Analysis of Influence of Terrain Relief Roughness on Dem Accuracy Generated From Lidar in the Czech Republic Territory

Abstract

ABSTRACT:Digital elevation models are today a common part of geographic information systems and derived applications. The way of their creation is varied. It depends on the extent of area, required accuracy, delivery time, financial resources and technologies available. The first model covering the whole territory of the Czech Republic was created already in the early 1980's. Currently, the 5th DEM generation is being finished. Data collection for this model was realized using the airborne laser scanning which allowed creating the DEM of a new generation having the precision up to a decimetre. Model of such a precision expands the possibilities of employing the DEM and it also offers new opportunities for the use of elevation data especially in a domain of modelling the phenomena dependent on highly accurate data. The examples are precise modelling of hydrological phenomena, studying micro-relief objects, modelling the vehicle movement, detecting and describing historical changes of a landscape, designing constructions etc. Due to a nature of the technology used for collecting data and generating DEM, it is assumed that the resulting model achieves lower accuracy in areas covered by vegetation and in built-up areas. Therefore the verification of model accuracy was carried out in five selected areas in Moravia. The network of check points was established using a total station in each area. To determine the reference heights of check points, the known geodetic points whose heights were defined using levelling were used. Up to several thousands of points were surveyed in each area. Individual points were selected according to a different configuration of relief, different surface types, and different vegetation coverage. The sets of deviations were obtained by comparing the DEM 5G heights with reference heights which was followed by verification of tested elevation model. Results of the analysis showed that the model reaches generally higher precision than the declared one in majority of areas. This applies in particular to areas covered by vegetation. By contrast, the larger deviations occurred in relation to the slope of the terrain, in particular in the micro-relief objects. The results are presented in this article.

Canopy Top, Height and Photosynthetic Pigment Estimation Using Parrot Sequoia Multispectral Imagery and the Unmanned Aerial Vehicle (UAV)

“…Tree height detection was based on the Canopy Height Model (CHM) described as the difference between treetop elevation and the underlying ground-level elevation [74]. In this study, the 5 th Digital Elevation Model (DEM) generation (DMR 5G; [61,75]) was used as a source of ground-level information, while treetop information was obtained from the digital surface model (DSM) that was obtained from the Parrot Sequoia camera multispectral images (Seq DSM):…”

Bayesian estimation of glacier surface elevation changes from DEMs

“…• Terrain morphology • Radar penetration for radar datasets • Low-contrast areas and cloud obscuration for optical datasets For both radar and optical sensors, terrain morphology and sample density have been documented as first-order controls of DEM error and uncertainty (Aguilar et al, 2005;Wise, 2011;Mukherjee et al, 2013;Hubacek et al, 2016;Hugonnet et al, 2022). We thus follow the findings of Heritage et al (2009); Wheaton et al (2010); Milan et al (2011) documenting the effect of terrain morphology on DEM errors and use terrain roughness (largest inter-cell difference for a central pixel and its surrounding cell (Darnell et al, 2008)-hereafter denoted r) as the main parameter for the DEM error model.…”

Fire Danger Observed from Space

“…For regional or local effects of topography on fire danger, high-resolution DEMs can also be generated from high-resolution sensors, for example using stereo imagery from Pleiades-1A and 1B (Bagnardi et al 2016;Nasir et al 2015), or from Worldview-2 (Wang et al 2019b). LiDAR data are also usually used for the generation of DEM maps (Hubacek et al 2016) or to fill voids in global DEMs (Schenk et al 2014), although at the moment these sensors are airborne and not located on satellites.…”