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

Abstract

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.

Partitioned by process: Measuring post‐fire debris‐flow and rill erosion with Structure from Motion photogrammetry

“…Johnson et al (2014) also compared SfM to ALS data as a reference and found error was <13 cm for 90% of points. Gillan et al (2017) compared UAV-derived SfM measurements to manual erosion bridge measurements along topographic transects and calculated an RMSE of~3 cm. Clapuyt et al (2016) tested the reproducibility of SfM topographic datasets and found 6 cm MAE within their workflow.…”

Measuring Change Using Quantitative Differencing of Repeat Structure-From-Motion Photogrammetry: The Effect of Storms on Coastal Boulder Deposits

“…The workflow implemented in this study is ideal for efficient repeat observations. This potential for producing four-dimensional data has already been realized in a range of studies [19,20,41,46,153,154]. Much of the procedure in Agisoft can be automated via custom scripts or the Batch Process option [105].…”

“…Second, SfM models generated by different imaging hardware can be successfully compared at sub-decimeter resolution, even when one of the camera systems has substantial lens distortion. This means that older image sets, which might not otherwise be considered of appropriate quality for co-analysis with more recent data, should not be ignored as data sources in long-term monitoring studies.Despite these improvements in protocols for data collection and analysis, quantifying change via repeat photogrammetry remains a challenge, generally requiring manipulation of digital terrain models (DTMs) using Geographic Information Systems (GIS) [45,46]. But recent innovations in 3D differencing software, as implemented in the open-source package CloudCompare (www.danielgm.net/cc/) enable rapid, objective, and replicable quantitative differencing [47].…”

“…Despite these improvements in protocols for data collection and analysis, quantifying change via repeat photogrammetry remains a challenge, generally requiring manipulation of digital terrain models (DTMs) using Geographic Information Systems (GIS) [45,46]. But recent innovations in 3D differencing software, as implemented in the open-source package CloudCompare (www.danielgm.net/cc/) enable rapid, objective, and replicable quantitative differencing [47].…”

UAV Remote Sensing Surveillance of a Mine Tailings Impoundment in Sub-Arctic Conditions

“…Numerous recent studies have focused on the accuracy, reproducibility, and optimization of UAV surveys utilizing SfM in variable locations [32][33][34][35][36][37][38][39] and can provide some insights. The accuracy is generally considered to be affected by various parameters, including camera parameters (e.g., focal length, sensor resolution, and exposure time), acquisition parameters (e.g., flight height and ground control network), environmental parameters (e.g., solar illumination and weather), and processing settings [40].…”