scholarly journals GLACIER VOLUME CHANGE MONITORING FROM UAV OBSERVATIONS: ISSUES AND POTENTIALS OF STATE-OF-THE-ART TECHNIQUES

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).

Author(s):  
Kazuo Oda ◽  
Satoko Hattori ◽  
Toko Takayama

This paper proposes movement detection method between point clouds created by SFM software, without setting any onsite georeferenced points. SfM software, like Smart3DCaputure, PhotoScan, and Pix4D, are convenient for non-professional operator of photogrammetry, because these systems require simply specification of sequence of photos and output point clouds with colour index which corresponds to the colour of original image pixel where the point is projected. SfM software can execute aerial triangulation and create dense point clouds fully automatically. This is useful when monitoring motion of unstable slopes, or loos rocks in slopes along roads or railroads. Most of existing method, however, uses mesh-based DSM for comparing point clouds before/after movement and it cannot be applied in such cases that part of slopes forms overhangs. And in some cases movement is smaller than precision of ground control points and registering two point clouds with GCP is not appropriate. Change detection method in this paper adopts CCICP (Classification and Combined ICP) algorithm for registering point clouds before / after movement. The CCICP algorithm is a type of ICP (Iterative Closest Points) which minimizes point-to-plane, and point-to-point distances, simultaneously, and also reject incorrect correspondences based on point classification by PCA (Principle Component Analysis). Precision test shows that CCICP method can register two point clouds up to the 1 pixel size order in original images. Ground control points set in site are useful for initial setting of two point clouds. If there are no GCPs in site of slopes, initial setting is achieved by measuring feature points as ground control points in the point clouds before movement, and creating point clouds after movement with these ground control points. When the motion is rigid transformation, in case that a loose Rock is moving in slope, motion including rotation can be analysed by executing CCICP for a loose rock and background slope independently.


Author(s):  
Kazuo Oda ◽  
Satoko Hattori ◽  
Toko Takayama

This paper proposes movement detection method between point clouds created by SFM software, without setting any onsite georeferenced points. SfM software, like Smart3DCaputure, PhotoScan, and Pix4D, are convenient for non-professional operator of photogrammetry, because these systems require simply specification of sequence of photos and output point clouds with colour index which corresponds to the colour of original image pixel where the point is projected. SfM software can execute aerial triangulation and create dense point clouds fully automatically. This is useful when monitoring motion of unstable slopes, or loos rocks in slopes along roads or railroads. Most of existing method, however, uses mesh-based DSM for comparing point clouds before/after movement and it cannot be applied in such cases that part of slopes forms overhangs. And in some cases movement is smaller than precision of ground control points and registering two point clouds with GCP is not appropriate. Change detection method in this paper adopts CCICP (Classification and Combined ICP) algorithm for registering point clouds before / after movement. The CCICP algorithm is a type of ICP (Iterative Closest Points) which minimizes point-to-plane, and point-to-point distances, simultaneously, and also reject incorrect correspondences based on point classification by PCA (Principle Component Analysis). Precision test shows that CCICP method can register two point clouds up to the 1 pixel size order in original images. Ground control points set in site are useful for initial setting of two point clouds. If there are no GCPs in site of slopes, initial setting is achieved by measuring feature points as ground control points in the point clouds before movement, and creating point clouds after movement with these ground control points. When the motion is rigid transformation, in case that a loose Rock is moving in slope, motion including rotation can be analysed by executing CCICP for a loose rock and background slope independently.


2012 ◽  
Vol 9 (1) ◽  
pp. 85-89 ◽  
Author(s):  
Chen Siying ◽  
Ma Hongchao ◽  
Zhang Yinchao ◽  
Zhong Liang ◽  
Xu Jixian ◽  
...  

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