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

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

Using ASTER and SRTM DEMs for studying geomorphology and glaciation in high mountain areas

For selected peaks in high mountains of diverse climatic regions (Andes, Hindu Kush, Tien Shan) digital elevation models (DEMs) have been generated from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data using PCI Geomatica 8.1/8.2 software. Artifacts in the ASTER DEM were eliminated using data from the Space Shuttle Radar Topography Mapping mission (SRTM). For two of the four case studies, accuracy was evaluated by comparison the ASTER/SRTM DEM with DEMs derived from contour maps. Whereas the SRTM DEM shows correct elevations in all altitudes, elevations in the ASTER DEM are slightly to low in higher altitudes and south-exposed aspects. Geomorphic analyses were undertaken using the software ArcInfo, ArcView and SAGA. Cluster analyses including tangetial, vertical curvature and slope combined with spectral information helped identifying debris-covered glaciers and geomorphologic forms and processes. Results show that ASTER/SRTM DEMs are useful for an interpretation of the macro- and mesorelief. The DEM scale sets limits for the level in analysis detail. Whereas SRTM DEMs offer more precise elevations, ASTER DEMs offer more geomorphologic details

Visualisation of Uncertainty in 30m Resolution Global Digital Elevation Models: SRTM v3.0 and ASTER v2

Geospatial visualisation presents us with innovative techniques of assessing uncertainty in digital elevation datasets. It gives the viewer immediate feedback on potential problems and heightens understanding of effects not easily appreciated when dealing with numerical statistics only. This study evaluated the performance of 30-metre resolution SRTM version 3.0 and ASTER GDEM version 2 over Lagos, Nigeria. Both datasets were examined by direct comparison with 176 highly accurate Ground Control Points (GCPs) coordinated by GPS (Global Positioning System) observation. The basis of comparison was on the elevation differences between the Digital Elevation Models (DEMs) and the GCPs at coincident points. The performance of both DEMs was visualised in 2D and 3D space by comparing pixel values and surface models. In the assessment, the absolute vertical accuracies of SRTM v3.0 and ASTER v2 are 4.23m and 28.73m respectively. The accuracy of SRTM for the study site proved to be higher than the value of 16m presented in the original SRTM requirement specification. ASTER did not meet up with its 17m overall accuracy specification.KEYWORDS: Uncertainty, Visualisation, Digital Elevation Model, SRTM, ASTER

Coupling potential of ICESat/GLAS and SRTM for the discrimination of forest landscape types in French Guiana

The Shuttle Radar Topography Mission (SRTM) has produced the most accurate nearly global elevation dataset to date. Over vegetated areas, the measured SRTM elevations are the result of a complex interaction between radar waves and tree crowns. In this study, waveforms acquired by the Geoscience Laser Altimeter System (GLAS) were combined with SRTM elevations to discriminate the five forest landscape types (LTs) in French Guiana. Two differences were calculated: (1) penetration depth, defined as the GLAS highest elevations minus the SRTM elevations, and (2) the GLAS centroid elevations minus the SRTM elevations. The results show that these differences were similar for the five LTs, and they increased as a function of the GLAS canopy height and of the SRTM roughness index. Next, a Random Forest (RF) classifier was used to analyze the coupling potential of GLAS and SRTM in the discrimination of forest landscape types in French Guiana. The parameters used in the RF classification were the GLAS canopy height, the SRTM roughness index, the difference between the GLAS highest elevations and the SRTM elevations and the difference between the GLAS centroid elevations and the SRTM elevations. Discrimination of the five forest landscape types in French Guiana was possible, with an overall classification accuracy of 81.3% and a kappa coefficient of 0.75. All forest LTs were well classified with an accuracy varying from 78.4% to 97.5%. Finally, differences of near coincident GLAS waveforms, one from the wet season and one from the dry season, were analyzed. The results showed that the open forest LT (LT12), in some locations, contains trees that lose leaves during the dry season. These trees allow LT12 to be easily discriminated from the other LTs that retain their leaves using the following three criteria: (1) difference between the GLAS centroid elevations and the SRTM elevations, (2) ratio of top energy in the wet season to top energy in the dry season, or (3) ratio of ground energy in the wet season to ground energy in the dry season

Towards quantifying the effects of resource extraction on land cover and topography through remote sensing analysis: Confronting issues of scale and data scarcity

This dissertation focuses on the mapping and monitoring of mineral mining activity using remotely sensed data. More specifically, it explores the challenges and issues associated with remote sensing-based analysis of land use land cover (LULC) and topographic changes in the landscape associated with artisanal and industrial-scale mining. It explores broad themes of image analysis, including evaluation of error in digital elevation models (DEMs), integration of multiple scales and data sources, quantification of change, and remote sensing classification in data-scarce environments. The dissertation comprises three case studies.;The first case study examines the LULC change associated with two scales of mining activity (industrial and artisanal) near Tortiya, Cote d\u27Ivoire. Industrial mining activity was successfully mapped in a regional LULC classification using Landsat multispectral imagery and support vector machines (SVMs). However, mapping artisanal mining required high-resolution imagery to discriminate the small, complex patterns of associated disturbance.;The second case study is an investigation of the potential for quantifying topographic change associated with mountain top removal mining and the associated valley-fill operations for a region in West Virginia, USA, using publicly available DEMs. A 1:24,000 topographic map data, the shuttle radar topography mission (SRTM) DEM, a state-wide photogrammetric DEM, and the Advanced Spaceborne Thermal Emission Radiometer (ASTER) Global DEM (GDEM) were compared to a lidar bare-earth reference DEM. The observed mean error in both the SRTM and GDEM was statistically different than zero and modeled a surface well above the reference DEM surface. Mean error in the other DEMs was lower, and not significantly different than zero. The magnitude of the root mean square error (RMSE) suggests that only topographic change associated with the largest topographic disturbances would be separable from background noise using global DEMS such as the SRTM. Nevertheless, regionally available DEMs from photogrammetric sources allow mapping of mining change and quantification of the total volume of earth removal.;Monitoring topographic change associated with mining is challenging in regions where publicly available DEMs are limited or not available. This challenge is particularly acute for artisanal mining, where the topographic disturbance, though locally important, is unlikely to be detected in global elevation data sets. Therefore, the third and final case study explored the potential for creating fine-spatial resolution bare-earth DEMs from digital surface models (DSMs) using high spatial resolution commercial satellite imagery and subsequent filtering of elevation artifacts using commercial lidar software and other spatial filtering techniques. Leaf-on and leaf-off DSMs were compared to highlight the effect of vegetation on derived bare-earth DEM accuracy. The raw leaf-off DSM was found to have very low error overall, with notably higher error in areas of evergreen vegetation. The raw leaf-on DSM was found to have a RMSE error much higher than the leaf-off data, and similar to that of the SRTM in dense deciduous forest. However, filtering using the commercial techniques developed for lidar notably reduced the error present in the raw DSMs, suggesting that such approaches could help overcome data scarcity in regions where regional or national elevation data sets are not available.;Collectively this research addressed data issues and methodological challenges in the analysis of 3D changes caused by resource extraction. Elevation and optical imagery are key data sets for mapping the disturbance associated with mining. The particular combination required regarding data spatial scale, and for elevation, accuracy, is a function of the type and scale of the mining

One Decade of Glacier Mass Changes on the Tibetan Plateau Derived from Multisensoral Remote Sensing Data

The Tibetan Plateau (TP) with an average altitude of 4,500 meters above sea level is characterized by many glaciers and ice caps. Glaciers are a natural indicator for climate variability in this high mountain environment where meteorological stations are rare or non-existent. In addition, the melt water released from the Tibetan glaciers is feeding the headwaters of the major Asian river systems and contributes to the rising levels of endorheic lakes on the plateau. As many people directly rely on the glacier melt water a continuous glacier monitoring program is necessary in this region. In situ measurements of glaciers are important, but are spatial limited due to large logistical efforts, physical constrains and high costs. Remote sensing techniques can overcome this gap and are suitable to complement in situ measurements on a larger scale. In the last decade several remote sensing studies dealt with areal changes of glaciers on the TP. However, glacier area changes only provide a delayed signal to a changing climate and the amount of melt water released from the glaciers cannot be quantified. Therefore it is important to measure the glacier mass balance. In order to estimate glacier mass balances and their spatial differences on the TP, several remote sensing techniques and sensors were synthesized in this thesis. In a first study data from the Ice Cloud and Elevation Satellite (ICESat) mission were employed. ICESat was in orbit between 2003 and 2009 and carried a laser altimeter which recorded highly accurate surface elevation measurements. As in mid-latitudes these measurements are rather sparse glaciers on the TP were grouped into eight climatological homogeneous sub-regions in order to perform a statistical sound analysis of glacier elevation changes. To assess surface elevation changes of a single mountain glacier from ICESat data, an adequate spatial sampling of ICESat measurements need to be present. This is the case for the Grosser Aletschgletscher, located in the Swiss Alps which served as a test site in this thesis. In another study data from the current TanDEM-X satellite mission and from the Shuttle Radar Topography Mission (SRTM) conducted in February 2000 were employed to calculate glacier elevation changes. In a co-authored study, these estimates could be compared with glacier elevation changes obtained from the current French Pléiades satellite mission. In order to calculate glacier mass balances, the derived elevation changes were combined with assumptions about glacier area and ice density in all studies. In this thesis contrasting patterns of glacier mass changes were found on the TP. With an ICESat derived estimate of -15.6±10.1 Gt/a between 2003 and 2009 the average glacier mass balance on the TP was clearly negative. However, some glaciers in the central and north-western part of the TP showed a neutral mass balance or a slightly positive anomaly which was also confirmed by data from the current TanDEM-X satellite mission. A possible explanation of this anomaly in mass balance could be a compensation of the temperature driven glacier melt due to an increase in precipitation