Resolution Digital Elevation Model Research Articles

Capabılıtıes of usıng UAVs to determıne forest road excavatıon volumes ın mountaınous areas

Decision support systems based on spatial data are widely used in some countries for planning production activitiesin forestry and designing forest roads. In recent years, Unmanned Aerial Vehicles (UAVs) have been playingan active role in the production of high resolution and accurate digital elevation models (DEMs) of forested areas.In the preparation of progress payments for forest road construction works, the workload is high, while the feethat has to be paid to the contractor company after the completion of the road construction is determined accordingto the final cost in the progress payment schedules. In this study, the applicability of UAV technology for determiningforest road cut volume and land classification was investigated. A 640-meter section of the forest roadcoded 001 of Taşlıyayla Forest Management Sub-Directorate (Bolu/Türkiye) was examined in the study. Flightswith an UAV (drone) were carried out in autonomous mode before and after the construction of the road examinedin the study. In addition, in order to determine the amount of cut, measurements of the geometric characteristicsof the road were made in the field after the road construction. As a result of the study, 2189 m³ cut volumewas found with the UAV data with an average error of 3 cm. According to the field measurement results, theamount of cut was determined as 2080 m³. A difference of +5% was found between the UAV data and the fieldmeasurement results. In addition, using the JavaScript coding language on the Google Earth Engine platform, soil(46%) and loose soil (54%) land classification was identified from the orthophoto images. The obtained resultsreveal the usability of UAVs in determining forest road volumes.

Influence of the spatial resolution of Digital Elevation Models on water-balance estimations in temporary shallow lakes

Many shallow lakes are strongly degraded and their hydroecological functioning has been altered. At the same time, their rate of disappearance is remarkably high compared to other ecosystem types. Moreover, due to their relatively small size and shallow depth these lakes are highly sensitive to meteorological variability, making them highly vulnerable to climate change. The accurate estimation of their water balance is key to identifying proper management strategies. To carefully model the water balance, it is necessary to use a Digital Elevation Model (DEM) of a high-enough spatial resolution to understand the bathymetric relationships of the basin; however, it is not always possible to generate DEMs with field data. Even when DEMs are available, it is still unclear what spatial resolution is adequate to obtain an accurate water balance in shallow lakes. The aim of this work was to analyze the influence that bathymetries from different DEMs may have on shallow lake-water-balance estimations, using Lake Alcahozo (central Spain) as a case study. To do this, hydrological balances were developed with Lidar-derived DEMs with different spatial resolutions, and these balances were compared with one developed from bathymetric field data. The hydrological models obtained were calibrated with limnimetric measurements and validated with estimates of flooded areas calculated via remote-sensing imagery. The main results showed that, to a certain extent, the different resolutions of bathymetries do not have a strong influence on the result of water-balance estimations: DEMs up to a spatial resolution of 50 m (a pixel size representing 0.3 % of the basin area) have shown similar goodness-of-fit in the modeling of the hydrological balances in our case study. Therefore, depending on the objective of the study, Lidar data with different spatial resolutions can be used, up to a minimal resolution that should be determined on a case-by-case basis.

What drives uncertainty surrounding riverine flood risks?

Designing strategies to manage flood risks is complicated by the often large uncertainties surrounding flood risk projections. Uncertainty surrounding riverine flood risks can stem from choices regarding boundary and initial conditions, model structures, and parameters as well as interactions among hazards, exposures, and vulnerabilities. Here we analyze a case study to rank the drivers of uncertainties surrounding riverine flood hazards and risks. Using Sobol' sensitivity analysis with a large number of simulations, we thoroughly explore the interactions among different sources of uncertainty. We find that the projected flood risk is most sensitive to factors associated with flood hazards, rather than exposure and vulnerability: upstream discharge, river bed elevation, channel roughness, and the digital elevation model resolution. Our results highlight the importance of uncertainty quantification in enhancing the reliability of flood models and risk assessments.

A global dataset of the shape of drainage systems

Abstract. Drainage basins delineate Earth's land surface into individual water collection units. Basin shape and river sinuosity determine water and sediment dynamics, affecting landscape evolution and connectivity between ecosystems and freshwater species. However, a high-resolution global dataset for the boundaries and geometry of basins is still missing. Using a 90 m resolution digital elevation model, we measured the areas, lengths, widths, aspect ratios, slopes, and elevations of basins over 50 km2 globally. Additionally, we calculated the lengths and sinuosities of the longest river channels within these 0.67 million basins. We built a new global dataset, Basin90m, to present the basins and rivers, as well as their morphological metrics. To highlight the use cases of Basin90m, we explored the correlations among morphological metrics, such as Hack's law. By comparing with HydroSHEDS, HydroATLAS, and Google Earth images, we demonstrated the high accuracy of Basin90m. Basin90m, available in shapefile format, can be used on various GIS platforms, including QGIS, ArcGIS, and GeoPandas. Basin90m has substantial application prospects in geomorphology, hydrology, and ecology. Basin90m is available at https://doi.org/10.5880/GFZ.4.6.2023.004 (He et al., 2023).

Assessing the Influence of Multiresolution DEMs on Soil Loss Prediction using the RUSLE Model in Central Morocco

Abstract The topography plays a significant role in determining the rate of soil loss. The Digital Elevation Model (DEM) retrieved from earth observation products can be used to calculate the slope length and steepness, which are important factors in the Revised Universal Soil Loss Equation (RUSLE) used to estimate soil erosion. In the present study, the effect of two DEM resolutions (30m and 90 m) on RUSLE model results was assessed and used to quantify the rate of soil loss in Settat province (Morocco), specifically in certain abandoned quarries. As a result, DEM-30 and DEM-90 soil erosion rates vary from 0 to 282.77 t.ha-1.yr-1 and 0 to 13.79 t.ha-1.yr-1, respectively. At the quarry level, the abandoned quarries showed soil loss rates ranging from 0 to 7.92 t.ha-1.yr-1 for DEM-30. Contrary to this, the soil loss rate for DEM-90 data was zero. To summarize, for more relevant and detailed conclusions, high-resolution data must be used to collect more information and describe the erosion process with as much detail as possible, especially for small areas.

SWAT Model Performance Using Spatially Distributed Saturated Hydraulic Conductivity (Ksat) and Varying-Resolution DEMs

Saturated hydraulic conductivity (Ksat) is a hydrologic flux parameter commonly used to determine water movement through the saturated soil zone. Understanding the influences of land-use-specific Ksat on the model estimation error of water balance components is necessary to advance model predictive certainties and land management practices. An exploratory modeling approach was developed in the physically based Soil and Water Assessment Tool (SWAT) framework to investigate the effects of spatially distributed observed Ksat on local water balance components using three digital elevation model (DEM) resolution scenarios (30 m, 10 m, and 1 m). All three DEM scenarios showed satisfactory model performance during calibration (R2 > 0.74, NSE > 0.72, and PBIAS ≤ ±13%) and validation (R2 > 0.71, NSE > 0.70, and PBIAS ≤ ±6%). Results showed that the 1 m DEM scenario provided more realistic streamflow results (0.315 m3/s) relative to the observed streamflow (0.292 m3/s). Uncertainty analysis indicated that observed Ksat forcings and DEM resolution significantly influence predictions of lateral flow, groundwater flow, and percolation flow. Specifically, the observed Ksat has a more significant impact on model predictive confidence than DEM resolution. Results emphasize the potential uncertainty of using observed Ksat for hydrological modeling and demonstrate the importance of finer-resolution spatial data (i.e., 1 m DEM) applied in smaller watersheds.

Impacts of Resampling and Downscaling Digital Elevation Model and Its Morphometric Factors: A Comparison of Hopfield Neural Network, Bilinear, Bicubic, and Kriging Interpolations

The digital elevation model (DEM) and its derived morphometric factors, i.e., slope, aspect, profile and plan curvatures, and topographic wetness index (TWI), are essential for natural hazard modeling and prediction as they provide critical information about the terrain’s characteristics that can influence the likelihood and severity of natural hazards. Therefore, increasing the accuracy of the DEM and its derived factors plays a critical role. The primary aim of this study is to evaluate and compare the effects of resampling and downscaling the DEM from low to medium resolution and from medium to high resolutions using four methods: namely the Hopfield Neural Network (HNN), Bilinear, Bicubic, and Kriging, on five morphometric factors derived from it. A geospatial database was established, comprising five DEMs with different resolutions: specifically, a SRTM DEM with 30 m resolution, a 20 m resolution DEM derived from topographic maps at a scale of 50,000, a 10 m resolution DEM generated from topographic maps at a scale of 10,000, a 5 m resolution DEM created using surveying points with total stations, and a 5 m resolution DEM constructed through drone photogrammetry. The accuracy of the resampling and downscaling was assessed using Root Mean Square Error (RMSE) and mean absolute error (MAE) as statistical metrics. The results indicate that, in the case of downscaling from low to medium resolution, all four methods—HNN, Bilinear, Bicubic, and Kriging—significantly improve the accuracy of slope, aspect, profile and plan curvatures, and TWI. However, for the case of medium to high resolutions, further investigations are needed as the improvement in accuracy observed in the DEMs does not necessarily translate to the improvement of the second derivative morphometric factors such as plan and profile curvatures and TWI. While RMSEs of the first derivatives of DEMs, such as slope and aspect, reduced in a range of 8% to 55% in all five datasets, the RMSEs of curvatures and TWI slightly increased in cases of downscaling and resampling of Dataset 4. Among the four methods, the HNN method provides the highest accuracy, followed by the bicubic method. The statistics showed that in all five cases of the experiment, the HNN downscaling reduced the RMSE and MAE by 55% for the best case and 10% for the worst case for slope, and it reduced the RMSE by 50% for the best case of aspect. Both the HNN and the bicubic methods outperform the Kriging and bilinear methods. Therefore, we highly recommend using the HNN method for downscaling DEMs to produce more accurate morphometric factors, slope, aspect, profile and plan curvatures, and TWI.

An innovative methodology for the adjustable use of energy line angle for susceptibility mapping by using cone propagation approach

Rockfall frequently occurs in the mountainous areas and threatens structures such as settlement areas, transportation lines, and agricultural field. The empirical approaches for rockfall mapping have been an attractive research topic in rock mechanics in the recent years, because producing rockfall maps of large areas by using the deterministic and the probabilistic analysis seems difficult due to the necessity of numerous inputs. The cone propagation approach is preferred as a practical tool particularly in the regional scale. The digital elevation model (DEM) of a region prone to rockfall is used for determination of possible propagation zones based on a simple geometric rule known as the energy line angle (reach angle). As a new term, ELAmax_stop was defined to represent the energy line angle that extends to the border of the propagation zone as the maximum run-out distance that is obtained from application of the cone approach to all points (pixels) in source area. The angle denoted as α refers to the threshold slope angle of the steep areas utilized to identify potential source areas by using DEM. Conceptually, the fallen rock blocks within a rockfall-prone region stop within the cone propagation zone, which is bounded by the energy line angles α and ELAmax_stop. While the value of α is susceptible to the resolution of DEM, ELAmax_stop, which exhibits a wide angle range as documented in the literature, is controlled by rock block features together with slope surface properties of the propagation zone. Due to the variability of ELAmax_stop and α depending on the studied region and the resolution of the DEM, the boundary value of the energy line angles between different susceptibility classes need to be adjusted by considering α and ELAmax_stop. By adopting the cone propagation approach to enable adjustable use of energy line angles for rockfall susceptibility mapping, a series of graphical presentations was prepared. These graphical presentations allowed for the prediction of energy line angles corresponding to various rockfall susceptibility classes including very low, low, medium, high, and very high. In addition to the graphical presentations, a series of practical equations were derived for the same purpose. In the final part of the study, a new rating system, namely the run-out distance rating (RDR), was introduced for the preliminary determination of ELAmax_stop. Due to the empirical structure of the methodology, the suggested supportive approach to the practitioners for determining ELAmax_stop should be considered as an initial step that opens to improvement. The proposed methodology in this study was implemented in the regions of Kargabedir Hill and Sivrihisar residential areas in Turkey to prepare rockfall susceptibility maps.

Modeling computer sight based on DEM data to detect terrain breaks caused by gully erosion on the loess Plateau

As a type of terrain break, the gully shoulder line is of great significance to locate the scope of surface failure and soil erosion. Based on the knowledge of the terrain structure of loess gullies, this paper modeled sight lines to calculate the three-dimensional maximum elevation angle (3D MEA). Then, a critical MEA value of 35° was determined via human vision to extract negative terrain and shoulder lines considering unmanned aerial vehicle (UAV)- and light detection and ranging (lidar)-based digital elevation models (DEMs). In addition to the DEM resolution, the uncertainties caused by the direction number, look distance and skip radius of computer sight were further examined. It was suggested that the process should involve observing at least 12 directions to detect complete information on terrain breaks. The simulation process required a relatively large look distance to analyze global terrain changes because a small distance could limit sight within the local window. The skip radius could result in the process ignoring nearby abrupt changes in terrain, thus causing negative terrain to be misclassified as positive terrain near shoulder lines. The concept of wall morphology was introduced to attribute the critical value to the repose angle of loess gullies. Eventually, the MEA was compared with many other parameters. It could be concluded that the slope is limited to the local scale, while the MEA could combine local and global scales using a large look distance. The hillshade is limited to one viewing direction, but the 3D MEA is a comprehensive layered index from multiple directions. This study is a novel exploration of interdisciplinary hydraulic engineering and information technology. This approach reduces terrain breaks from three-dimensional space into a one-dimensional index and simplifies shoulder lines as a critical 3D MEA value related to loess material properties. This research could be adopted for soil and water conservation.

Spatial distribution characteristics and influence factor analysis of landslides —case study of the Hanwang area in Qinba Mountains

The geological hazards of landslides in Hanwang Town, Ziyang County, Ankang City, Shaanxi Province, have emerged. Yet, the current understanding of the spatial distribution characteristics and influencing factors of landslides in this area remains unclear. Combining the results of remote sensing interpretation and field investigation, seven influencing factors, namely, elevation, slope direction, slope gradient, distance from rivers, distance from faults, engineering geologic lithology, and distance from roads, are selected for the study. The distribution characteristics of landslides in each influencing factor and the influence of the resolution of the Digital Elevation Model (DEM) on the results are statistically and analytically analyzed. Furthermore, two high-risk landslides within the study area were subjected to comprehensive analysis, integrating the findings from drilling and other field investigations in order to examine their deformation mechanisms. Based on this analysis, the following conclusions were derived: (1) 34 landslides in the study area, mainly small earth landslides, with a distribution density of 0.42/km2, threatening 414 people and property of about 55.87 million Yuan. (2)The landslides in the study area easily occur in the <400 ​m elevation range; the landslides are developed in all slope directions, the gradient is mainly concentrated in the range of 10°–40°, the distribution density of the landslides is higher in the closer distance from the river and the faults (0–200 ​m), the landslide-prone strata are mainly the softer and weaker metamorphic rocks, and the landslides are mainly around roads. (3) The resolution of the DEM should be selected based on the specific conditions of the study area, the requirements of the investigation, and the scale of the landslide. Opting for an appropriate DEM resolution is advantageous for understanding the patterns of landslides and conducting risk assessments in the region. (4) The Zhengjiabian landslide is a traction Landslide. The landslide body is a binary structure of gravel soil and slate weathering layer, and the damage process can be divided into three stages:①damage to the leading edge and stress release, ②continuous creep and cracking, ③rainfall infiltration and damage. The predominant slope material in the Brickyard landslide comprises clay, and the landslide is triggered by a combination of the traction effect resulting from the excavation at the slope's base and the nudging effect caused by the stacking load of the brick factory. Additionally, the Brickyard landslide exhibits persistent creep deformation. The study results provide a scientific basis for disaster prevention and mitigation in the Hanwang Township area.

D-SRCAGAN : DEM Super-resolution Generative Adversarial Network

High-resolution digital elevation models (DEMs) are widely used in many fields such as mapping, hydrology, meteorology and geology, where they can improve the accuracy and reliability of many geographic analysis applications as an input. However, due to the high cost and difficulty of acquiring high-resolution DEMs, as well as the problems of edge smoothing, data distortion and fractures in reconstructed ground surfaces with traditional super-resolution DEM reconstruction techniques. Inspired by the excellence of generative adversarial neural networks in super-resolution image analysis, this paper investigates an approach for DEM super-resolution reconstruction with deep residual generative adversarial network. An advanced DEM Super-resolution Generative Adversarial Network (D-SRCAGAN) is proposed in this paper, which can reconstruct a quadruple higher resolution DEM by using low-resolution DEM. Compared with the bicubic and SRGAN methods, the D-SRCAGAN method reconstruction results can retain more topographic features and obtain higher RMSE values.

An evaluation on the parameters used in flood susceptibility analysis

Taşkınlar her geçen gün artan büyüklük ve sıklıklarına bağlı olarak dünyada ve ülkemizde önemi giderek artan afetlerden birisidir. Bu çalışmadaki temel amaç, taşkın duyarlılık ile ilgili uluslararası ve ulusal literatürün değerlendirilmesi ve duyarlılık çalışmalarına yeni bir yaklaşım olarak sel ve taşkınların meydana geldiği yerleşmelerin su toplama havzaları temelli taşkın duyarlılık parametrelerinin belirlenmesini gerçekleştirmektir. Bu kapsamda çalışmada tarihsel taşkın envanterine bağlı olarak Bursa ili sınırları içerisinde vadi tabanı ve akarsu kenarında sel ve taşkınların yaşandığı yerleşmelerin havzalarına bağlı olarak taşkın duyarlılık analizi parametreleri belirlenmiştir. Çalışmada kullanılan temel altlık veriler, Bursa iline ait 5m çözünürlüklü Sayısal Yükseklik Modeli (SYM), 1956-2022 yılları arasını kapsayan envanter verileri, litoloji, hidrolojik toprak grupları (HTG) ve yağış (WorldClim) verileridir. Bursa il sınırları içerisinde meydana gelen tarihsel sel ve taşkın envanterine bağlı olarak 28 yerleşme ve bu yerleşmelerin su toplama havzaları belirlenmiş ve bu havzalara sel ve taşkının oluşmasında hazırlayıcı 12 parametre uygulanmıştır. Taşkın hazırlayıcı parametreler sınıflandırma aşamasında 0-1 arasında normalize edilerek ortaya çıkan sonuca göre taşkın duyarlılık için parametre katsayıları oluşturulmuştur. Sonuç olarak envantere göre maksimum etkiye sahip parametreler; çatallanma oranı (R_b), drenaj yoğunluğu (D_d), akım toplanma zamanı (T_c), eğim, topografik nemlilik indeksi, akarsu güç indeksi, hidrolojik toprak grupları, olarak belirlenmiştir. Bu çalışma ile taşkın duyarlılık analizinde önceki çalışmalardan farklı olarak envantere bağlı ve yerleşim temelli havzalardan taşkın duyarlılık parametreleri belirlenmiştir.

Evaluating the effects of DEM and soil data resolution on streamflow and sediment yield simulations in the Upper Blue Nile basin.

While the availability of "big data" on biophysical parameters through citizen science and/or from public/private sources is expected to help in addressing data scarcity issues, there is little understanding of whether and/or how such data will improve watershed simulations. This research aimed to evaluate whether improvements in resolutions of Digital Elevation Model (DEM) and soil data will enhance streamflow and sediment yield simulations and thereby improve soil and water management decisions. The study was conducted in two different-sized watersheds (Anjeni and Gilgel Abay with ~ 1 km2 and ~ 1655 km2 area, respectively) in the Upper Blue Nile basin in Ethiopia. Effects of DEM and soil data resolutions on streamflow and sediment yield were evaluated using the Soil and Water Assessment Tool (SWAT). The results showed that the effect of DEM and soil data resolution on streamflow and sediment yield simulation was scale dependent finer resolution DEM and soil datasets improved streamflow and sediment yield simulations in the smaller Anjeni watershed, whereasDEM resolution had no effect in the bigger Gilgel Abay watershed. Small watersheds are often used to understand watershed processes, and thus theuse of finer-resolution spatial data for watershed simulations could result in better results. Findings from the smaller Anjeni watershed suggested that the combined use of finer resolution DEM and soil data could potentially improve sediment yield simulations although the lack of observed sediment yield data did not allow verification of this at the larger Gilgel Abay watershed.

A field-validated ensemble species distribution model of Eriogonum pelinophilum, an endangered subshrub in Colorado, USA.

Understanding the suitable habitat of endangered species is crucial for agencies such as the Bureau of Land Management to plan management and conservation. However, few species distribution models are directly validated, potentially limiting their application in management. In preparation for a Species Status Assessment of clay-loving wild buckwheat (Eriogonum pelinophilum), an endangered subshrub found in southwest Colorado, we ran a series of species distribution models to estimate the species' potential occupied habitat and validated these models in the field. A 1-meter resolution digital elevation model derived from LiDAR and a high-resolution geology mapping helped identify biologically relevant characteristics of the species' habitat. We employed a weighted ensemble model based on two Random Forest and one Boosted Regression Tree model, and discrimination performance of the ensemble model was high (AUC-PR = 0.793). We then conducted a systematic field survey of model habitat suitability predictions, during which we discovered 55 new subpopulations of the species and demonstrated that new species observations were strongly associated with model predictions (p < .0001, Cliff's delta = 0.575). We further refined our original models by incorporating the additional species occurrences collected in the field survey, a new explanatory variable, and a more diverse set of models. These iterative changes marginally improved performance of the ensemble model (AUC-PR = 0.825). Direct validation of species distribution models is extremely rare, and our field survey provides strong validation of our model results. This helps increase confidence to utilize predictions in planning. The final model predictions greatly improve the Bureau of Land Management's understanding of the species' habitat and increase our ability to consider potential habitat in planning land use activities such as road development and travel management.

Machine learning insights of anthropogenic and natural influences on riverbed deformation in a large lowland river

As escalating environmental pressures threaten the world's river systems, understanding the driving factors influencing their geomorphological changes is of critical global importance. This study illuminates the complex interaction between natural and anthropogenic factors that drove geomorphological changes along the upper Apalachicola River (UAR) from 1960 to 2010. This study utilized LiDAR point cloud data and hydrographic surveys conducted between 1960 and 2010 to identify the primary factors driving riverbed changes. A triangular irregular network (TIN) was used to convert the combined datasets from each survey, following coordinate transformation, into a 1.5 m-resolution digital elevation model (DEM) raster. The DEM of difference (DEMoD) was computed by subtracting the 1960 DEM from that of 2010, and the total sediment loss and gain were then determined by multiplying the DEMoD by the DEM resolution. A random forest (RF) regression model was used to determine the most influential factors contributing to riverbed deformation. The results revealed substantial differences in sediment loss and gain across the river. The UAR, divided into three segments, experienced sediment loss of about −6.56 × 106 m3 between 1960 and 2010, with the farthest upstream section (RM 106 to 98) losing −3.14 × 106 m3, which is ~48 % of the sediment losses in the study area. Some sections of the channel have deepened significantly, with a pronounced depth difference of −17.20 m between RM 92 and 94, potentially caused by the presence of limestone and the enlargement of a sinkhole or spring on the riverbed. The RF model identified the dam (>28 %) as the most crucial anthropogenic factor in riverbed deformation, followed by the presence of dikes (~17—18 %), then historical dredging (~7—8 %), highlighting the substantial impact of artificial structures on riverbed deformation. In contrast, rock removal, while potentially impactful in certain locations, had negligible effects (>0.5 %) at the system scale. This study highlights the interaction of natural and anthropogenic factors in river geomorphology and the potential for machine learning to identify these dynamics and inform data-driven river management and conservation strategies.

Automated actual evapotranspiration estimation: Hybrid model of a novel attention based U-Net and metaheuristic optimization algorithms

Actual evapotranspiration (ETa) plays a crucial role in the water and energy cycles of the earth. An accurate estimate of the ETa is essential for management of the water resources, agriculture, and irrigation, as well as research on atmospheric variations. Despite the importance of accurate ETa values, estimating and mapping them remains challenging due to the physical and biological complexity of the ET process. As a novel approach for rapid and reliable estimation of ETa, the present study develops automated deep learning (AutoDL) models that incorporate a metaheuristic optimization algorithm for image processing, architectural design, and hyperparameter tuning. The proposed AutoDL models integrate three different spatial and channel attention mechanisms, including a novel activated spatial attention mechanism (ASPAM), with the U-Net architecture. Bypassing the need for meteorological inputs, the proposed framework uses Moderate Resolution Imaging Spectrometer (MODIS) products and Digital Elevation Model (DEM) data as inputs. To evaluate the performance of the models, they are applied to three study areas in the United States with various climatic characteristics. According to the results, during the spring and summer, when the target values have higher certainty, the estimations are highly promising, with R2 as high as 0.91 and MAPE as low as 6.40%. Furthermore, the proposed ASPAM module improves the accuracy of ETa estimations compared to attention gate (AG) and squeeze and excitation (SE) attention modules. The results also indicate that the MODIS raw products and derived vegetation and water indices can predict ETa within a reliable range of accuracy, with the addition of DEM data marginally enhancing the models' performance. The automatic workflow of this model makes it significantly easy to use, contributing to its applicability and generalizability for enhancing atmospheric research.

Evaluation of vertical accuracy of TanDEM-X Digital Elevation Model in Egypt

The study conducted aimed to examine the accuracy of Digital Elevation Models (DEMs) in Egypt, specifically the TanDEM-X mission's 30 m and 12 m resolution DEMs and the SRTM DEM with a 30 m resolution. The accuracy of DEMs is essential for various civil engineering and surveying applications, especially in geoscience applications. To ensure the comparison's accuracy, the study used ellipsoidal heights instead of orthometric heights, preventing errors caused by global geopotential models in the conversion process. The evaluation of the three DEMs was carried out using 352 GNSS points. The findings indicate that both TanDEM-X DEMs with 30 m and 12 m resolutions outperform the SRTM 30 m in terms of vertical accuracy, making them ideal for geomatic applications that require higher-resolution DEMs. The TanDEM-X 30 m generates a standard deviation (STD) and a Root Mean Square Error (RMSE) of approximately 3.03 m and 3.45 m, respectively. On the other hand, the TanDEM-X 12 m generates an STD and RMSE of approximately 2.86 m and 3.18 m, respectively. Comparatively, the SRTM 30 m produces an STD and RMSE of approximately 4.67 m and 5.35 m, respectively.

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.

Bridging the gap between airborne and spaceborne imaging spectroscopy for mountain glacier surface property retrievals

Observations of glacier albedo are sparse, despite being a first-order control on net solar radiation and melt rates. Recent and forthcoming airborne and satellite imaging spectrometer missions increase our ability to record changes in albedo over time and space but will require an understanding of uncertainties, especially in areas of rugged terrain. Some of these uncertainties arise from the timing of acquisition and resolution of digital elevation models (DEM) that are used for topographic correction. We use the fine-resolution data (2 m) from the Airborne Coastal Observatory (ACO), an imaging spectroscopy and lidar remote sensing platform, to quantify the expected error from other airborne and satellite scale platforms. First, we describe our retrieval workflow - the Imaging Spectroscopy Snow and Ice Algorithm (ISSIA) that yields spatial retrievals of broadband albedo (BBA), optical grain size, and radiative forcing by light absorbing particles (RFLAP) using the coincident 2 m lidar DEM over mountain glaciers. We use two acquisitions over Place Glacier, British Columbia, Canada that represent typical spring and summer conditions as a baseline from which synthetic datasets were generated to represent data from coarser-scale imaging spectroscopy platforms. Each synthetic dataset was motivated by specific airborne and satellite platform scenarios and quantifies four sources of error: (1) DEM spatial resolution and elevation accuracy, (2) coincident vs non-coincident DEM collection, (3) imagery spatial resolution, and (4) spectral resolution. Use of a 30 m DEM introduced the most error in steep mountain terrain, which primarily increased error in the BBA and RFLAP retrievals. The timing of DEM acquisition mattered most for high-resolution imagery and in areas surrounding the glacier toe where mass loss occurred more rapidly. Coarse-resolution imagery did not fully capture spatial variability in surface properties and topography, which lowered variance and increased overall errors. Coarsening of spectral resolution from 5 nm to 10 nm had minimal impact on surface property retrieval and associated errors were independent of DEM or imagery resolution. A simple energy analysis showed that surface albedo error, due to topographic correction with coarse and non-coincident DEMs, resulted in melt rate errors ranging from 6 to 20% for airborne and 11–20% for satellite scenarios.

Methods and insights on enabling geovisualisation for coastal communities of the North Shore of Vancouver

Contemporary sea level rise (SLR) research seldom considers enabling effective geovisualisation for the communities. This lack of knowledge transfer impedes raising awareness on climate change and its impacts. The goal of this study is to produce an online SLR map accessible to the public that allows them to interact with evolving high-resolution geospatial data and techniques. The study area was the North Shore of Vancouver, British Columbia, Canada. While typically coarser resolution (10 m+/pixel) Digital Elevation Models have been used by previous studies, we explored an open access airborne 1 m LiDAR which has a higher resolution and vertical accuracy and can penetrate tree cover at a higher degree than most satellite imagery. A bathtub method model with hydrologic connectivity was used to delineate the inundation zones for various SLR scenarios which allows for a not overly complex model and process using standard tools such as ArcGIS and QGIS with similar levels of accuracy as more complex models, especially with the high-resolution data. Deep learning and 3D visualizations were used to create past, present, and modelled future Land Use/Land Cover and 3D flyovers. Analysis of the possible impacts of 1, 2, 3, and 4 m SLR over the unique coastline, terrain, and land use was detailed. The generated interactive online map helps local communities visualize and understand the future of their coastlines. We have provided a detailed methodology and the methods and results are easily reproducible for other regions. Such initiatives can help popularize community-focused geovisualisation to raise awareness about SLR.