Use Of Digital Elevation Model Data Research Articles

Lake water storage estimation method based on similar characteristics of above-water and underwater topography

As the main supply of water for human activity, lakes make up 87% of all the liquid freshwater on the surface of the Earth and are essential to ecosystems. Water storage is a crucial characteristic in lake hydrological processes, with applications in scientific disciplines such as lake nutrient control, underwater aquatic evaluation, and so on. Due to environmental factors, including expenses associated with monitoring, and natural environmental circumstances, it is unknown how much water is stored in total within majority of lakes across the world. Based on the presumption of topographical continuity above and below the water’s surface, this study presents a 3-dimensional (3-D) lake topography simulation approach to estimate water storage via the use of a digital elevation model (DEM) of the lake basin. To assess the accuracy of this method, samples from twelve lakes on the Qinghai-Tibet Plateau were examined using collected underwater data. In the studied lakes, the findings showed a relative error of −11.99% for the total water storage simulation, with average and maximum relative errors for water depth of −19.50% and −5.64%, respectively. This approach, which simply needs the use of DEM data, makes it possible to estimate water storage in areas where there is little to no documented subsurface topography data.

Applying multidate Sentinel-2 data for forest-type classification in complex broadleaf forest stands

AbstractBiodiversity assessment and forest management require accurate tree species maps, which can be provided by remote sensing. Whereas the application of high-spatial resolution remote sensing data is constrained by high costs, Sentinel-2 (S2) satellites provide free imagery with appropriate spatial, spectral and temporal resolutions for mapping of various forest traits across larger spatial scales. Here we assessed the potential of multidate S2 as well as a Digital Elevation Model (DEM) in classifying tree species across a highly structured and heterogeneous broadleaf forest ecosystem in the Hyrcanian zone of northern Iran. We applied multidate S2 and DEM data as input to a variable selection using random forests algorithm for feature reduction. Ten forest types were classified using random forest algorithm and to evaluate the results we computed area-adjusted confusion matrices. Classifications based on single-date S2 data reached overall accuracies of 67–74 per cent, whereas results for multidate S2 images increased the accuracy by ~28 per cent. Joint use of DEM data along with multidate S2 images showed improvement of overall accuracy by ~3 per cent. In addition, we studied the effect of topographic correction of S2 data on classification performance. The results imply that applying topographically corrected imagery had no significant effect on the classification accuracy. Our results demonstrate the high potential of freely available multisource remotely sensed data for broadleaf tree species classification across complex broad-leaved forest landscapes.

Sensitivity Assessment of Spatial Resolution Difference in DEM for Soil Erosion Estimation Based on UAV Observations: An Experiment on Agriculture Terraces in the Middle Hill of Nepal

Soil erosion in the agricultural area of a hill slope is a fundamental issue for crop productivity and environmental sustainability. Building terrace is a very popular way to control soil erosion, and accurate assessment of the soil erosion rate is important for sustainable agriculture and environmental management. Currently, many soil erosion estimations are mainly based on the freely available medium or coarse resolution digital elevation model (DEM) data that neglect micro topographic modification of the agriculture terraces. The development of unmanned aerial vehicle (UAV) technology enables the development of high-resolution (centimeter level) DEM to present accurate topographic features. To demonstrate the sensitivity of soil erosion estimates to DEM resolution at this high-resolution level, this study tries to evaluate soil erosion estimation in the Middle Hill agriculture terraces in Nepal based on UAV derived high-resolution (5 × 5 cm) DEM data and make a comparative study for the estimates by using the DEM data aggregated into different spatial resolutions (5 × 5 cm to 10 × 10 m). Firstly, slope gradient, slope length, and topographic factors were calculated at different resolutions. Then, the revised universal soil loss estimation (RUSLE) model was applied to estimate soil erosion rates with the derived LS factor at different resolutions. The results indicated that there was higher change rate in slope gradient, slope length, LS factor, and soil erosion rate when using DEM data with resolution from 5 × 5 cm to 2 × 2 m than using coarser DEM data. A power trend line was effectively used to present the relationship between soil erosion rate and DEM resolution. The findings indicated that soil erosion estimates are highly sensitive to DEM resolution (from 5 × 5 cm to 2 × 2 m), and the changes become relatively stable from 2 × 2 m. The use of DEM data with pixel size larger than 2 × 2 m cannot detect the micro topography. With the insights about the influencing mechanism of DEM resolution on soil erosion estimates, this study provides important suggestions for appropriate DEM data selection that should be investigated first for accurate soil erosion estimation.

Digital elevation models (DEM) used to assess soil erosion risks: a case study in Boyaca, Colombia

The objective of this research was to develop a model for assessing the risk of erosion, exploring the potential of DEMs from SRTM, ASTER, ALOS PALSAR and one made with interpolation of a 1:25,000 contour map to calculate the variables of the relief that have greater impact on erosion. Several geomorphometric parameters, such as slope, aspect, profile and plan curvature, topographic wetness index, stream power index, and sediment transport capacity were computed from the DEM's elevation, some fuzzy logic functions proposed to evaluate the incidence of each parameter on erosion risk in a mountainous area of Colombia. The results showed that the use of DEM data is a relatively easy, uncostly method to identify, in a qualitative way, the risk of erosion and contribute to the enhancement of erosion information that is obtained with conventional general soil surveys.

Restoration of Information Obscured by Mountainous Shadows Through Landsat TM/ETM+ Images Without the Use of DEM Data: A New Method

Shadows in remotely sensed imagery occur when objects totally or partially occlude direct light from a source of illumination, generating great difficulty in land cover interpretation and classification because of the loss of spectral information of shaded pixels. In a mountainous environment with rough terrain, shadows are especially pronounced due to the differentiation of direct illumination between sunny and shady slopes. Topographic correction methods, which are widely used to adjust for differences in solar incidence angles, can partly alleviate the impacts of shadows. However, there are two limitations: one is that the contemporary topographic corrections have little effect on areas that have very low incidence angles and areas that are completely without direct solar illumination (cast shadow); another is that their effectiveness is restricted by the data quality and completeness, spatial resolution, and elevation accuracy of the Digital Elevation Model (DEM) data, which is not currently available in all parts of the world. Thus, noise and errors may be introduced in topographic correction during resampling and geometric registration of the target image. This paper proposes a new approach to restore the radiometric information of mountainous cast shadows using a spectral processing technique called “continuum removal” (CR) without the aid of DEM. The CR-based approach makes full use of the spectral information derived from both the shaded pixels and their neighboring nonshaded pixels of the same land cover type. Several Landsat TM images were used to assess the performance of the proposed method. Results indicated that the proposed method can effectively restore the spectral values of shaded pixels more accurately than the ATCOR_3 correction method, especially for very low incidence angle areas and cast shadows. By comparing data values of shaded pixels with nonshaded pixels (pure reference pixels) of their same class, images processed by the proposed method had the lowest average root mean square error (RMSE) between them in visible, NIR and SWIR bands, followed by the ATCOR_3 correction method and the original image. In addition, the proposed method achieved the best classification accuracy, higher than those from the original test image and the ATCOR_3 corrected image generated using 90 m or 30 m spatial resolution DEM. Therefore, the Continuum Removal method is a better alternative for restoring objects obscured by mountainous shadow when adequate DEM data are unavailable and the quality of DEM cannot satisfy the requirements of topographic correction algorithms.

Implications of mountain shading on calculating energy for snowmelt using unstructured triangular meshes

AbstractIn many parts of the world, snowmelt energetics is dominated by solar irradiance. This is particularly the case in the Canadian Rocky Mountains, where clear skies dominate the winter and spring. In mountainous regions, solar irradiance at the snow surface is not only affected by solar angles, atmospheric transmittance, the slope and aspect of immediate topography but also by shadows from surrounding terrain. Accumulation of errors in estimating solar irradiation can lead to significant errors in calculating the timing and rate of snowmelt owing to the seasonal storage of internal energy in the snowpack. Gridded methods, which are often used to estimate solar irradiance in complex terrain, work best with high‐resolution digital elevation models (DEMs), such as those produced using Light Detection and Ranging. However, such methods also introduce errors caused by the rigid nature of the mesh as well as limiting the ability to represent basin characteristics. Unstructured triangular meshes are more efficient in their use of DEM data than fixed grids when producing solar irradiance information for spatially distributed snowmelt calculations, and they do not suffer from the artefact problems of a gridded DEM. This paper demonstrates the increased accuracy of using a horizon‐shading algorithm model with an unstructured mesh versus standard self‐shading algorithms. A systematic over‐prediction in irradiance is observed when only self‐shadows are considered. The modelled results are diagnosed by comparison to measurements of mountain shadows by time‐lapse digital cameras and solar irradiance by a network of radiometers in Marmot Creek Research Basin, Alberta, Canada. Results show that, depending on the depth and aspect of the snowpack of the Mount Allan cirque, 6.0 to 66.4% of the pre‐melt snowpack could be prematurely melted. On average at a basin scale, there was a 14.4‐mm SWE difference in equivalent melt energy between the two shading algorithms with maximum differences over 100% of the total annual snowfall. Copyright © 2012 John Wiley & Sons, Ltd.

Sediment–landform assemblages and digital elevation data: Testing an improved methodology for the assessment of sand and gravel aggregate resources in north-western Britain

This paper uses the results of a series of sand and gravel resource assessment surveys to refine and improve the methods used for identifying aggregate resources in north-western Britain. Geomorphological surveys of the Cors Geirch and Rhosesmor areas of north Wales and the lower Ribble valley in Lancashire, carried out by 1:10,000 scale field mapping, are compared to resource assessments made using high resolution Digital Elevation Models (DEMs) of the same ground. The DEMs proved useful tools for remote geomorphological survey, but precision was significantly better for the Intermap NEXTMAP and, particularly, the Environment Agency LiDAR dataset. Only the LiDAR data revealed with any clarity small-scale and low relief features, for example scroll-bar palaeochannels on river terraces. The identification of sediment–landform assemblage zones from geomorphological survey permits the identification of the primary sedimentary process responsible for each assemblage and the attachment of an ideal sedimentary facies model to it. Each model can then be used to predict sedimentary composition, the optimal location of boreholes and the location of resource blocks. Combining the use of DEM data and the Spatial Analyst toolset within ArcGIS™ (ESRI) allows rapid accurate delineation of landforms and the use of cut and fill functions allows more accurate estimation of the volume of resources than the traditional calculations based on area and average thickness. These new methods improve the accuracy and cost-effectiveness of geomorphology-based aggregate assessments, notably where the mineral-bodies are concealed beneath minimal overburden and produce characteristic landforms.

Digital elevation model resolution: effects on terrain attribute calculation and quantitative soil-landscape modeling

The accuracy of digital elevation models (DEM) and DEM-derived products depends on several factors, including the horizontal resolution and vertical precision at which the elevation data are represented, and the source of the elevation data. This accuracy becomes increasingly important as we extend the use of DEM data for spatial prediction of soil attributes. Our objective was to compare terrain attributes and quantitative soil-landscape models derived from grid-based DEM represented at different horizontal resolutions (10 and 30 m), represented at different vertical precisions (0.1 and 1 m), and acquired from different sources. Decreasing the horizontal resolution of the field survey DEM produced lower slope gradients on steeper slopes, steeper slope gradients on flatter slopes, narrower ranges in curvatures, larger specific catchment areas in upper landscape positions, and lower specific catchment areas values in lower landscape positions. Overall, certain landscape features were less discernible on the 30-m DEM than on the 10-m DEM. Decreased vertical precision produced a large proportion of points with zero slope gradient and zero slope curvature, and a large number of steeply sloping and more highly curved areas. Differences among DEM from different sources were more significant, with less accurate representation of depressions and drainage pathways with the USGS DEM as compared to the field survey DEM. Empirical models developed from different DEM included similar predictive terrain attributes, and were equally successful in predicting A-horizon depth (AHD) in the validation data set.

Determination of actual snow-covered area using Landsat TM and digital elevation model data in Glacier National Park, Montana

AbstractIn the future, data from the moderate resolution imaging spectroradiometer (MODIS) will be employed to map snow in an automated environment at a resolution of 250 m to 1 km. Using Landsat thematic mapper (TM) data, an algorithm, SNOMAP, has been developed to map snow-covered area. This algorithm will be used, with appropriate modification, with MODIS data following the launch of the first Earth Observing System (EOS) platform in 1998. SNOMAP has been shown to be successful in mapping snow in a variety of areas using TM data. However, significant errors may be present in mountainous areas due to effects of topography. To increase the accuracy of mapping global snow-covered area in the future using MODIS data, digital elevation model (DEM) data have been registered to TM data for parts of Glacier National Park, Montana, so that snow cover on mountain slopes can be mapped. This paper shows that the use of DEM data registered to TM data increases the accuracy of mapping snow-covered area. Using SNOMAP on a subscene within the 14 March 1991 TM scene of northwestern Montana, 215 km2 of snow is mapped when TM data are used alone to map the snow cover. We show that about 1062 km2 of snow are actually present as measured when the TM and DEM data are registered. Approximately five times more snow is present when the effects of topography are considered for this subscene, which is in a rugged area in Glacier National Park. A simple model has been developed to determine the relationship between terrain relief and the amount of correction that must be applied to map actual snow-covered area in Glacier National Park using satellite data alone.