Tsunetaka et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-021-00419-6(2021) 8:22

Progress in Earth andPlanetary Science

RESEARCH ARTICLE

Open AccessComparison of length and dynamics ofwood pieces in streams covered withconiferous and broadleaf forests mappedusing orthophotos acquired by an
unmanned aerial vehicle
Haruka Tsunetaka1* , Slim Mtibaa1, Shiho Asano1, Takashi Okamoto1 and Ushio Kurokawa2

Abstract
As wood pieces supplied by landslides and debris flows are one of the main components of ecological andgeomorphic systems, the importance of quantifying the dimensions of the wood pieces is evident. However, thelow accessibility of disturbed channels after debris flows generally impedes accurate and quick wood-piece
investigations. Thus, remote-sensing measurements for wood pieces are necessitated. Focusing on sub-watersheds
in coniferous and broadleaf forests in Japan (the CF and BF sites, respectively), we measured the lengths of woodpieces supplied by landslides (> 0.2 m length and > 0.03 m diameter) from orthophotos acquired using a small
unmanned aerial vehicle (UAV). The measurement accuracy was analyzed by comparing the lengths derived fromthe UAV method with direct measurements. The landslides at the CF and BF sites were triggered by extremely
heavy rainfalls in 2017 and 2018, respectively. UAV flights were operated during February and September 2019 at
the CF site and during November 2018 and December 2019 at the BF site. Direct measurements of wood pieces
were carried out on the date of the respective second flight date in each site. When both ends of a wood piece
are satisfactorily extracted from an orthophoto acquired by the UAV, the wood-piece lengths at the CF site can bemeasured with an accuracy of approximately ±0.5 m. At the BF site, most of the extracted lengths were shorter
than the directly measured lengths, probably because the complex structures of the root wad and tree crown
reduced the visibility. Most wood pieces were discharged from landslide scars at the BF site, but at the CF site,
approximately 750 wood pieces remained in the landslide scars approximately 19 months after the landslide
occurrence. The number of wood pieces in the landslide scars of the CF site increased with increasing landslidearea, suggesting that some wood pieces can be left even if large landslides occur. The lengths and locations of theentrapped wood pieces at both sites were not significantly changed between the two UAV flight dates. However,during this period, the rainfall intensities around the CF site measured by the closest rain-gauge of the Japan
Meteorological Agency reached their second highest values from 1976 to 2019, which exceeded the 30-year return
period. This suggests that most of the entrapped wood pieces rarely migrated even under intense rainfall.
Keywords: UAV, Wood piece, Debris flow, Landslide* 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). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Tsunetaka et al. Progress in Earth and Planetary Science(2021) 8:22

1 Introduction
Debris flow causes the entrainment of stand woods located in initiation and riparian zones; consequently, it
may include ~10–60% of fluvial wood pieces by volume(Johnson et al. 2000; May and Gresswell 2003a; Lancaster et al. 2003). In addition to destructive impact on life
and infrastructure (e.g., Ruiz-Villanueva et al. 2013),
wood pieces in a debris flow can alter the flow regime
because of their irregular shapes, which can be entrapped around obstacles and lead to anomalous deposition of sediment and inundations (e.g., May 2002;
Lancaster et al. 2003; Tang et al. 2018; Booth et al.
2020). The accumulation of wood pieces results in structural peculiarities around channel networks (e.g., Keller
and Swanson 1979; Woodsmith and Buffington 1996;
Montgomery et al. 1996; Nakamura and Swanson 1993).
This contributes to changes in the ecosystem, channel
morphology, and sediment flux through logjam formation and the decay of wood pieces (e.g., Wallace andBenke 1984; Lisle 1995; Montgomery et al. 1995; Gurnell
et al. 2001; Comiti et al. 2006; Ruiz-Villanueva et al.
2016). Hence, the river form and function are determined by the interaction between water, sediment, andwood (Nakamura et al. 2017; Swanson et al. 2021).
Therefore, quantifying wood pieces is important forassessing their impacts on ecological, geomorphological,
and fluvial conditions.
Many previous studies focusing on wood pieces instreams have unraveled the role of wood pieces in various spatiotemporal scales and environmental settings(e.g., Nakamura et al. 2017; Swanson et al. 2021), but
most of these approaches required direct field measurements such as local monitoring (e.g., Manners et al.
2007), the tracking of wood pieces (e.g., Ravazzolo et al.
2015; Wyżga et al. 2017), and field experiments on artificial wood pieces (e.g., Haga et al. 2002). Taking into account the diversity of forests around channels (e.g., age,
species, and density of trees), the monitoring and fieldsurvey of wood pieces is evidently an effective approach.
However, the necessity of human effort in the field hinders data acquisition in inaccessible areas (e.g., headwater channels and disturbed areas immediately afterlandslides and debris flows). In practice, field-data acquisition is difficult over large-scale areas exceeding the sizeof a sub-basin. Therefore, improved field-measurement
techniques for wood pieces are urgently required.
To address the measurement issues for wood pieces,remote-sensing approaches using three-dimensional data
have been applied. The use of LiDAR (light detection
and ranging) data clearly reduces the processing time required for mapping logjams and individual wood pieces(Kasprak et al. 2012; Abalharth et al. 2015; Atha andDietrich 2016). However, this technology is expensive
and is therefore available only in some regions.

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Alternatively, photogrammetry based on structure frommotion multiview stereo (SfM-MVS) using images captured by an unmanned aerial vehicle (UAV) can produce
three-dimensional data (e.g., digital surface models andpoint clouds), which has been proven to be more timeefficient than classical field surveys (Sanhueza et al.
2019). This approach overcomes data availability issues
and is relatively low cost. Nevertheless, most tests havebeen conducted in lowlands and flood plains rather thanin low-accessibility areas such as steep channels (e.g.,
Sanhueza et al. 2019). As the accuracy of threedimensional data acquired by SfM-MVS is greatly influenced by complex surfaces and obstacles—steep slopes,
large reliefs, and vegetation coverage (e.g., Fonstad et al.
2013; James and Robson 2014)—many unresolved uncertainties remain in SfM-MVS analyses of threedimensional data of wood pieces in steep and complex
areas such as channels impacted by landslides and debris
flows.
Aerial photography is a traditional source of twodimensional data that may provide more-or-less meaningful information about wood pieces. Even satellite images from Google Earth are being used for accurate
wood-piece mapping (Atha 2013; Ulloa et al. 2015).
Hence, the efficacy of aerial photography is evident, but
the accuracy and effort of mapping depend on the image
quality. In this respect, it is expected that small UAVs
may allow the acquisition of high-resolution aerial photographs at low cost because of the lower flight altitudes
and higher portability of UAVs than conventionalmanned aerial vehicles. Moreover, because flights ofsmall UAVs can overcome inaccessibility issues andcover several kilometers on the horizontal scale (depending on the flight design), a UAV is a fairly attractive tool
for obtaining wood-piece measurements in lowaccessibility areas.
In addition, wood pieces are often quickly removed byriver administrators to avoid unexpected damages in thedownstream area resulting from its transport by streamflow after being supplied and entrapped in streams(hereinafter this is referred to as secondary transport).
The risk arising from the secondary transport of woodpieces (e.g., damages on facilities in residential regions)has not been evaluated properly so far because it has
been difficult to conduct field surveys of the wood-piece
supply immediately after intense rainfall. In contrast,
UAV flights can be conducted immediately after rainfall
events that result in a large number of wood pieces inchannels, which can potentially contribute to unravelingwood-piece dynamics after their supply and entrapment.
Therefore, an efficient mapping method of wood pieces
enables the investigations of wood-piece transport due
to subsequent rainfall, even if it is carried out with asimple method using aerial photography. Nevertheless,Tsunetaka et al. Progress in Earth and Planetary Science(2021) 8:22

the potential to measure wood pieces based on aerial
photographs acquired using UAV has not been thoroughly examined due to lack of sample cases.
In this study, we analyze the accuracy of UAV-based
measurements for wood-piece lengths. Two regions inJapan are selected representing two forest types, coniferous and broadleaf forests. In both regions, a large number of wood pieces were supplied to streams throughlandslides and debris flows triggered by a single rainfall
event. This research has two main objectives: (1) toanalyze the capability of orthophotos acquired via UAVs
and hence measure the lengths of wood pieces and (2)to investigate whether entrapped wood pieces moveagain in subsequent rainfall. Based on the results, we discuss the effectiveness of UAV measurements and how
wood pieces behave after their entrapment.

2 Study sites
To examine the influence of tree type on the accuracy ofwood-piece measurements obtained using the orthophotos acquired by the UAV, we selected two different sites
covered by different forest types (coniferous and broadleaf) in Japan (Fig. 1a). Hereafter, the former site is referred to as the CF (coniferous forest) site (Fig. 1b),whereas, the latter site is referred to as the BF (broadleaf
forest) site (Fig. 1c). In this study, we use the term “treetype” to indicate either coniferous or broadleaf trees rather than the detailed tree species.
2.1 Coniferous forest site

The CF site is a sub-watershed of the Otoishi watershedlocated in the northern part of Kyushu Island, Japan(Fig. 1a, b). Its highest point is approximately 385 m
above sea level (a.s.l.), and its lowest point isPage 3 of 16

approximately 225 m a.s.l. The total length of the main
channel is approximately 760 m with an average slope ofapproximately 12° (before the debris-flow occurrence),
and the drainage area is approximately 0.15 km2. To
prevent the destabilization of hillslopes and banks, fiveclosed-type check dams (check dams without slit andsteel lattice structures for trapping wood pieces andboulders, e.g., Piton and Recking 2016) were constructed
before the debris flow occurred in 2017 (Fig. 2a). Most
hillslopes are covered with artificial coniferous foreststhat consist mainly of Cryptomeria japonica and Chamaecyparis obtusa. The tree heights range from approximately 15 to 25 m.
In the region around the CF site, an intense rainfall on
July 5, 2017, triggered more than 2000 landslides, whichtransformed into debris flows, resulting in a large numberof wood pieces (Chigira et al. 2018). The rainfall event thattriggered these landslides has been recognized as extremely
heavy rainfall that led to intense rainfall intensity over 9 h
in nearly the same location (Kato et al. 2018). This event resulted from extremely tall convection (Tsuji et al. 2020)
generated by organized mesoscale convective systems(Unuma and Takemi 2021) and increased atmosphericmoisture convergence due to global warming (Imada et al.
2020). The sliding sediment layers were mainly granodiorite
and pelitic schist (Chigira et al. 2018). The intense rainfall
in July 2017 triggered six landslides at the CF site (Fig. 2a),but the existing check dams effectively prevented the descent of sediment and wood pieces. Hence, most of thewood pieces were trapped around the check dams andalong the channel, thereby preventing damage in the downstream residential areas. Because of this low impact on theresidences, the wood pieces were not removed and were left
in situ until the time we carried out observations.

Fig. 1 Study site: a overview, b locations of the coniferous forest (CF) site and the rain-gauge station (Asakura station), and c locations of thebroadleaf forest (BF) site and the rain-gauge station (Higashihiroshima station)

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Fig. 2 Orthophoto of the study sites: a the coniferous forest (CF) site and b the broadleaf forest (BF) site. The right side indicates the upper partof the channel (i.e., the streams descend from north to south). The white solid lines indicate the landslide scars. The white dotted line in panel a
indicates the boundary of sub-watershed

2.2 Broadleaf forest site

The BF site is located in Hiroshima Prefecture in thewestern part of Japan (Fig. 1c). Here, two channels areconfluent at an irrigation pond that is located at thelower ends of channels (~305 m a.s.l., Fig. 2b). The highest points of channels A and B are ~410 and ~480 ma.s.l., respectively. Their total lengths are ~880 and ~466m, respectively. The average slopes of the two channels
are similar, at ~11–12° (before the debris flow occurrence). The drainage areas of channels A and B are~0.07 and ~0.13 km2, respectively. Similar to the case of
the CF site, four closed-type check dams were constructed before the debris flow occurred in 2018 (Fig.
2b). The forest type completely differs from that at theCF site, and most of the hillslopes are covered by broadleaf forests with various tree types. The tree heights
range from approximately 10 to 20 m.
In the west part of Japan, a stationary front affected by
Typhoon Prapiroon caused heavy rainfall on July 5–7,
2018 (Tsuguti et al. 2019), and triggered approximately
8000 landslides in Hiroshima Prefecture due to the vulnerable geological setting, which is dominated by weathered granite (Kaibori et al. 2018). The extremely heavy
rainfall event that triggered these landslides resulted inhigh rainfall intensity over 72 h (Sueki and Kajikawa

2019), probably due to anomalies in temperature andspecific humidity (Tsuji et al. 2020) arising from mesoscale convective systems (Unuma and Takemi 2021) andglobal warming (Imada et al. 2020). Five landslides occurred at the BF site (Fig. 2b), but the check dams andthe pond effectively prevented the descent of sedimentand wood pieces. Similar to the situation at the CF site
after the disaster, the wood pieces produced in the BF
site were left in situ until we carried out theobservations.

3 Methods
This study consists of two parts: (1) an assessment of
the accuracy of the wood-piece measurements obtained
from ortho-photography acquired via the UAV and (2) arainfall analysis to investigate the possible transport ofentrapped wood pieces.
3.1 UAV flights and aerial-photograph processing

A small consumer-grade UAV (DJI Mavic 2 pro; Table 1)
was used for the flights covering the channels and landslides (Fig. 2). Two UAV flights were conducted at each of
the respective study sites (Table 2): (1) February 14 and
September 26, 2019, at the CF site and (2) November 16,
2018, and December 25, 2019, at the BF site. The flights

(2021) 8:22

Tsunetaka et al. Progress in Earth and Planetary Science

Table 1 Properties of the UAV and the installed camera
UAV
Model

DJI Mavic 2 pro

Weight907 g

Diagonal size

354 mmMax. duration of flight

31 minCameraNumber of pixels

20 M

Sensor size

13.2 × 8.8 mm

Focal length∞

were conducted manually at heights above ground between approximately 50 and 150 m, and most of the photographs were taken in the nadir direction. The flight path
provided at least 70% overlaps in the forward and side directions of the aerial photographs. To reduce the differences in shade and brightness among the acquired aerial
photographs, the flights were operated avoiding strong
sunlight.
The acquired images were processed using the SfMMVS photogrammetry software (Agisoft, Metashape
Professional version 1.5.1). Usually, artificial reference
markers are established, and their absolute coordinates
are measured with a GNSS (global navigation satellite
system), providing ground control points (GCPs) for
geo-referencing the SfM-MVS photogrammetry (e.g.,
Barnhart et al. 2019; Tsunetaka et al. 2020). However,immediately after a debris-flow disaster, the establishment of artificial reference markers is impeded by woodpieces, which behave as obstacles. The recently developed, high-accuracy, RTK (real time kinematic) GNSSbased direct geo-referencing system built into a UAV for
SfM-MVS photogrammetry processing makes it possible
to obtain accurate absolute coordinates without GCPs
(e.g., Carbonneau and Dietrich 2017). However, it is an
expensive technology, and the consumer-grade UAV
that we used does not include such an RTK-GNSS system. Because of these operational limitations, we focusedon the ability to make wood-piece measurements using
relatively low-cost UAV-based processing. Our intention
was thus to assess the accuracy of measurements using
Table 2 Dates of UAV flights and number of aerial images used
to provide orthophotos
Date

Number of aerial images

First flight

February 14, 2019159

Second flight

September 26, 2019284

CF siteBF siteFirst flight

November 16, 2018315

Second flight

December 25, 2019141

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only UAV flights without any other field operations (e.g.,setting artificial reference markers) or RTK-GNSS
systems.
The position data from the built-in GNSS that controlled the flight position were contained in exchangeable image file format data for each photograph. Using
these data, we preliminarily collected the coordinates ofthe processed orthophotos. Following standard SfMMVS photogrammetry workflow, the orthophotos were
produced with a spatial resolution of 0.03–0.05 m. Because the measurements were performed without establishing artificial reference markers, the coordinates of
the resulting orthophotos taken during the different
flight dates did not overlap accurately. As the principal
aim of using sequential orthophotos was to investigate
the transport of the wood pieces, it is necessary only tohave relative coordinate system overlap (i.e., absolute coordinate system overlap is not required). Thus, we
manually adjusted two sequential orthophotos using thecrests of the check dams as GCPs for which the coordinates were taken from the older orthophoto. Using theextracted coordinates, the orthophoto acquired on thelater date was transformed to fit the first one. The differences in the lengths of selected immobilized objects(e.g., large boulders and the crests of check dams) wereless than approximately 0.2 m. Therefore, we considered
the horizontal accuracy of the orthophotos to be lessthan 0.2 m.
3.2 Accuracy assessment

Because we used orthophotos (i.e., two-dimensionaldata) to quantify the wood pieces, the dimension ofwood pieces measured by the UAV is its length. First,
we extracted the length of the wood pieces, both as asingle piece and as an element of a jam-forming log
as line data from the acquired orthophotos. Note thatinvisible wood pieces below a jam-forming log are outof the measurement target. All visible wood pieces
were extracted at the CF site (the area surrounded by
the dotted white line in Fig. 2a), whereas only woodpieces around check dams A and B were investigatedat the BF site because of the low visibility of woodpieces originating from broadleaf trees (Fig. 2b). Accordingly, the measurement range differed from areach scale at the BF site to a sub-watershed scale at
the CF site.
The measurement accuracy of the extracted length ispresumably affected by visibility, which is related to thetypes and sizes of the trees (e.g., length and diameter).
Moreover, because wood pieces are often inclined andbroadleaf trees have a curved shape, orthogonal projection to obtain the extracted length may cause a measurement error (Fig. 3). Hence, we focused on the
differences among the tree types (i.e., coniferous or

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Fig. 3 Schematic representation of the components for the direct measurement of wood pieces: a the coniferous tree and b broadleaf tree. The
left side indicates the top of each type of tree (the side of the tree crown). The right side depicts the root wad

broadleaf trees), diameters, and entrapment inclinationsof the wood pieces.
To compare with the extracted lengths of the woodpieces, we directly measured wood pieces at the study
sites (Fig. 3). Based on the orthophotos from the firstflight, the visible wood pieces were labeled. When we
conducted the second flight, we found and directly measured some of the labeled wood pieces (50 wood pieces
at the CF site on September 26, 2019, and 131 woodpieces at the BF site on December 25, 2019). In thefound wood pieces, we measured the lengths—excluding
the root wads and twigs—directly using a tape (estimated precision < 0.05 m) (Fig. 3), similar to the approach of Sanhueza et al. (2019) and compared thosemeasurements with the lengths extracted from theorthophotos from the first UAV flight. Using the sametape, we measured the circumferences of the foundwood pieces at approximately 1.0–1.5 m height from theroot wad. For wood pieces less than approximately 1.5
m in length, because there were only slight differencesin the thickness of a single fragment, we measured thecircumference at an arbitrary location. From the measured circumferences, we calculated the diameters of thefound wood pieces. Note that, because the minimum
measurable circumference was approximately 0.1 m,
some of the calculated diameters of the wood pieces
were smaller than the estimated precision of the tapemeasurement (0.05 m). At the CF site, as the woodpieces retained relatively straight shapes, the entrapment
inclination was measured directly using an angle meter(estimated precision < ±1°, Fig. 3a).

The extraction of wood pieces depends on its visibility
in the orthophotos, and thus, the identification of its origin as well as the detection of small wood pieces of lessthan the minimum resolution of the orthophotos (i.e.,
0.03 m in diameter) was beyond the measurement purpose. In addition, the successful extraction of woodpieces from the orthophotos was constrained by the visibility of the wood-piece length, not by the visibility of
the wood-piece width (diameter). Considering the horizontal accuracy of the orthophotos (< 0.2 m), the minimum length of the extracted wood pieces was set to 0.2
m. A “large wood” piece is typically defined as a woodpiece > 0.1 m in diameter and > 1 m in length (e.g., Jackson and Sturm 2002; Tang et al. 2018). Note that, ourdataset included small wood pieces (> 0.03 m in diameter and > 0.2 m in length) because of the minimum
measured diameters and the lengths of the wood pieces.
Moreover, the extracted wood pieces may contain multiple pieces that originated from a single tree. Note that,as a single stand wood may result in several wood pieces,counting the extracted wood pieces involves uncertainty.
3.3 Analysis to determine the transport of wood pieces

To analyze whether or not wood pieces migrated
through time, we created the quantile–quantile (Q–Q)
plots of the probability density of the extracted length.
The CF site was divided into seven zones: six landslide
scars and the riparian zone with a channel approximately 760 m in length (Fig. 2a). Changes in the probability density of the wood-piece length were visuallytested using the Q–Q plots. Similarly, Q–Q plots were

Tsunetaka et al. Progress in Earth and Planetary Science(2021) 8:22

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made for channels A and B at the BF site (Fig. 2b). For
each zone, to investigate the coincidence of the probability
density of the extracted length between the two flight dates(Table 2)—(1) February and September, 2019 (i.e., approximately 7 months), at the CF site and (2) November, 2018,and December, 2019 (i.e., approximately 13 months), at theBF site—we conducted a goodness-of-fit test using the Kolmogorov–Smirnov test (e.g., Sane et al. 2018).
In addition, we investigated whether specific rainfall
characteristics triggered the secondary transport of woodpieces. For this reason, we used the hourly rainfall records from 1976 to 2019 obtained at the closest rain
gauges installed by the Japan Meteorological Agency, theAsakura station (for the CF site, Fig. 1b) and the Higashihiroshima station (for the BF site, Fig. 1c). The annual
maxima of rainfall intensity of various durations (1, 2, 3,
6, 12, 24, 48, and 72 h) were investigated from 1976 to2019 and compared with the intensity of the rainfall thattriggered the debris flows. In addition, we examined therecords of rainfall maxima after the debris-flow occurrence. The return periods were calculated based on theprobability density of each rainfall intensity fitted by aGumbel distribution based on the annual maxima (e.g.,
Koutsoyiannis et al. 1998; Sane et al. 2018). For this fitting process, the goodness-of-fit test (Kolmogorov–
Smirnov test) resulted in p-values exceeding 0.1, suggesting that the applied Gumbel distribution models well fitted the probability density of the observed rainfall.

4 Results
4.1 Lengths of the entrapped wood pieces

As expected, a comparison of the extracted and measured wood-piece lengths indicates that the measurement accuracy depends on the visibility of the woodpieces. Fifty wood pieces were directly measured at theCF site (Fig. 4a). Fourteen of them were trapped bystand woods located in the riparian zone, and they werepartially invisible from the sky due to the coverage by
the tree crowns. This low visibility caused underestimates of the extracted lengths, with errors in length being a maximum at ~10 m. In contrast, 36 other pieces
were fully visible, and their lengths ranged between
about 3 and 22 m. Despite this wide range, most measurements coincided well with the extracted lengths.
This clear dependence on the visibility of the woodpieces is reflected in the boxplot (Fig. 5), indicating thatthe interquartile ranges obviously differed between thevisible and covered wood pieces; the differences ranged
from −0.4 to 0.5 m and from 1.6 to 4.3 m, respectively(Fig. 5).
The lengths of 130 wood pieces without crown coverage were directly measured at the BF site, but they rarely
coincided with the extracted lengths, which were usually
underestimates (Fig. 4b). Moreover, the differences

Fig. 4 Comparison between extracted and measured lengths ofwood pieces: a the coniferous forest (CF) site and b the broadleaf
forest (BF) site

between the lengths varied. The related interquartile
range varied between approximately 0.3 and 2.1 m,
reflecting the lower accuracy and higher uncertainty
when measuring broadleaf trees than when measuringconiferous trees (Fig. 5).
Strictly speaking, the extracted length was the orthogonally projected length, and we therefore calculated the slope distance of the measured wood pieces
from the extracted length using the measured slope.
However, this correction did not significantly improvethe measurement accuracy because differences in theTsunetaka et al. Progress in Earth and Planetary Science(2021) 8:22

Fig. 5 Boxplot of differences between the extracted and measured
lengths of the wood pieces. Differences in length were calculated
by subtracting the extracted length from the measured length

visibility of the wood pieces depending on the difference in the tree types overwhelmed the influence arising from the approximation by the orthogonal
projection (Figs. 4a and 6). The differences in thewood-piece diameters indicate that the extracted
lengths of relatively narrow pieces (< ~0.2–0.3 m indiameter) at the BF site tend to be underestimated(Fig. 7), despite the absence of crown and logjam coverages. Note that, especially for wood pieces with diameters less than 0.1 m, direct measurements(estimated precision < 0.05 m) and orthophotos (estimated precision < 0.2 m) can contain significant
measurement error independent of the differences intree types. However, even for wood pieces with diameters between 0.1 and 0.2 m, the measurement errors
were higher at the BF site than at the CF site (without coverage, see Fig. 7). This suggests that the decreased visibility of small-diameter pieces was
responsible for the decreased measurement accuracy
at the BF site.
Most wood pieces were discharged from landslide
scars at the BF site, whereas a total of approximately 750
wood pieces (exceeding 50% of the total of ~1370 woodpieces) were left in the landslide scars at the CF site. At
the CF site, the interquartile of the wood-piece length
differed slightly among the landslide scars, but they weresimilar between the riparian zone and the average of alllandslides, 2.5 to 7.7 m and 2.6 to 8.3 m, respectively(Fig. 8a). The median length of the wood pieces ranged
from 3.4 (in landslide 4) to 7.8 m (in landslide 1). At the

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Fig. 6 Comparison between corrected extracted length andmeasured length of the wood pieces at the coniferous forest (CF)site. The corrected extracted length indicates that the inclined
length was corrected from the extracted length (i.e., orthogonallyprojected length) using the directly measured inclination of thewood pieces

BF site, the interquartile of the wood-piece length atcheck dams A and B were 1.1 to 3.1 m and 0.8 to 2.6 m,
respectively (Fig. 8b). The median lengths of the woodpieces at check dams A and B were similar: 1.8 and 1.5m, respectively. The median wood-piece length at theFig. 7 The differences between the extracted and measured lengthsof the wood pieces at both broadleaf forest (BF) and coniferous
forest (CF) sites as functions of the measured diameter. Differencesin length were calculated by subtracting the extracted length fromthe measured length, similar to Fig. 5

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Fig. 8 Boxplots of the extracted lengths: a the coniferous forest (CF) site, b the broadleaf forest (BF) site, and c all measured lengths. In panel a,the labels of the landslides correspond to Fig. 2a (“All” indicates the total number of landslides). The wood piece extracted in the area excluding
landslide scars was regarded as the riparian zone (Fig. 10)

CF site was ~4.5 m, which is evidently greater than thatat the BF site (Fig. 8c).
The areas of the landslide scars at the CF siteranged widely, from ~800 to 5400 m2 (Fig. 9). The
number of entrapped wood pieces tended to increase
with the increase in the area of the landslide scars(Fig. 9). Landslide 2 (~5400 m2) accounted for approximately 40% of the total area of the landslide
scars (i.e., the total landslide area at the CF site is~14,000 m2), and thus, this landslide was the predominant sediment source at the CF site. Despite thishuge sediment discharge, the number of entrappedwood pieces was highest among the landslide scars, atapproximately 350 wood pieces.

4.2 Transport of entrapped wood pieces

At both sites, there were only slight visible changes inthe spatial distribution of the wood pieces between
the two orthophotos (Figs. 10 and 11). The woodpieces were trapped in various areas—for instance, aswood pieces in the riparian zone, the check dams,
and the landslide areas. Despite these differences inthe entrapment zones, it seems that most of the woodpieces did not move even after about 7 months (atthe CF site, Fig. 10) and over 1 year (at the BF site,
Fig. 11). The Q–Q plots indicate that the probability
density of the extracted wood-piece length coincided
well with the measured length, regardless of the division of the entrapment zones (Fig. 12). Thus, the p-

Tsunetaka et al. Progress in Earth and Planetary Science(2021) 8:22

Fig. 9 Comparison between landslide area and the number ofremaining wood pieces at the coniferous forest (CF) site. The blackline is the linear regression line

values in the goodness-of-fit test exceeded 0.05, suggestingthatthechangeswerestatisticallyinsignificant.
The rainfall anomalies that triggered debris flows wererevealed by comparing the annual maxima of the rainfall
intensity in each case. At the CF site, the rainfall that triggered the debris flow in 2017 was obviously excessive rainfall, as the rainfall intensity was the highest compared withthe annual maxima of other years, regardless of the rainfall
duration (Fig. 13a). This suggests that the highly intenseand continuous rainfall resulted in the large number ofPage 10 of 16

wood pieces. At the BF site, for the rainfall that triggered
debris flows in 2018, rainfall intensities with 1–6 h durations were not higher than the other annual maxima since
1976, but those with 12–72 h durations were the most intense since 1976 (Fig. 13b). This suggests that a high rainfall intensity continuing over 12 h resulted in landslides
and debris flow accompanied by the propagation of woodpieces. Both rainfall events that triggered debris flows at
the CF and BF sites reached levels exceeding the 100-year
return period (Tables 3 and 4).
At the BF site, after the landslides and debris flows in
2018, the maximum annual rainfall intensity in 2019 was
not remarkable (Fig. 13b, Table 4). Thus, although mostwood pieces remained in place, it is difficult to analyze
the potential of secondary transport of entrapped woodpieces via intense rainfall. In contrast, at the CF site,
after the landslides and debris flows in 2017, the annual
rainfall intensity maxima in 2018 and 2019 were the second or third highest values since 1976 (Fig. 13a). Although the intensity of the triggering rainfall in 2017
significantly exceeded the annual maxima in 2018 and2019 in terms of the return period, some rainfall intensities in 2018 and 2019 exceeded the level of the 30-year
return period (Table 3). Such a heavy rainfall event occurred on July 21, 2019 (between the UAV flights at theCF site; Table 2).

5 Discussion
5.1 Implications for UAV-based wood-piece mapping

Our results demonstrate that the visibility and tree typesof the wood pieces determine the ability to measure

Fig. 10 Spatial distribution of extracted wood pieces at the coniferous forest (CF) site: a February 14, 2019, and b September 26, 2019

Tsunetaka et al. Progress in Earth and Planetary Science(2021) 8:22

Page 11 of 16

Fig. 11 Spatial distribution of extracted wood pieces at the broadleaf forest (BF) site: a check dam A on November 16, 2018, b check dam A on
December 25, 2019, c check dam B on November 16, 2018, and d check-dam B on December 25, 2019

their lengths accurately using orthophotos acquired via
UAVs. The lengths of most of the wood pieces originating from coniferous trees were measured with better
than ±0.5 m accuracy using the orthophotos acquired by
the UAV, but for the broadleaf trees, the lengths were
underestimated systematically despite the absence oftree-crown coverage reducing the visibility (Figs. 4b and5). Considering the complex structures of both ends ofthe broadleaf trees (i.e., wide and rounded crowns androot-wads; Fig. 3b), these underestimates may be

attributed to missing extractions due to the low visibilityof the broadleaf trees arising from the complex structures at the ends. In addition, the lengths of the entrapped wood pieces at the BF site was less than that at
the CF site (Fig. 8), implying the fragmentation of broadleaf trees during the debris flow. Although the characteristics of the debris flow also probably influence themagnitude of wood-piece fragmentation (e.g., Johnson
et al. 2000; Lancaster et al. 2003), the fragility of broadleaf trees may reduce the measurement accuracy based

Tsunetaka et al. Progress in Earth and Planetary Science(2021) 8:22

Page 12 of 16

Fig. 12 Q–Q plots for the lengths of the extracted wood pieces: a–g the coniferous forest (CF) site and h–i the broadleaf forest (BF) site. The pvalue indicates the result of the goodness-of-fit test (the Kolmogorov–Smirnov test)on the orthophoto at the BF site by decreasing the visibility of the wood pieces.
Hence, most of the factors that decrease the measurement accuracy of wood pieces depend on visibility.
Based on this interpretation, wood-piece measurements
via UAV have both positive and negative aspects. Several
components that affect the quality of the aerial photographs (e.g., black shadows, brightness involving overexposure, and insufficient overlap ratios among the aerial
photographs) depend on the weather conditions and theflight plan (e.g., the path, height, and direction ofshooting). For example, it is expected that low flight altitudes and flights under cloudy conditions can avoid
block-out shadows and blown-out highlights in the acquired UAV images, resulting in an increase in the visibility of wood pieces in the processed orthophotos.
Thus, an adequate flight plan may significantly improve
the accuracy of UAV-based wood-piece measurements,
without additional processing in the SfM-MVS
photogrammetry.
Conversely, our results reveal an obvious limitation oforthophoto-based measurements. In general, wood

Tsunetaka et al. Progress in Earth and Planetary Science(2021) 8:22

Page 13 of 16

Fig. 13 Curves for the annual maxima of rainfall intensity: a the coniferous forest (CF) site and b the broadleaf forest (BF) site

pieces are not only dispersed along a channel but also
result in logjam due to stacking and accumulation (e.g.,
Manners et al. 2007; Abalharth et al. 2015). However,the correlation between measurement accuracy and visibility from the sky demonstrates that the length
Table 3 Return period (RP) for the annual maxima of rainfall
intensities at the CF site
RP1h

RP2h

RP3h

RP6h

RP12h

RP24h

RP48h

RP72h

> 100

> 100

> 100

> 100

> 100

> 100

> 100

Table 4 Return period (RP) for the annual maxima of rainfall
intensities at the BF site

(year)

RP1h

2017

> 100

2018

1.66

2.68

3.47

10.84

17.60

20.70

33.67

17.05

2018

2019

10.32

11.98

14.18

33.46

32.22

15.86

11.17

11.31

2019

RP2h

RP3h

RP6h

RP12h

RP24h

RP48h

RP72h

15.60

14.67

11.88

13.70

> 100

> 100

> 100

> 100

3.77

2.76

1.90

2.36

2.13

2.00

1.77

1.67

(year)

Tsunetaka et al. Progress in Earth and Planetary Science(2021) 8:22

measurements of wood pieces covered by a logjam arebeyond the scope of orthophoto-based measurements.
Sanhueza et al. (2019) pointed out that vegetation in
streams also may cover wood pieces and thus decrease
the measurement accuracy of wood pieces based on images acquired by UAV. Indeed, the tree-crown coverage
decreased the measurement accuracy of wood-piece
length (Fig. 4a). Given these measurement limitations, it
is especially difficult to measure jam-forming woodpieces in streams deeply covered by vegetation (e.g., riparian trees) from the images acquired by a UAV.
In other words, the total number of wood pieces isprobably underestimated in all UAV-based measurements. In this respect, the entrapment of wood pieces inlandslide scars at the CF site may also have been underestimated, suggesting that high-ratio wood pieces wereleft even in slipped hillslopes. In some sub-watershedswith coniferous forests located around the CF site, nowood pieces were left in the landslide scars (Marutani
et al. 2017). Thus, the difference in tree type (i.e., coniferous or broadleaf trees) cannot account for the highratio of wood-piece entrapments in the landslide scars at
the CF site compared with that at the BF site. The median length of wood pieces varied among landslide scars(Fig. 8a), probably depending on differences in runout
processes and sediment volume among landslides. Considering this, the increase in the number of entrappedwood pieces with the increase in the landslide area (Fig.
9) indicates that wood pieces may remain in landslide
scars regardless of differences in size and the dynamics
of landslides. It seems that the outlets of landslide scars
at the CF site are relatively narrow (like bottle-neckedshapes) compared with those at the BF site (Fig. 2),
which may lead to high-ratio wood-piece entrapments.
5.2 Possibility of the transport of the entrapped woodpieces

The slight changes in the lengths and spatial distributions of the wood pieces suggest that the wood pieces
entrapped by the check dams were only slightly
shifted by the debris-flow disaster (Figs. 10, 11, and12). The elapsed time from the first flight was about
7 months for the CF site and over a year for the BF
site (Table 2), which may be insufficient to cause significant visible decay. This means that intact woodpieces still remain in the upper reaches of the channels. However, considering the slight differences inthe spatial distribution and Q–Q plot of the woodpieces at the CF site (Figs. 10 and 12), the high rainfall intensities in 2019 (Fig. 13a and Table 3) suggestthat most of the entrapped wood pieces remain inplace and rarely migrates, even when intense rainfall
exceeding the level of the 30-year return period occurs. This implies that most rainfall events that doPage 14 of 16

not cause debris flows and landslides cannot trigger
secondary transport of entrapped wood pieces.
Because the pre-existing check dams trapped woodpieces at both sites, differences in the trend between
such artificial trapping and natural entrapment of woodpieces should be noted. Even so, the low transportabilityof the entrapped wood pieces provides insight into therole of the wood pieces and sediment deposited in the
upstream channel after debris flows and landslides. The
efficacy of wood-piece trapping by closed-type checkdams (check dams without slit and steel lattice structures) is generally considered relatively lower than thatof open-type check dams (e.g., Piton and Recking 2016).
Nevertheless, the entrapment of wood pieces by closedtype check dams continued in the long term, due to thelow possibility of secondary transport at our study sites.
May and Gresswell (2003b) argued that wood pieces instreams can facilitate sediment deposition, thereby
resulting in an increase in sediment storage. Indeed, thelow transportability of the entrapped wood pieces in the
CF and BF sites indirectly indicates low erosion and discharge rates of the sediment stored after a landslide occurrence, suggesting that the entrapped wood pieces can
impede subsequent sediment transport. For this reason,
the trapping of wood pieces in low-order streams during
and immediately after a landslide occurrence is important for mitigating subsequent impacts of the woodpieces and sediment on the facilities in residential
regions.

6 Conclusions
We mapped wood pieces using orthophotos acquired bya UAV in two headwater channels covered by different
forest types in Japan (coniferous and broadleaf forests at
the CF and BF sites, respectively) after the landslide
events due to heavy rainfall events in 2017 and 2018, respectively. The aims were twofold: (1) to determine
whether or not ortho-photography acquired using a
UAV allows measurements of wood-piece lengths basedon comparisons between the UAV method and direct
measurements and (2) to investigate whether entrappedwood pieces can be transported by intense rainfall.
At the CF site, comparing the directly measured
lengths with the lengths extracted by UAV mapping indicates that aerial-photography measurements had approximately ±0.5 m accuracy when both ends of thewood pieces were satisfactorily visible. In contrast, somewood pieces were trapped by stand woods in the riparianzone, which were invisible underneath the tree crowns,
and this led to underestimation of the extracted lengths.
At the BF site, most of the extracted length values wereshorter than the lengths measured directly, probably due
to low visibility of the ends of the wood pieces due tocomplex tree structures in the root wads and tree

Tsunetaka et al. Progress in Earth and Planetary Science(2021) 8:22

crowns. Hence, these results suggest that the measurement accuracy of wood pieces via UAV strongly depends
on the visibility of the wood pieces arising from tree
types. In other words, although flight operations that adequately increase the visibility of wood pieces in UAV
images may improve the accuracy of wood-piece length
measurements, the UAV-image-based approach is fundamentally inadequate for measuring jam-forming woodpieces, especially in streams covered by riparian trees.
Most wood pieces did not remain in the landslide
scars at the BF site, whereas approximately 750 woodpieces were left in the landslide scars at the CF site. At
the CF site, the number of remained wood pieces in thelandslide scar tended to increase, depending on the increase in landslide area. Therefore, even large landslides
cannot transport all wood pieces from their slip hillslopes in some cases, suggesting that wood pieces may
remain in landslide scars regardless of the landslide area.
At both sites, changes in the frequency distribution of
the lengths and the locations with respect to entrappedwood pieces were slight even over 7 months had passed
since the first mapping. Nevertheless, at the CF site,
some rainfall intensities between the two flights reached
the second-highest values since 1976. Although the rainfall intensity that triggered the landslides in 2017 washigher than the annual maxima in 2019, some intensity
values in 2019 exceeded the 30-year return period. Thissuggests that most of the entrapped wood pieces in any
entrapment area remain and rarely re-migrate, evenunder intense rainfall.
Abbreviations
BF: Broadleaf forest; CF: Coniferous forest; GCP: Ground-control point;
GNSS: Global navigation satellite system; LiDAR: Light detection and ranging;
RTK: Real time kinematic; SfM-MVS: Structure from motion multiview stereo;
UAV: Unmanned aerial vehicle
Acknowledgements
The authors are grateful to the staff of Forest Agency, Japan, for theirassistance in the field observations. The constructive anonymous reviews andhelpful comments from the editor significantly improved the quality of thepaper and are greatly acknowledged.
Authors’ contributions
HT conducted field measurements using a UAV and data analysis anddrafted this manuscript. SM conducted field measurements and rainfall
processing. SA, TO, and UK carried out field measurements. All authors readand approved the final manuscript.
Funding
This work was supported by the project “Development of technology for
impacts, mitigation and adaptation to climate change in the sectors ofagriculture, forestry and fisheries” of the Agriculture, Forestry, and Fisheries
Research Council.
Availability of data and materials
The data used in this paper are available from the authors upon request.

Declarations
Competing interests
The authors declare that they have no competing interest.

Page 15 of 16

Author details
Forestry and Forest Products Research Institute, 1, Matsunosato, Tsukuba,
Ibaraki 305-8687, Japan. 2Kyushu Research Center, Forestry and Forest
Products Research Institute, 4-11-16, Kurokami, Kumamoto, Kumamoto
860-0862, Japan.
1

Received: 27 August 2020 Accepted: 12 March 2021

References
Abalharth M, Hassan M, Klinkenberg B, Leung V, McCleary R (2015) Using LiDAR
to characterize logjams in lowland rivers. Geomorphology 246:531–541.
https://doi.org/10.1016/j.geomorph.2015.06.036
Atha JB (2013) Identification of fluvial wood using Google Earth. River Researchand Applications 30:857–864
Atha JB, Dietrich JT (2016) Detecting fluvial wood in forested watersheds using
LiDAR data: a methodological assessment. River Research and Applications
32(7):1587–1596. https://doi.org/10.1002/rra.2989
Barnhart KR, Rengers FK, Ghent JN, Tucker GE, Coe JA, Kean JW, Smith JB, Staley
DM, Kleiber W, Wiens AM (2019) Topographic change detection at Chalk
Cliffs, Colorado, USA, using Airborne LiDAR and UAS-based Structure-fromMotion photogrammetry. In: Kean JW, Coe JA, Santi PM, and Guillen BK (eds)
Debris-flow hazards mitigation: mechanics, monitoring, modeling, andassessment, Association of Environmental and Engineering Geologists,
Special Publication #28, pp 85–92.
Booth AM, Sifford C, Vascik B, Siebert C, Buma B (2020) Large wood inhibits
debris flow runout in forested southeast Alaska. Earth Surface Processes andLandforms 45(7):1555–1568. https://doi.org/10.1002/esp.4830
Carbonneau PE, Dietrich JT (2017) Cost-effective non-metric photogrammetry
from consumer-grade sUAS: implications for direct georeferencing ofstructure from motion photogrammetry. Earth Surface Processes andLandforms 42(3):473–486. https://doi.org/10.1002/esp.4012
Chigira M, Ling S, Matsushi Y (2018) Landslide disaster induced by the 2017
northern Kyushu rainstorm. Disaster Prevention Research Institute Annals, A
61:28–35 (in Japanese, with English abstract).
Comiti F, Andreoli A, Lenzi MA, Mao L (2006) Spatial density and characteristics
of woody debris in five mountain rivers of the dolomites (Italian Alps).
Geomorphology 78(1-2):44–63. https://doi.org/10.1016/j.geomorph.2006.01.
021
Fonstad MA, Dietrich JT, Courville BC, Jensen JL, Carbonneau PE (2013)
Topographic structure from motion: a new development inphotogrammetric measurement. Earth Surface Processes and Landforms 38:
755–766
Gurnell AM, Petts GE, Hannah DM, Smith BPG, Edwards PJ, Kollmann J, Ward JV,
Tockner K (2001) Riparian vegetation and island formation along the gravelbed Riume Tagliamento, Italy. Earth Surface Processes and Landforms 26(1):31–62. https://doi.org/10.1002/1096-9837(200101)26:1<31::AID-ESP155>3.0.
CO;2-YHaga H, Kumagai T, Otsuki K, Ogawa S (2002) Transport and retention of coarse
woody debris in mountain streams: an in situ field experiment of log
transport and a field survey of coarse woody debris distribution. Water
Resource Research 38(8):1-1–1-16.
Imada Y, Kawase H, Watanabe M, Arai M, Shiogama H, Takayabu I (2020)
Advanced risk-based event attribution for heavy regional rainfall events. npj
Climate and Atmospheric Science 3:37.
Jackson CR, Sturm CA (2002) Woody debris and channel morphology in first andsecond order forested channels in Washington’s coast ranges. Water
Resource Research 38:1177–1190
James MR, Robson S (2014) Mitigating systematic error in topographic models
derived from UAV and ground-based image networks. Earth Surface
Processes and Landforms 39(10):1413–1420. https://doi.org/10.1002/esp.3609
Johnson AC, Swanston DN, McGee KE (2000) Landslide initiation, runout, anddeposition within clearcuts and old-growth forests of Alaska. Journal of theAmerican Water Resources Association 36(1):17–30. https://doi.org/10.1111/j.1
752-1688.2000.tb04245.x
Kaibori M, Hasegawa Y, Yamashita Y, Sakida H, Nakai S, Kuwada S, Hiramatsu S,
Jitousono T, Irasawa M, Shimizu O, Imaizumi F, Nakatani K, Kashiwabara Y,
Kato N, Torita E, Hirakawa Y, Yoshinaga S, Tanaka K, Hayashi S (2018)
Sediment related disaster due to heavy rainfall in Hiroshima prefecture in
July, 2018. Journal of the Japan Society of Erosion Control Engineering 71:
43–53 (in Japanese, with English abstract).

Tsunetaka et al. Progress in Earth and Planetary Science(2021) 8:22

Kasprak A, Magilligan FJ, Nislow KH, Snyder NP (2012) A Lidar-derived evaluationof watershed-scale large woody debris sources and recruitment mechanisms:
coastal Maine, USA. River Research and Applications 28(9):1462–1476. https://doi.org/10.1002/rra.1532
Kato R, Shimose K, Shimizu S (2018) Predictability of precipitation caused bylinear precipitation systems during the July 2017 Northern Kyushu heavy
rainfall event using a cloud-resolving numerical weather prediction model.
Journal of Disaster Research 13(5):846–859. https://doi.org/10.20965/jdr.2018.
p0846
Keller EA, Swanson FJ (1979) Effects of large organic material on channel form
and fluvial processes. Earth Surface Processes 4(4):361–380. https://doi.org/1
0.1002/esp.3290040406
Koutsoyiannis D, Kozonis D, Manetas A (1998) A mathematical framework forstudying rainfall intensity-duration-frequency relationships. Journal of
Hydrology 206(1-2):118–135. https://doi.org/10.1016/S0022-1694(98)00097-3
Lancaster ST, Hayes SK, Grant GE (2003) Effects of wood on debris flow runout insmall mountain watersheds. Water Resources Research 39:1168
Lisle TE (1995) Effects of coarse woody debris and its removal on a channel
affected by the 1980 eruption of Mount St. Helens, Washington. Water
Resources Research 31(7):1797–1808. https://doi.org/10.1029/95WR00734
Manners RB, Doyle MW, Small MJ (2007) Structure and hydraulics of natural
woody debris jams. Water Resources Research 43:W06432
Marutani T, Kaibori M, Jitousono T, Mizuno H, Ohno H, Shimizu O, Kubota T, Ue
H, Kanazawa A, Kawano T, Koga S, Kobayashi H, Kobayashi T, Sakashima T,
Sakatani Y, Sagara W, Shinohara Y, Suzuki Y, Takagi M, Torita E, Nakano K,
Fujisawa Y, Yamaguchi K, Yamada Y (2017) Sediment-related disasters by aheavy rainfall in the northern part of Kyushu-Island, Japan in July 2017.
Journal of the Japan Society of Erosion Control Engineering 70: 31–42 (inJapanese, with English abstract).
May CL (2002) Debris flows through different forest age classes in the central
Oregon Coast Range. Journal of the American Water Resources Association
38(4):1097–1113. https://doi.org/10.1111/j.1752-1688.2002.tb05549.x
May CL, Gresswell RE (2003a) Large wood recruitment and redistribution in
headwater streams in the Southern Oregon Coast Range, USA. Canadian
Journal of Forest Research 33(8):1352–1362. https://doi.org/10.1139/x03-023
May CL, Gresswell RE (2003b) Processes and rates of sediment and wood
accumulation in headwater streams of the Oregon Coast Range, USA. Earth
Surface Processes and Landforms 28(4):409–424. https://doi.org/10.1002/esp.4
50Montgomery DR, Abbe TB, Peterson NP, Buffington JM, Schmidt K, Stock JD(1996) Distribution of bedrock and alluvial channels in forested mountaindrainage basins. Nature 381(6583):587–589. https://doi.org/10.1038/381587a0
Montgomery DR, Buffington JM, Smith R, Schmidt K, Pess G (1995) Pool spacing
in forest channels. Water Resources Research 31(4):1097–1105. https://doi.
org/10.1029/94WR03285
Nakamura F, J-Il S, Akasaka T, Swanson FJ (2017) Large wood, sediment, and flow
regimes: their interactions and temporal changes caused by human impacts
in Japan. Geomorphology 279:176–187. https://doi.org/10.1016/j.geomorph.2
016.09.001
Nakamura F, Swanson FJ (1993) Effects of coarse woody debris on morphology
and sediment storage of a mountain stream system in western Oregon.
Earth Surface Processes and Landforms 18(1):43–61. https://doi.org/10.1002/
esp.3290180104
Piton G, Recking A (2016) Design of sediment traps with open check dams. II:
woody debris. Journal of Hydraulic Engineering 142:1–17
Ravazzolo D, Mao L, Picco L, Lenzi MA (2015) Tracking log displacement during
floods in the Tagliamento River using RFID and GPS tracker devices.
Geomorphology 228:226–233. https://doi.org/10.1016/j.geomorph.2014.09.
012
Ruiz-Villanueva V, Bodoque JM, Díez-Herrero A, Eguibar MA, Pardo-Igúzquiza E(2013) Reconstruction of a flash flood with large wood transport and its
influence on hazard patterns in an ungauged mountain basin. Hydrological
Processes 27(24):3424–3437. https://doi.org/10.1002/hyp.9433
Ruiz-Villanueva V, Piégay H, Gurnell AM, Marston RA, Stoffel M (2016) Recent
advances quantifying the large wood dynamics in river basins: new methods
and remaining challenges. Reviews of Geophysics 54(3):611–652. https://doi.
org/10.1002/2015RG000514
Sane Y, Panthou G, Bodian A, Vischel T, Lebel T, Dacosta H, Quantin G, Wilcox C,
Ndiaye O, Diongue-Niang A, Kane MD (2018) Intensity–duration–frequency(IDF) rainfall curves in Senegal. Natural Hazards and Earth System Sciences18(7):1849–1866. https://doi.org/10.5194/nhess-18-1849-2018

Page 16 of 16

Sanhueza D, Picco L, Ruiz-Villanueva V, Iroumé A, Ulloa H, Barrientos G (2019)
Quantification of fluvial wood using UAVs and structure from motion.
Geomorphology 345:106837. https://doi.org/10.1016/j.geomorph.2019.106837
Sueki K, Kajikawa Y (2019) Different precipitation systems between Hiroshima andKeihanshin during extreme rainfall event in western Japan in July 2018.
Journal of the Meteorological Society of Japan 97(6):1221–1232. https://doi.
org/10.2151/jmsj.2019-063
Swanson FJ, Gregory SV, Iroumé A, Ruiz-Villanueva V, Wohl E (2021) Reflection on
the history of research on large wood in rivers. Earth Surface Processes andLandforms 46(1):55–56. https://doi.org/10.1002/esp.4814
Tang YJ, Xu ZM, Yang TQ, Zhou ZH, Wang K, Ren Z, Yang K, Tian L (2018)
Impacts of small woody debris on slurrying, persistence, and propagation ina low-gradient channel of the Dongyuege debris flow in Nu River,Southwest China. Landslides 15(11):2279–2293. https://doi.org/10.1007/s1034
6-018-1036-7
Tsuguti H, Seino N, Kawase H, Imada Y, Nakaegawa T, Takayabu I (2019)
Meteorological overview and mesoscale characteristics of the Heavy Rain
Event of July 2018 in Japan. Landslides 16(2):363–371. https://doi.org/10.1
007/s10346-018-1098-6
Tsuji H, Yokoyama C, Takayabu YN (2020) Contrasting features of the July 2018
heavy rainfall event and the 2017 Northern Kyushu rainfall event in Japan.
Journal of the Meteorological Society of Japan 98(4):859–876. https://doi.
org/10.2151/jmsj.2020-045
Tsunetaka H, Hotta N, Hayakawa YS, Imaizumi F (2020) Spatial accuracy
assessment of unmanned aerial vehicle-based structures from motion multiview stereo photogrammetry for geomorphic observations in initiation zones
of debris flows, Ohya landslide, Japan. Progress in Earth and Planetary
Science 7: doi https://doi.org/10.1186/s40645-020-00336-0
Ulloa H, Iroumé A, Mao L, Andreoli A, Diez S, Lara LE (2015) Use of remote
imagery to analyse changes in morphology and longitudinal large wood
distribution in the Blanco River after the 2008 Chaitén volcanic eruption,southern Chile. Geografiska Annaler: Series A, Physical Geography 97(3):523–
541. https://doi.org/10.1111/geoa.12091
Unuma T, Takemi T (2021) Rainfall characteristics and their environmental
conditions during the heavy rainfall events over Japan in July of 2017 and2018. Journal of the Meteorological Society of Japan (in press).
Wallace JB, Benke AC (1984) Quantification of wood habitat in subtropical coastal
plain streams. Canadian Journal of Fisheries and Aquatic Science 41:1643–
1652.
Woodsmith RD, Buffington JM (1996) Multivariate geomorphic analysis of forest
streams: implications for assessment of land use impact on channel
condition. Earth Surface Processes and Landforms 21(4):377–393. https://doi.
org/10.1002/(SICI)1096-9837(199604)21:4<377::AID-ESP546>3.0.CO;2-2Wyżga B, Mikuś P, Zawiejska J, Ruiz-villanueva V, Kaczka RJ, Czech W (2017) Log
transport and deposition in incised, channelized, and multithread reaches ofa wide mountain river: tracking experiment during a 20-year flood.
Geomorphology 279:98–111. https://doi.org/10.1016/j.geomorph.2016.09.019

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