Progress in Earth andPlanetary Science

Kasai et al.
Progress in Earth and Planetary Science(2024) 11:21
https://doi.org/10.1186/s40645-024-00626-x

Open Access

RESEARCH ARTICLE

Quantification of the spatial distributionof individual mangrove tree species derived
from LiDAR point clouds
Katsumi Kasai1*, Hideaki Yanagisawa2 and Kazuhisa Goto1

Abstract
Mangrove forests have unquestionably high environmental and ecological value. Mangrove trees are believed
to have habitat zonation that is controlled mainly by the relative sea level. However, earlier discussions of mangrove
habitats have remained limited in terms of their quality and quantity because of a lack of high-resolution spatial
information of microtopography and trees. To clarify mangrove habitability over a wide forest area, we compoundedmobile laser scanning (MLS) and aerial laser scanning (ALS) LiDAR dataset of the Miyara River mangrove on Ishigaki
Island, Okinawa, Japan. The MLS provided sub-canopy data, while the unmanned aerial vehicle ALS data mainly
provided a point cloud of the canopy. We corrected point clouds and combined these data. The results indicatedthat ALS is unable to reconstruct the microtopography of the dense mangrove area well. Moreover, tree species were
not identifiable from the ALS data. However, by applying MLS to the mangrove forest, we obtained high-resolution
microtopography and tree information inside the forest, although the measurement area was limited to comparison
with ALS. By combining ALS and MLS point clouds, 3D point clouds of the forest were well reconstructed. From thesepoint clouds, a high-resolution digital elevation model was created. Subsequently, we segmented trees individuallyfrom composite MLS point clouds and identified each tree species. Consequently, the spatial distribution of thousands of mangrove trees was reconstructed at the Miyara River mouth. The spatial distribution of mangrove tree species together with earlier aerial photographs suggests that mangrove species have been segregated in accordancewith changes in their elevation and environment over 40 years. Our findings suggest that the distribution of the species changed sensitively along with dynamic variation of the microtopography.
Keywords LiDAR, UAV, SLAM, Bruguiera gymnorrhiza, Ishigaki Island, Mangrove, Rhizophora stylosa, Zonation, Miyara
River
1 Introduction
Mangroves characteristically inhabit areas above meansea level (MSL) in intertidal zones of tropical and subtropical coasts (Macnae 1969; Woodroffe et al. 2016). For*Correspondence:
Katsumi Kasai
kasai-katsumi0316@g.ecc.u-tokyo.ac.jp
1
Department of Earth and Planetary Science, The University of Tokyo,
7‑3‑1 Hongo, Bunkyo‑ku, Tokyo 113‑0033, Japan2
Department of Regional Communication Studies, Faculty of Regional
Studies, Tohoku Gakuin University, 3‑1 Shimizukoji, Wakabayashi‑ku,
Sendai, Miyagi 980‑8511, Japan

recently examined mangrove habitats, vertical accreting thick sequences of organic sediments occur primarily due to a rate of relative sea-level rise (Saintilan et al.
2020). Mangrove habitats typically show a zonation pattern: dominant species are mainly replaced depending onelevation change (Duke et al. 1998; Mochida et al. 1999).
Consequently, mangrove tree species inhabit limited
elevation ranges, which shifts over time in accordancewith the relative sea-level changes. Such relative sealevel changes might happen not only by absolute sea levelchanges but also by the microtopographic changes suchas uplift and subsidence of ground elevation by seismic© The Author(s) 2024. 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 theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence 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 thislicence, visit http://creativecommons.org/licenses/by/4.0/.

Kasai et al. Progress in Earth and Planetary Science(2024) 11:21

activities and by the deposition and erosion of the sediments in association with floods and other phenomena. To elucidate the mangrove habitat dynamics, somestudies have tracked the horizontal distribution areas ofmangrove forest by comparison with earlier aerial photographs (Do et al. 2022). Nevertheless, these studies
are few because it is difficult to map individual tree species using remote sensing techniques (Pham et al. 2019).
Moreover, understanding the microtopography withinmangrove forests is difficult because some mangrove species have overhanging prop roots; also, the forests often
have dense accumulations of trees and are submerged bytides every day (Yang et al. 2022; You et al. 2023). These
environments make it difficult to conventional surveying
in mangrove forests, and it is difficult to conduct topographic surveys efficiently and precisely in a short time.
Therefore, a small number of people and a short period ofsurveying cannot easily understand the microtopography
and forest conditions over a wide area. Although there
are some pioneering efforts to investigate the relationshipbetween microtopography and tree species distribution(e.g., Fujimoto et al. 1995), such works are very limited
nevertheless of the importance in mangrove science. Thisis probably because measurement of microtopography
and tree species is time-consuming and development ofefficient methods has long been awaited. Indeed, earlier field investigations have been mostly qualitative or,even when quantitative, based on limited informationof a narrow area. Therefore, the actual relation between
mangrove habitats and microtopography has not been
sufficiently elucidated yet. Obtaining sufficient quality and quantity of microtopography and tree data is thebarrier which must be hurdled for investigations of mangrove habitability.
To overcome these limitations of earlier studies, lightdetection and ranging (LiDAR) is the breakthrough survey technique. LiDAR technique can be divided into
three survey techniques: (1) aerial laser scanning (ALS),
which measures the ground from the sky using a small
aircraft or UAV; (2) mobile laser scanning (MLS), whichobtains measurements while a researcher walks using
handheld equipment; and (3) terrestrial laser scanning(TLS), which measures target objects from a fixed location (Donager et al. 2021). Yin and Wang (2019) recommended a combination of ALS and TLS to improve
surveys of mangrove forests. Unfortunately, TLS is
unsuitable for topographic and tree surveys of the mangrove forest over a wide area because of its requirement
for many scans at different points. In general, TLS-based
field measurements take approximately ten hours for aplot of 100 m × 100 m (Shao et al. 2020). Although ALS
is a promising technique to measure mangrove forests
and although it has been used for recent studies (Yin andPage 2 of 13

Wang 2019; You et al. 2023), its laser might not reach theground surface. In such cases, the quality of the obtained
topographic data would depend on tree canopy shading.
In addition, ALS might be unable to produce point cloudsof tree shapes because the tree canopy would shade tree
trunks and roots. Knight et al. (2009) stated that ALS iseffective in forests with low to medium canopy density,
but the ALS performance might be limited at dense mangrove forests. Tong et al. (2023) reported that the root
mean-squared error (RMSE) of DEM obtained using
UAV aerial photographs relative to ground control points(GCP) in the mangrove forest is 0.281 m. This accuracy
makes it difficult not only to discuss the habitat elevation
range of each mangrove species in a narrow intertidalzone, but also the microtopography in mangrove forests.
Therefore, using ALS alone for ecological studies such as
elucidating the spatial distribution of tree species is difficult. However, few studies have conducted MLS surveys of mangrove forests (Niwa et al. 2023; Yamamoto
et al. 2023). In Niwa et al. (2023); they applied two typesof LiDAR survey (ALS and MLS) in the mangrove forest.
They stated that combining point clouds from ALS and
MLS platforms in mangrove forest topographic surveys
can yield better accurate data. However, errors specific toeach platform have not been considered, and there is aneed for accuracy verification. Also, relationship between
microtopography and mangrove tree distributions havenot been explored in this research. The need exists toverify accuracy and to evaluate an appropriate processing scheme. If the point clouds obtained by ALS and MLS
can be combined accurately, then they might be able tocomplement each other’s blind spots and provide a representation (Nakata et al. 2023) with highly accurate 3D
data of mangrove forests, including relationship between
topography and distribution of tree species. Despite thatpromise, no report of the literature has described an
attempt to do so.
If the topography in mangrove forests can be reconstructed over a wide area with higher accuracy, and if
the spatial distribution of trees can be measured, thesedata will constitute a promising fundamental dataset for
future geomorphological and ecological studies in the
mangrove forest. The objective of this study is to ascertain the spatial distribution of individual mangrove tree
species quantitatively over a wide area and to identify thefactors affecting their habitability, especially in relation to
microtopography in mangrove forests. For this objective,
we obtained highly accurate microtopographic data inmangrove forests over a wide area by combining LiDAR
surveys of two types (ALS + MLS). The obtained point
clouds were corrected, combined, verified for their accuracy, and then created using high-resolution DEM. The
spatial distribution of each mangrove tree species wasKasai et al. Progress in Earth and Planetary Science(2024) 11:21

Page 3 of 13

also obtained by segmenting individual trees and visuallysorting from LiDAR data and DEM. Finally, vegetation
changes taking place over 40 years in the mangrove forest were discussed based on a comparison of past aerial
photograph and newly obtained spatial distributions ofmangrove tree species.

datum applied to the data is the Tokyo Peil (T.P.). The
MSL at the Ishigaki Port during 2017–2021, which was
the closest observation point to the study site and measured by Japan Meteorological Agency (JMA), is 15.9 cm
above T.P.

2 Methods

The point clouds of the ALS were acquired using the DJI
UAV LiDAR platform, specifically Matrice 300 RTK (DJI,
China) was used with the LiDAR equipment (Zenmuse
L1; DJI, China). It has a 20-megapixel camera, inertial
motion unit (IMU), and laser sensor installed in it. Therefore, point clouds and aerial photographs can be acquired
simultaneously during the same flight. The viewing angle
is 70.4°. The measurement range is within 190 m whenreflectivity is > 10%. The laser generates 160,000 per second. From 50 m altitude, the measurement error is within
10 cm in the horizontal direction and within 5 cm in thevertical direction. Point clouds were collected by RTK
positioning. A mobile station (D-RTK 2; DJI Inc., China)
was used as the base station. The coordinate of the basestation was determined by static positioning with a GNSS
receiver (Trimble R2; Trimble Navigation Ltd., USA).
Then, coordinates and elevations were assigned to theacquired point clouds and aerial photographs.
When flying at 50 m altitude from the base station
on July 14, 2022, the ALS data were acquired. Software(DJI Terra; DJI, China) was used for data post-processing. By providing the coordinate and elevation of thebase station, point cloud data were acquired with them.

2.1 Site settingThis research was conducted in the Miyara River mouth,
Ishigaki Island, Okinawa, Japan (Fig. 1). This forest was
selected because it is a compact area for ALS and because
it is walkable with MLS, although its canopy shading rate
is high. The Miyara River, which drains the southeastern part of Ishigaki Island, is about 12 km long, has an
approximately 36 ­km2 basin area. The mangrove forest
covers 0.15 ­km2 of estuary, with mangrove tree species
predominantly of Bruguiera gymnorrhiza, Rhizophorastylosa, and Kandelia obovata (Yamada et al. 2013).
2.2 LiDAR survey

We performed LiDAR surveys of two types: ALS fromthe sky using a real-time kinematic (RTK)-UAV, and MLS
within the forest using backpack-mounted LiDAR withsimultaneous localization and mapping (SLAM) technology. Because laser light cannot capture the underwater topography, surveys were conducted at the time ofspring low tide to capture intertidal topography as possible. The output coordinate system used for the data wasJapan Geodetic System 2011 (EPSG: 6684). The elevation

2.2.1 ALS

Fig. 1 a Map showing the Miyara River mangrove forest location on Ishigaki Island of the Sakishima Islands, Okinawa, Japan. b Aerial photographsof the study area in 1978 and 2015. The aerial photographs were provided by the Geospatial Information Authority of Japan. c View of the Miyara
River mangrove forest downstream from the Miyara Bridge. The tree species are being replaced from the river (left hand) to the inland area (righthand). d Small creek in the mangrove forest. It is not visible in aerial photographs because of canopy shadingKasai et al. Progress in Earth and Planetary Science(2024) 11:21

Orthophoto was created using Metashape (Agisoft LLC,
Russia) from aerial photographs acquired during the ALS
survey. After two flights (total about 45 min), ALS point
clouds from the UAV had been acquired and an area of
about 22 ha was surveyed.
2.2.2 MLS

A backpack-style LiDAR with a SLAM system was used
for MLS. SLAM can estimate its own position by matching characteristic points and allowing us to obtain accurate positions even in a forest that hampers the GNSS
signal (Hyyppä et al. 2020). The matching of characteristic points is based on photographs correspondence.
LiBackpack GC50 (GreenValley International, USA) wasused for the MLS survey. This equipment has a laser sensor, a GNSS receiver, and a 360° camera. The viewing
angle is vertical of ± 15° and fully 360° horizontally. The
measurement range is within 100 m at the place wherereflectivity is > 20%. The laser beam is 300,000 shots per
second. Measurement error is within 3 cm. A 360° camera was used to take movie files simultaneously withacquisition of the point cloud. Data acquisition was conducted on November 5 and 6, 2022, by a person carrying
MLS and walking in a loop through the mangrove forest
to avoid crossing the route. The routes were selected after
looking at ALS-DEM to cover the areas in which no ALSDEM data were obtained because of the high canopy
shielding rate. A GNSS base station was placed in a location with open sky for differential positioning by postprocessing. The starting and ending places of the walking
route were selected at locations with open sky where stable GNSS signals were able to be acquired. It is known
that SLAM accumulates cumulative error over measurement time (Xie et al. 2022). For this reason, to reduce thetime of each run, measurements were taken seven times
using different routes (four times on November 5, 2022;
three times on November 6, 2022). The measurement
time of each run was about 10 min.
For data processing, the software LiFuser BP (Green
Valley International, USA) was used. After the acquired
point cloud data were imported into the software, thecharacteristic points were matched before and after theframe. Then, by providing the location and elevation of
the base station, point cloud data were acquired withcoordinate and elevation. In addition, color information
was added from 360° camera images. Position correction was performed using GNSS information from thereference station. Then SLAM reanalysis was performed.
Also point clouds that were judged as unreliable, such as
points captured below the water surface where the laser
could not possibly reach, or scattered points at the edgeof the point cloud, were removed manually.

Page 4 of 13

2.2.3 Evaluation of precision

GNSS surveys were conducted in November 2022 andJune 2023 to measure the coordinates of reference points
for accuracy verification of ALS and MLS. Drogger(BizStation Corp., Japan) and Promark120 (Trimble Inc.,
USA) were used for surveying. Post-processing corrections were applied at the GNSS-based Control Stationof the Geospatial Information Authority of Japan. The
accuracy of Drogger is typically within a few centimeters
under environments in which they are recommended
for use (Ishikawa et al. 2022). We measured 12 points(Table 1). However, the area is covered mainly by theforest and artificial objects so that coordinates of somepoints were unsuitable for the evaluation because of canopy shielding or artificial metal objects. To evaluate theiraccuracies, we plotted the data on a GIS and compared
GNSS points with aerial photographs. Then, we excluded
survey points whose locations were clearly different(Table 1). Reference points P1, P3, P5, P9, and P12 were
excluded from ALS point accuracy evaluation because
they were clearly out of alignment with the measurement place when they were plotted on the GIS. In addition, points P7 indicating z values that differ from DEM(z value of point P7 is on the bridge, but z value of DEM
is on the ground under the bridge) and P2 falling outside
DEM area were excluded from the DEM accuracy evaluation. We used seven points as reference for ALS and five
points as accuracy verification for DEM.
2.3 Data analysis

For point cloud analyses, LiDAR360 (GreenValley International, USA) and Cloud Compare were used. With
LiDAR360, we corrected and combined point clouds,
classified ground points using high-density TIN filtering
based on Zhao et al. (2016), created a 0.5 m mesh DEM,
and did tree species segmentation. However, we realizedthat LiDAR360 alone was insufficient to classify groundpoints well. Therefore, to create a better quality of DEM,
we first classified ground points using the cloth simulation filter (CSF) based on Zhang et al. (2016) provided
with Cloud Compare. Then, we classified the groundpoints again using LiDAR360. Point clouds were also corrected in Cloud Compare.
2.3.1 Alignment and combination of point clouds

The seven MLS point clouds were combined as explained
hereinafter. First, two MLS point clouds upstream of the
Miyara Bridge and five MLS point clouds downstream of
the Miyara Bridge were combined. The MLS point clouds,
in which cumulative errors had accumulated, wereextracted by several time ranges. Then each was combined. When combining MLS point clouds, we manually− 181,823.644

21,480.24

21,404.474

P11

P12

− 181,805.46

21,406.48

21,431.488

P9

P10

− 181,737.606

21,463.275

21,369.714

P7P8− 181,796.9861− 181,782.139− 181,833.655− 181,818.516− 181,796.294− 181,922.091

21,468.486

21,366.45

P5− 181,925.6136− 181,819.1984− 181,826.9974

P621,427.9364

21,367.2226

P3

P421,417.7973

21,397.5107

P1

P2

2.406

0.2773− 0.0487

0.8242

1.97690.10617.31

0.2929

7.271

0.326

0.831

5.275

21,405.116

21,480.334

21,431.54

21,407.503

21,369.675

21,463.259

21,366.472

21,463.728

21,366.405

21,429.953

21,397.599

21,415.905

x

z

x

y

ALS

Reference point− 181,782.225− 181,833.577− 181,818.517− 181,805.776− 181,737.646− 181,796.339− 181,922.054− 181,796.13− 181,925.666− 181,819.101− 181,826.956− 181,824.063yTable 1 Accuracy evaluation of ALS point clouds and DEM by reference points

2.494

0.881

2.448

0.101

7.46

0.368

7.496

0.428

0.889

5.369

0.223

− 0.04z

0.15956− 0.0311

0.81866− 0.1335− 0.2133− 0.1786

0.346− 0.3383

0.395880.81702–0.64416z

DEM–

0.122147452

0.052009614–

0.055865911

0.047759816

0.043046486–

0.819277438–

0.097523587–

Horizontal

ALS error (m)–0.0087

0.0568–0.00510.15

0.0751

–

0.102

–

0.094

–

Vertical

Under the tree canopy
0.093092
0.148354612

DEM MAE (m)DEM RMSE (m)Alongside the bridge structure

On the bridge

On the bridge

Alongside the tree canopy

Point outside the DEM

Alongside the bridge structure

Remarks–

0.01756

0.00554–0.31938–

0.0531–

0.06988–––

DEM error (m)Kasai et al. Progress in Earth and Planetary Science(2024) 11:21
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Kasai et al. Progress in Earth and Planetary Science(2024) 11:21

selected up to four points, where are expected to represent the same points between alignment point clouds of
one MLS and reference point clouds of the other MLS,
and transformed alignment point clouds using a matrix.
This matrix applied to alignment point clouds minimized location errors resulting from the differences ofthese point clouds when combined. Alternatively, manual rotation and translation were used in combination, if
required. The GNSS information of the point clouds was
not obtained for the three MLS point clouds on November 6 because the base station had not functioned properly. These data were therefore combined with the point
cloud for the four surveys on November 5, which have
GNSS information by matrix transformation. The total
area of the composite MLS point clouds was about 2.5 ha.
Then, composite MLS point clouds that were made fromall seven MLS point clouds were corrected further by the
ALS point clouds, the latter of which cover a wider area.
Manual rotation and translation were used as alignment.
Finally, composite ALS + MLS point clouds were created.
2.3.2 Tree species segmentation

The Point Cloud Segmentation function of LiDAR360
was used for mangrove tree segmentation. Point Cloud
Segmentation function was developed originally as analgorithm to segment individual trees from TLS point
clouds (Li et al. 2012) but it is also applicable to MLS
point clouds. An earlier study using backpack LiDAR inforest to segment trees with this function reported an
88% detection rate with 83% segmentation accuracy (Liuet al. 2023). According to Niwa et al. (2023), it is statedthat the number of recognized trees in the segmentation
results of mangrove forest MLS point clouds, using thedefault parameter of this function, was approximately
twice that of visually identified results. We identified alltree species manually by shape from composite MLS
point clouds segmented using the Point Cloud Segmentation function. Although most trees were individually recognized well, multiple trees were detected
as one tree in some cases. To address that problem, we
manually checked and split them to assign each coordinate and species. Moreover, we used the 360° camera to
remove objects that were misidentified as trees or whichwere non-mangrove tree species. For the segmented andidentified trees, elevation values were obtained from theDEM data. The habitat altitude ranges were calculated
for each tree species.
2.3.3 Comparison with past aerial photographs

Although the LiDAR survey can provide detailed information about the current mangrove distribution and themicrotopography in mangrove forests, it cannot assess
the history because there are no past point cloud data

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of mangrove forests in the Miyara River. However, todiscuss the factors controlling mangrove habitats, it isimportant to understand the process by which the current mangrove forests have changed. Therefore, for thisstudy, by using past aerial photographs from the Geospatial Information Authority of Japan, the present distribution of mangrove forest was compared with the earlier
state of the mangrove forest.
By comparison of past aerial photographs, Nakaza et al.
(2011) pointed out that the distribution area of Miyara
River mangrove forest was increased and that some partof the river was partially blocked during 1967–2006. For
this study, earlier aerial color photographs taken in 1978,
which the mangrove forest locates in the center and noclouds were reflected in the image, were used for comparison. The earlier aerial photograph was rasterizedusing ArcGIS Pro (ESRI Inc., USA), and was overlaid
with the orthophoto that was created from aerial photographs by UAV. The Visible Atmospherically Resistant Index (VARI) values defined by Gitelson et al. (2002)
were used for comparison. Through the heuristic investigation, the locations where the value increased by morethan 0.15 were defined in this study as locations where
vegetation had increased.

3 Results
3.1 Results of LiDAR surveys and data combination

Comparison of the coordinates between the point clouds
obtained by the ALS (Fig. 2) and reference points showedthat the error was within 0.81 m (0.12 m, with one exceptional value) in horizontal direction and within 0.15 m invertical direction (Table 1). These values are similar withthe accuracy of the device itself. Therefore, the ALS point
clouds can be used as a reference to correct composite
MLS point clouds (Fig. 3a, b). Then, comparison of theelevations between the DEM created from composite
ALS + MLS point clouds and reference points showedthat the mean absolute error (MAE) was 0.0931 m andthat the RMSE was 0.148 m. These values have sufficiently high accuracy compared to those produced byearlier studies (Tong et al. 2023).
3.2 Reconstruction of microtopography in the mangrove
forestAt the study site, there was a “no data” area in the DEM
created solely from the ALS point clouds (Fig. 4). To create DEM, sufficient ground points must be obtained.
However, depending on the shielding ratio by tree canopies at the Miyara River mangrove forest, the laser was
sometimes unable to reach to the ground surface. For
that reason, sufficient ground points were not obtainedusing ALS. It is noteworthy that shapes associated witheach tree such as trunks and branches could not beKasai et al. Progress in Earth and Planetary Science(2024) 11:21

Page 7 of 13

Fig. 2 a ALS point clouds. Background grids are 50 m. An area of about 22 ha was surveyed. The red square marks the area surveyed by MLS
in Fig. 3. b Orthophoto created from aerial photographs obtained from an ALS survey

Fig. 3 Results of MLS surveys. Background grids are 50 m. a Seven point clouds obtained from each MLS survey. The three point clouds in the lower
panel did not have GNSS information at raw data. Therefore, they show different elevations and directions. b A composite MLS point clouds
for the location in Fig. 2a. The total area was about 2.5 ha. c Result of tree segmentation from composite MLS point clouds

detected from the ALS point clouds, simply because ALS
measures the trees perpendicularly from the sky.
This shortcoming of ALS can be complemented by
MLS point clouds. This complementarity can be achieved
because MLS can measure inside of the forest so that not
only the ground surface but also tree shapes are measurable, although tree canopies, which can be obtainable
by ALS, are not measurable by MLS. As a result of the

combination with ALS and MLS point clouds, groundpoints, where there were “no data” in the ALS-DEM,
were well complemented by composite MLS point clouds(Fig. 5).
The final DEM, which combined ALS + MLS point
clouds and which is 0.5 m mesh, shows that elevationof mangrove forest is approx. − 0.73 to 1.17 m. In thisDEM, microtopographical features such as small creeks

Kasai et al. Progress in Earth and Planetary Science(2024) 11:21

Page 8 of 13

Fig. 4 DEM created from ALS point clouds (left top), composite MLS point clouds (left bottom), and composite ALS + MLS point clouds (right). By
combining ALS and MLS point clouds, the area (red square) in which there were “no data” in the ALS-DEM was well complemented by composite
MLS point clouds

Fig. 5 Profile view in the mangrove forest. Transect position (left). Transect profiles of ALS point clouds (right top) and composite ALS + MLS point
clouds (right bottom). The combination of ALS and composite MLS point clouds shows the complementation of ground points and tree shapes.
Moreover, it is apparent that the elevation increases gradually from the river to land. Changes in tree species are visible

are recognizable throughout the mangrove forest. Thesemicrotopographies are not observable from aerial photographs because they were hidden by dense tree canopies.
It also shows that the elevation increases away from theriver and creeks. The elevation increases rapidly to over2 m at the inland edge of the forest.
3.3 Mangrove distribution

As a result of tree species segmentation (Fig. 3c), 4106
trees were detected in total: those are 2553 trees ofBruguiera gymnorrhiza, 1474 trees of Rhizophora stylosa,and 79 trees for which the tree species were judged as
saplings with undetermined species or were non-mangrove species (Fig. 6). Kandelia obovata was not foundby field observations in the MLS survey area. The spatial
distribution of tree species shows that Bruguiera gymnorrhiza is the dominant species and that it is widely distributed throughout the area. By contrast, Rhizophorastylosa is located around the river fronts and creek sides,
and around the boundaries between mangroves andKasai et al. Progress in Earth and Planetary Science(2024) 11:21

Fig. 6 Distribution of tree species on the orthophoto. Rhizophorastylosa (red) is distributed landward, at the riverside, and along creeks
to rim Bruguiera gymnorrhiza (blue). White circle indicates other trees(non-mangrove trees and unidentified trees)

non-mangrove trees. Rhizophora stylosa are also distributed as a rim surrounding the Bruguiera gymnorrhiza
area.
Because we have high-resolution DEM, the frequency
distribution of habitat altitude can be calculated foreach tree species (Fig. 7). Bruguiera gymnorrhiza inhabits − 0.47 to 1.12 m. Rhizophora stylosa inhabits − 0.73

Page 9 of 13

to 1.17 m. In this way, Rhizophora stylosa in the Miyara
River mangrove forest inhabits a slightly broader rangeof lower and higher elevations than Bruguiera gymnorrhiza does. In addition, Bruguiera gymnorrhiza shows anearly normal distribution with a sharp peak at around
0.4 m. By contrast, Rhizophora stylosa has multiple
peaks. In addition, Rhizophora stylosa individuals are
distributed preferentially at lower and higher elevations, where few Bruguiera gymnorrhiza individuals
live, but they are fewer at around 0.4 m elevation where
Bruguiera gymnorrhiza individuals are predominant. In
this survey area, about 89% of Bruguiera gymnorrhiza
trees and about 78% of Rhizophora stylosa trees wereabove MSL.
3.4 Comparison with the past aerial photograph

By comparing VARI values of aerial photographs of1978 and 2022, Fig. 8 shows the areas where the vegetations are thought to have grown during this period.
Indeed, as the figure shows, the vegetation area has
clearly advanced toward the riverside. It is also noteworthy that the vegetation area has advanced landward in inland areas away from the river. Based on fieldobservations, vegetation at these two expanded area
can be characterized as Rhizophora stylosa.

Fig. 7 Frequency distribution of habitat altitude for each mangrove tree species. a Bruguiera gymnorrhiza, b Rhizophora stylosa, c Bruguiera
gymnorrhiza and Rhizophora stylosa

Kasai et al. Progress in Earth and Planetary Science(2024) 11:21

Fig. 8 Map showing locations where the vegetation is regarded
as expanding over the last 40 years from the change of VARI values(yellow). Mangrove tree species distributions are also overlapped. The
vegetation is shown as expanding at both the riverside and landward.
Rhizophora stylosa is mainly distributed in vegetation expanded area

4 Discussion
4.1 Distribution characteristics of mangrove trees

The tree species segmentation in this study was conducted within the area which could be surveyed by MLS,
whereas areas close to the river were not measured wellbecause the area was not walkable. It is also likely thattree species are not counted accurately in areas where thepoint cloud density is low. These remaining issues notwithstanding, this study was able to use composite MLS
point clouds to elucidate the spatial distribution of mangrove tree species quantitatively over a wide area for the
first time ever reported. This achievement marks important progress in the study of tree distributions in mangrove forests: without such high-resolution point clouds,
earlier studies could have done similar assessments based
solely on field observations, transect survey results (e.g.,
Mochida et al. 1999; Fujimoto and Ohnuki 1995), andthe results of platforming for areal leveling on fixed plots(e.g., Fujimoto et al. 1995).
Using results of the tree species segmentation andhigh-resolution DEM data, several habitat characteristics can be quantified. For instance, habitat altitude ofmangrove trees at the Miyara River can be estimated as
ranging from − 0.73 to 1.17 m. According to the average tidal range from 2017 to 2021 for Ishigaki port, themean high water spring (MHWS) is 0.986 m, the mean
low water spring is − 0.817 m, and MSL is 0.159 m (JMA).
All these values fall within the mangrove habitat zone
at the Miyara River. Earlier results of studies suggestthat mangrove trees inhabit areas below the high waterlevel of spring tides (FAO 1994), between MSL and themean high water spring (Pugh 1987; Semeniuk 1994), or
between MSL and the mean higher high water (Zhang

Page 10 of 132004). In our findings, it was revealed that about 89%
of Bruguiera gymnorrhiza trees and about 78% of Rhizophora stylosa trees were above MSL in the Miyara River
mangrove forest. This confirms the suggestion made inprevious studies (e.g., Ellison et al. 2022) that the majority of these species inhabit above MSL. This is the study
that such elevation frequency by species has been better
quantified on a scale of thousands of trees.
In addition, the differences of ranges and peaks of thehabitat altitudes of different mangrove species were readily apparent (Fig. 7). The Rhizophora stylosa in the lower
elevations of the Miyara River mangrove forest mainly
grow along the river, whereas those at higher elevations
grow in inland areas. This tendency might be affected by
the ecological characteristics of the tree species. Bruguiera gymnorrhiza is a shade tree that can live in areas withlow sunlight exposure (Krauss and Allen 2003). By contrast, Rhizophora stylosa prefers places with high sunlight
exposure. Moreover, it is strongly tolerant of salt (Smith
1987; Zhang et al. 2021). Rhizophora stylosa is also wellknown as a pioneer tree species in expanding mangrove
forests (Kitao et al. 2003). For these differences of characteristics, it is expected that Rhizophora stylosa has difficult growing under a Bruguiera gymnorrhiza canopy.
4.2 Development process of the mangrove forest

It is also pointed out that changes in the zonation of tree
species are linked to the mangrove forest development
process (Nakasuga 1979). This study successfully quantified the elevation range of each mangrove tree species,
which had been surveyed earlier by transect surveys (Ellison et al. 2022) or quantitative surveys based on the areaused by each tree species community using image analysis and ALS point clouds (Zhu et al. 2019).
Comparison of 1978 and 2022 photographs shows thatmangrove trees are expanding their habitat in areas not
only along the river and creeks but also inland away fromthe river (Fig. 8). Unlike the orthophotograph, the aerial
photograph taken in 1978 includes distortions. In addition, value changes might not be the same between thesetwo photographs because of the differences of the sunlight at the times the aerial photographs were taken andbecause of the accuracy of the camera. Therefore, quantitative comparison might be difficult, although it is still
useful to examine the areas where vegetation has newly
increased. Our tree segmentation results revealed Rhizophora stylosa as the dominant species in the area where
the mangrove habitat area increased during 1978–2022.
The expansion of Rhizophora stylosa, which has highsalt tolerance and which seeks sunlight, toward the riverand creeks can imply that a new habitable area for mangrove trees was created because of the gain in elevation
with river sediment deposition and sediment trappingKasai et al. Progress in Earth and Planetary Science(2024) 11:21

by mangrove trees. This expansion represents a dynamictransition of mangrove forest, as generally considered inearlier studies (Mochida et al. 1999). However, because
the distribution of mangrove tree species is strictly controlled by salinity, which is tied to the altitude relative
to the tidal range (Marchand et al. 2011), the expansion of Rhizophora stylosa landward can be attributed to
sea-level rise. Indeed, Okinawa Regional Headquarters,
JMA report (2022) stated that the sea-level rise at Ishigaki Port is 2.4 mm per year. Alternatively, there mightalso be several other possible reasons, such as the groundelevation being lowered to enter the intertidal zone. Further detailed monitoring is required. In any case, it islikely that non-mangrove trees, which are not salt-tolerant, cannot continue to inhabit the area because of thesea-level rise and/or other factors such as the groundlowering. Rhizophora stylosa, which is salt-tolerant andwhich prefers sunlight, could have expanded its habitat
to inland areas as a pioneering mangrove tree seeking
sunlight.
Based on the findings obtained from this study, futurechanges of mangrove distributions can be predicted.
It can be expected that riverside and inland areas now
inhabited by Rhizophora stylosa will be surrendered
to the spread of Bruguiera gymnorrhiza. In addition,
Rhizophora stylosa might advance its distribution further inland and in riverward directions. However, landward advancement of Rhizophora stylosa might soon
halt because a steep slope exists at the landward edgeof the forest (Fig. 4). Similarly, riverward advancementof Rhizophora stylosa might confront some resistancebecause the area is frequently affected by large typhoons:sediments and trees along the river might constantly beaffected by floods.

5 Conclusions
This study was aimed to elucidate the microtopography and spatial distribution of mangrove forests at the
Miyara River. This contribution applied LiDAR surveys of two types (ALS and MLS) by focusing on mangrove species and distribution in the mangrove forest.
Results revealed that ALS and MLS complement eachweakness. Therefore, application of both survey methods is preferable to obtain an overall set of the point
clouds of the mangrove forest. By analyzing composite ALS + MLS point clouds, it was also possible toobtain high-precision microtopographic data in a wide
area and to understand the spatial distribution of tree
species: those were not obtainable using earlier conventional survey methods. The microtopography andspatial distribution of mangrove tree species are fundamental data supporting studies of geomorphology
and ecology. Indeed, these data are indispensable forPage 11 of 13

understanding not only the dynamics of the mangrove
forest but also for appropriate conservation measures
for mangrove forests.
We compared the elevations of tree species using
positions of trees with species segmentation and DEM
from LiDAR data. The results indicated that Rhizophorastylosa has a distribution from an area below the meantide level and has a wider range of habitat elevations
than shown by Bruguiera gymnorrhiza. This result
and comparison with aerial photographs revealed that
Rhizophora stylosa has been expanding over 40 years,
indicating that, within the restriction of the habitat altitude, the mangrove tree species are changing their habitat area according to the species characteristics under
the principle of survival of the fittest. Indeed, Rhizophora stylosa has high salt tolerance and prefers sunlight, whereas Bruguiera gymnorrhiza has high shadetolerance. Consequently, Rhizophora stylosa, which hasdifficulty growing under Bruguiera gymnorrhiza, prefers to expand its habitat areas not only to lower elevations along riverside areas but also to higher elevations
inland, seeking sunlight.
Although our dataset has sufficient accuracy to discuss
habitat altitudes of mangrove trees, the following should
be considered if one wants to obtain a higher accuracy
point cloud dataset: (1) Raise the density of ALS point
clouds by raising the overlap rates, which makes it easier
to infer characteristic points and to combine the MLS
point clouds. (2) Do the ALS survey at times when windspeeds are sufficiently low. Wind is regarded as the main
factor causing errors in ALS surveys related to device
positioning because UAVs are susceptible to wind gusts(Yang et al. 2017). During our ALS survey, the winds
were a bit strong (less than 10 m/s), which might haveaffected measurements. (3) Improve the SLAM accuracy
using the MLS survey. By dividing the survey into shortertime periods, error propagation might be reduced. However, it is noteworthy that errors occur when their point
clouds are combined.
Abbreviations
LiDAR	Light detection and ranging
SLAM	Simultaneous localization and mapping
UAV	Unmanned aerial vehicle
IMU	Inertial motion unit
ALS	Aerial laser scanning
MLS	Mobile laser scanning
DEM	Digital elevation model
MSL	Mean sea level
MHWS	Mean high water spring
RMSE	Root mean-squared error
MAE	Mean absolute error
VARI	Visible Atmospherically Resistant Index
Acknowledgements
We appreciate H. Takahashi, T. Katono, H. Shibuya, H. Kimura, and K. Nakata
for their support during field work and Ishigaki City for approving the survey.
Reviewers provided valuable comments on improving our manuscript.

Kasai et al. Progress in Earth and Planetary Science(2024) 11:21

Author contributions
KG proposed the topic and designed the research. All authors conducted fieldsurveys. KK and HY conducted data analyses. KK and KG prepared an originaldraft. All authors read and approved the final manuscript.
Funding
This research was supported by the Kurita Water and Environment Foundation, the Ministry of Education, Culture, Sports, Science and Technology(MEXT) of Japan, under its Second Earthquake and Volcano Hazards Observation and Research Program (Earthquake and Volcano Hazard Reduction
Research) and JSPS KAKENHI Grant Nos. 21H04508 and 21H04379.
Availability of data and materials
The datasets used and/or analyzed during this study are available from thecorresponding author on reasonable request.

Declarations
Competing interests
The authors declare no competing interests.
Received: 1 December 2023 Accepted: 7 April 2024

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