soil types and of ground vulnerability to earthquakes and the creation of seismic hazard maps. However, due to the resolution of digital elevation models (DEMs), the terrain classification data from previous studies could not discriminate small landforms such as plains at the bottom of narrow valleys and small rises in plains. Owing to the greater regional variation of small landforms, there is trade-off between DEMs of higher resolution and the creation of global geomorphological legends. To address this problem, we first merged regions with similar topographic characteristics using slope gradients and HAND (height above the nearest drainage) calculated by the 90-m-spatial-resolution DEMs interpolated from the multi-error-removed improved-terrain DEM (MERIT DEM), and united the polygons with the unit catchments of the MERIT-Basins dataset, so that the polygons contain calculated terrain measurements (slope gradient, HAND, surface texture, local convexity, sinks) and noise types as attributes, as well as the ID number of the unit catchment. In addition, we performed k-means clustering on the dataset using slope gradient, HAND, and surface texture, which can be combined with the dataset as a simple terrain classification. The clustering results were prepared in 15 and 40 global uniform clusters and 15 and 40 clusters for each basin to understand the global appearance of the terrain and provide zoning data for regional problem-solving. The 15 clusters were prepared to observe the outline of the terrain without any processing, whereas the 40 clusters were prepared to group and reclassify the polygons to create zoning data for each region. This dataset showed improvements in terms of capturing the small rises in plains compared to the authors' previous global terrain classification data. This dataset can be used as a proxy and is expected to contribute to modeling and estimation in various applications that are known to be related to topography. The datasets of this article are available at [https://gisstar.gsi.go.jp/terrain2021/].

Assessing the efficacy of offline water storage ponds for natural flood management

AbstractLand use changes, landscape modifications and changing climate have resulted in local to regional flood risk increases in recent decades. As an alternative to traditional engineering approaches, there has been a movement towards catchment‐based flood risk management, a subset of which is natural flood management (NFM). NFM aims to enhance flood resilience through the slowing and storing of runoff and flow, based on the restoration and augmentation of hydrological and morphological catchment features. However, despite research highlighting their potential benefits, there is a limited quantity of robust and science‐based empirical evidence on how these structures function in the landscape and their efficacy in reducing flood hazards. To address this knowledge gap and contribute to the growing NFM evidence base, this study evaluates the efficacy of offline water storage ponds for flow attenuation. Two contrasting pond sites in the Tone and Parrett catchment headwaters (SW‐England) were monitored from April 2018 to December 2020 for channel flow, pond volume, and rainfall. Field‐scale, high resolution (1.61 cm spatial support) digital elevation models from Structure‐from‐Motion and manual surveys were collected to quantify pond dynamics and connectivity. A comprehensive, storm definition methodology was developed for event separation, to enable quantification of the impact of pond behaviour on stream flow, across storm hydrographs. Results show that where offline ponds function as designed, with direct pond filling from the channel, peak flow attenuation can reach 7% (maximum event AEP of 83%). Smaller events, where pond filling occurs directly from rainfall or runoff display only a maximum of 3% peak flow reduction (event AEP from 83% –>99%). The ability of ponds to attenuate flow is heavily reliant on sufficient structural conditions for ponds to fill directly from the channel and to drain slowly following event peaks. This study provides an empirical database for future NFM applications, including key criteria for future design and for use as observational data in modelling applications, by upscaling the efficacy of ponds to reduce flood impacts at larger scales.

ON THE EFFECT OF DEM QUALITY FOR LANDSLIDE SUSCEPTIBILITY MAPPING

Abstract. Generating precise and up-to-date landslide susceptibility maps (LSMs) in landslide-prone areas is important to identify hazard potential in the future. The data quality and the method selection affect the accuracy of the LSMs. In this context, the accuracy and precision of the digital elevation models (DEMs) used as input are among the most important performance elements. Therefore, the influence of DEM accuracy and spatial resolution in producing LSMs was investigated here. A high accuracy DEM with 5 m grid spacing produced from aerial photographs and the EU-DEM v1.1 freely accessible from Copernicus Land Monitoring Service with 25 m spatial resolution were used for producing two different LSMs using the Random Forest (RF) method in this study. The RF method has proven success for this purpose. A total of eight conditioning factors, which include topographical and geological features, was used as model input. The landslide inventory was derived with the help of aerial stereo images with 20 cm and 30 cm ground sampling distances. The performances of the LSMs were assessed with receiver operating characteristics (ROC) area under curve (AUC) values. In addition, the results were compared with visual inspection. The results show that although the AUC values obtained from the aerial DEM (0.95) and EU-DEM v1.1 (0.93) were comparable; based on the visual assessments, the LSM obtained from the higher resolution DEM was found more successful in detecting the landslides and thus exhibited better prediction performance.

Surface drainage features identification using LiDAR DEM smoothing in agriculture area: a study case of Kebumen Regency, Indonesia

ABSTRACT Digital Elevation Model (DEM) is the most vital data to generate drainage networks and to provide critical terrain factors and hydrologic derivatives, such as slope, aspect, and streamflow. The accuracy of generated drainage features is extensively dependent on the quality and resolution of DEM, such as LiDAR-derived DEM. Contrary, it has a high level of roughness and complexity. Thus, smoothing methods are sometimes employed to conquer the roughness. This paper presents feature-preserving DEM smoothing (FPDEM-S) and edge-preserving DEM smoothing (EPDEM-S) approaches to smooth surface complexity in kind of preserving small drainage features using the 0.5 m – resolution LiDAR DEM of the Kedungbener River area in Kebumen Regency, Indonesia. Entangling linear morphometric factors, those smoothing approaches delivered a slight difference of stream number, with the FPDEM-S stream length ratio performing 7% better tendencies. The FPDEM-S method perormed better than EPDEM-S in this study area to provide an optimal smoothed LiDAR DEM at certain parameter values. Summarising that two smoothing methods approaches performed similar characteristics of watershed as an oval structure close to the circular shape. Also, it can be revealed that the watershed did not reach maturity phase.

An Evaluation of Wind Turbine-Induced Topographic Change in the Offshore Intertidal Sandbank Using Remote Sensing-Constructed Digital Elevation Model Data

With the rapid development of wind power generation, many marine wind farms have been developed on the offshore intertidal sandbank (OIS) along the coastal regions of Jiangsu Province, China during the last decade. In order to quantitatively assess the stability of offshore wind turbines and their induced topographic changes on the OIS, a digital elevation model (DEM)-based analysis supported by satellite remote sensing is adopted in the present study. Taking the Liangsha OIS at the middle of Jiangsu coast, China as the research area, we first used an enhanced waterline method (EWM) to construct the 30 m resolution DEMs for the years 2014 and 2018 with the embedment of tidal creeks to effectively express the detailed characteristics of the micro-terrain. Then, a hypothetical sandbank surface discrimination method (HSSDM) was proposed. By comparing the height difference between the hypothetical and the real terrain surface during the operation period, the wind turbine-induced topographic change rate (TCR) was estimated from the DEM of 2018. The results show that 73.47% of the 49 wind turbines in the Liangsha OIS have an erosional/depositional balanced influence on the intertidal sand body, 8.16% show a weak depositional influence, and 18.36% lead to weak erosion. The average erosional depth, 58.6 cm, reached nearly 6% to 10% of the maximum possible erosion estimated by the hydrodynamic model. Furtherly, using two DEMs for the years 2014 and 2018, the topographic change depths at the location of wind turbines were calculated. By comparing the wind turbine-induced terrain change with the naturally erosional/depositional depths of the OIS, the average contribution rate caused by the wind turbines achieved 42.17%, which meant that the impact of wind turbines on terrain changes could not be ignored. This work shows the potential of utilizing satellite-based remote sensing to monitor topographic changes in the OIS and to assess the influence of morphological variations caused by wind turbines, which will be helpful for offshore wind farm planning and intertidal environment protection.

Evaluating performance dependency of a geomorphologic instantaneous unit hydrograph-based hydrological model on DEM resolution

The digital elevation model (DEM) is a type of model that has been widely used in terrain analysis and hydrological modeling. DEM resolution influences the hydrological and geomorphologic features of delineated catchments and consequently affects hydrological simulations. This study investigated the impacts of DEM resolution on the performance of the XAJ-GIUH hydrological model, a model coupling the widely used Xinanjiang (XAJ) hydrological model with the geomorphologic instantaneous unit hydrograph (GIUH), in flood simulations in small and medium-sized catchments. To test the model performance, the model parameters were calibrated at a fine DEM resolution (30 m) and then directly transferred to the simulation runs using coarser DEMs. Afterwards, model recalibration was conducted at coarser DEM resolutions. In the simulation runs with the model parameters calibrated at the 30-m resolution, the DEM resolution slightly affected the overall shape of the simulated flood hydrographs but presented a greater impact on the simulated peak discharges in the two study catchments. The XAJ-GIUH model consistently underestimated the peak discharges when the DEM resolution became coarser. The qualified ratio of peak simulations decreased by 35% when the DEM resolution changed from 30 m to 600 m. However, model recalibration produced comparable model performances when DEMs with different resolutions were used. This study showed that the impact of DEM resolution on model performance can be mitigated by model recalibration to some extent, if the DEM resolution is not too coarse.