A new method for debris flow detection using multi‐temporal DEMs without ground control points

2006 ◽  
Vol 27 (21) ◽  
pp. 4911-4921 ◽  
Author(s):  
Tonggang Zhang ◽  
Minyi Cen ◽  
Guoqing Zhou ◽  
Xinghua Wu
Author(s):  
M. Di Rita ◽  
D. Fugazza ◽  
V. Belloni ◽  
G. Diolaiuti ◽  
M. Scaioni ◽  
...  

Abstract. Alpine glaciers play a key role in our society through the production of freshwater for domestic, industrial and agricultural use. As they are severely affected by climate change, it is of crucial importance to understand their behaviour and monitor their morphological evolution, with the primary aims to estimate ice volume and mass changes. However, the accurate retrieval of glacier morphology changes over time is not an easy task. In this context, the use of Unmanned Aerial Vehicles (UAVs) is of interest to the glaciological community because of their flexibility, fine spatial detail and ease of processing with state-of-the-art software packages, which makes them an ideal candidate to investigate glacier changes. The goal of this work is to assess the accuracy that can be obtained with UAVs observations when comparing volume changes computed from multi-temporal acquisitions on an Alpine glacier, on the basis of a photogrammetric pipeline implemented in Leica Infinity software. The study area is Forni Glacier in Raethian Alps, Italy. Two photogrammetric blocks were acquired in 2014 and 2016 using different UAVs: a fixed-wing drone in 2014 and an in-house multicopter in 2016. Ground Control Points (GCPs) were established only during the 2016 survey which was used to establish the reference datum. Different techniques to co-register the 2014 dataset to the 2016 dataset were applied and compared: 1) using points extracted from the 2016 Dense Point Cloud (DPC) as GCPs for the 2014 DPC generation; 2) shifting and rotating the raw 2014 DPC, using manually digitised common points from the 2014 and 2016 DPCs in Leica Infinity; 3) first manually shifting, then automatically roto-translating with the Iterative Closest Point (ICP) algorithm the raw 2014 DPC in CloudCompare. The investigation shows a good agreement of the three co-registration methods in terms of height and ice volume changes and the potential of UAV data processing with Leica Infinity for glacier monitoring even when the acquisition conditions are problematic (lack of ground control points, sub-optimal image quality).


2020 ◽  
Vol 12 (24) ◽  
pp. 4132
Author(s):  
Miguel Sánchez ◽  
Aurora Cuartero ◽  
Manuel Barrena ◽  
Antonio Plaza

This paper introduces a new method to analyze the positional accuracy of georeferenced satellite images without the use of ground control points. Compared to the traditional method used to carry out this kind of analysis, our approach provides a semiautomatic way to obtain a larger number of control points that satisfy the requirements of current standards regarding the size of the set of sample points, the positional accuracy of such points, the distance between points, and the distribution of points in the sample. Our methodology exploits high quality orthoimages, such as those provided by the Aerial Orthography National Plan (PNOA)—developed by the Spanish National Geographic Institute—and has been tested on spatial data from Landsat 8. Our method works under the current international standard (ASPRS 2014) and exhibits similar performance than other well-known methods to analyze the positional accuracy of georeferenced images based on the use of independent ground control points. More specifically, the positional accuracy achieved for a Landsat 8 dataset evaluated by the traditional method is 5.22 ± 1.95 m, and when evaluated with the proposed method, it exhibits a typical accuracy of 5.76 ± 0.50 m. Our experimental results confirm that the method is equally effective and less expensive than other available methods to analyze the positional accuracy of satellite images.


2011 ◽  
Vol 48 (3) ◽  
pp. 416-431 ◽  
Author(s):  
Ryan R. Jensen ◽  
Andrew J. Hardin ◽  
Perry J. Hardin ◽  
John R. Jensen

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