Spatiotemporal assessment of ephemeral gully characteristics using low altitude aerial imagery: an approach for quantifying

2020 ◽  
Author(s):  
Henrique Momm ◽  
Robert Wells ◽  
Carlos Castillo ◽  
Ronald Bingner
Keyword(s):  
Point Cloud ◽  
Ground Control ◽  
Control Points ◽  
Benchmark Point ◽  
Rainfall Events ◽  
Ground Control Points ◽  
Software Packages ◽  
Ephemeral Gully ◽  
Ephemeral Gullies ◽  
Low Altitude

<p>In agricultural fields, ephemeral gullies are defined as erosional channels formed primarily by overland flow from rainfall events. These channels are characterized by small dimensions, approximately 0.5 to 25 cm in depth, which allows their removal during regular farming operations. This dynamic characteristic coupled with their small size often can conceal soil losses by ephemeral gullies and poses challenges to efforts devised for soil loss quantification and mitigation. In this study, novel surveying and data processing techniques were employed to capture the small scale in topographic variation between two surveys and to assure that changes were due to erosional processes rather than survey miss-alignment. An agricultural field located in Iowa, U.S.A. with an area of approximately 54,500 m<sup>2</sup> was surveyed twice: right after the field was planted with corn and approximately one month later, following several rainfall events. A static benchmark point was established at the edge of the field and tied to public geodesic locations. A set of removable ground control points were spread throughout the field and surveyed in relation to the benchmark point. Low altitude aerial images were collected using a quadcopter UAS. Ground control points were used to aid in geospatial registration and to assess final survey accuracy. Standard off-the-shelf commercial software packages were unable compensate for less distortion and a new procedure using Micmac open-source photogrammetry software package was used to account for complex distortion patterns in the raw image data set. The undistorted images were then processed using Agisoft Photoscan for camera alignment, model georeferencing, and dense point cloud generation. Each point cloud representing a time period contained over 1 billion of points (file size > 100GB) and was processed using custom algorithms for filtering outliers and rasterization into a 2.5 cm raster grid (DEM). Analysis of differences between the two high spatial resolution DEMs revealed changes in the landscape due to natural (erosion/deposition) and anthropogenic (farming activities) factors. Specifically, for ephemeral gully analysis, morphological features in the form of headcut position and size, channel incision, sinuosity, lateral expansion, and depositional patterns were easily identified. Findings of this study shed light on potential pitfalls inherent to the utilization of off-the-shelf commercial software packages for such fine scale multi-temporal analysis, describe the need for standardization of procedures that assure accurate erosional response amongst different studies, and support the generation of accurate datasets critical in advancing our understanding of ephemeral gully processes needed for improved model development and validation.</p>

scholarly journals IMPROVING THE INTERNAL ACCURACY OF UAV-IMAGE BLOCKS USING LOCAL LOW-ALTITUDE FLIGHTS AND SCALE-BARS

2021 ◽  
Vol XLIII-B1-2021 ◽  
pp. 183-188
Author(s):  
M. Maboudi ◽  
A. Elbillehy ◽  
Y. Ghassoun ◽  
M. Gerke
Keyword(s):  
Ground Control ◽  
Accurate Estimation ◽  
Small Scale ◽  
Control Points ◽  
Accuracy Improvement ◽  
Object Space ◽  
Ground Control Points ◽  
Low Altitude ◽  
Uav Image ◽  
Small Parts

Abstract. Accurate image-based measurement based on UAV data is attracting attention in various applications. While the external accuracy of the UAV image blocks could be mainly affected by object-space information like number and distribution of ground control points and RTK-GNSS accuracy, its internal accuracy highly depends on camera specifications, flight height, data capturing setup and accuracy of scale estimation. For many small-scale projects accurate local measurements are highly demanded. This necessitates high internal accuracy of images block which could be transferred from model space to object space by accurate estimation of the scale parameter. This research aims at improving the internal accuracy of UAV image blocks using low-altitude flight(s) over small parts of the project area without using any ground control points. Possible further improvement by using calibrated scale-bars which serve as scale-constraints is also investigated. To this end, different scenarios of the flight configuration and distance measurements in the two photogrammetric blocks are also considered and the results are analyzed. Our investigations show 50% accuracy improvement achieved by performing local flights over small parts of the scene, given that RTK information is available. Moreover, adding accurate scale-bars increased the accuracy improvement to 67%. Furthermore, when RTK information is not available, adding local low-altitude flights and scale-bars decrease the error of local distance measurement form 1–3 meters to less than 4 centimeters.

scholarly journals Impact of UAV Surveying Parameters on Mixed Urban Landuse Surface Modelling

2020 ◽  
Vol 9 (11) ◽  
pp. 656
Author(s):  
Muhammad Hamid Chaudhry ◽  
Anuar Ahmad ◽  
Qudsia Gulzar
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Accuracy Assessment ◽  
Computational Cost ◽  
Three Dimensional ◽  
Point Clouds ◽  
Ground Control ◽  
Control Points ◽  
Ground Control Points ◽  
High Computational Cost

Unmanned Aerial Vehicles (UAVs) as a surveying tool are mainly characterized by a large amount of data and high computational cost. This research investigates the use of a small amount of data with less computational cost for more accurate three-dimensional (3D) photogrammetric products by manipulating UAV surveying parameters such as flight lines pattern and image overlap percentages. Sixteen photogrammetric projects with perpendicular flight plans and a variation of 55% to 85% side and forward overlap were processed in Pix4DMapper. For UAV data georeferencing and accuracy assessment, 10 Ground Control Points (GCPs) and 18 Check Points (CPs) were used. Comparative analysis was done by incorporating the median of tie points, the number of 3D point cloud, horizontal/vertical Root Mean Square Error (RMSE), and large-scale topographic variations. The results show that an increased forward overlap also increases the median of the tie points, and an increase in both side and forward overlap results in the increased number of point clouds. The horizontal accuracy of 16 projects varies from ±0.13m to ±0.17m whereas the vertical accuracy varies from ± 0.09 m to ± 0.32 m. However, the lowest vertical RMSE value was not for highest overlap percentage. The tradeoff among UAV surveying parameters can result in high accuracy products with less computational cost.

scholarly journals Geocoding Error Correction for InSAR Point Clouds

2018 ◽  
Vol 10 (10) ◽  
pp. 1523 ◽  
Author(s):  
Sina Montazeri ◽  
Fernando Rodríguez González ◽  
Xiao Zhu
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Ground Control ◽  
Control Points ◽  
Persistent Scatterer Interferometry ◽  
Sar Images ◽  
Localization Accuracy ◽  
Ground Control Points ◽  
Persistent Scatterer ◽  
The City

Persistent Scatterer Interferometry (PSI) is an advanced multitemporal InSAR technique that is capable of retrieving the 3D coordinates and the underlying deformation of time-coherent scatterers. Various factors degrade the localization accuracy of PSI point clouds in the geocoding process, which causes problems for interpretation of deformation results and also making it difficult for the point clouds to be compared with or integrated into data from other sensors. In this study, we employ the SAR imaging geodesy method to perform geodetic corrections on SAR timing observations and thus improve the positioning accuracy in the horizontal components. We further utilize geodetic stereo SAR to extract large number of highly precise ground control points (GCP) from SAR images, in order to compensate for the unknown height offset of the PSI point cloud. We demonstrate the applicability of the approach using TerraSAR-X high resolution spotlight images over the city of Berlin, Germany. The corrected results are compared with a reference LiDAR point cloud of Berlin, which confirms the improvement in the geocoding accuracy.

scholarly journals GEOREFERENCING UAS DERIVATIVES THROUGH POINT CLOUD REGISTRATION WITH ARCHIVED LIDAR DATASETS

2016 ◽  
Vol IV-2/W1 ◽  
pp. 195-199 ◽  
Author(s):  
M. S. L. Y. Magtalas ◽  
J. C. L. Aves ◽  
A. C. Blanco
Keyword(s):  
Point Cloud ◽  
Spatial Information ◽  
Aerial Images ◽  
Ground Control ◽  
Control Points ◽  
Model Errors ◽  
Validation Data ◽  
Positioning Systems ◽  
Ground Control Points ◽  
Skeleton Point

Georeferencing gathered images is a common step before performing spatial analysis and other processes on acquired datasets using unmanned aerial systems (UAS). Methods of applying spatial information to aerial images or their derivatives is through onboard GPS (Global Positioning Systems) geotagging, or through tying of models through GCPs (Ground Control Points) acquired in the field. Currently, UAS (Unmanned Aerial System) derivatives are limited to meter-levels of accuracy when their generation is unaided with points of known position on the ground. The use of ground control points established using survey-grade GPS or GNSS receivers can greatly reduce model errors to centimeter levels. However, this comes with additional costs not only with instrument acquisition and survey operations, but also in actual time spent in the field. This study uses a workflow for cloud-based post-processing of UAS data in combination with already existing LiDAR data. The georeferencing of the UAV point cloud is executed using the Iterative Closest Point algorithm (ICP). It is applied through the open-source CloudCompare software (Girardeau-Montaut, 2006) on a ‘skeleton point cloud’. This skeleton point cloud consists of manually extracted features consistent on both LiDAR and UAV data. For this cloud, roads and buildings with minimal deviations given their differing dates of acquisition are considered consistent. Transformation parameters are computed for the skeleton cloud which could then be applied to the whole UAS dataset. In addition, a separate cloud consisting of non-vegetation features automatically derived using CANUPO classification algorithm (Brodu and Lague, 2012) was used to generate a separate set of parameters. Ground survey is done to validate the transformed cloud. An RMSE value of around 16 centimeters was found when comparing validation data to the models georeferenced using the CANUPO cloud and the manual skeleton cloud. Cloud-to-cloud distance computations of CANUPO and manual skeleton clouds were obtained with values for both equal to around 0.67 meters at 1.73 standard deviation.

scholarly journals THE RATIONAL POLYNOMIAL COEFFICIENTS MODIFICATION USING DIGITAL ELEVATION MODELS

2015 ◽  
Vol XL-1-W5 ◽  
pp. 47-50 ◽  
Author(s):  
F. Alidoost ◽  
A. Azizi ◽  
H. Arefi
Keyword(s):  
Point Cloud ◽  
Digital Elevation Models ◽  
Ground Control ◽  
Control Points ◽  
Polynomial Coefficients ◽  
Ground Control Points ◽  
Rational Polynomial ◽  
Digital Elevation ◽  
Elevation Model ◽  
Parameters Calculation

The high-resolution satellite imageries (HRSI) are as primary dataset for different applications such as DEM generation, 3D city mapping, change detection, monitoring, and deformation detection. The geo-location information of HRSI are stored in metadata called Rational Polynomial Coefficients (RPCs). There are many methods to improve and modify the RPCs in order to have a precise mapping. In this paper, an automatic approach is presented for the RPC modification using global Digital Elevation Models. The main steps of this approach are: relative digital elevation model generation, shift parameters calculation, sparse point cloud generation and shift correction, and rational polynomial fitting. Using some ground control points, the accuracy of the proposed method is evaluated based on statistical descriptors in which the results show that the geo-location accuracy of HRSI can be improved without using Ground Control Points (GCPs).

scholarly journals Using Photogrammetric UAV Measurements as Support for Classical Topographical Measurements in Order to Obtain the Topographic Plan for Urban Areas

2017 ◽  
Vol 74 (2) ◽  
pp. 197
Author(s):  
Elemer Emanuel SUBA ◽  
Tudor SĂLĂGEAN ◽  
Ioana POP ◽  
Florica MATEI ◽  
Jutka DEAK ◽  
...  
Keyword(s):  
Digital Elevation Model ◽  
Urban Areas ◽  
Point Cloud ◽  
Ground Control ◽  
Control Points ◽  
Ground Control Points ◽  
Triangulation Network ◽  
Digital Elevation ◽  
Photogrammetric Method ◽  
Elevation Model

This article aims to highlight the benefits of UAV photogrammetric measurements in addition to classical ones. It will also deal with the processing and integration of the point cloud, respectively the digital elevation model in topo-cadastral works. The main purpose of this paper is to compare the results obtained using the UAV photogrammetric measurements with the results obtained by classical methods. It will briefly present the classical measurements made with the total station. In the present project, the closed-circuit traverse and the supported on the endings traverse were made using known coordinate points. Determining the coordinates of the points used for the traverses was done by GNSS methods. The area on which the measurements were made is 67942m2 and is covered by 31 determined station points. From these points, 13 were used as ground control points, respectively components of the aero-triangulation network and 17 points were used to control the obtained results by comparing their coordinates obtained by classical methods with those obtained by the UAV photogrammetric method. It was intended that the constraint points of the aero triangulation to be uniformly distributed on the studied surface.

Sign in / Sign up

Export Citation Format

Share Document