LU503402B1

LU503402B1 - Method for evaluating loss of urban tree ecosystem caused by typhoon and apparatus thereof

Info

Publication number: LU503402B1
Application number: LU503402A
Authority: LU
Inventors: Yunfeng Huang
Original assignee: Univ Jimei
Priority date: 2023-01-31
Filing date: 2023-01-31
Publication date: 2023-07-31

Abstract

One or more embodiments of the present description provide a method for evaluating the loss of an urban tree ecosystem caused by a typhoon and a device thereof. Firstly, lodging tree image information in a region is acquired, a lodging tree trunk sample point is acquired from the acquired information, and a threshold value of the lodging tree trunk sample point is calculated; the acquired information is firstly segmented, and then the segmented information is synchronously processed based on the threshold value to calculate the volume of the screened lodging tree; the total mass of the lodging trees in the region is calculated based on the volume of the screened lodging trees. Based on the total mass of the lodging trees in the region, the carbon sequestration value and oxygen release value lost by the lodging tree in the region are calculated.

Description

BL-5615

METHOD FOR EVALUATING LOSS OF URBAN TREE ECOSYSTEM LUs03402

CAUSED BY TYPHOON AND APPARATUS THEREOF

TECHNICAL FIELD

[01] One or more embodiments of the present description relate to the technical field of a method for evaluating the loss of an urban tree ecosystem caused by a typhoon, and more particularly to a method for evaluating the loss of an urban tree ecosystem caused by a typhoon and an apparatus thereof.

BACKGROUND ART

[02] Under the influence of global climate change, typhoons land more and more frequently in coastal cities, resulting in a serious impact on the socio-economic system, built-up area system, and natural ecosystem. Typhoons not only cause huge economic losses, but also have an important impact on the natural ecosystem of coastal cities. In 2012, the super-strong typhoon "Vicente" led to the damage of 115, 000 trees in

Shenzhen. The super-strong typhoon "Meranti" landed in Xiamen in 2016, resulting in 10.2 billion RMB of direct economic losses, and 90% of the city's trees in the city were damaged. Urban green space is an important part of the urban ecosystem, which has the functions of maintaining the carbon and oxygen balance of the urban atmosphere, regulating urban microclimate, eliminating air pollution, sterilizing and reducing noises, etc. The frequent occurrence of typhoons makes these ecological service functions at high risk.

[03] At present, the research related to typhoon disasters and losses focuses on typhoon disaster risk evaluation, the direct and indirect economic losses caused by typhoons, the evaluation of the relationship between typhoon disasters and disaster-causing factors, and the evaluation of typhoons to the urban forest ecosystem.

The technical means of evaluating the actual losses caused by typhoon disasters are diverse, and the combination of remote sensing technology and GIS is the most common method. By analyzing the changes of various indicators (NDVI, etc.) before and after a disaster by remote sensing technology, the losses caused by typhoons can be quantitatively evaluated from space. However, this method has certain limitations, and it is difficult to acquire high-quality remote sensing images in time without cloud cover after the disaster, and the values of various kinds of natural ecosystems are calculated mainly according to the ecosystem service value scale per unit area, leading to the lower speed and accuracy of the evaluation method.

SUMMARY

[04] On that account, it is an object of one or more embodiments of the present description to provide a method for evaluating the loss of an urban tree ecosystem caused by a typhoon and an apparatus thereof to solve the problem of low speed and accuracy of the evaluation method in the prior art.

[05] In view of the above object, one or more embodiments of the present description provide a method for evaluating the loss of urban tree ecosystem caused by a typhoon, comprising: 1

BL-5615

[06] acquiring lodging tree image information in a region, and pre-processing LU503402 acquired information to obtain pre-processed lodging tree image information;

[07] acquiring a lodging tree trunk sample point from pre-processed lodging tree image information, performing eigenvalue calculation on an acquired lodging tree trunk sample point, and setting a threshold value of the lodging tree trunk sample point according to the calculated mean value and standard deviation value;

[08] segmenting the pre-processed lodging tree image information, and performing lodging tree acquisition processing on segmented lodging tree image information at the same time based on the threshold value of the lodging tree trunk sample point;

[09] performing trunk judgment and screening on an acquired lodging tree in a region, and calculating a volume of a screened lodging tree;

[10] calculating a total mass of lodging trees in the region based on the volume of the screened lodging tree and a mean density of a basic wood in the region;

[11] and based on the total mass of lodging trees in the region, calculating a carbon sequestration value and an oxygen release value lost by the lodging tree in the region, and that being the loss of urban tree ecosystem caused by a typhoon.

[12] Optionally, the acquiring lodging tree image information in a region, and pre-processing acquired information comprise:

[13] in the planning of aerial photography region, selecting the region within a range of 1-2km from a center of the typhoon on a second day after the typhoon passes;

[14] as to the lodging tree image information in the region, utilizing an unmanned aerial vehicle to perform aerial photography in the region;

[15] and pre-processing the acquired information, and acquiring an aerial photography image in the region with a resolution of 8-10cm through image correction and splicing.

[16] Optionally, the acquiring a lodging tree trunk sample point from pre-processed lodging tree image information, performing eigenvalue calculation on an acquired lodging tree trunk sample point, and setting a threshold value of the lodging tree trunk sample point according to the calculated mean value and standard deviation value comprise:

[17] selecting a sample point at a trunk position of a lodging tree in the pre-processed lodging tree image information;

[18] acquiring color information for all sample points, including any one or more of the following: RGB, HSL, YMC, and a value of a band combination, including any one or more of the following: combining RG1, BG1, BR1, RG2, BG2, BR2;

[19] and calculating a maximum value, minimum value, mean value, and standard deviation of each band, and setting the threshold value according to the mean value and standard deviation value.

[20] Optionally, the threshold value of the lodging tree trunk sample point is BG2 and

BR2, being mean value +1.5 times of the standard deviation, RG2 is mean value +1 times of the standard deviation, and all other bands are mean value +2 times of the standard deviation.

[21] Optionally, segmenting the pre-processed lodging tree image information, and performing lodging tree acquisition processing on segmented lodging tree image 2

BL-5615 information at the same time based on the threshold value of the lodging tree trunk LU503402 sample point comprise:

[22] edge detection, using Canny algorithm to perform edge detection on the pre-processed lodging tree image information;

[23] color filtering, performing color filtering on the pre-processed lodging tree image information based on the threshold value to segment the lodging tree image information into 4 parts, using a multi-thread programming technology to realize that 4 threads traverse picture elements at the same time wherein pixel points within the threshold value will be set to be white, and remaining pixel points outside the threshold value will be set to be black, and finally, combining a filtering result with an edge detection result to obtain first lodging tree image information;

[24] edge line detection, wherein every three pixel points are traversed in the first lodging tree image information; if the pixel points have a point with the same characteristic value in a certain direction, the point is added to a line; finally, the line and coordinates of a starting point and an end point are returned to to obtain an edge line of the lodging tree image information;

[25] and judging that the edge lines are parallel, wherein if a slope of an acquired edge line is the same as the slope of an included angle of a coordinate axis, then the edge lines are considered to be parallel; if the edge lines are parallel and a distance between two lines is less than 145cm, then the parallel lines and a distance size are stored in a data set, and at the same time, an upper end and a lower end of the parallel lines will be closed to form a polygon, namely, the lodging tree in the region.

[26] Optionally, performing trunk judgment and screening on an acquired lodging tree in a region, and calculating a volume of a screened lodging tree comprise:

[27] classifying trunks according to an area of the polygon, and counting the polygon with an area of 2500cm?~40000cm? ;

[28] and calculating the volume of the screened lodging tree according to

DBH-volume conversion.

[29] A device for evaluating loss of urban tree ecosystem caused by a typhoon is disclosed, wherein the device is used to implement a method for evaluating loss of urban tree ecosystem caused by a typhoon, comprising:

[30] an image acquisition module: acquiring lodging tree image information in a region;

[31] an image processing module: pre-processing acquired information to obtain pre-processed lodging tree image information; acquiring a lodging tree trunk sample point from pre-processed lodging tree image information, performing eigenvalue calculation on an acquired lodging tree trunk sample point, and setting a threshold value of the lodging tree trunk sample point according to the calculated mean value and standard deviation value; and acquiring the lodging tree in the region from pre-processed lodging tree image information based on the threshold value of the lodging tree trunk sample point;

[32] and an image calculation module: performing trunk judgment and screening on an acquired lodging tree in the region, calculating a volume of a screened lodging tree, and calculating a total mass of the lodging tree in the region based on the volume of the 3

BL-5615 screened lodging tree and a mean density of a basic wood in the region; and calculating LU503402 a carbon sequestration value and an oxygen release value lost by the lodging tree in the region based on the total mass of the lodging trees in the region.

[33] It can be seen from the above that one or more embodiments of the present description provide a method for evaluating the loss of an urban tree ecosystem caused by a typhoon. Firstly, lodging tree image information in a region is acquired, and then a lodging tree trunk sample point is acquired from the acquired information, and a threshold value of the lodging tree trunk sample point is calculated; the acquired information is firstly segmented, and then the segmented information is synchronously processed based on the threshold value to calculate the volume of the screened lodging tree; the total mass of the lodging trees in the region is calculated based on the volume of the screened lodging tree. Based on the total mass of lodging trees in the region, the carbon sequestration value and an oxygen release value lost by the lodging tree in the region are calculated, and that is the loss of the urban tree ecosystem caused by a typhoon. In the process of information processing, firstly, the acquired information is segmented, and then the segmented information is synchronously processed, so as to reduce the difficulty of information processing and improve the speed of information processing, thereby improving the speed of the evaluation method. At the same time, the combination of high-division frequency conversion aerial photography technology is conducive to improving the definition of the acquired information and improving the accuracy of the evaluation method.

BRIEF DESCRIPTION OF THE DRAWINGS

[34] In order to more clearly illustrate one or more embodiments of the present description or technical solutions of the prior art, the drawings that need to be used in the embodiments are briefly described below. It is obvious that the drawings in the following description are only one or more embodiments of the description, and that those skilled in the art can obtain other drawings from these drawings without involving any inventive effort.

[35] Fig. 1 is a process diagram of an evaluation method according to one or more embodiments of the present description;

[36] Fig. 2 is a block diagram of an internal structure of an evaluation apparatus according to one or more embodiments of the present description.

DETAILED DESCRIPTION OF THE EMBODIMENTS

[37] In order to make the objects, technical solutions, and advantages of the present disclosure clearer, the present disclosure will be further described in detail below with reference to specific embodiments.

[38] In order to solve the problem of low evaluation speed and accuracy of the method for evaluating the loss of urban tree ecosystem by a typhoon in the prior art, one or more embodiments of the present description provide a method for evaluating the loss of urban tree ecosystem by a typhoon, comprising:

[39] acquiring lodging tree image information in a region, and pre-processing the acquired information to obtain pre-processed lodging tree image information; 4

BL-5615

[40] acquiring a lodging tree trunk sample point from the pre-processed lodging tree LU503402 image information, performing eigenvalue calculation on the acquired lodging tree trunk sample point, and setting a threshold value of the lodging tree trunk sample point according to the calculated mean value and standard deviation value;

[41] segmenting the pre-processed lodging tree image information, and performing lodging tree acquisition processing on the segmented lodging tree image information at the same time based on the threshold value of the lodging tree trunk sample point;

[42] performing trunk judgment and screening on the acquired lodging tree in the region, and calculating the volume of the screened lodging tree;

[43] calculating the total mass of the lodging trees in the region based on the volume of the screened lodging tree and the mean density of the basic wood in the region;

[44] and based on the total mass of lodging trees in the region, calculating the carbon sequestration value and oxygen release value lost by the lodging tree in the area, and that being the loss of urban tree ecosystem caused by a typhoon.

[45] In the process of information processing, firstly, the acquired information is segmented, and then the segmented information is synchronously processed, so as to reduce the difficulty of information processing and improve the speed of information processing, thereby improving the speed of the evaluation method. At the same time, the combination of high-division frequency conversion aerial photography technology is conducive to improving the definition of the acquired information and improving the accuracy of the evaluation method.

[46] Meanwhile, one or more embodiments of the present description also provide a device for evaluating the loss of an urban tree ecosystem caused by a typhoon, the device being used to implement a method for evaluating the loss of an urban tree ecosystem caused by a typhoon, comprising:

[47] image acquisition module: acquiring lodging tree image information in a region;

[48] image processing module: pre-processing the acquired information to obtain pre-processed lodging tree image information; acquiring a lodging tree trunk sample point from the pre-processed lodging tree image information, performing eigenvalue calculation on the acquired lodging tree trunk sample point, and setting a threshold value of the lodging tree trunk sample point according to the calculated mean value and standard deviation value; and acquiring the lodging tree in the region from pre-processed lodging tree image information based on the threshold value of the lodging tree trunk sample point;

[49] and image calculation module: performing trunk judgment and screening on the acquired lodging tree in the region, calculating the volume of the screened lodging tree, and calculating the total mass of the lodging trees in the region based on the volume of the screened lodging tree and the mean density of the basic wood in the region; calculating the carbon sequestration value and oxygen release value lost by the lodging tree in the region based on the total mass of the lodging trees in the region.

[50] It needs to be noted that the method according to one or more embodiments of the present description may be performed by a single apparatus, such as a computer or server, etc. The method of the present embodiment can also be applied to a distributed scenario to be completed by multiple apparatuses cooperating with each other. In the 5

BL-5615 case of such a distributed scenario, one apparatus of multiple apparatuses may only LU503402 execute one or more steps of the method according to one or more embodiments of the present description, and multiple apparatuses may interact with each other to complete the method.

[51] Specifically, one or more embodiments of the present description provide a method for evaluating the loss of urban tree ecosystem caused by a typhoon, the flow of which is as shown in Fig. 1, comprising the following steps.

[52] Step 101: acquiring lodging tree image information in a region, and pre-processing the acquired information to obtain pre-processed lodging tree image information.

[53] In an embodiment, step 101 may comprise the planning of an aerial photography region, and selecting a region within a range of 1-2km from the center of a typhoon the second day after the typhoon passes;

[54] as to the lodging tree image information in the region, an unmanned aerial vehicle is utilized to perform aerial photography in the region;

[55] the acquired information is pre-processed, and an aerial photography image in the region with a resolution of 8-10cm is acquired through image correction and splicing.

[56] For example, Jimei Street is selected as the studied region. Jimei Street is located in Jimei District of Xiamen, Fujian Province. The terrain of Jimei Street is low, and the topography is low on both sides and high in the middle. It belongs to the subtropical oceanic monsoon climate. The mean annual precipitation is about 1200mm. The green space is well configured, the area is about 0.87km°, and the fraction of coverage is about 24.2%. The typhoon “Meranti” landed in Xiamen on September 23, 2019, and the maximum wind speed of the typhoon center was 50m/s. The next day after the typhoon “Meranti” passed, a team was quickly organized to use the eBee unmanned aerial vehicle to carry out aerial photography. The aerial photography image of the studied region with the resolution of 8cm was acquired through post-image-correction-and-splicing.

[57] Step 102: acquiring a lodging tree trunk sample point from the pre-processed lodging tree image information, performing eigenvalue calculation on the acquired lodging tree trunk sample point, and setting a threshold value of the lodging tree trunk sample point according to the calculated mean value and standard deviation value.

[58] In an embodiment, step 102 may comprise selecting a sample point at a trunk position of a lodging tree in the pre-processed lodging tree image information;

[59] acquiring color information for all sample points, including any one or more of the following: RGB, HSL, YMC, and the value of the band combination, including any one or more of the following: combining RG1, BG1, BR1, RG2, BG2, BR2;

[60] and calculating the maximum value, minimum value, mean value, and standard deviation of each band, and setting the threshold value according to the mean value and standard deviation value.

[61] For example, in the pre-processed lodging tree image information, selecting a sample point at the lodging tree trunk position; and acquiring the values of RGB, HLS,

YMC and band combinations RG1, BG1, BR1, RG2, BG2, and BR2 of all the sample 6

BL-5615 points, calculating the maximum value, minimum value, mean value, and standard LU503402 deviation of each band, and setting a threshold value according to the mean value and standard deviation value. In this study, the threshold values of BG2 and BR2 are the mean value +1.5 times of the standard deviation, the threshold value of RG2 is the mean value +1 times of the standard deviation, and the threshold values of the remaining bands are the mean value £2 times of the standard deviation. The range of each band value of these sample points and the calculation result range of each band combination are used as the threshold value to prepare for the color filtering step of lodging tree detection.

[62] Segmenting the pre-processed lodging tree image information, and performing lodging tree acquisition processing on the segmented lodging tree image information at the same time based on the threshold value of the lodging tree trunk sample point. In an embodiment, the step may include:

[63] Step 103: segmenting the pre-processed lodging tree image information.

[64] Step 104: using the Canny algorithm to perform edge detection on the pre-processed lodging tree image information.

[65] Step 105: performing lodging tree acquisition processing on the segmented lodging tree image information at the same time based on the threshold value of the lodging tree trunk sample point.

[66] For example, the pre-processed lodging tree image information is segmented into four parts, and the Canny algorithm is used to perform edge detection on the pre-processed lodging tree image information, Canny algorithm is not easy to be disturbed by noises, and edge detection is clear, especially for weak edge detection effect. Color filtering is performed on the pre-processed lodging tree image information based on the threshold value, and a multi-thread programming technology is used to realize that four threads traverse picture elements at the same time. Pixel points within the threshold value will be set to be white, and the remaining pixel points outside the threshold value will be set to be black. Finally, the filtering result and the edge detection result are combined to obtain the first lodging tree image information.

[67] According to the edge line detection, every three pixel points are traversed in the first lodging tree image information. If the pixel points have a point with the same characteristic value in a certain direction, the point is added to the line, and finally, the line and the coordinates of the starting point and the end point are returned to to obtain an edge line of the lodging tree image information.

[68] Judging that the edge lines are parallel. If the slope of the acquired edge line is the same as the slope of the included angle of the coordinate axis, then the edge lines are considered to be parallel; if the edge lines are parallel and the distance (diameter at breast height) between the two lines is less than 145cm (equivalent to 18 picture elements in an aerial photograph with a spatial resolution of 8cm), then the parallel lines and the distance (diameter at breast height) size are stored in the data set, and at the same time, the upper end and lower end of the parallel lines will be closed to form a polygon, namely, the lodging tree in the region.

[69] Step 106: performing trunk judgment and screening on the acquired lodging tree in the region, and calculating the volume of the screened lodging tree. 7

BL-5615

[70] In an embodiment, for example, step 106 may comprise classifying the trunks LU503402 according to the areas of the polygons. Only the polygons with an area between 2500cm’ (an area of about 38 picture elements with a resolution of 8.1cm) and 40000cm’? (an area of about 614 picture elements with a resolution of 8.1cm) are counted, and the remaining ones with an area less than 2500em° are classified as secondary tree trunks, which are not considered.

[71] According to the DBH-volume conversion, the DBH of the lodging tree is converted to obtain the occupied land volume. Since a considerable part of the occupied land volume is air, the volume of the screened lodging tree is calculated assuming that the air accounts for 50% and the volumes of the trunk and branch of the lodging tree are 50%.

[72] Step 107: calculating the total mass of the lodging trees in the region based on the volume of the screened lodging trees and the mean density of the basic wood in the region.

[73] In an embodiment, for example, the total mass of the lodging trees is estimated based on the volume of the lodging trees and the mean density of the basic wood of

Xiamen.

[74] Step 108: based on the total mass of lodging trees in the region, calculating the carbon sequestration value and oxygen release value lost by lodging trees in the region, which is the loss of the urban tree ecosystem caused by a typhoon.

[75] In an embodiment, for example, studies have shown that plants can fix 1.63g of carbon dioxide and release 1.20g of oxygen by producing per 1.00g of dry matter. By using the Swedish tax rate of USD 40.94. t', in which the exchange rate of USD to

RMB is 1:6.6), the carbon sequestration value lost by the lodging tree is calculated. The industrial oxygen production method (0.4 RMB. kg“) is used to calculate the value of the oxygen released by the lodging tree. That is the loss of the urban tree ecosystem caused by the typhoon.

[76] In an embodiment, for example, (1) the steps of the validity check of the lodging tree sample point were as follows: the trunk sample points of 67 lodging trees in the aerial image were uniformly selected as the input data of the algorithm, the eigenvalues of the sample points were selected as the judging conditions of the lodging trees in the studied region, and the validity was verified. Calculating the RGB, HLS, YMC band values and band combinations of RG1, BG1, BR1, RG2, BG2, and BR2 of each sample point, and calculating to obtain the threshold value and confidence level of each band (the proportion of the band value of each sample point falling within the threshold value) according to the mean value and standard deviation, the results are as shown in Table 1.

The confidence level was high for all bands except the RG2 band where the confidence level was less than 80%, indicating that the validity of the sample was verified and color filtering could be performed.

[77] Table 1 The band statistical results of lodging tree sample points

Mean Standard Threshold Confidence

Band 2 Formula value deviation value level 116.38~

Red 158.31 20.96 200.23 98.46% 8

BL-5615 -_______________ iloii0- ________ 10— LU503402

Green 148,55 22.23 : 100% Directly acquiring a 193.01 picture 82.21~ 0

Blue 123.95 20.87 165.70 95.38%

Hue 42.56 0.64 23.29—61.84 96.92% tne) = Yo) 100.33 ~ M +m . 0 L-

Luminance 141.18 20.42 182.02 98.46% ra . _ CM +m)

Saturation 41.51 11.92 17.68~6534 96.92% SE M TM) 221.32— 0 =R+

Yellow 306.86 42.77 392.40 100% Y=R+G 200.62~ 0 =R+

Magenta 282.26 4082 363.91 95.38% M=R +B 188.32~ 0 =

Cyan 272.51 42.09 356.69 98.46% C=G+B

RG1 = (R-G)/(R

RG1 0.03 0.02 0.00~0.08 92.31% 1G) ( Xe

BG1 = (B-G)(B

BG1 0.09 0.03 0.02—0.16 95.38% ( N +G)

BR1 = (B-R)/(B +

BR1 0.12 0.04 0.05~0.20 93.85% R) ( M

RG2 9.94 5.82 4.12~15.76 66.15% RG2=R-G

BG2 24.60 9.36 10.55~38.65 89.23% BG2 = B-G

BR2 34.35 9.16 20.62~48.09 84.62% BR2 = B-R

[78] (2) Lodging trees and the loss of carbon sequestration and oxygen release value thereof. A total of 1510 lodging trees were detected. The diameter at breast height, volume, and amount of loss of carbon sequestration and oxygen release value are shown in Table 2. Combining 1: 1000 digital topographic map of Jimei district in Xiamen with

ArcGIS 10.2 software, vector layers of roads, parks, and schools were created, and lodging trees within the range of each polygon layer were extracted through the road buffer zone, park, and school polygons. It can be concluded that the descending order of the source of lodging trees is successively school (50%)>urban living area (25%)> roads (15%)>parks (10%). The descending order of the loss of carbon sequestration and oxygen release value is successively urban living area (57%) >schools (25%)> roads (11%) > parks (7%).

[79] Table2 The statistical result of lodging trees

Lodging The number Lodging tree (Carbon Oxygen The total value of tree source of lodging volume (cm?) sequestration release value carbon trees value (RMB) (RMB) sequestration and oxygen release (RMB)

Roads 221 4969417500 954270.19 1039999.69 1994269.88 9

BL-5615

Schools 760 15853256500 3044278.31 1741673.25 4785951.56 LUS03402

Parks 146 3362257500 645649.53 703653.25 1349302.78

Urban 383 26957325000 5176576.81 5641628.98 10818205.79 living area

Total 1510 51142256500 9820774.84 9126955.17 18947730.01

[80] Based on the above-mentioned method, one or more embodiments of the present description also provide a device for evaluating the loss of the urban tree ecosystem caused by a typhoon. The device is used for implementing the method for evaluating the loss of the urban tree ecosystem caused by a typhoon. The block diagram of the internal structure is as shown in Fig. 2, comprising: an image acquisition module 201, an image processing module 202, and an image calculation module 203.

[81] The image acquisition module 201: in the planning of aerial photography region, the region within a range of 1~2km from the center of the typhoon on the second day after the typhoon passes is selected; as to the lodging tree image information in the region, an unmanned aerial vehicle is utilized to perform aerial photography in the region.

[82] The image processing module 202: the acquired information is pre-processed; an aerial image in a region with a resolution of 8-10cm is acquired through image correction and splicing such that the pre-processed lodging tree image information is obtained; a lodging tree trunk sample point is acquired from the pre-processed lodging tree image information, eigenvalue calculation is performed on the acquired lodging tree trunk sample point, and a threshold value of the lodging tree trunk sample point is set according to the calculated mean value and standard deviation value; and the lodging tree in the region is acquired from pre-processed lodging tree image information based on the threshold value of the lodging tree trunk sample point.

[83] The image calculation module 203: performing trunk judgment and screening on the acquired lodging tree in the region, calculating the volume of the screened lodging tree, and calculating the total mass of the lodging trees in the region based on the volume of the screened lodging tree and the mean density of the basic wood in the region; and calculating the carbon sequestration value and oxygen release value lost by the lodging tree in the region based on the total mass of the lodging trees in the region.

[84] The foregoing describes particular embodiments of the present description.

Other embodiments are within the scope of the appended claims. In some cases, the acts or steps recited in the claims may be executed out of the order in the embodiments and still achieve the desired result. Additionally, the process depicted in the drawings does not necessarily require the particular order or sequential order shown to achieve the desired result. Multi-tasking and parallel processing are also possible or may be advantageous in some implementation modes.

[85] For the convenience of description, the above device is described in terms of functions divided into various modules. Of course, when implementing one or more embodiments of the present description, the functions of each module may be implemented in the same one or more software and/or hardware.

[86] The device of the above-described embodiments is used to implement the corresponding method of the foregoing embodiments, and has the advantageous effects 10

BL-5615 . . . . ; ; ; ; LU503402 of the corresponding method embodiments, which will not be described in detail herein.

[87] The one or more embodiments of the description are intended to cover all such alternatives, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omission, modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of the present description should be included within the scope of the present disclosure. 11

Claims

BL-5615 CLAIMS LU503402

1. A method for evaluating a loss of an urban tree ecosystem caused by a typhoon, characterized by comprising: acquiring lodging tree image information in a region, and pre-processing acquired information to obtain pre-processed lodging tree image information; acquiring a lodging tree trunk sample point from pre-processed lodging tree image information, performing eigenvalue calculation on an acquired lodging tree trunk sample point, and setting a threshold value of the lodging tree trunk sample point according to a calculated mean value and standard deviation value; segmenting the pre-processed lodging tree image information, and performing lodging tree acquisition processing on segmented lodging tree image information at the same time based on the threshold value of the lodging tree trunk sample point; performing trunk judgment and screening on an acquired lodging tree in a region, and calculating a volume of a screened lodging tree; calculating a total mass of lodging trees in the region based on the volume of the screened lodging trees and a mean density of a basic wood in the region; and based on the total mass of lodging trees in the region, calculating a carbon sequestration value and an oxygen release value lost by the lodging tree in the region, and that being the loss of the urban tree ecosystem caused by a typhoon.

2. The method for evaluating a loss of an urban tree ecosystem caused by a typhoon according to claim 1, characterized in that the acquiring lodging tree image information in a region, and pre-processing acquired information comprise: in a planning of aerial photography region, selecting the region within a range of 1—2km from a center of the typhoon on a second day after the typhoon passes; as to the lodging tree image information in the region, utilizing an unmanned aerial vehicle to perform aerial photography in the region; and pre-processing the acquired information, and acquiring an aerial photography image in the region with a resolution of 8-10cm through image correction and splicing.

3. The method for evaluating a loss of an urban tree ecosystem caused by a typhoon according to claim 1, characterized in that the acquiring a lodging tree trunk sample point from pre-processed lodging tree image information, performing eigenvalue calculation on an acquired lodging tree trunk sample point, and setting a threshold value of the lodging tree trunk sample point according to a calculated mean value and standard deviation value comprise: selecting a sample point at a trunk position of a lodging tree in the pre-processed lodging tree image information; acquiring color information for all sample points, including any one or more of the following: RGB, HSL, YMC, and a value of a band combination, including any one or more of the following: combining RG1, BG1, BR1, RG2, BG2, BR2; and calculating a maximum value, minimum value, mean value, and standard deviation of each band, and setting the threshold value according to the mean value and standard deviation value.

4. The method for evaluating a loss of an urban tree ecosystem caused by a 12

BL-5615 typhoon according to claim 3, characterized in that the threshold value is BG2 and BR2, LU503402 being mean value +1.5 times of the standard deviation, RG2 is mean value +1 times of the standard deviation, and all other bands are mean value +2 times of the standard deviation.

5. The method for evaluating a loss of an urban tree ecosystem caused by a typhoon according to claim 1, characterized in that the segmenting the pre-processed lodging tree image information, and performing lodging tree acquisition processing on segmented lodging tree image information at the same time based on the threshold value of the lodging tree trunk sample point comprise: edge detection, using Canny algorithm to perform edge detection on the pre-processed lodging tree image information; color filtering, performing color filtering on the pre-processed lodging tree image information based on the threshold value to segment the lodging tree image information into 4 parts, using a multi-thread programming technology to realize that 4 threads traverse picture elements at the same time wherein pixel points within the threshold value will be set to be white, and remaining pixel points outside the threshold value will be set to be black, and finally, combining a filtering result with an edge detection result to obtain first lodging tree image information; edge line detection, wherein every three pixel points are traversed in the first lodging tree image information; if the pixel points have a point with a same characteristic value in a certain direction, the point is added to a line; finally, the line and coordinates of a starting point and an end point are returned to to obtain an edge line of the lodging tree image information; and judging that the edge lines are parallel, wherein if a slope of an acquired edge line is the same as the slope of an included angle of a coordinate axis, then the edge lines are considered to be parallel; if the edge lines are parallel and a distance between two lines is less than 145cm, then the parallel lines and a distance size are stored in a data set, and at the same time, an upper end and a lower end of the parallel lines will be closed to form a polygon, namely, the lodging tree in the region.

6. The method for evaluating a loss of an urban tree ecosystem caused by a typhoon according to claim 1, characterized in that performing trunk judgment and screening on an acquired lodging tree in a region, and calculating a volume of a screened lodging tree comprise: classifying trunks according to an area of the polygon, and counting the polygon with an area of 2500cm?>~40000cm? ; and calculating the volume of the screened lodging tree according to DBH-volume conversion.

7. A device for evaluating a loss of an urban tree ecosystem caused by a typhoon, characterized in that the device is used to implement a method for evaluating the loss of the urban tree ecosystem caused by a typhoon, comprising: an image acquisition module: acquiring lodging tree image information in a region, an image processing module: pre-processing acquired information to obtain pre-processed lodging tree image information; acquiring a lodging tree trunk sample point from pre-processed lodging tree image information, performing eigenvalue 13

BL-5615 calculation on an acquired lodging tree trunk sample point, and setting a threshold value LU503402 of the lodging tree trunk sample point according to a calculated mean value and standard deviation value; and acquiring the lodging tree in the region from pre-processed lodging tree image information based on the threshold value of the lodging tree trunk sample point; and an image calculation module: performing trunk judgment and screening on an acquired lodging tree in the region, calculating a volume of a screened lodging tree, and calculating a total mass of the lodging tree in the region based on the volume of the screened lodging tree and a mean density of a basic wood in the region; and calculating a carbon sequestration value and an oxygen release value lost by the lodging tree in the region based on the total mass of the lodging trees in the region.

14