Tsunetaka et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-020-00336-0(2020) 7:24

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open Access

Spatial accuracy assessment of unmanned
aerial vehicle-based structures from motion
multi-view stereo photogrammetry forgeomorphic observations in initiation zones
of debris flows, Ohya landslide, Japan
Haruka Tsunetaka1* , Norifumi Hotta2, Yuichi S. Hayakawa3 and Fumitoshi Imaizumi4

Abstract
Fluctuations in sediment storage arising from sediment discharge and recharge in headwater channels are an
important factor influencing changes in landforms in mountainous areas, but the frequency of surveys islimited because of access difficulties and complex topography. Although unmanned aerial vehicle-based
structure-from-motion photogrammetry (UAV-SfM) may be effective for topographic measurement, its
utilization in headwater channels has not been fully examined. We assessed the accuracy and reproducibilityof a digital elevation model acquired via UAV-SfM (DEMSfM) in a headwater channel within the Ohya landslidearea, Japan, using a DEM acquired via terrestrial laser scanning (DEMTLS). The results indicate that differencesin the measured elevation between DEMSfM and DEMTLS in the vicinity of the channel bed ranged fromabout 0.4 to −0.4 m, with a median of 0.06 m. Hence, the profiles acquired via DEMSfM coincide well withthose acquired via DEMTLS, and the spatial distributions and histograms of the measured surface slope were
nearly the same for UAV-SfM and TLS. However, part of the DEMSfM indicates low elevation compared with
DEMTLS, probably because of topical distortion arising from technical problems in UAV-SfM. The positive andnegative differences in volume between DEMSfM and DEMTLS were approximately 200 and −30 m3,
respectively. To remedy this bias, an alignment of the UAV-SfM point cloud using the TLS point cloud in thehillslope sections was conducted, based on an iterative closest point (ICP) algorithm. Consequently, themedian of the elevation differences decreased to −0.002 m, resulting in the positive and negative differences
becoming approximately 100 m3. This demonstrates that ICP-based alignment can lead to a reduction of thedeviation of differences in the estimated volume. In terms of eliminating biases due to topical distortion inelevation, this approach would be valid for the estimation of volumetric changes using UAV-SfM.
Keywords: UAV-SfM, TLS, ICP, Debris flow

* Correspondence: tsunetakaharuka@ffpri.affrc.go.jp
1
Forestry and Forest Products Research Institute, 1, Matsunosato, Tsukuba,
Ibaraki 305-8687, Japan
Full list of author information is available at the end of the article© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
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Tsunetaka et al. Progress in Earth and Planetary Science(2020) 7:24

Introduction
In headwater channels, sediment production persists for along time because of weathering promoted by freeze–thaw
processes, complex geological features, and steep topography (Imaizumi et al. 2015; Nishii et al. 2018). Such sediments are discharged as torrential flows, e.g., debris flows
and debris floods, by heavy rainfall events (Coe et al. 2008;
Bel et al. 2017). This fluctuation in sediment storage due
to sediment recharge and discharge is related to the sediment cascade system on the watershed scale (Gallo andLavé 2014). Moreover, the discharge of channel deposits
via the descent of torrential flows leads to the process ofchannelization (McCoy et al. 2013). Therefore, changes in
sediment storage in headwater channels are an importantfactor in geomorphic processes.
Previously, changes in sediment storage have been detected using DEMs acquired via airborne laser scanning(ALS) due to the inaccessibility of headwater channels(Schlunegger et al. 2009; Berger et al. 2011a). Because
ALS is an expensive measurement method and radiates
lasers from high attitudes, the observation intervals, andspatial resolutions of such DEMs are generally several
years and several meters, respectively. Torrential flows
often occur several times per year (Coe et al. 2008; Imaizumi et al. 2017), with the result that the characteristics
of channel deposits can be spatially and temporally altered within a few months because of the entrainmentand deposition of channel deposits (Blasone et al. 2014;
Hayakawa et al. 2016). Moreover, changes in the elevation of channel deposits vary widely, from dozens ofcentimeters to several meters (McCoy et al. 2010, 2012;
Berger et al. 2011b; Staley et al. 2011). Therefore, an increase in measurement frequency and spatial resolution
is necessary to further examine changes in sedimentstorage and the distribution of channel deposits.
Structure-from-motion multi-view stereo (SfM-MVS)
photogrammetry makes it possible to provide highresolution topographic data at low cost compared withconventional topographical surveys (Westoby et al. 2012;
James and Robson 2012; Fonstad et al. 2013; Stumpf
et al. 2015). In particular, unmanned aerial vehicle-based
SfM-MVS photogrammetry (UAV-SfM) can lead to improvements in geomorphic observation because it is immune to surface obstructions and inaccessibility due tocomplex topography and structures (Westoby et al.
2015; Saito et al. 2018). UAV-SfM has the potential to
improve the spatiotemporal resolution of topographic
measurement in headwater channels, but its accuracy insuch areas has not been thoroughly examined.
UAV-SfM can provide a DEM with a resolution andaccuracy of less than a few decimeters if suitable georeferencing is used based on accurate ground control
points (GCPs) (De Haas et al. 2014). Differences in coordinates among time series DEMs focusing on the same

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site hinge on the GCP locations and the measurement
accuracy of their coordinates when only GCPs are used
for georeferencing (Clapuyt et al. 2016). Consequently,
the setting of GCPs is an important factor for the accurate detection of time series differences in elevation
resulting from sediment erosion and deposition, but thelocations of GCPs in headwater channels are restricted
to the vicinity of the channel bed because of steep andinaccessible surface conditions in the hillslope sections.
Moreover, DEMs acquired via UAV-SfM include not
only rotation errors arising from aerial photographs but
also scale errors relating to the uncertainty of georeferencing (Carbonneau and Dietrich 2017). These lead tospatial differences in measurement accuracy between
measurement periods. Consequently, topical validations
using point data, such as elevation measured by global
navigation satellite system (GNSS), are not sufficient,and spatial validations of UAV-SfM are necessary. However, previous topographic data, such as DEMs acquired
via ALS, cannot be used for such validations because of
frequent fluctuations in sediment storage. Therefore, theaccuracy of UAV-SfM in headwater channels has not yet
been fully examined.
With this in mind, the accuracy of UAV-SfM in a
headwater channel was investigated, based on a comparison of topographic data acquired via UAV-SfM andterrestrial laser scanning (TLS) during a similar measurement period. Using a comparison of the DEMs, wevalidated the reproducibility of complex topography via
UAV-SfM photogrammetry. Moreover, the applicabilityof point cloud registration using the iterative closest
point (ICP) algorithm was examined with a view to increasing the volume estimation accuracy of channel deposits. On the basis of these results, we discuss theapplicability of UAV-SfM for topographic measurements
in headwater channels.

Methods
Study site

Our study site was located at the upper Ichinosawa torrentof the Ohya landslide in the Southern Japanese Alps (Fig. 1a,b). The Ohya landslide was initiated by an earthquake in
1707, with an estimated total sediment volume of 120 million
m3 (Tsuchiya and Imaizumi 2010). The prominent geologicalfeature in the area is Paleogene strata, which are comprisedof highly fractured shale and well-jointed sandstone, and theannual precipitation is approximately 3400 mm (Imaizumi
et al. 2016).
The Ichinosawa torrent is located at the northern endof the Ohya landslide. The highest point is 1905 m above
sea level (a.s.l.), while the lowest point is a waterfall, located at 1450 m a.s.l. The total length of the channel isapproximately 650 m, the average channel inclination isapproximately 27°, and the drainage area is 0.22 km2

Tsunetaka et al. Progress in Earth and Planetary Science(2020) 7:24

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Fig. 1 Study site. a Overview. b The Ohya landslide. c The upper Ichinosawa torrent(Fig. 1c). The vast majority of the slopes in the upper parts of
the hillslopes exceed 38°. During the winter season (December–March), sediment moves from the hillslope to the channel bed via freeze–thaw processes, which promote dry ravel
and rock-fall (Fig. 2a) (Imaizumi et al. 2006). In the summer
season (June–September), channel deposits are discharged asdebris flows during storm events (Fig. 2b). Debris flows occur
approximately three or four times per year in this area (Imaizumi et al. 2017, 2019). This area is one of the most active
debris flow torrents in Japan. Although sparse vegetation
covers part of the hillslopes during the summer–fall season(Fig. 2b), the percentage coverage and vegetative height areless than 30% and 10 m, respectively.
UAV-SfM measurements

We carried out UAV-SfM photogrammetry and TLS during similar periods (September 4 and 5, 2016) in the study
site. In nearly all cases, it is expected that the accuracy ofa DEM acquired via laser scanning will be higher than that
acquired via SfM-MVS photogrammetry (e.g., James andRobson 2012). Therefore, we assumed that the topographic data acquired via TLS was more accurate.

A UAV (DJI Phantom4) was used for the flight on September 4, 2016, covering the channel bed of the upper
Ichinosawa torrent (Fig. 1c). In 2016, a total of three debris flow events were observed by monitoring cameras from
May to August (Imaizumi et al. 2019). Consequently, thetopography of this area in the measurement period consisted of various surface slopes suitable for validating the
reproducibility of complex topography.
The weight of the UAV including its battery and propellers was 1380 g, the diagonal size excluding the propellers
was 350 mm, the max flight time was approximately 28
min, and a three-axes (pitch, roll, and yaw) gimbal control
camera was installed on the bottom of its body. The digital
camera sensor (CMOS) size was 1/2.3″, the effective pixel
size was 12.4 megapixels, the field of view was 94 degrees
20 mm (35 mm format equivalent), the F measure was 2.8,
and the focus was at infinity.
The flight was conducted manually at elevations between 50 m and 200 m above the channel bed. The flightpath accounted for an overlap in the photographs of atleast 70% to produce an accurate point cloud. We primarily focused on a comparison of the channel deposits

Tsunetaka et al. Progress in Earth and Planetary Science(2020) 7:24

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Fig. 2 Photos of channel deposits taken at point A in Fig. 1c during (a) the sediment supply season (February 19, 2015) and (b) the debris flow
season (September 10, 2016). The red circle indicates a large boulder (approximately 2 m) located at the same position in both photos

rather than the hillslopes; accordingly, nearly all thephotographs were taken from the vertical direction forthe channel bed. Because the weather conditions werecloudy, the amounts of shade, and spatial variations in
the brightness were slight in the obtained photographs.
The photographs were processed using the SfM-MVS
photogrammetry software (Agisoft PhotoScan Professional),
which can produce accurate DEMs if suitable photographs
and GCPs are used, for example, it has been used for debris
flow fans (De Haas et al. 2014), forest canopies (Jensen andMathews 2016), and landslide areas (Saito et al. 2018). In
the first step of processing, the shooting camera positions
for all acquisition photographs (approximately 300 photographs) were detected using the exchangeable image fileformat data of each photograph. Consequently, the estimated errors of the relative camera positions for eachphotograph were detected (the average value of the relative
estimated errors was 1.262 m), and photographs with estimated errors exceeding 2 m were removed. This process

reduced the number of photographs to approximately 170,
and the average value of the relative estimated errors decreased to approximately 0.6 m. Following the standard
SfM-MVS photogrammetry workflow, a point cloud comprising over 50 million points was produced (see Table 1
for properties of the point cloud).
The georeferencing of the point cloud was conductedusing four GCPs (Fig. 1c), and the geographic coordinates of the GCPs were measured using a GNSS receiver(Hemisphere A325). The GCPs were dispersed in domains with fewer undulations (e.g., without large boulders and vegetation) because the reproducibility of SfMMVS is known to decrease at the boundary of a characteristic surface, such as a structure (James and Robson2014). The root-mean-square deviation (RMS) of the
GNSS measurement for the GCPs ranged from 0.005 to
0.033 m (Table 2). The serial number of the GCPs indicates the order of the GCPs from upper to lower in Fig.
1c. After georeferencing and eliminating points of

Tsunetaka et al. Progress in Earth and Planetary Science(2020) 7:24

Table 1 Point cloud properties acquired via UAV-SfM and TLS
Measurement type

UAV-SfM

Point density (pt/m2)

483.6

TLS
281.1

Standard deviation in point density (pt/m2)

112.6

173.2

Average point spacing (mm)

45.5

59.6

Marge of scans

Tie point matchingーPrecision of marge (mm)<6ーInterpolator creating DEM

TIN

TIN

uncertainty and noise, the point cloud consisted of approximately 37 million points and the average projection
error of the GCPs was approximately 0.004 m. From thispoint cloud, a DEM (DEMSfM) and orthophotographwere produced, with spatial resolutions of 0.1 m and0.015 m, respectively. Triangulated irregular network(TIN) interpolation was used to create the DEM (Table 1).
Terrestrial laser scanning

We obtained point cloud data on September 4 and 5, 2016,
via TLS to validate the accuracy of the results acquired via
UAV-SfM (Table 1). Two TLS units (Topcon GLS-1500 and
GLS-2000) were used. The maximum measurable ranges ofthe scanners were 500 m (for a target object with a 90% reflectance), with distance accuracies of 4 mm (GLS-1500) and3.5 mm (GLS-2000), respectively, at a range of 150 m andangle accuracies of 6″. A total of five point-cloud data was
acquired. A total of eight reference targets were installed andused for the registration of the point cloud data via a target
matching (tie-point) method, with accuracies of 0.5–6 mm.
Two targets were used for the georeferencing and geographic
coordinates as measured by GNSS receivers (Trimble
GeoExplorer 6000XH). The overall errors of the registeredpoint cloud, including scanning, registration, and georeferencing, were considered to be on the order of centimeters(Hayakawa et al. 2016). After eliminating noise, a DEM witha 0.1 m resolution (DEMTLS) was produced from the point
cloud using TIN interpolation.
Point cloud registration

The alignment of point clouds using the ICP algorithm
has been applied to reduce the deviation in the

Table 2 Accuracies and RMS of the GCPs used for thegeoreferencing of UAV-SfM
GCPs

Accuracyx (m)Accuracyy (m)Accuracy
z (m)

RMS
(m)

GCP1

0.007

0.010

0.018

0.032

GCP2

0.005

0.006

0.012

0.031

GCP3

0.008

0.010

0.019

0.010

GCP4

0.012

0.013

0.033

0.029

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registration error between point clouds acquired via TLS(Teza et al. 2007; Schürch et al. 2011; Hayakawa et al.
2017, Obanawa and Hayakawa 2018). The point cloudacquired via UAV-SfM contains rotation errors (Carbonneau and Dietrich 2017), but it is possible that an ICPbased alignment could decrease such errors. We therefore examined the effectiveness of ICP-based alignment
for topographic measurements via UAV-SfM in the
headwater channel.
The ICP algorithm fits a point cloud to a reference
point cloud using a transformation matrix (Besl and
McKay 1992; Bergevin et al. 1996). The transformation
matrix is iteratively computed as a matrix that minimizes the distance between the nearest points on key
features of two-point clouds based on similar morphological features in the overlapping area. The hillslope
section of a point cloud acquired via TLS was set as thereference point cloud because the hillslope morphology
is relatively stable during the summer season. We calculated the transformation matrix and aligned the point
cloud acquired via UAV-SfM to fit the reference point
cloud using the CloudCompare software (GirardeauMoutaut et al. 2005). After ICP-based alignment, DEMSfM-ICP was generated via TIN interpolation, and the resolution was set to 0.1 m.
DEM comparison

We computed the DEMs of the differences (DoD) between
DEMTLS and DEMSfM or DEMSfM-ICP to validate the accuracy of UAV-SfM. DoDTLS − SfM was calculated by subtracting
DEMSfM from DEMTLS. DoDTLS − SfM-ICP was calculated bysubtracting DEMSfM-ICP from DEMTLS. To investigate the efficacy of ICP-based alignment, volumetric differences were
calculated with respect to the positive and negative domainsof DoDTLS − SfM and DoDTLS − SfM-ICP using the equation
below, similar to Staley et al. (2014):
ΔV ¼

AV 

Pn

ði; jÞ¼1 ΔZ ði; jÞ

n

;

ð1Þ

where Av is the area of the volume being calculated in
m2 (i.e., total area of positive or negative domains), ΔZ(i,j)
is the change in elevation at each pixel (i,j; given coordinates of each pixel in meter), and n is the total numberof pixels in Av (Staley et al. 2014). The calculated volumetric differences were compared with already-known
erosion volume in sediment storage resulting from debris flow, referring to the time series TLS survey by Hayakawa et al. (2016). To investigate the representability ofthe topography, we compared the spatial distribution
and a histogram of the surface slope for each DEM. For
the comparison of the spatial distribution of the surface
slope, the slope domains were divided into three domains based on the balance between the shear strengthTsunetaka et al. Progress in Earth and Planetary Science(2020) 7:24

and the shear stress, similar to Imaizumi et al. (2017):
saturated sediment (< 21°), unsaturated sediment (21–38°), and bedrock (> 38°, the angle of static friction).
The comparison area was restricted to the vicinity of
the channel deposits (Fig. 3c) for the following three reasons: (1) the accuracy around the edge of a DEM produced via SfM-MVS is generally low because of
distortion (Carbonneau and Dietrich 2017), (2) the vegetation on the hillslope section causes topical differencesin the elevation of DEMSfM compared with the actual
topographic surface, and (3) DEMTLS does not sufficiently cover the upper part of the hillslopes because thereflected density of the radiated laser on the upper partof the hillslopes is relatively low. Accordingly, we cannot
investigate the accuracy of UAV-SfM including the ridge
surrounding the headwater channel. However, nearly allof the topographic changes induced by fluctuations inPage 6 of 14

the sediment storage are induced in the vicinity of thechannel (e.g., Imaizumi et al. 2006; Berger et al. 2011a).
Therefore, the comparison area is reasonable to validate
and discuss the measurement accuracy for sedimentstorage in headwater channels.

Results and discussion
Figure 3 shows the orthophotograph acquired via UAVSfM and the DoDs between DEMTLS and DEMSfM or
DEMSfM-ICP. While both DoDs indicated a spatial variation in the elevation differences (Fig. 3b, c), the DoDs
around the edges of the DEMs exceeded 2 m. DoDTLS −SfM in part of the right bank and channel bed exceeded
0.1 m (Fig. 3b), whereas the domain where the DoD
exceeded 0.1 m was relatively small for DoDTLS − SfM-ICP(Fig. 3c). From the comparison between the DoDs andthe orthophotograph, we see that domains with

Fig. 3 Results of the UAV-SfM measurements. a Orthophotograph of the entire observation area and DEMs of the differences (DoD). b Between
DEMTLS and DEMSfM. c Between DEMTLS and DEMSfM-ICP. Because both DoDs were computed such that DEMSfM or DEMSfM-ICP was subtracted fromDEMTLS, the cold (positive value) and warm (negative value) colors indicate domains where the elevations of DEMSfM or DEMSfM-ICP are relatively
high and low compared to DEMTLS, respectively. The TLS measurement focused on the downstream side of the initiation zone; therefore, there
are blank domains indicated by the black color on the upstream side. The domain surrounded by the dotted red line indicates the area used forthe validation

Tsunetaka et al. Progress in Earth and Planetary Science(2020) 7:24

substantial DoDs exceeding ± 2 m correspond to domains covered by vegetation (Fig. 3).
Figure 4 shows histograms for the DoDs in the vicinityof the channel bed (the domain surrounded by the red
dotted line in Fig. 3c). Nearly all DoDs were within ±0.4 m. For DoDTLS − SfM (Fig. 4a), the median was 0.06
m and, therefore, the elevation of the UAV-SfM photogrammetry was relatively low compared to that of TLS.
After the ICP-based alignment (Fig. 4b), because the deviation in the DoDs was reduced, the median decreased
to −0.002 m.

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The difference in volume based on the DoDs varied
between DEMSfM and DEMSfM-ICP (Fig. 5). For DoDTLS
3− SfM, the positive difference in volume exceeded 200 m ,whereas the negative difference was less than 30 m3.
Conversely, the positive and negative differences in volume for DoDTLS − SfM-ICP were similar at 100 m3. As aconsequence of ICP-based alignment, the net differencein volume (i.e., the absolute value of the sum of the positive and negative values of volumetric differences) was
reduced to 6.9 m3 (Fig. 6). This was low compared with
the net difference in volume with respect to DEMSfM

Fig. 4 Histograms of the DoDs in the vicinity of the channel deposits. a The DoD between DEMTLS and DEMSfM. b The DoD between DEMTLS and
DEMSfM-ICP. The red line and dashed blue line indicate the cumulative distribution and the median value, respectively

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Fig. 5 Difference in volume in the vicinity of the channel bed. Similar to Fig. 2, the cold (positive value) and warm (negative value) colors
indicate that the volume of the accumulated domains, DEMSfM or DEMSfM-ICP, is relatively high or low compared with DEMTLS, respectively

Fig. 6 Difference in net volume in the vicinity of the channel bed. The left two bars indicate the net difference in the volume of DEMSfM or DEM
SfM-ICP versus DEMTLS (i.e., the absolute value of the sum of positive and negative volume in Fig. 5). The right two bars indicate the volume oferoded sediment due to debris flows in our study site investigated by Hayakawa et al. (2016)

Tsunetaka et al. Progress in Earth and Planetary Science(2020) 7:24

(approximately 180 m3). Moreover, during two different
periods, May–August 2012 and August–November
2013, two debris flows occurred, and their eroded volume was approximately 3000 m3 by TLS measurement(Hayakawa et al. 2016). Considering this, the net difference in volume using UAV-SfM was obviously low compared with the volumetric changes caused by debris flows.
The profiles of the channel bed were similar for thetwo DEMs because the DoDs along the channel were
mostly less than ± 0.2 m (Fig. 7a). Regardless of location,DoDTLS − SfM tended to be high compared with DoDTLS− SfM-ICP. After ICP-based alignment, the spatial trendsof the DoDs were similar, and the DoDs approachedzero in the channel bed section. Both DoDs in part of
the area around 1100–1120 m downward were as much
as 0.8 m, which was confirmed to correspond with topical depressions in the channel deposits, using theorthophotograph.
Because nearly all the DoDs of the cross section werealso within ± 0.2 m, the profiles of the DEMs were similar (Fig. 7b). On the right bank, DoDTLS − SfM tended tobe larger than DoDTLS − SfM-ICP. After ICP-based alignment, the DoDs on the right bank were close to zero,
resulting in a decrease in the DoDs around the channel
bed. The DoDs in parts of the right and left banks weregreater than those around the channel bed, probably due
to steep hillslopes. However, the vast majority of the
DoDs corresponded to the change point in the profile
shape (inclination), e.g., at locations 20–45 m from theright bank.
From the viewpoint of differences in surface type, thespatial distribution of the slope domains was similar for theDEMs (Fig. 8). Moreover, histograms of the slope computed from the DEMs show the similarity in slope distribution (Fig. 9), indicating that the medians were similar, atapproximately 35°. The surface topography included steep
sections exceeding 38° (the angle of static friction for sediment particles, Imaizumi et al. 2017), but the density ofsuch steep areas was similar for the two DEMs.
Accuracy of topographic data acquired via UAV-SfM

The comparison of the DoDs around the channel demonstrates that all elevation differences between DEMSfM
and DEMTLS varied between about −0.4 and 0.4 m (Fig.
4). In addition, the spatial distributions and histogramsof the slope calculated using each DEM were similar(Figs. 8 and 9). Therefore, in the vicinity of the channel
bed in the headwater channel, the representability ofcomplex topography with UAV-SfM photogrammetry iscomparable to that with TLS.
Because DEMs acquired via TLS generally include
registration errors resulting from the uncertainty ofgeoreferencing and point matching, the true value of the
topography on the order of centimeters is unknown

Page 9 of 14(Obanawa and Hayakawa 2018). However, because therepresentative grain size of observed debris flows at thissite is approximately 0.1–0.3 m (Imaizumi et al. 2016),
topographic changes caused by flows nearly always exceed values on the order of decimeters (Hayakawa et al.
2016). The accuracy of DEMSfM is therefore sufficient todetect the entrainment and deposition of channel deposits induced by debris flows. Considering that it has
been reported that the grain size of debris flows is over
0.1 m in various debris flow torrents (e.g., Berti et al.
1999; McArdell et al. 2007), UAV-SfM may be able toaccurately detect changes in the sediment storage caused
by debris flows in many headwater channels.
A relatively large DoD (approximately 0.8 m) was topicallydetected around a small hole in the channel bed (Fig. 7a),probably due to the limitation of the TLS footprint. Undulations on the surface often behave as obstructions and occlusions in response to scanning positions (Schürch et al. 2011).
In this respect, shaded areas result in domains without measured points in the point cloud. This lack of points is generally interpolated based on the elevation of the surrounding
points when the point cloud is converted to a DEM. Considering this, the large DoD around the hole may result from inadequate interpolation arising from a lack of measurement
points by TLS. This suggests that in some cases, UAV-SfM
photogrammetry may surpass TLS for the detection of
microtopography in headwater channels.
Estimation of volume changes and effectiveness of the
ICP alignment

Considering the steep slope of hillslopes (Figs. 8 and 9),
topical differences in elevation may lead to over- or
under-estimation regarding channel deposits. As the elevations of DEMSfM in part of the right bank and channel
bed were low compared with those of DEMTLS (Fig. 3b),
the positive difference in volume was greater than thenegative difference (Fig. 5). As shown in Fig. 3b, theDEM acquired via UAV-SfM includes spatial heterogeneous DoDs due to topical distorted shapes caused by
the location and accuracy of the GCPs, camera shooting
directions, surface structures, and environmental conditions such as solar radiation and shade (James and Robson 2014). In other words, the DEM acquired by UAVSfM includes more or less uncertain measurement accuracy. However, when considering that the erosion volumes of channel deposits resulting from debris flows
obviously exceeded the differences in volume between
DEMSfM and DEMTLS (Fig. 6), the measurement accuracy of UAV-SfM is presumably adequate to investigatechanges in sediment storage in response to a single debris flow event.
By contrast, the analysis via multi-time series DEM acquired by UAV-SfM should be noted in terms of the accumulation of differences arising from topicalTsunetaka et al. Progress in Earth and Planetary Science(2020) 7:24

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Fig. 7 Comparison of the elevation and DoDs along (a) a profile of the channel bed and (b) a cross section through the channel, as indicated inFig. 1. The inset of (a) indicates the topical depressions of channel deposits

distortions. The topography of a channel bed is spatiallyaltered because of sediment discharge and recharge(Blasone et al. 2014; Imaizumi et al. 2017). Hence, eventhough we distributed four GCPs in the area (Fig. 1c),
the GCPs were compelled to shift to other stable positions in response to topographic changes. This makes it
impossible to control the spatial distribution and coordinate accuracy (e.g., the GNSS measurement) of the
GCPs, despite the condition of the GCPs being one of

the key factors for the reproducibility of UAV-SfM
photogrammetry (Clapuyt et al. 2016). Because of theunavoidable uncertainty arising from the technical limitations of UAV-SfM and the difference in measurement
accuracy of GCPs, the spatial distribution of such distortion will be different for each measurement date. These
topical differences in elevation are accumulated via theintegration of the sediment volumes calculated from thetime series DEMs acquired via UAV-SfM. Therefore,

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Fig. 8 Spatial distribution of slope domains. a DEMTLS. b DEMSfM. c DEMSfM-ICP. Slope domains were refereed, similar to Imaizumi et al. (2017)

there is a risk of over- or under-estimation or ostensible
offset of changes in sediment storage when time series
DEMs acquired via UAV-SfM are used.
Domains with DoDs exceeding 0.1 m in part of theright bank and channel decreased after ICP-based alignment (Fig. 3b, c). Therefore, the median of the DoDs in
the vicinity of the channel tended toward zero (Fig. 4),
and consequently, the difference in the estimated volume between the negative and positive values was
smaller compared with the DoDs excluding the ICPbased alignment (Fig. 5). When considering that the
DoDs on the right bank significantly decreased after
ICP-based alignment (Fig. 7b), these results indicate that
this alignment could decrease the topical distortion
caused by SfM-MVS. The hillslope topography in thevicinity of the channel bed was relatively unchanged overa long period, suggesting that ICP-based alignment using
hillslope topography acquired via previous TLS measurements would alleviate the accumulation of errors suchas topical distortions. Therefore, an ICP-based alignment
using stable topography acquired via TLS could be an effective process for investigating long-term changes in

sediment storage in a headwater channel based on
UAV-SfM.

Conclusions
The accuracy of a DEM acquired via UAV-SfM for complex topography in a headwater channel was validated
using a DEM and point cloud acquired via TLS. The results indicate that nearly all of the differences in themeasured elevation between UAV-SfM and TLS were
within ± 0.4 m, with a median of 0.06 m. Therefore, theshapes of the profiles acquired via UAV-SfM were similar to those acquired via TLS. Moreover, the spatial distributions and medians of the surface slope were similar.
For the differences in volume calculated by subtracting
the DEM acquired via UAV-SfM from that acquired via
TLS, the positive difference was 200 m3, and the negative
difference was −30 m3 because part of the elevation acquired via UAV-SfM was low compared with that acquired via TLS. After ICP-based alignment of the point
cloud acquired via UAV-SfM using a reference (TLS)
point cloud for the hillslope sections, the median of thedifferences in elevation decreased to −0.002 m.

Tsunetaka et al. Progress in Earth and Planetary Science(2020) 7:24

Page 12 of 14

Fig. 9 Histograms of the surface slope in the vicinity of the channel deposits. a DEMTLS. b DEMSfM. c DEMSfM-ICP. Similar to Fig. 4, the red line anddashed blue line indicate the cumulative distribution and the median value, respectively

Consequently, the deviation of the estimated volume
was reduced, resulting in both positive and negative differences of approximately 100 m3. This demonstrates
that the accuracy of DEMs acquired via UAV-SfM iscomparable with those acquired via TLS and that ICPbased alignment using point clouds acquired via TLS iseffective for the accurate estimation of time series volumetric changes with respect to channel deposits.
Abbreviations
ALS: Airborne laser scanning; GCP: Ground control point; GNSS: Global
navigation satellite system; ICP: Iterative closest point; RMS: Root-meansquare deviation; SfM-MVS: Structure from motion multi-view-stereo;

Tsunetaka et al. Progress in Earth and Planetary Science(2020) 7:24

TIN: Triangulated irregular network; TLS: Terrestrial laser scanning;
UAV: Unmanned aerial vehicle
Acknowledgements
The authors are grateful to Takeshi Masui, Yushi Yokota, Yuichi Sakai, and thestudents at Shizuoka University for their assistance in the field observations.
Authors’ contributions
HT conducted field measurements using an UAV and data analysis, anddrafted this manuscript. NH collaborated with the corresponding author in
the construction of the manuscript. YSH carried out TLS measurements inthe field and data analysis. FI helped field measurements. All authors readand approved the final manuscript.
Funding
This work was supported by JSPS KAKENHI grant numbers 16 J02197, 18
J01961, and 25702014.
Availability of data and materials
The data used in this paper are available from the authors on request.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Forestry and Forest Products Research Institute, 1, Matsunosato, Tsukuba,
Ibaraki 305-8687, Japan. 2Faculty of Agriculture, The University of Tokyo,
1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan. 3Faculty of Environmental
Earth Science, Hokkaido University, N10W5, Sapporo, Hokkaido 060-0810,
Japan. 4Faculty of Agriculture, Shizuoka University, 836, Ohya, Suruga-ku,
Shizuoka 422-8529, Japan.
Received: 5 December 2019 Accepted: 18 May 2020

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