Vertical Accuracy Assessment of ALOS PALSAR, GMTED2010, SRTM and Topodata Digital Elevation Models

Ferreira, Z. A., & Cabral, P. (2021). Vertical Accuracy Assessment of ALOS PALSAR, GMTED2010, SRTM and Topodata Digital Elevation Models. In Proceedings of the 7th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2021) (pp. 116-124). SCITEPRESS - Science and Technology Publications, Lda. https://doi.org/10.5220/0010404001160124Three-dimensional data of the Earth's surface can support several types of studies, such as hydrological, geomorphological, environmental monitoring, among many others. But, due to the difficulty of acquiring these data in the field, freely available Digital Elevation Models (DEM) have been widely used, and therefore, it is increasingly necessary to check their accuracy to ensure their correct applicability according to the appropriate scale. However, there are no studies which have assessed specifically the vertical accuracy of the ALOS PALSAR, GMTED2010, SRTM and Topodata DEMs according to Brazilian Cartographic Accuracy Standard (PEC). In this sense, this paper aims to evaluate the quality of the above-mentioned DEMs by using the official high accuracy altimetric network data of the Brazilian Geodetic System. Statistical analysis of errors results demonstrated that the DEMs have applications compatible with 1:100,000 scales or smaller than this, and although the GMTED2010 presented a lower accuracy than the other DEMs, it also could be classified in the same accuracy category according to the Brazilian PEC. We conclude that DEMs assessment is very important to ensure their correct application as they can be used in many researches since these data are available for practically all areas of the planet.publishersversionpublishe

Vertical accuracy evaluation of freely available latest high-resolution (30 m) global digital elevation models over Cameroon (Central Africa) with GPS/leveling ground control points.

Digital Elevation Models (DEMs) contain topographic relief data that are vital for many geoscience applications. This study relies on the vertical accuracy of publicly available latest high-resolution (30 m) global DEMs over Cameroon. These models are (1) the ALOS World 3D-30 m (AW3D30), (2) the Shuttle Radar Topography Mission 1 Arc-Second C-Band Global DEM (SRTM 1) and (3) the Advanced Spaceborne Thermal Emission and Reflection Global DEM Version 2 (ASTER GDEM 2). After matching their coordinate systems and datums, the horizontal positional accuracy evaluation was carried out and it shows that geolocation errors significantly influence the vertical accuracy of global DEMs. After this, the three models are compared among them, in order to access random and systematic effects in the elevation data each of them contains. Further, heights from 555 GPS/leveling points distributed all over Cameroon are compared to each DEM, for their vertical accuracy determination. Traditional and robust statistical measures, normality test, outlier detection and removal were used to describe the vertical quality of the DEMs. The test of the normality rejected the hypothesis of normal distribution for all tested global DEMs. Overall vertical accuracies obtained for the three models after georeferencing and gross error removal in terms of Root Mean Square (RMS) and Normalized Median Absolute Deviation (NMAD) are: AW3D30 (13.06 m and 7.75 m), SRTM 1 (13.25 m and 7.41 m) and ASTER GDEM 2 (18.87 m and 13.30 m). Other accuracy measures (MED, 68.3% quantile, 95% quantile) supply some evidence of the good quality of AW3D30 over Cameroon. Further, the effect of land cover and slope on DEM vertical accuracy was also analyzed. All models have proved to be worse in the areas dominated by forests and shrubs areas. SRTM 1 and AW3D30 are more resilient to the effects of the scattering objects respectively in forests and cultivated areas. The dependency of DEMs accuracy on the terrain roughness is evident. In all slope intervals, AW3D30 is performing better than SRTM 1 and ASTER GDEM 2 over Cameroon. AW3D30 is more representative of the external topography over Cameroon in comparison with two others datasets and SRTM 1 can be a serious alternative to AW3D30 for a range of DEM applications in Cameroon

An ınvestigation on accuracy analysis of global and regional (high resolution) digital elevation models

Topografik yükseklikler birçok mühendislik uygulamasında ve yerbilimlerine ilişkin araştırmalarda kullanılmaktadır. Yüksek çözünürlüklü Sayısal Yükseklik Modelleri (SYM), günümüzde yükseklik verilerini elde etmenin en pratik ve ekonomik yoludur. SYM’lerinin üretiminde farklı yöntemler uygulanır. Bu modeller çeşitli hata kaynaklarından etkilenirler. Bu nedenle, SYM verilerini kullanmadan önce çalışma alanlarındaki performanslarını test etmek çalışmada gereksinim duyulan yükseklik doğruluğunun sağlanması için önemlidir. Genel bir yaklaşım olarak, Sayısal Yükseklik Modellerinin doğruluk analizinde topografyaya uygun dağılmış kontrol noktalarında Global Navigation Satellite System (GNSS) ve/veya nivelman yükseklikleri ile modelden elde edilen yükseklik farklarının karesel ortalama hata (k.o.h.) değerleri dikkate alınır. Bu çalışmada, yüksek çözünürlüklü global SYM’leri: ASTER GDEM (Advanced Spaceborne Thermal Emission and Reflection Radiometer-Gelişmiş Uzay Kaynaklı Termal Emisyon ve Yansıma Radyometresi), SRTM (Shuttle Radar Topography Mission-Mekik Radar Topografya Misyonu) ile bölgesel HGM DTED2 modellerinin doğrulukları GNSS/nivelman verileri kullanılarak analiz edilmiştir. Bu amaçla farklı topografik özellikteki alanları temsil eden üç ayrı GNSS/nivelman veri seti kullanılarak kontrol noktalarının dağılımının yanı sıra test alanı topografyasının SYM doğruluğuna etkisi incelenmiştir. Sonuçlar Türkiye'nin kuzeybatısındaki test edilen global ve bölgesel SYM’lerinin doğruluğunu topografyanın değişen özelliklerine bağlı olarak karşılaştırmakta ve değerlendirmektedir. Testler sonucu global SYM’lerinin doğruluğu 8.0 m iken bölgesel DTED2 SYM doğruluğu 6.0 m olarak bulunmuştur.The topographical heights are required in practice for a number of engineering applications as well as their specific use in many Earth science disciplines. Using a high-resolution Digital Elevation Model (DEM) is the most practical and economical way for obtaining the height data nowadays. These models include errors. So, it is important to clarify the quality as well as the accuracy of the DEM in the study areas before using its data. In general, validating DEMs using independent point-wise data such as GNSS and leveling heights provide an overall accuracy measure in terms of root means square error (r.m.s.e.) of the DEM derived heights. In this study three high-resolution digital elevation models ASTER, SRTM and Turkey Digital Topographic Data (DTED2) in 1 and 3 resolutions are assessed using GNSS/leveling data. Using three different sets of GNSS/leveling data invalidations it is aimed to clarify the role of the distribution of the ground-control points as well as the region’s characteristics, such as roughness of topography, land-cover, etc., in the validation results. The conclusions report the accuracy of the validated DEMs in northwest Turkey and hence include a categorization of DEM performances, generated from remotely sensed data and terrestrial techniques, depending on the topographical characteristics. In the test results the accuracies for global DTMs is 8.0 m, and for regional DTED2 DEM is 6.0 m

Vertical Accuracy of Freely Available Global Digital Elevation Models (ASTER, AW3D30, MERIT, TanDEM-X, SRTM, and NASADEM)

https://www.mdpi.com/2072-4292/12/21/348

Uncertainties in Digital Elevation Models: Evaluation and Effects on Landform and Soil Type Classification

Digital elevation models (DEMs) are a widely used source for the digital representation of the Earth's surface in a wide range of scientific, industrial and military applications. Since many processes on Earth are influenced by the shape of the relief, a variety of different applications rely on accurate information about the topography. For instance, DEMs are used for the prediction of geohazards, climate modelling, or planning-relevant issues, such as the identification of suitable locations for renewable energies. Nowadays, DEMs can be acquired with a high geometric resolution and over large areas using various remote sensing techniques, such as photogrammetry, RADAR, or laser scanning (LiDAR). However, they are subject to uncertainties and may contain erroneous representations of the terrain. The quality and accuracy of the topographic representation in the DEM is crucial, as the use of an inaccurate dataset can negatively affect further results, such as the underestimation of landslide hazards due to a too flat representation of relief in the elevation model. Therefore, it is important for users to gain more knowledge about the accuracy of a terrain model to better assess the negative consequences of DEM uncertainties on further analysis results of a certain research application. A proper assessment of whether the purchase or acquisition of a highly accurate DEM is necessary or the use of an already existing and freely available DEM is sufficient to achieve accurate results is of great qualitative and economic importance. In this context, the first part of this thesis focuses on extending knowledge about the behaviour and presence of uncertainties in DEMs concerning terrain and land cover. Thus, the first two studies of this dissertation provide a comprehensive vertical accuracy analysis of twelve DEMs acquired from space with spatial resolutions ranging from 5 m to 90 m. The accuracy of these DEMs was investigated in two different regions of the world that are substantially different in terms of relief and land cover. The first study was conducted in the hyperarid Chilean Atacama Desert in northern Chile, with very sparse land cover and high elevation differences. The second case study was conducted in a mid-latitude region, the Rur catchment in the western part of Germany. This area has a predominantly flat to hilly terrain with relatively diverse and dense vegetation and land cover. The DEMs in both studies were evaluated with particular attention to the influence of relief and land cover on vertical accuracy. The change of error due to changing slope and land cover was quantified to determine an average loss of accuracy as a function of slope for each DEM. Additionally, these values were used to derive relief-adjusted error values for different land cover classes. The second part of this dissertation addresses the consequences that different spatial resolutions and accuracies in DEMs have on specific applications. These implications were examined in two exemplary case studies. In a geomorphometric case study, several DEMs were used to classify landforms by different approaches. The results were subsequently compared and the accuracy of the classification results with different DEMs was analysed. The second case study is settled within the field of digital soil mapping. Various soil types were predicted with machine learning algorithms (random forest and artificial neural networks) using numerous relief parameters derived from DEMs of different spatial resolutions. Subsequently, the influence of high and low resolution DEMs with the respectively derived land surface parameters on the prediction results was evaluated. The results on the vertical accuracy show that uncertainties in DEMs can have diverse reasons. Besides the spatial resolution, the acquisition technique and the degree of improvements made to the dataset significantly impact the occurrence of errors in a DEM. Furthermore, the relief and physical objects on the surface play a major role for uncertainties in DEMs. Overall, the results in steeper areas show that the loss of vertical accuracy is two to three times higher for a 90 m DEM than for DEMs of higher spatial resolutions. While very high resolution DEMs of 12 m spatial resolution or higher only lose about 1 m accuracy per 10° increase in slope steepness, 30 m DEMs lose about 2 m on average, and 90 m DEMs lose more than 3 m up to 6 m accuracy. However, the results also show significant differences for DEMs of identical spatial resolution depending on relief and land cover. With regard to different land cover classes, it can be stated that mid-latitude forested and water areas cause uncertainties in DEMs of about 6 m on average. Other tested land cover classes produced minor errors of about 1 – 2 m on average. The results of the second part of this contribution prove that a careful selection of an appropriate DEM is more crucial for certain applications than for others. The choice of different DEMs greatly impacted the landform classification results. Results from medium resolution DEMs (30 m) achieved up to 30 % lower overall accuracies than results from high resolution DEMs with a spatial resolution of 5 m. In contrast to the landform classification results, the predicted soil types in the second case study showed only minor accuracy differences of less than 2 % between the usage of a spatial high resolution DEM (15 m) and a low resolution 90 m DEM. Finally, the results of these two case studies were compared and discussed with other results from the literature in other application areas. A summary and assessment of the current state of knowledge about the impact of a particular chosen terrain model on the results of different applications was made. In summary, the vertical accuracy measures obtained for each DEM are a first attempt to determine individual error values for each DEM that can be interpreted independently of relief and land cover and can be better applied to other regions. This may help users in the future to better estimate the accuracy of a tested DEM in a particular landscape. The consequences of elevation model selection on further results are highly dependent on the topic of the study and the study area's level of detail. The current state of knowledge on the impact of uncertainties in DEMs on various applications could be established. However, the results of this work can be seen as a first step and more work is needed in the future to extend the knowledge of the effects of DEM uncertainties on further topics that have not been investigated to date