Spatio-temporal measurements of landform evolution provide the basis for process-based theory formulation and validation. Over time, field measurements of landforms have increased significantly worldwide, driven primarily by the availability of new surveying technologies. However, there is no standardized or coordinated effort within the scientific community to collect morphological data in a dependable and reproducible manner, specifically when performing long-term small-scale process investigation studies. Measurements of the same site using identical methods and equipment, but performed at different time periods, may lead to incorrect estimates of landform change as a result of three-dimensional registration errors. This work evaluated measurements of an ephemeral gully channel located on agricultural land using multiple independent survey techniques for locational accuracy and their applicability in generating information for model development and validation. Terrestrial and unmanned aerial vehicle photogrammetry platforms were compared to terrestrial lidar, defined herein as the reference dataset. Given the small scale of the measured landform, the alignment and ensemble equivalence between data sources was addressed through postprocessing. The utilization of ground control points was a prerequisite to three-dimensional registration between datasets and improved the confidence in the morphology information generated. None of the methods were without limitation; however, careful attention to project preplanning and data nature will ultimately guide the temporal efficacy and practicality of management decisions.
Spatio-temporal measurements of landform evolution provide the basis for process-based theory formulation and validation. Field measurements of landforms have increased significantly worldwide, driven by the availability of new surveying technologies. Recent improvements include, but are not limited to, aerial and terrestrial light detection and ranging (lidar) systems (Kukko et al., 2012; Vinci et al., 2015; Eitel et al., 2016; Hawdon et al., 2016), integrated unmanned aerial vehicles (UAVs) utilizing both photogrammetric and lidar payloads (Bachrach et al., 2012; Bry et al., 2015; Honkavaara et al., 2016), real-time kinematics (RTK; Rietdorf et al., 2006), terrestrial photogrammetric systems (James and Robson, 2014; Gómez-Gutiérrez et al., 2014; Di Stefano et al., 2016; Marzolff, 2016), and low-cost and/or freeware coupled structure-from-motion (SfM) and multi-view stereo (MVS) photogrammetric software (Castillo et al., 2012, 2015; Smith and Vericat, 2015; Piermattei et al., 2016). However, the buyer must be aware that these systems can be prone to misinterpretation (Wheaton et al., 2010), and even the “high-resolution” equipment can provide misleading information (e.g., Fig. 13b in Vinci et al., 2015). Research efforts should focus on a standardized and/or coordinated effort within the scientific community to collect morphological data in a dependable and reproducible manner, specifically when performing long-term process investigation studies (Castillo et al., 2016).
Ephemeral gullies are often defined as small channels on the order of a few centimeters in depth, predominantly in agricultural fields (Soil Science Society of America, 2008). The emergence, evolution, and persistence of these concentrated flow path erosion features is controlled by the combined effects of flow, slope, soil properties, topography, and vegetation characteristics (Zevenbergen, 1989; Castillo et al., 2016). The term ephemeral refers to the fact that agricultural producers often erase these channels during regular farming operations (Foster, 2005); flow within these channels is also often cyclical. The combination of a highly dynamic lifespan with the relatively small-scale channel features requires high-accuracy measurements with high temporal and spatial resolution.
Many studies have been conducted to assess the topographical accuracy of
ephemeral or classical gully morphological measurements using a wide range of
systems (e.g., Casalí, et al., 2006; Gómez-Gutiérrez et al.,
2014; Di Stefano et al., 2016). Among them, lidar data have been used as the
reference for the evaluation of secondary remote sensing systems and physical
contact systems. Traditional airborne lidar studies have primarily focused on
quantifying locational error from datasets generated by airborne systems,
in which locational variations are the result of coalesced errors generated by
inaccuracies in the global positional system (GPS), aircraft inertial
measurement unit (IMU), and overall timing of the system (Hodgson and
Bresnahan, 2004). Lidar positional errors can also be the result of an
interaction between the laser pulse and features with sharp relief change or
occlusions that result in multiple returns from one laser pulse
(Milenković et al., 2015). Evaluations of the accuracy of topographical
information using airborne lidar are often compared with discrete sample
locations and/or man-made targets with known coordinates (Hodgson and
Bresnahan, 2004; Csanyi et al., 2005). Despite the large number of studies and
methods developed to quantify positional errors in traditional airborne lidar
surveys, this type of survey does not offer the temporal and spatial
resolution necessary for the quantitative monitoring of small-scale
geomorphological characteristics (i.e., ephemeral gullies) in terms of
process description; however, recent developments in UAV lidar systems
provide 10 mm of survey-grade accuracy, one million measurements per second,
and a 360
At a finer scale, investigation of ground-based and terrestrial lidar has
demonstrated a high locational accuracy (
Studies involving various surveying techniques of concentrated flow paths have revealed a wide range of quality, accuracy, cost, and field campaign effort (Momm et al., 2011, 2013b; Castillo et al., 2012; Wells et al., 2016). Among the surveying techniques considered, photogrammetry has been shown to provide simple but robust measurements of small-scale changes in geomorphologic characteristics within agricultural fields (Castillo et al., 2012; Gesch et al., 2015; Wells et al., 2016). Further, a wide variety of platforms and techniques have been used to capture images, including kites (Marzolff et al., 2003), backpacks (Wells et al., 2016) and UAVs (Ries and Marzolff, 2003; Bachrach et al., 2012; James and Robson, 2014; Cook, 2017). Erosion monitoring programs based on photogrammetry have several advantages compared to other surveying techniques. Photogrammetric field surveys do not interfere with farming operations, as they are nonobstructive; field campaigns are also extremely efficient and often do not require specialized technical skill sets to implement (James and Robson, 2012). However, photogrammetric results can vary as a function of the controlling parameters used during data collection and processing (Eltner et al., 2016).
Datasets generated by three distinct surveying methods for the purpose of quantifying locational uncertainty in gully studies.
A particular point of interest is the general query posed by Wheaton et al. (2010) concerning real geomorphic change. With these evolving technologies, our ability to collect topographical information is seemingly limitless. At what point can we agree that the results describe “real” change over noise? The alignment of temporal topographical elements is the most critical step when planning small-scale erosion studies (Smith and Vericat, 2015). Reliance on control points is the foundation of classical surveying. All surveys must close with a shot back to the initial occupation point. This is also the initiation of error propagation. A multitude of solutions exist for each set of photos and/or lidar points; however, the unique solution is bounded by the spatial and vertical positioning of the control points (Micheletti et al., 2015). Provided that alignment can be controlled, the next operation typically involves a culling process of some sort as the data shift into organized units.
The conversion of irregularly sampled point clouds into regular grids, referred to as digital elevation models (DEMs), is extremely common as most flow routing algorithms and soil erosion modeling technologies based on a geographic information system (GIS) are designed to work using these digital representations. As a result, a large number of studies have been conducted to evaluate DEM representation as affected by sampling intervals, interpolation algorithms (Aguilar et al., 2005; Ziadat, 2007; Bater and Coops, 2009; James and Robson, 2012, 2014), and DEM spatial resolution (Zhang and Montgomery, 1994; Kienzle, 2004; Momm et al., 2013a).
The majority of previous studies have focused on accuracy evaluation of a specific photogrammetric survey method at a single time period. Varying sensors, platforms, and processing methods can yield different results (variations in sampling densities, gaps, and noise). Furthermore, measurements of the same site using identical methods and equipment, but performed at different time periods, can also lead to three-dimensional registration errors. Therefore, the scope of this work was to evaluate multiple survey techniques and provide a framework for temporal studies of ephemeral gully channels. Three surveying platforms with varying parameters were independently evaluated for locational accuracy and applicability in generating information for model development and validation. The objectives of this study are twofold: to quantify the overall accuracy of the different survey configurations and to develop practical guidelines for the design and implementation of future ephemeral gully monitoring studies.
Study site location used in the evaluation of close-range photogrammetric surveys of ephemeral gully channels.
The study site was located in the northwest corner of Webster County, Iowa,
USA (Fig. 1). Farming is the dominant enterprise in Webster County. The crop
rotation was a corn–soybean rotation. Total annual precipitation is about
873 mm, 70 % of which usually falls between April and September. The
area of interest (AOI) within the field survey was a small reach
(1.9
Field surveys were conducted using three independent modes: ground-based and terrestrial lidar, ground-based and terrestrial photogrammetry, and airborne photogrammetry. The surveys yielded 12 datasets (Table 1; dashed lines represent the delineation between survey modes). The terrestrial lidar was considered the reference dataset due to perceived superior accuracy. All surveys were run independently of each other and completed on the same day. Each dataset was represented using the NAD83 UTM 15N coordinate system.
In this study, the terrestrial lidar point cloud was generated using Topcon
ScanMaster software
(
Site preparation began by locating a state monument point (Fig. 2a) and
laying out 406
The terrestrial lidar survey was conducted using a Topcon GLS 1500
(Topcon Corporation, Tokyo, Japan; 4 mm of single point and 2 mm of surface
accuracy with a spot size
Given the level of user control over the input parameters and the high locational accuracy of terrestrial lidar systems, this survey method was selected as the reference to which all other survey methods were compared. However, it is important to acknowledge that this survey method does have limitations.
In surveys with a high sampling intensity, it is common for the same location
on the ground to be hit by multiple laser pulses. This yields datasets with
a high sampling intensity but a range of elevation values for the same
location (i.e., fluff) given the vertical accuracy of the system. In this
study, this elevation variability is estimated to be approximately
Limitations of the terrestrial lidar survey used herein as a reference
dataset.
Terrestrial photogrammetry was conducted using a Nikon D7000 16.2 MP camera
(Nikon Inc., Melville, NY) with a calibrated 20 mm lens (Gesch et al., 2015;
Wells et al., 2016). The camera was mounted to a backpack frame connected to
an iPad mini (Apple, Cupertino, CA) through a WiFi CamRanger hub (Camranger
LLC;
Two UAV platforms were used to collect airborne photography
(
Still images captured by the UAVs were transformed into point clouds using Pix4DMapper Pro photogrammetric software. Initial data processing included camera calibration, aerial triangulation and bundle adjustment, camera position, and orientation. Following initial processing, field GCP positions (i.e., global external geometry) were included to optimize point cloud accuracy.
Determination of affine transformation matrices using iterative closest point (ICP) methodology.
Both fixed-wing and quadrotor UAV systems were deployed with a fixed path and common photograph overlap percentage. All missions and deployments were preplanned using flight planning and control software provided by the manufacturer. A mission block and a specific area or point of interest were selected, including preferred ground resolution, camera head angle (quadrotor only), and flight altitude. Flight lines for aerial coverage, circular paths with a horizontal plane around objects of interest (quadrotor only), image capture points, and waypoints were then generated prior to deployment. Key flight parameters were displayed in real time, along with the battery level and image acquisition progress, while the autopilot continuously analyzed onboard control data to optimize the flight.
To ensure the highest three-dimensional alignment among all point clouds and consistent spatial coverage by all methods, a three-step preprocessing approach was developed. First, the set of four channel GCPs (white square targets in Fig. 2b) were used to generate a rectangular polygon to subset the point clouds in all surveys and ensure that all surveys cover exactly the same ground position. Second, a smaller polygon subset was created by generating a polygon with a 50 mm reduction on all sides. This was performed to exclude areas close to the channel GCPs and ensure the elimination of shadowing and occlusion created by the slightly elevated channel GCPs. Third, the sampling void within the lidar dataset, created by the presence of a thin film of water within the channel (Fig. 3a), was manually digitized into another polygon and used to remove points from all photogrammetrically generated datasets to ensure uniformity among all datasets. Essentially, instead of using interpolated data within this void in the reference dataset, we simply placed a void in all datasets; therefore, we do not introduce bias into the calculations with regard to the water film void within the lidar data (e.g., Gómez-Gutiérrez et al., 2014).
Subsequently, since each surveying method was performed using the same set of
field and channel GCPs, a manual inspection of measured points located
coincident with channel GCPs (white square targets in Fig. 2b) was used to
generate planes (10 total), one for each dataset with the exception of the
Fixed_61m and Fixed_122m datasets, in which no points were located on top
of the channel GCPs. The four GPS-surveyed coordinates of the center location
of the channel GCPs were used to fit a reference plane to be matched by all
surveys (black squares in Fig. 4). Three-dimensional locational differences
between the reference plane generated using the GPS survey (black squares in
Fig. 4) and the planes of each surveyed dataset (lidar and photogrammetry)
were calculated using the iterative closest point (ICP) algorithm (Besl and
Mckey, 1994; James and Robson, 2012; Micheletti et al., 2015) implemented in
Matlab (MathWorks Inc., Natick, Massachusetts). Since no scale issues
were observed, no scaling factor was implemented in the ICP. The ICP
algorithm minimizes the locational differences between two sets of
three-dimensional point clouds and outputs a 3
One of the problems in looking at the data in the original point cloud format was the large difference between the total number of points within datasets (i.e., hundreds to millions), which tends to bias the results; therefore, in the sections that follow, the investigation of point cloud data is complemented by an analysis of gridded data. In the point cloud analysis, each point within the photogrammetry surveys was compared to that within the lidar survey. The analysis is carried out in two ways: point normal to the plane (each photogrammetry point was projected normal to a fitted plane of lidar points at the nearby position) and spot elevation to triangular irregular network (TIN; each photogrammetry point was projected up or down to intersect the TIN surface of the lidar points). In the gridded data analysis, a volume difference and cross-sectional assessment are performed. This type of data structure (i.e., raster grid) is a common format used to estimate soil loss volumes and generate cross sections for modeling exercises (Dabney et al., 2014). The gridded data introduce a common means of discussing differences between the survey methods.
Schematic representation of the positional accuracy analysis. Black
dots represent the reference dataset (lidar) and the green circle represents the point
being evaluated from photogrammetry.
Sampling intensity is defined as the number of points per unit of area. Investigation of the sampling intensity spatial variation can reveal oversampled or undersampled locations. Undersampled locations may be potential sources of error in quantifying geomorphologic change (i.e., cross-sectional areas or volumes), especially in surfaces with high relief. Sampling intensity was evaluated using the quadrant method (Dodd, 2011), in which a virtual regular grid of 1 cm was imposed on each dataset and the number of lidar and photogrammetry points falling within each grid was counted and recorded. Similarly, the local elevation variance was evaluated by calculating the elevation range (difference between the maximum and minimum) within each grid. The local elevation variance is a function of the terrain characteristics, sampling intensity, survey method, and postprocessing parameters.
Two metrics were used to quantify the spatial pattern distribution: distances
between events and between events and random points not in the pattern (void
space). The
Furthermore, points with the same
The vertical and horizontal displacement between the lidar and photogrammetry
measurements in each dataset was quantified using the normal projection of
each photogrammetry point into a plane fitted to the nearest lidar points
(Fig. 5a). For each photogrammetry point (green circle in Fig. 5a), the
nearest (within a 25 mm sphere) lidar points were selected (black dots in
Fig. 5a), a plane was fitted to the selected lidar point cloud points, the
photogrammetry point was normally projected onto the plane, the coordinates
of the intersection point (red circle in Fig. 5a) were recorded, and statistics
were generated. This analysis was performed for all datasets using an
in-house-developed Python script, where
The three-dimensional point cloud representing the reference dataset (lidar)
was converted into a TIN (Fig. 5b). Each point in the photogrammetry dataset
(green circle in Fig. 5b) was compared to the lidar TIN by fixing the
photogrammetry
All point clouds were converted into 5
Metrics used in the ranking analysis of the photogrammetric measurements.
Sampling intensity using the quadrat method for each dataset
considered. The individual colors represent point sampling count intervals
within a 1
Elevation range (difference between the minimum and maximum elevation)
represented as individual colors within a 1
Results of the
At 122 m of flight altitude, fixed-wing spot elevation comparison
(left column) and normal to plane comparison (right column) of
photogrammetry and lidar point cloud data with a fitted line through the
elevations (blue; spot) and the
Four-photo pair (Ground_8A) spot elevation comparison (left
column) and normal to plane comparison (right column) of photogrammetry and
lidar point cloud data with a fitted line through the elevations (blue; spot)
and the
Illustration of three-dimensional point cloud interpolation into
raster grids for volume and cross-sectional analysis of gully monitoring and
geomorphologic quantification. Direct comparison of Ground_8A
photogrammetry
Gully modeling technologies often use cross sections as basic modeling
units. With the objective of assessing the error introduced by each survey to
a
cross-sectional analysis, the raster grid surfaces were used to generate nine cross sections. For each cross section, various assessments were
conducted, including minimum elevation, maximum elevation, mean elevation,
variance, linear modeling (
Since there is no true standard of judging the performance of the
measurements provided herein, a system of scoring was developed to grade the
photogrammetry data with regard to the lidar data (Table 2). Scores between 1
and 11 were assigned to each evaluation category using both point cloud and
gridded data. For example, the difference in absolute volume was assigned
decreasing scores (1
The point clouds evaluated here had a large variability in sampling intensity
(from 1 to > 250 points cm
Point pattern analysis was examined using the
Graphical representations of both spot elevation to TIN and normal to fitted
plane for the fixed-wing flight at
Statistics comparing photogrammetry and terrestrial lidar using the point normal projected into the fitted plane analysis. Residual values were calculated based on the coordinate difference of all raster grid cells.
Statistics comparing photogrammetry and terrestrial lidar using
the point vertical spot to TIN approach (
Very similar results were obtained with these two methods. All datasets had
negligible mean displacement in the
Simple statistics of comparative cross-sectional elevations generated using different surveying methods. Values were calculated for nine cross sections individually and then averaged.
Most of what is reported here is due to point cloud alignment during the preprocessing step discussed earlier (i.e., GCP alignment for each, except fixed-wing flights). The mean elevation difference was approximately 5 mm for all datasets (Tables 3 and 4). Positive values indicate that the lidar data was, on average, higher than the photogrammetry data and negative values indicate that the lidar data was, on average, lower than the photogrammetry data. Similarly, when contrasting the photogrammetry elevation with the elevation of the normal point through linear regression (Table 3), the slope of the fitted line is very close to unity for all methods except Fixed_122, indicating spatially variable discrepancies (higher elevation differences at one region than the rest of the study site).
The conversion of point clouds with irregularly spaced points and spatially
varying sampling intensity point clouds into regular raster grids affected
each dataset differently (Fig. 11). For example, the lidar dataset contained
a high sampling intensity (>100 points cm
One of the most important measurements for gully monitoring is the volume
difference between surfaces (Table 6). Given the small scale of this type of
erosional feature (on the order of a few centimeters), it is vital to have a good
understanding of the expected error for each method. Among similar collection
methods (terrestrial photogrammetry), the absolute volume difference
(Table 6) ranged from 1 to 52 % in comparison to Ground_8A, although these
differences were extremely small in reality (i.e., a range of 0.0062 to
0.0105 m
Volume difference between photogrammetry and terrestrial lidar raster grids generated from three-dimensional point clouds.
Cross-sectional evaluation comparison between photogrammetry and lidar.
Results from the ranking analysis based on the difference metrics of multiple photogrammetric surveys applied to gully channel monitoring.
Selected cross sections generated from interpolating point clouds
into a 5
In the gridded elevation evaluations (min, max, mean; Table 5; Fig. 12), the
absolute difference from lidar was less than 0.02 %, and these differences
were only seen in the fixed-wing flights. The variance (i.e., roughness),
however, shows that the absolute differences from lidar were 131 %
(Fixed_122), 23 % (Fixed_61), and 9 % (Grround_4A). A comparison of
the
elevation information (Table 7; Fig. 12) between photogrammetric
cross sections and lidar cross sections through linear regression indicates
a coefficient of determination larger than
The gridded data performance was led by Ground_8A and Quad_35. Combined category score points ranged from 49 (Ground_8A) to 6 (Fixed_122), terrestrial photogrammetry ranged from 49 to 20, quadrotor ranged from 42 to 35, and the fixed wing ranged from 9 to 6 (Table 8). For the point cloud analysis, the scoring results were, for the most part, very similar. The terrestrial photogrammetry surveys all score very high, with the quadrotor falling in the middle and the fixed wing at the bottom. Overall, scoring ranged from 140 to 24 with the terrestrial photogrammetry leading the group. As the number of photos increased, so did the sample density; however, the four-photo pair (Ground_8A) was less dense than the six-photo pair (Ground_6A) or the two-photo pair (Ground_4B, Ground_4C), which may be associated with higher accuracy in pixel matching or the addition of inferior images to the project. However, it is noteworthy to add that sampling intensity increases as the UAV altitude decreased, although the Quad_35 outperformed the Quad_20 in a number of categories.
Two photogrammetric software packages (Pix4DMapper Pro and PhotoModeler
Scanner) were used to generate solutions for the UAV platform and terrestrial
photogrammetry surveys. Pix4DMapper Pro uses a larger number
(
An alarming concern in this analysis was the realization that rotation and translation (Fig. 4) were required to ensure that all data were properly aligned. The lidar global coordinates were the same as those used for the fixed-wing and quadrotor flights (i.e., field GCPs). The channel GCPs were also utilized to optimize the lidar point cloud solution. In all terrestrial photogrammetry point cloud solutions, the same set of global coordinates (channel GCPs) were used. One might expect the solutions to converge without the need to manipulate the point clouds in postprocessing; however, not one of the solutions contained the exact positions of the channel GCPs, including the solutions generated using the same platform but with varying processing parameters. For example, three-dimensional registration discrepancies were detected between lidar solutions and solutions from the quadrotor platform at 20 and 35 m, the fixed-wing platform at 61 and 122 m, and the terrestrial photogrammetry surveys. This realization presents extreme difficulty for temporal studies of ephemeral erosion processes, no matter the choice of resolution, platform, or processing parameters.
Initially, an attempt was made to analyze all datasets in their original
form; however, two limitations to the approach were noted: the lack of
three-dimensional registration between the datasets skewed efforts to
quantify individual point accuracy and, more importantly, reduced confidence
in the geomorphology information generated. The difference in point
sampling density, ranging from hundreds (fixed-wing platform) to millions
(lidar), biased the results. Therefore, the discussion presented herein relates
to solutions that have been altered from the original solutions produced by
the respective software packages. A comparison of the measured
The normal projection and vertical spot analysis place the mean elevation for
quadrotor flights at
Another interesting finding was the difference between solutions from
terrestrial photogrammetry (varying number and/or orientation of photo
pairs). The solutions from the Ground_2A and Ground_2B datasets both used only one
photo pair; however, the results from the analysis indicate a superior solution
generated from the Ground_2B pairing (i.e., upstream or downstream orientation;
Table 1). This could be potentially attributed to the orientation of the
images in relation to the channel, in which differences in illumination could
hamper the photogrammetric process of automated pixel matching between each
photo pair (Marzolff and Poesen, 2009). Additionally, increasing the number of
photo pairs used in the solution seems to yield improved solutions.
Results from the volumetric analysis show that the Quad_35 was a very close
approximation (0.0002 m
The long-term photogrammetric monitoring of ephemeral gullies should be performed
with systems and procedures that strongly consider the following.
Provide a minimum sampling density to capture the overall and local
terrain characteristics based on the study objectives (i.e., a temporal headcut
migration process understanding may require data with sub-centimeter resolution,
while a temporal channel meander process understanding may only require
decimeter resolution; James and Robson, 2012; Gómez-Gutiérrez et
al., 2014). The planning phase of the project must consider the physical
characteristics of the process to be investigated, the study site physical and
environmental variables, and the available hardware and software. Utilize static ground control points visible in comparable photo pairs in all
time-step surveys (i.e., fixed known points within the scene provide checks to
ensure proper three-dimensional registration of temporal data; e.g., Smith
and Vericat, 2015). An organized scheme for control points must be realized
for a detailed multi-temporal quantitative assessment. Small variations in
alignment within temporal surveys will introduce error into length, width,
cross-sectional area, and volume estimates (e.g., Casalí et al., 2015).
Repeated realizations of GCP coordinates will always reduce error in survey
solutions. Collect the same number of photo pairs using the same sensor and
with the same orientation in all time-step surveys (i.e., data collection
strategies should not vary temporally and new sensors must be carefully
calibrated to preexisting datasets). Consistency in photo collection (i.e.,
scheduling and number of photo pairs) will enhance the comparison of temporal
solutions (Gómez-Gutiérrez et al., 2014). Also, consider site visits
at a particular time of day. Process and generate
photogrammetric solutions using the same software package and similar input
processing parameters. A calibrated camera will always yield better
solutions.
Comparative evaluations were completed using terrestrial lidar and photogrammetry, both terrestrial and aerial (UAV). None of these methods were without limitation, and the ultimate goal of the data collection effort should guide the planning phase of the project. One cautionary note: without GCP there is no reasonable expectation that temporal activities will be successful. Although GCP may increase the workload during data acquisition, this is the only realization that will ensure global alignment, minimize project error, and enhance process theory development. While adherence to conventional ground methods for GCP establishment is essential for accurate temporal terrain characterization, the results presented herein are transferrable to larger survey areas with different terrain and surface characteristics. In terms of survey choice, all results point to financial and temporal questions. What is the project goal? What are the data expectations? A temporal assessment of gully channels and most geomorphic process descriptions can be accomplished with a camera and a few GCPs, whether on the ground or airborne. Each of the survey methods provided herein performed very well; although the scoring was not spectacular, the Fixed_61 data would be satisfactory for most static model evaluations. As expectations rise, so will the planning and technology.
The data is available at:
The authors declare that they have no conflict of interest.
The use of trade, firm, or corporation names in this paper is for the information and convenience of the reader. Such use does not constitute an official endorsement or approval by the United States Department of Agriculture or the Agricultural Research Service of any product or service to the exclusion of others that may be suitable.
This research was partially funded by grant 1359852 from the US National Science Foundation. The authors would like to acknowledge the support provided by Nathan Stein (remote pilot for the quadcopter UAV), Daniel Murphy (remote pilot for the fixed-wing UAV), Justin Hobart, Tom Buman, Bob Buman, Sarah Anderson, and Rick Cruse. Edited by: Anette Eltner Reviewed by: Álvaro Gómez-Gutiérrez and one anonymous referee