Digital Elevation Model Products Research Articles

Qinghai-Tibet Plateau Glacier-DEM product derived from the Chinese stereo mapping satellites

Abstract. The Digital Elevation Model (DEM) is an important basic data for glacier research on the Qinghai-Tibet Plateau. With the rapid development of China 's stereo mapping satellite, it is possible to obtain high-precision DEM in the glacier area of the Qinghai-Tibet Plateau. In this study, the stereo images and laser altimetry data of ZY-3 and GF-7 satellites were comprehensively used, and the matching and joint adjustment methods of laser elevation control points and stereo images were proposed. A high-precision DEM product in glacier area was established, and the elevation accuracy of DEM was verified by using UAV data and ICESat-2 data. The results show that the accuracy of GF-7 DEM is better than 1.5 m when the slope is less than 6 degrees, and the overall accuracy is better than 2.8 m. The accuracy of ZY-3 DEM is better than 3.5 m when the slope is less than 6 degrees, and the overall accuracy is better than 5.0 m. Compared with the open source DEM with medium spatial resolution, the DEM elevation accuracy based on domestic stereo mapping satellite images is better, and the grid is finer and more detailed to describe the texture characteristics of the end of the glaciers. Compared with the high spatial resolution data set HMA DEM, the ZY-3 DEM is slightly worse and the GF-7 DEM is better, and both the ZY-3 DEM and GF-7 DEM are better than the HMA DEM in terms of coverage integrity in the Qinghai- Tibet Plateau. Chinese stereo mapping satellites can generate high-precision DEM in the glacier area, and can provide more accurate and reliable terrain reference data than the existing open free data for the study of glaciers in the Qinghai-Tibet Plateau.

Topographical dynamics based on global and UAV-SfM derived DEM products: a case study of transboundary Teesta River, Bangladesh

ABSTRACT Topographical changes in riverine floodplains need to be measured for assessing geomorphological dynamics and protecting floodplain areas, although topographic evaluation in disaster-prone floodplain in South Asia has been limited due to the lack of multitemporal, high-definition digital elevation models (DEMs) derived from modern techniques including airborne laser scanning, structure-from-motion (SfM) photogrammetry accompanied with Unmanned Aerial Vehicle (UAV, often referred as drone), and field-based mapping approaches. Here we conducted a preliminary study at two locations of Teesta River (Brahmaputra’s tributary) in Bangladesh using the UAV-SfM techniques and generated high-resolution DEMs. The selected locations represent dynamic changes of sediment and water on the floodplain over the years, and the UAV-SfM approach can be an effective method for monitoring those changes, but an archive of the past data has been unavailable. Here we evaluated the topographic changes by comparing the UAV-SfM-derived DEMs of 2022 with global DEM products (NASADEM of 1999), which are often the only available choice of DEMs in this river floodplain. The elevation differences of these two sets of DEMs were in the range of −5.23 to −84.66 m, and volumetric changes of −4.11 ± 0.15 to −86.25 ± 0.20 million m3, likely dominated by erosional processes towards the left side bank where the elevation errors are supposed to be several meters for UAV-DEM and ca. 5–12 m for NASADEM. Although it is not easy to accurately evaluate the absolute values of the changes, these changes may be associated with the upper basin’s morphodynamics.

Enhancing Digital Elevation Model Accuracy for Flood Modelling – A Case Study of the Ciberes River in Cirebon Indonesia

Topographic conditions represented by the Digital Elevation Model (DEM) are essential in flood inundation models. The DEM which is categorised as a Digital Surface Model (DSM) stores the height information, be-sides the ground and non-ground elevation required for preprocessing before being employed in hydrologic applications, particularly in relation to flood modelling by removing non-ground elevation along the flood plain and river channels. The improvement in the accuracy of flood inundation modelling is crucial in reduc-ing the impact of flood disasters. This study aims to compare the accuracy level of the DEM based on Ter-raSAR-X data with the filtering process using slope-based filtering and combining the cross-sectional river profile from the field measurement to the filtered DEM. The result confirms that the accuracy of the DEM product is improved via filtering to remove non-ground elevations and a significant improvement in accuracy by means of fused river profile information to filtered DEM. The results of adding river information to the DEM could provide a representation closer to the cross-sectional profile of the river based on field measure-ments within the accuracy level Mean Absolute Error 2.51 m, 2.72 m and 1.91 m in the left overbank, right overbank and centre of the river, respectively. The performance results of the 2-dimensional flood hydrody-namic modelling using HEC-RAS derived from the DEM before filtering, after filtering, and the addition of river information show increasing accuracy in flood depth at each stage of the DEM processing. There is an improvement in accuracy in flood depth of approximately 11.67% using the filtered DEM, besides an in-crease in accuracy in flood depth by 24.98% utilising the filtered DEM with added river channel information.

Performance Analysis of a Drone Development Kit-derived Digital Elevation Model

Surface modeling constitutes is a crucial aspect in numerous engineering inquiries and earth observation endeavors. In contemporary times, the acquisition of geospatial data essential for the digital representation of local regions is increasingly facilitated through drone-based methodologies, supplanting conventional terrestrial data gathering techniques. The market presently hosts a plethora of cost-effective, "ready-to-fly" unmanned aerial vehicles (UAVs), offering users the capability to generate photogrammetric outputs, including high geometric precision Digital Elevation Models (DEMs). Moreover, modularly structured drone development kits, designed for multifarious applications, are readily accessible for purchase. These drone kits offer an economically advantageous platform that users can customize to suit their specific needs. Nevertheless, the geometric precision of DEMs created using these kits hinges upon the capabilities of the imaging and navigation systems, in addition to the stabilization of the platform during autonomous flight. In this study, using a drone development kit and a commercial drone, simultaneous image acquisition was performed for the same study area and two different DEMs were produced. The efficacy of the DEM generated using the drone development kit was assessed through a comparative analysis with the DEM obtained from a commercial drone. In addition, geometric accuracy assessment was conducted for both DEMs using ground control points. The findings reveal the usability of drone development kits in precision DEM production, as well as their limitations.

Research on Landslide Trace Recognition by Fusing UAV-Based LiDAR DEM Multi-Feature Information

Landslide traces are crucial geomorphological features of landslides. Through the recognition of landslide traces, a better grasp of the topographical features of landslides can be achieved, thereby aiding in the enhancement of capabilities for the prevention, response, and management of landslides. Aiming at the complex topographic features of landslide traces, only using a single DEM product could provide a complete and comprehensive recognition of landslide traces. A method of landslide tracing recognition based on the fusion of multi-feature information from the Unmanned Aerial Vehicle-based Light Detection and Ranging (UAV-based LiDAR) Digital Elevation Model (DEM) is proposed. First, a high-precision DEM is constructed by using the LiDAR point cloud data. Based on the DEM, four multi-feature images that can enhance the landslide geomorphology are generated: hillshading, slope, positive openness, and sky-view factor. Furtherore, the DEM multi-feature images were fused using the Visualization for Archaeological Topography (VAT) method to obtain the DEM Multi-Feature Fusion Image (DEM-DFFI). Finally, the landslide traces were extracted from the DEM-DFFI based on fractal theory. The method presented in this paper makes full use of DEM multi-feature images and fuses them, which can accurately and clearly show the topographic and geomorphological features of landslides. Based on this, it helps improve landslide trace recognition accuracy.

Analysing the spatial context of the altimetric error pattern of a digital elevation model using multiscale geographically weighted regression

ABSTRACT Many freely available Digital Elevation Models (DEM) have increasingly been used worldwide due to the difficulty in acquiring accurate elevation data in some regions, emphasizing the need to investigate their accuracy and the factors that may influence their uncertainties. We performed an accuracy analysis of the Topodata DEM in the hydrographic region of Uruguay (Brazil) assuming that its vertical accuracy may be related to terrain characteristics. Multiscale Geographically Weighted Regression (MGWR) was applied to investigate the spatial scales over which terrain characteristics affect local variations in altimetric errors. MGWR outperformed Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR). MGWR results also showed that aspect, curvature, and artificial areas operate at much smaller scales than elevation and have a higher influence in areas with high positive altimetric errors. The model explains about 41% of the total variation of the altimetric error of the Topodata DEM in the study area. Our findings enrich the understanding of the global and local processes affecting the accuracy of the Topodata DEM and shed light on the importance of local terrain characteristics in effective DEM product development.

SRTM DEM Correction Using Ensemble Machine Learning Algorithm

The Shuttle Radar Topographic Mission (SRTM) digital elevation model (DEM) is a widely utilized product for geological, climatic, oceanic, and ecological applications. However, the accuracy of the SRTM DEM is constrained by topography and vegetation. Using machine learning models to correct SRTM DEM with high-accuracy reference elevation observations has been proven to be useful. However, most of the reference observation-aided approaches rely on either parametric or non-parametric regression (e.g., a single machine learning model), which may lead to overfitting or underfitting and limit improvements in the accuracy of SRTM DEM products. In this study, we presented an algorithm for correcting SRTM DEM using a stacking ensemble machine learning algorithm. The proposed algorithm is capable of learning how to optimally combine the predictions from multiple well-performing machine learning models, resulting in superior performance compared to any individual model within the ensemble. The proposed approach was tested under varying relief and vegetation conditions in Hunan Province, China. The results indicate that the accuracy of the SRTM DEM productions improved by approximately 46% using the presented algorithm with respect to the original SRTM DEM. In comparison to two conventional algorithms, namely linear regression and artificial neural network models, the presented algorithm demonstrated a reduction in root-mean-square errors of SRTM DEM by 28% and 12%, respectively. The approach provides a more robust tool for correcting SRTM DEM or other similar DEM products over a wide area.

Consumer‐grade UAV solid‐state LiDAR accurately quantifies topography in a vegetated fluvial environment

AbstractUnoccupied aerial vehicles (UAVs) with passive optical sensors have become popular for reconstructing topography using Structure from Motion (SfM) photogrammetry. Advances in UAV payloads and the advent of solid‐state LiDAR have enabled consumer‐grade active remote sensing equipment to become more widely available, potentially providing opportunities to overcome some challenges associated with SfM photogrammetry, such as vegetation penetration and shadowing, that can occur when processing UAV‐acquired images. We evaluate the application of a DJI Zenmuse L1 solid‐state LiDAR sensor on a Matrice 300 RTK UAV to generate digital elevation models (DEMs). To assess flying height (60–80 m) and speed parameters (5–10 ms−1) on accuracy, four point clouds were acquired at a test site. These point clouds were used to develop a processing workflow to georeference, filter and classify the point clouds to produce a raster DEM product. A dense control network showed that there was no significant difference in georeferencing from differing flying height or speed. Building on the test results, a 3 km reach of the River Feshie was surveyed, collecting over 755 million UAV LiDAR points. The Multiscale Curvature Classification algorithm was found to be the most suitable classifier of ground topography. GNSS check points showed a mean vertical residual of −0.015 m on unvegetated gravel bars. Multiscale Model to Model Cloud Comparison (M3C2) residuals compared UAV LiDAR and Terrestrial Laser Scanner point clouds for seven sample sites demonstrating a close match with marginally zero residuals. Solid‐state LiDAR was effective at penetrating sparse canopy‐type vegetation but was less penetrable through dense ground‐hugging vegetation (e.g. heather, thick grass). Whilst UAV solid‐state LiDAR needs to be supplemented with bathymetric mapping to produce wet–dry DEMs, by itself, it offers advantages to comparable geomatics technologies for kilometre‐scale surveys. Ten best practice recommendations will assist users of UAV solid‐state LiDAR to produce bare earth DEMs.

Accuracy Assessment of High-Resolution Globally Available Open-Source DEMs Using ICESat/GLAS over Mountainous Areas, A Case Study in Yunnan Province, China

The Open-Source Digital Elevation Model (DEM) is fundamental data of the geoscientific community. However, the variation of its accuracy with land cover type and topography has not been thoroughly studied. This study evaluates the accuracy of five globally covered and open-accessed DEM products (TanDEM-X90 m, SRTEM, NASADEM, ASTER GDEM, and AW3D30) in the mountain area using ICESat/GLAS data as the GCPs. The robust evaluation indicators were utilized to compare the five DEMs’ accuracy and explore the relationship between these errors and slope, aspect, landcover types, and vegetation coverage, thereby revealing the consistency differences in DEM quality under different geographical feature conditions. The Taguchi method is introduced to quantify the impact of these surface characteristics on DEM errors. The results show that the slope is the main factor affecting the accuracy of DEM products, accounting for about 90%, 81%, 85%, 83%, and 65% for TanDEM-X90, SRTM, NASADEM, ASTER GDEM, and AW3D30, respectively. TanDEM-X90 has the highest accuracy in very flat areas (slope < 2°), NASADEM and SRTM have the greatest accuracy in flat areas (2 ≤ slope < 5°), while AW3D30 accuracy is the best in other cases and shows the best consistency on slopes. This study makes a new attempt to quantify the factors affecting the accuracy of DEM, and the results can guide the selection of open-source DEMs in related geoscience research.

Validation of FABDEM, a global bare-earth elevation model, against UAV-lidar derived elevation in a complex forested mountain catchment

Space-based, global-extent digital elevation models (DEMs) are key inputs to many Earth sciences applications. However, many of these applications require the use of a ‘bare-Earth’ DEM versus a digital surface model (DSM), the latter of which may include systematic positive biases due to tree canopies in forested areas. Critical topographic features may be obscured by these biases. Vegetation-free datasets have been created by using statistical relationships and machine learning to train on local-scale datasets (e.g., lidar) to de-bias the global-extent datasets. Recent advances in satellite platforms coupled with increased availability of computational resources and lidar reference products has allowed for a new generation of vegetation- and urban-canopy removals. One of these is the Forest And Buildings removed Copernicus DEM (FABDEM), based on the most recent and most accurate global DSM Copernicus-30. Among the more challenging landscapes to quantify surface elevations are densely forested mountain catchments, where even airborne lidar applications struggle to capture surface returns. The increasing affordability and availability of UAV-based lidar platforms have resulted in new capacity to fly modest spatial extents with unrivalled point densities. These data allow an unprecedented ability to validate global sub-canopy DEMs against representative UAV-based lidar data. In this work, the FABDEM is validated against up-scaled lidar data in a steep and forested mountain catchment considering elevation, slope, and Terrain Position Index (TPI) metrics. Comparisons of FABDEM with SRTM, MERIT, and the Copernicus-30 dataset are made. It was found that the FABDEM had a 24% reduction in elevation RMSE and a 135% reduction in bias compared to the Copernicus-30 dataset. Overall, the FABDEM provides a clear improvement over existing deforested DEM products in complex mountain topography such as the MERIT DEM. This study supports the use of FABDEM in forested mountain catchments as the current best-in-class data product.

A comparative study of automatic drainage network extraction using ASTER GDEM, SRTM DEM and Cartosat-1 DEM in parts of Kosi basin, Bihar, India

The present study attempts to evaluate and compare the different open source 30 m resolution spaceborne digital elevation models (DEM) based on their automatic drainage network extraction efficiency. Digital elevation models from three satellite data i.e., ASTER GDEM, SRTM DEM and Cartosat-1 DEM have been employed for the extraction of stream networks. The study was carried out in the watershed situated in parts of Saharsa, Madhepura and Supaul district along the river Kosi, Bihar. Analysis was performed in ArcMap10.3 software wherein, three different thresholds of flow accumulation were used to assess the comparative evaluation. Morphometric parameters were analysed using geographical information system (GIS). The evaluation and quantification of morphometric parameters provide an effective basis for the comparison of extracted stream networks based on hydrological and morphological characteristics. Comparison and selection of appropriate DEM products for quantitative and qualitative hydrological studies are lacking in the present study area. This comparative study is helpful in selecting suitable DEM products out of the three commonly available free datasets for delineating streams in this floodplain with little variation in elevation and slope. The findings can be beneficial for using appropriate DEM data in various morphometric, prioritization, management and other GIS applications in parts of Kosi basin.

Historical Structure from Motion (HSfM): Automated processing of historical aerial photographs for long-term topographic change analysis

Precisely measuring the Earth’s changing surface on decadal to centennial time scales is critical for many science and engineering applications, yet long-term records of quantitative landscape change are often temporally and geographically sparse. Archives of scanned historical aerial photographs provide an opportunity to augment these records with accurate elevation measurements that capture the historical state of the Earth surface. Structure from Motion (SfM) photogrammetry workflows produce high-quality digital elevation models (DEMs) and orthoimage mosaics from these historical images, but time-intensive tasks like manual image preprocessing (e.g., fiducial marker identification) and ground control point (GCP) selection impede processing at scale. We developed an automated method to process historical images and generate self-consistent time series of high-resolution (0.5–2 m) DEMs and orthomosaics, without manual GCP selection. The method relies on SfM to correct camera interior and exterior orientation and a robust multi-stage co-registration approach using modern reference terrain datasets for geolocation refinement. We demonstrate the method using scanned images from the North American Glacier Aerial Photography (NAGAP) archive collected between 1967 and 1997. We present results for two sites with variable photo acquisition geometry and overlap — Mount Baker and South Cascade Glacier in Washington State, USA. The automated method corrects initial camera position errors of several kilometers and produces accurately georeferenced, high-resolution DEMs and orthoimages, regardless of camera configuration, acquisition geometry, terrain characteristics, and reference DEM properties. The average RMS reprojection error following bundle adjustment optimization was 0.67 px (0.15 m) for the 261 images contributing to 10 final DEM mosaics between 1970 and 1992 at Mount Baker, and 0.65 px (0.13 m) for the 243 images contributing to 18 individual DEMs between 1967 and 1997 at South Cascade Glacier. The relative accuracy of elevation values in the historical time series stacks was 0.68 m at Mount Baker and 0.37 m at South Cascade Glacier. Our products have reduced systematic error and improved accuracy compared to DEM products generated using SfM with manual GCP selection. Final elevation change measurement precision was ∼0.7–1.0 m over a 30-year period, enabling the study of processes with rates as low as ∼1-3 cm/yr. Our results demonstrate the potential of this scalable method to rapidly process archives of historical imagery and deliver new quantitative insights on long-term geodetic change and Earth surface processes.

Quantification of global Digital Elevation Model (DEM) – A case study of the newly released NASADEM for a river basin in Central Vietnam

Study regionLai Giang River basin, Central Vietnam Study focusThe Digital Elevation Models (DEM) is essential in hydrological modeling and water cycle understanding. There are several unanswered questions regarding the impact of DEM on the models’ quality of hydrological applications. This study develops a comprehensive framework for evaluating impacts of six global DEM products: MERIT, NASA, SRTM, ASTER GDEM2, AW3D30, and TanDEM-X using the semi-distributed model SWAT for the Lai Giang River basin, Central Vietnam, in which four criteria have been used (i) statistical analysis, (ii) river basin characteristics, (iii) effects of DEM uncertainty on streamflow using hydrological simulations, and (iv) cross-validation’s impacts on hydrological applications. New hydrological insights for the regions(1) NASA and SRTM 30 DEM were ranked as most accurate DEMs among the chosen products; (2) ASTER GDEM2 performed the worst, with more uncertainty in river networks delineation; (3) MERIT, ASTER GDEM2, and SRTM 90 m provided inaccurate basin delineations, particularly for the intersection subbasin; (4) DEM resolution and source are insensitive to most of the hydrological components, except for lateral flow, groundwater flow, water percolation, and sediment yield. Moreover, a proposed cross-validation approach for the DEM performance has been introduced, showing that the calibrating parameters of higher-resolution DEM would positively impact the hydrological simulations of lower-resolution DEMs and the opposite, especially in terms of flood peak observation.

Quantifying the impacts of DEM uncertainty on clear-sky surface shortwave radiation estimation in typical mountainous areas

Accurate estimation of shortwave radiation in mountains will advance our knowledge of climate change effects, especially on mountain ecosystems. Recently, some approaches have been developed to estimate shortwave radiation parameters in mountains with satellite data, but few attempts were made to understand the impacts of digital elevation model (DEM) uncertainty on estimates. Our study investigates such impacts quantitatively in clear-sky conditions at multiple spatial and temporal scales (30–3000 m, instantaneous to daily). We employed a retrieval algorithm to estimate instantaneous and daily mean clear-sky downward shortwave radiation (DSR) and net shortwave radiation (NSR), as a proxy for our evaluation. The accuracy of our method based on accurate terrain data was verified against in-situ measurements with root-mean-square errors (RMSEs) of 65.9 W/m2 and 65.1 W/m2 for instantaneous DSR and NSR, and 21.2 W/m2 and 22.5 W/m2 for daily mean values, respectively. When using satellite DEM products, the DSR estimation uncertainty could increase by 64.0% for instantaneous values and 46.2% for daily mean values. Using AW3D30 and SRTM DEMs for DSR estimation led to a maximum difference of 16.8% (103.6 W/m2) and 13.0% (25.8W/m2) on instantaneous and daily mean values, respectively. That estimation difference of shortwave radiation decreased with an increase in spatial scale, with RMS deviation lower than 2% for spatial resolution beyond 3000 m. In addition, the evaluation of introducing random errors into AW3D30 DEM showed that the shortwave radiation uncertainty caused by DEM may exceed the algorithm uncertainty itself with DEM mean absolute error (MAE) equaling about 5.0 m. Considering the current DEM accuracy, the impacts of DEM errors on shortwave radiation in mountains cannot be ignored. This study emphasizes the potential impacts of DEM uncertainty on surface shortwave radiation estimation, which is crucial in using satellite-derived datasets for energy balance calculation and climate change applications in mountains.

Effect of interpolation methods on quantifying terrain surface roughness under different data densities

Terrain surface roughness (TSR) is an important parameter in various geoscience applications. TSR can be easily estimated from digital elevation models (DEMs), which are generally derived from the interpolation of Light Detection and Ranging (LiDAR) point clouds. Thus, the quality of TSR is inevitably influenced by data density and interpolation method. However, to what extent the data density can be reduced and which method is more accurate than the others for quantifying TSR are still ambiguous. Thus, this paper evaluated the performance of five classical interpolation methods (ordinary kriging (OK), thin plate spline (TPS), natural neighbor (NN), Delaunay with linear interpolation (TIN) and inverse distance weighting (IDW)) for quantifying TSR under different airborne LiDAR data densities (90 %, 70 %, 50 %, 30 % and 10 % of the original data) in three study sites (samp1, samp2 and samp3) with different terrain characteristics. Results demonstrate that regardless of data density and study sites, TPS is consistently more accurate than the other methods for DEM production in terms of root mean square error (RMSE) and normalized median absolute deviation (NMAD), while IDW produces the worst results. Moreover, the reduction of data density to 50 % of the original data results in no obvious accuracy loss of DEMs for all the interpolation methods. For quantifying TSRs from DEMs, IDW shows the highest accuracy when data density is larger than 50 % in samp1 and samp2, and larger than 70 % in samp3, while TPS obtains the best results in the other densities; however, the surfaces of IDW are very coarse, which makes terrain details unrecognizable. Additionally, the DEM-based TSRs of all the interpolation methods can endure the reduction of LiDAR data to 50 % of the original samples without considerable accuracy decreases, especially for TPS. Overall, TPS can be considered as a promising method for LiDAR DEM production and TSR quantification.

ANN-Based DEM Fusion and DEM Improvement Frameworks in Regions of Assam and Meghalaya using Remote Sensing Datasets

The Digital Elevation Models (DEMs) are a key and primary input to a large number of modelling processes such as disaster risk monitoring, flood modelling, hydrology, geology, geomorphology, climatology, and environmental study applications. The DEM serves as an important source of topographic information representing the continuous surface of the earth in 3 dimensions with x, y and z coordinates of any point in a grided raster form or a vector TIN form. This study is based on developing a new method using the universal approximation capability of neural networks for the fusion and improvement of L-band and X-band SAR (Synthetic Aperture Radar) DEMs in the complex terrain of Assam and Meghalaya states of the Indian geographic region using the DEM fusion technique. The high-spatial-resolution ALOS PALSAR RTC HR 12.5 m DEM products are used in a fusion framework designed with neural network models. The network adaptively learns the terrain information to produce fused DEM products. The neural network models generate the relationship between the input elevation information from ALOS PALSAR DEMs and precise reference elevation from ICESat-2 spaceborne altimetry as target data. The training and testing data samples are prepared and filtered by checking the correct range of elevation from the toposheets of this region. Different models are used to separately train for the relatively plain valley portion of the Assam region and mountainous portions of the highland Shillong plateau. The obtained fused DEMs from the developed neural net structure are assessed for their quality and accuracy by estimating the RMSE parameter. The fused DEM attains an RMSE value of 7 meters for the complete region which is a significant improvement over the input DEM RMSE of approximately 11 meters. The plain area points and mountainous region points are assessed separately to analyze the predictions from neural nets in the two types of terrains observed in this study site. Moreover, TanDEM-X 90 m DEM is improved using the neural network modeling in the geometric distortions affected areas, which shows an improvement of around 33% overall at the study site. The assessment of plain and mountainous region points for near-ground points shows an improvement of 47% and 55% respectively. The fusion framework designed using the neural network models is an effective and efficient method for obtaining fused DEMs as well as for the improvement of the existing DEMs for complex terrain.

Accuracy Comparison of Digital Elevation Models Produced from Ascending and Descending Orbits TerraSAR-X Data

Sayısal Yükseklik Modeli (SYM) verisi birçok mühendislik disiplini için arazi yükseklik bilgileri içermesinden dolayı oldukça önemlidir. Uzaktan algılamada mikrodalga görüntüler yardımı ile SYM üretiminde, stereoskopik ve interferometrik yöntemler kullanılmaktadır. Mikrodalga görüntülerin bu alanda kullanılmasındaki en büyük etken ise gece-gündüz alım yapabilmeleri ve kar, yağmur gibi hava koşulundan etkilenmemeleridir. Mikrodalga algılayıcı sistemler, yörünge etrafında alçalan veya yükselen yönde hareket ederek algılama yaparlar. Bu çalışmada TerraSAR-X verileri ile Erciyes Dağı’nda seçilen bir pilot bölgede SYM üretimi gerçekleştirilmiştir. Aynı zamanda SAR görüntülerinin uyumluluk verileri kullanılarak iki yörüngeye ait veriler füzyon edilmiştir. Alçalan, yükselen yörünge ve füzyon görüntüler için ayrı ayrı üretilen SYM’ler arazi ölçmelerinden elde edilen yükseklik bilgileri ile karşılaştırılmıştır. Karşılaştırma işlemlerinde istatistiksel analizler uygulanmıştır. İstatistiksel analizler sonucunda sırasıyla yükselen yörünge, alçalan yörünge ve füzyon korelasyon katsayısı değerleri 0.892, 0.894 ve 0.934 bulunmuştur. Elde edilen sonuçlar incelendiğinde, kullanılan füzyon yönteminin sonuçları pozitif yönde iyileştirdiği görülmüştür.

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.

EVALUATION OF DEM DERIVED BY REPEAT-PASS X-BAND STRIPMAP MODE PAZ DATA

Abstract. This paper, presents the initial results of digital elevation model (DEM) extraction from PAZ Synthetic Aperture Radar (SAR) satellite images using repeat-pass interferometric analysis. We used a multi-temporal high-resolution strip-map mode X-band satellite image that has a single polarization. Five main classes, i.e., volcanic structures, agriculture, settlement, sand dune and plain bareland are considered depending on the structure of the region. Within the category, the coherence value and DEM value are evaluated. In the accuracy assessment analysis, a reference map produced from aerial photogrammetry is used. Additionally, global DEM TanDEM-X data is also tested in the study region. In the analysis, quality metrics, mean error (ME), root means square error (RMSE), standard deviation (STD), and the normalized median absolute deviation (NMAD) are used. The results showed that as the temporal baseline increases the coherence values and the quality of the DEM product decrease. The RMSE values range between 2.36 m to 7.09 m in different classes. The TanDEM-X data provided high accuracies over each class range from 0.88 m to 2.40 m. Since the study area is vulnerable to sinkhole formation, sinkhole-like signals were also observed in the interferograms obtained from different and sequential pairs. The high-resolution repeat-pass PAZ data pointed out its potential for interferometric products generation.