Fusion of high-resolution DEMs for water flow modeling

Abstract

Background: New technologies for terrain reconstruction have increased the availability of topographic data at a broad range of resolutions and spatial extents. The existing digital elevation models (DEMs) can now be updated at a low cost in selected study areas with newer, often higher resolution data using unmanned aerial systems (UAS) or terrestrial sensors. However, differences in spatial coverage and levels of detail often create discontinuities along the newly mapped area boundaries and subsequently lead to artifacts in results of DEM analyses or models of landscape processes. Methods: To generate a seamless updated DEM, we propose a generalized approach to DEM fusion with a smooth transition while preserving important topographic features. The transition is controlled by distance-based weighted averaging along the DEMs' blending overlap with spatially variable width based on elevation differences. Results: We demonstrate the method on two case studies exploring the effects of DEM fusion on water flow modeling in the context of precision agriculture. In the first case study, we update a lidar-based DEM with a fused set of two digital surface models (DSMs) derived from imagery acquired by UAS. In the second application, developed for a tangible geospatial interface, we fuse a georeferenced, physical sand model continuously scanned by a Kinect sensor with a lidar-based DEM of the surrounding watershed in order to computationally simulate and test methods for controlling storm water flow. Conclusions:The results of our experiments demonstrate the importance of seamless, robust fusion for realistic simulation of water flow patterns using multiple high-resolution DEMs.

Vertical Accuracy and Calibration of Digital Elevation Models (DEMs) for the Piranhas-Assu River Basin, Rio Grande do Norte, Brazil

“…A utilização de modelos digitais de elevação (MDEs) com alta acurácia vertical para subsidiar estudos ambientais, tem aumentado significativamente nos ultimos anos, principalmente em estudos com enfoques hidrológicos e modelagem de inundações e enchentes (e.g., Valeriano & Carvalho Júnior, 2003;Pires et al, 2005;Medeiros et al, 2009;Costa et al, 2010;Moura et al, 2014;Ribeiro & Ferreira, 2014;Chen et al, 2017;Petrasova et al, 2017). Entretanto, observa-se uma carência na oferta de modelos com alta acurácia vertical para recortes regionais e locais, principalmente nos modelos disponibilizados gratuitamente.…”

Staring Spotlight TerraSAR-X SAR Interferometry for Identification and Monitoring of Small-Scale Landslide Deformation

“…Baseline-dependent errors in the deformation phase stemming from DSM inaccuracies can be removed via time series analysis [31], but for shorter series, particularly the simple D-InSAR case, it is crucial to use as good a DSM surface as can be constructed. A standard problem is the merging of continuous existing DSMs with lower resolution and accuracy (such as SRTM) with newer higher resolution DSMs (often from photogrammetry and Light Detection and Ranging (LiDAR)) to generate the DSM surface used by the InSAR processing chain [32]. To avoid artifacts in the topographically-corrected phase across where DSMs were joined together, smooth/differentiable transitions are desirable.…”

Quality Features for the Integration of Terrestrial and Uav Images

“…In the field of Geomatics and Geoinformatics, data integration (or sensor integration) is understood as the combination of data acquired with various sensors in order to derive more accurate information which cannot be deduced from one sensor alone (Zhang, 2010;Gasparovic and Malaric, 2012;Petrasova et al, 2017). The integration of heterogeneous (2D or 3D) data acquired with different sensors and platforms is a relevant and open issue in various fields and applications: 3D modelling of man-made environments, heritage documentation, medicine, autonomous driving, etc.…”