scholarly journals Using Digital Surface Models from UAS Imagery of Fire Damaged Sphagnum Peatlands for Monitoring and Hydrological Restoration

Drones ◽  
2018 ◽  
Vol 2 (4) ◽  
pp. 45 ◽  
Author(s):  
Shannon de Roos ◽  
Darren Turner ◽  
Arko Lucieer ◽  
David M.J.S. Bowman
Keyword(s):  
Unmanned Aerial Systems ◽  
Control Points ◽  
Hydrological Models ◽  
Slow Recovery ◽  
Ground Control Points ◽  
Surface Models ◽  
Good Potential ◽  
Northern Peatlands ◽  
Aerial Systems

The sub-alpine and alpine Sphagnum peatlands in Australia are geographically constrained to poorly drained areas c. 1000 m a.s.l. Sphagnum is an important contributor to the resilience of peatlands; however, it is also very sensitive to fire and often shows slow recovery after being damaged. Recovery is largely dependent on a sufficient water supply and impeded drainage. Monitoring the fragmented areas of Australia’s peatlands can be achieved by capturing ultra-high spatial resolution imagery from an unmanned aerial systems (UAS). High resolution digital surface models (DSMs) can be created from UAS imagery, from which hydrological models can be derived to monitor hydrological changes and assist with rehabilitation of damaged peatlands. One of the constraints of the use of UAS is the intensive fieldwork required. The need to distribute ground control points (GCPs) adds to fieldwork complexity. GCPs are often used for georeferencing of the UAS imagery, as well as for removal of artificial tilting and doming of the photogrammetric model created by camera distortions. In this study, Tasmania’s northern peatlands were mapped to test the viability of creating hydrological models. The case study was further used to test three different GCP scenarios to assess the effect on DSM quality. From the five scenarios, three required the use of all (16–20) GCPs to create accurate DSMs, whereas the two other sites provided accurate DSMs when only using four GCPs. Hydrological maps produced with the TauDEM tools software package showed high visual accuracy and a good potential for rehabilitation guidance, when using ground- controlled DSMs.

scholarly journals Accuracy of 3D Landscape Reconstruction without Ground Control Points Using Different UAS Platforms

Drones ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 13 ◽  
Author(s):  
Margaret Kalacska ◽  
Oliver Lucanus ◽  
J. Pablo Arroyo-Mora ◽  
Étienne Laliberté ◽  
Kathryn Elmer ◽  
...  
Keyword(s):  
Low Cost ◽  
Ground Control ◽  
Unmanned Aerial Systems ◽  
Control Points ◽  
Model Errors ◽  
Positional Accuracy ◽  
Ground Control Points ◽  
Digital Elevation ◽  
High Level ◽  
Aerial Systems

The rapid increase of low-cost consumer-grade to enterprise-level unmanned aerial systems (UASs) has resulted in the exponential use of these systems in many applications. Structure from motion with multiview stereo (SfM-MVS) photogrammetry is now the baseline for the development of orthoimages and 3D surfaces (e.g., digital elevation models). The horizontal and vertical positional accuracies (x, y and z) of these products in general, rely heavily on the use of ground control points (GCPs). However, for many applications, the use of GCPs is not possible. Here we tested 14 UASs to assess the positional and within-model accuracy of SfM-MVS reconstructions of low-relief landscapes without GCPs ranging from consumer to enterprise-grade vertical takeoff and landing (VTOL) platforms. We found that high positional accuracy is not necessarily related to the platform cost or grade, rather the most important aspect is the use of post-processing kinetic (PPK) or real-time kinetic (RTK) solutions for geotagging the photographs. SfM-MVS products generated from UAS with onboard geotagging, regardless of grade, results in greater positional accuracies and lower within-model errors. We conclude that where repeatability and adherence to a high level of accuracy are needed, only RTK and PPK systems should be used without GCPs.

scholarly journals Assessing the Accuracy of Digital Surface Models Derived from Optical Imagery Acquired with Unmanned Aerial Systems

Drones ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 15 ◽  
Author(s):  
Salvatore Manfreda ◽  
Petr Dvorak ◽  
Jana Mullerova ◽  
Sorin Herban ◽  
Pietro Vuono ◽  
...  
Keyword(s):  
3D Model ◽  
Low Cost ◽  
Three Dimensional ◽  
Careful Consideration ◽  
Ground Control ◽  
Unmanned Aerial Systems ◽  
Control Points ◽  
Earthen Dam ◽  
Ground Control Points ◽  
Aerial Systems

Small unmanned aerial systems (UASs) equipped with an optical camera are a cost-effective strategy for topographic surveys. These low-cost UASs can provide useful information for three-dimensional (3D) reconstruction even if they are equipped with a low-quality navigation system. To ensure the production of high-quality topographic models, careful consideration of the flight mode and proper distribution of ground control points are required. To this end, a commercial UAS was adopted to monitor a small earthen dam using different combinations of flight configurations and by adopting a variable number of ground control points (GCPs). The results highlight that optimization of both the choice and combination of flight plans can reduce the relative error of the 3D model to within two meters without the need to include GCPs. However, the use of GCPs greatly improved the quality of the topographic survey, reducing error to the order of a few centimeters. The combined use of images extracted from two flights, one with a camera mounted at nadir and the second with a 20° angle, was found to be beneficial for increasing the overall accuracy of the 3D model and especially the vertical precision.

Application of Postprocessing Kinematic Methods with UAS Remote Sensing in Forest Ecosystems

2021 ◽  
Author(s):  
Zachary M Miller ◽  
Joseph Hupy ◽  
Aishwarya Chandrasekaran ◽  
Guofan Shao ◽  
Songlin Fei
Keyword(s):  
Remote Sensing ◽  
Data Collection ◽  
Ground Control ◽  
Unmanned Aerial Systems ◽  
Control Points ◽  
Gps Technology ◽  
Ground Control Points ◽  
Forested Areas ◽  
Rapid Deployment ◽  
Aerial Systems

Abstract Unmanned Aerial Systems (UAS) serve as an excellent remote-sensing platform to fulfill an aerial imagery data collection niche previously unattainable in forestry by satellites and manned aircraft. However, for UAS-derived data to be spatially representative, a precise network of ground control points (GCP) is often required, which can be tedious and limit the logistical benefits of UAS rapid deployment capabilities, especially in densely forested areas. Therefore, methods for efficient data collection without GCPs are highly desired in UAS remote sensing. Here, we demonstrate the use of postprocessing kinematic (PPK) technology to obtain subcentimeter precision in datasets of forested areas without the need for placing GCPs. We evaluated two key measures, positional variability and time efficiency, of the PPK technology by comparing them to traditional GCP methods. Results show that PPK displays consistently higher positional precision than traditional GCP approaches. Moreover, PPK surveys and processing take less time to complete than traditional GCP methods and require fewer logistical steps, especially in image acquisition. The time and resource savings with PPK as compared to GCP processing are undeniable. We conclude that PPK technology provides a practical means to produce precise aerial forest surveys. Study Implications Unmanned Aerial Systems (UAS) have enormous potential for lowering costs and streamlining practices in the forestry management and research community. Despite this potential, however, UAS forestry applications have been limited in scope and precision because of a reliance on using ground-based GPS technology to survey ground control points (GCP), which are time intensive and require an open view of the sky. Such a need for a ground-based GCP survey, along with forest canopy serving to limit and scatter incoming GPS signals, diminishes the potential for rapid deployment and precision mapping offered by UAS. Fortunately, Postprocessing-Kinematic (PPK) GPS technology lowers these barriers by providing the means to seamlessly gather highly precise UAS imagery without needing to conduct time-intensive ground-based surveys. This study compares the precision and time-effectiveness between traditional GCP marker surveys and PPK correction methods.

scholarly journals LOW-COST UAS PHOTOGRAMMETRY FOR ROAD INFRASTRUCTURES’ INSPECTION

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1145-1150
Author(s):  
L. Pinto ◽  
F. Bianchini ◽  
V. Nova ◽  
D. Passoni
Keyword(s):  
Low Cost ◽  
Laser Scanner ◽  
Unmanned Aerial Systems ◽  
Control Points ◽  
The Past ◽  
Ground Control Points ◽  
General Rules ◽  
Aerial Systems ◽  
Cloud Of Points ◽  
Non Destructive

Abstract. All over the world, road infrastructures are getting closer to their life cycle and need to be constantly inspected: a consistent number of bridges are structurally deficient, and the risk of collapse can no longer be excluded. In contrast with the past, the interest in structure durability has recently grown rapidly. In order to make bridges durable, it is necessary to carry out ordinary maintenance, preceded by inspection activities, which can be traditionally divided in two categories: destructive and non-destructive (NDT). All the NDT inspections (visual, IR thermography, GPR) can be conducted by using UAS (Unmanned Aerial Systems), a technology that makes bridges inspections quicker, cheaper, objective and repeatable. This study presents the visual inspection and survey of two bridges by using a UAS DJI Mavic 2 Pro, equipped with a 20Mpixel Hasselblad camera that records 60fps 4K video and a 10bit radiometric resolution. Starting from the acquired data, a 3D model of each structure was built by using Structure from Motion (SfM) principles and software. To validate the two models, each of them characterized by a centimetric accuracy, the UAS camera generated cloud of points and was co-registered with the point cloud of a terrestrial laser-scanner using Ground Control Points (GCPs). To make this, CloudCompare comparison software was used; the plugin M3C2 automatically calculates the distance between the points of two compared clouds. Finally, some general rules concerning the UAS main characteristics for inspection of bridges and software for data processing are proposed.

Detecting Geo-Positional Bias in Imagery Collected Using Small UASs

2021 ◽  
Vol 87 (9) ◽  
pp. 631-638
Author(s):  
Jonathan B. Thayn ◽  
Aaron M. Paque ◽  
Megan C. Maher
Keyword(s):  
Global Positioning System ◽  
Unmanned Aerial Systems ◽  
Control Points ◽  
Positional Accuracy ◽  
Directional Bias ◽  
Unequal Variances ◽  
Ground Control Points ◽  
Positional Bias ◽  
Global Positioning ◽  
Aerial Systems

Statistical methods for detecting bias in global positioning system (<small>GPS</small>) error are presented and applied to imagery collected using three common unmanned aerial systems (<small>UASs</small>). Imagery processed without ground control points (<small>GCPs</small>) had horizontal errors of 1.0–2.5 m; however, the errors had unequal variances, significant directional bias, and did not conform to the expected statistical distribution and so should be considered unreliable. When <small>GCPs</small>were used, horizontal errors decreased to less than 5 cm, and the errors had equal variances, directional uniformity, and they conformed to the expected distribution. The analysis identified a longitudinal bias in some of the reference data, which were subsequently excluded from the analysis. Had these data been retained, the estimates of positional accuracy would have been unreliable and inaccurate. These results strongly suggest that examining <small>GPS</small> data for bias should be a much more common practice.

scholarly journals Shallow snow depth mapping with unmanned aerial systems lidar observations: A case study in Durham, New Hampshire, United States

2020 ◽  
Author(s):  
Jennifer M. Jacobs ◽  
Adam G. Hunsaker ◽  
Franklin B. Sullivan ◽  
Michael Palace ◽  
Elizabeth A. Burakowski ◽  
...  
Keyword(s):  
Snow Depth ◽  
Laser Scanning ◽  
Coniferous Forest ◽  
Potential Method ◽  
Unmanned Aerial Systems ◽  
Lidar System ◽  
Aerial Vehicle ◽  
Aerial Systems ◽  
Lidar Observations

Abstract. Shallow snowpack conditions, which occur throughout the year in many regions as well as during accumulation and ablation periods in all regions, are important in water resources, agriculture, ecosystems, and winter recreation. Terrestrial and airborne (manned and unmanned) laser scanning and structure from motion (SfM) techniques have emerged as viable methods to map snow depths. Lidar on an unmanned aerial vehicle is also a potential method to observe field and slope scale variations of shallow snowpacks. This paper describes an unmanned aerial lidar system, which uses commercially available components, for snow depth mapping on the landscape scale. The system was assessed in a mixed deciduous and coniferous forest and open field for a shallow snowpack (

scholarly journals Can 3D Point Clouds Replace GCPs?

2014 ◽  
Vol II-5 ◽  
pp. 347-354 ◽  
Author(s):  
G. Stavropoulou ◽  
G. Tzovla ◽  
A. Georgopoulos
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
Field Work ◽  
Surface Model ◽  
Control Points ◽  
3D Point Clouds ◽  
Surface Models

Over the past decade, large-scale photogrammetric products have been extensively used for the geometric documentation of cultural heritage monuments, as they combine metric information with the qualities of an image document. Additionally, the rising technology of terrestrial laser scanning has enabled the easier and faster production of accurate digital surface models (DSM), which have in turn contributed to the documentation of heavily textured monuments. However, due to the required accuracy of control points, the photogrammetric methods are always applied in combination with surveying measurements and hence are dependent on them. Along this line of thought, this paper explores the possibility of limiting the surveying measurements and the field work necessary for the production of large-scale photogrammetric products and proposes an alternative method on the basis of which the necessary control points instead of being measured with surveying procedures are chosen from a dense and accurate point cloud. Using this point cloud also as a surface model, the only field work necessary is the scanning of the object and image acquisition, which need not be subject to strict planning. To evaluate the proposed method an algorithm and the complementary interface were produced that allow the parallel manipulation of 3D point clouds and images and through which single image procedures take place. The paper concludes by presenting the results of a case study in the ancient temple of Hephaestus in Athens and by providing a set of guidelines for implementing effectively the method.

scholarly journals Archival Aerial Images Georeferencing: A Geostatistically-Based Approach for Improving Orthophoto Accuracy with Minimal Number of Ground Control Points

2020 ◽  
Vol 12 (14) ◽  
pp. 2232
Author(s):  
Manuela Persia ◽  
Emanuele Barca ◽  
Roberto Greco ◽  
Maria Immacolata Marzulli ◽  
Patrizia Tartarino
Keyword(s):  
Southern Italy ◽  
Aerial Images ◽  
Ground Control ◽  
Control Points ◽  
Territorial Planning ◽  
Ground Control Points ◽  
Study Results ◽  
Open Issue ◽  
Historical Photographs

Georeferenced archival aerial images are key elements for the study of landscape evolution in the scope of territorial planning and management. The georeferencing process proceeds by applying to photographs advanced digital photogrammetric techniques integrated along with a set of ground truths termed ground control points (GCPs). At the end of that stage, the accuracy of the final orthomosaic is assessed by means of root mean square error (RMSE) computation. If the value of that index is deemed to be unsatisfactory, the process is re-run after increasing the GCP number. Unfortunately, the search for GCPs is a costly operation, even when it is visually carried out from recent digital images. Therefore, an open issue is that of achieving the desired accuracy of the orthomosaic with a minimal number of GCPs. The present paper proposes a geostatistically-based methodology that involves performing the spatialization of the GCP errors obtained from a first gross version of the georeferenced orthomosaic in order to produce an error map. Then, the placement of a small number of new GCPs within the sub-areas characterized by the highest local errors enables a finer georeferencing to be achieved. The proposed methodology was applied to 67 historical photographs taken on a geo-morphologically complex study area, located in Southern Italy, which covers a total surface of approximately 55,000 ha. The case study showed that 75 GCPs were sufficient to garner an orthomosaic with coordinate errors below the chosen threshold of 10 m. The study results were compared with similar works on georeferenced images and demonstrated better performance for achieving a final orthomosaic with the same RMSE at a lower information rate expressed in terms of nGCPs/km2.

scholarly journals Determining the Optimal Number of Ground Control Points for Varying Study Sites through Accuracy Evaluation of Unmanned Aerial System-Based 3D Point Clouds and Digital Surface Models

Drones ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 49 ◽  
Author(s):  
Jae Jin Yu ◽  
Dong Woo Kim ◽  
Eun Jung Lee ◽  
Seung Woo Son
Keyword(s):  
Image Processing ◽  
Point Clouds ◽  
Optimal Number ◽  
Ground Control ◽  
Control Points ◽  
Widespread Application ◽  
Ground Control Points ◽  
3D Point Clouds ◽  
Surface Models ◽  
Study Sites

The rapid development of drone technologies, such as unmanned aerial systems (UASs) and unmanned aerial vehicles (UAVs), has led to the widespread application of three-dimensional (3D) point clouds and digital surface models (DSMs). Due to the number of UAS technology applications across many fields, studies on the verification of the accuracy of image processing results have increased. In previous studies, the optimal number of ground control points (GCPs) was determined for a specific area of a study site by increasing or decreasing the amount of GCPs. However, these studies were mainly conducted in a single study site, and the results were not compared with those from various study sites. In this study, to determine the optimal number of GCPs for modeling multiple areas, the accuracy of 3D point clouds and DSMs were analyzed in three study sites with different areas according to the number of GCPs. The results showed that the optimal number of GCPs was 12 for small and medium sites (7 and 39 ha) and 18 for the large sites (342 ha) based on the overall accuracy. If these results are used for UAV image processing in the future, accurate modeling will be possible with minimal effort in GCPs.

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