Registration of Uav Data and Als Data Using Point to Dem Distances for Bathymetric Change Detection

Boerner, R.; Xu, Yangsheng; Hoegner, L.; Stilla, Uwe

doi:10.5194/isprs-archives-xlii-1-51-2018

Cited by 2 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The following parameters were measured by the GNSS receiver: the position and elevation of the channel and the water's edge in the rivers, the elevation of the lower and upper reaches of the beaver dams, their width and their length. This method has been tested by various researchers ( [30][31][32]38], etc. ).…”

Section: Methodsmentioning

confidence: 99%

Channel Gradient as a Factor in the Distribution of Beaver Dams and Ponds on Small Rivers: A Case Study in the Northern Extremity of the Volga Upland, the East European Plain

Sharifullin

Gusarov

Lavrova

et al. 2023

Water

View full text Add to dashboard Cite

A very noticeable increase in the beaver population (mainly the Eurasian beaver (Castor fiber L.)) in the East European Plain since the end of the 20th century through reintroduction contributed to the emergence of a large number of beaver dams and ponds, which resulted in the transformation of the channels and the hydrological regime of small rivers. In this paper, for the first time in the Middle Volga region, regularities in the distribution of beaver dams and ponds on small rivers were revealed based on a topographic survey of the river channel, regulated by beaver activity in recent decades. The materials of the paper were obtained during field (geodetic) work in August and September of 2022 in the valleys of two small rivers typical for the north of the Volga Upland, Morkváshinka River (16.6 km) and Morkváshka River (7.8 km), with subsequent statistical processing. According to the results obtained, all the dams we encountered were located in the sections of their channels with relatively small (up to 1%) and medium (1–3%) average gradients. In the upper reaches of the rivers, the expansion of beaver activity is limited to the large gradients of their channels. Along the entire length, there is an alternation of sectors with a “natural” (not regulated by beavers) channel and a cascade of beaver ponds. Drained (at the time of the survey) sectors without dams, as well as those with preserved and destroyed dams, were also identified on the Morkváshinka River. On the whole, 26–48% of the length of the studied rivers has been transformed by beaver activity. The average number of dams per kilometer of the watercourse is 3.7–5.1. The most favorable channel gradients for beaver activity are 3.6–16.6 m/km, or 0.36–1.66%. The average gradient of the channel, above which no beaver dams and related ponds were found in these rivers, is 30 m/km, or 3%. An increase in the channel gradient upstream of the rivers is reflected in the size of beaver dams and ponds. The beaver dams and ponds are slightly larger in the sections of the rivers with a relatively small average gradient than in the sections with a medium average gradient of the channel.

show abstract

Section: Methodsmentioning

confidence: 99%

Channel Gradient as a Factor in the Distribution of Beaver Dams and Ponds on Small Rivers: A Case Study in the Northern Extremity of the Volga Upland, the East European Plain

Sharifullin

Gusarov

Lavrova

et al. 2023

Water

View full text Add to dashboard Cite

show abstract

“…with: In contrast to memory and time-consuming global optimization [3], this local interpolation estimates the height of each cell with inverse distance weighting. This allows calculation of large-scale grids in an adaptive manner and avoids the squared complexity of the global optimization.…”

Section: Methodsmentioning

confidence: 99%

“…The calculation method can generate DEMs for large-scale data such as the whole flight strip and saves memory and computation time. The evaluation in the pilot study [3] was done only on a single scene which shows degrees of freedom along the rotation angle around the z-axis and the translation along the x-axis. To acquire knowledge about the performance of the proposed method in different scenarios and at different level of detail of the data, this paper shows a more complex evaluation of the method.…”

Section: Introductionmentioning

confidence: 99%

Registration of Multi-Sensor Bathymetric Point Clouds in Rural Areas Using Point-to-Grid Distances

Boerner

Baran³

et al. 2019

IJGI

Self Cite

View full text Add to dashboard Cite

This article proposes a method for registration of two different point clouds with different point densities and noise recorded by airborne sensors in rural areas. In particular, multi-sensor point clouds with different point densities are considered. The proposed method is marker-less and uses segmented ground areas for registration.Therefore, the proposed approach offers the possibility to fuse point clouds of different sensors in rural areas within an accuracy of fine registration. In general, such registration is solved with extensive use of control points. The source point cloud is used to calculate a DEM of the ground which is further used to calculate point to raster distances of all points of the target point cloud. Furthermore, each cell of the raster DEM gets a height variance, further addressed as reconstruction accuracy, by calculating the grid. An outlier removal based on a dynamic threshold of distances is used to gain more robustness against noise and small geometry variations. The transformation parameters are calculated with an iterative least-squares optimization of the distances weighted with respect to the reconstruction accuracies of the grid. Evaluations consider two flight campaigns of the Mangfall area inBavaria, Germany, taken with different airborne LiDAR sensors with different point density. The accuracy of the proposed approach is evaluated on the whole flight strip of approximately eight square kilometers as well as on selected scenes in a closer look. For all scenes, it obtained an accuracy of rotation parameters below one tenth degrees and accuracy of translation parameters below the point spacing and chosen cell size of the raster. Furthermore, the possibility of registration of airborne LiDAR and photogrammetric point clouds from UAV taken images is shown with a similar result. The evaluation also shows the robustness of the approach in scenes where a classical iterative closest point (ICP) fails.

show abstract

Registration of Uav Data and Als Data Using Point to Dem Distances for Bathymetric Change Detection

Cited by 2 publications

References 15 publications

Channel Gradient as a Factor in the Distribution of Beaver Dams and Ponds on Small Rivers: A Case Study in the Northern Extremity of the Volga Upland, the East European Plain

Channel Gradient as a Factor in the Distribution of Beaver Dams and Ponds on Small Rivers: A Case Study in the Northern Extremity of the Volga Upland, the East European Plain

Registration of Multi-Sensor Bathymetric Point Clouds in Rural Areas Using Point-to-Grid Distances

Contact Info

Product

Resources

About