Comparison of low-altitude UAV photogrammetry with terrestrial laser scanning as data-source methods for terrain covered in low vegetation

Gruszczyński, Wojciech; Matwij, Wojciech; Ćwiąkała, Paweł

doi:10.1016/j.isprsjprs.2017.02.015

Cited by 66 publications

(57 citation statements)

References 26 publications

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“…This uncertainty depends directly on the method used to al., 2016; Khan et al, 2014) are often utilized to make observations of surface deformation. Unmanned aerial vehicles (UAVs) (Gruszczyński et al, 2017) have also been employed to conduct studies on surface deformation. However, in Poland and many other countries, conventional measurements from points stabilized on observation lines remain the most common technique for characterizing land surface changes.…”

Section: Introductionmentioning

confidence: 99%

Influence of model parameter uncerainties on forecasted subsidence

Gruszczyński¹

2018

Acta Geodynamica Et Geomaterialia

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Surface deformation due to underground exploitation affects the safety of overlying structures. Forecasting can predict risks to surface structures and facilitates actions designed to improve their resilience and reduce the potential impact of mining activities. However, forecasting accuracy is limited. Therefore, in practice, model parameters are determined within a certain margin to ensure that critical values of deformation indicators for surface objects are not exceeded. For economic reasons, it is important to minimize these margins while also ensuring that safety is maintained. One important factor influencing forecasting accuracy is the uncertainty in deformation model parameters used for calculations. Therefore, it is critical to adopt an appropriate methodology for determining and addressing the uncertainties in deformation model parameters used in forecasting. This study presents methods for estimating the Knothe's model parameters needed to forecast surface deformation caused by underground mining and defining the uncertainties in those forecasts. Depending on the parameter uncertainties, one of two methods for propagation is proposed: the Monte Carlo method or the law of propagation of uncertainty. Using this approach, it is possible to account for uncertainty and reduce forecast margins. A case study of hard coal mining in the Upper Silesian Coal Basin region of Poland is presented. ARTICLE INFO

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Section: Introductionmentioning

confidence: 99%

Influence of model parameter uncerainties on forecasted subsidence

Gruszczyński¹

2018

Acta Geodynamica Et Geomaterialia

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“…3D model, in the shape of point cloud or grid models, is created from a group of images done by hand held non-calibrated camera [16], [17]. Structure-from-motion algorithms allow the recognition of high-contrast features (of the object), following their movement through series of pictures and produce a sparse point cloud basing on features placement in the image series [16].…”

Section: Survey Methodologymentioning

confidence: 99%

“…In order to add scale and global coordinates, similarly as with laser scanners, easy to identify points of known coordinates must be given. In case of photogrammetric survey those points are almost always black and white chessboards [16], [17].…”

Section: Survey Methodologymentioning

confidence: 99%

“…Agisoft automatically sorts uploaded pictures into smaller groups that consist of images done with the same camera parameters, focal length etc. [17] The next process, Align photos, allows finding the same elements on two or more images. This results in a sparse point cloud of complementary points and visualisation of estimated camera location.…”

Section: Data Pre-processingmentioning

confidence: 99%

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The measurements of snow cover volume in the area of Szrenicki Cirque

2018

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Abstract. The purpose of this project is to determine a fast way of calculating the volume and distribution of snow mantle, which is located in wide terrain concavities in mountain areas. Our study area was so-called Szrenicki Cirque (Kocioł Szrenicki), which is the nival recess, located in Karkonosze Mountains, Poland. We analyzed modern technologies, that are designed to generate 3D-models: terrain laser scanning and close-range photogrammetry (including structure-from-motion technique). There were two major reasons for our research. First, analyzing if a structure-frommotion based software is capable of creating a 3D model of snow cover since potential tie points for adjoin pictures are scarce. The second was to establish the quality and accuracy of this model in relation to potentially more accurate terrestrial laser scanning results. An important issue was also is to estimate the fastest, simplest and least expensive methodology that can be implemented as a daily task of Karkonosze National Park workers. A proper, fast, safe and accurate method of calculating the snow cover volume would incise the safety and avalanche risk evaluation in the vicinity of Karkonosze Mountains. In addition, the developed method can be used to monitor the risk of local spring floods.

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“…As a result, the low-cost system and automated mapping software have allowed users to conveniently apply UAV to various applications including change detection [31], leaf area index (LAI) mapping [32], vegetation cover estimation [33], vegetation species mapping [34], water stress mapping [35], power line surveys [36], building detection and damage assessment [37,38], and comparison with terrestrial LiDAR surveys [39]. For more details, Watts et al [40] gave a thorough review about UAV technologies with an emphasis on hardware and technique specs.…”

Section: Introductionmentioning

confidence: 99%

Photogrammetric UAV Mapping of Terrain under Dense Coastal Vegetation: An Object-Oriented Classification Ensemble Algorithm for Classification and Terrain Correction

et al. 2017

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Photogrammetric UAV sees a surge in use for high-resolution mapping, but its use to map terrain under dense vegetation cover remains challenging due to a lack of exposed ground surfaces. This paper presents a novel object-oriented classification ensemble algorithm to leverage height, texture and contextual information of UAV data to improve landscape classification and terrain estimation. Its implementation incorporates multiple heuristics, such as multi-input machine learning-based classification, object-oriented ensemble, and integration of UAV and GPS surveys for terrain correction. Experiments based on a densely vegetated wetland restoration site showed classification improvement from 83.98% to 96.12% in overall accuracy and from 0.7806 to 0.947 in kappa value. Use of standard and existing UAV terrain mapping algorithms and software produced reliable digital terrain model only over exposed bare grounds (mean error = −0.019 m and RMSE = 0.035 m) but severely overestimated the terrain by~80% of mean vegetation height in vegetated areas. The terrain correction method successfully reduced the mean error from 0.302 m to −0.002 m (RMSE from 0.342 m to 0.177 m) in low vegetation and from 1.305 m to 0.057 m (RMSE from 1.399 m to 0.550 m) in tall vegetation. Overall, this research validated a feasible solution to integrate UAV and RTK GPS for terrain mapping in densely vegetated environments.

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Comparison of low-altitude UAV photogrammetry with terrestrial laser scanning as data-source methods for terrain covered in low vegetation

Cited by 66 publications

References 26 publications

Influence of model parameter uncerainties on forecasted subsidence

Influence of model parameter uncerainties on forecasted subsidence

The measurements of snow cover volume in the area of Szrenicki Cirque

Photogrammetric UAV Mapping of Terrain under Dense Coastal Vegetation: An Object-Oriented Classification Ensemble Algorithm for Classification and Terrain Correction

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