Erosion processes in calanchi in the Upper Orcia Valley, Southern Tuscany, Italy based on multitemporal high-resolution terrestrial LiDAR and UAV surveys

Neugirg, Fabian; Stark, Manuel; Kaiser, Andreas; Vláčilová, Markéta; Seta, Marta Della; Vergari, Francesca; Schmidt, Jürgen; Becht, Michael; Haas, Florian

doi:10.1016/j.geomorph.2016.06.027

Cited by 126 publications

(88 citation statements)

References 57 publications

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“…This lower precision was possibly due to less precise GNSS coordinates of check points and sparser point clouds, so it is not directly comparable to our study. Neugirg et al [28] reported repeatability of DTMs having a standard deviation of 6.2 cm which was less precise than our DTMs (2.3 cm).…”

Section: Comparison With Other Studiescontrasting

confidence: 86%

Fine-Resolution Repeat Topographic Surveying of Dryland Landscapes Using UAS-Based Structure-from-Motion Photogrammetry: Assessing Accuracy and Precision against Traditional Ground-Based Erosion Measurements

et al. 2017

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Structure-from-motion (SfM) photogrammetry from unmanned aerial system (UAS) imagery is an emerging tool for repeat topographic surveying of dryland erosion. These methods are particularly appealing due to the ability to cover large landscapes compared to field methods and at reduced costs and finer spatial resolution compared to airborne laser scanning. Accuracy and precision of high-resolution digital terrain models (DTMs) derived from UAS imagery have been explored in many studies, typically by comparing image coordinates to surveyed check points or LiDAR datasets. In addition to traditional check points, this study compared 5 cm resolution DTMs derived from fixed-wing UAS imagery with a traditional ground-based method of measuring soil surface change called erosion bridges. We assessed accuracy by comparing the elevation values between DTMs and erosion bridges along thirty topographic transects each 6.1 m long. Comparisons occurred at two points in time (June 2014, February 2015 which enabled us to assess vertical accuracy with 3314 data points and vertical precision (i.e., repeatability) with 1657 data points. We found strong vertical agreement (accuracy) between the methods (RMSE 2.9 and 3.2 cm in June 2014 and February 2015, respectively) and high vertical precision for the DTMs (RMSE 2.8 cm). Our results from comparing SfM-generated DTMs to check points, and strong agreement with erosion bridge measurements suggests repeat UAS imagery and SfM processing could replace erosion bridges for a more synoptic landscape assessment of shifting soil surfaces for some studies. However, while collecting the UAS imagery and generating the SfM DTMs for this study was faster than collecting erosion bridge measurements, technical challenges related to the need for ground control networks and image processing requirements must be addressed before this technique could be applied effectively to large landscapes.

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Section: Comparison With Other Studiescontrasting

confidence: 86%

Fine-Resolution Repeat Topographic Surveying of Dryland Landscapes Using UAS-Based Structure-from-Motion Photogrammetry: Assessing Accuracy and Precision against Traditional Ground-Based Erosion Measurements

et al. 2017

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“…In geology, the technique has, for example, been used to monitor erosion rates [6] and landslide dynamics [7,8]. In agricultural and horticultural studies, it has proven to be a reliable tool for estimating crop [9,10] or tree [11] height.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Processing of UAV-Based Thermal Imagery

2017

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Abstract:The current standard procedure for aligning thermal imagery with structure-from-motion (SfM) software uses GPS logger data for the initial image location. As input data, all thermal images of the flight are rescaled to cover the same dynamic scale range, but they are not corrected for changes in meteorological conditions during the flight. This standard procedure can give poor results, particularly in datasets with very low contrast between and within images or when mapping very complex 3D structures. To overcome this, three alignment procedures were introduced and tested: camera pre-calibration, correction of thermal imagery for small changes in air temperature, and improved estimation of the initial image position by making use of the alignment of RGB (visual) images. These improvements were tested and evaluated in an agricultural (low temperature contrast data) and an afforestation (complex 3D structure) dataset. In both datasets, the standard alignment procedure failed to align the images properly, either by resulting in point clouds with several gaps (images that were not aligned) or with unrealistic 3D structure. Using initial thermal camera positions derived from RGB image alignment significantly improved thermal image alignment in all datasets. Air temperature correction had a small yet positive impact on image alignment in the low-contrast agricultural dataset, but a minor effect in the afforestation area. The effect of camera calibration on the alignment was limited in both datasets. Still, in both datasets, the combination of all three procedures significantly improved the alignment, in terms of number of aligned images and of alignment quality.

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“…Geological and mainly the lithological factors have the main influence on the evolution of these landforms (FarabegoliAgostini 2000;Neugirg et al 2016) since the occurrence of badlands is significant on soft materials (Caraballo-Arias et al 2015).…”

Section: Introductionmentioning

confidence: 99%

UAS photogrammetry and object-based image analysis (GEOBIA): erosion monitoring at the Kazár badland, Hungary

Bertalan¹,

Túri²,

Szabó³

2016

Landsc. environ.

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A remarkable badland valley is situated near Kazár, NE-Hungary, where rhyolite tuff outcrops as greyish white cliffs and white barren patches. The landform is shaped by gully and rill erosion processes. We performed a preliminary state UAS survey and created a digital surface model and ortophotograph. The flight was operated with manual control in order to perform a more optimal coverage of the aerial images. The overhanging forests induced overexposed photographs due to the higher contrast with the bare tuff surface. The multiresolution segmentation method allowed us to classify the ortophotograph and separate the tuff surface and the vegetation. The applied methods and final datasets in combination with the subsequent surveys will be used for detecting the recent erosional processes of the Kazár badland.

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Erosion processes in calanchi in the Upper Orcia Valley, Southern Tuscany, Italy based on multitemporal high-resolution terrestrial LiDAR and UAV surveys

Cited by 126 publications

References 57 publications

Fine-Resolution Repeat Topographic Surveying of Dryland Landscapes Using UAS-Based Structure-from-Motion Photogrammetry: Assessing Accuracy and Precision against Traditional Ground-Based Erosion Measurements

Fine-Resolution Repeat Topographic Surveying of Dryland Landscapes Using UAS-Based Structure-from-Motion Photogrammetry: Assessing Accuracy and Precision against Traditional Ground-Based Erosion Measurements

Optimizing the Processing of UAV-Based Thermal Imagery

UAS photogrammetry and object-based image analysis (GEOBIA): erosion monitoring at the Kazár badland, Hungary

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