Meso- to macro-scale landscape modelling with SfM-MVS photogrammetry: a case study from the Urft Lake water reservoir (Eifel Mountains, western Germany)

3D landscape reconstruction derived from imagery acquired by unmanned aerial systems (UAS) is an increasingly applied method within the field of geosciences. Low-cost UAS and subsequent Structure from Motion (SfM) and multi-view stereo (MVS) processing provides the opportunity to study landforms and processes in high detail; for instance mapping of river terraces (Li et al. 2019) or landslide monitoring (Devoto et al. 2020).Due to an almost complete drainage of the Urft Lake reservoir in the northern Eifel mountains (W-Germany) in the autumn of 2020, the lake&#8217;s entire ground could be surveyed using a low-cost UAS.The lake stretches for 12 km and has a maximum impoundment volume of approximately 45 million m&#179;. Its shape is characterized by multiple fluvial bends and steep slopes, which required an elaborated flight layout. A DJI Phantom 4 Pro V2.0 was used. Each flight was carried out in two parallel heights (90 and 120 m), 80&#176; camera inclination, and in double-grid pattern. Five full days of surveying yielded over 6,000 aerial images. Despite the difficulty to access the drained reservoir, 154 evenly distributed ground control points were taken using a Leica RTK dGPS instrument (accuracy <5 cm). SfM-MVS photogrammetric processing was conducted with Agisoft Metashape Professional 1.6, using an optimized workflow based on USGS (2017) and James et al. (2020).The resulting 3D model features high accuracy and precision making it suitable for further detailed stationary as well as multi-temporal geomorphologic analyses. The derived DEM features a spatial resolution of <6 cm and will be used to calculate geometric changes of the reservoir body since its construction in 1905; in particular, due to sedimentation and mass movements along the hillslopes. Moreover, the products can be used to study the anthropogenic influences of the water reservoir on the fluvial morphology of the Urft.&#160;References:Devoto, S., Macovaz, V., Mantovani, M., Soldati, S., Furlani, S., 2020. Advantages of Using UAV Digital Photogrammetry in the Study of Slow-Moving Coastal Landslides.&#160; Remote Sensing 2020, 12, 3566. https://doi.org/10.3390/rs12213566&#160;&#160;James, M.R., Antoniazza, G., Robson, S., Lane, S.N., 2020. Mitigating systematic error in topographic models for geomorphic change detection: accuracy, precision and considerations beyond off-nadir imagery. Earth Surface Processes and Landforms 45, 2251&#8211;2271. https://doi.org/10.1002/esp.4878Li, H., Lin, C., Wang, Z., Yu, Z., 2019. Mapping of River Terraces with Low-Cost UAS Based Structure-from-Motion Photogrammetry in a Complex Terrain Setting. Remote Sensing 2019, 11, 464. https://doi.org/10.3390/rs11040464United States Geological Survey (USGS), 2017. Unmanned Aircraft Systems Data Post Processing: Structure-from-Motion Photogrammtery. Section 2 &#8211; MicaSense 5-band MultiSpectral Imagery. USGS National UAS Project Office. https://uas.usgs.gov/nupo/pdf/PhotoScanProcessingMicaSenseMar2017.pdf (Retrieved: 24 July 2020).

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AggieAir: A Low-Cost Unmanned Aircraft System for Remote Sensing

Remote Sensing and Actuation Using Unmanned Vehicles ◽

10.1002/9781118377178.ch2 ◽

2012 ◽

pp. 15-52

Keyword(s):

Remote Sensing ◽

Low Cost ◽

Unmanned Aircraft ◽

Unmanned Aircraft System ◽

Aircraft System

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Improved Architecture Designs for a Low Cost Personal Remote Sensing Platform: Flight Control and Safety

Volume 3: 2011 ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications, Parts A and B ◽

10.1115/detc2011-48167 ◽

2011 ◽

Cited By ~ 5

Author(s):

Calvin Coopmans ◽

Long Di ◽

Austin Jensen ◽

Aaron A. Dennis ◽

YangQuan Chen

Keyword(s):

Remote Sensing ◽

Flight Control ◽

Large Scale ◽

Current System ◽

Low Cost ◽

Flight Test ◽

Unmanned Aircraft ◽

Personal Remote Sensing ◽

Sensing Platform ◽

Fail Safe

Remote sensing is a field traditionally dominated by expensive, large-scale operations. This paper presents our efforts to improve our unmanned aircraft (UA) platforms for low-cost personal remote sensing purposes. Safety concerns are first emphasized regarding the local airspace and multiple fail-safe features are shown in the current system. Then the AggieAir unmanned system architecture is briefly described including the Paparazzi UA autopilot, AggieAir JAUS implementation, AggieNav navigation unit and payload integration. Some preliminary flight test results and images acquired using an example thermal IR payload system are also shown. Finally Multi-UAV and heterogeneous platform capabilities are discussed with respect to their applications. Based on our approaches on the new architecture design, personal remote sensing on smaller-scale operations can be more beneficial and common.

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Mapping of River Terraces with Low-Cost UAS Based Structure-from-Motion Photogrammetry in a Complex Terrain Setting

Remote Sensing ◽

10.3390/rs11040464 ◽

2019 ◽

Vol 11 (4) ◽

pp. 464 ◽

Cited By ~ 6

Author(s):

Hui Li ◽

Lin Chen ◽

Zhaoyang Wang ◽

Zhongdi Yu

Keyword(s):

Structure From Motion ◽

Complex Terrain ◽

Laser Scanning ◽

Low Cost ◽

Unmanned Aerial Systems ◽

Surface Model ◽

Simple Method ◽

River Terraces ◽

Geomorphic Features ◽

Topography Data

River terraces are the principal geomorphic features for unraveling tectonics, sea level, and climate conditions during the evolutionary history of a river. The increasing availability of high-resolution topography data generated by low-cost Unmanned Aerial Systems (UAS) and modern photogrammetry offer an opportunity to identify and characterize these features. In this paper, we assessed the capabilities of UAS-based Structure-from-Motion (SfM) photogrammetry, coupled with a river terrace detection algorithm for mapping of river terraces over a 1.9 km2 valley of complex terrain setting, with a focus on the performance of this latest technology over such complex terrains. With the proposed image acquisition approach and SfM photogrammetry, we constructed a 3.8 cm resolution orthomosaic and digital surface model (DSM). The vertical accuracy of DSM was assessed against 196 independent checkpoints measured with a real-time kinematic (RTK) GPS. The results indicated that the root mean square error (RMSE) and mean absolute error (MAE) were 3.1 cm and 2.9 cm, respectively. These encouraging results suggest that this low-cost, logistically simple method can deliver high-quality terrain datasets even in the complex terrain, competitive with those obtained using more expensive laser scanning. A simple algorithm was then employed to detect river terraces from the generated DSM. The results showed that three levels of river terraces and a high-level floodplain were identified. Most of the detected river terraces were confirmed by field observations. Despite the highly erosive nature of fluvial systems, this work obtained good results, allowing fast analysis of fluvial valleys and their comparison. Overall, our results demonstrated that the low-cost UAS-based SfM technique could yield highly accurate ultrahigh-resolution topography data over complex terrain settings, making it particularly suitable for quick and cost-effective mapping of micro to medium-sized geomorphic features under such terrains in remote or poorly accessible areas. Methods discussed in this paper can also be applied to produce highly accurate digital terrain data over large spatial extents for some other places of complex terrains.

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Performance of Hyperspectral Imaging with Drone Swarms

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118775876 ◽

2018 ◽

Vol 2672 (45) ◽

pp. 36-44

Author(s):

Raj Bridgelall ◽

James B. Rafert ◽

Denver D. Tolliver

Keyword(s):

Remote Sensing ◽

Hyperspectral Imaging ◽

Spatial Resolution ◽

Low Cost ◽

Unmanned Aircraft ◽

Unmanned Aircraft Systems ◽

Aircraft Systems ◽

Sensing Platforms ◽

Spatial Coverage ◽

Ground Coverage

The ongoing proliferation and diversification of remote sensing platforms offer greater flexibility to select from a range of hyperspectral imagers as payloads. The emergence of low-cost unmanned aircraft systems (drones) and their launch flexibility present an opportunity to maximize spectral resolution while scaling both daily spatial coverage and spatial resolution simultaneously by operating synchronized swarms. This article presents a model to compare the performance of hyperspectral-imaging platforms in their spatial coverage and spatial resolution envelope. The authors develop a data acquisition framework and use the model to compare the achievable performance among existing airborne and spaceborne hyperspectral imaging vehicles and drone swarms. The results show that, subject to cost and operational limitations, a platform implemented with drone swarms has the potential to provide greater spatial resolution for the same daily ground coverage compared with existing airborne platforms.

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AggieAir: Towards Low-cost Cooperative Multispectral Remote Sensing Using Small Unmanned Aircraft Systems

Advances in Geoscience and Remote Sensing ◽

10.5772/8333 ◽

2009 ◽

Cited By ~ 10

Author(s):

Haiyang Chao ◽

Austin M. ◽

Yiding Han ◽

YangQuan Chen ◽

Mac McKee

Keyword(s):

Remote Sensing ◽

Low Cost ◽

Unmanned Aircraft ◽

Unmanned Aircraft Systems ◽

Aircraft Systems ◽

Multispectral Remote Sensing

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sUAS for 3D Tree Surveying: Comparative Experiments on a Closed-Canopy Earthen Dam

Forests ◽

10.3390/f12060659 ◽

2021 ◽

Vol 12 (6) ◽

pp. 659

Author(s):

Cuizhen Wang ◽

Grayson Morgan ◽

Michael E. Hodgson

Keyword(s):

Remote Sensing ◽

Low Cost ◽

Tree Height ◽

Unmanned Aircraft ◽

Height Measurement ◽

Earthen Dam ◽

Local Maxima ◽

Lower Accuracy ◽

Closed Canopy ◽

Good Potential

Defined as “personal remote sensing”, small unmanned aircraft systems (sUAS) have been increasingly utilized for landscape mapping. This study tests a sUAS procedure of 3D tree surveying of a closed-canopy woodland on an earthen dam. Three DJI drones—Mavic Pro, Phantom 4 Pro, and M100/RedEdge-M assembly—were used to collect imagery in six missions in 2019–2020. A canopy height model was built from the sUAS-extracted point cloud and LiDAR bare earth surface. Treetops were delineated in a variable-sized local maxima filter, and tree crowns were outlined via inverted watershed segmentation. The outputs include a tree inventory that contains 238 to 284 trees (location, tree height, crown polygon), varying among missions. The comparative analysis revealed that the M100/RedEdge-M at a higher flight altitude achieved the best performance in tree height measurement (RMSE = 1 m). However, despite lower accuracy, the Phantom 4 Pro is recommended as an optimal drone for operational tree surveying because of its low cost and easy deployment. This study reveals that sUAS have good potential for operational deployment to assess tree overgrowth toward dam remediation solutions. With 3D imaging, sUAS remote sensing can be counted as a reliable, consumer-oriented tool for monitoring our ever-changing environment.

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