The New Landsat Collection-2 Digital Elevation Model

Digital elevation models (DEMs) are the most obvious data sources in landslide susceptibility assessment. Many landslide casual factors are often generated from DEMs. Most studies on landslide susceptibility assessments rely on freely available DEMs. However, very little is known about the performance of different DEMs with varying spatial resolutions on the accurate assessment of landslide susceptibility. This study compared the performance of four different DEMs including 30 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM), 30–90 m Shuttle Radar Topographic Mission (SRTM), 12.5 m Advanced Land Observation Satellite (ALOS) Phased Array Type L band Synthetic Aperture Radar (PALSAR), and 25 m Survey of Bangladesh (SOB) DEM in landslide susceptibility assessment in the Rangamati district in Bangladesh. This study used three different landslide susceptibility assessment techniques: modified frequency ratio (bivariate model), logistic regression (multivariate model), and random forest (machine-learning model). This study explored two scenarios of landslide susceptibility assessment: using only DEM-derived causal factors and using both DEM-derived factors as well as other common factors. The success and prediction rate curves indicate that the SRTM DEM provides the highest accuracies for the bivariate model in both scenarios. Results also reveal that the ALOS PALSAR DEM shows the best performance in landslide susceptibility mapping using the logistics regression and the random forest models. A relatively finer resolution DEM, the SOB DEM, shows the lowest accuracies compared to other DEMs for all models and scenarios. It can also be noted that the performance of all DEMs except the SOB DEM is close (72%–84%) considering the success and prediction accuracies. Therefore, anyone of the three global DEMs: ASTER, SRTM, and ALOS PALSAR can be used for landslide susceptibility mapping in the study area.

Digital Elevation Models (DEMs) contribute to geomorphological and hydrological applications. DEMs can be derived using different remote sensing-based datasets, such as Interferometric Synthetic Aperture Radar (InSAR) (e.g., Advanced Land Observing Satellite (ALOS) Phased Array type L-band SAR (PALSAR) and Shuttle Radar Topography Mission (SRTM) DEMs). In addition, there is also the Digital Surface Model (DSM) derived from optical tri-stereo ALOS Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) imagery. In this study, we evaluated satellite-based DEMs, SRTM (Global) GL1 DEM V003 28.5 m, ALOS DSM 28.5 m, and PALSAR DEMs 12.5 m and 28.5 m, and their derived channel networks/orders. We carried out these assessments using Light Detection and Ranging (LiDAR) Digital Surface Models (DSMs) and Digital Terrain Models (DTMs) and their derived channel networks and Strahler orders as reference datasets at comparable spatial resolutions. We introduced a pixel-based method for the quantitative horizontal evaluation of the channel networks and Strahler orders derived from global DEMs utilizing confusion matrices at different flow accumulation area thresholds (ATs) and pixel buffer tolerance values (PBTVs) in both ±X and ±Y directions. A new Python toolbox for ArcGIS was developed to automate the introduced method. A set of evaluation metrics—(i) producer accuracy (PA), (ii) user accuracy (UA), (iii) F-score (F), and (iv) Cohen’s kappa index (KI)—were computed to evaluate the accuracy of the horizontal matching between channel networks/orders extracted from global DEMs and those derived from LiDAR DTMs and DSMs. PALSAR DEM 12.5 m ranked first among the other global DEMs with the lowest root mean square error (RMSE) and mean difference (MD) values of 4.57 m and 0.78 m, respectively, when compared to the LiDAR DTM 12.5 m. The ALOS DSM 28.5 m had the highest vertical accuracy with the lowest recorded RMSE and MD values of 4.01 m and −0.29 m, respectively, when compared to the LiDAR DSM 28.5 m. PALSAR DEM 12.5 m and ALOS DSM 28.5 m-derived channel networks/orders yielded the highest horizontal accuracy when compared to those delineated from LiDAR DTM 12.5 m and LiDAR DSM 28.5 m, respectively. The number of unmatched channels decreased when the PBTV increased from 0 to 3 pixels using different ATs.

Topographical information is fundamental to many geo-spatial related information and applications on Earth. Remote sensing satellites have the advantage in such fields because they are capable of global observation and repeatedly. Several satellite-based digital elevation datasets were provided to examine global terrains with medium resolutions e.g. the Shuttle Radar Topography Mission (SRTM), the global digital elevation model by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER GDEM). A new global digital surface model (DSM) dataset using the archived data of the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS, nicknamed “Daichi”) has been completed on March 2016 by Japan Aerospace Exploration Agency (JAXA) collaborating with NTT DATA Corp. and Remote Sensing Technology Center, Japan. This project is called “ALOS World 3D” (AW3D), and its dataset consists of the global DSM dataset with 0.15 arcsec. pixel spacing (approx. 5 m mesh) and ortho-rectified PRISM image with 2.5 m resolution. JAXA is also processing the global DSM with 1 arcsec. spacing (approx. 30 m mesh) based on the AW3D DSM dataset, and partially releasing it free of charge, which calls “ALOS World 3D 30 m mesh” (AW3D30). The global AW3D30 dataset will be released on May 2016. This paper describes the processing status, a preliminary validation result of the AW3D30 DSM dataset, and its public release status. As a summary of the preliminary validation of AW3D30 DSM, 4.40 m (RMSE) of the height accuracy of the dataset was confirmed using 5,121 independent check points distributed in the world.

TerraSAR-X add-on for Digital Elevation Measurements (TanDEM-X) mission is designed to generate 3-D images of the Earth as the first bistatic synthetic aperture radar (SAR). However, few quantitative studies of TanDEM-X digital elevation model (DEM) quality validation have been conducted specifically in China. This article presents an iterative method to generate high-resolution TanDEM-X DEMs and assesses the vertical accuracy with high-accuracy GPS observations, 1 arc second global DEMs available [Advanced Land Observing Satellite World 3-D-30 m v2.2, Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) v3, Shuttle Radar Topography Mission (SRTM) v3.1, NASADEM, and X-band SRTM], and TanDEM-X 90 m DEM. The results demonstrate remarkable elevation quality and consistency in coastal areas with root mean square error of 1.7 m and 90% linear error (LE90) of 0.4 m, whereas 3–4 times weaker accuracies in steep mountainous areas. A positive bias of 1–2 m for an overall LE90 measure exists in the dense vegetation and steep-slope mountainous areas. TanDEM-X DEM-based SAR interferometry deformation uncertainty simulation indicates a low or even negligible topographic error contribution of 2–4 mm in mountainous areas and less than 1 mm in coastal areas. It indicates that the TanDEM-X DEM performs better than other global DEMs overall and shows a better elevation consistence with SRTM C-band DEM in the coastal area. As an excellent source of up-to-date information, the TanDEM-X DEM are expected be an advantage for understanding dynamic land use changes and improving identification and delineation of coastal lands, mountainous landslides, and earthquakes disasters.

Urban flood models that use Digital Elevation Models (DEMs) to simulate extent and depth of flood inundation rely on the accuracy of DEMs for predicting flood events. Despite recent advances in developing vegetation corrected DEMs, the effect of building height and density errors in global DEMs in urban areas are still poorly understood, and their correction remains a challenge. In this research we developed a methodology for building error correction that can be applied to any other case study, where building density data and a local reference DEM data are available. This methodology was applied to Nairobi, Kenya using six global DEMs (SRTM, MERIT, ALOS, NASADEM, TanDEM-X 12 m, and TanDEM-X 90 m DEM). Our results show building error at highest building density varying between 1.25 m and 5.07 m for the DEMs used, with the MERIT DEM showing the smallest vertical height error from the reference DEM. The six DEMs were corrected by deriving a linear relationship between building density and DEM error. Our findings show that the removal of building density error resulted in the improvement of the vertical height accuracy of the global DEMs of up to 45% for MERIT and 40% for ALOS. This methodology was also applied to the Central Business District (CBD) area of Nairobi, characterized by taller buildings and high building density. The error parameters in the CBD area resulted to be between 15 to 45% higher than those of the Nairobi city wide area for the six global DEMs, thus providing further insights into the contribution of building heights to errors in global DEMs. Building height data is still unavailable on a global scale and our results show that global DEMs can be usefully corrected for building density errors in urban areas, even where specific building height data are not available.

Digital elevation models (DEMs) are fundamental data infrastructure that support a large range of applications in environmental modelling. The distribution of solar radiation, patterns of wetness and water movement, mobility of animals and evolution of soils are all intimately linked to topographic form. DEMs for the Australian continent have progressed from 1/10 th degree resolution in 1982 to 1/40 th degree in 1991, 9 th degree) in 1996 and now 1 second (about 30 m) derived from the NASA Shuttle Radar Topographic Mission (SRTM). Each improvement in resolution and accuracy enables new applications as more detail and structure are revealed. The 1 second SRTM-derived DEMs for Australia are now publicly available through the Geoscience Australia National Elevation Data Framework (NEDF) portal (http://nedf.ga.gov.au). The raw SRTM data has been processed through a series of steps to provide several products suitable for different purposes: • DEM-S is a bare-earth elevation model with stripes and voids removed, offsets due to trees treated and noise substantially reduced in flatter areas by adaptive smoothing, which is suitable for most applications where connectivity of flow is not the primary concern such as calculation of slopes and topographic position • DEM-H is a hydrologically conditioned elevation model derived from DEM-S with drainage enforcement using 1:250,000 scale stream lines, which is suitable for applications where hydrological connectivity is of primary concern such as calculation of contributing areas and catchment delineation • DEM is the pre-cursor to DEM-S before adaptive smoothing, which may be useful when the retention of detail is more important than smoothness. There is also a cleaned digital surface model (DSM) that is the precursor to DEM-S before tree offsets are removed, which is restricted to Australian government use only. Most users of DEMs need information about the shape of the landscape, not just height, so we are producing a set of standard derived products including slope, aspect, curvatures, topographic position, relief, contributing area and wetness index. These derived surfaces are key inputs for continental scale prediction of soil properties and ecological patterns. Predictions of a suite of key soil properties on a 3 second grid for the Australian continent, using the 1 second terrain layers, are being developed and will contribute to the GlobalSoilMap.net global grid of soil properties. Catchments and stream lines derived from DEM-H will form the basis for future versions of the Australian Hydrological Geospatial Fabric (http://www.bom.gov.au/water/geofabric/index.shtml). Digital elevation data continues to improve as measurement technology improves. Airborne lidar already provides high precision terrain data with vertical accuracy of 20 cm or better at 1 m horizontal resolution or better. This data is available in parts of Australia where the better accuracy justifies the high cost, such as urban areas, coastal lowlands, wetlands and floodplains. We need to develop ways of using high resolution data covering limited areas in combination with lower resolution data covering the continent. New global DEMs are being acquired and will replace SRTM in the years ahead. The TANDEM-X satellite radar system will potentially produce global elevations at 2-3 times better resolution and accuracy than SRTM, and other systems will follow. Higher resolutions bring further technical challenges, not least in data volume: each 1 second DEM product for Australia is a 40 GB dataset and finer resolutions will be correspondingly larger.

ABSTRACTOne of the most essential inputs in flood inundation mapping is the geometric description of the floodplains and river channel that often derives from the digital elevation models (DEMs). By increasing the satellite-based technologies during the past 30 years, several DEM sources ranging from fine-resolution and accurate, but costly, to low-cost and low-resolution have been developed. In most parts of the world, especially developing countries and data sparse regions, the coarse resolution DEMs is the only available data set for hydraulic modelling and flood inundation mapping. This research addressed the usefulness and efficiency of the recently released Advanced Land Observing Satellite (ALOS) DEM in flood inundation mapping using 1D Hydrologic Engineering Centre- River Analysis System (HEC-RAS) model. In addition, other DEM sources such as Shuttle Radar Topography Mission (SRTM-90 m), SRTM-30 m, and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER-30 m) are used to carry out a comprehensive evaluation of remotely sensed DEMs for flood inundation mapping. Findings indicate that using ALOS-30m for hydraulic simulation approximately leads to the similar results as well as ground-based DEM (GDEM). For example, the Mean Absolute Percentage Error (MAPE) in simulating mean Water Surface Elevation (WSE) and mean inundated extents based on this dataset, within the cross-sections, is lower than 8% and 13% for SojasRood river, respectively, while for Sarbaz river these values are 9% and 2%. Moreover, in both rivers, SRTM-30 m relative to ASTER-30 m and SRTM-90 m DEMs presents better results in deriving the geometric model and hydraulic simulation. Also, Hydraulic modelling based on ASTER-30 m, even relative to SRTM-90 m as a coarser resolution DEM, shows a significant discrepancy compared to GDEM. Moreover, in both rivers, the MAPE in predicting inundated extents, within the reaches, is higher than 38%.

Abstract. In the diverse domains of earth observation, elevation data are essential for a wide range of applications with various technical requirements and use cases. The Advanced Spaceborne Thermal Emission and Reflection Radiometer-Global Digital Elevation Model (ASTER GDEM), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), Shuttle Radar Topography Mission (SRTM), and other projects have made a large number of global Digital Elevation Model (DEM) datasets for environmental modelling and studies freely available. Global DEMs have undergone an accuracy review to measure their inherent vertical uncertainty to show how accurate information should be considered while planning and analysing. Comparing the DEMs with highly accurate geodetic control points as the independent reference data one of the best methods in the evaluation process. SRTM 30m, SRTM 90m, ALOS World 3D-30, Aster-GDEM, GMTED2010, and NASADEM are among the worldwide DEMs that were examined. Comparisons are made between 793 geodetic control points values and those from SRTM 30m, SRTM 90m, ALOS World 3D-30, Aster-GDEM, GMTED2010, and NASADEM. The statistical analysis of global DEMs from GPS reference elevations gave us that the accuracy of the ALOS World 3D-30m is much better than other models with RMSE and STD values of 1.2497 and 1.235 m, respectively. In contrast, Aster-GDEM exhibited the highest RMSE and residual error of STD values of 5.793 m and 3.394 m, respectively.

High-quality seamless DEM generation blending SRTM-1, ASTER GDEM v2 and ICESat/GLAS observations

Digital elevation models (DEMs) are the cornerstone for hydrological and geomorphological modeling. Herein, two Nile-tributary catchments (Wadi Al Rishrash and Wadi Atfeh) in Egypt are selected to examine the contribution of different DEMs to the accuracy of hydrological and geomorphological analyses in the hyper-arid Sahara. DEMs sources include: Advanced Land Observing Satellite-1 (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) (12.5 m resolution), ALOS World 3D with 30 m resolution (AW3D30), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER DEM with 30 m resolution) and the Shuttle Radar Topography Mission (SRTM with 30 and 90 m resolution), in addition to topographic map-derived DEM (90 m resolution). Using a hypothetical uniformly-distributed 10 mm rainfall event, the estimated parameters, including: flow duration, time to peak and peak discharge rates, are almost similar for the different DEMs and thus technical aspects related to sources and resolutions of the datasets impose insignificant control on quantitative flash-flood analyses. Conversely, variations in geological and geomorphological characteristics of the catchments show more significant control on the hydrograph magnitudes as indicated by the different parameters of the two catchments. These findings indicate that understanding the geological and hydrological evolution of the catchment is essential for integrated management strategies of floods especially in the Saharan–Arabian deserts and in similar conditions of hyper-aridity and scarce in situ data worldwide.

Formulation of the problem. Water erosion of soils is the most widespread and dangerous soil degradation process in Ukraine. The development of an effective system of soil protection measures requires the use of spatially distributed mathematical models of soil erosion losses. This, in turn, highlights the problem of spatially distributed source data, which adequately reflect the spatial differentiation of factors of the erosion process, among which the main one is the relief. The purpose of the article. Assessing the adequacy of available spatially distributed source data, including cartographic and freely distributed global digital elevation models (DEMs), for spatially distributed quantitative assessment of soil erosion losses at the local level of territorial coverage is the aim of the article. Assessing from this point of view the scale of the original cartographic data, different global DEMs and their spatial resolution, as well as the degree of spatial generalization of the original data. Materials and methods. The solution of the set tasks was performed by the method of simulation modeling with the use of physical-statistical GIS-realized mathematical model of soil erosion-accumulation, developed at Odessa I. I. Mechnikov National University. Source data arrays were tested with DEMs SRTM90 and SRTM30 with a spatial resolution of 3 and 1 angular seconds, respectively, and AW3D30 with a spatial resolution of 1 angular second, as well as with cartographic DEMs based on topographic maps of scale 1:10000 and 1:25000. For testing the initial data, three test plots with an area of 2.67, 0.59 and 0.21 km2 were selected. The plots are located in the Balta district of Odessa region on the southern spurs of the Podolska upland. Results. It is established that freely distributed global digital elevation models SRTM and AW3D30 in the conditions of flat terrain do not always allow to adequately display the structure of slope runoff and, accordingly, to correctly perform calculations of soil erosion losses. The maximum deviation of the average soil erosion losses calculated for the test plots using global DEMs from the soil losses calculated using the reference DEM for SRTM30 and AW3D30 was 27%, for SRTM90 – almost 70%. The distribution of soil losses over the area of test plots obtained using different global DEMs differs even more. When using DEM based on topographic maps, reducing the scale of the original maps from 1: 10000 to 1: 25000 leads to a decrease in the average value of soil erosion losses by about 20% due mainly to reducing the magnitude and area of distribution of maximum soil losses, and on slopes of complex shape also due to changes in the area of accumulation zones. The degree of spatial generalization of the initial data significantly affects the results of the assessment of soil erosion losses both in relation to the average values and their distribution over the area. For small areas, the use of raster cells larger than 50 m is impractical. Scientific novelty and practical significance. It has been shown for the first time that in the conditions of flat terrain at the local level of spatial coverage, the freely distributed global DEM SRTM and AW3D30 are not always hydrologically correct. The reasons and conditions of violation of this correctness are specified. It has been established that the global DEM AW3D30 has local instrumental errors that may make it impossible to use it. The most realistic values of soil erosion losses are provided by DEM SRTM with a spatial resolution of 1 angular second.

Accurate elevation data are essential for effective oil palm plantation planning, hydrological modeling, and environmental management particularly in tropical regions characterized by complex topography and dense vegetation. While LiDAR-derived Digital Elevation Models (DEMs) offer high vertical precision, their acquisition costs often hinder adoption in resource-limited settings. Open-source DEMs provide accessible alternatives but are frequently affected by vegetation interference and coarse resolution, leading to reduced vertical accuracy. This study proposes a hybrid correction framework that integrates a random forest (RF) machine learning algorithm and a geographically weighted regression (GWR) a spatially adaptive statistical method to enhance the vertical accuracy of open-source DEMs for terrain-sensitive applications. The study used 377 high-precision Ground Control Points (GCPs) and LiDAR data to evaluate and correct six global DEMs: Advanced Land Observing Satellite (ALOS), TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X), Copernicus GLO-30, Shuttle Radar Topography Mission (SRTM), Forest And Buildings removed Copernicus DEM (FABDEM), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) GDEM, and a high-resolution drone-derived DEM. RF was used to identify key topographic predictors, including aspect, slope, curvature, Topographic Position Index (TPI), and Terrain Ruggedness Index (TRI), while GWR applied spatially adaptive corrections to the RF residuals. The integrated RF–GWR model significantly improved the vertical accuracy across all DEMs. The post-correction R² values reached 0.914 for TanDEM-X, 0.910 for ALOS, and increased from 0.608 to 0.914 for Copernicus, with the residual standard deviations reduced by up to 75% and near-zero mean bias. These results highlight the model’s ability to correct both systematic and spatially varying elevation errors. The framework presents a scalable and alternative to LiDAR for use in precision agriculture, flood risk modeling, and infrastructure planning. Future work should explore integration with deep learning to improve the temporal responsiveness and operational scalability.

Freely available Global Digital Elevation Models (GDEMs) are essential for many scientific and humanitarian applications. Recently, TanDEM-X 90 has been released with a global coverage at 3 arc sec resolution. Its release is sure to generate keen interest as it provides an alternative to the widely used Shuttle Radar Topography Mission (SRTM) DEM, especially for flood risk management as for low slope floodplains height errors can become particularly significant. Here, we provide a first accuracy assessment of TanDEM-X 90 for selected floodplain sites and compare it to other popular global DEMs – the Shuttle Radar Topography Mission (SRTM) and the error-reduced version of SRTM called Multi-Error-Removed-Improved-Terrain (MERIT) DEM. We characterize vertical height errors by comparing against high resolution LiDAR DEMs for 32 floodplain locations in 6 continents. Results indicate that the average vertical accuracy of TanDEM-X 90 and MERIT are similar and are both a significant improvement on SRTM. We further our analysis by assessing vertical accuracy by landcover, with our results suggesting that TanDEM-X 90 is the most accurate global DEM in all landcover categories tested except short vegetation and tree-covered areas where MERIT is demonstrably more accurate. Lastly, we present the first characterization of the spatial error structure of any TanDEM-X DEM product, and find the spatial error structure is similar to MERIT, with MERIT generally having lower sill values and larger ranges than TanDEM-X 90 and SRTM. Our findings suggest that TanDEM-X 90 has the potential to become the benchmark global DEM in floodplains with careful removal of errors from vegetation, and at this stage should be used alongside MERIT in any flood risk application.

With the realization of the Shuttle Radar Topographic Mission (SRTM) and the distribution of the 3 arcsec (90 m) data over Europe, a high-resolution digital terrain model (DTM) became available for Greece. Until today, high-resolution DTMs for Greece were generated by the Hellenic Military Geographic Service (HMGS) only and present variable resolutions with the finest one being set to 100 m. These DTMs were usually determined by digitizing topographic maps and are thus of variable and sometimes unknown accuracy. When a high-resolution, e.g., 0.5 - 1 arcmin geoid is needed, it is absolutely necessary to employ a very high resolution DTM to compute the terrain effects to gravity and the indirect effect to the geoid. If this information is not available and a coarser DTM is used, then the topographic effects computed are aliased, due to the insufficient resolution of the topographic data used. The scope of this work is twofold. First, a validation and accuracy assessment of the SRTM 90 m DTM over Greece is performed through comparisons with existing global models, like GLOBE and GTOPO30, as well as with the Greek 450 m DTMs delivered by HMGS. Whenever a misrepresentation of the topography is identified in the SRTM data, it is “corrected” using the local 100 m DTM. This processes resulted in an improved SRTM DTM called SRTMG, which was then used to determine terrain and RTM effects to gravity field quantities. Then, all available DTMs were used to compute terrain effects on both gravity anomalies and geoid heights at variable spatial resolutions. From the results acquired, the performance of the SRTMG model with respect to geoid modeling was assessed and conclusions on the effect of the DTM resolution were drawn.

TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurements) is an Earth observation radar mission that consists of a SAR interferometer built by two almost identical satellites flying in close formation [1]–[4]. With a typical separation between the satellites of 120 to 500 m a global Digital Elevation Model (DEM) with 2 m relative height accuracy at 12 m posting is being generated. While the main mission phase for DEM data acquisition has been finished in 2014, the processing of the global TanDEM-X DEM will be concluded in autumn 2016. Final DEMs are well within specifications and feature a low percentage of void areas. Following the DEM data acquisition the capabilities of this unique mission for new scientific application have been demonstrated. In the meantime the continuation of the joint TerraSAR-X/TanDEM-X mission was approved and will be dedicated to the generation of DEMs with even higher accuracy for selected areas and further scientific experiments.