Accuracy assessment of airborne photogrammetrically derived high-resolution digital elevation models in a high mountain environment

Müller, Johann; Gärtner‐Roer, Isabelle; Thee, P.; Ginzler, Christian

doi:10.1016/j.isprsjprs.2014.09.015

Cited by 43 publications

(43 citation statements)

References 27 publications

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“…Looking at the influence of topography, a decrease in accuracy with increasing slope inclination was found. This is in line with a study by Bühler et al [42] in high alpine terrain comparing ADS80 stereo image elevation values and ALS data and with the findings of Müller et al [43] in a high mountain environment. Contrary to our expectations, no difference in accuracy was found for different aspect classes.…”

Section: Accuracy Of the Digital Surface Model (Dsm)supporting

confidence: 92%

Countrywide Stereo-Image Matching for Updating Digital Surface Models in the Framework of the Swiss National Forest Inventory

2015

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Surface models provide key knowledge of the 3-d structure of forests. Aerial stereo imagery acquired during routine mapping campaigns covering the whole of Switzerland (41,285 km 2 ), offers a potential data source to calculate digital surface models (DSMs). We present an automated workflow to generate a nationwide DSM with a resolution of 1 × 1 m based on photogrammetric image matching. A canopy height model (CHM) is derived in combination with an existing digital terrain model (DTM). ADS40/ADS80 summer images from 2007 to 2012 were used for stereo matching, with ground sample distances (GSD) of 0.25 m in lowlands and 0.5 m in high mountain areas. Two different image matching strategies for DSM calculation were applied: one optimized for single features such as trees and for abrupt changes in elevation such as steep rocks, and another optimized for homogeneous areas such as meadows or glaciers. The country was divided into 165,500 blocks, which were matched independently using an automated workflow. The completeness of successfully matched points was high, 97.9%. To test the accuracy of the derived DSM, two reference data sets were used: (1) topographic survey points (n = 198) and (2) stereo measurements (n = 195,784) within the framework of the Swiss National Forest Inventory (NFI), in order to distinguish various land cover types. An overall median accuracy of 0.04 m with a normalized median absolute deviation (NMAD) of 0.32 m was found using the topographic survey points. The agreement between the stereo measurements and the values of the DSM revealed acceptable NMAD values between 1.76 and 3.94 m for forested areas. A good correlation (Pearson's r = 0.83) was found between terrestrially OPEN ACCESSRemote Sens. 2015, 7 4344 measured tree height (n = 3109) and the height derived from the CHM. Optimized image matching strategies, an automatic workflow and acceptable computation time mean that the presented approach is suitable for operational usage at the nationwide extent. The CHM will be used to reduce estimation errors of different forest characteristics in the Swiss NFI and has high potential for change detection assessments, since an aerial stereo imagery update is available every six years.

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Section: Accuracy Of the Digital Surface Model (Dsm)supporting

confidence: 92%

Countrywide Stereo-Image Matching for Updating Digital Surface Models in the Framework of the Swiss National Forest Inventory

2015

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“…For the four periods of data generated using aerial photogrammetry, the errors of σ total ranged from 0.22 m to 0.40 m ( Table 3). The comparison with the image GSD values indicated that σ total error was 0.98~2.1 times that of GSD, which was in good agreement with similar previous studies [37,38]. This suggested that the precision of generated DTM data in this work based on historical aerial images and UAV images using aerial photogrammetry was suitable for landslide analysis.…”

Section: Use Of Data and Error Estimatessupporting

confidence: 90%

“…Then their differences were the errors [37], as shown in Table 3. Errors in this paper were all given in the form of root-mean-square error (RMSE), which is a commonly used error expression [38,39]. The terrain change could be found by comparing data from different periods.…”

Section: Error Estimationmentioning

confidence: 99%

“…For terrain data produced using aerial photogrammetry, the elevation error is, generally speaking, about 0.2~1.3 m [37,38]. In a normal processing approach with sufficient ground control points, errors are mostly determined by the value of GSD [38]. Elevation error of ALS, on the other hand, is about 0.15~0.3 m [24,39,47].…”

Section: Use Of Data and Error Estimatesmentioning

confidence: 99%

“…This is especially true for those flight parameters regarding the target area that would affect data resolution and errors, such as flying height, flying speed, flight direction, and overlapping strips. For terrain data produced using aerial photogrammetry, the elevation error is, generally speaking, about 0.2~1.3 m [37,38]. In a normal processing approach with sufficient ground control points, errors are mostly determined by the value of GSD [38].…”

Section: Use Of Data and Error Estimatesmentioning

confidence: 99%

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Digital Elevation Model Differencing and Error Estimation from Multiple Sources: A Case Study from the Meiyuan Shan Landslide in Taiwan

Hsieh

Chan

2016

Remote Sensing

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In this study, six different periods of digital terrain model (DTM) data obtained from various flight vehicles by using the techniques of aerial photogrammetry, airborne LiDAR (ALS), and unmanned aerial vehicles (UAV) were adopted to discuss the errors and applications of these techniques. Error estimation provides critical information for DTM data users. This study conducted error estimation from the perspective of general users for mountain/forest areas with poor traffic accessibility using limited information, including error reports obtained from the data generation process and comparison errors of terrain elevations. Our results suggested that the precision of the DTM data generated in this work using different aircrafts and generation techniques is suitable for landslide analysis. Especially in mountainous and densely vegetated areas, data generated by ALS can be used as a benchmark to solve the problem of insufficient control points. Based on DEM differencing of multiple periods, this study suggests that sediment delivery rate decreased each year and was affected by heavy rainfall during each period for the Meiyuan Shan landslide area. Multi-period aerial photogrammetry and ALS can be effectively applied after the landslide disaster for monitoring the terrain changes of the downstream river channel and their potential impacts.

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From consumer to enterprise grade: How the choice of four UAS impacts point cloud quality

Stark

Heckmann

Piermattei

et al. 2021

Earth Surf Processes Landf

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Uncrewed aerial systems (UAS), combined with structure-from-motion photogrammetry, has already proven to be very powerful for a wide range of geoscience applications and different types of UAS are used for scientific and commercial purposes.However, the impact of the UAS used on the accuracy of the point clouds derived is not fully understood, especially for the quantitative analysis of geomorphic changes in complex terrain. Therefore, in this study, we aim to quantify the magnitude of systematic and random error in digital elevation models derived from four commonly used UAS (XR6/Sony α6000, Inspire 2/X4s, Phantom 4 Pro+, Mavic Pro) following different flight patterns. The vertical error of each elevation model is evaluated through comparison with 156 GNSS reference points and the normal distribution and spatial correlation of errors are analysed. Differences in mean errors (À0.4 to À1.8 cm) for the XR6, Inspire 2 and Phantom 4 Pro are significant but not relevant for most geomorphological applications. The Mavic Pro shows lower accuracies with mean errors up to 4.3 cm, thus showing a higher influence of random errors. QQ plots revealed a deviation of errors from a normal distribution in almost all data. All UAS data except Mavic Pro exhibit a pure nugget semivariogram, suggesting spatially uncorrelated errors. Compared to the other UAS, the Mavic Pro data show trends(i.e. differences increase with distance across the survey-doming) and the range of semivariances is 10 times greater. The lower accuracy of Mavic Pro can be attributed to the lower GSD at the same flight altitude and most likely, the rolling shutter sensor has an effect on the accuracy of the camera calibration. Overall, our study shows that accuracies depend highly on the chosen data sampling strategy and that the survey design used here is not suitable for calibrating all types of UAS camera equally.

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Accuracy assessment of airborne photogrammetrically derived high-resolution digital elevation models in a high mountain environment

Cited by 43 publications

References 27 publications

Countrywide Stereo-Image Matching for Updating Digital Surface Models in the Framework of the Swiss National Forest Inventory

Countrywide Stereo-Image Matching for Updating Digital Surface Models in the Framework of the Swiss National Forest Inventory

Digital Elevation Model Differencing and Error Estimation from Multiple Sources: A Case Study from the Meiyuan Shan Landslide in Taiwan

From consumer to enterprise grade: How the choice of four UAS impacts point cloud quality

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