A typology of global relief classes derived from digital elevation models at 1 arcsec resolution

The Geoscience Laser Altimeter System (GLAS) instrument onboard the Ice, Cloud and land Elevation Satellite (ICESat) provides elevation data with very high accuracy which can be used as ground data to evaluate the vertical accuracy of an existing Digital Elevation Model (DEM). In this article, we examine the differences between ICESat elevation data (from the 1064 nm channel) and Shuttle Radar Topography Mission (SRTM) DEM of 3 arcsec resolution (90 m) and map-based DEMs in the Qinghai-Tibet (or Tibetan) Plateau, China. Both DEMs are linearly correlated with ICESat elevation for different land covers and the SRTM DEM shows a stronger correlation with ICESat elevations than the map-based DEM on all land-cover types. The statistics indicate that land cover, surface slope and roughness influence the vertical accuracy of the two DEMs. The standard deviation of the elevation differences between the two DEMs and the ICESat elevation gradually increases as the vegetation stands, terrain slope or surface roughness increase. The SRTM DEM consistently shows a smaller vertical error than the map-based DEM. The overall means and standard deviations of the elevation differences between ICESat and SRTM DEM and between ICESat and the map-based DEM over the study area are 1.03 ± 15.20 and 4.58 ± 26.01 m, respectively. Our results suggest that the SRTM DEM has a higher accuracy than the map-based DEM of the region. It is found that ICESat elevation increases when snow is falling and decreases during snow or glacier melting, while the SRTM DEM gives a relative stable elevation of the snow/land interface or a glacier elevation where the C-band can penetrate through or reach it. Therefore, this makes the SRTM DEM a promising dataset (baseline) for monitoring glacier volume change since 2000.

Digital elevation models (DEMs) are becoming increasingly important components in national and regional spatial data infrastructure. High-quality DEMs can now be derived directly from airborne light detection and ranging (LiDAR) point-cloud data of high spatial density if the derivation process can be verified. However, LiDAR is relatively new compared with other technologies for terrain data collection, and, although offering the potential for providing better spatial resolution than those that have been routinely available before, will not diffuse among DEM users until the results of meeting the verification challenge are favourable enough to inspire re-organisation of spatial data in decision support for catchment management and other third-tier-of-government authorities. By way of exemplification, the research presented in this thesis concerned ways of improving the processing of the airborne LiDAR data for high-quality DEM generation in terms of both accuracy and efficiency, and explored the applications of LiDAR-derived DEMs in the region of the Corangamite Catchment Management Authority, Victoria, Australia. This thesis begins with a review of the traditional technologies for terrain data collection and DEM generation and compares them with the LiDAR technology. Accordingly, a review of the recently-reported advances in LiDAR data deployment for DEM generation is followed by reports of experiments designed to improve selection and deployment of LiDAR data filtering, modelling methods and data reduction, and the achievement of vertical accuracy for different land covers. Also reported are results of deployment of LiDAR data for ground truthing, and application of LiDAR data for the extraction of drainage networks on an area of deranged drainage: the Victorian Volcanic Plain. The show that: (a) the issues of filtering, modelling techniques, interpolation methods, DEM resolution, and data reduction are critical and must be considered carefully when using LiDAR data for a high-quality DEM generation; (b) it is efficient to use survey marks for the accuracy assessment of LiDAR data. Normal distribution must be tested in order to select a suitable measure for the accuracy assessment of LiDAR data over different land covers; (c) LiDAR data reduction can improve the terrain production efficiency without compromising the product quality. The deployment of breaklines made a significant contribution to improving the accuracy of terrain models while allowing for data reduction; (d) it demonstrated the practical feasibility of applying ground control points from LiDAR intensity image and LiDAR-derived DEM in image orthorectification. The resultant orthoimage accuracy was shown to be superior to that achieved by using (lower accuracy) data sources such as those from Vicmap data; and (e) the LiDAR-derived DEM offers the capability of extracting and delineating the drainage networks in much more detail in low¬relief terrain, including areas in which drainage is barely coherent; The advantages of using LiDAR-derived DEM over the lower-accuracy DEM emerge in terms of stream order, stream number and stream length.

The main objective of the study is Digital Terrain Model (DTM) generation from aerial photogrammetric data and identify and map the potential avalanche prone zones in Manali region. Avalanche is a dynamic hazardous phenomenon in the snow-bound mountainous terrain. Mapping of avalanche prone terrain is crucial to minimize the avalanche hazard. Nowadays, airborne data capturing technology, such as large-format Photogrammetry, has opened new vistas for the mapping of complex and inaccessible mountainous areas. In the present study, large format digital Photogrammetry data of 20 cm ground sample distance (GSD) have been used to generate high-resolution and accurate Digital Elevation Model and ortho-images. Digital terrain model along with its derivative terrain products and land cover map generated from land cover classification of derived ortho-photo is analyzed to locate the probable avalanche zone. The terrain characteristics, snow-pack condition and prevailing meteorological conditions are the groups of variables that influence the occurrence of avalanche. Amongst these, the terrain characteristics is the most influencing factor, and easier to map due to its stable nature along the time. Therefore advanced geo-informatics techniques have been used by mixing terrain property, Digital Elevation Model (DEM) and satellite imagery to determine the different geographical factors that affect the avalanche triggering. Also the derived information was combined in Analytic Hierarchy Process to extract a map of the avalanche prone zones in the study area standard mapping techniques as coarse-resolution data are not very appropriate for such studies.

Digital Elevation Models (DEMs) and land cover products are primary inputs for hydrologic models of surface runoff that affects infiltration, erosion, and evapotranspiration. DEM and land cover play important role in determining the runoff characteristics of specific catchment areas. Recently, at local level, a number of data sources have been used to derive land cover products for high resolution studies. These studies have been carried out for a number of different applications, including estimation of biomass and vegetation mapping. A hydrologic land cover classification includes information not only about vegetation species, but also about the land surface and what classes are important hydrologically. This kind of classification must therefore incorporate information on elevation, slope, aspect, surface roughness, as well as vegetation species derived from satellite added-value products. The main problems when generating hydrologic land cover maps is the lack of accurate DEMs and the confusion of spectral responses from different features. In this study, a Terra/ASTER image acquired over the region of Heraklion, Crete, Greece was used. ASTER stereo imagery is used for DEM production because it gives a strong advantage in terms of radiometric variations versus the multi-date stereo-data acquisition with across-track stereo, which can then compensate for the weaker stereo geometry. GCPs (Ground Control Points) derived from differential GPS measurements were also used for absolute DEM production. A hydrologic land cover classification scheme was developed by combining ASTER multispectral imagery, ASTER DEM products and the spectral signatures derived from field observations at predefined training sites.

Chemical and biological composition of surface materials and physical structure and arrangement of those materials determine the intrinsic reflectance of Earth's land surface. The apparent reflectance—as measured by a spaceborne or airborne sensor that has been corrected for atmospheric attenuation—depends also on topography, surface roughness, and the atmosphere. Especially in Earth's mountains, estimating properties of scientific interest from remotely sensed data requires compensation for topography. Doing so requires information from digital elevation models (DEMs). Available DEMs with global coverage are derived from spaceborne interferometric radar and stereo‐photogrammetry at ∼30 m spatial resolution. Locally or regionally, lidar altimetry, interferometric radar, or stereo‐photogrammetry produces DEMs with finer resolutions. Characterization of their quality typically expresses the root‐mean‐square (RMS) error of the elevation, but the accuracy of remotely sensed retrievals is sensitive to uncertainties in topographic properties that affect incoming and reflected radiation and that are inadequately represented by the RMS error of the elevation. The most essential variables are the cosine of the local solar illumination angle on a slope, the shadows cast by neighboring terrain, and the view factor, the fraction of the overlying hemisphere open to the sky. Comparison of global DEMs with locally available fine‐scale DEMs shows that calculations with the global products consistently underestimate the cosine of the solar angle and underrepresent shadows. Analyzing imagery of Earth's mountains from current and future spaceborne missions requires addressing the uncertainty introduced by errors in DEMs on algorithms that analyze remotely sensed data to produce information about Earth's surface.

Digital elevation models (DEM) are of fundamental importance for remote sensing. With a DEM the three-dimensional positioning, requiring a stereo model can be reduced to a two-dimensional solution just based on a single image. With the free of charge availability of the SRTM-height models, covering the land area from 56° southern up to 60.25° northern latitude a nearly world wide coverage is given. But especially in mountainous regions and dry sand deserts the original SRTM DEMs have gaps in the original SRTM data. Now with the also free of charge available ASTER GDEM the area from 83° southern up to 83° northern latitude is covered. For areas where both height models exist, it is the question which height model should be preferred. Outside the USA the SRTM height data have a spacing of 3 arcsec (nearly 90m), while the ASTER GDEM has a spacing of just 1 arcsec (nearly 30m). The decision for the selection of the DEM is based on accuracy, homogeneity, reliability, completeness and morphologic details. In test areas with precise reference height models, located in the USA, Germany, France, Poland, Turkey and Jordan and with different morphology as mountainous, rolling, flat and urban and also with different land classes, the ASTER GDEM has been analyzed and compared with SRTM DEM as well as with SPOT 5 HRS and Cartosat 1 height models. ASTER GDEM in most cases shows improved accuracy with a higher number of number of stacks (number of images used for overlapping height models). But the accuracy improvement with more stacks is smaller as it should be for random data. The number of used stacks per DEM-point varies strongly depending upon the area. Especially in areas with low cloud coverage and higher imaging priority a high number of stacks have been used opposite to areas often covered by clouds and having lower imaging priority, where the dominating number of DEM-points may be located only in 2 stacks. Based on own matching results with ASTER images quite more morphologic details have been expected in ASTER GDEM having 1 arcsec point spacing as in SRTM height models with 3 arcsec spacing, but the analyzed data show only slightly more morphologic details as the SRTM 3” height model. SRTM as well as ASTER height models are strongly depending upon the morphology and the land coverage, so not a homogenous accuracy can be expected. In addition, as all height models, the accuracy depends usually linear upon the tangent of terrain slope, so the standard deviation of height (SZ) should be given in the form SZ = a + b∗tan(terrain slope). Not only the standard deviation is important, the height models have different systematic errors (bias). The bias in X, Y and Z is larger for ASTER GDEM as for SRTM DEMs. Horizontal shifts have been determined by adjustment of the ASTER GDEMs against the reference height model. In general the SRTM height models are slightly more accurate as the ASTER GDEM.

Uncertainty can be apprehended as lack of knowledge about a certain phenomenon. Decisions about whether and how to react to this uncertainty depend on a number of factors. These factors include the ability to estimate the amount of uncertainty and thus estimate the involved risk, available options to decrease either the uncertainty or its relevance, and the costs for responding or ignoring uncertainty.
\nIn GIScience, the modelling of processes is subject to uncertainties from a number of sources. Above all, the abstraction inherent in any model results in uncertainty,
\ncreated from the assumptions made to simplify complex processes and interrelations in order to formalise and model them. Additionally, uncertainty in any input data
\npropagates through a model into the results. For topography-based models, i.e. models characterising and detecting topographic form, or models simulating processes that act upon this topography, digital elevation models (DEMs) are a potential source of uncertainty. DEMs consist of measured or digitised elevation values, and as such are
\nsubject to any error in the data capturing process. Widespread DEMs such as GLOBE or SRTM are distributed with accuracy figures that only give global measures such as
\nroot mean square error (RMSE) lacking any information on the spatial distribution of error. Where uncertainty from DEM accuracy has to be modelled to assess its impact on the results of associated topographic models, assumptions have to be made about the spatial distribution of uncertainty. Within this dissertation it has been shown that these assumptions influence the impact of uncertainty on modelled ice sheets. Besides DEM accuracy, a number of factors in handling DEM data introduce additional uncertainty. These factors include the choice of data model, processing such as projecting and resampling of a DEM data, as well as algorithms used to extract and process elevation based information.
\nWithin this dissertation, the influence of resampling on uncertainty in topography has been explored. This was done by assessing the variation in resampled DEMs introduced
\nby changing the source and target resolution, choice of resampling algorithms and resampling origin. When these uncertainties were modelled and added to input topographies for the GLIMMER ice sheet model, they had noticeable influence on modelled ice sheet configurations. Where higher accuracy reference data for a DEM is available, error can be derived and analysed to provide information about spatial autocorrelation and possible dependencies of error with topographic attributes such as elevation, slope or roughness. Within the course of this dissertation, an uncertainty model was developed which allows modelling of GLOBE DEM uncertainty for areas without higher accuracy reference data such as Scandinavia. The model is based on derived dependencies of GLOBE error with topographic attributes, derived from areas where SRTM data was available to be used as a reference. The model includes both deterministic and stochastic components and reproduces GLOBE DEM uncertainty well for different test areas.
\nThe developed uncertainty model was applied to investigate the impact of DEM uncertainty on different types of models in three case studies. The first case study applied a geomorphologic and hydrologic model (TARDEM), the second case study used two snow melt models, and in the third case study the GLIMMER ice sheet model was employed. Results showed the impact of uncertainty to be depending on a number of facts. Generally, modelled DEM uncertainty had less impact on derived global topographic variables such as mean slope length or the number of derived watersheds when
\napplied to a hydrological model. Higher impacts were recorded where the model focus was on local processes, such as the delineation of a certain watershed and calculation
\nof associated parameters such as hypsometry. For process models like the ice sheet model, factors such as terrain configuration (smooth vs. rough topography, abundant
\nridges or valleys) influenced the impact of DEM uncertainty on ice sheet model (ISM)results.
\nAdditionally, the amount of uncertainty and its spatial correlation, as well as the relative influence of topography within a model were found to play key roles. This implies that for process models, the impact of uncertainty can vary over time. In the case of the ice sheet model, uncertainty had the greatest impact on ice sheet configuration during phases of inception and retreat, and its impact was shown to be dependent on the overall size of the ice masses.
\nIn another set of experiments, a range of sensitivity tests using different ISM parameters and input data were conducted, and the results of these tests were used to
\nconduct a full parametric uncertainty analysis (PUA) for a steady-state climate scenario on Fennoscandia. Results from this analysis allowed the comparison of the influence
\nof uncertainty in other parameters to that of DEM uncertainty, which was found to be equivalent to a 1degC change in climate. The impact of DEM uncertainty was found to be comparable to that of various ‘internal’ ISM parameters. However modelled DEM uncertainty resulted in significantly different ice sheet configurations. This underlines the importance of DEM uncertainty to be considered in ice sheet modelling.
\nUsing different temperature index models (TIM) to model potential snow melt across different resolutions revealed significant impact of scale and resampling on modelled melt rates. This effect was substantially decreased by the use of subgrid model approaches. While it was shown that these subgrid approaches are subject to an increased susceptibility to DEM uncertainty, this effect was more than compensated for by an increased performance in terms of modelled melt rates.
\nIn summary, the results of this dissertation underline the necessity of detailed information on the statistical and spatial distribution of DEM uncertainty to be included
\nwith the data. Additionally, in topographic modelling, uncertainty from other sources such as resampling have shown to be of importance, and modellers and end-users
\nshould account for these uncertainties introduced into model results.

After several years of development, mid-2025 should see the launch of the CO3D1 (Optical Constellation in 3D) mission. The CO3D project is a public-private partnership between CNES (French space agency) and AIRBUS (aerospace company). The mission aims to launch pairs of optical satellites to photogrammetrically reconstruct the Digital Surface Model (DSM) of the Earth's land surface. A notable innovation is the ability to manage stereo acquisition in synchronous mode for capturing moving elements. As part of the CO3D program, CNES is developing the 2D and 3D product processing chains integrated into the mission's ground segment, alongside an Image Calibration Center (ICC) for radiometric and geometric calibration. These systems leverage a suite of open-source tools created by CNES teams. CNES chooses to release these tools prior to the mission's launch to gather user feedback and improve the quality of final CO3D products. CARS2 is the Multiview Stereo Framework (MVS). It produces DSM from satellite images acquired from different view angles. PANDORA3 is the stereo matching framework. It combines both switchable similarity measures at the pixel level and regularizers. The CARS pipeline integrates PANDORA. BULLDOZER4 removes above-ground elements (e.g., trees, buildings) from DSM generated by CARS to produce Digital Terrain Models (DTM). XDEM5 evaluates and validates the 3D quality of the generated models (i.e., the CARS DSM and the Bulldozer DTM). SLURP6 produces land cover masks from very high-resolution (VHR) images. The CARS pipeline integrates these masks to generate sharper 3D reconstruction. CO3D mission represents a significant step forward in Earth observation, offering innovative tools for producing accurate digital surface models. By engaging with the user community early, CNES aims to ensure the mission delivers results that fit user needs. [1] Lebegue, L. et al. (2024). CO3D Products Qualification Forecast. IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 8555-8559.[2] Youssefi, D. et al. (2020). CARS: A Photogrammetry Pipeline Using Dask Graphs to Construct A Global 3D Model. IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 453-456.[3] Cournet, M. et al. (2020). Ground-truth generation and disparity estimation for optical satellite imagery. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.[4] Lallement, D. et al. (2023). Bulldozer, a free open source scalable software for DTM extraction. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.[5] Hugonnet, R. et al. (2022). Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain. IEEE Journal Selected Topics in Applied Earth Observations and Remote Sensing, 15, 6456–6472.[6] Tanguy, Y. et al. (2024). Smart Land Use Masks: A Simple and Robust Approach to Produce Low/High Vegetation Masks from a Single High Resolution Satellite Image. IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 4164-4168.

Landforms with similar surface matter compositions, endogenic and exogenic forces, and development histories tend to exhibit significant degrees of self-similarity in morphology and spatial variation. In loess hill-gully areas, ridges and hills have similar topographic relief characteristics and present nearly periodic variations of similar repeating structures at certain spatial scales, which is termed the topographic relief period (TRP). This is a relatively new concept, which is different from the degree of relief, and describes the fluctuations of the terrain from both horizontal and vertical (cross-section) perspectives, which can be used for in-depth analysis of 2-D topographic relief features. This technique provides a new perspective for understanding the macro characteristics and differentiation patterns of loess landforms. We investigate TRP variation features of different landforms on the Loess Plateau, China, by extracting catchment boundary profiles (CBPs) from 5 m resolution digital elevation model (DEM) data. These profiles were subjected to temporal-frequency analysis using the ensemble empirical mode decomposition (EEMD) method. The results showed that loess landforms are characterized by significant regional topographic relief; the CBP of 14 sample areas exhibited an overall pattern of decreasing TRPs and increasing topographic relief spatial frequencies from south to north. According to the TRPs and topographic relief characteristics, the topographic relief of the Loess Plateau was divided into four types that have obvious regional differences. The findings of this study enrich the theories and methods for digital terrain data analysis of the Loess Plateau. Future study should undertake a more in-depth investigation regarding the complexity of the region and to address the limitations of the EEMD method.

Topographic conditions play an important role in controlling land cover dynamic processes. In this study, remotely sensed data and the geographic information system were applied to analyze the changes in land cover along topographic gradients from 1978 to 2001 in Beijing, a rapidly urbanized mega city in China. The study was based on five periods of land cover maps derived from remotely sensed data: Landsat MSS for 1978, Landsat TM for 1984, 1992, 1996 and 2001, and the digital elevation model (DEM) derived from 1:250,000 topographic map. The whole area was divided into ten land cover types: conifer forest, broadleaf forest, mixed forest, shrub, brushwood, meadow, farmland, built-up, water body and bare land. The results are summarized as follows. (1) Shrub, forest, farmland and builtup consist of the main land cover types of the Beijing area. The most significant land cover change from 1978 to 2001 was the decrease of the farmland and expansion of the builtup area. Farmland decreased from 6354 to 3813 km2 in the 23 years, while the built-up area increased from 421 to 2642 km2. Meanwhile, the coverage of forest increased from 17.2% to 24.7% of the total area. The conversion matrix analysis indicated that the transformation of farmland to the built-up area was the most significant process and afforestation was the primary cause of the replacement of shrub to forest. (2) Topographic conditions are of great importance to the distribution of land cover types and the process of land cover changes. Elevation has an intensive impact on the distribution of land cover types. The area below 100 m mostly consists of farmland and built-up areas, while the area above 100 m is mainly covered by shrub and forest. Shrub has the maximum frequency in areas between 100 and 1000 m, while forest has dominance in areas above 800 m. According to the analysis of land cover changes in different ranges of elevation, the greatest change below 100 m was the process of urbanization. The process of the main land cover change occurred above 100 m was the transformation from shrub to forest. This result was consistent with the vertical change of natural vegetation distribution in Beijing. (3) Slope has a great influence on the distribution of land cover. Farmland and built-up areas are mostly distributed in flat areas, while shrub and forest occupy steeper areas compared with other land cover types. Forest frequency increased with the increasing slope. Land cover changes differed from the slope gradients. In the plain area, the land cover change occurred as the result of urbanization. With the increasing of the slope gradient, afforestation, which converts shrub to forest, was the process of the primary land cover change.

Studies of the Earth's land surface involving scatterometers are becoming an increasingly important application field of microwave remote sensing. Similarly to scatterometer observations of ocean waves, the backscattering coefficient (sigma <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> ) response of land surfaces depends on both the incidence and azimuth angle under which the observations are made. In order to retrieve geophysical parameters from scatterometer data, it is necessary to account for azimuthal-modulation effects of the backscattered signal. In the present study, this paper localizes the regions affected by a strong azimuthal signal dependence when observed with the European Remote Sensing Satellite Scatterometer and the SeaWinds Scatterometer on QuikSCAT (QSCAT). The possible physical reasons for the azimuthal effects, relating the very detailed QSCAT azimuthal response to the spatial orientation of special topographic features and land cover within the sensor footprint, were then discussed. Different methods for normalizing the backscattering coefficient with respect of observation azimuth angle were also proposed and evaluated. First, the mean local incidence angle of the sensor footprint using the shuttle radar topography mission digital elevation model (DEM) were modeled and concluded that the resolution of the DEM is too coarse to characterize most of the observed azimuthal effects. A more effective way of normalizing the backscatter with respect to azimuth is then found to be by using historical backscatter observations to statistically determine the expected backscatter at each observation azimuth and incidence angle as well as time of the year. The efficiency of this method is limited to the availability of past measurements for each location on the Earth

Digital elevation models (DEMs) have shown much potential for use in the extraction of land surface parameters and analysis of the relationship between land surface units and vegetation cover. However, there is lack of studies on the use of SRTM-3 DEM in vegetation studies of mountainous regions. This study is therefore an attempt to relate land surface parameters to vegetation cover in the Obudu mountain region using SRTM-3 DEM and Landsat data. Geomorphometric classification of the land surface was done using an unsupervised ISOCLUST algorithm while vegetation cover classification was done using the supervised approach based on the Maximum Likelihood algorithm. The resultant land surface units and vegetation cover maps were then related using grid-based statistic within the geographic information systems. The overall measure of difference between the two maps yielded a chi-square (d.f. = 24) = 1.9154, p &gt; 0.05. This implies that there is no significant difference between the land surface units and the vegetation cover in the study area. This findings support the use of SRTM-3 for land surface and vegetation mapping where there is no higher quality data, or the cost of obtaining one is inhibitive; a situation that is faced by many developing economies like Nigeria. However, this results should be interpreted and used within the context of the uncertainty that is contained in the SRTM-3 DEM.

The 3D products derived from drone-based remote sensing provide digital surface models (DSMs) with ultra-high resolution at a landscape scale. Some critical land surface processes can be quantitatively identified by using these DSMs. This paper aims to gain three land surface indicators (i.e., vegetation biomass, land surface height, and dust flux) from the 3D product (DSM). Vegetation biomass and dust flux were estimated from the established models (i.e., canopy volume model and wind erosion (WEMO) model) using the structural land surface indicators from DSMs. Land surface height was directly retrieved from DSMs. As a result, a drone-based estimate of these three indicators has a high correlation compared to the field measurement. In the future, these three indicators can be used to describe the occurred land surface processes in drylands.

The TanDEM-X mission has opened a new era in spaceborne radar remote sensing. Its primary objective, the acquisition and generation of a global Digital Elevation Model (DEM) with 12 m horizontal resolution and 2 m relative height accuracy, has&#13;\nbeen completed in autumn 2016. Since the end of 2010 the two twin synthetic aperture radar (SAR) satellites TerraSAR-X and TanDEM-X have been flying in a controlled close orbit formation. The resulting large single-pass SAR interferometer allowed to map the complete Earth’s land surface at least twice. Once the acquisition of the data set for the generation of the global DEM has been completed, the flexibility offered by both SAR instruments in terms of imaging, interferometric, and polarization modes, has been further exploited to serve several secondary mission objectives based on along-track interferometry&#13;\nas well as new bistatic SAR techniques. In this paper, we present the actual status and activities within the mission and the quality assessment of the TanDEM-X final global&#13;\nDEM.

Flow routing algorithm,which is often used to calculate the terrain parameters such as catchment area,specific catchment area,topographic wetness index and so on,is a key point in distributed hydrologic models,soil erosion modeling and other geoscience fields.The results obtained by the flow routing algorithms have distinct effects on hydrological and soil erosion modeling process.So evaluating flow routing algorithms has become a focus in these fields. In this paper,five flow routing algorithms,which are known as D8(Deterministic eight-node),Rho8(Random eight-node),Dinf(D-infinite),MFD(Multiple flow direction algorithm),and DEMON(Digital Elevation Model Networks),are selected to compare and analyze quantitatively on two Digital Elevation Model(DEM) with 5m,10m and 25m horizontal resolution respectively.Coefficient of relative difference,cumulative frequency distributions and XY scatter plot of the Total Catchment Area(TCA) are designed to evaluate the similarities within the selected flow path algorithms.Also the effect of DEM resolution on TCA,obtained from the selected five algorithms,is fully discussed. The results of the paper are summarized as follows: 1) differences between catchment area values estimated by the selected five flow routing algorithms were the greatest along side slopes area,and the differences decreased where the terrain became more convergent or along the channel;2) the difference exists in any DEM with various resolutions,but is more sensitive to the high resolution DEM;and 3) multiple flow direction(MFD) algorithm is more suitable to estimate catchment area in complex terrain area than single flow direction(SFD) algorithm does that.So if the condition permits or accurate results are needed,high resolution DEM and fine MFD should be used effectively.This study also points out that the choice of flow routing algorithm has potentially important consequences for the calculation of upslope contributing areas,sediment transport capacity,topographic wetness,and several other topographic indices.