CN115563448A - Coastal beach ecological vulnerability evaluation system and method - Google Patents
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Abstract
The invention discloses a coastal beach ecological vulnerability evaluation system and method, which is characterized in that a coastal beach ecological vulnerability evaluation index system is constructed based on exposure, sensitivity and adaptability; an evaluation index weight is calculated based on a processing method of an improved expert weighting method of data variation conditions, a sensitivity index VI is obtained pixel by pixel based on phytoplankton diversity, zooplankton diversity, large zooplankton diversity, swimming animal diversity, identification of important habitat and an ecological system with special protection value, and a fitness index AI is obtained pixel by pixel based on chlorophyll a concentration, zooplankton density, roe density and larval and juvenile fish density; and performing weighted calculation to obtain an ecological vulnerability comprehensive index, and obtaining the vulnerability grade according to the ecological vulnerability comprehensive index. The method has objective evaluation results, and can provide technical basis for marine ecological protection, marine ecological red line planning and coastal mud flat planning management.
Description
Technical Field
The invention relates to a coastal mudflat ecological vulnerability evaluation method, and belongs to the field of environmental monitoring.
Background
The ocean is an important component of an earth life support system, the diversity of marine organisms is seriously threatened due to the influence of factors such as pollution, over-fishing and destructive marine operation, a series of problems such as over-development of coastal beaches, overlarge sea reclamation scale, reduction of shallow sea biological resources, deterioration of near-shore seawater quality and the like exist in the near-shore sea area, the fragility of ocean ecology is gradually increased, and a support system or a management tool is urgently needed to deal with the marine ecological environment problem caused by the pressure, so that the sustainable development of the ocean is promoted. The study of scholars at home and abroad shows that the ecological vulnerability is the result of the combined action of the ecological system and human beings. The scientific and reasonable identification of the marine ecological vulnerability is the basis for defining the ecological protection red line, is the main basis for determining the marine main body functional area and is the important basis for realizing the control of the marine ecological space, so the evaluation of the ecological vulnerability is very important for the development and protection of marine mudflats.
In general, the ecological vulnerability assessment is composed of 5 steps including assessment unit selection, index selection, weight calculation, comprehensive assessment, and vulnerability classification. The difference of different methods is mainly reflected in the difference of the evaluation unit size, the index construction mode and the index weight determination method. The evaluation unit determines that administrative regions, drainage basins, landscapes and the like are mainly used as the evaluation unit, and the ecological vulnerability evaluation index system mainly comprises a sensitive-elastic-pressure index system, a pressure-state-response index system, an exposure-sensitive-adaptability index system and the like. The index weight calculation mainly comprises an analytic hierarchy process, an entropy value process, a fuzzy comprehensive evaluation method, a principal component analysis method and the like.
The method for evaluating the ecological vulnerability of the ocean is more, the method is rarely used for evaluating the ecological vulnerability of coastal zones and sea island regions, and a consistent technical method is not formed yet, so that the related technology is blank or deficient at present:
firstly, the evaluation scale of the marine ecological vulnerability mostly takes administrative regions, drainage basins and landscapes as evaluation units, and the evaluation scale is difficult to finely reflect the size of the marine ecological vulnerability. Secondly, the existing marine ecological vulnerability indexes lack specific quantitative depiction of marine ecological vulnerability by marine development activities, and the evaluation indexes lack important habitat distribution and ecological system indexes with humanistic special protection. Thirdly, the ecological vulnerability of the ocean mainly aims at open ocean, and a method for evaluating the ecological vulnerability of coastal mudflats with greater human disturbance is lacked. 4. At present, weight calculation mainly adopts different expert empowerments and adopts an equal-weight mode for determination, the consistency of the expert empowerments is difficult to control, the error of the expert empowerments is difficult to discriminate, and the weight adaptability is poor.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides a coastal beach ecological vulnerability evaluation system and method, which take grid pixels as evaluation units, more finely depict the difference of marine ecological vulnerability, and construct a coastal beach ecological vulnerability evaluation method based on exposure, sensitivity and adaptability from sea area use types, seabed landforms, important habitats and special ecological systems. In addition, the invention provides a processing method for improving the expert empowerment method based on the data variation condition, aiming at the problem that the expert empowerment is inconsistent due to the reasons of expert cognitive level or filling errors and the like in the expert empowerment method, and the problems of objective errors and supervisor cognitive deviation of the expert empowerment are reduced. The invention can provide technical basis for marine ecological protection, marine ecological red line planning and coastal beach planning management.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the technical scheme that:
a coastal beach ecological vulnerability evaluation method comprises the following steps:
s1, constructing a coastal beach ecological vulnerability evaluation index system based on the exposure degree, the sensitivity and the adaptability.
Step S2, calculating the weight of the evaluation index, wherein the method comprises the following steps:
step S21: selecting n experts in the field of vulnerability evaluation of coastal mudflat, evaluating and weighting m indexes, and constructing an index weight matrix W (1) 。
wherein ,wij And (3) representing the evaluation empowerment of the j index by experts in the ith coastal beach vulnerability evaluation field, wherein i =1,2,3, \8230;, n, j =1,2,3, \8230;, m.
Step S22: and calculating the average weight of the expert group by using an average value method.
wherein ,
represents the average weight of the j-th index.
Step S23: the degree of deviation between the individual expert's assigned weights and the other expert's weights is calculated.
wherein ,
denotes the ith 0 The expert in the field of evaluation of vulnerability of coastal mudflat empowers the evaluation of the jth index,
denotes the ith 1 Bit edgeThe expert in the field of evaluation of the vulnerability of the beach empowers the evaluation of the jth index,
indicates the ith for the jth index 0 The expert in the field of the evaluation of the vulnerability of the coastal mudflat empowers the evaluation of the mth index,
for the jth index ith 1 The evaluation of the mth index is empowered by experts in the field of evaluation of the vulnerability of coastal mudflats, A k,j Represents the deviation degree between the assigned weight of the kth expert for the jth index and the weights of other experts, w k,j Represents the evaluation empowerment of the expert in the k-th coastal beach vulnerability evaluation field to the j index, w i+1,j Showing that the evaluation of the jth index is empowered by experts in the field of the vulnerability evaluation of the (i + 1) th coastal beach,
the average weight of the j-th index, k =1,2, … n, is shown.
Step S24: calculating the deviation degree lambda between experts i And normalizing the deviation degree of the weight evaluation of the n experts to obtain an influence factor W of the weight (q) 。
W (q) ＝(λ 1 ,λ 2 ,λ 3 ,…,λ n ) T
Step S25: evaluation weight matrix W according to n experts (1) Combining the influence factor W of the weights (q) Finally, the weight vector W of m indexes is obtained (2) 。
W (2) A weight vector representing m indices.
And S3, collecting basic data of the research area, preprocessing the data, unifying the data to the same grid pixel size, and facilitating spatial operation.
And S4, carrying out exposure evaluation, carrying out interference degree and coast erosion evaluation, and obtaining an evaluation unit exposure index EI pixel by pixel.
And S5, developing sensitivity evaluation, and acquiring a sensitivity index VI pixel by pixel based on phytoplankton diversity, zooplankton diversity, large benthonic animal diversity, swimming animal diversity, identification of important habitat and ecological system with special protection value.
And S6, carrying out fitness evaluation, and obtaining a fitness index AI by pixel based on the chlorophyll a concentration, the zooplankton density, the roe density and the fry density.
And S7, performing weighted calculation on the exposure index obtained in the step S4, the sensitivity index obtained in the step S5 and the fitness index obtained in the step S6 to obtain an ecological vulnerability comprehensive index, and obtaining a vulnerability grade according to the ecological vulnerability comprehensive index.
Preferably: in the step S1, the constructed coastal beach ecological vulnerability evaluation index system comprises the following evaluation indexes: interference degree, coast erosion rate, phytoplankton diversity, zooplankton diversity, large benthos diversity, swimming animal diversity, important habitat, ecosystem of special protection value, chlorophyll a concentration, zooplankton density, roe density, larval fish density.
Preferably, the following components: in the step S3, the collected basic data comprise important habitat distribution and important wetlands such as ocean basic geography, sea area development and utilization, coastlines, phytoplankton, zooplankton, large benthonic animals, swimming animals, chlorophyll a, roe and fry density monitoring data, rare or endangered species habitat and the like.
Preferably, the following components: in step S4, the exposure evaluation includes a disturbance factor C1 and a coastal erosion factor C2.
And evaluating the interference factor C1, and performing assignment determination according to the sea area use type and the topographic features. The undeveloped shallow sea water area outside the 0m and other depth lines is assigned 1 as no disturbance. The undeveloped sea areas from the coastline to the 0m and other deep lines are assigned as low interference and are assigned as 2, and the open sea for breeding, the sea for artificial fish reefs and the sea for coastal protection engineering are assigned as medium interference and are assigned as 1. The fish, shrimp, crab culture sea area in the depth line of 0m and the like and the enclosure culture sea area outside the depth line of 0m and the like are taken as strong interference, and the value is assigned to 4. The fishery infrastructure uses the sea, the industrial sea, the transportation sea, the travel and entertainment sea, the sea for the seabed engineering, the pollution discharge dumping sea, and the land building engineering sea as the strong interference, and the value is 5.
The coastal erosion factor C2 is evaluated, and the calculation formula is as follows:
where D1 and D2 are shoreline locations at times T1 and T2, respectively, and EPR represents the shoreline erosion rate. For silt and silt, the coastal erosion rate is more than or equal to 10m/a, and the coastal erosion factor C 2 Value 5 is assigned. For silt and silt, the coast erosion rate is 1-10 m/a, and the coast erosion factor C 2 Value 3 is assigned. For silt, the coastal erosion rate is less than 1.0m/a, and the coastal erosion factor C 2 Value 1 is assigned. For sandy shoreline, the coastal erosion rate is more than or equal to 2.0m/a, and the coastal erosion factor C 2 Value 5 is assigned. For sandy shoreline, the coastal erosion rate is 0.5-2.0 m/a, and the coastal erosion factor C 2 Value 3 is assigned. For sandy coastlines, a coastline erosion rate of < 0.5m/a, the coastline erosion factor C2 is assigned a value of 1.
And evaluating the exposure degree, wherein the calculation formula is as follows:
wherein EI is exposure index, C 1 As a function of the interference factor, and,
as weights of interference factors, C 2 Is a coastal erosion factor, and the water quality of the water is improved,
Preferably, the following components: in step S5, the sensitivity evaluation method is as follows:
and (3) evaluating the diversity of phytoplankton, wherein the calculation formula is as follows:
wherein H' is a Shanon-Weaver diversity index value, R is the total number of species, P r The number of individuals of the r-th species is expressed as a proportion of the total number of all the individuals.
And calculating the phytoplankton diversity index, the zooplankton diversity index, the large benthonic animal diversity index and the swimming animal diversity index according to a formula 3. The obtained index of diversity of phytoplankton is given in Table 4 for the diversity of phytoplankton C 3 Evaluation and assignment are carried out, and the obtained zooplankton diversity index is used for zooplankton diversity C according to the table 4 4 Evaluating and assigning to obtain diversity index of large benthonic animals according to Table 4 5 Evaluation and assignment are carried out, and the obtained swimming animal diversity index is used for swimming animal diversity C according to the table 4 6 Evaluation assignments are made. To important habitat C 7 Evaluating important habitats including habitat of rare or endangered species, migratory bird migration channel and three-channel-one-channel distribution area, and if the grid unit is positioned in the important habitat area, the important habitat C 7 The value is 5, otherwise the important habitat C 7 Is 1. Other Special ecosystems C 8 Evaluating if the grid unit is located within the range of the important habitat area, and other special ecosystems C 8 The value is 5, otherwise, other special ecosystems C 8 Is 1.
And evaluating the sensitivity, wherein the calculation formula is as follows:
wherein, VI is a sensitivity index,
denotes the ith 2 Weight of C 3 、C 4 、C 5 、C 6 、C 7 、C 8 Respectively phytoplankton diversity, zooplankton diversity, large-scale zoobenthos diversity, swimming animal diversity, important habitat and other special ecological systems.
Preferably: in step S6, the fitness evaluation method is as follows:
the pixel value after chlorophyll a concentration interpolation is assigned according to the table 5 to obtain a chlorophyll a concentration factor C 9 。
Assigning the pixel value after the zooplankton density interpolation according to the table 5 to obtain a zooplankton density factor C 10 。
Assigning the pixel value after roe density interpolation according to table 5 to obtain roe density factor C 11 。
Assigning the pixel value after the fry density interpolation according to the table 5 to obtain a fry density factor C 12 。
And evaluating the fitness, wherein the calculation formula is as follows:
wherein, AI is a fitness index,
denotes the ith 3 Weight of C 9 、C 10 、C 11 、C 12 Chlorophyll a concentration factor, zooplankton density factor, roe density factor and fry density factor.
Preferably, the following components: the calculation formula of the ecological vulnerability comprehensive index EVI in the step S7 is as follows:
EVI＝EI+VI+AI (6)
wherein EVI is a ecological fragile composite index. EI. VI and AI are respectively exposure index, sensitivity index and fitness index.
Preferably: step S7, the marine ecological vulnerability is classified into 5 levels of extreme vulnerability, high vulnerability, medium vulnerability, low vulnerability and non-vulnerability, wherein extreme vulnerability: the ecological fragile comprehensive index is 4.21-5.0. High fragility: the ecological fragile comprehensive index is 3.41-4.2. Medium fragility: the ecological vulnerability comprehensive index is 2.61-3.4. Low vulnerability: the ecological fragile comprehensive index is 1.81-2.6. Not fragile: the ecological vulnerability comprehensive index is 1-1.8.
The utility model provides a coastal beach ecological vulnerability evaluation system, includes input module, evaluation index weight calculation module, exposure evaluation module, sensitivity evaluation module, fitness evaluation module, vulnerability grade module, output module, wherein:
the input module is used for inputting basic data of a research area, preprocessing the data and unifying the data to the same grid pixel size.
And the evaluation index weight calculation module is used for calculating to obtain a weight vector of each index.
The exposure degree evaluation module is used for obtaining an exposure degree index EI according to the interference degree and the coast erosion in the basic data input into the research area and the weight vector calculated by the evaluation index weight calculation module.
The sensitivity evaluation module is used for obtaining a sensitivity index VI according to the phytoplankton diversity, the zooplankton diversity, the large zoobenthos diversity, the swimming animal diversity, the ecological system for identifying the important habitat and the special protection value in the basic data input into the research area and the weight vector calculated by the evaluation index weight calculation module.
And the fitness evaluation module is used for obtaining a fitness index AI according to the chlorophyll a concentration, the zooplankton density, the roe density and the fry density in the basic data input into the research area and the weight vector calculated by the evaluation index weight calculation module.
And the vulnerability grade module is used for weighting and calculating according to the exposure index EI, the sensitivity index VI and the fitness index AI to obtain an ecological vulnerability comprehensive index and obtaining the vulnerability grade according to the ecological vulnerability comprehensive index.
And the output module is used for outputting the obtained vulnerability grade.
Compared with the prior art, the invention has the following beneficial effects:
according to the coastal beach ecological vulnerability evaluation method based on exposure, sensitivity and fitness, the evaluation indexes fully consider the interference of ocean development activities on ecological vulnerability and the sensitivity of important habitats. The invention provides a processing method for improving an expert weighting method, a calculation model of a weight influence factor is constructed, weighting calculation is carried out on the existing expert assignment weight, and expert weighting errors caused by subjectivity and cognition can be overcome.
Drawings
FIG. 1 is a flow chart of the method for evaluating ecological vulnerability of coastal mudflat in the embodiment of the invention.
Fig. 2 shows an evaluation index weight determination method.
FIG. 3 is a result diagram of ecological vulnerability assessment of Jiangsu coastal beaches.
Detailed Description
The present invention is further illustrated by the following description in conjunction with the accompanying drawings and the specific embodiments, it is to be understood that these examples are given solely for the purpose of illustration and are not intended as a definition of the limits of the invention, since various equivalent modifications will occur to those skilled in the art upon reading the present invention and fall within the limits of the appended claims.
A method for evaluating ecological vulnerability of coastal beaches is shown in figure 1 and comprises the following steps:
s1, constructing a coastal beach ecological vulnerability evaluation index system based on the exposure degree, the sensitivity and the adaptability.
The constructed ecological vulnerability evaluation index system of the coastal mudflat comprises the following 12 evaluation indexes as shown in table 1: interference degree, coast erosion rate, phytoplankton diversity, zooplankton diversity, large benthos diversity, zooplankton diversity, important habitat, ecosystem of special protection value, chlorophyll a concentration, zooplankton density, roe density, larval fish.
TABLE 1 evaluation index System
And S2, calculating the weight of the evaluation index.
When the evaluation of the weight of the index by one expert is consistent with the results of most experts, the expert should have a larger decision power, and on the contrary, when the evaluation result of the expert is deviated from the evaluation result of most experts, the decision power of the expert should be smaller, that is, the weight of the decision problem can be determined by the difference degree of decision data, so that the weight given by the expert can be distributed again by constructing a deviation model, and the influence of the abnormal value evaluated by the expert on the evaluation result can be reduced. Based on the index weight determination method, the index weight determination method based on the index deviation degree is provided. The content specifically comprises the following contents:
and (4) supposing that n experts carry out weight evaluation on the coastal beach corrosion vulnerability index, wherein the index number is recorded as m.
Step S21: selecting n experts in the field of vulnerability evaluation of coastal mudflat, evaluating and weighting m indexes, and constructing an index weight matrix W (1) 。
wherein ,wij And (3) representing the evaluation empowerment of the j index by experts in the ith coastal beach vulnerability evaluation field, wherein i =1,2,3, \8230;, n, j =1,2,3, \8230;, m.
Step S22: and calculating the average weight of the expert group by using an average value method.
wherein ,
represents the average weight of the j-th index.
Step S23: the degree of deviation between the individual expert assigned weights and the other expert weights is calculated.
wherein ,
denotes the ith 0 The expert in the field of evaluation of the vulnerability of the coastal mudflat empowers the evaluation of the jth index,
denotes the ith 1 The expert in the field of evaluation of the vulnerability of the coastal mudflat empowers the evaluation of the jth index,
indicates the ith for the jth index 0 The expert in the field of the evaluation of the vulnerability of the coastal mudflat empowers the evaluation of the mth index,
for the jth index ith 1 The evaluation of the mth index is empowered by experts in the field of evaluation of the vulnerability of coastal mudflats, A k,j Representing the degree of deviation between the assigned weight of the kth expert for the jth index and the weights of other experts, w k,j Showing the evaluation of j index by experts in the k coastal beach vulnerability evaluation fieldPrice assignment, w i+1,j Showing that the evaluation of the jth index is empowered by experts in the field of the vulnerability evaluation of the (i + 1) th coastal beach,
the average weight of the j-th index, k =1,2, … n, is shown.
Step S24: calculating the degree of deviation lambda between experts i And normalizing the deviation degree of the weight evaluation of the n experts to obtain an influence factor W of the weight (q) 。
W (q) ＝(λ 1 ,λ 2 ,λ 3 ,…,λ n ) T
Step S25: according to the evaluation weight matrix W of n experts (1) Combining the influence factor W of the weights (q) Finally, the weight vector W of m indexes is obtained (2) 。
W (2) A weight vector representing m indices.
And S3, collecting basic data of the research area, preprocessing the data, unifying the data to the same grid pixel size, and facilitating spatial operation.
The collected basic data comprises marine basic geography, sea area development and utilization, coastlines, phytoplankton, zooplankton, large benthos, swimming animal, chlorophyll a, roe and fry density monitoring data, important habitat distribution of rare or endangered species habitat and the like, and ecological system data of special protection values of important wetlands and the like.
And sequentially carrying out format conversion, projection conversion, cutting, resampling and reclassification on the data, and unifying the data to a coordinate system and the same image resolution after preprocessing.
In this embodiment, the coastal mudflat of Jiangsu is taken as an example, wherein the sea area development data comes from the national sea area use dynamic management data, the water depth data comes from the chart data, and the data is rasterized into a grid unit of 100m × 100 m. The data of phytoplankton, zooplankton, large benthos and swim animals are from site data monitored by actual investigation, and the biodiversity index of each grid unit is obtained by adopting a kriging interpolation method. The important habitat data and the ecosystem data with special protection value come from the range of the complex protection area and are subjected to rasterization processing.
And S4, carrying out exposure evaluation, carrying out interference degree and coast erosion evaluation, and obtaining an evaluation unit exposure index EI pixel by pixel.
In step S4, the exposure evaluation includes an interference factor C 1 And coastal erosion factor C 2 。
The exposure degree refers to the risk brought to the marine tidal flat ecosystem by human activities, including sea reclamation, sewage discharge and the like which influence the balance of the marine ecosystem, and two indexes of an interference degree index and coastal erosion are selected. The interference degree index represents the influence degree of the ecological system in the area under the artificial interference, and the higher the interference degree index is, the higher the artificial interference degree is, the higher the ecological vulnerability degree is. The interference degree calculation method combines the sea area using mode and the sea water depth determination, and divides the interference degree index into 5 types: the method comprises the steps of no interference, low interference, medium interference, strong interference and extremely strong interference, and the values are respectively assigned to 1,2,3, 4 and 5. The method comprises the following specific steps:
interference factor C 1 And evaluating, and performing assignment determination according to the sea area use type and the topographic features. The undeveloped shallow sea water area outside the 0m deep line is assigned as 1 as no disturbance. The undeveloped sea areas from the coastline to the 0m and other deep lines are assigned as low interference and are assigned as 2, and the open sea for breeding, the sea for artificial fish reefs and the sea for coastal protection engineering are assigned as medium interference and are assigned as 1. The fish, shrimp, crab culture sea area in the depth line of 0m and the like and the enclosure culture sea area outside the depth line of 0m and the like are taken as strong interference, and the value is assigned to 4. Sea for fishery foundation, industrial sea, transportation sea, travel and entertainment seaThe sea for bottom engineering, the sea for pollution discharge dumping and the sea for land making engineering are used as strong interference, and the value is 5.
TABLE 2 interference calculation
The coast erosion is calculated by different types of coastlines and annual advance and retreat amplitude of the coastlines, the higher the coast erosion degree is, the higher the ecological vulnerability degree is, and the coast erosion factor C is subjected to 2 The evaluation is carried out, and the calculation formula is as follows:
where D1 and D2 are the shoreline locations at times T1 and T2, respectively, and EPR represents the coastal erosion rate. As shown in Table 3, for silt, the coastal erosion rate is not less than 10m/a, the coastal erosion factor C 2 Value 5 is assigned. For silt and silt, the coast erosion rate is 1-10 m/a, and the coast erosion factor C 2 Value 3 is assigned. For silt, the coastal erosion rate is less than 1.0m/a, and the coastal erosion factor C 2 The value is assigned 1. For sandy shoreline, the coastal erosion rate is more than or equal to 2.0m/a, and the coastal erosion factor C 2 Value 5 is assigned. For sandy shoreline, the coastal erosion rate is 0.5-2.0 m/a, and the coastal erosion factor C 2 Value 3 is assigned. For sandy shoreline, the coastal erosion rate is less than 0.5m/a, and the coastal erosion factor C 2 The value is assigned 1.
TABLE 3 coastal erosion vulnerability
And evaluating the exposure degree, wherein the calculation formula is as follows:
wherein EI is exposure index, C 1 As a function of the interference factor, and,
as an interference factor weight, C 2 Is a coastal erosion factor, and the water quality of the water is improved,
weighting the coast erosion factor.
And S5, carrying out sensitivity evaluation, and acquiring a sensitivity index VI pixel by pixel on the basis of phytoplankton diversity, zooplankton diversity, large benthonic animal diversity, swimming animal diversity, identification of important habitat and ecological system with special protection value.
According to the characteristics of the coastal beach marine ecosystem, indexes capable of reflecting intertidal zone biological sensitivity (the occurrence probability of protected organisms), habitat sensitivity (water environment and deposition environment) and structural sensitivity (phytoplankton diversity) are selected to construct a coastal beach sensitivity index system. 6 indexes such as plankton diversity, benthos diversity, whether the benthos is a habitat of rare or endangered species, migratory bird migration channels, three-field one-channel distribution areas of important economic fishes and the like are selected.
The sensitivity evaluation method was as follows:
evaluating the diversity of phytoplankton, wherein the calculation formula is as follows:
wherein H' is a Shanon-Weaver diversity index value, R is the total number of species, P r The number of individuals of the r-th species is expressed as a ratio of the total number of all individuals.
And calculating the phytoplankton diversity index, the zooplankton diversity index, the large benthos diversity index and the swim animal diversity index according to a formula 3. The obtained index of diversity of phytoplankton is given in Table 4 for the diversity of phytoplankton C 3 Evaluating and assigning to obtain zooplankton diversity index according to the table 4Diversity C 4 Evaluating and assigning to obtain diversity index of large benthonic animals according to Table 4 5 Evaluation and assignment are carried out, and the obtained swimming animal diversity index is used for swimming animal diversity C according to the table 4 6 Evaluation assignments are made. To important habitat C 7 Evaluating important habitats including a rare or endangered species habitat, a migratory bird migration channel and a three-field one-channel distribution area, and if the grid unit is positioned in the range of the important habitat area, the important habitat C 7 The value is 5, otherwise the important habitat C 7 Is 1. Other Special ecosystems C 8 Evaluating if the grid unit is located within the range of the important habitat area, and other special ecosystems C 8 The value is 5, otherwise, other special ecosystems C 8 Is 1.
And evaluating the sensitivity, wherein the calculation formula is as follows:
wherein, VI is a sensitivity index,
denotes the ith 2 Weight of each, C 3 、C 4 、C 5 、C 6 、C 7 、C 8 The species of the zooplankton are phytoplankton diversity, zooplankton diversity, large benthos diversity, swimming animal diversity, important habitat and other special ecosystems.
TABLE 4 sensitivity assignment criteria
And S6, carrying out fitness evaluation, and obtaining a fitness index (AI) pixel by pixel on the basis of the chlorophyll a concentration, the zooplankton density, the roe density and the fry density.
The adaptability refers to the capacity of an ecological system for dealing with the external pressure, and mainly comes from the recovery capacity of the tidal flat determined by the structural property of the tidal flat. The adaptability reflects the self-regulation capability of an ecological system, and indexes of chlorophyll a concentration, zooplankton density, roe and fry density are selected.
The fitness index evaluation method comprises the following steps:
assigning the pixel value after chlorophyll a concentration interpolation according to the table 5 to obtain a chlorophyll a concentration factor C 9 。
Assigning the pixel value after the zooplankton density interpolation according to the table 5 to obtain a zooplankton density factor C 10 。
Assigning the pixel value after roe density interpolation according to table 5 to obtain roe density factor C 11 。
Assigning the pixel value after the fry density interpolation according to the table 5 to obtain a fry density factor C 12 。
Table 5 fitness assignment criteria
Note: and B value determination, wherein if the sampling data is B value 10 in spring, the sampling data is B value 5 in summer.
And evaluating the fitness, wherein the calculation formula is as follows:
wherein, AI is a fitness index,
denotes the ith 3 Weight of each, C 9 、C 10 、C 11 、C 12 Chlorophyll a concentration factor, zooplankton density factor, roe density factor and fry density factor.
And S7, performing weighted calculation on the exposure index obtained in the step S4, the sensitivity index obtained in the step S5 and the fitness index obtained in the step S6 to obtain a marine ecological vulnerability evaluation index and a vulnerability grade.
Vulnerability consists of three dimensions of exposure, sensitivity and fitness. The exposure represents the extrinsic risk involved in the development activity of the ecosystem. The sensitivity is from the perspective of biological and habitat sensitivity, and the biological and habitat sensitivity is considered in index selection. Fitness is derived from the ability of the ecosystem to handle actual or expected stresses. The ecological vulnerability index is composed of two positive indicators and one negative indicator, the exposure and the sensitivity play a positive role in the vulnerability, and the fitness plays a negative role. The calculation formula is as follows:
EVI＝EI+VI+AI (6)
wherein EVI is a ecological vulnerability composite index. EI. VI and AI are respectively exposure index, sensitivity index and fitness index.
Marine ecological vulnerability was classified into 5 grades of extreme vulnerability, high vulnerability, medium vulnerability, low vulnerability and no vulnerability, wherein extreme vulnerability: 4.21-5.0, high fragility: 3.41-4.2, medium fragile: 2.61-3.4, low fragility: 1.81-2.6, not fragile: 1-1.8.
The utility model provides a coastal mud flat ecological vulnerability evaluation system, includes input module, evaluation index weight calculation module, exposure evaluation module, sensitivity evaluation module, fitness evaluation module, vulnerability grade module, output module, wherein:
the input module is used for inputting basic data of a research area, preprocessing the data and unifying the data to the same grid pixel size.
And the evaluation index weight calculation module is used for calculating to obtain a weight vector of each index.
The exposure degree evaluation module is used for obtaining an exposure degree index EI according to the interference degree and the coast erosion in the basic data input into the research area and the weight vector calculated by the evaluation index weight calculation module.
The sensitivity evaluation module is used for obtaining a sensitivity index VI according to the phytoplankton diversity, the zooplankton diversity, the large zooplankton diversity, the swimming animal diversity, the ecological system for identifying important habitat and special protection value in the basic data input into the research area and the weight vector obtained by the evaluation index weight calculation module.
And the fitness evaluation module is used for obtaining a fitness index AI according to the chlorophyll a concentration, the zooplankton density, the roe density and the fry density in the basic data input into the research area and the weight vector calculated by the evaluation index weight calculation module.
And the vulnerability grade module is used for weighting and calculating according to the exposure index EI, the sensitivity index VI and the fitness index AI to obtain an ecological vulnerability comprehensive index and obtaining the vulnerability grade according to the ecological vulnerability comprehensive index.
And the output module is used for outputting the obtained vulnerability grade.
As shown in fig. 3, which is a diagram of the evaluation result of ecological vulnerability of Jiangsu coastal beaches according to the embodiment of the present invention, the evaluation index fully considers the interference of ocean development activities on ecological vulnerability and the sensitivity of important habitat, and the evaluation result is objective.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (9)
1. The method for evaluating the ecological vulnerability of the coastal mudflat is characterized by comprising the following steps:
s1, constructing a coastal beach ecological vulnerability evaluation index system based on exposure, sensitivity and fitness;
step S2, calculating the weight of the evaluation index, wherein the method comprises the following steps:
step S21: selecting n experts in the field of evaluation of brittleness of coastal mudflat, evaluating and weighting m indexes, and constructing an index weight matrix W (1) ；
wherein ,wij The evaluation empowerment of the j index by experts in the ith coastal beach vulnerability evaluation field is shown, i =1,2,3, \ 8230; n, j =1,2,3, \ 8230; m;
step S22: calculating the average weight of the expert group by adopting an average value method;
wherein ,
represents the average weight of the j index;
step S23: calculating the deviation degree between the assignment weight of a single expert and the weights of other experts;
wherein ,
denotes the ith 0 The expert in the field of evaluation of vulnerability of coastal mudflat empowers the evaluation of the jth index,
denotes the ith 1 The expert in the field of evaluation of the vulnerability of the coastal mudflat empowers the evaluation of the jth index,
indicates the ith for the jth index 0 Ridge for evaluating brittleness of coastal mudflatExperts in the domain empower the evaluation of the mth index,
for the jth index ith 1 The evaluation of the mth index is empowered by experts in the field of evaluation of the vulnerability of coastal mudflats, A k,j Representing the degree of deviation between the assigned weight of the kth expert for the jth index and the weights of other experts, w k,j Showing that the evaluation empowerment of the j index by the expert in the k coastal beach vulnerability evaluation field, w i+1,j Showing that experts in the field of the vulnerability assessment of the i +1 th coastal beach assign weights to the assessment of the j index,
represents the average weight of the j index, k =1,2, \8230; n;
step S24: calculating the degree of deviation lambda between experts i And normalizing the deviation degree of the n-bit expert weight evaluation to obtain the weight influence factor W (q) ；
W (q) ＝(λ 1 ,λ 2 ,λ 3 ,…,λ n ) T
Step S25: according to the evaluation weight matrix W of n experts (1) Combining the influence factor W of the weights (q) Finally, the weight vector W of m indexes is obtained (2) ；
W (2) A weight vector representing m indices;
s3, collecting basic data of a research area, preprocessing the data, and unifying the data to the same grid pixel size so as to facilitate spatial operation;
s4, carrying out exposure evaluation, carrying out interference degree and coast erosion evaluation, and obtaining an evaluation unit exposure index EI pixel by pixel;
s5, developing sensitivity evaluation, and acquiring a sensitivity index VI pixel by pixel based on phytoplankton diversity, zooplankton diversity, large benthonic animal diversity, swimming animal diversity, identification of important habitat and ecological system with special protection value;
s6, carrying out fitness evaluation, and obtaining a fitness index AI pixel by pixel based on chlorophyll a concentration, zooplankton density, roe density and fry density;
and S7, carrying out weighted calculation on the exposure index obtained in the step S4, the sensitivity index obtained in the step S5 and the fitness index obtained in the step S6 to obtain an ecological vulnerability comprehensive index, and obtaining a vulnerability grade according to the ecological vulnerability comprehensive index.
2. The ecological vulnerability assessment method of coastal mudflat according to claim 1, characterized in that: in the step S1, the constructed coastal beach ecological vulnerability evaluation index system comprises the following evaluation indexes: interference degree, coast erosion rate, phytoplankton diversity, zooplankton diversity, large benthos diversity, swimming animal diversity, important habitat, ecosystem of special protection value, chlorophyll a concentration, zooplankton density, roe density, larval and juvenile fish density.
3. The ecological vulnerability assessment method of coastal mudflat according to claim 2, characterized in that: in the step S3, the collected basic data comprise important habitat distribution and important wetlands such as ocean basic geography, sea area development and utilization, coastlines, phytoplankton, zooplankton, large benthonic animals, swimming animals, chlorophyll a, fish egg and fish larva density monitoring data, rare or endangered species habitat and the like.
4. The coastal beach ecological vulnerability assessment method according to claim 3, characterized in that: in step S4, the exposure evaluation includesInterference factor C 1 And coastal erosion factor C 2 ；
Interference factor C 1 Evaluating, and carrying out assignment determination according to the sea area use type and the topographic features; an undeveloped shallow sea water area outside the 0 m-depth line is used as a non-interference area, and is assigned with the value of 1; the undeveloped sea areas from the coastline to the depth line of 0m and the like are used as low interference and are assigned as 2, and the open sea for cultivation, the sea for artificial fish reefs and the sea for coastal protection engineering are used as medium interference and are assigned as 1; taking fish, shrimp and crab culture sea areas in the depth lines from the coastline to 0m and the like and enclosure culture sea areas outside the depth lines from 0m and the like as strong interference, and assigning a value of 4; the fishery infrastructure uses the sea, the industrial sea, the transportation sea, the travel and entertainment sea, the seabed engineering sea, the pollution discharge and dumping sea, the land making engineering sea as the strong interference, and the value is 5;
erosion factor C to coast 2 The evaluation is carried out, and the calculation formula is as follows:
wherein D1 and D2 are the shoreline positions at times T1 and T2, respectively, and EPR represents the coastal erosion rate; for silt and silt, the coastal erosion rate is more than or equal to 10m/a, and the coastal erosion factor C 2 Assigning a value of 5; for silt and silt, the coast erosion rate is 1-10 m/a, and the coast erosion factor C 2 Assigning 3; for silt, the coastal erosion rate is less than 1.0m/a, and the coastal erosion factor C 2 Assigning a value of 1; for sandy shoreline, the coastal erosion rate is more than or equal to 2.0m/a, and the coastal erosion factor C 2 Assigning a value of 5; for sandy shoreline, the coastal erosion rate is 0.5-2.0 m/a, and the coastal erosion factor C 2 Assigning 3; for sandy shoreline, the coastal erosion rate is less than 0.5m/a, and the coastal erosion factor C 2 Assigning a value of 1;
and evaluating the exposure degree, wherein the calculation formula is as follows:
wherein EI is exposure index, C 1 In order to be a factor of the degree of interference,
as an interference factor weight, C 2 Is a coastal erosion factor, and the water quality of the water is improved,
weighting the coastal erosion factor.
5. The coastal beach ecological vulnerability assessment method according to claim 4, characterized in that: in step S5, the sensitivity evaluation method is as follows:
evaluating the diversity of phytoplankton, wherein the calculation formula is as follows:
wherein H' is the Shanon-Weaver diversity index value, R is the total number of species, P r Expressing the ratio of the number of the r species to the total number of all the species;
TABLE 4 sensitivity assignment criteria
Calculating a phytoplankton diversity index, a zooplankton diversity index, a large benthos diversity index and a swim animal diversity index according to a formula 3; the obtained index of diversity of phytoplankton is given in Table 4 for the diversity of phytoplankton C 3 Evaluation and assignment are carried out, and the obtained zooplankton diversity index is used for zooplankton diversity C according to the table 4 4 Evaluating and assigning to obtain diversity index of large benthonic animals according to Table 4 5 Evaluation and assignment were performed, and the obtained swimming animal diversity index was for swimming animal diversity C according to Table 4 6 Evaluating and assigning; to important habitat C 7 Evaluating important habitats including a rare or endangered species habitat, a migratory bird migration channel and a three-field one-channel distribution area, and if the grid unit is positioned in the range of the important habitat area, the important habitat C 7 The value is 5, otherwise the important habitat C 7 Is 1; other special ecosystems C 8 Evaluating if the grid unit is within the important habitat area, other special ecosystems C 8 The value is 5, otherwise other special ecosystems C 8 Is 1;
and evaluating the sensitivity, wherein the calculation formula is as follows:
wherein VI is a sensitivity index,
denotes the ith 2 Weight of C 3 、C 4 、C 5 、C 6 、C 7 、C 8 Respectively phytoplankton diversity, zooplankton diversity, large-scale zoobenthos diversity, swimming animal diversity, important habitat and other special ecological systems.
6. The coastal beach ecological vulnerability assessment method according to claim 5, characterized in that: in step S6, the fitness evaluation method is as follows:
assigning the pixel value after chlorophyll a concentration interpolation according to the table 5 to obtain a chlorophyll a concentration factor C 9 ；
Assigning the pixel value after the zooplankton density interpolation according to the table 5 to obtain a zooplankton density factor C 10 ；
Assigning the pixel value after roe density interpolation according to table 5 to obtain roe density factor C 11 ；
Assigning the pixel value after the fry density interpolation according to the table 5 to obtain the fryDensity factor C 12 ；
Table 5 fitness assignment criteria
And evaluating the fitness, wherein the calculation formula is as follows:
wherein, AI is a fitness index,
denotes the ith 3 Weight of C 9 、C 10 、C 11 、C 12 Chlorophyll a concentration factor, zooplankton density factor, roe density factor and larval fish density factor.
7. The coastal beach ecological vulnerability assessment method of claim 6, which is characterized in that: the calculation formula of the ecological vulnerability comprehensive index EVI in the step S7 is as follows:
EVI＝EI+VI+AI (6)
wherein EVI is a ecological fragile composite index; EI. VI and AI are respectively exposure index, sensitivity index and fitness index.
8. The coastal beach ecological vulnerability assessment method according to claim 7, characterized in that: step S7, the marine ecological vulnerability is classified into 5 grades of extremely vulnerable, high vulnerable, medium vulnerable, low vulnerable and non-vulnerable, wherein the extremely vulnerable: the ecological vulnerability comprehensive index is 4.21-5.0; high fragility: the ecological fragile comprehensive index is 3.41-4.2; medium vulnerability: the ecological vulnerability comprehensive index is 2.61-3.4; low vulnerability: the ecological vulnerability comprehensive index is 1.81-2.6; not fragile: the ecological vulnerability comprehensive index is 1-1.8.
9. An evaluation system based on the coastal beach ecological vulnerability evaluation method of claim 1, characterized in that: the system comprises an input module, an evaluation index weight calculation module, an exposure evaluation module, a sensitivity evaluation module, a fitness evaluation module, a vulnerability grade module and an output module, wherein:
the input module is used for inputting basic data of a research area, preprocessing the data and unifying the data to the same grid pixel size;
the evaluation index weight calculation module is used for calculating to obtain a weight vector of each index;
the exposure degree evaluation module is used for obtaining an exposure degree index EI according to the interference degree and the coast erosion in the basic data input into the research area and the weight vector calculated by the evaluation index weight calculation module;
the sensitivity evaluation module is used for obtaining a sensitivity index VI according to the phytoplankton diversity, the zooplankton diversity, the large zoobenthos diversity, the swimming animal diversity, the ecological system for identifying important habitat and special protection value in the basic data input into the research area and the weight vector obtained by the evaluation index weight calculation module;
the fitness evaluation module is used for obtaining a fitness index AI according to the chlorophyll a concentration, the zooplankton density, the roe density and the fry density in basic data input into the research area and a weight vector calculated by the evaluation index weight calculation module;
the vulnerability grade module is used for weighting and calculating according to the exposure index EI, the sensitivity index VI and the fitness index AI to obtain an ecological vulnerability comprehensive index and obtaining the vulnerability grade according to the ecological vulnerability comprehensive index;
and the output module is used for outputting the obtained vulnerability grade.
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