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Measurement of volume and accuracy analysis of standing trees using Forest Survey Intelligent Dendrometer
Computers and Electronics in Agriculture 169 (2020) 105211
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Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag
Measurement of volume and accuracy analysis of standing trees using Forest Survey Intelligent Dendrometer
T
Guangpeng Fana, Wenxin Fengb, Feixiang Chena, , Danyu Chena, Yanqi Donga, Zhiming Wanga ⁎
a b
School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China Henan Geology Vocational College, Zhengzhou 450007, China
ARTICLE INFO
ABSTRACT
Keywords: Forest Electronic instrument Standing tree Stand volume Precision analysis
The standing tree volume is very important for accurately evaluating stand growth. This paper introduced a new type of high precision portable electronic instrument - Forest Survey Intelligent Dendrometer (FSID). For the instrument, we developed guidelines for measuring the standing tree volume in the sample plot and explained the principles and methods. The instrument can use the integrated angle and distance measuring device to determine the height of standing trees based on the low-power Bluetooth data transmission, extract the coordinates of the characteristic points of the standing trees, calculate the diameter of the tree trunk at any height, and realize the real-time measurement of tree volume in single photo and multiple photo solutions. To verify and analyze the accuracy of the instrument, we measured 181 standing trees in a 45x45m temporary plot in Haidian District, Beijing. The experimental results showed that the accuracy of the FSID for measuring the standing tree volume was 96.89%. Different tree heights lead to different optimal observation distances, and the optimal observation distance of this experiment was approximately 8 m. The method in this paper has the characteristics of real-time, fast, efficient and non-destructive, which can meet the requirements of forest survey, especially in the forest areas where logging is prohibited or restricted. The method shows great potential in forest survey and environmental protection.
1. Introduction Forest ecosystems are vital to the survival of animals and humans, and they provide services not only in the carbon cycle, soil and water conservation, climate regulation and biodiversity, but also in providing food, wood and energy to humans (Grassi et al., 2018). The stand volume is the total volume of all standing trees volume in the forest. The stand volume is an important factor for assessing the status of forest resources and the level of forest management and an important indicator for assessing a forest’s ability to reduce climate change risks (Avitabile and Camia, 2018). Therefore, the stand volume is one of the main factors for forest surveys. Measuring the standing tree volume is the basis for accurately assessing the stand volume, and it has also been the focus of tree research for a long time (Liu et al., 2019). In the early stage, the standing tree volume was measured mainly by the trunk analysis method. In this method, after cutting down the wood, the trunk is analyzed, and the modeling samples needed to compile the volume table are obtained, which is time consuming and laborious and destructive to the forest (Feng et al., 2005). Accordingly, some researchers proposed the establishment of nondestructive tree volume
⁎
estimation methods, such as the experimental form factor method, the from reference point method, the normal form factor and the pressler method (Feng et al., 2005; Zhang, 2004; Zhang and She, 2010; Cao et al., 2015). However, due to cumbersome operation and low measurement accuracy, the use of these methods is limited. Therefore, domestic and foreign scholars hope to establish a nondestructive testing method that can quickly and accurately measure standing tree volume. In recent years, ground observation technology, remote sensing, photogrammetry technology, and 3D laser scanning technology have provided effective solutions for forest resource inventory and have been used to extract various forestry attributes (Astola et al., 2019; Jayathunga et al., 2018; Vaglio Laurin et al., 2019). However, because of the high cost and difficult data processing, such as 3D laser scanners and terrestrial laser radar, it is more suitable for large-scale forest resource surveys (Jayathunga et al., 2018; Magnussen et al., 2018; Oveland et al., 2018; Vauhkonen et al., 2016). In China, instruments such as hand-held electronic tree survey gun, CCD smart station, mapping recorder, miniature over station, laser photography tree survey and other multifunctional comprehensive forest survey instruments have emerged, forming a relatively complete forest ground
Corresponding author. E-mail address: [email protected] (F. Chen).
https://doi.org/10.1016/j.compag.2020.105211 Received 13 October 2019; Received in revised form 1 January 2020; Accepted 2 January 2020 0168-1699/ © 2020 Elsevier B.V. All rights reserved.
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survey technology system (Yu et al., 2016; Feng et al., 2015; Liu et al., 2016; Xu et al., 2013; Yang et al., 2018; Huang, 2016). Although the above techniques can solve the measurement problem of the standing tree volume, these related measuring instruments require high cost or professional training. The current forestry observation equipment still has the disadvantages of high cost, low measurement accuracy, low integration, poor portability and single functionality. The development of mobile computing and advances in computer vision have helped researchers develop methods that are well suited for forest surveys, with a high accuracy and a low price (Vastaranta et al., 2015; Hyyppä et al., 2018; Jaakkola et al., 2010; Kangas et al., 2015; Tomaštík et al., 2017; Zhou et al., 2016). The feasibility of obtaining the standing tree volume through computer vision and ground observation technology has been verified (Bauwens et al., 2017; Dean, 2003; Gaffrey et al., 2001; Kansanen et al., 2016; Luhmann et al., 2016). However, the equipment used in this technology is usually a stand-alone digital camera, which needs to import the image into computer software for processing, and the measurement results cannot be solved in the field (Berveglieri et al., 2017; Mikita et al., 2016; Mokroš et al., 2018). In the existing forest survey methods, especially in the calculation of standing tree volume, there are still problems such as low measurement accuracy, complicated operation, cumbersome equipment or high cost. These factors not only limit the promotion and application of such equipment in forest surveys, but also restrict the computerization of modern forestry. This paper focused on the necessity of standing tree volume measurement and the actual situation of forestry investigation. The FSID is designed and developed. It highly integrates a mobile phone with a Bluetooth data transmission function and a laser rangefinder. It combines the forest survey software running on mobile phone and is based on the Java language to integrate the functions of the measuring angle and distance, which can measure the standing tree volume in real time, rapidly and accurately. The advantage of the FSID is that it is cheaper than the 3D laser scanner, easy to carry, and simple to operate, and it does not require post processing. Compared with the conventional method, the method in this paper reduces the on-site working time and workload, and one can operate it independently without any additional personnel assistance to realize noncontact real-time measurement. In addition, the volume data of the standing tree measured by the total station and the FSID were compared and analyzed, and the feasibility of using the FSID to accurately measure the standing tree volume was verified.
Fig. 1. Forest Survey Intelligent Dendrometer.
software was developed using the Java programming language (see Fig. 2). The Java native interface (JNI) technology was used to call the Open Source Computer Vision Library (OpenCV) interface to process photos of standing trees. This software can measure the angle and distance and measure the standing tree volume in real time. According to the actual work needs, the user can also splice multiple photos of the photographed standing trees. The user can adjust the instrument according to the actual measurement environment and the measurement work requirements, and process the angle data, distance data and pictures through information transmission and integration between the modules. All the measured data can be stored through an SQLite database or exported in (.csv) format. 2.2. Research area The research in this paper was carried out in an artificial forest located in Haidian District (40°00′N, 116°19′E), Beijing, China (Fig. 3). The altitude of the study area is 45 m and the terrain is flat. It belongs to a temperate humid monsoon climate zone, with four distinct seasons, hot summers, cold winters and low precipitation. It is a plantation planted with tree species such as poplar (Populus tomentosa Carrière), cypress (Platycladus orientalis (L.)), Chinese scholar tree (Sophora japonica Linn.) and eucalyptus (Ulmus pumila L.), with no or only a small amount of undergrowth and bushes. The density of trees in plantations is about 1000 trees per hectare. In order to verify the accuracy of the FSID, the volume of 181 standing trees was measured in a 45x45m temporary marking plot using the method of this paper and the total station measuring volume method.
2. Materials and methods 2.1. Measuring device 2.1.1. Hardware composition The total weight of the electronic forest tree measuring instrument in this paper is less than 2 kg. The instrument is easy to carry, simple to operate and highly integrated with a mobile phone, laser rangefinder and photography platform (see Fig. 1). The mobile phone is the core module of the whole instrument. It is equipped with Android 8.0 operating system and has various sensors such as angle sensor, camera and Bluetooth. It is a platform for software operation and real-time data processing and controls interactions with other hardware modules. The laser rangefinder has a ranging function and uses BLE4.0 low-power Bluetooth communication technology to directly provide high-precision distance data to the mobile phone. The photography platform is a tripod with an integrated hardware connection for placing and securing the mobile phone. It can rotate 360° during operation, and the height to the ground can be arbitrarily adjusted. Appropriate tests are conducted before operation.
2.3. Principle and method of measurement The two-site observation method is used to accurately measure the standing trees by selecting the measuring points with a clear view and a visible canopy (Zhao et al., 2014). Because the trunk portion is not circular or approximately oval, the angle between the two observation sites and the tree is guaranteed to be 90°. The average value of the two observation sites is taken to make the measurement more accurate. The specific measurement process is as follows: (1) At observation site No. 1, set up a forest electronic tree measuring instrument. Open the forest survey software, use the laser rangefinder to project the laser point onto the trunk of the standing tree to be measured, and obtain the horizontal distance L from the observation site to the trunk. Transfer horizontal distance L to the mobile phone via Bluetooth. Then, using the rotating platform, the line of the crosshair in the center of the camera is aligned at point T at the top of the tree and point B at the bottom of the tree (the part where the trunk and the
2.1.2. Software function On the Android Studio 3.3 development platform, a forest survey 2
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Fig. 2. Operation flow of calculating volume.
ground contact) to obtain the angle of the observation site and the tree top and the angle of the observation site and the tree bottom, respectively. The tree height H of the standing tree can be obtained by the principle of the trigonometric function (1).
H = L (tan + tan )
sections. To ensure that the photos of the standing tree are not deformed after shooting, keep the main optical axis of the camera parallel to the ground as much as possible. After taking a picture of the standing tree, a red calibration line appears automatically in the software interface, and the trunk is divided into several sections. First, according to the shape of the trunk, slide the calibration line along the vertical direction in the program interface to segment the trunk. Then, by sliding along the horizontal direction, the left and right intersection points of each trunk and the calibration line are
(1)
(2) A photo of the standing tree to be measured is taken with the method of normal case photography, and the red calibration line automatically appears in the interface to divide the tree trunk into several
Fig. 3. Study area location map. 3
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determined, and the coordinates of the intersection points of each section of trunk and the calibration line were obtained manually. Finally, the software automatically calculates the diameter Di of the bottom of each section of the trunk, the coordinates of the midpoint of the calibration line according to formulas (2) and (3), and the vertical height hi between each two calibration lines according to formula (4).
H (PTx
PBx ) 2 + (PTy
PBy ) 2
=
Di (PiLx
PiRx ) 2 + (PiLy
PiRy )2
PiTx = (PiLx + PiRx )/2 PiTy = (PiLy + PiRy )/2
(3)
H (PTx
PBx ) 2 + (PTy
(2)
PBy ) 2
=
hi (PiTx
PiBx ) 2 + (PiTy
PiBy ) 2
(4)
H represents for tree height. (PTx , PTy ) and (PBx , PBy) are the coordinates of the top of the tree and the bottom of the tree, respectively. Di is the diameter of the bottom of the trunk at the i-th point. (PiLx , PiLy) and (PiRx , PiRy) represent the coordinates of the intersection of the two sides of the trunk at the i-th and the calibration line, respectively. hi represents the vertical height between two adjacent calibration lines of the trunk at the i-th point. (PiTx , PiTy ) and (PiBx , PiBy ) represent the midpoint coordinates of two adjacent calibration lines of the trunk at the ith point, respectively. (3) The measurement parameters include the angle between the observation site and the tree top, the angle between the observation site and the tree bottom, the horizontal distance L between the instrument and the standing tree, and several intersections of the calibration lines and the trunk in the photo of the standing tree (see Fig. 4). Then, another site around the standing tree is selected, and the step of the observation site 1 is re-executed. After the system generates volume data, standing tree photo and other measurement parameters, the system saves the data as a file in the database by entry, which can be viewed, modified, deleted or otherwise changed, or the data can be exported in spreadsheet form by using JXL technology. The main principle is to use the FSID to approximate the parts of the whole tree based on geometry and the characteristics of the shape of the trunk. The entire trunk is divided into n segments, and the average segment length is 0.5 m to 3 m. The crown part is treated as a circular cone, and the parts below the crown are considered as the frustum of a cone. The volume calculation formula of the Frustum of a cone (Eq. (5)) and the volume calculation formula of a circular cone (Eq. (6)) are as follows. Finally, the volume of each segment is added to obtain the volume of the entire standing tree. The specific algorithm is as follows (see Fig. 5): The formula for calculating the volume of frustum of a cone is:
Fig. 5. Model of standing tree volume.
Vi = h (DU2 + DU DD + DD2 )/12
(5)
(i = 1, 2, , n 1) is the volume of the frustum of a cone. h is the height of the segmented frustum of a cone. DU is the cross-sectional diameter of the top surface of the frustum of a cone, and DD is the crosssectional diameter of the bottom surface of the frustum of a cone. The formula for calculating the circular cone volume is: (6)
Vn = hn 1 Dn2 1/12
Vn is the volume of the circular cone. hn 1 is the height of the circular cone (the length of the crown), and Dn 1 is the diameter of the bottom of the circular cone (the diameter of the lowest part of the cone). The total volume formula (7) can be expressed as: V = V1 + V2 + V3 +V4 + =
hn
2 1 Dn
1
+
+ Vn n 2 i=1
hi + 1 (Di2 + Di Di + 1 + Di2+ 1) /12
(7)
V represents the total volume of the standing tree trunk, and Vi (i = 1, 2, 3 n) represents the volume of each segment. hi (i = 1, 2, 3 n 1) is the height of each segment, and Di (i = 1, 2, 3 n 1) is the diameter of each segment. 2.4. Photography mode Although a strict observation plan was designed in this paper, difficult conditions were often encountered due to the complex environment in the forest. For example, when the observation site is close to the standing tree or when the standing tree to be measured is very high, it is impossible to include complete information of the standing tree by taking a photo. Therefore, it is necessary to change the photography mode of the FSID to take multiple photos of trees with overlapping areas. The software developed in this paper corrects the obtained photos one by one by tilting them horizontally. Multiple photos with overlapping portions are stitched together to form a photo containing the complete information of the standing timber. As shown in Fig. 6, two or three photos with overlapping areas are merged after the tree height is obtained using the method in 2.3 when the tree volume cannot be measured from one photo. Although precise measurement method was used in this paper, some incorrect or abnormal data will inevitably appear in the collected sample data. Abnormal data have a great impact on the selection of the model and the accuracy of the final model, which may lead to the distortion of objective laws. To express volume models more
Fig. 4. Principle of standing tree observation for FSID. H is the height of the tree, hi is the height of the trunk segment, Di is the diameter of the trunk segment, L is the horizontal distance from the observation site to standing tree. is the angle from the observation site to the tree top, and is the angle from the observation site to the tree bottom, i = 1, 2, , n 1, where Dn = 0. 4
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2.6. Accuracy evaluation method When measuring the standing tree volume with the FSID, error is inevitable and will affect the accuracy of the measurement results. When evaluating the accuracy of the method used in this paper to measure the standing tree volume, the measuring test of standing tree volume was carried out with NTS–372R total station instrument (South Surveying & Mapping Technology CO., LTD.) and the FSID. Because the theoretical accuracy of the total station for the nondestructive measurement of trees is much higher than the precision requirement of forestry surveys, the volume data measured by the FSID are compared with the volume data obtained by the total station. Formula (8)–(13) are used to calculate absolute error e , relative error δ', average relative error ¯' , absolute value of the relative error , average absolute value of the relative error ¯ and precision P .
e = Vm '= Fig. 6. Photography mode of merging multiple photos.
¯ '=
objectively, it is necessary to eliminate abnormal data that deviate from most data distribution rules.
=
Vm n i=1
× 100%
'
|Vm
Vs | Vs
(9) (10)
n
P=1
(1) The effect of distance and photography mode (number of photos) on the measurement accuracy was analyzed without considering the shape of standing tree and other factors. This paper specifically selects a poplar crown that has been intercepted as the sample tree. The sample tree has a small surface roughness, a straight trunk and a nearly conical top. The diameter at breast height (DBH) of the sample tree measured using calipers is 15.8 cm, and the ground diameters in different directions were measured multiple times, with an average value of 25.9 cm. After 5 measurements on the ground, the height of the sample tree measured by total station averaged 10.968 m, and the volume of the sample tree was 0.1586 m3, with the above measured value as the reference value. After repeated deduction and test, when the distance between the FSID and the sample tree is less than 8.3 m, two photos should be taken. When the distance between the instrument and the standing tree is less than 3.8 m, to include complete information of the standing tree and ensure the measurement accuracy as much as possible, three standing tree photos can be taken. The surveyors can use the “photo correction” and “photo merge” functions in the software. By observing each photo and selecting 2–3 feature points, the software will automatically merge the photo according to the feature points. This paper suggests that the repeated area of each photo should account for up to 30% of the total area of the photo. The nearest distance is approximately 3 m, with an increase of approximately 1 m each time. A total of 20 observation sites were set. (2) The effect of various unstable factors on the measurement results is considered. Therefore, tree species, tree height, DBH, trunk shape and other factors are introduced into the actual measurement situation, and the accuracy and stability of the method in practical application are verified. In this paper, the accuracy of the FSID was verified and analyzed in a 45x45m temporary marked sample plot. The volume measurement method of the total station was used three times for measurement, and the average value was taken as the reference value of the standing trees volume. In the field work of this paper, when the total station was used to measure the standing trees volume as the reference value, the FSID was used to collect the data on the same side.
Vs Vs
¯=
2.5. Experiments design
(8)
Vs
× 100%
n i=1
n
¯
(11) (12) (13)
In formula (8)–(13), Vm is the measured value of the FSID in the forest. Vs is the calculated reference value corresponding to Vm . 3. Results 3.1. The impact of observation distance on measurement accuracy In the process of measuring the sample tree, we found that when the instrument was 3–8 m away from the sample tree, it was difficult to obtain the information of the complete sample tree through a photo due to the field of view. After testing, it is found that the observation effect is the best when it is at 8 m, and a photo containing the complete sample tree information can be obtained. When it is not possible to obtain the complete information of the sample tree through one photo, we can only obtain the information of the standing tree by taking 2 or 3 photos. In the process of measuring the sample tree, a total of 174 groups of sample tree photos were taken, the distance range measured by the instrument was 3.38 m ~ 15.581 m, the absolute error range of the sample tree volume measurement was −0.0065 m3 ~ 0.0077 m3 , the relative error range was −5.29%~5.42%, the average relative error was 0.86%, the average absolute value of relative error was 2.21%, and the measurement accuracy was 97.79%. As shown in Fig. 7, within the range of 3–8 m, the measurement accuracy becomes higher as the distance increases and tends to be the highest when the observation distance is approximately 8 m. When the observation distance is greater than 8 m, the measurement accuracy of the FSID starts to gradually decrease because in the shooting of standing trees, three photos are first taken and combined into one photo containing complete information for the standing tree. With the increase of distance, two photos can be taken and combined into one photo containing complete information for the standing tree. When the observation distance is approximately 8 m, one photo can be taken to contain complete information for the standing tree. When the observation distance exceeded 10 m, the edge points formed by the standing tree on the photo gradually blurred, resulting in a decrease in the number of pixels for the feature points. Therefore, when the 5
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Fig. 7. The impact of observation distance on the measurement accuracy of sample tree volume.
observation distance is approximately 8 m, the obtained data has higher precision.
the number of photos increases, the error will increase, resulting in a lower measurement accuracy.
3.2. The impact of photography mode on accuracy
3.3. The comprehensive analysis of measurement accuracy of the FSID
In this paper, the measurement results with single photo are compared with those with multiple photos. To verify the influence of the number of photos on the measurement accuracy, a circle with a radius of 8 m was set up with the sample tree as the center. At the border of the circle, the following three methods are used to obtain a photo containing the complete information of the standing tree: take one photo, take two photos and merge them into one, and take three photos and merge them into one. Of the 174 groups of photos measured, 85 were measured by one photo, 54 were measured by merging two photos and 35 were measured by merging three photos. It can be seen from the experimental results in Fig. 8 that the working scheme of a single photo has the highest accuracy. As the number of photos increases, the volume measurement accuracy decreases. The volume measurement accuracy of the single photo was 97.96%, the volume measurement accuracy of the combined two photos was 97.77%, and the volume measurement accuracy of the combined three photos was 96.29%. When the overlapping areas of two or three photos are superimposed on each other to form a photo that contains complete information about the tree, the process produces error that increases or decreases the height of the tree trunk compared to the height of the normal tree. As
In the actual measurement, different tree species, tree heights, DBH values, trunk shape and so on will be encountered. To analyze the impact of these factors on the accuracy of instrument measurement, 181 standing trees were measured, including 40 Chinese arborvitae trees (Platycladus orientalis (L.) Franco), 25 Chinese red pines (Pinus tabuliformis Carr.), 36 elms (Ulmus pumila L.), 44 Chinese scholar trees (Sophora japonica Linn.) and 36 poplars (Populus tomentosa Carrière). The data measured by total station are taken as the reference value, and the data measured by the FSID are compared with the reference values. The absolute error range of the standing tree volume of the 181 standing trees is −0.0620 m3 ～0.0834 m3 , the relative error range is −7.42% ~ 6.58%, the average relative error is 0.05%, the average absolute value of the relative error is 3.11%, and the measurement accuracy of standing tree volume is 96.89%. Therefore, the measurement method of standing tree volume in this paper can meet the requirements of forest survey. According to Fig. 9, we can analyze the impact of the DBH, species and observation distance on measurement accuracy. There is no significant difference between the measurement accuracy of the FSID and that of the total station at the same direction. As the number of photos increases, the measurement accuracy decreases gradually. The reason for this result is that the overlapping areas are merged too much or too little during the merging of two or three photos, resulting in incomplete information of the merged standing tree. As the number of photos increases, the error of merging also increases. It is also possible that the resulting image is distorted, resulting in a lower resolution. According to the measurement results of 3.1, the optimal observation distance is 8 m through the analysis of error causes. Therefore, when using the FSID to measure the standing trees volume, it should preferably take a photo of the stand for measurement, then increase the observation distance and finally increase the number of photos. In field work, surveyors should avoid poor measuring environment, observe the operating specifications of instruments and make accurate measurements according to the procedures. 4. Discussion As the public's attention to forests continues to increase, more and more people use the “forest growing stock” to understand the total scale and level of forest resources in a country or region. The stand volume is also an important basis to reflect the richness of forest resources and estimate the quality of forest ecological environment (Didion et al.,
Fig. 8. The impact of number of photos on the measurement accuracy of sample tree volume. 6
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volume of standing trees without any close contact with the trees, it can perform calculations in real time to make the work more coherent, and the measurement results are automatically stored. It saves working time, has the characteristics of low cost, high integration, simple operation and easily carried, and it is very suitable for the application of low-cost, small-scale forest survey work. Compared with the conventional measurement method (Forsman et al., 2018; Pierzchała et al., 2018), the FSID has no obvious advantages in terms of measurement accuracy, but its measurement efficiency is higher, the measurement cost is greatly reduced, and damagefree monitoring is achieved, which is not destructive to the forest. Secondly, 3D laser scanners, total stations, and other high-precision measuring instruments are gradually being applied to volume measurement (Del Perugia et al., 2019; Oveland et al., 2018; Pierzchała et al., 2018; Vaglio Laurin et al., 2019; Wagner et al., 2018). Their accuracy advantages are obvious, but these advanced instruments are much less portable than the FSID, so the cost of measurement is much higher. The third advantage of the electronic forest tree tester is that it can quickly measure the volume of standing trees without the need to go back to the office for data post-processing, and it does not need close contact with the trees when working, which makes the work more coherent in real time. Friendly user interaction enables us to freely assemble and improve the equipment composition according to the survey requirements. The instrument is light weight, easy to carry and to operate, and improves the measurement efficiency and data quality. In addition, the instrument is reasonably priced and works independently of electric fields and environmental conditions. Although the device is a new option for forest surveys, it still has some limitations. The optimal observation distance is different due to other factors such as the hardware configuration (such as different field angles) or the stand growth environment, so 8 m is not absolute. In 3.1 and 3.2, due to the height of the sample tree and the angle of view of the camera, the optimal observation distance for this experiment was 8 m. The best observation accuracy can be obtained by taking a picture at 8 m. This distance is only the conclusion of this paper after several measurements, which provides a reference value of the observation distance for the surveyors. The error of instrument calibration (including the calibration of the mobile phone camera, the sensitivity of the angle sensor and the integrated calibration of the instrument) and the environmental factors with high density of trees will affect the measurement accuracy. To ensure the accuracy of the measurement results, the correct calibration parameters of the instrument should be ensured and the measurement error caused by human factors such as the selection of touch points on the terminal screen should be avoided. When the visibility of the instrument observation is disturbed, try to solve it by adjusting the observation site. However, due to the complex and diverse conditions in the forest, the observer must determine whether the displayed treetop position is appropriate during the measurement, which may be affected by the trunk shape or crown occlusion and the subjective decision of the observer. Otherwise, the observation position should be adjusted according to the need. When it is not possible to take a single picture with complete information about the stand by adjusting the observation position, two or three photos with overlapping areas of the same standing tree can be taken, and the photos can be combined to obtain complete tree information for measurements. Therefore, compared with the measurement of high-density forests, the method in this paper is more suitable for the accurate monitoring of small-scale low-density or medium-density forests.
Fig. 9. Impact of the DBH, tree species, observation distance and photography mode on the standing tree error. In the Fig. 9(a), the X axis is the observation distance, the Y axis is the number of photos, and the Z axis is the relative measurement error (%). In the Fig. 9(b), X axis is the DBH of the standing trees, the Y axis is the tree species of standing trees, and the Z axis is the relative measurement error (%).
2019; Scolforo et al., 2019). The accurate measurement of the standing trees volume is the basis of accurately evaluation of stand volume and has always been a hot spot in forestry research (Grassi et al., 2018). This paper introduces a high precision, portable and highly integrated measuring instrument for the standing volume. Through independent observations, we verified the impact of the observation distance and the number of photos on the measurement accuracy of the instrument. Through the observation of 181 trees in the 45 × 45 m temporary calibration sample plot, the measured value was compared with the reference value obtained by the total station. The measurement accuracy of volume was 96.89%, and the optimal observation distance was approximately 8 m. The measurement accuracy of a single photo was better than that of fusing multiple photos. The experimental results show that the accuracy of the FSID further demonstrates the feasibility of using the device to measure the volume of standing trees. In the experiment of this paper, the FSID is compared with the conventional volume measuring instrument in forest survey (as shown in Table 1). This instrument can quickly and accurately measure the
5. Conclusion This paper proposes an electronic tree measuring instrument, a new type of portable high-precision standing trees electronic measuring instrument. It combines the laser rangefinder and tree software running on a smart phone to realize the function of measuring the standing trees volume. It can quickly and accurately measure the volume of standing 7
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Table 1 Comparison of cost and time between this article’s mode and the current mode. Work mode
Volume
Survey costs
The average time of measuring a tree
3d
Able
0.2–4 million USD.at least two people to operate
Total station
Unable to measure in real time
1500–7000 USD. Needs one person to operate
Traditional methods (altimeter and calipers)
Unable to measure in real time
Digital camera + total station
Unable to measure in real time
71–1500 USD. Needs one person to operate with different instruments 1500–7000 USD, two people to operate.
The FSID
Able to measure in real time
Heavy workload on site. The equipment is heavy and inconvenient to carry, the internal work is complex, and the measurement results cannot be obtained in real time. It needs to go back to the office for post-processing with professional software, which takes approximately 18 min on average. It has a high accuracy and is suitable for large-scale forest point cloud data acquisition. Heavy workload on site. Requires continuous work of both eyes. It easily causes fatigue and is unsuitable for continuous operation. It takes an average of 12 min. It has a high accuracy. Heavy workload on site. Independent tools. Simple operation. Cannot operate continuously. Data cannot be recorded directly, needs to be read manually, belongs to contact measurement, and has a high accuracy. Heavy workload on site. Unable to obtain measurement results in real time. Needs to go back to the office with professional software for post processing. It takes an average of 8 min. Light workload on site. Measure and automatically store data in real time. Easy to carry. The operation is simple and continuous. It takes an average of 3 min. Suitable for small-scale, low-density forest surveys.
Laser scanner
600 USD, one person to operate
trees at a work site and display and store the measured data of the standing trees in real time, without going back to the office for data post-processing. However, the instrument has some limitations, such as in high density forest or areas where the canopy of trees is heavily shaded, which can reduce the measurement accuracy of the instrument. In general, the instrument provides a new idea for small-scale and lowcost forest survey, and its measuring accuracy basically meets the requirements of forestry survey.
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CRediT authorship contribution statement Guangpeng Fan: Formal analysis, Investigation, Software, Validation, Writing - review & editing. Wenxin Feng: Data curation, Visualization, Writing - original draft. Feixiang Chen: Visualization, Supervision, Resources. Danyu Chen: Investigation, Writing - review & editing. Yanqi Dong: Data curation. Zhiming Wang: Data curation, Investigation. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgement This research was jointly supported by the Fundamental Research Funds for the Central Universities (TD2014-02). We gratefully acknowledge the reviewers for their insightful comments of the manuscript, thank other team members for help with the experiment. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.compag.2020.105211. References Astola, H., Häme, T., Sirro, L., Molinier, M., Kilpi, J., 2019. Comparison of Sentinel-2 and Landsat 8 imagery for forest variable prediction in boreal region. Remote Sens. Environ. 223, 257–273. https://doi.org/10.1016/j.rse.2019.01.019. Avitabile, V., Camia, A., 2018. An assessment of forest biomass maps in Europe using harmonized national statistics and inventory plots. For. Ecol. Manage. 409, 489–498. https://doi.org/10.1016/j.foreco.2017.11.047. Bauwens, S., Fayolle, A., Gourlet-Fleury, S., Ndjele, L.M., Mengal, C., Lejeune, P., 2017. Terrestrial photogrammetry: a non-destructive method for modelling irregularly shaped tropical tree trunks. Methods Ecol. Evol. 8, 460–471. https://doi.org/10. 1111/2041-210X.12670.
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Evaluation of a smartphone app for forest sample plot measurements. Forests 6, 1179–1194. https://doi.org/10.3390/f6041179. Vauhkonen, J., Holopainen, M., Kankare, V., Vastaranta, M., Viitala, R., 2016. Geometrically explicit description of forest canopy based on 3D triangulations of airborne laser scanning data. Remote Sens. Environ. 173, 248–257. https://doi.org/ 10.1016/j.rse.2015.05.009. Wagner, B., Ginzler, C., Bürgi, A., Santini, S., Gärtner, H., 2018. An annually-resolved stem growth tool based on 3D laser scans and 2D tree-ring data. Trees 32, 125–136. https://doi.org/10.1007/s00468-017-1618-3. Xu, W., Feng, Z., Su, Z., Xu, H., Jiao, Y., Fan, J., 2013. Development and experiment of handheld digitalized and multi-functional forest measurement gun. Trans. Chin. Soc. Agric. Eng. 29, 90–99. https://doi.org/10.3969/j.issn.1002-6819.2013.03.013. Yang, L., Feng, Z., Fan, G., Wu, F., 2018. Design and experiment of laser photogrammetric instrument for measuring forest. Trans. Chin. Soc. Agric. Mach. 49 (1), 211–218. https://doi.org/10.6041/j.issn.1000-1298.2018.01.026. Yu, D., Feng, Z., Cao, Z., Jiang, J., 2016. Error analysis of measuring diameter at breast height and tree height and volume of standing tree by total station. Trans. Chin. Soc. Agric. Eng. 32, 160–167. https://doi.org/10.11975/j.issn.1002-6819.2016.17.022. Zhang, M., 2004. Study on volume measurement of single trees. Forest Resour. Manage. 24–26. https://doi.org/10.3969/j.issn.1002-6622.2004.01.006. Zhang, M., She, G., 2010. Study on estimation of tree volumes and final decision for deforestation and illegal felling of forests. J. Nanjing Forestry Univ. (Nat. Sci. Ed.) 34, 85–90. https://doi.org/10.3969/j.issn.1000-2006.2010.01.018. Zhao, F., Feng, Z., Gao, X., Zheng, J., Wang, Z., 2014. Measure method of tree height and volume using total station under canopy cover condition. Nongye Gongcheng Xuebao/Trans. Chin. Soc. Agricult. Eng. 30, 182–190. https://doi.org/10.3969/j.issn. 1002-6819.2014.02.024. Zhou, K., Wang, Y., Li, J., Jiang, G., Xu, A., 2016. A study of tree measurement systems based on Android platform. J. Nanjing Forestry Univ. (Nat. Sci. Ed.) 40, 95–100. https://doi.org/10.3969/j.issn.1000-2006.2016.04.015.
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Measurement of volume and accuracy analysis of standing trees using Forest Survey Intelligent Dendrometer
T
Guangpeng Fana, Wenxin Fengb, Feixiang Chena, , Danyu Chena, Yanqi Donga, Zhiming Wanga ⁎
a b
School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China Henan Geology Vocational College, Zhengzhou 450007, China
ARTICLE INFO
ABSTRACT
Keywords: Forest Electronic instrument Standing tree Stand volume Precision analysis
The standing tree volume is very important for accurately evaluating stand growth. This paper introduced a new type of high precision portable electronic instrument - Forest Survey Intelligent Dendrometer (FSID). For the instrument, we developed guidelines for measuring the standing tree volume in the sample plot and explained the principles and methods. The instrument can use the integrated angle and distance measuring device to determine the height of standing trees based on the low-power Bluetooth data transmission, extract the coordinates of the characteristic points of the standing trees, calculate the diameter of the tree trunk at any height, and realize the real-time measurement of tree volume in single photo and multiple photo solutions. To verify and analyze the accuracy of the instrument, we measured 181 standing trees in a 45x45m temporary plot in Haidian District, Beijing. The experimental results showed that the accuracy of the FSID for measuring the standing tree volume was 96.89%. Different tree heights lead to different optimal observation distances, and the optimal observation distance of this experiment was approximately 8 m. The method in this paper has the characteristics of real-time, fast, efficient and non-destructive, which can meet the requirements of forest survey, especially in the forest areas where logging is prohibited or restricted. The method shows great potential in forest survey and environmental protection.
1. Introduction Forest ecosystems are vital to the survival of animals and humans, and they provide services not only in the carbon cycle, soil and water conservation, climate regulation and biodiversity, but also in providing food, wood and energy to humans (Grassi et al., 2018). The stand volume is the total volume of all standing trees volume in the forest. The stand volume is an important factor for assessing the status of forest resources and the level of forest management and an important indicator for assessing a forest’s ability to reduce climate change risks (Avitabile and Camia, 2018). Therefore, the stand volume is one of the main factors for forest surveys. Measuring the standing tree volume is the basis for accurately assessing the stand volume, and it has also been the focus of tree research for a long time (Liu et al., 2019). In the early stage, the standing tree volume was measured mainly by the trunk analysis method. In this method, after cutting down the wood, the trunk is analyzed, and the modeling samples needed to compile the volume table are obtained, which is time consuming and laborious and destructive to the forest (Feng et al., 2005). Accordingly, some researchers proposed the establishment of nondestructive tree volume
⁎
estimation methods, such as the experimental form factor method, the from reference point method, the normal form factor and the pressler method (Feng et al., 2005; Zhang, 2004; Zhang and She, 2010; Cao et al., 2015). However, due to cumbersome operation and low measurement accuracy, the use of these methods is limited. Therefore, domestic and foreign scholars hope to establish a nondestructive testing method that can quickly and accurately measure standing tree volume. In recent years, ground observation technology, remote sensing, photogrammetry technology, and 3D laser scanning technology have provided effective solutions for forest resource inventory and have been used to extract various forestry attributes (Astola et al., 2019; Jayathunga et al., 2018; Vaglio Laurin et al., 2019). However, because of the high cost and difficult data processing, such as 3D laser scanners and terrestrial laser radar, it is more suitable for large-scale forest resource surveys (Jayathunga et al., 2018; Magnussen et al., 2018; Oveland et al., 2018; Vauhkonen et al., 2016). In China, instruments such as hand-held electronic tree survey gun, CCD smart station, mapping recorder, miniature over station, laser photography tree survey and other multifunctional comprehensive forest survey instruments have emerged, forming a relatively complete forest ground
Corresponding author. E-mail address: [email protected] (F. Chen).
https://doi.org/10.1016/j.compag.2020.105211 Received 13 October 2019; Received in revised form 1 January 2020; Accepted 2 January 2020 0168-1699/ © 2020 Elsevier B.V. All rights reserved.
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survey technology system (Yu et al., 2016; Feng et al., 2015; Liu et al., 2016; Xu et al., 2013; Yang et al., 2018; Huang, 2016). Although the above techniques can solve the measurement problem of the standing tree volume, these related measuring instruments require high cost or professional training. The current forestry observation equipment still has the disadvantages of high cost, low measurement accuracy, low integration, poor portability and single functionality. The development of mobile computing and advances in computer vision have helped researchers develop methods that are well suited for forest surveys, with a high accuracy and a low price (Vastaranta et al., 2015; Hyyppä et al., 2018; Jaakkola et al., 2010; Kangas et al., 2015; Tomaštík et al., 2017; Zhou et al., 2016). The feasibility of obtaining the standing tree volume through computer vision and ground observation technology has been verified (Bauwens et al., 2017; Dean, 2003; Gaffrey et al., 2001; Kansanen et al., 2016; Luhmann et al., 2016). However, the equipment used in this technology is usually a stand-alone digital camera, which needs to import the image into computer software for processing, and the measurement results cannot be solved in the field (Berveglieri et al., 2017; Mikita et al., 2016; Mokroš et al., 2018). In the existing forest survey methods, especially in the calculation of standing tree volume, there are still problems such as low measurement accuracy, complicated operation, cumbersome equipment or high cost. These factors not only limit the promotion and application of such equipment in forest surveys, but also restrict the computerization of modern forestry. This paper focused on the necessity of standing tree volume measurement and the actual situation of forestry investigation. The FSID is designed and developed. It highly integrates a mobile phone with a Bluetooth data transmission function and a laser rangefinder. It combines the forest survey software running on mobile phone and is based on the Java language to integrate the functions of the measuring angle and distance, which can measure the standing tree volume in real time, rapidly and accurately. The advantage of the FSID is that it is cheaper than the 3D laser scanner, easy to carry, and simple to operate, and it does not require post processing. Compared with the conventional method, the method in this paper reduces the on-site working time and workload, and one can operate it independently without any additional personnel assistance to realize noncontact real-time measurement. In addition, the volume data of the standing tree measured by the total station and the FSID were compared and analyzed, and the feasibility of using the FSID to accurately measure the standing tree volume was verified.
Fig. 1. Forest Survey Intelligent Dendrometer.
software was developed using the Java programming language (see Fig. 2). The Java native interface (JNI) technology was used to call the Open Source Computer Vision Library (OpenCV) interface to process photos of standing trees. This software can measure the angle and distance and measure the standing tree volume in real time. According to the actual work needs, the user can also splice multiple photos of the photographed standing trees. The user can adjust the instrument according to the actual measurement environment and the measurement work requirements, and process the angle data, distance data and pictures through information transmission and integration between the modules. All the measured data can be stored through an SQLite database or exported in (.csv) format. 2.2. Research area The research in this paper was carried out in an artificial forest located in Haidian District (40°00′N, 116°19′E), Beijing, China (Fig. 3). The altitude of the study area is 45 m and the terrain is flat. It belongs to a temperate humid monsoon climate zone, with four distinct seasons, hot summers, cold winters and low precipitation. It is a plantation planted with tree species such as poplar (Populus tomentosa Carrière), cypress (Platycladus orientalis (L.)), Chinese scholar tree (Sophora japonica Linn.) and eucalyptus (Ulmus pumila L.), with no or only a small amount of undergrowth and bushes. The density of trees in plantations is about 1000 trees per hectare. In order to verify the accuracy of the FSID, the volume of 181 standing trees was measured in a 45x45m temporary marking plot using the method of this paper and the total station measuring volume method.
2. Materials and methods 2.1. Measuring device 2.1.1. Hardware composition The total weight of the electronic forest tree measuring instrument in this paper is less than 2 kg. The instrument is easy to carry, simple to operate and highly integrated with a mobile phone, laser rangefinder and photography platform (see Fig. 1). The mobile phone is the core module of the whole instrument. It is equipped with Android 8.0 operating system and has various sensors such as angle sensor, camera and Bluetooth. It is a platform for software operation and real-time data processing and controls interactions with other hardware modules. The laser rangefinder has a ranging function and uses BLE4.0 low-power Bluetooth communication technology to directly provide high-precision distance data to the mobile phone. The photography platform is a tripod with an integrated hardware connection for placing and securing the mobile phone. It can rotate 360° during operation, and the height to the ground can be arbitrarily adjusted. Appropriate tests are conducted before operation.
2.3. Principle and method of measurement The two-site observation method is used to accurately measure the standing trees by selecting the measuring points with a clear view and a visible canopy (Zhao et al., 2014). Because the trunk portion is not circular or approximately oval, the angle between the two observation sites and the tree is guaranteed to be 90°. The average value of the two observation sites is taken to make the measurement more accurate. The specific measurement process is as follows: (1) At observation site No. 1, set up a forest electronic tree measuring instrument. Open the forest survey software, use the laser rangefinder to project the laser point onto the trunk of the standing tree to be measured, and obtain the horizontal distance L from the observation site to the trunk. Transfer horizontal distance L to the mobile phone via Bluetooth. Then, using the rotating platform, the line of the crosshair in the center of the camera is aligned at point T at the top of the tree and point B at the bottom of the tree (the part where the trunk and the
2.1.2. Software function On the Android Studio 3.3 development platform, a forest survey 2
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Fig. 2. Operation flow of calculating volume.
ground contact) to obtain the angle of the observation site and the tree top and the angle of the observation site and the tree bottom, respectively. The tree height H of the standing tree can be obtained by the principle of the trigonometric function (1).
H = L (tan + tan )
sections. To ensure that the photos of the standing tree are not deformed after shooting, keep the main optical axis of the camera parallel to the ground as much as possible. After taking a picture of the standing tree, a red calibration line appears automatically in the software interface, and the trunk is divided into several sections. First, according to the shape of the trunk, slide the calibration line along the vertical direction in the program interface to segment the trunk. Then, by sliding along the horizontal direction, the left and right intersection points of each trunk and the calibration line are
(1)
(2) A photo of the standing tree to be measured is taken with the method of normal case photography, and the red calibration line automatically appears in the interface to divide the tree trunk into several
Fig. 3. Study area location map. 3
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determined, and the coordinates of the intersection points of each section of trunk and the calibration line were obtained manually. Finally, the software automatically calculates the diameter Di of the bottom of each section of the trunk, the coordinates of the midpoint of the calibration line according to formulas (2) and (3), and the vertical height hi between each two calibration lines according to formula (4).
H (PTx
PBx ) 2 + (PTy
PBy ) 2
=
Di (PiLx
PiRx ) 2 + (PiLy
PiRy )2
PiTx = (PiLx + PiRx )/2 PiTy = (PiLy + PiRy )/2
(3)
H (PTx
PBx ) 2 + (PTy
(2)
PBy ) 2
=
hi (PiTx
PiBx ) 2 + (PiTy
PiBy ) 2
(4)
H represents for tree height. (PTx , PTy ) and (PBx , PBy) are the coordinates of the top of the tree and the bottom of the tree, respectively. Di is the diameter of the bottom of the trunk at the i-th point. (PiLx , PiLy) and (PiRx , PiRy) represent the coordinates of the intersection of the two sides of the trunk at the i-th and the calibration line, respectively. hi represents the vertical height between two adjacent calibration lines of the trunk at the i-th point. (PiTx , PiTy ) and (PiBx , PiBy ) represent the midpoint coordinates of two adjacent calibration lines of the trunk at the ith point, respectively. (3) The measurement parameters include the angle between the observation site and the tree top, the angle between the observation site and the tree bottom, the horizontal distance L between the instrument and the standing tree, and several intersections of the calibration lines and the trunk in the photo of the standing tree (see Fig. 4). Then, another site around the standing tree is selected, and the step of the observation site 1 is re-executed. After the system generates volume data, standing tree photo and other measurement parameters, the system saves the data as a file in the database by entry, which can be viewed, modified, deleted or otherwise changed, or the data can be exported in spreadsheet form by using JXL technology. The main principle is to use the FSID to approximate the parts of the whole tree based on geometry and the characteristics of the shape of the trunk. The entire trunk is divided into n segments, and the average segment length is 0.5 m to 3 m. The crown part is treated as a circular cone, and the parts below the crown are considered as the frustum of a cone. The volume calculation formula of the Frustum of a cone (Eq. (5)) and the volume calculation formula of a circular cone (Eq. (6)) are as follows. Finally, the volume of each segment is added to obtain the volume of the entire standing tree. The specific algorithm is as follows (see Fig. 5): The formula for calculating the volume of frustum of a cone is:
Fig. 5. Model of standing tree volume.
Vi = h (DU2 + DU DD + DD2 )/12
(5)
(i = 1, 2, , n 1) is the volume of the frustum of a cone. h is the height of the segmented frustum of a cone. DU is the cross-sectional diameter of the top surface of the frustum of a cone, and DD is the crosssectional diameter of the bottom surface of the frustum of a cone. The formula for calculating the circular cone volume is: (6)
Vn = hn 1 Dn2 1/12
Vn is the volume of the circular cone. hn 1 is the height of the circular cone (the length of the crown), and Dn 1 is the diameter of the bottom of the circular cone (the diameter of the lowest part of the cone). The total volume formula (7) can be expressed as: V = V1 + V2 + V3 +V4 + =
hn
2 1 Dn
1
+
+ Vn n 2 i=1
hi + 1 (Di2 + Di Di + 1 + Di2+ 1) /12
(7)
V represents the total volume of the standing tree trunk, and Vi (i = 1, 2, 3 n) represents the volume of each segment. hi (i = 1, 2, 3 n 1) is the height of each segment, and Di (i = 1, 2, 3 n 1) is the diameter of each segment. 2.4. Photography mode Although a strict observation plan was designed in this paper, difficult conditions were often encountered due to the complex environment in the forest. For example, when the observation site is close to the standing tree or when the standing tree to be measured is very high, it is impossible to include complete information of the standing tree by taking a photo. Therefore, it is necessary to change the photography mode of the FSID to take multiple photos of trees with overlapping areas. The software developed in this paper corrects the obtained photos one by one by tilting them horizontally. Multiple photos with overlapping portions are stitched together to form a photo containing the complete information of the standing timber. As shown in Fig. 6, two or three photos with overlapping areas are merged after the tree height is obtained using the method in 2.3 when the tree volume cannot be measured from one photo. Although precise measurement method was used in this paper, some incorrect or abnormal data will inevitably appear in the collected sample data. Abnormal data have a great impact on the selection of the model and the accuracy of the final model, which may lead to the distortion of objective laws. To express volume models more
Fig. 4. Principle of standing tree observation for FSID. H is the height of the tree, hi is the height of the trunk segment, Di is the diameter of the trunk segment, L is the horizontal distance from the observation site to standing tree. is the angle from the observation site to the tree top, and is the angle from the observation site to the tree bottom, i = 1, 2, , n 1, where Dn = 0. 4
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2.6. Accuracy evaluation method When measuring the standing tree volume with the FSID, error is inevitable and will affect the accuracy of the measurement results. When evaluating the accuracy of the method used in this paper to measure the standing tree volume, the measuring test of standing tree volume was carried out with NTS–372R total station instrument (South Surveying & Mapping Technology CO., LTD.) and the FSID. Because the theoretical accuracy of the total station for the nondestructive measurement of trees is much higher than the precision requirement of forestry surveys, the volume data measured by the FSID are compared with the volume data obtained by the total station. Formula (8)–(13) are used to calculate absolute error e , relative error δ', average relative error ¯' , absolute value of the relative error , average absolute value of the relative error ¯ and precision P .
e = Vm '= Fig. 6. Photography mode of merging multiple photos.
¯ '=
objectively, it is necessary to eliminate abnormal data that deviate from most data distribution rules.
=
Vm n i=1
× 100%
'
|Vm
Vs | Vs
(9) (10)
n
P=1
(1) The effect of distance and photography mode (number of photos) on the measurement accuracy was analyzed without considering the shape of standing tree and other factors. This paper specifically selects a poplar crown that has been intercepted as the sample tree. The sample tree has a small surface roughness, a straight trunk and a nearly conical top. The diameter at breast height (DBH) of the sample tree measured using calipers is 15.8 cm, and the ground diameters in different directions were measured multiple times, with an average value of 25.9 cm. After 5 measurements on the ground, the height of the sample tree measured by total station averaged 10.968 m, and the volume of the sample tree was 0.1586 m3, with the above measured value as the reference value. After repeated deduction and test, when the distance between the FSID and the sample tree is less than 8.3 m, two photos should be taken. When the distance between the instrument and the standing tree is less than 3.8 m, to include complete information of the standing tree and ensure the measurement accuracy as much as possible, three standing tree photos can be taken. The surveyors can use the “photo correction” and “photo merge” functions in the software. By observing each photo and selecting 2–3 feature points, the software will automatically merge the photo according to the feature points. This paper suggests that the repeated area of each photo should account for up to 30% of the total area of the photo. The nearest distance is approximately 3 m, with an increase of approximately 1 m each time. A total of 20 observation sites were set. (2) The effect of various unstable factors on the measurement results is considered. Therefore, tree species, tree height, DBH, trunk shape and other factors are introduced into the actual measurement situation, and the accuracy and stability of the method in practical application are verified. In this paper, the accuracy of the FSID was verified and analyzed in a 45x45m temporary marked sample plot. The volume measurement method of the total station was used three times for measurement, and the average value was taken as the reference value of the standing trees volume. In the field work of this paper, when the total station was used to measure the standing trees volume as the reference value, the FSID was used to collect the data on the same side.
Vs Vs
¯=
2.5. Experiments design
(8)
Vs
× 100%
n i=1
n
¯
(11) (12) (13)
In formula (8)–(13), Vm is the measured value of the FSID in the forest. Vs is the calculated reference value corresponding to Vm . 3. Results 3.1. The impact of observation distance on measurement accuracy In the process of measuring the sample tree, we found that when the instrument was 3–8 m away from the sample tree, it was difficult to obtain the information of the complete sample tree through a photo due to the field of view. After testing, it is found that the observation effect is the best when it is at 8 m, and a photo containing the complete sample tree information can be obtained. When it is not possible to obtain the complete information of the sample tree through one photo, we can only obtain the information of the standing tree by taking 2 or 3 photos. In the process of measuring the sample tree, a total of 174 groups of sample tree photos were taken, the distance range measured by the instrument was 3.38 m ~ 15.581 m, the absolute error range of the sample tree volume measurement was −0.0065 m3 ~ 0.0077 m3 , the relative error range was −5.29%~5.42%, the average relative error was 0.86%, the average absolute value of relative error was 2.21%, and the measurement accuracy was 97.79%. As shown in Fig. 7, within the range of 3–8 m, the measurement accuracy becomes higher as the distance increases and tends to be the highest when the observation distance is approximately 8 m. When the observation distance is greater than 8 m, the measurement accuracy of the FSID starts to gradually decrease because in the shooting of standing trees, three photos are first taken and combined into one photo containing complete information for the standing tree. With the increase of distance, two photos can be taken and combined into one photo containing complete information for the standing tree. When the observation distance is approximately 8 m, one photo can be taken to contain complete information for the standing tree. When the observation distance exceeded 10 m, the edge points formed by the standing tree on the photo gradually blurred, resulting in a decrease in the number of pixels for the feature points. Therefore, when the 5
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Fig. 7. The impact of observation distance on the measurement accuracy of sample tree volume.
observation distance is approximately 8 m, the obtained data has higher precision.
the number of photos increases, the error will increase, resulting in a lower measurement accuracy.
3.2. The impact of photography mode on accuracy
3.3. The comprehensive analysis of measurement accuracy of the FSID
In this paper, the measurement results with single photo are compared with those with multiple photos. To verify the influence of the number of photos on the measurement accuracy, a circle with a radius of 8 m was set up with the sample tree as the center. At the border of the circle, the following three methods are used to obtain a photo containing the complete information of the standing tree: take one photo, take two photos and merge them into one, and take three photos and merge them into one. Of the 174 groups of photos measured, 85 were measured by one photo, 54 were measured by merging two photos and 35 were measured by merging three photos. It can be seen from the experimental results in Fig. 8 that the working scheme of a single photo has the highest accuracy. As the number of photos increases, the volume measurement accuracy decreases. The volume measurement accuracy of the single photo was 97.96%, the volume measurement accuracy of the combined two photos was 97.77%, and the volume measurement accuracy of the combined three photos was 96.29%. When the overlapping areas of two or three photos are superimposed on each other to form a photo that contains complete information about the tree, the process produces error that increases or decreases the height of the tree trunk compared to the height of the normal tree. As
In the actual measurement, different tree species, tree heights, DBH values, trunk shape and so on will be encountered. To analyze the impact of these factors on the accuracy of instrument measurement, 181 standing trees were measured, including 40 Chinese arborvitae trees (Platycladus orientalis (L.) Franco), 25 Chinese red pines (Pinus tabuliformis Carr.), 36 elms (Ulmus pumila L.), 44 Chinese scholar trees (Sophora japonica Linn.) and 36 poplars (Populus tomentosa Carrière). The data measured by total station are taken as the reference value, and the data measured by the FSID are compared with the reference values. The absolute error range of the standing tree volume of the 181 standing trees is −0.0620 m3 ～0.0834 m3 , the relative error range is −7.42% ~ 6.58%, the average relative error is 0.05%, the average absolute value of the relative error is 3.11%, and the measurement accuracy of standing tree volume is 96.89%. Therefore, the measurement method of standing tree volume in this paper can meet the requirements of forest survey. According to Fig. 9, we can analyze the impact of the DBH, species and observation distance on measurement accuracy. There is no significant difference between the measurement accuracy of the FSID and that of the total station at the same direction. As the number of photos increases, the measurement accuracy decreases gradually. The reason for this result is that the overlapping areas are merged too much or too little during the merging of two or three photos, resulting in incomplete information of the merged standing tree. As the number of photos increases, the error of merging also increases. It is also possible that the resulting image is distorted, resulting in a lower resolution. According to the measurement results of 3.1, the optimal observation distance is 8 m through the analysis of error causes. Therefore, when using the FSID to measure the standing trees volume, it should preferably take a photo of the stand for measurement, then increase the observation distance and finally increase the number of photos. In field work, surveyors should avoid poor measuring environment, observe the operating specifications of instruments and make accurate measurements according to the procedures. 4. Discussion As the public's attention to forests continues to increase, more and more people use the “forest growing stock” to understand the total scale and level of forest resources in a country or region. The stand volume is also an important basis to reflect the richness of forest resources and estimate the quality of forest ecological environment (Didion et al.,
Fig. 8. The impact of number of photos on the measurement accuracy of sample tree volume. 6
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volume of standing trees without any close contact with the trees, it can perform calculations in real time to make the work more coherent, and the measurement results are automatically stored. It saves working time, has the characteristics of low cost, high integration, simple operation and easily carried, and it is very suitable for the application of low-cost, small-scale forest survey work. Compared with the conventional measurement method (Forsman et al., 2018; Pierzchała et al., 2018), the FSID has no obvious advantages in terms of measurement accuracy, but its measurement efficiency is higher, the measurement cost is greatly reduced, and damagefree monitoring is achieved, which is not destructive to the forest. Secondly, 3D laser scanners, total stations, and other high-precision measuring instruments are gradually being applied to volume measurement (Del Perugia et al., 2019; Oveland et al., 2018; Pierzchała et al., 2018; Vaglio Laurin et al., 2019; Wagner et al., 2018). Their accuracy advantages are obvious, but these advanced instruments are much less portable than the FSID, so the cost of measurement is much higher. The third advantage of the electronic forest tree tester is that it can quickly measure the volume of standing trees without the need to go back to the office for data post-processing, and it does not need close contact with the trees when working, which makes the work more coherent in real time. Friendly user interaction enables us to freely assemble and improve the equipment composition according to the survey requirements. The instrument is light weight, easy to carry and to operate, and improves the measurement efficiency and data quality. In addition, the instrument is reasonably priced and works independently of electric fields and environmental conditions. Although the device is a new option for forest surveys, it still has some limitations. The optimal observation distance is different due to other factors such as the hardware configuration (such as different field angles) or the stand growth environment, so 8 m is not absolute. In 3.1 and 3.2, due to the height of the sample tree and the angle of view of the camera, the optimal observation distance for this experiment was 8 m. The best observation accuracy can be obtained by taking a picture at 8 m. This distance is only the conclusion of this paper after several measurements, which provides a reference value of the observation distance for the surveyors. The error of instrument calibration (including the calibration of the mobile phone camera, the sensitivity of the angle sensor and the integrated calibration of the instrument) and the environmental factors with high density of trees will affect the measurement accuracy. To ensure the accuracy of the measurement results, the correct calibration parameters of the instrument should be ensured and the measurement error caused by human factors such as the selection of touch points on the terminal screen should be avoided. When the visibility of the instrument observation is disturbed, try to solve it by adjusting the observation site. However, due to the complex and diverse conditions in the forest, the observer must determine whether the displayed treetop position is appropriate during the measurement, which may be affected by the trunk shape or crown occlusion and the subjective decision of the observer. Otherwise, the observation position should be adjusted according to the need. When it is not possible to take a single picture with complete information about the stand by adjusting the observation position, two or three photos with overlapping areas of the same standing tree can be taken, and the photos can be combined to obtain complete tree information for measurements. Therefore, compared with the measurement of high-density forests, the method in this paper is more suitable for the accurate monitoring of small-scale low-density or medium-density forests.
Fig. 9. Impact of the DBH, tree species, observation distance and photography mode on the standing tree error. In the Fig. 9(a), the X axis is the observation distance, the Y axis is the number of photos, and the Z axis is the relative measurement error (%). In the Fig. 9(b), X axis is the DBH of the standing trees, the Y axis is the tree species of standing trees, and the Z axis is the relative measurement error (%).
2019; Scolforo et al., 2019). The accurate measurement of the standing trees volume is the basis of accurately evaluation of stand volume and has always been a hot spot in forestry research (Grassi et al., 2018). This paper introduces a high precision, portable and highly integrated measuring instrument for the standing volume. Through independent observations, we verified the impact of the observation distance and the number of photos on the measurement accuracy of the instrument. Through the observation of 181 trees in the 45 × 45 m temporary calibration sample plot, the measured value was compared with the reference value obtained by the total station. The measurement accuracy of volume was 96.89%, and the optimal observation distance was approximately 8 m. The measurement accuracy of a single photo was better than that of fusing multiple photos. The experimental results show that the accuracy of the FSID further demonstrates the feasibility of using the device to measure the volume of standing trees. In the experiment of this paper, the FSID is compared with the conventional volume measuring instrument in forest survey (as shown in Table 1). This instrument can quickly and accurately measure the
5. Conclusion This paper proposes an electronic tree measuring instrument, a new type of portable high-precision standing trees electronic measuring instrument. It combines the laser rangefinder and tree software running on a smart phone to realize the function of measuring the standing trees volume. It can quickly and accurately measure the volume of standing 7
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Table 1 Comparison of cost and time between this article’s mode and the current mode. Work mode
Volume
Survey costs
The average time of measuring a tree
3d
Able
0.2–4 million USD.at least two people to operate
Total station
Unable to measure in real time
1500–7000 USD. Needs one person to operate
Traditional methods (altimeter and calipers)
Unable to measure in real time
Digital camera + total station
Unable to measure in real time
71–1500 USD. Needs one person to operate with different instruments 1500–7000 USD, two people to operate.
The FSID
Able to measure in real time
Heavy workload on site. The equipment is heavy and inconvenient to carry, the internal work is complex, and the measurement results cannot be obtained in real time. It needs to go back to the office for post-processing with professional software, which takes approximately 18 min on average. It has a high accuracy and is suitable for large-scale forest point cloud data acquisition. Heavy workload on site. Requires continuous work of both eyes. It easily causes fatigue and is unsuitable for continuous operation. It takes an average of 12 min. It has a high accuracy. Heavy workload on site. Independent tools. Simple operation. Cannot operate continuously. Data cannot be recorded directly, needs to be read manually, belongs to contact measurement, and has a high accuracy. Heavy workload on site. Unable to obtain measurement results in real time. Needs to go back to the office with professional software for post processing. It takes an average of 8 min. Light workload on site. Measure and automatically store data in real time. Easy to carry. The operation is simple and continuous. It takes an average of 3 min. Suitable for small-scale, low-density forest surveys.
Laser scanner
600 USD, one person to operate
trees at a work site and display and store the measured data of the standing trees in real time, without going back to the office for data post-processing. However, the instrument has some limitations, such as in high density forest or areas where the canopy of trees is heavily shaded, which can reduce the measurement accuracy of the instrument. In general, the instrument provides a new idea for small-scale and lowcost forest survey, and its measuring accuracy basically meets the requirements of forestry survey.
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CRediT authorship contribution statement Guangpeng Fan: Formal analysis, Investigation, Software, Validation, Writing - review & editing. Wenxin Feng: Data curation, Visualization, Writing - original draft. Feixiang Chen: Visualization, Supervision, Resources. Danyu Chen: Investigation, Writing - review & editing. Yanqi Dong: Data curation. Zhiming Wang: Data curation, Investigation. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgement This research was jointly supported by the Fundamental Research Funds for the Central Universities (TD2014-02). We gratefully acknowledge the reviewers for their insightful comments of the manuscript, thank other team members for help with the experiment. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.compag.2020.105211. References Astola, H., Häme, T., Sirro, L., Molinier, M., Kilpi, J., 2019. Comparison of Sentinel-2 and Landsat 8 imagery for forest variable prediction in boreal region. Remote Sens. Environ. 223, 257–273. https://doi.org/10.1016/j.rse.2019.01.019. Avitabile, V., Camia, A., 2018. An assessment of forest biomass maps in Europe using harmonized national statistics and inventory plots. For. Ecol. Manage. 409, 489–498. https://doi.org/10.1016/j.foreco.2017.11.047. Bauwens, S., Fayolle, A., Gourlet-Fleury, S., Ndjele, L.M., Mengal, C., Lejeune, P., 2017. Terrestrial photogrammetry: a non-destructive method for modelling irregularly shaped tropical tree trunks. Methods Ecol. Evol. 8, 460–471. https://doi.org/10. 1111/2041-210X.12670.
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