High Accuracy Digital Elevation Models Research Articles

Open-access digital elevation model (DEM) selection for flood inundation modelling using HEC-RAS in Capital City of Nusantara

Flood is a classic but complex issue in most regions of Indonesia. Nusantara, the future capital of Indonesia, is now undergoing construction but the site continues to experience flooding due to high intensity rainfall. To overcome this challenge, identification of flood points in those areas needs to be done. Digital Elevation Models (DEM) are the most important component in modeling flood inundation in data-sparse areas. High-accuracy DEMs have been found to give better flood estimation but the availability of such data is very limited. Therefore, open-access DEMs, such as SRTM, DEMNAS, and MERIT-Hydro, are the most common choices used in modeling flood inundation in Indonesia. Each data set may provide varying model outcomes, due to different data processing methods. These results often lead to confusion as to which result should be used in subsequent studies. In this study, flood inundation in Nusantara will be generated using different DEMs and HEC-RAS hydraulic modeling with 25-, 50-, and 100-year return period rainfall events. The return period rainfall was obtained by calibrating Global Precipitation Measurement (GPM) satellite rain data. The results provide various flood inundation conditions, MERIT-Hydro tends to present higher flood depths. Followed by DEMNAS and SRTM which have lower flood depths. On the other hand, the use of return period variation gives a linear increase in flood depth for the DEMNAS model. While a decline in increment occurred in the SRTM and MERIT-Hydro models when the two big year return periods were applied. This research can be considered for the Nusantara development planning.

A newly-developed multi-algorithm integration technique for mapping the potentially mineralized alteration zones

The present work introduces a newly-developed mathematical-based Multi-Algorithm Integration (MAI) technique, GIS weighted overlay model, and field investigations for delineating the potentially mineralized alteration zones. The introduced technique was tested on Semna area in Egypt as a case study. Three different algorithms were utilized to map the abundance of six alteration indicator minerals. The introduced MAI technique enabled combining the outputs of the utilized mapping algorithms allowing the delineation of only the localities that were successfully mapped as the potential for alteration zones occurrence by all the three algorithms together. Localities delineated by only one or even two techniques are not included in the output of the MAI technique, giving rise to a higher accuracy than that of individual techniques. The structural linear features were automatically depicted from a hill-shade image generated from a high-accuracy Digital Elevation Model (DEM). A Geographic Information System (GIS) Weighted Overlay Model (WOM) was then implemented to integrate the alteration maps resulting from the MAI technique with the density map of lineaments. A final hydrothermal-related mineralization potentiality map was obtained and validated through fieldwork and spectral analyses for confirming the occurrence of alteration indicator minerals. Samples were collected from the delineated potentially mineralized localities. The spectral signatures of the collected samples were measured, and spectral analyses were performed to reveal the identity of the existing alteration minerals. Various index alteration minerals were recorded in the measured samples including; kaolinite, montmorillonite, sericite, chlorite, carbonates (dolomite), and iron oxides/hydroxide minerals.

High-Accuracy Filtering of Forest Scenes Based on Full-Waveform LiDAR Data and Hyperspectral Images

Airborne light detection and ranging (LiDAR) technology has been widely utilized for collecting three-dimensional (3D) point cloud data on forest scenes, enabling the generation of high-accuracy digital elevation models (DEMs) for the efficient investigation and management of forest resources. Point cloud filtering serves as the crucial initial step in DEM generation, directly influencing the accuracy of the resulting DEM. However, forest filtering presents challenges in dealing with sparse point clouds and selecting appropriate initial ground points. The introduction of full-waveform LiDAR data offers a potential solution to the problem of sparse point clouds. Additionally, advancements in multi-source data integration and machine learning algorithms have created new avenues that can address the issue of initial ground point selection. To tackle these challenges, this paper proposes a novel filtering method for forest scenes utilizing full-waveform LiDAR data and hyperspectral image data. The proposed method consists of two main steps. Firstly, we employ the improved dynamic graph convolutional neural network (IDGCNN) to extract initial ground points. In this step, we utilize three types of low-correlation features: LiDAR features, waveform features, and spectral features. To enhance its accuracy and adaptability, a self-attention module was incorporated into the DGCNN algorithm. Comparative experiments were conducted to evaluate the effectiveness of the algorithm, demonstrating that the IDGCNN algorithm achieves the highest classification accuracy with an overall accuracy (OA) value of 99.38% and a kappa coefficient of 95.95%. The second-best performer was the RandLA-net algorithm, achieving an OA value of 98.73% and a kappa coefficient of 91.68%. The second step involves refining the initial ground points using the cloth simulation filter (CSF) algorithm. By employing the CSF algorithm, non-ground points present in the initial ground points are effectively filtered out. To validate the efficacy of the proposed filtering method, we generated a DEM with a resolution of 0.5 using the ground points extracted in the first step, the refined ground points obtained with the combination of the first and second steps, and the ground points obtained directly using the CSF algorithm. A comparative analysis with 23 reference control points revealed the effectiveness of our proposed method, as evidenced by the median error of 0.41 m, maximum error of 0.75 m, and average error of 0.33 m.

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.

Assessment of ICESat-2’s Horizontal Accuracy Using an Iterative Matching Method Based on High-Accuracy Terrains

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), launched in September 2018, has been widely used in forestry and surveying. A high-accuracy digital elevation model (DEM)/digital surface model (DSM) for terrain matching can effectively evaluate the ICESat-2 design requirements and provide essential data support for further study. The conventional terrain-matching methods regard the laser ground track as a whole, ignoring the individual differences caused by the interaction of photons during flight. Therefore, a novel terrain-matching method using a two-dimensional affine transformation model was proposed to describe the deformation of laser tracks. The least-square optimizes the model parameters with the high-accuracy terrain data to obtain the best matching result. The results in McMurdo Dry Valley (MDV), Antarctica, and Zhengzhou (ZZ), China, demonstrate that the proposed method can verify geolocation accuracy and indicate that the average horizontal accuracy of ICESat-2 V5 data is about 3.86 m in MDV and 4.67 m in ZZ. It shows that ICESat-2 has good positioning accuracy, even in mountainous areas with complex terrain. Additionally, the random forest (RF) model was calculated to analyze the influence of four factors on geographic location accuracy. The slope and signal-to-noise ratio (SNR) are considered the crucial factors affecting the accuracy of ICESat-2 data.

Evaluating the Performance of High Spatial Resolution UAV-Photogrammetry and UAV-LiDAR for Salt Marshes: The Cádiz Bay Study Case

Salt marshes are very valuable and threatened ecosystems, and are challenging to study due to their difficulty of access and the alterable nature of their soft soil. Remote sensing methods in unmanned aerial vehicles (UAVs) offer a great opportunity to improve our knowledge in this type of complex habitat. However, further analysis of UAV technology performance is still required to standardize the application of these methods in salt marshes. This work evaluates and tunes UAV-photogrammetry and UAV-LiDAR techniques for high-resolution applications in salt marsh habitats, and also analyzes the best sensor configuration to collect reliable data and generate the best results. The performance is evaluated through the accuracy assessment of the corresponding generated products. UAV-photogrammetry yields the highest spatial resolution (1.25 cm/pixel) orthomosaics and digital models, but at the cost of large files that require long processing times, making it applicable only for small areas. On the other hand, UAV-LiDAR has proven to be a promising tool for coastal research, providing high-resolution orthomosaics (2.7 cm/pixel) and high-accuracy digital elevation models from lighter datasets, with less time required to process them. One issue with UAV-LiDAR application in salt marshes is the limited effectiveness of the autoclassification of bare ground and vegetated surfaces, since the scattering of the LiDAR point clouds for both salt marsh surfaces is similar. Fortunately, when LiDAR and multispectral data are combined, the efficiency of this step improves significantly. The correlation between LiDAR measurements and field values improves from R2 values of 0.79 to 0.94 when stable reference points (i.e., a few additional GCPs in rigid infrastructures) are also included as control points. According to our results, the most reliable LiDAR sensor configuration for salt marsh applications is the nadir non-repetitive combination. This configuration has the best balance between dataset size, spatial resolution, and processing time. Nevertheless, further research is still needed to develop accurate canopy height models. The present work demonstrates that UAV-LiDAR technology offers a suitable solution for coastal research applications where high spatial and temporal resolutions are required.

An Improved Independent Parameter Decomposition Method for Gaofen-3 Surveying and Mapping Calibration

The Gaofen-3 (GF-3) satellite can provide digital elevation model (DEM) data from its interferogram outputs. However, the accuracy of these data cannot be ensured without applying a surveying and mapping (SAM) calibration process, thus necessitating geometric and interferometric calibration technologies. In this paper, we propose an independent parameter decomposition (IPD) method to conduct SAM calibration on GF-3 data and generate high-accuracy DEMs. We resolved the geometric parameters to improve the location accuracy and resolved the interferometric parameters to improve the height accuracy. First, we established a geometric calibration model, analyzed the Range–Doppler (RD) model and resolved the initial imaging time error as well as the initial slant range error. Then, we established a three-dimensional reconstruction (TDR) model to analyze the height error sources. Finally, the interferometric phase error and baseline vector error were precisely estimated to ensure the vertical accuracy of the interferometric results by establishing the interferometric calibration model. We then used the GF-3 interferometric data derived on the same orbit in a north–south distribution to conduct the calibration experiment. The results show that the plane positioning accuracy was 5.09 m following geometric calibration, that the vertical accuracy of the interferometric results was 4.18 m following interferometric calibration and that the average absolute elevation accuracy of the derived DEM product was better than 3.09 m when using the GF-3 SAR data, thus confirming the correctness and effectiveness of the proposed GF-3 IPD calibration method. These results provide a technical basis for SAM calibration using GF-3 interferograms at the 1:50,000 scale in China.

TECHNOLOGY ON HIGH-ACCURACY DEM EXTRACTION FROM AIRBORNE INTERFEROMETRIC SAR

Abstract. Remote sensing image mapping (aviation, aerospace, UAV) is the main form of current mapping. Optical image mapping has been a certain research foundation and played an important role in the mapping task. However, cloud, ice, snow, rain, and haze conditions seriously affect the interpretation and processing of optical images, making it difficult to meet the needs of rapid emergency response. Synthetic Aperture Radar (SAR) is currently the only method that can achieve all-day, all-weather rapid imaging under various weather conditions such as day, night, cloud, snow, rain, etc. With unique advantages unmatched by traditional optical remote sensing technology, it is an important technical guarantee for improving emergency response capabilities and promoting remote sensing and geographic information industries, and an earth observation technology that has been invested and vigorously developed internationally. Digital Elevation Model (DEM) plays an increasingly important role in the application of terrain information acquisition such as rapid acquisition of sudden disasters and surface deformation information acquisition. Rapid acquisition of large area high-accuracy DEM is one of the important research contents of SAR mapping. Based on interferometric SAR data combined with sensor orbit data, external DEM and other auxiliary data, DEM is finally generated through complex image registration, interference image generation, removal of flat ground effects, interference phase filtering, phase unwrapping, interference calibration and geocoding. The registration accuracy of multiple images greatly affects the accuracy of DEM. This paper proposes an improved registration method based on the conventional rough registration, pixel-level registration and sub-pixel registration. When the motion compensation of the sensor platform is unknown during imaging, it can meet the requirements of high-accuracy and large-scale rapid registration of dual-antenna polarization interferometric SAR. The experimental data is X-band airborne SAR data in Weinan region of Shaanxi Province, and DEM products with the elevation accuracy better than 0.5m is generated, which verifies the effectiveness, accuracy and fast processing ability of the method.

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.

Use of noise reduction filters on stereo images for improving the accuracy and quality of the digital elevation model

Recently, there has been increasing attention to the digital elevation model (DEM) because of its ability to learn the Earth’s surface’s topography. Freely accessible DEMs such as the Cartosat-1, shuttle radar topography mission (SRTM), light detection and ranging, and advanced spaceborne thermal emission and reflection radiometer DEM, contain large vertical errors. The errors are aggravated over multifaceted geography and cannot rectify microgeographic deviations in the moderately flat landscape. As high-accuracy DEMs have limited availability, dated low-accuracy DEMs are still used in various models, specifically in the data-sparse areas. However, it is necessary to enhance the quality of these DEMs before their use in the geomorphometric analysis. We aim to investigate the effect of noise reduction filters on DEM’s accuracy and quality. The noise reduction filters such as weighted average filter, median filter, sharpen filter, Lee sigma filter, and local sigma filter are used for noise reduction in the stereo images. The quality and accuracy of the generated DEMs are further improved by selecting an optimum number of tie points in the image matching process. The effects are assessed by correlating the surface profiles for the final DEM obtained and SRTM DEM as a reference with a resolution of 1 arc sec.

A Novel Airborne Dual-Antenna InSAR Calibration Method for Backprojection Imaging Model

The interferometric synthetic aperture radar (InSAR) elevation inversion method based on the backprojection (BP) imaging model simplifies the interferometric processing chain where image registration and phase unwrapping are not required. Meanwhile, BP algorithm is suitable for radar with different imaging modes and geometry. However, the existing calibration methods are all aimed at traditional InSAR which adopts frequency domain imaging algorithm, but the calibration method for backprojection InSAR elevation inversion model has not been published. In this paper, we propose a novel InSAR calibration method which combines Fast Fourier Transform (FFT) estimation method with sensitivity equation method based on backprojection imaging model. FFT is used to process the interferometric phase which has removed the terrain phase of external digital elevation model (DEM) under BP imaging, and the estimation of interferometric parameters is realized through the phase fringe frequency. In the calibration method based on sensitivity equations, the deviations of interferometric parameters are calculated by solving sensitivity equations according to the three-dimensional information of ground control points. FFT estimation method not only generates the initial baseline parameters for sensitivity equation method, which is conducive to obtaining the global optimal solution, but also separates the calibration of phase offset and baseline parameters, which makes it possible to minimize the condition number of sensitivity matrix in sensitivity equation method. Finally, we obtain the calibrated interferometric parameters through iteration of sensitivity equation method and realize high accuracy DEM inversion. Both simulation experiment and airborne dual-antenna InSAR data processing are performed to verify the effectiveness of this method.

Estimation of soil loss by an integrated approach of USLE and geospatial techniques—a case study of Kumulur watershed

A study was conducted to estimate soil loss by an integrated approach of USLE and geospatial techniques from Kumulur watershed, Trichy district, Tamil Nadu, India, located at 10° 56′ 41.50″–10° 55′ 57.85″ N latitude and 78° 50′ 4.34″–78° 50′ 4.58″ E longitude with an areal extent of 848,694 m2. The factors in USLE were prepared as different thematic layers using GIS. The R factor was calculated using 28 years of rainfall data (1991–2018), and the average R factor was found to be 1862.892 MJ mm ha-1 h-1 yr-1. The K factor was generated through textural classes of soil by collection of soil samples. The high-accuracy digital elevation model (DEM) (4-m resolution) was created using hand-held GPS instrument and Google Earth. The contour, aspect, and slope maps were derived from DEM. The slope classes revealed to be moderately steep sloping having 32.248 and 17.873°, and the LS factor was found to be 0 to 219.33. The C and P factors were obtained from different land uses, viz., barren rocky, built-up area, fallow land, open forest, and water bodies prepared from Google Earth image (2-m resolution—2018 image). The average annual soil loss was estimated as 4.927 t/ha/year which ranged between 0 and 1.469 t/ha/year for each grid cell. To arbitrate the soil loss, the surface runoff of the watershed was also estimated by the SCS-CN method. The runoff depth (2018 rainfall) for CN I, II, and III was found to be 52.19, 120.09, and 664.46 mm, respectively, which shows moderately high runoff potential and moderately low infiltration rate.

Fusion of public DEMs based on sparse representation and adaptive regularization variation model

Global or quasi-global digital elevation model (DEM) datasets provide three-dimensional information on terrain surface, and they have been extremely useful in geoscience research and applications. However, the wide application of DEMs is constrained by differences in the means of observation and processing, and in the resolution of global public DEM datasets. An adaptive regularization variation model based on sparse representation is proposed to generate a high-quality DEM by fusing multi-source DEMs. First, since the sparse representation method has a powerful capability to reconstruct information based on a small amount of information, prior terrain information is extracted from the 90-m TanDEM-X DEM (TDM90) with unprecedented global accuracy using a so-called sparse representation. In this step, an intermediate DEM (termed STDM30) is first extracted from TDM90 that preserves maximum terrain details, thereby preventing the degradation of the DEM accuracy induced by resampling. Then, the designed regularization framework based on terrain slope can constrain the DEM spatial information during fusing multiple datasets. STDM30 is combined with the ALOS Global Digital Surface Model “ALOS World 3D 30 m” (AW3D30) and the 1 arc-second Shuttle Radar Topography Mission Digital Elevation Model (SRTM1) through the designed adaptive regularization variation model to generate a high-accuracy DEM product with a resolution of 30 m. The results of the proposed method were verified by a model-to-model comparison in South Dakota as well as by validation against GPS benchmarks in Southern California. The RMSE, MAE, and SD of the fused DEM are all lower than those of the existing public DEMs, especially in terms of removing topographic noise and refining terrain details. The GPS validation showed that the fused DEM has an RMSE of 3.04 m, with the highest absolute accuracy among the four studied DEMs, and its errors are almost equal to the normal distribution. These experimental results confirm that the multi-scale and multi-source DEM fusion strategy combining sparse representation and an adaptive regularization variation model can utilize existing public datasets and effectively improve the quality of global DEM products.

Observations of dune interactions from DEMs using through-water Structure from Motion

The role of three-dimensionality (3D) in modulating both flow and sediment transport remains poorly understood. 3D bed elevation measurements are difficult to obtain due to irregular dune shapes and submergence. Using photogrammetric tools for topographic reconstruction has become popular in surface studies, yet water refraction makes through-water image capturing of submerged dunes still challenging. We show that through-water images and Structure from Motion (SfM) tools can produce accurate Digital Elevation Models (DEMs) of a measured dune field, with a high level of detail. It is a low-cost and non-intrusive alternative for submerged bed elevation measurements. We provide a detailed SfM workflow, introduce and discuss the use of camera coordinates and underwater ground control points, and processing steps needed to obtain high accuracy DEMs. Our method results in DEMs with mm resolution in controlled laboratory conditions. New data for 23 dune field DEMs from 6 experiments were obtained and are presented.The dune field DEM time series provide insights on dune interaction processes. In our experiments, merging and defect creation were most common. Merging decreases the number of bedforms, and defect creation is responsible for more bedforms. Dunes continue to interact and jostle after achieving equilibrium. Simple crests are much more frequently observed than irregular, sinuous or double crests. Two-dimensional (2D) crest shapes were observed, although crest shapes were generally of 3D nature. Observed dune migration rates are not steady and vary in time and space. Bedform splitting speeds up dune migration, whilst neighbouring dunes tend to accelerate or decelerate. We identify a cascade of processes and show that 3D DEMs at high frequency are required to resolve observed rapid deformations and interaction of dunes. The identified dune interaction processes are inherent to sediment flux (variability), whereas the temporal component of the underlying sediment transport processes is still not well understood in relation to average transport.

Inundation Exposure Assessment for Majuro Atoll, Republic of the Marshall Islands Using A High-Accuracy Digital Elevation Model

Majuro Atoll in the central Pacific has high coastal vulnerability due to low-lying islands, rising sea level, high wave events, eroding shorelines, a dense population center, and limited freshwater resources. Land elevation is the primary geophysical variable that determines exposure to inundation in coastal settings. Accordingly, coastal elevation data (with accuracy information) are critical for assessments of inundation exposure. Previous research has demonstrated the importance of using high-accuracy elevation data and rigorously accounting for uncertainty in inundation assessments. A quantitative analysis of inundation exposure was conducted for Majuro Atoll, including accounting for the cumulative vertical uncertainty from the input digital elevation model (DEM) and datum transformation. The project employed a recently produced and validated DEM derived from structure-from-motion processing of very-high-resolution aerial imagery. Areas subject to marine inundation (direct hydrologic connection to the ocean) and low-lying lands (disconnected hydrologically from the ocean) were mapped and characterized for three inundation levels using deterministic and probabilistic methods. At the highest water level modeled (3.75 ft, or 1.143 m), more than 34% of the atoll study area is likely to be exposed to inundation (68% chance or greater), while more than 20% of the atoll is extremely likely to be exposed (95% chance or greater). The study demonstrates the substantial value of a high-accuracy DEM for assessing inundation exposure of low-relief islands and the enhanced information from accounting for vertical uncertainty.

An Operational Forecasting System for Flash Floods in Mountainous Areas in Taiwan

Flash floods are different from common floods because they occur rapidly over short time scales, and they are considered to be one of the most devastating natural hazards worldwide. Mountainous areas with high population densities are particularly threatened by flash floods because steep slopes generate high flow velocities. Therefore, there is a great need to develop an operational forecasting system (OFS) for better flash flood prediction and warning in mountainous regions. This study developed an OFS through the integration of meteorological, hydrological, and hydrodynamic models. Airborne light detection and ranging (LiDAR) data were used to generate a digital elevation model (DEM). The OFS employs high-density and high-accuracy airborne LiDAR DEM data to simulate rapid water level rises and flooding as the result of intense rainfall within relatively small watersheds. The water levels and flood extent derived from the OFS are in agreement with the measured and surveyed data. The OFS has been adopted by the National Science and Technology Center for Disaster Reduction (NCDR) for forecasting flash floods every six hours in a mountainous floodplain in Taiwan. The 1D and 2D visualization of the OFS is performed via the National Center for Atmospheric Research Command Language (NCL).

How Well can Spaceborne Digital Elevation Models Represent a Man-Made Structure: A Runway Case Study

In this case study, an active runway of a civilian airport in Zonguldak, Turkey was used to assess the suitability of spaceborne digital elevation models (DEMs) to model an anthropogenic structure. The tested DEMs include the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the Advanced Land Observing Satellite (ALOS) World 3D 30 m (AW3D30), the Shuttle Radar Topography Mission (SRTM)-1”, the SRTM-3”, the SRTM-X, the TanDEM-3”, and the WorldDEMTM. A photogrammetric high accuracy DEM was also available for the tests. As a reference dataset, a line-leveling survey of the runway using a Leica Sprinter 150/150M instrument was performed. The selection of a runway as a testbed for this type of investigation is justified by its unique characteristics, including its flat surface, homogenous surface material, and availability for a ground survey. These characteristics are significant because DEMs over similar structures are free from environment- and target-induced error sources. For our test area, the most accurate DEM was the WorldDEMTM followed by the SRTM-3” and TanDEM-3”, with vertical errors (LE90) equal to 1.291 m, 1.542 m, and 1.56 m, respectively. This investigation uses a method, known as the runway method, for identifying the vertical errors in DEMs.

Utilizing Flood Inundation Observations to Obtain Floodplain Topography in Data-Scarce Regions

Flood models predict inundation extents, and can be an important source of information for flood risk studies. Accurate flood models require high resolution and high accuracy digital elevation models (DEM); current global DEMs do not capture the topographic details in floodplains, and this often leads to inaccurate prediction of flood extents by flood models. Flood extents obtained from remotely sensed data provide indirect information about topography. Here, we attempt to use this information along with model predictions to produce better floodplain topography. The algorithm we describe is a two-step process: first, we reduce the noise along the observed flood boundaries for all particles. Then, the model predictions from these modified DEMs are assimilated with observations using a particle batch smoother. We implemented the algorithm for a synthetic test case. For the nominal case, we observed a significant improvement in accuracy in terms of RMSE (35% reduction), bias (20%) and standard deviation (40%). We conducted sensitivity analysis by using priors of varying bias (0.5 m, 1 m, 2 m) and standard deviation (1 m, 2 m, 4 m). The bias reduced to ~0.5 m or below in all the cases: the reduction in bias varied from 11% to 76%. The standard deviation of errors in the final estimate was almost half of the prior: the reduction varied from 40% to 49%. The reduction in RMSE ranged between 35% and 67%. For the case with 2 m bias and 4 m standard deviation (SRTM-like error levels), bias went down to 0.48 m (76% reduction), and standard deviation reduced to 2.24 m (44% reduction). Flood inundation maps produced from the final estimate DEMs also improved on its prior. For the 2 m bias cases, true positive rate (TPR) for peak inundation went from ~30% to more than 57% in all three cases. The algorithm produces promising results, and this type of analysis can be performed in data-poor floodplains where high resolution DEMs do not exist.

Overcoming data scarcity in flood hazard assessment using remote sensing and artificial neural network

Many urban cities in Southeast Asia are vulnerable to climate change. However, these cities are unable to take effective countermeasures to address vulnerabilities and adaptation due to insufficient data for flood analysis. Two important inputs required in flood analysis are high accuracy Digital Elevation Model (DEM), and long term rainfall record. This paper presents an innovative and cost-effective flood hazard assessment using remote sensing technology and Artificial Neural Network (ANN) to overcome such lack of data. Shuttle Radar Topography Mission (SRTM) and multispectral imagery of Sentinel-2 are used to derive a high-accuracy DEM using ANN. The improvement of SRTM’s DEM is significant with a 42.3% of reduction on Root Mean Square Error (RMSE) which allows the flood modelling to proceed with confidence. The Intensity Duration Frequency (IDF) curves that were constructed from precipitation outputs from a Regional Climate Model (RCM) Weather Research and Forecasting (WRF) were used in this study. Design storms, calculated from these IDF curves with different return periods were then applied to numerical flood simulations to identify flood prone areas. The approach is demonstrated in a flood hazard study in Kendal Regency, Indonesia. Flood map scenarios were generated using improved SRTM and design storms of 10-, 50- and 100-year re-turn periods were constructed using the MIKE 21 hydrodynamic model. This novel approach is innovative and cost-effective for flood hazard assessment using remote sensing and ANN to overcome lack of data. The results are useful for policy makers to understand the flood issues and to proceed flood mitigation adaptation/measures in addressing the impacts of climate change.

Spatial Enhancement of DEM Using Interpolation Methods: A Case Study of Kuwait’s Coastal Zones

Digital elevation models (DEMs) are essential tools utilized in several branches of science, including environmental, geological, and geospatial studies. Unfortunately, high-accuracy DEM data such as LiDAR are not publicly available, and the coverage is limited. Therefore, the use of alternative methods, such as interpolation techniques (i.e., kriging, inverse distance weighting, radial basis functions), is greatly advantageous for the production of enhanced DEMs. The results of this study show that interpolated DEMs had minimal errors (RMSE = 1.44) with an increase of about 28% from the original DEM. However, the spatial resolution of interpolated DEM data was enhanced significantly by 83%. The deterministic interpolation methods provided more accurate estimations for producing DEMs in the coastal zones of Kuwait than geostatistical interpolation methods. The reference elevation data were collected using GPS and accurate topographic maps (1:25,000), and elevation points from the interpolated DEM were matched significantly (R2 = 0.88; R2 = 94, respectively). Given the lack of accurate DEM data, the interpolated DEM produced in this study are held in high regard and highly recommended for use in the coastal zone of Kuwait.