A Comprehensive Review on Tree Detection Methods Using Point Cloud and Aerial
Imagery from Unmanned Aerial Vehicles

Weijie Kuang1, Hann Woei Ho1,*, Ye Zhou1, Shahrel Azmin Suandi2, and

Farzad Ismail1

1 School of Aerospace Engineering, Engineering Campus, Universiti Sains
Malaysia, 14300 Nibong Tebal, Pulau Pinang, Malaysia

2School of Electrical and Electronic Engineering, Engineering Campus,
Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, Malaysia

*Corresponding author. Email<EMAIL_ADDRESS>
###### Abstract

Unmanned Aerial Vehicles (UAVs) are considered cutting-edge technology with
highly cost-effective and flexible usage scenarios. Although many papers have
reviewed the application of UAVs in agriculture, the review of the application
for tree detection is still insufficient. This paper focuses on tree detection
methods applied to UAV data collected by UAVs. There are two kinds of data,
the point cloud and the images, which are acquired by the Light Detection and
Ranging (LiDAR) sensor and camera, respectively. Among the detection methods
using point-cloud data, this paper mainly classifies these methods according
to LiDAR and Digital Aerial Photography (DAP). For the detection methods using
images directly, this paper reviews these methods by whether or not to use the
Deep Learning (DL) method. Our review concludes and analyses the comparison
and combination between the application of LiDAR-based and DAP-based point
cloud data. The performance, relative merits, and application fields of the
methods are also introduced. Meanwhile, this review counts the number of tree
detection studies using different methods in recent years. From our statics,
the detection task using DL methods on the image has become a mainstream trend
as the number of DL-based detection researches increases to 45 % of the total
number of tree detection studies up to 2022. As a result, this review could
help and guide researchers who want to carry out tree detection on specific
forests and for farmers to use UAVs in managing agriculture production.

Keywords: UAVs; Individual Tree Detection; LiDAR; Digital Aerial Photography;
Deep Learning

##  1 Introduction

Forest plays a crucial role in the global ecosystem. Protecting and managing
forests is essential for the environment and sustainable development. In the
management of forests, the Individual Tree Detection (ITD) could provide data
support in biomass estimation, growing situation monitoring, and precise
spraying [81]. Traditional tree detection relies on the field data and
statistical estimators. [11]. However, the conventional forest inventory, as
field measurements, is time-intensive and expensive to get accurate data from
small-region plantations [46]. Therefore, highly accurate, cost-effective,
rapid forest inventory techniques are needed to enable tree detection to
address the increasing demand for efficient forestry management. Although
remote sensing images from satellites have been used for acquiring forest
information for many years, the low quality of the freely available satellite
images or expensive high resolution images restricts its application in the
small-scale forest management [114].

Unmanned Aerial Vehicles (UAVs), originally developed for military purposes as
cutting-edge technology, are now steadily making their way into commercial
applications within the agroforestry sector [42]. UAVs offer distinct
advantages over traditional remote sensing methods, primarily owing to their
various designs [115], making them versatile tools in this context. Compared
to other forms of remote sensing, UAVs are capable of acquiring image data
with minimal atmospheric interference, at a lower cost, with greater
flexibility in usage timing, and boasting higher spatial resolution.
Consequently, UAV-captured remote sensing data is particularly well-suited for
agroforestry applications, especially in smaller plantations. In the realm of
tree detection, two primary models emerge: point cloud-based and spectral
image-based detection. Notably, point cloud-based detection leverages data
collected by Light Detection And Ranging (LiDAR) sensors or cameras mounted on
UAVs, showcasing the adaptability and potential for customization inherent in
UAV designs.

LiDAR could determine the distance of objects by measuring the interval time
between the laser pulse and the reflected signal without being affected by the
weather [44]. The developments of the flight control, the motor and the
battery on UAVs enhance the ability of LiDAR to collect three-dimensional
structure data of forests. Besides, LiDAR could detect the terrain information
of the forest floor directly due to the penetration of laser beam on forest
canopy [94]. However, the high cost of LiDAR makes it challenging to be used
in small area forests universally. Meanwhile, the emergence of image
stereomatching makes the reconstruction of the 3D structure of trees possible
in the field of Digital Aerial Photography (DAP) [77]. Among them, Structure
from Motion (SfM) is a common technique reproduces the 3D structure by
processing the two-dimensional overlapping photos which target on the same
object from different directions. Generally, the tree detection approaches use
the point cloud data in two steps, i.e., tree localization and segmentation.
For example, to find the location of the tree top, a Local Maximum (LM)
algorithm is first applied on Canopy Height Models (CHM) which represents the
residual distance between the ground and the object top [90]. Then, a
segmentation algorithm, Watershed Algorithm (WA), is applied to separate an
individual tree from other trees, or background [122]. Finally, the ITD is
achieved with the localization of the tree top and the segmentation of the
tree crown area.

Moreover, the LM was applied on the CHM In detecting the treetop in
mountainous forests. The results showed that 85 % of the treetop could be
detected when the UAV was flown following terrain [31]. In the ITD of the
crown over pure ginkgo, one approach used the WA on the CHM obtained from
LiDAR for individual tree segmentation, and then the LM was used to localize
the tree top. However, the detection using WA did not show good results since
the WA often divides a single tree into several trees [117]. To evaluate and
compare the performance of LiDAR-based and DAP-based point cloud data on
Eucaluptus tree detection, another study also implemented LM with a fixed
window size [32]. The results showed that the UAV images had a similar
capability with LiDAR-based point clouds on the ITD. Therefore, point clouds
generated from UAV images could be a robust and low-cost alternative for
expensive laser scanning sensors to be used in tree detection. Usually, the
tree detection is completed by combining several methods, such as Point Cloud-
Based Cluster Segmentation, Layer Stacking, and k-means clustering, which will
be introduced in the following parts [71]. However, the points derived from
aerial images do not have good qualities in the alpine forests. Because the
irregular topography makes the aerial images taken inconsistent. And the high
tree density hinders the reconstruction of the single tree.

Rather than detecting trees on the point cloud data, with the advances of
computer vision technology, more and more researchers devoted to conducting
the detection task on the UAV image solely [2, 20]. The methods used in tree
detection could be divided into classical and Deep Learning (DL) methods. The
classical methods, such as support vector machine, scale-invariant feature
transform, superpixel, optical flow, and morphological operation, often do the
detection task based on the pixel level. For instance, in the automated
delineation of crop crown research, researchers used morphological operations
to segment the UAV images to separate the trees from each other [89]. Besides
static images, by detecting and tracking features in consecutive images,
optical flow representing the relative motion to detected features can be
obtained for identifying objects underneath UAVs [40, 39]. On the other hand,
DL methods usually use multiple networks to detect trees based on the object
[13, 54]. Furthermore, many DL methods take candidate boxes into use and then
give them scores toward different classes [85]. For instance, object detection
DL algorithms, such as Faster-RCNN and YOLO-V4, were applied to realise the
recognition of the tree with the pine wilt disease. Results showed that the
Faster R-CNN has a higher detection accuracy of 66.7 % than the YOLO-V4 as
63.55 % [124]. Apart from the object-based DL methods, DL networks are also
focused on scoring each pixel. For example, one study used the convolutional
neural networks (CNN) which is based on the shared-weight architecture of the
convolution kernels to identify citrus trees on UAV images [16]. At first, the
CNN will generate the heat map to show the probability that each pixel belongs
to the tree. To reduce the error induced by the multiple detection of large
canopies during the CNN, simple linear iterative clustering is then applied to
the heat map to aggregate all the objects representing the same tree.

Fig. 1 shows the number of articles published on tree detection using UAVs in
recent years. All articles were counted according to the following four
categories, which have been explained in the above content detailedly: (1)
LiDAR represents the research that uses LiDAR sensors to collect the point
cloud data; (2) DAP represents the research that generates the point cloud
data from UAV images; (3) Classic represent the researches that apply the
classical methods on UAV images directly; (4) DL represent the researches that
use the DL methods on UAV images directly. As can be seen from the line chart
in 1, the tree detection on LiDAR and DL make up the majority. Moreover, the
quantity of LiDAR-based tree detection increased continuously from 2017 to
2020 whereas it began to reduce from 2020 to 2022.

Furthermore, the total number of studies using point cloud data for tree
detection is decreasing, but the number of studies using LiDAR data has
remained significant. This is due to the high precision of the point cloud
data collected by LiDAR sensor, even if it is more expensive than other
methods. For DL-based tree detection, the number of it has increased
dramatically due to the significant progress made in object detection
algorithms in the DL research area [50]. Meanwhile, the number of research on
classic-based tree detection remains the lowest. It decreases every year
caused by the high dependency of handcraft methods on hyperparameters, which
are usually set manually and restricted to a specific application. The pie
chart shows that the research on DL-based tree detection increases to 45.5 %
of the total number of studies up to 2022. Moreover, even though the
percentage of LiDAR-based tree detection decreases, it is still one of the
main directions of tree detection research.

Fig. 1: Quantity of articles published on tree detection in recent years

Recently, many researchers have reviewed the application of UAVs in forestry,
agriculture, and agroforestry, as summarised in Table. 1. This summary
includes the year, keywords, does the article contain tree detection, and
published papers. Even though some reviews include tree detection, it is only
a small supporting part, not its subject matter. Therefore, to the best of our
knowledge, there is a literature gap related to review articles combining the
application of UAVs and tree detection. The main goal of this review is to
analyze, conclude, and look to the future of the application of UAVs, as a
rapidly growing research field, on tree detection. Our review aims to
introduce existing methods and common shortcomings. In short, the
contributions of this paper are the following:

  * •

The statistics on published articles in scientific sources according to the
publication year, data type and method type are provided. And the statistical
results on the development trend of UAV application on tree detection are
analysed.

  * •

The presentation of the principle, application scenario and shortcomings of
existing tree detection methods is given. And the solutions for the
shortcoming of the earlier methods are introduced.

  * •

The challenge of tree detection conducted by the UAV systems in the
complicated environment are described. The possible solutions and future
perspectives are offered.

Table 1: Summary of reviews relating to forestry, agriculture, and agroforestry Reference |  Year |  Keywords |  Tree detection |  Journal
---|---|---|---|---
[2] |  2017 |  Hyperspectral; UAS; UAV; Hyperspectral sensors; Hyperspectral data processing; Agriculture; Forestry; Agroforestry |  ✗ |  Remote sensing
[118] |  2020 |  Unmanned aerial system (UAS); Sensors; Plant; Phenotyping; Traits |  ✗ |  Computers and Electronics in Agriculture
[42] |  2020 |  Unmanned Aerial Vehicle (UAV); Lidar; DJI Livox; Low cost; Forest inventory |  ✓ |  Remote Sensing
[24] |  2021 |  Unmanned aerial vehicle; Remote sensing; High-throughput phenotyping; Sensors; Applications review |  ✗ |  Computers and Electronics in Agriculture
[85] |  2021 |  Convolutional neural networks; Remote sensing imagery; Unmanned aerial vehicles |  ✓ |  International Journal of Applied Earth Observations and Geoinformation
[20] |  2021 |  Deep learning; UAV; Forestry; Literature review; Practical applications; RGB |  ✓ |  Remote Sensing
[3] |  2022 |  Operational cost; Electric tractor; Artificial intelligence; Plowing; Forestry; Multi-robot |  ✗ |  Computers and Electronics in Agriculture
[86] |  2022 |  Computer vision; Deep learning; Image processing; Machine learning; Machine vision; Plant stand count |  ✓ |  Computers and Electronics in Agriculture
[95] |  2022 |  Drones; UAV; Precision agriculture; Internet of Things; Bibliometric |  ✗ |  Computers and Electronics in Agriculture
|  |  |  |

##  2 Object Detection based on 3D Point Cloud

The 3D point cloud data has been commonly used on the ITD in remote sensing.
The main ways to obtain 3D point cloud data of the plantation are through
LiDAR and DAP. The former obtains three-dimensional information about the
forest directly by transmitting and receiving laser pulses using active
sensors, Whereas DAP extracts the 3D point cloud data by processing aerial
images using passive sensors. Fig. 2 is the typical study workflow of tree
detection using 3D point clouds. In the process, the raw point cloud data will
be pre-processed to remove the noise at first and then separated into ground
points and non-ground points by a filter. The non-ground points will further
be interpreted to generate different models, e.g., Digital Terrain Model
(DTM), Digital Surface Model (DSM), and normalized Digital Surface Model
(nDSM). Fig. 3 illustrated the DTM, DSM, and nDSM schematically. The DTM is
depicted by the blue curve which is the bare ground surface with removing all
other features. The DSM is represented by the red curve which includes both
the natural and artificial features. And the orange curve shows the nDSM which
represents the absolute height of all objects after removing the ground point,
is characterized by subtracting DTM from DSM. When nDSM is applied in forest
areas, nDSM is also called the Canopy Height Model (CHM) because most objects
are trees. Subsequently, individual tree segmentation and localization will be
conducted based on these models. In the end, the ITD is achieved by combining
the results from segmentation and localization

Fig. 2: Typical study workflow of the tree detection based on 3D point cloud
[10, 9, 70] Fig. 3: Schematic illustration of DSM, DTM, and CHM

###  2.1 Point cloud data collection and pre-processing

The forest canopy structure could be reconstructed in forestry management by
high-quality point cloud data. Classifying by different platforms, LiDAR-based
point cloud collection methods can be divided into different kinds of LiDAR
scanning, e.g., spaceborne, airborne, UAV, and terrestrial scanning. This
review focuses on UAV LiDAR scanning that mounts a sensor on a UAV and
acquires data rapidly and conveniently in a top-down approach. In the data
acquisition process, the main parameters are the flight altitude, flight belt
interval, flight speed, emission frequency of the laser pulse, scanning angle,
and overlap. For the result of point cloud data, the key parameter is point
cloud density. Typically, the point cloud density is between 100 and 300
$pts/m^{2}$. Researchers sometimes use a very high overlap rate of 90 % for
acquiring high point densities of more than 1000 $pts/m^{2}$.

On the other hand, point cloud data can also be acquired from UAV images by
SfM techniques [30, 31, 52]. In the flight plans, flight altitude, flight
speed, overlap rate, camera parameters, and ground control points play the
important role in acquiring UAV images. Among them, ground control points are
points on the surface as known locations and used for geo-reference data [119,
21]. Moreover, most SfM processes are completed by commercial software, such
as Agisoft Metashape software [9, 88] and Pix4Dmapper software [49, 43, 105].

Due to the multifarious types of the terrain and plant, the original point
cloud data are complex and diverse. Therefore, the data used for the ITD
should be normalized at heights and only includes trees. The first step of
data process is to filter the noise of the data. Most researches use the
commercial software for data processing, especially denoising data, such as
Lidar 360 software [42, 72, 71], rLiDAR and LiDAR packages in software R [97,
17], FUSION/LDV software [76, 32, 79], CloudCompare software [34, 30],
LASTools software [15, 119, 43] and Terrasolid software [92].

After reducing the noise of the data, a ground point filter will separate the
ground points and non-ground points, which can be further used to generate DTM
and DSM. The mainstream approaches for ground point filters are as follows:

As developed in year 2016 [127], Cloth Simulation Filtering (CSF) has now
become one of the most commonly used ground-filtering algorithms with few
parameters to be set, high accuracy [64, 98, 56]. Before introducing CSF, the
meaning of cloth simulation should be known first. Based on Mass-Spring Model
[91], cloth simulation can build the cloth with many interconnected particles
with mass. The location of the particles determines the position and shape of
the cloth. The interconnection between particles is a virtual spring which
obeys Hooke’s law and has three types, such as traction spring, shear spring,
and flexion spring. The relationship between position and forces on particles
could be calculated using Newton’s second law of motion.

In general, the CSF used as a ground-filtering algorithm could be summarized
in four steps: (1) put a cloth on the inverted surface, which is transformed
from turning upside down the point cloud. This cloth includes particles and
interconnections; (2) under the force of gravity, particles drop along the
terrain, and the displacement can be calculated; (3) particles intersect with
the terrain will be set as unmovable ground points, and the position of
particles can be modified based on the internal forces; (4) the final shape of
the cloth can separate ground points and non-ground points and further build a
DTM. Additionally, some conditions are used to simplify the model and make it
feasible. The particle is constrained to move only in the vertical direction,
so the collision can be detected by comparing the height values. The mass of
particles and rigidness are both constants. However, the cloth will make
significant errors at the place with the sparse point cloud. In order to
overcome this shortcoming, a modified CSF algorithm make the rigidness of each
particle adaptively changes with the point density of the initially bare earth
point cloud [60]. Even using the sparse point cloud, the improved adaptive CSF
algorithm still shows a good performance to generate the high quality DTM.

The second ground point filter is the progressive Triangular Irregular
Networks (TIN) filtering algorithm [5]. In this algorithm, original data is
filtered for only keeping the measurements belonging to the ground surface.
Firstly, using all collected point cloud data, an initial sparse TIN is
generated from the original lowest seed points. Then, the TIN adapts the data
iteratively and is constrained under the curvature set in advance in the
model. Points can be added to the TIN if the value is below threshold values,
such as distance and angle. Finally, the TIN is gradually densified in the
iterative process, and all points are classified as the ground or object.
Because the progressive TIN filtering algorithm is robust in processing the
random noise point cloud, this algorithm is suitable for filtering the 3D UAV
point cloud [128, 125]. However, the traditional TIN filtering algorithm is
usually applied on specific sites, such as flat and low-vegetated regions.
Therefore, an Improved Progressive TIN Densification (IPTD) filtering
algorithm was developed and performed well in various complex forested
landscapes [129]. The IPTD filtering algorithm has been used in many
researches [109, 132]. In this improved algorithm, instead of choosing the
lowest points, a morphological opening operation is used to acquire more
ground seed points that are evenly distributed. To avoid the generation of
redundant triangles, it also adds the simulated ground points to the original
TIN to improve the quality of the TIN.

###  2.2 Individual tree detection

Since the tree point cloud is distinct from other tiny object point clouds in
many scenarios, individual tree localization and segmentation can be seen as a
kind of individual tree detection. This review presents some standard
algorithms used in the ITD. Generally, the detection of trees requires the
combination of several algorithms which play roles of segmentation and
localization during the process.

LM algorithm is used as a localization algorithm based on the hypothesis that
the highest value is on the tree apex in CHM [17, 71]. A window with given
size is set to traverse the CHM, generating a local maximum point for each
move. If the local maximum is higher than the threshold representing the
minimum height of a tree, this local maximum point is taken as a treetop. This
process means that one tree is detected. In this algorithm, CHM resolution and
window size play essential roles in tree detection. For example, one tree will
be regarded as two separate trees when the window size is too tiny [121]
because the big and tall tree will be detected more than two times during the
move of the small window [32, 62].

WA is a segmentation algorithm often used with LM to achieve the ITD [117].
This method is based on simulating immersion and could sense subtle changes
and generate closed contour lines around objects [87]. In the ITD, it can
build a barrier at canopy boundaries as watershed boundaries. Such boundaries
prevent the merging of adjacent waters. In other words, they can segment the
adjacent tree crowns. In WA, the core parameter is the threshold value of
minimum height and sigma which influence on the number of segmented trees
directly.

However, WA does not perform well in the forest area with higher tree
densities because the segmentation line of the overlapping tree cannot
accurately represent the actual canopy area. After that, a progressive
watershed segmentation algorithm is proposed to improve the segmentation
performance in such situations as identifying the overlapped crown areas [72].
Identifying overlapped areas is based on observing the relationship between
the point cloud density and the enclosed irregular curve. The theoretical
radius of the curve and the derivative of the radius increment are essential
parameters during the identification. For instance, one area can be considered
as a single independent tree when the derivative of the increment of the
theoretical radius is equal to zero.

Usually, the ITD includes two steps as tree top detection and tree crown
segmentation. A one-step algorithm called Region-based Hierarchical Cross-
section Analysis (RHCSA) is proposed to complete the detection and
segmentation together [130]. The algorithm treats the tree crown as a 3D
topographic surface, which can be represented as a mountain-like uplift on the
CHM. In this approach, the CHM is scanned vertically by a horizontal plane
within the specified interval, as shown in Fig. 4. During the scanning
process, when the plane passes through trees, the cross-section areas will be
created from the higher tree to the lower tree in sequence. After the scan,
the CHM is decomposed into a horizontal crown area of different heights. This
algorithm will automatically determine whether the cross-section appears
first. When different individual tree crowns do not have contact with each
other, the cross sections show multiple circulars; when they interact with
each other, the cross sections usually show an irregular shape. Users can
define the specified interval as starting and ending heights.

Moreover, the iteration time could be calculated with the height of the level
cutting in a single scan. The scanning accuracy could be improved by
increasing the step subdivision. In the end, the output of this algorithm is
delineated as individual tree crowns after the iteration. However, this
algorithm is restricted by the specified interval that it cannot delineate the
small trees which are outside the starting height and ending point [64, 96].

Fig. 4: Schematic diagram of RHCSA algorithm [130]

Point Cloud-Based Cluster Segmentation (PCS) is an algorithm that uses a
spacing threshold, a minimum spacing rule, and a horizontal profile of the
tree profile [58]. This algorithm classifies points by comparing the distance
between the target point and other treetop points. The distance greater than a
specified threshold from the treetop points will also be excluded. In order to
solve the problem in classifying the lower level points, which may involve the
overlapping area, points are classified from the highest and lowest
sequentially. Moreover, it can also add a shape index to reduce the error when
classifying whether points belong to an elongated branch or a separate
treetop. One of the shortcomings of PCS is that it is highly dependent on the
value of the spacing threshold. Therefore, the PCS does not perform well in a
complex forest with many overlapping trees [42, 68].

A forest tree segmentation algorithm combined k-means clustering, PCS, and LM
was proposed as named Layer Stacking (LS) [6]. In the LS, all objects are
layered at one-meter intervals horizontally through the full height. LM is
used to find the maximum height point in CHM representing each tree top. And
then, every layer is applied with k-means clustering using maximmu points as
seed points for clustering points. After clustering, each cluster is placed
with a polygonal buffer around it. The purposes of the buffer are to be an
additional round of clustering to reduce the classification errors and to be a
means to connect the points and vectorize the clusters. A rasterized map of
the overlapping polygons is then generated. Next to that, the overlap map is
smoothed to remove different overlapping areas in the tree that might
represent branches. In the end, LM is used again on the smoothed overlapping
map to find the centers of trees. Each local maximum is placed with the buffer
and takes its centroid as the new center point for the tree. This step helps
in merging local maxima to prevent them, which are too close to each other,
from being classified into separate trees. The last local maxima are regarded
as the centers of each tree and will be used for tree segmentation later.

A study compared the performance of Watershed, PCS, and LS in three tree
datasets, i.e., leaf-on, leaf-off, and fused (combine leaf-on and leaf-off)
point cloud [12]. In this research, three individual tree segmentation
algorithms were applied to the four forest types with three datasets. As shown
in the results, WA, PCS, and LS performed similarly in leaf-on and leaf-off
datasets. Regarding fused point cloud data, the LS algorithm had the higher
F-score than that of the WA and PCS algorithms. For example, the segmentation
had 15.7 % more trees with the fused data than leaf-off data in the mixed
broadleaved forest. The high point density caused by fused point cloud data
includes more small trees and is responsible for the improved segmentation.

Another research analyzed the sensitivity of critical parameters in WA, LM,
PCS, and LS for choosing a superior method in different situations [71]. The
WA and LM methods were implemented based on the data from the CHM when the PCS
and LS were based on the Normalized Point Cloud (NPC). Results indicated that
the NPC-based algorithm showed a better capability than the CHM-based
algorithm in tree segmentation. For instance, the average detection rate r of
the NPC-based method is 87.06 % which is higher than that of the CHM-based
algorithm with 81.28 %. The limited pixel resolution of CHM rasters causes a
lower detection rate.

On the one hand, the core parameters, such as the sigma value in WA, the
window size in LM, and the distance threshold in PCS, showed a negative
correlation with the detection rate performance. On the other hand, the layer
thickness in LS was positively correlated with the detection rate. Meanwhile,
the results also showed that the WA and LM did not have the advantage on the
tree segmentation with a high-density point cloud. The PCS algorithm was good
at segmenting many types of forests. Furthermore, the LS algorithm achieved
better results in multilayer and extremely complex heterogeneous forests.

Some methods perform on basis of point density without relying on the tree
height [120]. For example, Adaptive Mean Shift 3D (AMS3D) segmentation [25] is
first used in the point cloud collected from airborne laser scanning data. As
a multi-modal distribution, it classifies the point cloud as many models where
each model represents an individual crown with the maximum local value in
density and height. There are two steps in this approach. In the first step, a
3D kernel is moved to denser regions iteratively until convergence for
acquiring multiple modes from the point cloud. In the second step, the points
belonging to the same mode are gathered to compute the 3D clusters of the
individual tree crown. Because AMS3D is not a parametric algorithm, it does
not pre-set the contours of objects in the data and can be used to analyze
complex forests with different structures and shapes. The only influential
parameter is the size of the 3D kernel named bandwidth. For example, the small
bandwidth will divide the giant tree into smaller trees due to the search for
the local maxima in a narrow section. The single bandwidth cannot meet the
requirements to process the point cloud data with different sizes of the tree
crown. Therefore, a study established a self-calibrated bandwidth model which
can be optimized with different forest structures [26]. That is to say, the
bandwidth increases from the lower points to the higher points [98]. Moreover,
the segmentation accuracy of the algorithm could also be improved by using
different canopy structure attributes to calibrate the bandwidth adaptively
[56]. To verify the generalization of the improved algorithm in different
species, it is applied in a subtropical forest with an overall precision
greater than 0.72.

The algorithms, which rely on the local maximum height to find the tree top
location, do not perform well when dealing with trees without apparent tree
tops or irregular tree tops. As a very robust method, the Random Sample
Consensus (RANSAC) algorithm can address such problems [7]. Initially, Roth
and Levine [28] proposed RANSAC to extract geometric primitives in model-based
computer vision. This algorithm searches the model by comparing the point
cloud with a specified minimal set of points, the smallest number of points
needed to define a geometric primitive. The remaining points are used to fit
the shape after the minimum number of points has been selected[88]. Because
RANSAC carries out the whole point cloud data, extracting all the shapes
similar to the parametric model, the candidate Region of Interest (RoI) is
needed to process only the most relevant point cloud to increase its
efficiency and reduce its computational cost.

Another common problem in tree detection is that many algorithms detect the
tree based on a top-down structure. For such algorithms, the tree top is
localized followed by segmenting the tree crown. And then, the tree trunk can
be delineated in the end. However, it is difficult for this approach to
perform well in forest with many interlacing tree branches. Since it takes
much work to complete the tree top localization or tree crown segmentation,
some researchers design their algorithm based on the bottom-up scheme [69,
59]. The basic flow of the bottom-up algorithm is to first locate and
delineate the trunk, and the tree crown can then be delineated by clustering
the points around the trunk. Moreover, the core concept of trunk localization
is that the trunk corresponds with higher point density and elevation [8].
Subsequently, the tree crown segmentation is conducted based on the fact that
points belonging to its trunk have a smaller distance than any other trunk.
This concept is similar to the PCS algorithm. Since the distance between the
points and the trunks is a core parameter, the algorithm still needs to be
improved when a branch reaches into the crown of another tree.

Nowadays, more and more researchers conduct tree detection based on machine
learning, especially DL. For example, based on CHM, a DL framework is used to
detect trees in the forest with complex structures with mining defining
features [70]. There are three steps in the tree detection stage: Firstly, to
obtain the vertical structure information in a different layer, the point
cloud is sliced into several parts in a top-down way; Secondly, three
properties, i.e., height, height gradient, point density, as a three-channel
representation are inputted to the DL network simultaneously for providing
valuable information. The height gradient could show gaps between the adjacent
tree crown. The point density helps extracting the tree because the center of
the tree has a higher point density. Thirdly, a multibranch network is built
to extract trees from grid images. This research uses region growing algorithm
and Mask RCNN to compare the performance with the proposed multibranch network
method. Results show that the new method performs well than the other two
methods achieving a mean precision of 85.99%, a mean recall of 89.23%, and a
mean F1-score of 87.04%.

###  2.3 Comparison and combination of tree detection between LiDAR and DAP

Both the point cloud data acquired from the DAP and LiDAR work well in tree
detection. Therefore, some researches explored to compare the differences
between them and combine them to find superior detection performance in
different scenarios.

There are some studies focused on combining DAP and LiDAR technology for
improving the performance in extracting the individual tree crown information.
For example, in a multi-resolution segmentation algorithm, researchers
combined the RGB information from photogrammetric images and CHM from LiDAR-
normalized point clouds for tree crown segmentation. In this algorithm, the
weight of the CHM layer is three times higher than that of the RGB layer [80].
However, results show a lower correlation between field-measured and multi-
resolution segmented crown areas for all species. The complex crown structures
in deciduous species and the varied spectra may cause inaccurate individual
tree crown segmentation. Many previous studies also proved that it was
difficult for tree crown detection algorithms to perform well when the object
was broadleaf species [108, 113].

Another study combined the DAP-based DSM and the LiDAR-based DTM [52].
Although such a combination increases the height accuracy of the normalized
surface model, the accuracy of tree crowns height and detection do not
improve. Therefore, the researchers conclude that DAP could replace LiDAR
system when the duty performs a task with frequently changing sites to save
costs. Furthermore, combining two types of data lead to the better quality of
the DTM.

On the other hand, one research compared two data acquisition methods, i.e.,
DAP and LiDAR on the difference of the point cloud quality and the estimated
tree crown parameters over an urban park [30]. The result shows that the types
of point clouds are highly correlated with the statistical measure of fit R2
up to 99.54 %. Moreover, the estimated tree height, diameter, area, and volume
results correlate that R2 is higher than 95 %.

It is not feasible to compare the points directly because of the
differentiation between the center positions of two corresponding LiDAR and
DAP points. So, a study compares the quadratic rasterized points and CHMs
generated from LiDAR and DAP points. In the raster, the highest height of
points is set as the value within each cell [107]. Results indicate a high
correlation rate between LiDAR and DAP data. Compared to the LiDAR data, the
DAP data has more detailed information in geometry which causes the advantage
in tree detection. In the same area, with the same algorithm, the detection
rate using DAP data is 93.2 % which is higher than that of LiDAR data at 78 %.
There are two main reasons for this phenomenon. Firstly, the DAP data has a
higher point density which includes more detailed information about the
plantation. Secondly, the penetration of the LiDAR pulses is slight, which
ignores the small targets with lower reflection.

##  3 Object Detection based on Spectral Channel in Images

The methods used in the ITD with spectral images could be categorized into the
DL-based and classical methods. Most DL methods applied in detection are based
on the object, such as the algorithm of the YOLO series, R-CNN series, and
SSD, which mark the target with boxes on the image. On the other hand, The
classical methods detect trees based on the pixel and classify every pixel
into different classes.

###  3.1 Spectral image data acquisition

The spectral image could be roughly divided into RGB images and multispectral
images, which are collected by RGB, e.g., senseFly.S.O.D.A, and multispectral,
e.g., Parrot Sequoia and RedEdge-M, cameras, respectively [84, 48]. In
general, the acquisition location, weather, and the model and parameters of
the UAVs should be clearly described [106]. The flight mission, altitude,
speed, and spatial resolution are the main parameters in acquiring spectral
images [66, 126]. The spectral images used in DL methods are divided into
training, validating, and testing datasets. In the training and validation
datasets, all the images should be labeled with bounding boxes [106]. In order
to decrease the influence of weather, equipment, and suspended particles on
the quality of images, a balance contrast enhancement technique could be used
[99]. In this technique, the histogram pattern of the input image remains the
same, with the contrast that can be stretched and compressed [33].

###  3.2 Individual tree detection

Fig. 5 shows the workflow of the tree detection with spectral images. As shown
in the workflow, the classical method includes object segmentation, e.g.,
superpixel and WA, and object classification, e.g., k-mean, mean-shift and
SVM. In addition, there are still extraordinary examples. For instance, in the
DL methods, a study used the ResNet-18 network for semantic segmentation by
classifying pixels into different palm trees, i.e., a pixel-based method,
[27]. Moreover, in the classical methods, as an object-based method, the
Template Matching (TM) method finds targets by comparing the template with
objects in the image [4]. Since the pixel-based detection method classifies
every pixel singly, the variations in spectral patterns from changing weather
and season impact the accuracy of the method performance significantly[22].
Tree detection results by DL and classical methods are also shown in Fig. 5.
Generally, the detection results acquired by the DL method usually used the
boxes to mark the trees, while the results by the classical method marked the
trees with specific contours.

Fig. 5: Typical study workflow of tree detection based on spectral images
[101, 65]

####  3.2.1 Detection with DL methods

The DL methods have been widely used in the object detection duty due to the
technological advances in graphics processing unit. Generally, DL-based tree
detection is supervised approaches and need a large training set to learn. The
quality of the training set is crucial to the performance of the detection.
Among them, Yolo series algorithms and RCNN series algorithms are applied on
the tree detection popularly. Other than that, some detection methods will
process the image before applying the DL methods to achieve a better detection
result. Commonly, the processing methods are noise removal and potential areas
selection.

##### 3.2.1.1 YOLO series algorithms

As one of the one-stage object detection methods with the highest performance,
the YOLO series algorithms take a single forward pass on the input images and
outputs the detection results. In the results, the detected targets are marked
with the bounding box and class probabilities [55, 116]. Due to the excellent
performance, the YOLO series algorithms have been applied in detecting
plantation widely [78, 14, 38]. For example, one research used YOLO-V5 to
detect palm trees from RGB images and compared it with YOLO-V3, YOLO-V4, and
SSD300 for evaluating their performance [48]. In particular, YOLO-V5 has four
sub-versions as V5s (small), V5m (medium), V5l (large), and V5x (extra-large),
which have the same network structure but with different depths from small to
large. The results showed that the YOLO-V5m had the maximum mAP values of
92.34 %. Furthermore, the YOLO-V5 method had the shortest average detection
time compared with YOLO-V3, YOLO-V4, and SSD300.

In order to explore the superiority of the YOLO algorithms, many researchers
conducted experiments to compare tree detection performance between YOLO with
other deep leanring networks. For instance, one study compared the performance
of Faster R-CNN, YOLO-V3, and SSD on the detection of loblolly pine crowns
[66]. Moreover, another investigation applied the SSD, YOLO-v3, YOLO-v4, and
Fast-RCNN in a spruce forest area to compare their performance on the tree
detection [23]. Meanwhile, in another research of the assessment of CNN-based
methods for the ITD, Faster R-CNN, YOLO-V3 and RetinaNet were compared by
being applied on the UAV images [103]. RetinaNet obtained the most accurate
results with an average precision of 92.53 %. YOLO-V3 achieved the fastest
computation speed compared to its counterpart. Moreover, Faster R-CNN has the
lowest accuracy and highest computational cost.

Although the YOLO algorithms achieves good accuracy in tree detection [99],
some structures can still be improved and optimized. For example, even if
YOLO-V4 can extract the feature effectively, the complex backbone network,
i.e., CSPDarknet53, in YOLO-V4 increases the detection time. Therefore, one
study improved the YOLO-v4 in three steps [106]. Firstly, the CSPDarknet53 was
replaced by MobileNetv2 as a backbone network to increase efficiency while
maintaining its accuracy. Secondly, Convolutional Block Attention Module
(CBAM) was added to ignore the redundant features, prioritize the essential
features, and further optimize the detection processes. Thirdly, for deepening
the network depth and reducing the complexity of the calculation, the original
3 $\times$ 3 convolution kernel for information integration on the YOLO-V4 was
replaced with 1 $\times$ 3 and 3 $\times$ 1 convolution kernels. Subsequently,
this improved DL network was used to detect pine wilt nematode. The results
showed that the improved algorithm had a better performance than its
counterparts, such as Faster R-CNN, SSD, and YOLO-V4 regarding average
precision, training time, parameter size, and test time.

The YOLO series algorithms commonly concentrate on detecting trees with large
sizes and outstanding features. For the detection with a small size and few
features, one research proposed a lightweight, small object detection method
based on the YOLO network toward the detection of dead trees on mobile
terminals [111]. The proposed LDS-YOLO algorithm made improvements in the
following aspects. At first, to resolve the problem of overfitting caused by
the small-scale dataset, the dense connection module was added in the backbone
part. By combining different feature layers in dense connection modules, the
high-dimensional feature reused the low-dimensional features to improve the
regularization and smooth decision boundaries. Second, a variant of pooling
SoftPool [104] was inserted into the network to minimize the pooling loss and
computation cost. In the end, depth-wise separable convolution was used in the
network to reduce the number of parameters and further improve the efficiency
of the detection. As a result, the number of parameters was decreased by using
1 $\times$ 1 $\times$ 3 convolution kernel.

##### 3.2.1.2 R-CNN series algorithms

As a two-stage detection algorithm, R-CNN series algorithms with consisting
two parts have been applied to detect trees in many studies [65]. The first
part aims to find the proposed regions on the images, and the second part is
then used to process the proposal regions for scoring. For example, Faster
R-CNN was used to recognize and extract pine crown [66] and to extract the
spruce tree locations, numbers, and diameters of the spruce tree [23].

However, for specific detection problems, Faster R-CNN could still be
optimized. To detect and observe the oil palm trees, a Multi-class Oil Palm
Detection (MOPAD) approach was formulated based on the Multi-level Region
Proposal Network (RPN), which plays a vital role in Faster R-CNN [131]. Based
on Faster R-CNN, MOPAD added a Refined Pyramid Feature (RPF) module and a
hybrid class-balanced loss module to the detecting process. Benefiting from
integrating the multi-level features in the RPF, the network could classify
similar classes and detect small objects. The proposed MOPAD method was
evaluated using oil palm plantation images and compared to the original Faster
R-CNN algorithm. The results indicated that the new method improved the
average F1-score by 17.09 %.

In another example, a study inherited from the Faster R-CNN algorithm focused
on the detection and localization of dead pine wilt trees [19]. This research
improved the Faster R-CNN algorithm in three ways. The first one, because the
goal of the algorithm was only to detect dead trees as the binary
classification problem. The loss function was changed from the Softmax
function to the sigmoid function to add the accuracy of the loss value. The
second one, the anchor size was optimized according to the small size of
detected trees because the anchors were crucial for the pre-selected boxes in
the RPN. The last one, the original backbone VGG16 was replaced with the
ResNet101 network to deepen the network and prevent the gradient
disappearance. As results shown in Fig. 6, the detection time increased from
0.649 s to 1.129 s with accuracy increased from 66.2 % to 70.8 % by replaced
the backbone network with ResNet101. The improved loss function could reduce
the calculation time along with the increased time in data augmentation.
Meanwhile, the improved RPN network decreased the detection time. In the end,
compared with the original Faster R-CNN algorithm, the accuracy of the
optimized algorithm was increased from 66.2 % to 89.1 %.

Fig. 6: Comparison of the performance in terms of the accuracy and processing
time of different improved models [19]

Instead of just providing candidate boxes, Mask-RCNN can segment the image to
find the same region of the object for achieving image segmentation and
classification at the same time in one task[37, 83, 35]. To delineate trees’
properties and observe their growth status, the Mask R-CNN was applied in one
study for detecting and mapping the height and the crown width of the
individual tree [102]. Results indicated that the Mask R-CNN showed a good
performance in detecting the tree crown with an overall accuracy of over 70 %.
However, in the original Mask R-CNN, the path between the high-level features
and low-level features in the FPN for the feature fusion is too long, which
leads to the problem in multi-target segmentation [63]. For improving the
algorithm and using it on the large-scale multi-target tree detection, another
study modified the fusion style by applying a bottom-up approach which reduced
the path of feature fusion between lower and higher layer features [126].

##### 3.2.1.3 The two-steps methods and other kinds of method

In addition to the direct application of the DL network on images for tree
detection, some methods pre-process the image and then apply the detection
algorithm to the result. This two-step method could be classified into two
categories, as shown in Fig. 7. The first class is to remove noise or
backgrounds in advance, while the second class is to select the potential
regions before classification.

Fig. 7: Schematic of the two-steps detection methods [100, 41]

For instance, in detecting diseased pine trees, MobileNet was first applied to
images to reduce interference by removing backgrounds as only interested
objects were left in the image [41]. Faster R-CNN was then used to classify
the different kinds of trees. This method showed a better performance as
higher recall values and lower detection error on tree detection were found,
compared to the traditional methods. Moreover, in the research of the
detection of diseased pine trees, it used MobileNet to remove backgrounds at
first due to the disturbance from other objects, such as shadows and roads.
Faster R-CNN was then selected to achieve the detection of disease pine trees.

The second kind of research processed the image first to select the potential
regions. The DL network was then used on the selected region to detect the
individual tree. For example, in finding the candidate area, the UAV RGB
images were transformed into grayscale color palettes at first [100].
Moreover, the grayscale images were blurred and further applied with a
threshold function to create the binary images with thier segmented areas
known as the potential regions of trees. After this, the CNN classification
model would analyze each potential area. The results showed that the proposed
method achieved a better performance than the most famous CNN models in tree
detection, such as ResNet, VGG, and DenseNet.

In another example, the results of LM and WA were input to the Faster R-CNN
[74]. The outputs of LM and WA are the information of tree top points and the
segmented images as interesting elements. Furthermore, the Faster R-CNN was
applied based on the segmented images for tree detection. Compared with the
direct application, such as pure classification, this method could increase
the efficiency and accuracy of the detection by ignoring the area without
trees. Similarly, in the research on individual sick fir tree identification,
some DL methods, i.e., Alexnet, Squeezenet, Vgg, Resnet, and Densenet,
classified healthy and sick trees on the images which have been segmented
based on LM [82].

Most DL-based methods conduct object detection based on candidate anchors,
meaning that the final prediction results are the score of each box. Some tree
detection algorithms do not rely on the candidate anchors but directly make
the prediction on the pixels of the image for classification. For instance, to
detect and classify the amazonian palms using UAV images, one study used
ResNet-18 with DeepLabv3+ on RGB images at first for acquiring score maps with
five categories based on each pixel [27]. The network can only conduct patch-
level semantic segmentation, so it cannot distinguish too close trees.
Therefore, the score maps were then performed with morphological operations
for the ITD to solve such a problem. The steps of the morphological operations
are erosion, opening, dilation, and regional maxima. The results showed that
the detection accuracy of the Euterpe precaoria tree and Iriartea deltoidea
tree are high, i.e., 98.6 % and 96.6 %, respectively. Furthermore, the
proposed method can identify two very close trees, which other conventional
algorithms cannot achieve.

In another research, a CNN approach is input with RGB images and outputs the
predictions of a confidence map [84]. In building the training dataset, te
confidence map as a dense map is generated in the following ways: (1) Compare
the values of every location with each plant location at first, (2) Select the
value with the most negligible difference as the value in the confidence map,
(3) In the confidence map, the location of the trees could be calculated by
using a local maximum algorithm. From the comparison results predicted by
proposed approach and other object-based methods, i.e., RetinaNet and Faster
R-CNN, the proposed approach showed the highest precision of 0.95 and recall
rate of 0.96.

All the methods introduced above can be classified as 2D-CNN, which conducts
the tree detection on the RGB images with two-dimensional feature maps. In
addition to 2D-CNN algorithms, some researchers used the 3D-CNN on the
Hyperspectral Imagery (HI) both in spatial and spectral dimensions. The former
usually losses the spectral information in the HI while the latter keeps it
[123]. Furthermore, most datasets presented so far are RGB images. However,
multispectral images are also used in tree detection and delineation, i.e.,
tree height and crown radius [124]. For example, one study applied the Mask
R-CNN on the detection using six different band combination data as multi
band-DSM, RGB-DSM, NDVI-DSM, Multi band-CHM, and NDVI-CHM combination [35].

####  3.2.2 Detection with classical methods

Most of the classical methods complete the tree detection based on pixel
information. In general, the classical methods cannot detect the desired
targets directly and alone [53]. Morphological operation is one of the
efficient ways to detect trees initially as it aims to extract interesting
structures of the image [27, 89]. However, it only can have a good accuracy
toward the simple detection due to the single function. Therefore, it is
suitable for use in combination with other methods to improve the robust and
applicability in the complex detection task. Fig. 8 illustrates the typical
morphological operations to get clear binary image of the oil palm trees from
the RGB image. Firstly, the RGB image is converted into the HSV image. The
background image is obtained by applying a morphological opening operation on
the value image from the channel of value in the HSV image. Secondly, the
background image is subtracted from the value image. Thirdly, the last image
is transformed into the binary image and applied with morphological closing
operation. Both opening and closing operations are performed with a square
structuring element. Obviously, the main trunk of the oil palm leaves can be
extracted well when the background and most of the noise are removed. The
original RGB image is acquired from the data supported by an oil palm
detection research [93].

Fig. 8: Illustration of the typical morphological operations of the oil palm
tree

For example, the morphological operation is applied to find the potential
regions that have a high possibility to include a tree [100]. The input images
were transformed from RGB to grayscale in the morphological operations. Then,
the grayscale images would be blurred by the Gaussian high-pass filter to
reduce the noise. Subsequently, the binary images could be created by changing
the optimal brightness. In the end, image erosion and dilation could generate
candidate boxes of potential regions. And then, the images with candidate
regions would be input into a convolutional neural network for classification.

Referring to pixel-based approaches, the superpixel method is used in
detection in many ways. For example, one study input the image to a CNN
algorithm for getting an initial classification of the trees [16]. Generally,
CNN would cause the single large trees to be detected multiple times, thus
treating a tree as multiple. In this research, a classification refinement was
based on the superpixel after the initial classification transforms the over-
segment imagery into low-level groups. The superpixel was used on the heatmap
generated from the results of CNN, and Normalized Difference Vegetation Index
(NDVI) layer, i.e., green and bare soil layer using a simple linear iterative
clustering algorithm [1]. After the classification refinement, the detection
showed a higher accuracy of 96.24 %, a precision of 94.59 %, and a recall of
97.94 %.

Moreover, the Vegetation Index (VI) is often used in the classification of
plantations as it performs well in distinguishing the vegetation and non-
vegetation areas, e.g., builds, roads, and bare soil, in images than the
standard segmentation techniques [29, 61, 75, 132]. Fig. 9 shows two examples
of image segmentation with and without using VI images. As shown in Example 1,
compared with RGB images, a good segmentation result with more exact detection
area and the less over-detection area is achieved on VI images. Moreover, in
Example 2, the vegetation pixels extraction method based on the R and B bands
of the Normalized Difference Vegetation Index (NrbVI) could segment the
vegetation area.

Fig. 9: Two examples of image segmentation with and without using vegetation
index [61, 75]

Among the pixel-based methods, one type is based on the local image gradient
computed from the pixels. For example, as a feature descriptor, the Histogram
of Oriented Gradient (HOG) is used to calculate the statistical value of the
directional message of the local image gradient [18]. In the research of
automatic detection of individual oil palm trees in UAV images [112], the
pixels in the images were initially classified into vegetation and non-
vegetation by SVM. And then, for the vegetation regions, another SVM would be
used on the features extracted by HOG for detecting the oil palm tree.
Moreover, the images are scaled to different sizes for detecting oil palm
trees of various sizes. Finally, the results indicated that the overall
accuracy in all test sites was over 99.00 %. Another method, the Level Sets
(LSs), also used the local image gradient to sketch the outline of an object.
It was applied with other methods together for tree detection in followed
research.

By combining several classical algorithms, an efficient framework for palm
tree detection was proposed[73]. There were four parts in the framework. At
first, the Scale-Invariant Feature Transform (SIFT) was applied to the
original images to extract the key points. SIFT can detect and match local
features in images by comparing the Euclidean distance between the database
features and the new image [67]. This efficient framework indicated the
feasibility of SIFT in detecting oil palm trees when it is rarely applied in
this research area. Then, these key points were initially classified into the
palm, and non-palm types by an Extreme Learning Machine (ELM), which was an
excellent classification used in tree detection [51]. Subsequently, LSs were
used to connect the points for sketching the contours of the trees. Finally,
the pattern of the LSs region was further classified by the Local Binary
Patterns (LBPs) to find the oil palm tree accurately. LBPs could distinguish
the oil palm tree from other green plants by comparing the gray-scale
invariant texture efficiently [110].

One of the core parts of the SIFT method is the creation of scale-space
creation which is commonly applied in detection [57]. In simple terms, the
scale space is a function of and continuous with the scale parameter as the
independent variable. Furthermore, it can represent the original intensity
image in a multi-scale way. Another research combined the scale-space with
other classical methods to detect the papaya tree with UAV images [47]. At
first, the RGB image was transformed to Lab color space to create a high
contrast between the trees and backgrounds. The Lab color space has three
channels with L for lightness, a for red-green, and b for yellow-blue. Then,
the scale-space was created to generate the images with blobs symbolizing the
object. To select the trees in all the objects, a local maximum filtering was
applied to compare the pixel with its eight neighbors. Meanwhile, only the
pixel with a value higher than a specified threshold could be classified into
the tree. Finally, the results showed that the proposed method reaches a high
accuracy in detecting trees as an F-score larger than 0.94.

Different from other classical methods, which are pixel-based, TM is an
object-based method and has been used in tree detection [101]. The manually
built template plays a crucial role in the detection and will influence the
results directly. Furthermore, the template is a standard model and will be
used for comparison with the target. The target can be detected by finding the
most similar region or the area with the slightest difference. Classical TM
methods are Normalized with the Cross Correlation (NCC), the square root of
the Sum of Squared Differences (SSD), and the Sum of Absolute Differences
(SAD). In detecting oil palm trees, the researcher applied the NCC method to
evaluate the agreement degree between the template tree and the actual tree in
the image [36].

The traditional template matching methods used in tree detection depends
highly on the grey-intensity image. Such methods perform well when the image
only includes trees without other interfering objects, such as buildings and
cars. In order to overcome the limitations and increase the detection
accuracy, one study implemented tree detection based on different vision
levels including spectra, shape, and context [45]. The detection model has two
most distinctive features. The first one considers the tree shadow information
during the detection. The second one, rather than only matching the grayscale
intensity image, the method matches the template in the object class, and
shadow class separately and then combines the results for the final decision.

##  4 Conclusion and outlook

This paper reviewed the recent application of UAVs equipped with LiDAR sensors
or cameras for tree detection. The methods of tree detection can be divided
into two categories based on data type, i.e., point cloud data and image data.
Furthermore, the point cloud data can be collected by LiDAR sensors directly
or transformed from UAV images. Since the information collected by the camera
cannot penetrate the crown like the LiDAR sensor, the point cloud data
contains only a minimal amount of terrain structure information. Thanks to the
development of computer vision, tree detection could be conducted directly on
the UAV image spectral channel. Moreover, depending on the option to use a DL-
based algorithm, the methods applied to UAV images are classified as classical
and DL-based. The specific conclusions derived from this review are the
following:

  * •

According to our statistics, the number of research based on LiDAR data kept
increasing up to the year 2020 and has remained significant till now due to
the high precision of LiDAR point cloud data. Meanwhile, caused by significant
advances in object detection in recent years, DL-based tree detection research
accounted for the most considerable portion, i.e., 45 % of total research in
2022.

  * •

Whether using point cloud data or image data, the two main steps of most tree
detection using classical methods are segmentation and localization or
classification. Meanwhile, most DL-based tree detection methods are based on
the object, meaning the detection results show the location and class of trees
with only one step. Although the segmentation can show the exact area of
individual trees, it is time-consuming and cannot be used in real-time
detection.

  * •

In the detection based on point cloud data, it is mainly completed based on
the following attributes: CHM, the distance between points, point density, and
model represented by a minimal set of points. All methods have limitations
according to the attribute, such as that CHM-based detection is unsuitable for
the tree without the obvious highest point in the crown. Furthermore, the
distance between points is unsuitable for tree detection when the tree
branches are intertwined.

  * •

In the detection based on image data, the DL-based methods highly rely on the
training data. Robust DL-based detection methods need to include as many tree
samples as possible in the training data, e.g., under different lighting
conditions, ages, health levels, and camera angles. Furthermore, the classical
methods are focused on the image pixel information, such as local pixel
gradient and binary image. Usually, the classical method often performs well
in one specific type of tree due to the manually defined parameters.

Although extensive research on tree detection has been carried out with
different types of methods, many fundamental problems still need to be solved
as have been identified from our review. For the ITD in complex forest
environments with multiple types of trees, real-time detection of individual
trees for agricultural applications have yet to yield significant results.
Therefore, the detailed analysis on the challenges and possibilities must be
considered in the future study. Although the camera is an affordable
alternative to the LiDAR sensor in collecting point cloud data, the LiDAR-
based method is still necessary when detecting densely vegetated areas, as it
can penetrate the tree canopy to obtain bottom information. Therefore, the
effective ways to separate the crossed branches from the point cloud data in
the densely vegetated areas is a fundamental problem of the ITD for the
future.

According to our statistics, there is no doubt that the DL-based method is
suitable and becoming increasingly popular in tree detection. However, the
performance of DL-based detection highly relies on the architecture of the
neural network and training data, which simultaneously restrict its
application in different forests. Meanwhile, DL-based methods usually need
more pertinence, i.e., detecting the tree with unique properties does not
achieve good results. Therefore, how to make DL methods more general to be
applied to a region containing multiple trees, such as tropical rainforest, is
a question that people still need to consider in the future.

The classical method has yet to attract much attention due to its high
dependence on manually set parameters in recent years. Because of such
characteristics, the classical methods could be a promising solution for tree
detection with specific features. Meanwhile, due to the possibility of
building a classical method with a simple structure and high efficiency, real-
time tree detection could be achieved in agricultural applications, such as
precision spraying, pollination, and harvest.

## Acknowledgement

The authors would like to thank Malaysian Ministry of Higher Education (MOHE)
for providing the Fundamental Research Grant Scheme (FRGS) (Grant number:
FRGS/1/2020/TK0/USM/03/3).

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