CN111652893A - Method and equipment for evaluating loss of typhoon to urban tree ecosystem - Google Patents
Method and equipment for evaluating loss of typhoon to urban tree ecosystem Download PDFInfo
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Abstract
One or more embodiments of the present disclosure provide a method and an apparatus for evaluating a loss of a typhoon to an urban tree ecosystem, where image information of a lodging tree in an area is obtained, a lodging tree trunk sample point is obtained from the obtained information, a threshold of the lodging tree trunk sample point is calculated, the obtained information is segmented, the segmented information is processed synchronously based on the threshold, a volume of the selected lodging tree is calculated, and a total mass of the lodging tree in the area is calculated based on the volume of the selected lodging tree; the carbon fixation value and the oxygen release value of the loss of the lodging trees in the area are calculated based on the total mass of the lodging trees in the area, in the information processing process, the obtained information is segmented, the segmented information is synchronously fed and discharged, the information processing difficulty is reduced, and the information processing speed is increased, so that the speed of the evaluation method is increased, and meanwhile, the high-resolution frequency conversion aerial photography technology is combined, so that the information obtaining definition is improved, and the accuracy of the evaluation method is improved.
Description
Technical Field
One or more embodiments of the present disclosure relate to the technical field of methods for evaluating the loss of typhoon to an urban tree ecosystem, and in particular, to a method and an apparatus for evaluating the loss of typhoon to the urban tree ecosystem.
Background
Under the influence of global climate change, typhoons frequently land in coastal cities, and have serious influence on social and economic systems, built-up area systems and natural ecosystems. Typhoon not only causes huge economic loss, but also causes non-negligible influence on the natural ecosystem of coastal cities. The ultrastrong typhoon "weinstat" in 2012 caused 11.5 million trees in Shenzhen city to be damaged. In 2016, the super-strong typhoon Molandi lands on the city of mansion, which causes direct economic loss of 102 million yuan and 90% of trees in the whole city are damaged. The urban green land is an important component of an urban ecological system, and has the functions of maintaining the carbon-oxygen balance of urban atmosphere, adjusting urban microclimate, eliminating atmospheric pollution, sterilizing and reducing noise, and the frequent occurrence of typhoons makes the ecological service functions in a high-risk state.
At present, research contents related to typhoon disaster damage are concentrated on the aspects of typhoon disaster risk assessment, direct and indirect economic losses caused by typhoons, evaluation of the relationship between typhoon disasters and disaster-causing factors and evaluation of urban forest ecological systems caused by typhoons. The technical means for evaluating the actual loss caused by typhoon disasters are diversified, the remote sensing technology is combined with the GIS to be the most common method, the remote sensing technology is used for analyzing the change of indexes (NDVI and the like) before and after the disasters to quantitatively evaluate the loss of each aspect caused by typhoon in space, but the method has certain limitation, the method is difficult to obtain a high-quality remote sensing image without cloud shielding in time after the disasters, and the value of various natural ecosystems is mainly calculated according to a unit area ecosystem service value scale, so that the evaluation method is low in speed and precision.
Disclosure of Invention
In view of the above, one or more embodiments of the present disclosure are directed to a method and an apparatus for evaluating a loss of an urban tree ecosystem caused by typhoon, so as to solve the problem of low speed and accuracy of an evaluation method in the prior art.
In view of the above, one or more embodiments of the present disclosure provide a method for assessing typhoon loss to an urban tree ecosystem, comprising:
acquiring lodging tree image information in the area, and preprocessing the acquired information to obtain preprocessed lodging tree image information;
acquiring lodging tree trunk sample points from the preprocessed lodging tree image information, performing characteristic value calculation on the acquired lodging tree trunk sample points, and setting a threshold value of the lodging tree trunk sample points according to the calculated average value and standard deviation value;
dividing the preprocessed image information of the lodging trees, and acquiring lodging trees from the divided image information of the lodging trees based on the threshold values of the trunk sample points of the lodging trees;
judging and screening trunks of the lodging trees in the obtained area, and calculating the volume of the screened lodging trees;
calculating the total mass of the lodging trees in the area based on the screened lodging tree volume and the average density of the basic wood in the area;
and calculating the carbon fixation value and the oxygen release value of the loss of the lodging trees in the region based on the total mass of the lodging trees in the region, namely the loss of the typhoon to the urban tree ecosystem.
Optionally, the acquiring image information of the lodging tree in the region and preprocessing the acquired information include:
planning an aerial photography area, namely selecting an area which is within a range of 1-2 km away from the center of typhoon the next day after the typhoon passes through the area;
utilizing an unmanned aerial vehicle to carry out aerial photography in the region according to the image information of the lodging trees in the region;
preprocessing the acquired information, and acquiring an aerial image in an area with the resolution ratio of 8-10 cm through image correction and splicing.
Optionally, obtain lodging tree trunk sample point in the lodging tree image information after follow preliminary treatment, carry out the eigenvalue to the lodging tree trunk sample point that obtains and calculate, set up the threshold value of lodging tree trunk sample point according to the value of the average value and the standard deviation that calculate, include:
selecting a sample point at the trunk position of the lodging tree in the preprocessed lodging tree image information;
acquiring color information of all sample points comprises any one or more of the following: the values of RGB, HSL, YMC, and band combinations include any one or more of: synthetic RG1, BG1, BR1, RG2, BG2, BR 2;
and calculating the maximum value, the minimum value, the average value and the standard deviation of each waveband, and setting a threshold according to the values of the average value and the standard deviation.
Optionally, the threshold values of the sample points of the lodging tree trunk are BG2 and BR2 which are mean values ± 1.5 times standard deviation, RG2 which is mean values ± 1 times standard deviation, and the rest wave bands are mean values ± 2 times standard deviation.
Optionally, cut apart the lodging tree image information after the preliminary treatment, based on the threshold value of lodging tree trunk sample point, the lodging tree image information after cutting apart simultaneously acquires the processing of lodging tree, includes:
edge detection, namely performing edge detection on the preprocessed image information of the lodging trees by adopting a Canny algorithm;
color filtering, namely performing color filtering on the preprocessed lodging tree image information based on a threshold value, dividing the lodging tree image information into 4 parts, traversing pixels by 4 threads simultaneously through a multithreading programming technology, setting the pixels within the threshold value to be white, setting the rest pixels outside the threshold value to be black, and finally merging a filtering result and an edge detection result to obtain first lodging tree image information;
detecting edge lines, traversing every 3 pixel points in the first lodging tree image information, if the pixel points have the same characteristic value in a certain direction, adding the points into the lines, and finally returning the lines, the coordinates of the starting point and the end point to obtain lodging tree image information edge lines;
and judging whether the edge lines are parallel, judging that the slope of the obtained edge lines is the same as the slope of an included angle of a coordinate axis, if the edge lines are parallel and the distance between the two lines is less than 145cm, storing the parallel lines and the distance into a data set, and closing the upper end and the lower end of the parallel lines to form a polygon, namely the lodging tree in the area.
Optionally, carry out trunk judgement and screening to the lodging tree in obtaining the region, calculate the volume of the lodging tree of selecting, include:
classifying the trunks according to the area of the polygon, wherein the statistical area is 2500cm2～40000cm2A polygon of (2);
and calculating the volume of the screened lodging tree according to the breast diameter-volume conversion.
An apparatus for assessing typhoon loss to urban tree ecosystem, the apparatus for implementing a method of assessing typhoon loss to urban tree ecosystem, comprising:
an image acquisition module: acquiring lodging tree image information in the area;
the image processing module: preprocessing the acquired information to obtain preprocessed image information of the lodging trees; acquiring lodging tree trunk sample points from the preprocessed lodging tree image information, performing characteristic value calculation on the acquired lodging tree trunk sample points, and setting a threshold value of the lodging tree trunk sample points according to the calculated average value and standard deviation value; acquiring a lodging tree in the area from the preprocessed lodging tree image information based on the threshold value of the lodging tree trunk sample point;
the image calculation module: judging and screening trunks of the lodging trees in the obtained area, calculating the volume of the screened lodging trees, and calculating the total mass of the lodging trees in the area based on the volume of the screened lodging trees and the average density of the basic wood in the area; and calculating the carbon fixation value and the oxygen release value of the lodging trees in the region based on the total mass of the lodging trees in the region.
As can be seen from the above, in the method for evaluating the loss of typhoon to the urban tree ecosystem, provided in one or more embodiments of the present specification, image information of a lodging tree in an area is obtained, then a lodging tree trunk sample point is obtained from the obtained information, a threshold of the lodging tree trunk sample point is calculated, the obtained information is segmented first, the segmented information is processed synchronously based on the threshold, the volume of the selected lodging tree is calculated, and the total mass of the lodging tree in the area is calculated based on the volume of the selected lodging tree; the method comprises the steps of calculating the carbon fixation value and the oxygen release value of the loss of the lodging trees in the area based on the total mass of the lodging trees in the area, namely the loss of typhoon to an urban tree ecosystem, in the information processing process, firstly dividing the acquired information, and then synchronously entering and exiting the divided information, so that the information processing difficulty is reduced, the information processing speed is increased, the speed of the evaluation method is increased, and meanwhile, the method is combined with a high-frequency-division variable aerial photography technology, so that the information acquisition definition is improved, and the accuracy of the evaluation method is improved.
Drawings
In order to more clearly illustrate one or more embodiments or prior art solutions of the present specification, the drawings that are needed in the description of the embodiments or prior art will be briefly described below, and it is obvious that the drawings in the following description are only one or more embodiments of the present specification, and that other drawings may be obtained by those skilled in the art without inventive effort from these drawings.
FIG. 1 is a process diagram of an evaluation method according to one or more embodiments of the disclosure;
FIG. 2 is a block diagram of an internal structure of an evaluation device according to one or more embodiments of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the present disclosure more apparent, the present disclosure is further described in detail below with reference to specific embodiments.
In order to solve the problem of low evaluation speed and precision of a method for evaluating the loss of typhoon to an urban tree ecosystem in the prior art, one or more embodiments of the present specification provide a method for evaluating the loss of typhoon to the urban tree ecosystem, including:
acquiring lodging tree image information in the area, and preprocessing the acquired information to obtain preprocessed lodging tree image information;
acquiring lodging tree trunk sample points from the preprocessed lodging tree image information, performing characteristic value calculation on the acquired lodging tree trunk sample points, and setting a threshold value of the lodging tree trunk sample points according to the calculated average value and standard deviation value;
dividing the preprocessed image information of the lodging trees, and acquiring lodging trees from the divided image information of the lodging trees based on the threshold values of the trunk sample points of the lodging trees;
judging and screening trunks of the lodging trees in the obtained area, and calculating the volume of the screened lodging trees;
calculating the total mass of the lodging trees in the area based on the screened lodging tree volume and the average density of the basic wood in the area;
and calculating the carbon fixation value and the oxygen release value of the loss of the lodging trees in the region based on the total mass of the lodging trees in the region, namely the loss of the typhoon to the urban tree ecosystem.
In the process of information processing, firstly, the acquired information is segmented, and then the segmented information is synchronously processed in and out, so that the difficulty of information processing is reduced, the speed of the information processing is increased, the speed of the evaluation method is increased, and meanwhile, the definition of the acquired information is favorably improved and the accuracy of the evaluation method is improved by combining the high-resolution frequency conversion aerial photography technology.
Meanwhile, one or more embodiments of the present specification further provide an apparatus for evaluating a loss of typhoon to an urban tree ecosystem, the apparatus being used for implementing a method for evaluating a loss of typhoon to an urban tree ecosystem, the method including:
an image acquisition module: acquiring lodging tree image information in the area;
the image processing module: preprocessing the acquired information to obtain preprocessed image information of the lodging trees; acquiring lodging tree trunk sample points from the preprocessed lodging tree image information, performing characteristic value calculation on the acquired lodging tree trunk sample points, and setting a threshold value of the lodging tree trunk sample points according to the calculated average value and standard deviation value; acquiring a lodging tree in the area from the preprocessed lodging tree image information based on the threshold value of the lodging tree trunk sample point;
the image calculation module: judging and screening trunks of the lodging trees in the obtained area, calculating the volume of the screened lodging trees, and calculating the total mass of the lodging trees in the area based on the volume of the screened lodging trees and the average density of the basic wood in the area; and calculating the carbon fixation value and the oxygen release value of the lodging trees in the region based on the total mass of the lodging trees in the region.
It should be noted that the method of one or more embodiments of the present disclosure may be performed by a single device, such as a computer or server. The method of the embodiment can also be applied to a distributed scene and completed by the mutual cooperation of a plurality of devices. In such a distributed scenario, one of the devices may perform only one or more steps of the method of one or more embodiments of the present disclosure, and the devices may interact with each other to complete the method.
Specifically, one or more embodiments of the present disclosure provide a method for evaluating a loss of an urban tree ecosystem due to typhoon, where the flow is shown in fig. 1, and the method includes the following steps:
step 101: acquiring lodging tree image information in the region, and preprocessing the acquired information to obtain preprocessed lodging tree image information.
In one embodiment, the step 101 may include planning an aerial photography area, and selecting an area within a range of 1-2 km away from the center of the typhoon the next day after the typhoon passes through the area;
utilizing an unmanned aerial vehicle to carry out aerial photography in the region according to the image information of the lodging trees in the region;
preprocessing the acquired information, and acquiring an aerial image in an area with the resolution ratio of 8-10 cm through image correction and splicing.
For example, the research area selects street of beauty collection, street of beauty collectionThe street is located in a United states region of buildings and cities in Fujian province, the topography of the United states street is low, the terrain is high in the middle and low on two sides, the street belongs to subtropical marine monsoon climate, the annual average rainfall is about 1200mm, the green space configuration is perfect, and the area is about 0.87km2The coverage was about 24.2%. And (3) landing the typhoon 'Molandi' on a mansion gate in 2019, 9 and 23, rapidly organizing a team at the maximum wind speed of a typhoon center of 50m/s on the next day after the typhoon 'Molandi' passes the border, and carrying out aerial photography by using an eBee unmanned aerial vehicle. And acquiring an aerial image of the research area with the resolution of 8cm by later-stage image correction and splicing.
Step 102: acquiring lodging tree trunk sample points from the preprocessed lodging tree image information, performing characteristic value calculation on the acquired lodging tree trunk sample points, and setting the threshold of the lodging tree trunk sample points according to the calculated average value and standard deviation value.
In one embodiment, step 102 may include selecting a sample point at a lodging tree trunk location in the preprocessed lodging tree imagery information;
acquiring color information of all sample points comprises any one or more of the following: the values of RGB, HSL, YMC, and band combinations include any one or more of: synthetic RG1, BG1, BR1, RG2, BG2, BR 2;
and calculating the maximum value, the minimum value, the average value and the standard deviation of each waveband, and setting a threshold according to the values of the average value and the standard deviation.
For example, in the preprocessed image information of the lodging tree, a sample point is selected at the trunk position of the lodging tree; the method comprises the steps of obtaining values of RGB, HLS and YMC of all sample points and wave band combinations RG1, BG1, BR1, RG2, BG2 and BR2, calculating a maximum value, a minimum value, an average value and a standard deviation of each wave band, setting a threshold according to the values of the average value and the standard deviation, setting the thresholds of BG2 and BR2 in the research to be the average value +/-1.5 times of the standard deviation, setting the thresholds of RG2 to be the average +/-1 times of the standard deviation, setting the thresholds of the rest wave bands to be the average +/-2 times of the standard deviation, and taking the wave band value ranges and the wave band combination calculation result ranges of the sample points as the thresholds to prepare for a color filtering step of lodging tree detection.
Segmenting the preprocessed image information of the lodging tree, and acquiring the lodging tree based on the threshold value of the trunk sample point of the lodging tree and the segmented image information of the lodging tree, wherein in one embodiment, the step can comprise:
step 103: and segmenting the preprocessed lodging tree image information.
Step 104: and performing edge detection on the preprocessed lodging tree image information by adopting a Canny algorithm.
Step 105: and based on the threshold value of the trunk sample point of the lodging tree, simultaneously carrying out the processing of obtaining the lodging tree by the segmented lodging tree image information.
For example, dividing the preprocessed image information of the lodging tree by 4 parts, and performing edge detection on the preprocessed image information of the lodging tree by adopting a Canny algorithm; the Canny algorithm is not easily interfered by noise, the edge detection is clear, and the method has a good effect on weak edge detection. Carrying out color filtering on the preprocessed lodging tree image information based on a threshold value, traversing pixels by 4 threads simultaneously through a multithreading programming technology, setting the pixels within the threshold value to be white, setting the other pixels outside the threshold value to be black, and finally merging the filtering result and the edge detection result to obtain first lodging tree image information;
detecting edge lines, traversing every 3 pixel points in the first lodging tree image information, if the pixel points have the same characteristic value in a certain direction, adding the points into the lines, and finally returning the lines, the coordinates of the starting point and the end point to obtain lodging tree image information edge lines;
and (3) judging the parallel of the edge lines, judging the obtained edge lines to be parallel if the slope of the obtained edge lines is the same as the slope of an included angle of a coordinate axis, if the edge lines are parallel and the distance (breast diameter) between the two lines is less than 145cm (equivalent to 18 pixels in an aerial image with 8cm spatial resolution), storing the parallel lines and the distance (breast diameter) in a data set, and closing the upper end and the lower end of the parallel lines to form a polygon, namely the lodging tree in the area.
Step 106: and (4) judging and screening trunks of the lodging trees in the acquired area, and calculating the volume of the screened lodging trees.
In one embodiment, for example, step 106 may include classifying the trunk according to the area of the polygon, with only the statistical area being 2500cm2(about 38 areas with resolution of 8.1cm size pixels) and 40000cm2(area of about 614 picture elements with resolution of 8.1 cm) and remaining area of less than 2500cm2Is classified as a second-level tree trunk and is not considered;
and converting the breast diameter of the lodging tree into the occupied land volume according to the breast diameter-volume conversion, and calculating the volume of the screened lodging tree on the assumption that the occupied land volume is a considerable part of air and the air accounts for 50 percent and the volumes of the lodging tree trunk and branches account for 50 percent.
Step 107: calculating the total mass of the lodging trees in the area based on the screened lodging tree volume and the average density of the basic wood in the area;
in one embodiment, the total weight of the lodging trees is estimated based on the volume of the lodging trees and the average density of the base wood in Xiamen, for example.
Step 108: and calculating the carbon fixation value and the oxygen release value of the loss of the lodging trees in the region based on the total mass of the lodging trees in the region, namely the loss of the typhoon to the urban tree ecosystem.
In one example, studies have shown that plants can fix 1.63g of carbon dioxide and release 1.20g of oxygen per 1.00g of dry matter produced. Using Swedish tax rate of $ 40.94. t-1Wherein the carbon fixation value of lodging tree loss is calculated according to the exchange rate of exchanging the dollar into the Renminbi according to 1:6.6), and an industrial oxygen generation method (0.4 yuan.kg)-1) And calculating the value of oxygen released by the lodging trees. Namely the loss of the urban tree ecosystem caused by typhoon.
In one embodiment, for example, (1) the lodging tree sample point validity checking steps are as follows: trunk sample points of 67 lodging trees in the aerial image are uniformly selected as algorithm input data, sample point characteristic values are selected as judgment conditions of the lodging trees in the research area, and validity verification is carried out on the sample points. RGB, HLS, and YMC band values and RG1, BG1, BR1, RG2, BG2, and BR2 band combinations of the sample points are calculated, and a threshold value and a confidence (a ratio of the band value of each sample point falling within the threshold value) of each band are calculated from the average value and the standard deviation, and the results are shown in table 1. The confidence of the bands other than the RG2 band is lower than 80%, and the confidence of the other bands is higher, which indicates that the sample is valid and can be subjected to color filtering.
TABLE 1 lodging tree sample points each band statistical results
(2) 1510 lodging trees are detected together, the diameter at breast, the volume and the loss amount of the carbon-fixing oxygen-releasing value are shown in table 2, a road, a park and a school vector layer are created by combining a digital topographic map 1:1000 in the centralized and American area of the Xiamen city and ArcGIS 10.2 software, and the lodging trees in the range of each polygonal layer are extracted through a road buffer area, a park and a school polygon. The lodging tree source is obtained from high to low sequentially from school (50%) > urban living area (25%) > road (15%) > park (10%). The loss of the carbon-fixing oxygen-releasing value is sequentially from high to low in urban living areas (57%) > schools (25%) > roads (11%) > parks (7%).
TABLE 2 lodging tree statistics results
Based on the above method, one or more embodiments of the present specification further provide an apparatus for evaluating the loss of typhoon to the urban tree ecosystem, where the apparatus is used to implement the method for evaluating the loss of typhoon to the urban tree ecosystem, and an internal structural block diagram is shown in fig. 2, and includes: the image acquisition module 201, the image processing module 202 and the image calculation module 203.
The image acquisition module 201: planning an aerial photography area, namely selecting an area which is within a range of 1-2 km away from the center of typhoon the next day after the typhoon passes through the area; the image information of the lodging trees in the region is utilized to carry out aerial photography in the region by utilizing an unmanned aerial vehicle.
The image processing module 202: preprocessing the acquired information, and acquiring aerial images in an area with the resolution ratio of 8-10 cm through image correction and splicing to obtain preprocessed image information of the lodging trees; acquiring lodging tree trunk sample points from the preprocessed lodging tree image information, performing characteristic value calculation on the acquired lodging tree trunk sample points, and setting a threshold value of the lodging tree trunk sample points according to the calculated average value and standard deviation value; acquiring a lodging tree in the area from the preprocessed lodging tree image information based on the threshold value of the lodging tree trunk sample point;
the image calculation module 203: judging and screening trunks of the lodging trees in the obtained area, calculating the volume of the screened lodging trees, and calculating the total mass of the lodging trees in the area based on the volume of the screened lodging trees and the average density of the basic wood in the area; and calculating the carbon fixation value and the oxygen release value of the lodging trees in the region based on the total mass of the lodging trees in the region.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, the functionality of the modules may be implemented in the same one or more software and/or hardware implementations in implementing one or more embodiments of the present description.
The apparatus of the foregoing embodiment is used to implement the corresponding method in the foregoing embodiment, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
It is intended that the one or more embodiments of the present specification embrace all such alternatives, modifications and variations as fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements, and the like that may be made without departing from the spirit and principles of one or more embodiments of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (7)
1. A method for evaluating the loss of typhoon to an urban tree ecosystem is characterized by comprising the following steps:
acquiring lodging tree image information in the area, and preprocessing the acquired information to obtain preprocessed lodging tree image information;
acquiring lodging tree trunk sample points from the preprocessed lodging tree image information, performing characteristic value calculation on the acquired lodging tree trunk sample points, and setting a threshold value of the lodging tree trunk sample points according to the calculated average value and standard deviation value;
dividing the preprocessed image information of the lodging trees, and acquiring lodging trees from the divided image information of the lodging trees based on the threshold values of the trunk sample points of the lodging trees;
judging and screening trunks of the lodging trees in the obtained area, and calculating the volume of the screened lodging trees;
calculating the total mass of the lodging trees in the area based on the screened lodging tree volume and the average density of the basic wood in the area;
and calculating the carbon fixation value and the oxygen release value of the loss of the lodging trees in the region based on the total mass of the lodging trees in the region, namely the loss of the typhoon to the urban tree ecosystem.
2. The method of claim 1, wherein the acquiring image information of lodging trees in the area and preprocessing the acquired information comprises:
planning an aerial photography area, namely selecting an area which is within a range of 1-2 km away from the center of typhoon the next day after the typhoon passes through the area;
utilizing an unmanned aerial vehicle to carry out aerial photography in the region according to the image information of the lodging trees in the region;
preprocessing the acquired information, and acquiring an aerial image in an area with the resolution ratio of 8-10 cm through image correction and splicing.
3. The method of claim 1, wherein the step of obtaining the lodging tree trunk sample points from the preprocessed lodging tree image information, the step of calculating the characteristic values of the obtained lodging tree trunk sample points, and the step of setting the threshold values of the lodging tree trunk sample points according to the calculated average value and standard deviation values comprises the steps of:
selecting a sample point at the trunk position of the lodging tree in the preprocessed lodging tree image information;
acquiring color information of all sample points comprises any one or more of the following: the values of RGB, HSL, YMC, and band combinations include any one or more of: synthetic RG1, BG1, BR1, RG2, BG2, BR 2;
and calculating the maximum value, the minimum value, the average value and the standard deviation of each waveband, and setting a threshold according to the values of the average value and the standard deviation.
4. The method of claim 3, wherein the thresholds are BG2, BR2 mean + 1.5 standard deviations, RG2 mean + 1 standard deviations, and the rest of the bands mean + 2 standard deviations.
5. The method of claim 1, wherein the segmenting the preprocessed image information of the lodging tree, and the acquiring the lodging tree based on the threshold value of the sample point of the trunk of the lodging tree and the segmented image information of the lodging tree, comprises:
edge detection, namely performing edge detection on the preprocessed image information of the lodging trees by adopting a Canny algorithm;
color filtering, namely performing color filtering on the preprocessed lodging tree image information based on a threshold value, dividing the lodging tree image information into 4 parts, traversing pixels by 4 threads simultaneously through a multithreading programming technology, setting the pixels within the threshold value to be white, setting the rest pixels outside the threshold value to be black, and finally merging a filtering result and an edge detection result to obtain first lodging tree image information;
detecting edge lines, traversing every 3 pixel points in the first lodging tree image information, if the pixel points have the same characteristic value in a certain direction, adding the points into the lines, and finally returning the lines, the coordinates of the starting point and the end point to obtain lodging tree image information edge lines;
and judging whether the edge lines are parallel, judging that the slope of the obtained edge lines is the same as the slope of an included angle of a coordinate axis, if the edge lines are parallel and the distance between the two lines is less than 145cm, storing the parallel lines and the distance into a data set, and closing the upper end and the lower end of the parallel lines to form a polygon, namely the lodging tree in the area.
6. The method of claim 1, wherein the trunk determination and screening of the lodging trees in the acquisition area, and the calculation of the volume of the screened lodging trees comprises:
classifying the trunks according to the area of the polygon, wherein the statistical area is 2500cm2～40000cm2A polygon of (2);
and calculating the volume of the screened lodging tree according to the breast diameter-volume conversion.
7. An apparatus for assessing typhoon loss to urban tree ecosystem, the apparatus for implementing a method of assessing typhoon loss to urban tree ecosystem, comprising:
an image acquisition module: acquiring lodging tree image information in the area;
the image processing module: preprocessing the acquired information to obtain preprocessed image information of the lodging trees; acquiring lodging tree trunk sample points from the preprocessed lodging tree image information, performing characteristic value calculation on the acquired lodging tree trunk sample points, and setting a threshold value of the lodging tree trunk sample points according to the calculated average value and standard deviation value; acquiring a lodging tree in the area from the preprocessed lodging tree image information based on the threshold value of the lodging tree trunk sample point;
the image calculation module: judging and screening trunks of the lodging trees in the obtained area, calculating the volume of the screened lodging trees, and calculating the total mass of the lodging trees in the area based on the volume of the screened lodging trees and the average density of the basic wood in the area; and calculating the carbon fixation value and the oxygen release value of the lodging trees in the region based on the total mass of the lodging trees in the region.
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