CN117078033A - Typhoon disaster influence assessment method, typhoon disaster influence assessment device and computer - Google Patents
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Abstract
The invention provides a typhoon disaster influence assessment method, a typhoon disaster influence assessment device and a computer, wherein the typhoon disaster influence assessment method comprises the following steps: acquiring a remote sensing image of an evaluation area; according to the remote sensing image of the evaluation area, combining with a mangrove vector base map of the evaluation area, calculating the area of the evaluation area by using a pixel dichotomy, and constructing an area change data set of the evaluation area with long time sequence in a history period; screening historical typhoon disasters affecting the evaluation area according to the area change data set of the evaluation area; calculating the damaged area of the mangrove forest according to the historical typhoon disasters, and constructing a vulnerability curve by combining the typhoon disaster intensity; mangrove partitions and species damage were evaluated based on the vulnerability profile. The invention adopts the satellite image data with long time sequence and the historical typhoon data with long time sequence, and can quantitatively monitor the influence of mangrove typhoon disasters based on the vulnerability curve theory.
Description
Technical Field
The invention relates to the technical field of disaster risk assessment, in particular to a typhoon disaster influence assessment method, a typhoon disaster influence assessment device and a computer.
Background
In the research content of the mangrove typhoon influence, the foreign research fields are wide, including post-disaster investigation of the mangrove typhoon influence, influence factor analysis (tree species, stand structure, geographic position, typhoon intensity, age and the like) of the damage degree of the mangrove typhoon disaster, monitoring of the damage of the mangrove typhoon disaster and research of recovery. The domestic research content is mainly focused on investigation of the damage condition of mangrove after typhoon disasters and investigation of the windproof effect of mangrove species.
In research, in-situ investigation and spot monitoring over a period of time before and after typhoons occur is the most accurate method to obtain hand data of typhoons on mangrove forest damage. Satellite imaging offers the possibility of typhoon monitoring due to the development of geographic information technology and remote sensing technology, but most still requires the help of field survey data. There are also models to simulate typhoon paths, wind speeds, etc. to restore typhoon processes, but there are few reports. The indexes for measuring typhoon damage are area, density, net primary productivity, net carbon dioxide exchange amount and the like.
Most overseas monitoring the whole loss or recovery process through long-term sampling points, and domestic research on monitoring mangrove forest under the influence of typhoons is not available, and most of the research is site investigation after typhoons occur. The influence study of typhoon disasters on mangrove forests at home and abroad is concentrated on one typhoon, and the study on historical typhoon disasters with long time sequences is less. Moreover, researches on evaluating the influence of mangrove typhoon disasters through vulnerability curve theory are very visible at home and abroad.
Disclosure of Invention
The invention aims to solve the technical problem of providing a typhoon disaster influence assessment method, a typhoon disaster influence assessment device and a computer, which can quantitatively monitor the influence of mangrove typhoon disasters by adopting long-time-sequence satellite image data and long-time-sequence historical typhoon data based on a vulnerability curve theory.
In order to solve the technical problems, the technical scheme of the invention is as follows:
in a first aspect, a typhoon disaster impact assessment method includes:
acquiring a remote sensing image of an evaluation area;
according to the remote sensing image of the evaluation area, combining with a mangrove vector base map of the evaluation area, calculating the area of the evaluation area by using a pixel dichotomy, and constructing an area change data set of the evaluation area with long time sequence in a history period;
screening historical typhoon disasters affecting the evaluation area according to the area change data set of the evaluation area;
calculating the damaged area of the mangrove forest according to the historical typhoon disasters, and constructing a vulnerability curve by combining the typhoon disaster intensity;
mangrove partitions and species damage were evaluated based on the vulnerability profile.
Further, calculating the area of the evaluation area by using a pixel dichotomy, and constructing an area change data set of the evaluation area with long time sequence in a history period, including:
The mangrove vegetation coverage of unit pixel is FVC, bare soil coverage is 1-FVC, and pixel information of pure vegetation coverage is S veg The pixel information covered by the bare soil is S soil Then:
Sv＝FVC×Sveg，Ss＝(1-FVC)×Ssoil
FVC＝(S-Ssoil)/(Sveg-Ssoil)；
combining the EVI with the pixel bipartite model to obtain a vegetation coverage pixel bipartite model based on an EVI index,
wherein, EVI soil Enhanced vegetation index for bare soil, EVI veg Vegetation enhancing finger for vegetationThe number S is satellite remote sensing information of the mixed pixel, S v Is vegetation information S s S=s as bare soil information v +S s EVI is enhanced vegetation index, evi=2.5 (dNIR-dRed)/(dnir+c1dred-c2dblue+l), where d NIR Is the near infrared reflectance value corrected by the atmosphere, d Red Is the reflection value of the red wave band corrected by the atmosphere, d Blue Is the blue wave band reflection value corrected by the atmosphere, L=1, is the soil regulating parameter, parameter C 1 And C 2 6.0 and 7.5, respectively, for describing the correction of the effect of the atmosphere on the red band by the blue band.
Further, the calculation formula of the mangrove unit pixel area is as follows:
A＝FVC×900
wherein A is mangrove unit pixel area, unit is square meter, and FVC is mangrove vegetation coverage of unit pixel.
Further, calculating the area of the evaluation area by using a pixel dichotomy, and constructing an area change data set of the evaluation area with long time sequence in a history period, the method further comprises:
Generating 32-day mangrove area synthesis series data of Landsat series images;
according to the mangrove area synthesis series data, generating mangrove area year synthesis series data of Landsat series images by adopting a maximum synthesis method;
synthesizing series data according to mangrove area years of Landsat series images, and classifying and dividing according to the area of unit pixels;
and obtaining a long-time sequence mangrove forest area space distribution map in a history period according to the classification regions.
Further, according to the area change data set of the evaluation area, screening historical typhoon disasters affecting the evaluation area, including:
connecting typhoon data points according to the serial numbers of typhoon time, and recovering a typhoon outlet path;
taking the longitude and latitude of the center of the evaluation area as a center point, and taking a radius of 1/2 scale as a buffer area for analysis, wherein when a typhoon path is tangent to or intersected with the buffer area, typhoon events affect mangrove forests of the evaluation area;
based on the vertical distance from the connecting line of two adjacent points on each typhoon path to the central point of the evaluation area, the judgment is carried out by combining the correlation theorem, and the three situations can be specifically divided:
when two adjacent typhoon event points fall in the buffer zone, the judgment can be carried out according to a formula between the two points;
When two adjacent typhoons are in time points, one typhoons fall in the buffer area, and the other typhoons fall outside the buffer area, the two typhoons are judged according to a formula between the two typhoons;
when two adjacent typhoon time points are outside the buffer area, the judgment is carried out according to the cosine law.
Further, according to the area change data set of the evaluation area, screening historical typhoon disasters affecting the evaluation area, and further including:
screening out the year of typhoons in the evaluation area, using V i ＝S i -S i-1 Calculating the change area of mangrove forest every year compared with the last year in the year in which typhoons occur, wherein V i For the i-th year change area, S i Is the area of the ith year, S i-1 Is the area of the i-1 th year;
calculating the correlation between the change area of the mangrove forest and the maximum typhoon wind speed, wherein the correlation calculation formula is as follows:wherein R is a correlation coefficient, X and Y are two groups of samples respectively, the annual change area and the typhoon near-center maximum wind speed are indicated, the value range of R is (-1, 1), and N is the number of samples.
Further, calculating a mangrove forest damaged area according to the historical typhoon disasters, and constructing a vulnerability curve by combining typhoon disaster intensity, wherein the method comprises the following steps:
calculating the unit pixel area change before and after typhoons occur according to the historical typhoons, and spatially analyzing the distribution area characteristics of the mangrove typhoons damaged by the typhoons;
According to the vulnerability curve theory, constructing a vulnerability curve by taking the maximum wind speed near the center of typhoons as a disaster factor and taking the average damage rate of mangrove forest areas as a loss rate.
In a second aspect, a typhoon disaster impact assessment device includes:
the acquisition module is used for acquiring the remote sensing image of the evaluation area; according to the remote sensing image of the evaluation area, combining with a mangrove vector base map of the evaluation area, calculating the area of the evaluation area by using a pixel dichotomy, and constructing an area change data set of the evaluation area with long time sequence in a history period;
the processing module is used for screening historical typhoon disasters affecting the evaluation area according to the area change data set of the evaluation area; calculating the damaged area of the mangrove forest according to the historical typhoon disasters, and constructing a vulnerability curve by combining the typhoon disaster intensity; mangrove partitions and species damage were evaluated based on the vulnerability profile.
In a third aspect, a computing device includes:
one or more processors;
and a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the above-described methods.
In a fourth aspect, a computer readable storage medium stores a program that when executed by a processor implements the above method.
The scheme of the invention at least comprises the following beneficial effects:
according to the scheme, based on the historical typhoon disaster data with the long time sequence and the mangrove image data with the long time sequence, the damage area condition (ordinate) of the historical typhoon disaster of the mangrove is represented through EVI, and the vulnerability curve is constructed by combining the typhoon disaster intensity (abscissa), so that the influence of the mangrove disaster of the typhoon disaster is quantitatively evaluated.
Drawings
Fig. 1 is a flow chart of a typhoon disaster impact assessment method provided by an embodiment of the present invention.
Fig. 2 is a schematic diagram of a typhoon disaster impact assessment device provided by an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As shown in fig. 1, an embodiment of the present invention proposes a typhoon disaster impact assessment method, which includes:
step 11, acquiring a remote sensing image of an evaluation area;
step 12, calculating the area of the evaluation area by using a pixel dichotomy according to the remote sensing image of the evaluation area and combining a mangrove vector base map of the evaluation area, and constructing an area change data set of the evaluation area with long time sequence in a history period;
step 13, screening historical typhoon disasters affecting the evaluation area according to the area change data set of the evaluation area;
step 14, calculating the damaged area of the mangrove forest according to the historical typhoon disasters, and constructing a vulnerability curve by combining the typhoon disaster intensity;
and step 15, evaluating mangrove forest partitions and species damage according to the vulnerability curves.
In the embodiment of the invention, based on long-time-sequence historical typhoon disaster data and long-time-sequence mangrove image data, the damage area condition (ordinate) of the historical typhoon disaster of the mangrove is represented by EVI, and a vulnerability curve is constructed by combining typhoon disaster intensity (abscissa), so that the influence of the mangrove disaster of the typhoon disaster is quantitatively evaluated.
It should be noted that the remote sensing image used in the present invention is derived from the NASA land satellite (Landsat) series, and the estimated mangrove area change dataset in 1985-2018 is based on multi-phase Landsat image generation, and the image used has 1157 scenes. The method comprises 604 scenes of Landsat TM images in 1985-2011, 389 scenes of Landsat ETM images in 1999-2016, and 164 scenes of Landsat OLI in 2013-2018.
The Landsat series satellites have large remote sensing data quantity, image data with small cloud quantity is preferentially selected when the remote sensing images are selected in order to reflect mangrove information, each year at least comprises an image in summer, the summer is in a plant growing season, and the remote sensing images have rich spectral information, so that vegetation identification is facilitated.
Because of the limitations of space, spectrum, time and radiation resolution in the remote sensing image acquisition process, it is difficult to accurately record complex surface information, and errors inevitably exist. In order to reduce errors, the image needs to be preprocessed, which specifically comprises the following steps:
(1) The radiometric calibration and the atmospheric correction firstly inhibit noise, and image noise is interference to images, so that the image quality is reduced, and the extraction quantity of effective image information is reduced. Radiometric calibration and atmospheric correction are then performed, as the remote sensing satellites receive reflected electromagnetic wave images of terrestrial objects, during which atmospheric effects distort the images obtained by the sensors. The two main causes of radiation errors are absorption and scattering of radiation by the atmosphere. Therefore, the FLAASH model is selected to perform atmospheric correction on the remote sensing image so as to eliminate the influence of factors such as water vapor particles in the atmosphere, and the image is closer to the actual condition of the earth surface.
(2) Landsat ETM+ image stripe repair, ETM+ sensor failure of Landsat7 results in data stripe loss of images after month 5 and 31 2003. In order to acquire the image data of the complete time sequence, especially for screening the image data before and after typhoon disasters occur, the requirement on the time resolution of the images is extremely high. Therefore, band repair is required for Landsat EMT+ images.
(3) The scanning area of Landsat series satellites is 185km multiplied by 185km, the area of the mangrove protection area of the evaluation area is only 3337.6ha, and the proportion of the mangrove protection area in a remote sensing image is too small, so that the mangrove vegetation information is not easy to be highlighted, and the image needs to be cut. And cutting the image according to the protection area range by IDL software. Then, the image Mask (Mask) is used for masking the image, and the image Mask (Mask) is used for selecting from the corresponding other image according to the determined area and area code of the image by using a masking method to generate a plurality of output images. The invention adopts a fixed extracted mangrove vegetation range, the mangrove area of the extracted 2015 evaluation area is subjected to batch mask output, and the flooding process is realized based on ENVI-IDL software.
Examples of Landsat images used in the present invention are shown in table 1:
| Sensor for detecting a position of a body | Stripe number/line number | Imaging date | Imaging time |
| TM5 | 123/46 | 1987.01.13 | 10:18:44 |
| TM5 | 123/46 | 1988.04.05 | 10:29:10 |
| TM5 | 123/46 | 1999.10.13 | 10:35:43 |
| TM5 | 123/46 | 2002.05.14 | 10:36:01 |
| TM7 | 123/46 | 2004.12.05 | 10:48:27 |
| TM7 | 123/46 | 2005.08.02 | 10:48:36 |
| TM8 | 124/46 | 2013.09.08 | 10:34:42 |
| TM8 | 124/46 | 2014.05.22 | 10:31:36 |
| TM8 | 124/46 | 2015.04.07 | 10:25:40 |
The typhoon disaster data set adopted by the invention is from the satellite analysis tropical cyclone scale data (Liu et al, 2017) of 1985-2016 of the tropical cyclone data center of the China weather department. The satellite inversion tropical cyclone scale data comprises all tropical cyclones captured by satellites in North of the equator and North of the west of 180 degrees, and the data comprises typhoon time, typhoon center longitude and latitude, typhoon center lowest air pressure, typhoon center maximum wind speed, typhoon scale and the like.
In a preferred embodiment of the present invention, the step 12 may include:
step 121, mangrove vegetation coverage of unit pixel is FVC, bare soil coverage is 1-FVC, and pixel information of pure vegetation coverage is S veg The pixel information covered by the bare soil is S soil Then:
Sv＝FVC×Sveg，Ss＝(1-FVC)×Ssoil
FVC＝(S-Ssoil)/(Sveg-Ssoil)；
step 122, combining the EVI with the pixel bipartite model to obtain a vegetation coverage pixel bipartite model based on the EVI index, wherein,EVI soil enhanced vegetation index for bare soil, EVI veg Is the enhanced vegetation index of vegetation, S is satellite remote sensing information of mixed pixel, S v Is vegetation information S s S=s as bare soil information v +S s EVI is enhanced vegetation index, evi=2.5 (dNIR-dRed)/(dnir+c1dred-c2dblue+l), where d NIR Is the near infrared reflectance value corrected by the atmosphere, d Red Is the reflection value of the red wave band corrected by the atmosphere, d Blue Is the blue wave band reflection value corrected by the atmosphere, L=1, is the soil regulating parameter, parameter C 1 And C 2 6.0 and 7.5, respectively, for describing the correction of the effect of the atmosphere on the red band by the blue band; the calculation formula of the mangrove unit pixel area is as follows:
A＝FVC×900
wherein A is mangrove unit pixel area, unit is square meter, and FVC is mangrove vegetation coverage of unit pixel.
In the embodiment of the invention, an evaluation area mangrove forest annual change data set is constructed and the time-space change is analyzed based on the collected Landsat remote sensing data set, satellite analysis tropical cyclone scale data, unmanned aerial vehicle images and other data. An assessment area historical typhoon disaster dataset is constructed and typhoon profiles are introduced. Then, the relationship between the annual change of the mangrove area and the typhoon disaster intensity is tried to be analyzed, and the long-time-sequence historical data analysis result lays a foundation for the follow-up quantitative research of the damage condition of the mangrove caused by the typical typhoon disaster event.
In the embodiment of the invention, the EVI eliminates the influence of aerosol in the atmosphere to a large extent, well overcomes the variation caused by soil background, and reduces the saturation effect of a high vegetation coverage. At home and abroad, a plurality of researchers use the NDVI index to monitor the typhoon disaster area and vegetation restoration dynamics, and the EVI index is less. In consideration of the characteristic of dense growth of mangrove vegetation in an evaluation area, the EVI is used as a typhoon disturbance index, and the change of typhoon influence of the mangrove vegetation in a research area is monitored, so that a EVI long time sequence data set in 1985-2018 is obtained by calculating cut Landsat series of image data through ENVI-IDL. For pure bare earth pixels, EVI soil Should be close to 0 in theory. But in reality, EVI soil Can change with time and space according to different areas or ground environments, and the change range is generally-0.1-0.2. For pure vegetation pixels, the EVI is caused by vegetation type, vegetation spatial distribution and vegetation growing season change veg Spatiotemporal variation of values. The maximum value and the minimum value of the EVI are respectively selected from the maximum value of the EVI in the mangrove area and the minimum value of the EVI in the soil area corresponding to the high-resolution image of the unmanned aerial vehicle. Because the spatial resolution of the Landsat series image data applied by the invention is 30m, the calculation of the mangrove area multiplies the obtained vegetation coverage data set by the pixel area 900m of the Landsat image 2 A mangrove area dataset of unit pixels can be obtained.
The method is used for verifying the accuracy of the mangrove area data set and also providing guarantee for the accuracy of the damage area of the mangrove typhoon disaster in subsequent calculation. The invention adopts the image data shot by the unmanned aerial vehicle in the field in 12 months 2017 and 7 months 2018, the data range is an evaluation area, and the data spatial resolution is as high as 5.91cm. And comparing the areas of the two images with the images corresponding to Landsat8 data at the same time, and judging the precision error. And performing atmospheric correction and radiation correction on the Landsat8 image and the unmanned aerial vehicle image, performing geometric correction, and cutting out the same tower city area range. The unmanned aerial vehicle has high image space resolution, so that the mangrove area is calculated by adopting a classification extraction method, the mangrove area of the tower city area is extracted, and the total area of the mangrove area is calculated. The area calculation method of the Landsat8 image still adopts the steps, and the calculation is performed through a pixel bipartite model. Finally, the image area (583.37 ha) of the unmanned aerial vehicle in 2017 and the image area (590.28 ha) of the Landsat8 are different by 6.91ha, the image area (674.78 ha) of the unmanned aerial vehicle in 2018 and the image area (680.87 ha) of the Landsat8 are different by 6.09ha, and the calculation precision of the mangrove area is 98.83% and 99.11% respectively. The mangrove area obtained by the two data sources and the method has smaller difference, and the result obtained by the Landsat image data and pixel bipartite model method has high precision, which indicates that the mangrove area data set obtained by the method has higher precision.
In a preferred embodiment of the present invention, the step 12 may further include:
step 123, generating 32-day mangrove area synthesis series data of Landsat series images;
step 124, according to the mangrove area synthesis series data, generating mangrove area year synthesis series data of Landsat series images by adopting a maximum synthesis method;
step 125, synthesizing series data according to the mangrove area of Landsat series images, and classifying and dividing according to the area of unit pixels;
and step 126, obtaining a long-time sequence mangrove area space distribution map in a history period according to the classification division.
In the embodiment of the invention, in order to solve the background situation of the mangrove growth in the evaluation area, the mangrove unit pixel area annual space distribution change in the mangrove protection area 1985-2018 in the evaluation area is further generated by utilizing the obtained mangrove area long time sequence data set. The method used in the step is maximum synthesis (MVC), and can effectively reduce the influences of haze, cloud and solar altitude in the atmosphere and EVI data in the data synthesis process. Firstly, generating 32-day mangrove area synthesis series data of Landsat series imagesNext, based on the 32-day synthetic data, mangrove area annual synthetic series data of Landsat series images were also generated using the maximum synthetic method. Tidal interference is automatically eliminated because the tidal water EVI value at high tide levels is less than the EVI value of mangrove at low tide levels, and the maximum synthesis is employed, thus tidal factors are not considered here. The obtained 1985-2018 long time sequence mangrove area data are classified and divided into five grades by ArcGIS software according to the area of unit pixel >700m 2 ，700-500m 2 ，300-500m 2 ，100-300m 2 ，<100m 2 ) So as to facilitate the observation of the change condition of the area of the differential mangrove forest.
In a preferred embodiment of the present invention, the step 13 may include:
step 131, connecting typhoon data points according to the serial numbers of typhoon time, and recovering the typhoon outlet path;
step 132, analyzing a buffer area by taking the longitude and latitude of the center of the evaluation area as a center point and the radius of the center of the evaluation area as 1/2 scale, wherein when the typhoon path is tangent to or intersected with the buffer area, typhoon events affect mangrove forests of the evaluation area;
step 133, based on the vertical distance from the connection line between two adjacent points on each typhoon path to the central point of the evaluation area, the judgment is performed by combining the correlation theorem, and the method can be specifically divided into three cases:
step 134, when two adjacent typhoon event points fall in the buffer zone, the judgment can be performed according to a formula between the two points;
step 135, when two adjacent typhoons are time-points, one of which falls in the buffer area and one of which falls outside the buffer area, judging according to a formula between the two points;
in step 136, when two adjacent typhoons are outside the buffer area, the judgment is performed according to the cosine law.
In the embodiment of the invention, the typhoon data set of the tropical cyclone scale data analyzed by the original satellite contains more than 1 ten thousand typhoon data points, and the latitude range spans the whole coastal zone area of China, so that the original typhoon data set is required to be screened, and typhoon events which seriously affect the mangrove protection area of the evaluation area are selected. According to the method, scale data in satellite analysis tropical cyclone scale data are taken as the basis, the data comprise tropical cyclone 6-hour position, intensity and scale (based on 34 sea/hour wind circle radius) information, and because the wind speed influence range is smaller for a long-distance scale due to the fact that the wind speed influence range is selected as a near-center maximum wind speed, 1/2 scale is selected as a judging index, and typhoon events within 1/2 scale from an evaluation area are considered to influence mangrove forests in the evaluation area. Taking the longitude and latitude of the center of the evaluation area as a center point, and if the vertical distance from the line connecting all typhoon paths to the point is smaller than 1/2 scale, taking the typhoon into an effective typhoon disaster event set.
In a preferred embodiment of the present invention, the step 13 may include:
step 137, screening out the year of typhoons in the evaluation area, using V i ＝S i -S i-1 Calculating the change area of mangrove forest every year compared with the last year in the year in which typhoons occur, wherein V i For the i-th year change area, S i Is the area of the ith year, S i-1 Is the area of the i-1 th year;
step 138, calculating the correlation between the change area of the mangrove forest and the maximum typhoon wind speed, wherein the correlation calculation formula is as follows:wherein, R is a correlation coefficient, X and Y are two groups of samples respectively, the annual change area and the typhoon near-center maximum wind speed are indicated, the value range of R is (-1, 1), N is the number of samples, R is smaller than 0 and represents negative correlation, R is larger than 0 and represents positive correlation, R=1 represents complete correlation, and R=0 represents uncorrelation. Calculated that the correlation coefficient of typhoon maximum wind speed and annual area change is-0.155, and the significance test (p<0.05 The results indicate that typhoon wind speed and area change of mangrove in the year are inversely related, namely, the larger typhoon wind speed occurs, the more the area of mangrove in the year is reduced.
In the embodiment of the invention, the background analysis is carried out on the mangrove growth condition and typhoon disaster condition of the evaluation area. The mangrove area calculation is based on a Landsat dataset in 1985-2018, calculates the vegetation coverage of the mangrove in long time sequence by means of an EVI and pel bipartite model method, and further converts the vegetation coverage into the mangrove area in long time sequence. The introduction of typhoon disasters includes screening typhoon disaster events which have serious influence on an evaluation area and analyzing basic characteristics of the typhoon disaster events. Finally, based on the long time sequence change values of the two, preliminary researches are carried out on typhoon disaster response of the annual change of mangrove forest areas. The main results are as follows: (1) The growth situation is worse before 1992 on the area space distribution of the mangrove protection area in the evaluation area in 1985-2018. Mangrove area has gradually increased after 1993, but has been significantly reduced in 1997. (2) There are 29 typhoon disaster events which have serious influence on the evaluation area in 1985-2016, and an average of 0.91 typhoons per year. The near-center maximum wind speed ranges from a minimum of 17.6m/s to a maximum of 60.4m/s, with an affected scale ranging from 103km to 288km. (3) The relationship between the annual change area of mangrove and the near-center maximum wind speed maximum value in all typhoons in the year is negative, which shows from historical data that typhoons influence the growth condition of mangrove to a certain extent.
In a preferred embodiment of the present invention, the step 14 may include:
step 141, calculating the area change of unit pixels before and after typhoons occur according to historical typhoons, and spatially analyzing the characteristics of the distribution areas of the mangrove forest typhoons damaged by the typhoons;
step 142, constructing a vulnerability curve by taking the maximum wind speed near the center of typhoons as a disaster factor and the average damage rate of mangrove forest areas as a loss rate according to the vulnerability curve theory
In the embodiment of the invention, in order to quantitatively evaluate the influence of mangrove typhoon disasters, the invention introduces a vulnerability curve theory. Vulnerability (vulnerabilities) measures the damage degree of disaster-bearing bodies, is an important link of disaster damage estimation and risk assessment, and is a bridge for connecting disaster factors with disaster conditions. When the vulnerability of the disaster-bearing body is focused on the disaster level caused by the disaster, the vulnerability is usually expressed by a relation Curve or equation between the disaster causing (h) and the disaster causing (d), namely v=f (h, d), and also called a vulnerability Curve (Vulnerability Curve) or a disaster Damage (rate) Curve (function) (Damage/Loss Curve) for measuring the relation between the intensity of different disaster species and the corresponding Loss (rate) thereof, and the relation is mainly expressed in the form of a Curve, a curved surface or a table. For the mangrove forest, which is a special typhoon disaster carrier, the mangrove forest is a non-economic forest, so that historical disaster information is lacking for the loss condition after the disaster. The method takes the calculated damaged area of the typhoon on the mangrove forest as historical disaster data, takes the maximum wind speed of the center near the typhoon of 16 fields as historical disaster factor data, and constructs a relation curve of the two, namely a vulnerability curve pointed by the research.
Tidal fluctuation is one of mangrove habitat characteristics, the tide type of an estimated area belongs to irregular half daily tides, the estimated area is in a climax stage when the mangrove is affected by the tide, and errors are generated when the actual area of the mangrove is calculated due to the fact that the mangrove is submerged. The Beijing time of the satellite image is basically about 10:30, so that when images before and after disasters corresponding to typhoon disaster event occurrence time are selected, images of an evaluation area in climax time are removed by comparing the image shooting time and tide level height (from a national ocean science data sharing service platform). Combining the effective typhoon data set obtained by screening with the mangrove forest area long time sequence data set, removing the images in the climax stage, finding out the image data with the best quality before and after occurrence corresponding to typhoons, and obtaining 16 typhoons disaster events with the best image quality after screening. Typhoons are instant disasters and have the characteristics of strong damage but short time on the influence of mangrove forest, so that in order to avoid being influenced by growth factors of the mangrove forest, images in a time period as short as possible are selected to represent the state of typhoons before and after occurrence of typhoons, and the image time is preferably selected to be within a week to a month before and after occurrence of typhoons.
The damaged area of the mangrove is the change quantity of the unit pixel area in the mangrove taking the EVI as the typhoon disturbance index. Specifically, firstly, images which are not more than 1 month away from typhoons are selected, because the tropics at the mangrove sites are evergreen broad-leaved forests, the change of EVI values in the year is not great, the image acquisition time before and after typhoons is controlled within one month, the change of EVI values caused by the growth of the mangroves can be ignored, then, the areas of the mangroves before and after typhoons occur are calculated respectively by using a pixel bipartite model, the areas after typhoons occur are subtracted from the areas before typhoons, and the obtained value is regarded as the damage area of the typhoons in the field to the mangroves. According to the method, the damage distribution of the mangrove area of each 16 typhoons can be obtained, so that the influence of typhoons on mangrove forests is measured.
In order to analyze the general rule of mangrove affected by typhoons, the damage area map of 16 typhoons to the mangrove is overlapped, and in an evaluation area, the region of the mangrove, which is greatly affected, is intensively distributed at the edge of the mangrove distribution and the region close to tidal ditches and water areas. This is mainly because mangrove forests in the border areas are often pioneer vegetation against typhoons and are affected by typhoons for the first time, and mangrove forests in the border areas have smaller stand density and high tree collapse rate. Strong wind is easy to gather in a narrow area near the tidal ditch, so that the damage to mangrove vegetation is stronger.
The screening is carried out to obtain the wind speed of 16 typhoons and the damaged area of mangrove forest, the damage to the mangrove forest caused by small typhoon grades is small, and when the typhoon grade is 11-12 grades or more wind power, the damage to the mangrove forest can be obviously caused. Thus, the impact pattern is relatively consistent with the Logistic model. After exponential, logarithmic, power function, piecewise linear, and Logistic model analysis of the intensity-area impairment data, it was found that the Logistic fit was best (R 2 ＝0.6898，P<0.001 Second, a linear model, and other models fit relatively poorly. As the typhoon wind speed value increases, the damage rate of the mangrove forest area caused by typhoons is larger, the typhoon wind speed is 13-50m/s, the change of the mangrove forest area damage rate is most sensitive, and typhoons exceeding 50m/s can cause damage to mangrove forest of more than 16% of the total area of the east village harbor mangrove forest.
And calculating the proportion of the damaged area of each community species in the damaged grade of each mangrove to the total area of the community. Typhoons affect the total area of the mangrove forest high-loss area to be 2.87ha, and account for 0.18% of the total area of the mangrove forest. Wherein the damage ratio of the Kandelia candel is highest (0.55%), the white-bone soil community (0.45%), the sea Sang Qunla (0.45%), the sea lotus-spinosa-tinospora community (0.16%), the red-sea-olive community (0.15%), the valve-free sea Sang Qunla (0.13%), the fruit tree community (0.13%), the tung tree community (0.12%), the semi-red tree community (0.11%), the olive Li Qunla (0.09%), the sea lacquer community (0.04%), and the halogen-mouse trifoliate acanthus community are not damaged basically.
Typhoons affect the total area of damaged areas in mangrove to be 13.22ha, which accounts for 0.83% of the total area of the mangrove. Wherein the damage proportion of the Bruguiera gymnorrhiza community is highest (1.24%), the autumn eggplant community (1.04%), the cornus community (0.99%), the semi-red tree community (0.85%), the tung tree community (0.85%), the avicennia community (0.83%), the petalous sea Sang Qunla (0.72%), the sea Sang Qunla (0.62%), the sea lotus-petalous sea lotus-Bruguiera gymnorrhiza community (0.59%), the sea lacquer community (0.35%), the halogen syncope-mouse acanthus community (0.32%), the elemi Li Qunla (0.28%).
Typhoons affect the area of the mangrove forest low-loss area to be 23.03ha and account for 1.45% of the total area of the mangrove forest. Wherein the damage proportion of the tung tree community is the highest (1.97%), the red sea olive community (1.79%), the cornus bigelovii community (1.77%), the sea lotus-spikenard-muelet community (1.47%), the agastache rugosa Sang Qunla (1.38%), the semi-red tree community (1.06%), the olive Li Qunla (0.94%), the halosyncope-acanthus trifolia community (0.81%), the sea Sang Qunla (0.53%), the sea lacquer community (0.53%), the okara community (0.43%), the avicennia community (0.39%).
As can be seen from comparison of the areas of species, damaged areas and percentage of damaged areas of each community, the average single typhoons have small damaged areas on mangroves, but are seriously damaged in local areas. Under the influence of typhoon disasters, the communities of Kawakame, avicennia marina and sea Sang Jihai lotus-petiole-tinospora are the most susceptible to typhoons, while the communities of Brussels, non-petiole, cornus and tung flowers are less affected by typhoons.
And superposing the typhoon disaster mangrove forest damaged grade map and the east village harbor partition map to obtain mangrove forest damaged areas with different typhoon grades in five areas of the evaluation area. And then according to the formula:
wherein: z is a comprehensive index of the damage degree of mangrove forest; a is the damaged area percentage of mangrove forest; d is the damage rating 1-3.
The method is used for grading according to the size of the damaged area to obtain a high-damage area, a medium-damage area and a low-damage area of the mangrove forest. And then, combining the mangrove community species distribution condition and the five administrative region division conditions of the east village harbor, calculating the community species composition conditions under different grades, and analyzing the typhoons comprehensive evaluation index results of all the regions. The following results were obtained:
(1) The mangrove high-loss area is distributed at the edge of the mangrove area and is close to the area of the tidal range and the water area; the middle damaged area is also distributed along the tidal channel; the low-loss area occupies a large part of the whole east village harbor mangrove forest area, and the total area is 418ha and the area percentage is 56.35%.
(2) The mangrove forest has greater area percentage of Kandelia candel and avicennia marina in high-loss areas, kandelia candel and Kandelia candel in medium-loss areas, and Tung flower tree and Kandelia candel in low-loss areas. According to analysis, typhoon disasters have a large influence on the communities of Kandelia candel, mulberry, hailot-Hemsleya amabilis-Tinospora cordifolia and a small influence on the communities of Bryonia pilosa, horseradish, heterous sinensis and Tung flower trees. This is mainly related to factors such as distance of each community species from typhoon landing point, stand density, community structure, plant height, crown width, species root system type, trunk hardness and the like.
As shown in fig. 2, an embodiment of the present invention further provides a typhoon disaster impact assessment device 20, including:
an acquisition module 21, configured to acquire a remote sensing image of the evaluation area; according to the remote sensing image of the evaluation area, combining with a mangrove vector base map of the evaluation area, calculating the area of the evaluation area by using a pixel dichotomy, and constructing an area change data set of the evaluation area with long time sequence in a history period;
a processing module 22, configured to screen a historical typhoon disaster that affects the evaluation area according to an area change data set of the evaluation area; calculating the damaged area of the mangrove forest according to the historical typhoon disasters, and constructing a vulnerability curve by combining the typhoon disaster intensity; mangrove partitions and species damage were evaluated based on the vulnerability profile.
Optionally, calculating the area of the evaluation area by using a pixel dichotomy, and constructing an area change data set of the evaluation area with long time sequence in a historical period of time, including:
the mangrove vegetation coverage of unit pixel is FVC, bare soil coverage is 1-FVC, and pixel information of pure vegetation coverage is S veg The pixel information covered by the bare soil is S soil Then:
Sv＝FVC×Sveg，Ss＝(1-FVC)×Ssoil
FVC＝(S-Ssoil)/(Sveg-Ssoil)；
combining the EVI with the pixel bipartite model to obtain a vegetation coverage pixel bipartite model based on an EVI index,
Wherein, EVI soil Enhanced vegetation index for bare soil, EVI veg Is the enhanced vegetation index of vegetation, S is satellite remote sensing information of mixed pixel, S v Is vegetation information S s S=s as bare soil information v +S s EVI is enhanced vegetation index, evi=2.5 (dNIR-dRed)/(dnir+c1dred-c2dblue+l), where d NIR Is the near infrared reflectance value corrected by the atmosphere, d Red Is the reflection value of the red wave band corrected by the atmosphere, d Blue Is the blue wave band reflection value corrected by the atmosphere, L=1, is the soil regulating parameter, parameter C 1 And C 2 6.0 and 7.5, respectively, for describing the correction of the effect of the atmosphere on the red band by the blue band.
Optionally, the calculation formula of the mangrove unit pixel area is as follows:
A＝FVC×900
wherein A is mangrove unit pixel area, unit is square meter, and FVC is mangrove vegetation coverage of unit pixel.
Optionally, calculating the area of the evaluation area by using a pixel dichotomy, and constructing an area change data set of the evaluation area with long time sequence in a historical period of time, further including:
generating 32-day mangrove area synthesis series data of Landsat series images;
according to the mangrove area synthesis series data, generating mangrove area year synthesis series data of Landsat series images by adopting a maximum synthesis method;
Synthesizing series data according to mangrove area years of Landsat series images, and classifying and dividing according to the area of unit pixels;
and obtaining a long-time sequence mangrove forest area space distribution map in a history period according to the classification regions.
Optionally, screening the historical typhoon disasters affecting the evaluation area according to the area change data set of the evaluation area, including:
connecting typhoon data points according to the serial numbers of typhoon time, and recovering a typhoon outlet path;
taking the longitude and latitude of the center of the evaluation area as a center point, and taking a radius of 1/2 scale as a buffer area for analysis, wherein when a typhoon path is tangent to or intersected with the buffer area, typhoon events affect mangrove forests of the evaluation area;
based on the vertical distance from the connecting line of two adjacent points on each typhoon path to the central point of the evaluation area, the judgment is carried out by combining the correlation theorem, and the three situations can be specifically divided:
when two adjacent typhoon event points fall in the buffer zone, the judgment can be carried out according to a formula between the two points;
when two adjacent typhoons are in time points, one typhoons fall in the buffer area, and the other typhoons fall outside the buffer area, the two typhoons are judged according to a formula between the two typhoons;
When two adjacent typhoon time points are outside the buffer area, the judgment is carried out according to the cosine law.
Optionally, screening the historical typhoon disasters affecting the evaluation area according to the area change data set of the evaluation area, and further including:
screening out the year of typhoons in the evaluation area, using V i ＝S i -S i-1 Calculating the change area of mangrove forest every year compared with the last year in the year in which typhoons occur, wherein V i For the i-th year change area, S i Is the area of the ith year, S i-1 Is the area of the i-1 th year;
calculating the correlation between the change area of the mangrove forest and the maximum typhoon wind speed, wherein the correlation calculation formula is as follows:wherein R is a correlation coefficient, X and Y are two groups of samples respectively, the annual change area and the typhoon near-center maximum wind speed are indicated, the value range of R is (-1, 1), and N is the number of samples.
Optionally, calculating a mangrove forest damaged area according to the historical typhoon disaster, and constructing a vulnerability curve by combining typhoon disaster intensity, including:
calculating the unit pixel area change before and after typhoons occur according to the historical typhoons, and spatially analyzing the distribution area characteristics of the mangrove typhoons damaged by the typhoons;
according to the vulnerability curve theory, constructing a vulnerability curve by taking the maximum wind speed near the center of typhoons as a disaster factor and taking the average damage rate of mangrove forest areas as a loss rate.
It should be noted that the apparatus is an apparatus corresponding to the above method, and all implementation manners in the above method embodiment are applicable to this embodiment, so that the same technical effects can be achieved.
Embodiments of the present invention also provide a computing device comprising: a processor, a memory storing a computer program which, when executed by the processor, performs the method as described above. All the implementation manners in the method embodiment are applicable to the embodiment, and the same technical effect can be achieved.
Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a method as described above. All the implementation manners in the method embodiment are applicable to the embodiment, and the same technical effect can be achieved.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.
Furthermore, it should be noted that in the apparatus and method of the present invention, it is apparent that the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention. Also, the steps of performing the series of processes described above may naturally be performed in chronological order in the order of description, but are not necessarily performed in chronological order, and some steps may be performed in parallel or independently of each other. It will be appreciated by those of ordinary skill in the art that all or any of the steps or components of the methods and apparatus of the present invention may be implemented in hardware, firmware, software, or a combination thereof in any computing device (including processors, storage media, etc.) or network of computing devices, as would be apparent to one of ordinary skill in the art after reading this description of the invention.
The object of the invention can thus also be achieved by running a program or a set of programs on any computing device. The computing device may be a well-known general purpose device. The object of the invention can thus also be achieved by merely providing a program product containing program code for implementing said method or apparatus. That is, such a program product also constitutes the present invention, and a storage medium storing such a program product also constitutes the present invention. It is apparent that the storage medium may be any known storage medium or any storage medium developed in the future. It should also be noted that in the apparatus and method of the present invention, it is apparent that the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention. The steps of executing the series of processes may naturally be executed in chronological order in the order described, but are not necessarily executed in chronological order. Some steps may be performed in parallel or independently of each other.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the present invention.
Claims (10)
1. A typhoon disaster impact assessment method, the method comprising:
acquiring a remote sensing image of an evaluation area;
according to the remote sensing image of the evaluation area, combining with a mangrove vector base map of the evaluation area, calculating the area of the evaluation area by using a pixel dichotomy, and constructing an area change data set of the evaluation area with long time sequence in a history period;
screening historical typhoon disasters affecting the evaluation area according to the area change data set of the evaluation area;
calculating the damaged area of the mangrove forest according to the historical typhoon disasters, and constructing a vulnerability curve by combining the typhoon disaster intensity;
mangrove partitions and species damage were evaluated based on the vulnerability profile.
2. The typhoon disaster impact assessment method according to claim 1, wherein calculating the area of the assessment area by using a pixel dichotomy to construct an area change dataset of the assessment area with long time sequence over a history period of time, comprising:
the mangrove vegetation coverage of unit pixel is FVC, bare soil coverage is 1-FVC, and pixel information of pure vegetation coverage is S veg The pixel information covered by the bare soil is S soil Then:
Sv＝FVC×Sveg，Ss＝(1-FVC)×Ssoil
FVC＝(S-Ssoil)/(Sveg-Ssoil)；
combining the EVI with the pixel bipartite model to obtain a vegetation coverage pixel bipartite model based on an EVI index,
Wherein, EVI soil Enhanced vegetation index for bare soil, EVI veg Is the enhanced vegetation index of vegetation, S is satellite remote sensing information of mixed pixel, S v Is vegetation information S s S=s as bare soil information v +S s EVI is enhanced vegetation index, evi=2.5 (dNIR-dRed)/(dnir+c1dred-c2dblue+l), where d NIR Is the near infrared reflectance value corrected by the atmosphere, d Red Is the reflection value of the red wave band corrected by the atmosphere, d Blue Is the blue wave band reflection value corrected by the atmosphere, L=1, is the soil regulating parameter, parameter C 1 And C 2 6.0 and 7.5, respectively, for describing the correction of the effect of the atmosphere on the red band by the blue band.
3. The typhoon disaster impact assessment method according to claim 2, wherein the calculation formula of the mangrove unit pixel area is:
A＝FVC×900
wherein A is mangrove unit pixel area, unit is square meter, and FVC is mangrove vegetation coverage of unit pixel.
4. A typhoon disaster impact assessment method according to claim 3, wherein the area of the assessment area is calculated by using a pixel dichotomy, and an area change data set of the assessment area with long time sequence in a history period is constructed, further comprising:
generating 32-day mangrove area synthesis series data of Landsat series images;
According to the mangrove area synthesis series data, generating mangrove area year synthesis series data of Landsat series images by adopting a maximum synthesis method;
synthesizing series data according to mangrove area years of Landsat series images, and classifying and dividing according to the area of unit pixels;
and obtaining a long-time sequence mangrove forest area space distribution map in a history period according to the classification regions.
5. The typhoon disaster impact assessment method according to claim 4, wherein screening historical typhoon disasters affecting said assessment area according to an area change dataset of said assessment area comprises:
connecting typhoon data points according to the serial numbers of typhoon time, and recovering a typhoon outlet path;
taking the longitude and latitude of the center of the evaluation area as a center point, and taking a radius of 1/2 scale as a buffer area for analysis, wherein when a typhoon path is tangent to or intersected with the buffer area, typhoon events affect mangrove forests of the evaluation area;
based on the vertical distance from the connecting line of two adjacent points on each typhoon path to the central point of the evaluation area, the judgment is carried out by combining the correlation theorem, and the three situations can be specifically divided:
when two adjacent typhoon event points fall in the buffer zone, the judgment can be carried out according to a formula between the two points;
When two adjacent typhoons are in time points, one typhoons fall in the buffer area, and the other typhoons fall outside the buffer area, the two typhoons are judged according to a formula between the two typhoons;
when two adjacent typhoon time points are outside the buffer area, the judgment is carried out according to the cosine law.
6. The typhoon disaster impact assessment method according to claim 5, wherein historical typhoon disasters affecting said assessment area are screened based on an area change dataset of said assessment area, further comprising:
screening out the year of typhoons in the evaluation area, using V i ＝S i -S i-1 Calculating the change area of mangrove forest every year compared with the last year in the year in which typhoons occur, wherein V i For the i-th year change area, S i Is the area of the ith year, S i-1 Is the area of the i-1 th year;
calculating the correlation between the change area of the mangrove forest and the maximum typhoon wind speed, wherein the correlation calculation formula is as follows:wherein R is a correlation coefficient, X and Y are two groups of samples respectively, the annual change area and the typhoon near-center maximum wind speed are indicated, the value range of R is (-1, 1), and N is the number of samples.
7. The typhoon disaster impact assessment method according to claim 6, wherein calculating a mangrove forest damaged area from a historical typhoon disaster and constructing a vulnerability curve in combination with typhoon disaster intensity comprises:
Calculating the unit pixel area change before and after typhoons occur according to the historical typhoons, and spatially analyzing the distribution area characteristics of the mangrove typhoons damaged by the typhoons;
according to the vulnerability curve theory, constructing a vulnerability curve by taking the maximum wind speed near the center of typhoons as a disaster factor and taking the average damage rate of mangrove forest areas as a loss rate.
8. A typhoon disaster impact assessment device, comprising:
the acquisition module is used for acquiring the remote sensing image of the evaluation area; according to the remote sensing image of the evaluation area, combining with a mangrove vector base map of the evaluation area, calculating the area of the evaluation area by using a pixel dichotomy, and constructing an area change data set of the evaluation area with long time sequence in a history period;
the processing module is used for screening historical typhoon disasters affecting the evaluation area according to the area change data set of the evaluation area; calculating the damaged area of the mangrove forest according to the historical typhoon disasters, and constructing a vulnerability curve by combining the typhoon disaster intensity; mangrove partitions and species damage were evaluated based on the vulnerability profile.
9. A computing device, comprising:
one or more processors;
one or more processors;
Storage means for storing one or more programs which when executed by the one or more processors cause the one or more processors to implement the method of any of claims 1-7.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a program which, when executed by a processor, implements the method according to any of claims 1-7.
Priority Applications (1)
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| CN118211053A (en) * | 2024-04-11 | 2024-06-18 | 浙江工业大学 | Typhoon prediction method based on multi-mode double-prompt diffusion model |
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