# Application of High-Resolution Remote Sensing Image for Individual Tree Identification of Pinus sylvestris and Pinus tabulaeformis

Authors: Hong Li; Wunian Yang
Journal: Wireless Communications and Mobile Computing
 (2021)

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## Abstract

Aiming at applying unmanned aerial vehicle (UAV) remote sensing technology in extracting individual standing tree information, a new automatic single-tree information extraction method is proposed in this paper. The spectral information enhancement processing was performed on the original UAV image to highlight detailed local features; by importing DBI index, the optimal cluster number of theK-means clustering was automatically determined, and image pixels were then marked; Gauss Markov random field (GMRF) model was employed to segment the image further; by mathematical morphology, operators to postprocess the segmentation results to obtain the individual standing tree crown information, and individual standing tree position was calculated through image geometric moment as the basis for its identification. The results show that with the proposed extraction method, the overall accuracy of standing tree identification for the Pinus sylvestris and Pinus tabulaeformis forest areas are 95.65% and 89.52%; the single-tree crown extraction accuracy is 95.65% and 81.90%, respectively. This method exhibits good applicability while it does not require a large amount of manual intervention and prior knowledge, which significantly improves the automation of information extraction.


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## Body

## 1. Introduction

The extraction of forestland information is one of the most important fields of remote sensing technology applications. The information extraction technology of individual standing trees mainly refers to the identification of individual standing tree and extraction of individual tree crowns, which provides a practical and effective scientific basis for investigating the forestland tree species [1], density estimation, planting survival rate [2, 3], change monitoring, ecological resource protection, etc. [4]. The traditional machine vision features include hog, sift, surf, orb, LBP, and Haar. These features can only extract limited information, not enough to support the subsequent detection task.The standing tree information is traditionally obtained by manual field measurement, which is slow with high cost [5].The application of remote sensing technology and methods not only improves the efficiency of an individual [6, 7] standing tree information extraction but also provides a broader range of data sources including high spatial resolution satellite image data [7, 8], airborne hyperspectral, multispectral image data, and airborne radar data (such as IKONOS, QuickBird, and WorldView series) [9]. For some remote and barren research areas, it is often challenging to acquire corresponding high spatial resolution satellite remote sensing image in time, which limits its application in monitoring [10], and the method of airborne sensors is complicated and risky. In recent years, UAV (unmanned aerial vehicle) remote sensing technology has been in rapid development. UAV has the outstanding characteristics of convenient usage and maintenance, small equipment size, low cost, low loss, low risk, fast image acquisition speed, and high resolution [7, 11], which is practically advantageous in regional remote sensing monitoring. In addition, the flying height of UAVs is generally low, so they are rarely affected by the cloud in the process of data acquisition, and the flight route can be flexibly planned and particularly designed [12–14]. Target detection technologies mainly include one-stage and two-stage technologies, including RCNN, fast RCNN, and fast RCNN in one stage, and Yolo and SSD in the other stage.At present, scholars have carried out researches on the extraction methods for individual standing trees; most studies are based on different types of remote sensing data from various perspectives. From high spatial resolution satellite image data, the researchers used the extracted local maximum value to identify individual standing trees. The local maximum method usually takes the maximum value of the local spectrum as the centre of the tree crown and regards it as the position of the tree. This conventional method is relatively simple, and the extraction speed is fast; however, when the image brightness value changes much, or the image background is complex, it generates unsatisfactory recognition output [15]. Other studies report by combining object-oriented method and hydrological analysis technique [16], single-tree information extraction method is applied to high spatial resolution satellite image data. However, the traditional object-oriented methods often require excessive manual interventions [5, 14]; thus, it is difficult to fulfil the automatic extraction [17] of individual standing tree information. Spatial resolution refers to the size or size of the smallest unit that can be distinguished in detail on the remote sensing image, which is used to characterize the image to distinguish the details of ground targets. It is usually expressed by pixel size, resolution, or field of view.In this paper, based on high spatial resolution UAV remote sensing image data, a new method for extracting standing tree information from a single plant was proposed; the individual standing tree information was extracted and analyzed, in comparison with other data sources and traditional theoretical methods. The accuracy of the method is verified and analyzed by selecting a specific research area.


## 2. Study Area and Data

### 2.1. Overview of the Study Area

The study area is located in the Shandong mining area in Daliuta Town, Shenmu County, Yulin City, Shaanxi Province. The satellite base map is the Gaofen-2 satellite image of the study area on July 08, 2020. The Shandong mining area is rich in mineral resources with sparse vegetation, fragile ecological environment, frequent sandstorms, droughts, and severe wind erosion.Since its development and construction in the 1980s, its ecological restoration has been implemented through the combination of water and soil conservation and biological measures. Artificial afforestation is mainly conducted by planting evergreen trees such asPinus sylvestris and Pinus tabulaeformis and shrubs such as sea buckthorn. In this paper, two representative areas are selected as experimental areas, as shown in Figure 1. The geographical range of experimental area 1 (with Pinus sylvestris) is 110° 12′ 13″ E-l10° 12′ 21″ E, 39° 18′ 36″ N-39° 18′ 42″ N, 0.03 km2, an average crown width of 1.0 m; the geographic area of experimental area 2 (with Pinus tabulaeformis) is 110° 13′ 09″ E—110° 13′ 20″ E, 39° 15′ 31″ N-39° 15′ 41″ N, 0.08 km2, and an average crown width of 1.5 m.Figure 1
Pictures of experimental areas 1 and 2.
(a)
Pinus sylvestris(b)
Pinus tabulaeformis


### 2.2. Data Acquisition and Processing

This research uses a DJI Phantom 4 PRO drone, which is a consumer-grade quadrotor drone product. This mode is battery powered with a hovering shooting function and can fly for about 30 mins. The UAV platform is equipped with a CMOS (complementary metal oxide semiconductor) digital camera with a field view 84°; the recording resolution is1920×1080 pixels with R (red light), G (green light), B (blue light) spectral channels.Two field drone aerial photography experiments were carried out in August 2020 and September 2020, respectively. During the experiment, the weather was clear, and the wind was low. The data is collected by video shooting; the flying height of the drone during aerial photography is about 50 m.By the MATLAB R2017a software platform, a video frame is extracted from UAV aerial video, and the TIFF format image file is saved as the image data for research. The data only contains three spectral channels of R, G, and B [18]; the spatial image resolutions of Pinus tabulaeformis area and Pinus tabulaeformis area are about 0.05 m and 0.03 m. The typical regional aerial image obtained is shown in Figure 2, which is further used as the image data for the extraction of tree information in this paper. TIFF (tag image file format) image file is one of the commonly used formats in graphics and image processing. Its image format is very complex, but because it stores image information flexibly, it can support many colour systems, and it is independent of the operating system, so it has been widely used. In various applications such as GIS, photogrammetry, and remote sensing, the image is required to have geocoding information, such as the coordinate system, scale, coordinates of points on the image, longitude and latitude, length unit, and angle unit.Figure 2
Aerial images of areas 1 and 2.
(a)
Pinus sylvestris(b)
Pinus tabulaeformis


## 2.1. Overview of the Study Area

The study area is located in the Shandong mining area in Daliuta Town, Shenmu County, Yulin City, Shaanxi Province. The satellite base map is the Gaofen-2 satellite image of the study area on July 08, 2020. The Shandong mining area is rich in mineral resources with sparse vegetation, fragile ecological environment, frequent sandstorms, droughts, and severe wind erosion.Since its development and construction in the 1980s, its ecological restoration has been implemented through the combination of water and soil conservation and biological measures. Artificial afforestation is mainly conducted by planting evergreen trees such asPinus sylvestris and Pinus tabulaeformis and shrubs such as sea buckthorn. In this paper, two representative areas are selected as experimental areas, as shown in Figure 1. The geographical range of experimental area 1 (with Pinus sylvestris) is 110° 12′ 13″ E-l10° 12′ 21″ E, 39° 18′ 36″ N-39° 18′ 42″ N, 0.03 km2, an average crown width of 1.0 m; the geographic area of experimental area 2 (with Pinus tabulaeformis) is 110° 13′ 09″ E—110° 13′ 20″ E, 39° 15′ 31″ N-39° 15′ 41″ N, 0.08 km2, and an average crown width of 1.5 m.Figure 1
Pictures of experimental areas 1 and 2.
(a)
Pinus sylvestris(b)
Pinus tabulaeformis


## 2.2. Data Acquisition and Processing

This research uses a DJI Phantom 4 PRO drone, which is a consumer-grade quadrotor drone product. This mode is battery powered with a hovering shooting function and can fly for about 30 mins. The UAV platform is equipped with a CMOS (complementary metal oxide semiconductor) digital camera with a field view 84°; the recording resolution is1920×1080 pixels with R (red light), G (green light), B (blue light) spectral channels.Two field drone aerial photography experiments were carried out in August 2020 and September 2020, respectively. During the experiment, the weather was clear, and the wind was low. The data is collected by video shooting; the flying height of the drone during aerial photography is about 50 m.By the MATLAB R2017a software platform, a video frame is extracted from UAV aerial video, and the TIFF format image file is saved as the image data for research. The data only contains three spectral channels of R, G, and B [18]; the spatial image resolutions of Pinus tabulaeformis area and Pinus tabulaeformis area are about 0.05 m and 0.03 m. The typical regional aerial image obtained is shown in Figure 2, which is further used as the image data for the extraction of tree information in this paper. TIFF (tag image file format) image file is one of the commonly used formats in graphics and image processing. Its image format is very complex, but because it stores image information flexibly, it can support many colour systems, and it is independent of the operating system, so it has been widely used. In various applications such as GIS, photogrammetry, and remote sensing, the image is required to have geocoding information, such as the coordinate system, scale, coordinates of points on the image, longitude and latitude, length unit, and angle unit.Figure 2
Aerial images of areas 1 and 2.
(a)
Pinus sylvestris(b)
Pinus tabulaeformis


## 3. Research Methods

### 3.1. Individual Tree Information Extraction

The flow chart of the information extraction of individual standing trees based on the high spatial resolution UAV remote sensing image is shown in Figure3, the left columns display technical methods used, and the operational targets to be achieved are listed in the middle.Figure 3
The flow chart of the information extraction.


#### 3.1.1. Image Spectral Information Enhancement

The extraction of a single tree based on remote sensing images can essentially be regarded as the process of separating foreground and background on images [19], that is, image segmentation. In this study, the foreground refers to the area covered by a single standing tree, while the background refers to the soil, grassland, and other areas. The image enhancement technology is considered conducive to the separation of the foreground and background in the image [18–20]. The method of image enhancement is to add some information or transform data to the original image by certain means, selectively highlight the features of interest in the image, or suppress (mask) some unnecessary features in the image, so as to make the image match the visual response characteristics. In the process of image enhancement, the reason of image degradation is not analyzed, and the processed image is not necessarily close to the original image. Image enhancement technology can be divided into two categories: spatial domain-based algorithm and frequency domain-based algorithm.Since this study uses true colour image data, there exists a high degree of correlation between various bands [21]. When the traditional contrast stretching method is used to enhance the image, it can only improve the image chroma and brightness, but cannot perfect the image colour saturation degree. However, the decorrelation stretching method with a principal of component transformation can enlarge the coupling degree of the image information within the high band, thereby increasing the image colour saturation. In addition, the colours of the relevant areas of the image are more vivid and prominent, while the dark areas will become brighter. Therefore, this paper adopts the decorrelation stretching spectrum enhancement method to operate the original images.


#### 3.1.2. Image Pixel Mark

For the purpose of separation for image foreground and background, further image pixel labelling is required on the basis of image enhancement processing. During this process, theK-means clustering is used in the image pixel labelling process. The purpose of image pixel clustering is to group or classify the pixels in the images. The clustering algorithm is to divide a data set into multiple classes or clusters by a certain standard, such as distance, so that the data objects in the same cluster are similar, and the data objects in different clusters are different as much as possible. In addition, the commonly used clustering algorithms include K-means, K-modes, K-prototypes, dmscan density clustering, GMM, and hierarchical clustering.Assuming the image is a spatial data setX, the elements xii=1,2,3,⋯,N in the data set correspond to the pixels in the image [22]. In the theory of K-means clustering, K initial clustering centres μkk=1,2,3,⋯,K are to be found in the sum of squares of Euclidean distances between clustering centres and image pixels reaches the minimum, and the pixels are assigned to the corresponding cluster centres. The objective function is as follows:
(1)D=∑i=1N∑k=1Kxi−μk.For the conventionalK-means clustering method, it is necessary to artificially set the initial clustering centre several times to obtain the optimal result, which reduces efficiency and is affected by human subjectivity to a certain extent. Therefore, the introduction of the DBI index in this study can automatically determine the optimal number of clusters, avoid subjective judgments, and improve clustering accuracy. DBI mathematically calculate the ratio of the intraclass distance and the interclass distance between any two categories [23]. The larger the DBI value is, the smaller the intraclass distance and the larger the interclass distance would be. The calculation formula is
(2)di,j=vi−vj,(3)Si=1Ci∑x∈Cix−vi,(4)DBI=1K∑i,j∈1,kmaxSi+Sjdi,j.In the formula,x represents data objects in the ith class; vi and vj denote the centroids of the i and j classes, respectively; Ci is the number of data objects in the ith class; di,j represents the Euclidean distance between the centroid of the ith class and jth class; Si and Sj are the class distance between each data object in ith and jth class and centroids vi and vj, and K is the number of clusters.Therefore, in this study, the process of image pixel labelling method is described as follows:(1)
PresetK value (K∈Kmin,Kmax), where Kmin is 2, and Kmax is 15, that is, the image clustering categories are divided into at least two categories, and at most 15 categories(2)
Initialize the cluster centreCk, (k=1，2,3,⋯,K) and calculate the D value, then redistribute the image pixels(3)
Calculate the new cluster centre according to the formula below:(5)μk′=1Ck∑x∈Ckx(4)
Repeat steps (2) and (3) until the cluster centre no longer changes. Record the corresponding DBI values, and label the corresponding category of image pixels, mark value off∈1,K(5)
Produce DBI, when the number of clusters corresponds to the minimum value of DBI, the optimal number of clustersKs is obtained, and output of corresponding image pixel marking result is given. The shortcoming of K-means are as follows: (1) The selection of K value is not easy to grasp. (2) It is difficult to converge for nonconvex data sets. (3) If the data of each hidden category is unbalanced, such as the data quantity of each hidden category is seriously unbalanced, or the variance of each hidden category is different, the clustering effect is not good. (4) Using iterative method, the result is only local optimal


#### 3.1.3. Image Foreground/Background Separation

When using theK-means clustering method for image pixel labelling, only the grey value information of image pixels is considered, while the statistical dependence between neighbouring pixels is not taken into account, which renders some certain errors of labelling results. Hence, according to the Gaussian Markov random field (GMRF) theory [24], the spatial characteristics of image pixels were also considered, and image segmentation was carried out again on the marked image results. GMF is an image segmentation model built on the image neighborhood system; that is, through a neighborhood system, the pixels in the image and other pixels in its neighborhood are connected. The image to be segmented is described by constructing two random fields, a marker field and a feature field [22]. It should be noted that the prior probability is the probability of current event based on historical experience, posterior probability is the probability of an event based on data or evidence, and likelihood probability is the probability of the occurrence of data or evidence when the probability of an event is known. The marker field is used to mark image pixels and to describe the result of image segmentation, denoted as Y=Y∣y1,y2,⋯,yn; besides, the feature field can effectively reflect spatial feature information of each pixel, describing the original image data, denoted as X=X∣x1,x2,⋯,xn. According to the Bayesian posterior probability formula [25], the image segmentation can be expressed as
(6)PY=y∣X=x=PX=x∣Y=yPY=yPX=x,wherePY=y∣X=x is the posterior probability of the marker field Y=y under the condition of the given image characteristic X=x, PX=x∣Y=y is the probability distribution of characteristic field X=x under the condition of given observation data Y=y, PY=y represents the prior probability, and PX=x is a known constant so it can be ignored. The maximum posterior probability can be approximated:
(7)y^=argmaxPY=y∣X=x.During parameter solving process, the image probability calculation actually can be converted into a problem of energy solution, i.e., to minimize the sum of the current mark field energy and the characteristic field energy, namely,(8)Ey,x=minEYy+EXy,x.In the formula,EYy is the marker field energy, and EXy,x is the characteristic field energy.Then, the energy potential functions of the marker field can be defined as(9)EYyI=∑k=1svI,k,(10)vI,k=f,yI≠yk,0,yI=yk,whereI is a centre pixel of the neighborhood and K is the pixel contained in the neighborhood.Assuming that the probability distribution of the image is a normal distribution, the mean and variance ofk in each category are μk and σk, respectively; EXy,x can be expressed as
(11)EXy,x=∑ln2π×σk+x−μk22σk2.Iterative condition modes (ICM) are used to iterate, the program is finalized, and the final segmentation result is recorded once the termination condition is met.


#### 3.1.4. Single-Wood Separation, Extraction, and Postprocessing

In order to obtain the tree information from segmentation results, it is postprocessed by mathematical morphological operators and other methods. The major processing steps [26] are described as follows:
(1)
The segmentation result area is separated: separate the areas containing tree information from the segmentation result and save in TIFF format(2)
Isolated nodes removal: the primary purpose is to remove the isolated noise information in the segmentation result(3)
Patchwork elimination: mainly remove the sporadic small fragmented noise in the non-tree areas(4)
Hole filling: fill the holes in the tree pattern to form a complete closed polygon for further extraction of the tree crown(5)
Morphological operator processing: basic morphological operators in this study include expansion operator (⊕), corrosion operator (Θ), open operator (∘), and closure operators (•) [27]. The principles expressed by assuming binary image A and structure element B are shown in equations (12)–(15). The expansion operator can increase the number of pixels on the border; the erosion operator can reduce the number of pixels on the border. The open operator is first corroded and then expanded; the closed operator is first expanded and then corroded(12)A⊕B=∪A+b∣b∈B,(13)AΘB=∩A−b∣b∈B,(14)A∘B=AΘB⊕B,(15)A•B=A⊕BΘBThis work adopts the processing strategy of first corrosion and consequently expansion, i.e., open operators for morphological processing, which could smooth the boundary contour of the pattern, break the narrow connections, and remove the small protrusion on edges.(6)
Treatment of abnormal segmentation patches: due to the fact that two or more tree spots might be connected together in the segmentation results, which reduces the extraction accuracy. Thus, it is separated based on the rules of patch area and patch roundnessAccording to the patch area statistics from the processing results, the patches whose area is more than 90% of the total patch area are screened out and recorded asA_u, and the remaining patches are recorded as A_d; then, the patch roundness is calculated, and spots with roundness less than 0.5 is marked as C_d, and the remaining patches are marked as C_u. The C_d patches were corroded and then combined with C_u and A_d to produce final segmentation result.


#### 3.1.5. Extraction of Individual Standing Trees

For individual standing tree identification, based on the principle of geometric image moments, the morphological postprocessing results are calculated to obtain the centroid position of the segmentation pattern, which is used as a marker [28] for the crown position, and the number of markers is the exact number of trees extracted. The calculation formulae are
(16)x=C10C00=∑i=1M∑j=1Ni×fi,j∑i=1M∑j=1Nfi,j,(17)y=C01C00=∑i=1M∑j=1Nj×fi,j∑i=1M∑j=1Nfi,j.fi,j is the grey value of the image pixel; C10 and C01 are the first-order moments of the image, representing the cumulative sum of the product of x-coordinate value and pixel grey value of the patch area and the cumulative sum of the product of the y-coordinate value of the patch area and the pixel grey value; C00 is the zero-order moment of the image, which represents the cumulative sum of pixel grey values in the pattern area. Additionally, by vectorizing the final morphological postprocessing results, the corresponding single-tree canopy range is obtained. Note that now, moment technology has been widely used in image retrieval and recognition, image matching, image reconstruction, digital compression, digital watermarking, and moving image sequence analysis. Common moment descriptors can be divided into the following categories: geometric moment, orthogonal moment, complex moment, and rotational moment.


### 3.2. Verification and Evaluation of Individual Standing Tree Information Extraction

When evaluating the methods for extracting information from individual trees, relevant reference data, including the position of individual trees and the range of individual tree crowns, should be considered for accuracy evaluation. Due to the lack of measured reference data, this study employs visual interpretation instead of reference data for accuracy verification.


#### 3.2.1. Evaluation of Recognition Accuracy of Individual Standing Trees

The evaluation accuracy indicators of individual standing tree identification are basically overall accuracy, commission error, and omission error [29]. The number of trees obtained by artificial recognition is used as the reference tree Nr. The number of trees extracted is recorded as Na, and the number of trees extracted correctly is Nc. The overall accuracy refers to the percentage of the number of correctly extracted trees to the number of reference trees, see equation (18). Commission error refers to the percentage of the difference between the number of reference trees and correctly extracted trees in the number of reference trees, as shown in equation (19). Omission error refers to the percentage of the difference between the number of extracted trees and correctly extracted trees in the number of reference trees, see formula (20). In this paper, all the experiments have gone through a lot of experiments, and we have carried out three experiments, respectively, under each working condition, and then obtained the final results by using the average value technology, so the results are very effective.
(18)Overallaccuracy=OA=NcNr×100%,(19)Commissionerror=CE=Nr−NcNr×100%,(20)Omissionerror=OE=Na−NcNa×100%.


#### 3.2.2. Evaluation of Accuracy of Single-Tree Crown Extraction

The range of the canopy was extracted manually by professional interpreters as a reference for verifying the results of single-tree crown extraction. This article quantitatively evaluates the accuracy of single-tree crown extraction based on the following criteria:(1)
Matching: the extracted single-tree canopy overlaps with the reference canopy by more than 50%, and the centre point of the reference canopy is located in the extracted canopy(2)
Merge: the extracted single tree canopy contains two or more reference canopies, and the centre of the reference crown is also included in the extracted crown(3)
Separation: more than 50% of the reference tree canopy is occupied by one or more single-tree canopies(4)
Loss: the overlap between the reference canopy and the extracted single tree canopy is less than 50% [30], and the centre of the reference canopy does not belong to any extracted single-tree crown


## 3.1. Individual Tree Information Extraction

The flow chart of the information extraction of individual standing trees based on the high spatial resolution UAV remote sensing image is shown in Figure3, the left columns display technical methods used, and the operational targets to be achieved are listed in the middle.Figure 3
The flow chart of the information extraction.


### 3.1.1. Image Spectral Information Enhancement

The extraction of a single tree based on remote sensing images can essentially be regarded as the process of separating foreground and background on images [19], that is, image segmentation. In this study, the foreground refers to the area covered by a single standing tree, while the background refers to the soil, grassland, and other areas. The image enhancement technology is considered conducive to the separation of the foreground and background in the image [18–20]. The method of image enhancement is to add some information or transform data to the original image by certain means, selectively highlight the features of interest in the image, or suppress (mask) some unnecessary features in the image, so as to make the image match the visual response characteristics. In the process of image enhancement, the reason of image degradation is not analyzed, and the processed image is not necessarily close to the original image. Image enhancement technology can be divided into two categories: spatial domain-based algorithm and frequency domain-based algorithm.Since this study uses true colour image data, there exists a high degree of correlation between various bands [21]. When the traditional contrast stretching method is used to enhance the image, it can only improve the image chroma and brightness, but cannot perfect the image colour saturation degree. However, the decorrelation stretching method with a principal of component transformation can enlarge the coupling degree of the image information within the high band, thereby increasing the image colour saturation. In addition, the colours of the relevant areas of the image are more vivid and prominent, while the dark areas will become brighter. Therefore, this paper adopts the decorrelation stretching spectrum enhancement method to operate the original images.


### 3.1.2. Image Pixel Mark

For the purpose of separation for image foreground and background, further image pixel labelling is required on the basis of image enhancement processing. During this process, theK-means clustering is used in the image pixel labelling process. The purpose of image pixel clustering is to group or classify the pixels in the images. The clustering algorithm is to divide a data set into multiple classes or clusters by a certain standard, such as distance, so that the data objects in the same cluster are similar, and the data objects in different clusters are different as much as possible. In addition, the commonly used clustering algorithms include K-means, K-modes, K-prototypes, dmscan density clustering, GMM, and hierarchical clustering.Assuming the image is a spatial data setX, the elements xii=1,2,3,⋯,N in the data set correspond to the pixels in the image [22]. In the theory of K-means clustering, K initial clustering centres μkk=1,2,3,⋯,K are to be found in the sum of squares of Euclidean distances between clustering centres and image pixels reaches the minimum, and the pixels are assigned to the corresponding cluster centres. The objective function is as follows:
(1)D=∑i=1N∑k=1Kxi−μk.For the conventionalK-means clustering method, it is necessary to artificially set the initial clustering centre several times to obtain the optimal result, which reduces efficiency and is affected by human subjectivity to a certain extent. Therefore, the introduction of the DBI index in this study can automatically determine the optimal number of clusters, avoid subjective judgments, and improve clustering accuracy. DBI mathematically calculate the ratio of the intraclass distance and the interclass distance between any two categories [23]. The larger the DBI value is, the smaller the intraclass distance and the larger the interclass distance would be. The calculation formula is
(2)di,j=vi−vj,(3)Si=1Ci∑x∈Cix−vi,(4)DBI=1K∑i,j∈1,kmaxSi+Sjdi,j.In the formula,x represents data objects in the ith class; vi and vj denote the centroids of the i and j classes, respectively; Ci is the number of data objects in the ith class; di,j represents the Euclidean distance between the centroid of the ith class and jth class; Si and Sj are the class distance between each data object in ith and jth class and centroids vi and vj, and K is the number of clusters.Therefore, in this study, the process of image pixel labelling method is described as follows:(1)
PresetK value (K∈Kmin,Kmax), where Kmin is 2, and Kmax is 15, that is, the image clustering categories are divided into at least two categories, and at most 15 categories(2)
Initialize the cluster centreCk, (k=1，2,3,⋯,K) and calculate the D value, then redistribute the image pixels(3)
Calculate the new cluster centre according to the formula below:(5)μk′=1Ck∑x∈Ckx(4)
Repeat steps (2) and (3) until the cluster centre no longer changes. Record the corresponding DBI values, and label the corresponding category of image pixels, mark value off∈1,K(5)
Produce DBI, when the number of clusters corresponds to the minimum value of DBI, the optimal number of clustersKs is obtained, and output of corresponding image pixel marking result is given. The shortcoming of K-means are as follows: (1) The selection of K value is not easy to grasp. (2) It is difficult to converge for nonconvex data sets. (3) If the data of each hidden category is unbalanced, such as the data quantity of each hidden category is seriously unbalanced, or the variance of each hidden category is different, the clustering effect is not good. (4) Using iterative method, the result is only local optimal


### 3.1.3. Image Foreground/Background Separation

When using theK-means clustering method for image pixel labelling, only the grey value information of image pixels is considered, while the statistical dependence between neighbouring pixels is not taken into account, which renders some certain errors of labelling results. Hence, according to the Gaussian Markov random field (GMRF) theory [24], the spatial characteristics of image pixels were also considered, and image segmentation was carried out again on the marked image results. GMF is an image segmentation model built on the image neighborhood system; that is, through a neighborhood system, the pixels in the image and other pixels in its neighborhood are connected. The image to be segmented is described by constructing two random fields, a marker field and a feature field [22]. It should be noted that the prior probability is the probability of current event based on historical experience, posterior probability is the probability of an event based on data or evidence, and likelihood probability is the probability of the occurrence of data or evidence when the probability of an event is known. The marker field is used to mark image pixels and to describe the result of image segmentation, denoted as Y=Y∣y1,y2,⋯,yn; besides, the feature field can effectively reflect spatial feature information of each pixel, describing the original image data, denoted as X=X∣x1,x2,⋯,xn. According to the Bayesian posterior probability formula [25], the image segmentation can be expressed as
(6)PY=y∣X=x=PX=x∣Y=yPY=yPX=x,wherePY=y∣X=x is the posterior probability of the marker field Y=y under the condition of the given image characteristic X=x, PX=x∣Y=y is the probability distribution of characteristic field X=x under the condition of given observation data Y=y, PY=y represents the prior probability, and PX=x is a known constant so it can be ignored. The maximum posterior probability can be approximated:
(7)y^=argmaxPY=y∣X=x.During parameter solving process, the image probability calculation actually can be converted into a problem of energy solution, i.e., to minimize the sum of the current mark field energy and the characteristic field energy, namely,(8)Ey,x=minEYy+EXy,x.In the formula,EYy is the marker field energy, and EXy,x is the characteristic field energy.Then, the energy potential functions of the marker field can be defined as(9)EYyI=∑k=1svI,k,(10)vI,k=f,yI≠yk,0,yI=yk,whereI is a centre pixel of the neighborhood and K is the pixel contained in the neighborhood.Assuming that the probability distribution of the image is a normal distribution, the mean and variance ofk in each category are μk and σk, respectively; EXy,x can be expressed as
(11)EXy,x=∑ln2π×σk+x−μk22σk2.Iterative condition modes (ICM) are used to iterate, the program is finalized, and the final segmentation result is recorded once the termination condition is met.


### 3.1.4. Single-Wood Separation, Extraction, and Postprocessing

In order to obtain the tree information from segmentation results, it is postprocessed by mathematical morphological operators and other methods. The major processing steps [26] are described as follows:
(1)
The segmentation result area is separated: separate the areas containing tree information from the segmentation result and save in TIFF format(2)
Isolated nodes removal: the primary purpose is to remove the isolated noise information in the segmentation result(3)
Patchwork elimination: mainly remove the sporadic small fragmented noise in the non-tree areas(4)
Hole filling: fill the holes in the tree pattern to form a complete closed polygon for further extraction of the tree crown(5)
Morphological operator processing: basic morphological operators in this study include expansion operator (⊕), corrosion operator (Θ), open operator (∘), and closure operators (•) [27]. The principles expressed by assuming binary image A and structure element B are shown in equations (12)–(15). The expansion operator can increase the number of pixels on the border; the erosion operator can reduce the number of pixels on the border. The open operator is first corroded and then expanded; the closed operator is first expanded and then corroded(12)A⊕B=∪A+b∣b∈B,(13)AΘB=∩A−b∣b∈B,(14)A∘B=AΘB⊕B,(15)A•B=A⊕BΘBThis work adopts the processing strategy of first corrosion and consequently expansion, i.e., open operators for morphological processing, which could smooth the boundary contour of the pattern, break the narrow connections, and remove the small protrusion on edges.(6)
Treatment of abnormal segmentation patches: due to the fact that two or more tree spots might be connected together in the segmentation results, which reduces the extraction accuracy. Thus, it is separated based on the rules of patch area and patch roundnessAccording to the patch area statistics from the processing results, the patches whose area is more than 90% of the total patch area are screened out and recorded asA_u, and the remaining patches are recorded as A_d; then, the patch roundness is calculated, and spots with roundness less than 0.5 is marked as C_d, and the remaining patches are marked as C_u. The C_d patches were corroded and then combined with C_u and A_d to produce final segmentation result.


### 3.1.5. Extraction of Individual Standing Trees

For individual standing tree identification, based on the principle of geometric image moments, the morphological postprocessing results are calculated to obtain the centroid position of the segmentation pattern, which is used as a marker [28] for the crown position, and the number of markers is the exact number of trees extracted. The calculation formulae are
(16)x=C10C00=∑i=1M∑j=1Ni×fi,j∑i=1M∑j=1Nfi,j,(17)y=C01C00=∑i=1M∑j=1Nj×fi,j∑i=1M∑j=1Nfi,j.fi,j is the grey value of the image pixel; C10 and C01 are the first-order moments of the image, representing the cumulative sum of the product of x-coordinate value and pixel grey value of the patch area and the cumulative sum of the product of the y-coordinate value of the patch area and the pixel grey value; C00 is the zero-order moment of the image, which represents the cumulative sum of pixel grey values in the pattern area. Additionally, by vectorizing the final morphological postprocessing results, the corresponding single-tree canopy range is obtained. Note that now, moment technology has been widely used in image retrieval and recognition, image matching, image reconstruction, digital compression, digital watermarking, and moving image sequence analysis. Common moment descriptors can be divided into the following categories: geometric moment, orthogonal moment, complex moment, and rotational moment.


## 3.1.1. Image Spectral Information Enhancement

The extraction of a single tree based on remote sensing images can essentially be regarded as the process of separating foreground and background on images [19], that is, image segmentation. In this study, the foreground refers to the area covered by a single standing tree, while the background refers to the soil, grassland, and other areas. The image enhancement technology is considered conducive to the separation of the foreground and background in the image [18–20]. The method of image enhancement is to add some information or transform data to the original image by certain means, selectively highlight the features of interest in the image, or suppress (mask) some unnecessary features in the image, so as to make the image match the visual response characteristics. In the process of image enhancement, the reason of image degradation is not analyzed, and the processed image is not necessarily close to the original image. Image enhancement technology can be divided into two categories: spatial domain-based algorithm and frequency domain-based algorithm.Since this study uses true colour image data, there exists a high degree of correlation between various bands [21]. When the traditional contrast stretching method is used to enhance the image, it can only improve the image chroma and brightness, but cannot perfect the image colour saturation degree. However, the decorrelation stretching method with a principal of component transformation can enlarge the coupling degree of the image information within the high band, thereby increasing the image colour saturation. In addition, the colours of the relevant areas of the image are more vivid and prominent, while the dark areas will become brighter. Therefore, this paper adopts the decorrelation stretching spectrum enhancement method to operate the original images.


## 3.1.2. Image Pixel Mark

For the purpose of separation for image foreground and background, further image pixel labelling is required on the basis of image enhancement processing. During this process, theK-means clustering is used in the image pixel labelling process. The purpose of image pixel clustering is to group or classify the pixels in the images. The clustering algorithm is to divide a data set into multiple classes or clusters by a certain standard, such as distance, so that the data objects in the same cluster are similar, and the data objects in different clusters are different as much as possible. In addition, the commonly used clustering algorithms include K-means, K-modes, K-prototypes, dmscan density clustering, GMM, and hierarchical clustering.Assuming the image is a spatial data setX, the elements xii=1,2,3,⋯,N in the data set correspond to the pixels in the image [22]. In the theory of K-means clustering, K initial clustering centres μkk=1,2,3,⋯,K are to be found in the sum of squares of Euclidean distances between clustering centres and image pixels reaches the minimum, and the pixels are assigned to the corresponding cluster centres. The objective function is as follows:
(1)D=∑i=1N∑k=1Kxi−μk.For the conventionalK-means clustering method, it is necessary to artificially set the initial clustering centre several times to obtain the optimal result, which reduces efficiency and is affected by human subjectivity to a certain extent. Therefore, the introduction of the DBI index in this study can automatically determine the optimal number of clusters, avoid subjective judgments, and improve clustering accuracy. DBI mathematically calculate the ratio of the intraclass distance and the interclass distance between any two categories [23]. The larger the DBI value is, the smaller the intraclass distance and the larger the interclass distance would be. The calculation formula is
(2)di,j=vi−vj,(3)Si=1Ci∑x∈Cix−vi,(4)DBI=1K∑i,j∈1,kmaxSi+Sjdi,j.In the formula,x represents data objects in the ith class; vi and vj denote the centroids of the i and j classes, respectively; Ci is the number of data objects in the ith class; di,j represents the Euclidean distance between the centroid of the ith class and jth class; Si and Sj are the class distance between each data object in ith and jth class and centroids vi and vj, and K is the number of clusters.Therefore, in this study, the process of image pixel labelling method is described as follows:(1)
PresetK value (K∈Kmin,Kmax), where Kmin is 2, and Kmax is 15, that is, the image clustering categories are divided into at least two categories, and at most 15 categories(2)
Initialize the cluster centreCk, (k=1，2,3,⋯,K) and calculate the D value, then redistribute the image pixels(3)
Calculate the new cluster centre according to the formula below:(5)μk′=1Ck∑x∈Ckx(4)
Repeat steps (2) and (3) until the cluster centre no longer changes. Record the corresponding DBI values, and label the corresponding category of image pixels, mark value off∈1,K(5)
Produce DBI, when the number of clusters corresponds to the minimum value of DBI, the optimal number of clustersKs is obtained, and output of corresponding image pixel marking result is given. The shortcoming of K-means are as follows: (1) The selection of K value is not easy to grasp. (2) It is difficult to converge for nonconvex data sets. (3) If the data of each hidden category is unbalanced, such as the data quantity of each hidden category is seriously unbalanced, or the variance of each hidden category is different, the clustering effect is not good. (4) Using iterative method, the result is only local optimal


## 3.1.3. Image Foreground/Background Separation

When using theK-means clustering method for image pixel labelling, only the grey value information of image pixels is considered, while the statistical dependence between neighbouring pixels is not taken into account, which renders some certain errors of labelling results. Hence, according to the Gaussian Markov random field (GMRF) theory [24], the spatial characteristics of image pixels were also considered, and image segmentation was carried out again on the marked image results. GMF is an image segmentation model built on the image neighborhood system; that is, through a neighborhood system, the pixels in the image and other pixels in its neighborhood are connected. The image to be segmented is described by constructing two random fields, a marker field and a feature field [22]. It should be noted that the prior probability is the probability of current event based on historical experience, posterior probability is the probability of an event based on data or evidence, and likelihood probability is the probability of the occurrence of data or evidence when the probability of an event is known. The marker field is used to mark image pixels and to describe the result of image segmentation, denoted as Y=Y∣y1,y2,⋯,yn; besides, the feature field can effectively reflect spatial feature information of each pixel, describing the original image data, denoted as X=X∣x1,x2,⋯,xn. According to the Bayesian posterior probability formula [25], the image segmentation can be expressed as
(6)PY=y∣X=x=PX=x∣Y=yPY=yPX=x,wherePY=y∣X=x is the posterior probability of the marker field Y=y under the condition of the given image characteristic X=x, PX=x∣Y=y is the probability distribution of characteristic field X=x under the condition of given observation data Y=y, PY=y represents the prior probability, and PX=x is a known constant so it can be ignored. The maximum posterior probability can be approximated:
(7)y^=argmaxPY=y∣X=x.During parameter solving process, the image probability calculation actually can be converted into a problem of energy solution, i.e., to minimize the sum of the current mark field energy and the characteristic field energy, namely,(8)Ey,x=minEYy+EXy,x.In the formula,EYy is the marker field energy, and EXy,x is the characteristic field energy.Then, the energy potential functions of the marker field can be defined as(9)EYyI=∑k=1svI,k,(10)vI,k=f,yI≠yk,0,yI=yk,whereI is a centre pixel of the neighborhood and K is the pixel contained in the neighborhood.Assuming that the probability distribution of the image is a normal distribution, the mean and variance ofk in each category are μk and σk, respectively; EXy,x can be expressed as
(11)EXy,x=∑ln2π×σk+x−μk22σk2.Iterative condition modes (ICM) are used to iterate, the program is finalized, and the final segmentation result is recorded once the termination condition is met.


## 3.1.4. Single-Wood Separation, Extraction, and Postprocessing

In order to obtain the tree information from segmentation results, it is postprocessed by mathematical morphological operators and other methods. The major processing steps [26] are described as follows:
(1)
The segmentation result area is separated: separate the areas containing tree information from the segmentation result and save in TIFF format(2)
Isolated nodes removal: the primary purpose is to remove the isolated noise information in the segmentation result(3)
Patchwork elimination: mainly remove the sporadic small fragmented noise in the non-tree areas(4)
Hole filling: fill the holes in the tree pattern to form a complete closed polygon for further extraction of the tree crown(5)
Morphological operator processing: basic morphological operators in this study include expansion operator (⊕), corrosion operator (Θ), open operator (∘), and closure operators (•) [27]. The principles expressed by assuming binary image A and structure element B are shown in equations (12)–(15). The expansion operator can increase the number of pixels on the border; the erosion operator can reduce the number of pixels on the border. The open operator is first corroded and then expanded; the closed operator is first expanded and then corroded(12)A⊕B=∪A+b∣b∈B,(13)AΘB=∩A−b∣b∈B,(14)A∘B=AΘB⊕B,(15)A•B=A⊕BΘBThis work adopts the processing strategy of first corrosion and consequently expansion, i.e., open operators for morphological processing, which could smooth the boundary contour of the pattern, break the narrow connections, and remove the small protrusion on edges.(6)
Treatment of abnormal segmentation patches: due to the fact that two or more tree spots might be connected together in the segmentation results, which reduces the extraction accuracy. Thus, it is separated based on the rules of patch area and patch roundnessAccording to the patch area statistics from the processing results, the patches whose area is more than 90% of the total patch area are screened out and recorded asA_u, and the remaining patches are recorded as A_d; then, the patch roundness is calculated, and spots with roundness less than 0.5 is marked as C_d, and the remaining patches are marked as C_u. The C_d patches were corroded and then combined with C_u and A_d to produce final segmentation result.


## 3.1.5. Extraction of Individual Standing Trees

For individual standing tree identification, based on the principle of geometric image moments, the morphological postprocessing results are calculated to obtain the centroid position of the segmentation pattern, which is used as a marker [28] for the crown position, and the number of markers is the exact number of trees extracted. The calculation formulae are
(16)x=C10C00=∑i=1M∑j=1Ni×fi,j∑i=1M∑j=1Nfi,j,(17)y=C01C00=∑i=1M∑j=1Nj×fi,j∑i=1M∑j=1Nfi,j.fi,j is the grey value of the image pixel; C10 and C01 are the first-order moments of the image, representing the cumulative sum of the product of x-coordinate value and pixel grey value of the patch area and the cumulative sum of the product of the y-coordinate value of the patch area and the pixel grey value; C00 is the zero-order moment of the image, which represents the cumulative sum of pixel grey values in the pattern area. Additionally, by vectorizing the final morphological postprocessing results, the corresponding single-tree canopy range is obtained. Note that now, moment technology has been widely used in image retrieval and recognition, image matching, image reconstruction, digital compression, digital watermarking, and moving image sequence analysis. Common moment descriptors can be divided into the following categories: geometric moment, orthogonal moment, complex moment, and rotational moment.


## 3.2. Verification and Evaluation of Individual Standing Tree Information Extraction

When evaluating the methods for extracting information from individual trees, relevant reference data, including the position of individual trees and the range of individual tree crowns, should be considered for accuracy evaluation. Due to the lack of measured reference data, this study employs visual interpretation instead of reference data for accuracy verification.


### 3.2.1. Evaluation of Recognition Accuracy of Individual Standing Trees

The evaluation accuracy indicators of individual standing tree identification are basically overall accuracy, commission error, and omission error [29]. The number of trees obtained by artificial recognition is used as the reference tree Nr. The number of trees extracted is recorded as Na, and the number of trees extracted correctly is Nc. The overall accuracy refers to the percentage of the number of correctly extracted trees to the number of reference trees, see equation (18). Commission error refers to the percentage of the difference between the number of reference trees and correctly extracted trees in the number of reference trees, as shown in equation (19). Omission error refers to the percentage of the difference between the number of extracted trees and correctly extracted trees in the number of reference trees, see formula (20). In this paper, all the experiments have gone through a lot of experiments, and we have carried out three experiments, respectively, under each working condition, and then obtained the final results by using the average value technology, so the results are very effective.
(18)Overallaccuracy=OA=NcNr×100%,(19)Commissionerror=CE=Nr−NcNr×100%,(20)Omissionerror=OE=Na−NcNa×100%.


### 3.2.2. Evaluation of Accuracy of Single-Tree Crown Extraction

The range of the canopy was extracted manually by professional interpreters as a reference for verifying the results of single-tree crown extraction. This article quantitatively evaluates the accuracy of single-tree crown extraction based on the following criteria:(1)
Matching: the extracted single-tree canopy overlaps with the reference canopy by more than 50%, and the centre point of the reference canopy is located in the extracted canopy(2)
Merge: the extracted single tree canopy contains two or more reference canopies, and the centre of the reference crown is also included in the extracted crown(3)
Separation: more than 50% of the reference tree canopy is occupied by one or more single-tree canopies(4)
Loss: the overlap between the reference canopy and the extracted single tree canopy is less than 50% [30], and the centre of the reference canopy does not belong to any extracted single-tree crown


## 3.2.1. Evaluation of Recognition Accuracy of Individual Standing Trees

The evaluation accuracy indicators of individual standing tree identification are basically overall accuracy, commission error, and omission error [29]. The number of trees obtained by artificial recognition is used as the reference tree Nr. The number of trees extracted is recorded as Na, and the number of trees extracted correctly is Nc. The overall accuracy refers to the percentage of the number of correctly extracted trees to the number of reference trees, see equation (18). Commission error refers to the percentage of the difference between the number of reference trees and correctly extracted trees in the number of reference trees, as shown in equation (19). Omission error refers to the percentage of the difference between the number of extracted trees and correctly extracted trees in the number of reference trees, see formula (20). In this paper, all the experiments have gone through a lot of experiments, and we have carried out three experiments, respectively, under each working condition, and then obtained the final results by using the average value technology, so the results are very effective.
(18)Overallaccuracy=OA=NcNr×100%,(19)Commissionerror=CE=Nr−NcNr×100%,(20)Omissionerror=OE=Na−NcNa×100%.


## 3.2.2. Evaluation of Accuracy of Single-Tree Crown Extraction

The range of the canopy was extracted manually by professional interpreters as a reference for verifying the results of single-tree crown extraction. This article quantitatively evaluates the accuracy of single-tree crown extraction based on the following criteria:(1)
Matching: the extracted single-tree canopy overlaps with the reference canopy by more than 50%, and the centre point of the reference canopy is located in the extracted canopy(2)
Merge: the extracted single tree canopy contains two or more reference canopies, and the centre of the reference crown is also included in the extracted crown(3)
Separation: more than 50% of the reference tree canopy is occupied by one or more single-tree canopies(4)
Loss: the overlap between the reference canopy and the extracted single tree canopy is less than 50% [30], and the centre of the reference canopy does not belong to any extracted single-tree crown


## 4. Results and Discussion

### 4.1. DBI Optimization Results

Based on the UAV remote sensing image data of thePinus sylvestris forest and the Pinus tabulaeformis forest, the corresponding DBI index is calculated through the program for different preset classification numbers. The statistical results are shown in Table 1.Table 1
The statistical results of DBI.
Number of categoriesDBIPinus sylvestris forest areaPinus tabulaeformis forest area21.435371.4435131.154841.1896541.134411.0229551.242261.0886361.078971.1831871.122761.1429981.106501.2335791.207271.18306101.131711.14993111.175261.11194121.187791.10884131.141181.09068141.127981.07564151.113441.07782It can be seen that the minimum DBI of thePinus sylvestris area is 1.07897, and the number of classifications is 6, and it is taken as the optimal number of classifications; The minimum value of DBI is 1.02295 of the Pinus tabulaeformis forest, and the number of classifications is 4, which is regarded as the optimal number of classifications.


### 4.2. Segmentation Result Comparison and Analysis

The segmentation results of UAV remote sensing image ofPinus sylvestris forest and Pinus tabulaeformis forest are greyscaled image. The UAV remote sensing images of Pinus sylvestris forest are divided into 6 and 4 categories, respectively. It is difficult to distinguish different divided regions in the greyscale image visually; therefore, in order to facilitate observation, the colours of the different segmented areas are, respectively, matched, see Figure 4.Figure 4
The segmented areas of the algorithm of the study.
(a)
Pinus sylvestris(b)
Pinus tabulaeformisSince the UAV remote sensing image in this paper has a high spatial resolution, a transparent shadow area can be seen from the original image. It mainly consists of two parts. One part is self-shadow, and the shadow area comes from the branches or leaves in the crown; the other part is cast shadow, and the shadow area caused by the sun’s rays on the ground or other trees. From the segmentation results, it can be seen that the segmentation algorithm in this paper can suppress or eliminate the shadows of trees to a certain extent. Moreover, casting shadows can also be better distinguished and separated. It shows that this algorithm has better robustness to the shadow part.In order to further verify the segmentation effect of this article, the object-oriented segmentation method is applied to segment the experimental images. Based on the software platform eCognition Developer-10.2, a multiscale segmentation, the segmentation parameters include image layer weights, segmentation scale, shape factor, and compactness factor [31]. The weight of the band can be determined according to the importance of the band; the segmentation scale determines the size of the segmented object and the quality of the segmentation; the shape factor determines the proportion of the spectral value relative to the shape during the segmentation; the compactness factor determines the shape characteristics of generated object.After several trials of the segmentation process, the optimal segmentation parameters of different UAV remote sensing images are determined, see Table2. The final segmentation results are shown in Figure 5. In order to compare the segmentation effects, the tree objects in the segmentation area are highlighted.Table 2
Segmentation parameters.
Experimental areaBand weightSegmentation scaleShape factorCompactness factorPinus sylvestris area1 1 1200.80.2Pinus tabulaeformis area1 1 1600.650.5Figure 5
Segmentation results of object-oriented method.
(a)
Pinus sylvestris(b)
Pinus tabulaeformisBy comparing the segmentation results of the algorithm in this paper with that of the object-oriented method, the algorithm of this paper has a better outcome of the restoration for original tree contours. Due to the complexity of the features in the original image, when multiscale segmentation [32] is performed with different segmentation parameters, the segmentation effect is usually better for some particularly segmented areas, and it is always hard to achieve a balanced segmentation effect for the whole.


### 4.3. Individual Standing Tree Identification and Accuracy Evaluation

The results of individual standing tree identification by UAV remote sensing images ofPinus sylvestris forest and Pinus tabulaeformis forest are shown in Figure 6. The yellow dots “•” are the reference tree position for artificial visual interpretation, and the red signs “+” are the automatic extraction of the tree position. To improve visual observation, a red circle equivalent to the area of the extracted tree crown is used as marking for the fitting tree range. If the position of the reference tree for manual visual interpretation is within the range of the fitted range, it is regarded as a correct extraction; otherwise, it is regarded as an incorrect one.Figure 6
The results of individual standing tree identification ofPinus sylvestris and Pinus tabulaeformis.
(a)
Pinus sylvestris(b)
Pinus tabulaeformisThe accuracy evaluation of the individual standing tree recognition results is shown in Table3. For relatively sparse Pinus sylvestris forest, the overall accuracy of individual standing tree identification reached 95.66%, the commission error was 4.37%, and the omission error was 2.89%; for the densely growing Pinus sylvestris forest area, the overall accuracy of individual standing wood identification reached 89.55%, commission error was 10.39%, and omission error was 8.81%.Table 3
The accuracy evaluation of the individual standing tree recognition.
Experimental areaNrNaNcOA (%)CE (%)OE (%)Pinus sylvestris forest area69676695.664.372.89Pinus tabulaeformis forest area1051039489.5510.398.81


### 4.4. Single-Tree Crown Extraction and Accuracy Evaluation

The single-tree crown extraction results ofPinus sylvestris and Pinus tabulaeformis forest areas are shown in Figure 7. The yellow polygons are the reference crown range obtained by manual visual interpretation, and the red polygons are the automatic extraction range of single-tree crown.Figure 7
The single-tree crown extraction results ofPinus sylvestris and Pinus tabulaeformis.
(a)
Pinus sylvestris(b)
Pinus tabulaeformisThe accuracy evaluation for individual tree crown extraction is shown in Table4. The number of reference tree crowns for visual interpretation of Pinus sylvestris forest is 69, the matching number is 66, the number of separation is 1, the missing number is 3, and the corresponding extraction accuracy is 95.66%; the number of reference tree crowns for visual interpretation of Pinus tabulaeformis forest is 105, the matching number is 82, the combined number is 10, the missing number is 8, and the corresponding extraction accuracy is 81.92%.Table 4
Evaluation of the accuracy of single tree crown extraction.
Experimental areaReference canopyMatchMergeSeparationLossExtraction accuracy (%)Pinus sylvestris forest696601395.66Pinus tabulaeformis forest1058680981.92The method in this paper has higher accuracy for single-tree crown extraction in sparse forest areas, but relatively low extraction accuracy for denser forest areas. The main reason is that the vegetation types of the selectedPinus tabulaeformis forest experimental area are complex and diverse, and a large number of shrubs and trees grow together, which are difficult to differentiate. The method in this paper is mainly based on the spectral feature information of ground and mathematical geometry principles to extract single-tree information; the overlapping growth of branches between trees or between trees and shrubs cannot be eliminated [33], which limits the extraction accuracy to a certain extent.


## 4.1. DBI Optimization Results

Based on the UAV remote sensing image data of thePinus sylvestris forest and the Pinus tabulaeformis forest, the corresponding DBI index is calculated through the program for different preset classification numbers. The statistical results are shown in Table 1.Table 1
The statistical results of DBI.
Number of categoriesDBIPinus sylvestris forest areaPinus tabulaeformis forest area21.435371.4435131.154841.1896541.134411.0229551.242261.0886361.078971.1831871.122761.1429981.106501.2335791.207271.18306101.131711.14993111.175261.11194121.187791.10884131.141181.09068141.127981.07564151.113441.07782It can be seen that the minimum DBI of thePinus sylvestris area is 1.07897, and the number of classifications is 6, and it is taken as the optimal number of classifications; The minimum value of DBI is 1.02295 of the Pinus tabulaeformis forest, and the number of classifications is 4, which is regarded as the optimal number of classifications.


## 4.2. Segmentation Result Comparison and Analysis

The segmentation results of UAV remote sensing image ofPinus sylvestris forest and Pinus tabulaeformis forest are greyscaled image. The UAV remote sensing images of Pinus sylvestris forest are divided into 6 and 4 categories, respectively. It is difficult to distinguish different divided regions in the greyscale image visually; therefore, in order to facilitate observation, the colours of the different segmented areas are, respectively, matched, see Figure 4.Figure 4
The segmented areas of the algorithm of the study.
(a)
Pinus sylvestris(b)
Pinus tabulaeformisSince the UAV remote sensing image in this paper has a high spatial resolution, a transparent shadow area can be seen from the original image. It mainly consists of two parts. One part is self-shadow, and the shadow area comes from the branches or leaves in the crown; the other part is cast shadow, and the shadow area caused by the sun’s rays on the ground or other trees. From the segmentation results, it can be seen that the segmentation algorithm in this paper can suppress or eliminate the shadows of trees to a certain extent. Moreover, casting shadows can also be better distinguished and separated. It shows that this algorithm has better robustness to the shadow part.In order to further verify the segmentation effect of this article, the object-oriented segmentation method is applied to segment the experimental images. Based on the software platform eCognition Developer-10.2, a multiscale segmentation, the segmentation parameters include image layer weights, segmentation scale, shape factor, and compactness factor [31]. The weight of the band can be determined according to the importance of the band; the segmentation scale determines the size of the segmented object and the quality of the segmentation; the shape factor determines the proportion of the spectral value relative to the shape during the segmentation; the compactness factor determines the shape characteristics of generated object.After several trials of the segmentation process, the optimal segmentation parameters of different UAV remote sensing images are determined, see Table2. The final segmentation results are shown in Figure 5. In order to compare the segmentation effects, the tree objects in the segmentation area are highlighted.Table 2
Segmentation parameters.
Experimental areaBand weightSegmentation scaleShape factorCompactness factorPinus sylvestris area1 1 1200.80.2Pinus tabulaeformis area1 1 1600.650.5Figure 5
Segmentation results of object-oriented method.
(a)
Pinus sylvestris(b)
Pinus tabulaeformisBy comparing the segmentation results of the algorithm in this paper with that of the object-oriented method, the algorithm of this paper has a better outcome of the restoration for original tree contours. Due to the complexity of the features in the original image, when multiscale segmentation [32] is performed with different segmentation parameters, the segmentation effect is usually better for some particularly segmented areas, and it is always hard to achieve a balanced segmentation effect for the whole.


## 4.3. Individual Standing Tree Identification and Accuracy Evaluation

The results of individual standing tree identification by UAV remote sensing images ofPinus sylvestris forest and Pinus tabulaeformis forest are shown in Figure 6. The yellow dots “•” are the reference tree position for artificial visual interpretation, and the red signs “+” are the automatic extraction of the tree position. To improve visual observation, a red circle equivalent to the area of the extracted tree crown is used as marking for the fitting tree range. If the position of the reference tree for manual visual interpretation is within the range of the fitted range, it is regarded as a correct extraction; otherwise, it is regarded as an incorrect one.Figure 6
The results of individual standing tree identification ofPinus sylvestris and Pinus tabulaeformis.
(a)
Pinus sylvestris(b)
Pinus tabulaeformisThe accuracy evaluation of the individual standing tree recognition results is shown in Table3. For relatively sparse Pinus sylvestris forest, the overall accuracy of individual standing tree identification reached 95.66%, the commission error was 4.37%, and the omission error was 2.89%; for the densely growing Pinus sylvestris forest area, the overall accuracy of individual standing wood identification reached 89.55%, commission error was 10.39%, and omission error was 8.81%.Table 3
The accuracy evaluation of the individual standing tree recognition.
Experimental areaNrNaNcOA (%)CE (%)OE (%)Pinus sylvestris forest area69676695.664.372.89Pinus tabulaeformis forest area1051039489.5510.398.81


## 4.4. Single-Tree Crown Extraction and Accuracy Evaluation

The single-tree crown extraction results ofPinus sylvestris and Pinus tabulaeformis forest areas are shown in Figure 7. The yellow polygons are the reference crown range obtained by manual visual interpretation, and the red polygons are the automatic extraction range of single-tree crown.Figure 7
The single-tree crown extraction results ofPinus sylvestris and Pinus tabulaeformis.
(a)
Pinus sylvestris(b)
Pinus tabulaeformisThe accuracy evaluation for individual tree crown extraction is shown in Table4. The number of reference tree crowns for visual interpretation of Pinus sylvestris forest is 69, the matching number is 66, the number of separation is 1, the missing number is 3, and the corresponding extraction accuracy is 95.66%; the number of reference tree crowns for visual interpretation of Pinus tabulaeformis forest is 105, the matching number is 82, the combined number is 10, the missing number is 8, and the corresponding extraction accuracy is 81.92%.Table 4
Evaluation of the accuracy of single tree crown extraction.
Experimental areaReference canopyMatchMergeSeparationLossExtraction accuracy (%)Pinus sylvestris forest696601395.66Pinus tabulaeformis forest1058680981.92The method in this paper has higher accuracy for single-tree crown extraction in sparse forest areas, but relatively low extraction accuracy for denser forest areas. The main reason is that the vegetation types of the selectedPinus tabulaeformis forest experimental area are complex and diverse, and a large number of shrubs and trees grow together, which are difficult to differentiate. The method in this paper is mainly based on the spectral feature information of ground and mathematical geometry principles to extract single-tree information; the overlapping growth of branches between trees or between trees and shrubs cannot be eliminated [33], which limits the extraction accuracy to a certain extent.


## 5. Conclusions

Aiming for the high spatial resolution UAV remote sensing image data application, a new method for extracting the standing tree information of forest land is proposed in this paper. By comparing with the object-oriented method, it is found that the extraction effect of this method is better. Through the accuracy verification and evaluation of the extraction results, it is found that the test images for thePinus sylvestris forest area and the Pinus tabulaeformis forest area have reached a higher level of accuracy, which shows the method of this paper is both feasible and effective and renders specific application values in remote sensing fields.The advantages of the method mainly lie in the fact that it does not require a large amount of manual intervention and input of prior knowledge and also avoids the inefficiency of traditional manual extraction. It dramatically improves the degree of automation and universal utilization for the method. Furthermore, it makes full use of the spatial correlation between the grey value of the image pixel and the pixel. It combines the filtering rules based on area and roundness in the postprocessing process, which dramatically improves the extraction effect, accuracy, and shadow inhibition. Discussions were carried out from two aspects (research image data and traditional theoretical method application), focusing on the analysis of the difficulties and limitations of traditional high spatial resolution remote sensing image data in practical application. However, this article only uses high spatial resolution UAV remote sensing image data to extract the information of a single tree. Therefore, how to combine multisource remote sensing data to achieve complementary advantages between data to compensate for the defects of UAV image data and to improve further extraction accuracy of forest information needs further study. In addition, because the algorithm proposed in this paper is more complex, although it can run on the computer with graphics card, it is difficult to guarantee the real-time performance. In this case, this is the place where we need to break through in our next work.



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*Source: 7672762-2021-10-19.xml*