Method for determining preferential recovery area of marsh wetland

Abstract

The invention discloses a marsh wetland priority recovery area determination method, and belongs to the technical field of ecological system priority recovery area determination. Comprising the following steps: acquiring multi-stage data of a research area, and constructing a marsh wetland priority recovery area evaluation index system; determining the evaluation index weight of the marsh wetland priority recovery area in the evaluation index system of the marsh wetland priority recovery area by adopting an entropy weight method and an AHP (Analytic Hierarchy Process); calculating evaluation indexes of each marsh wetland priority recovery area; performing normalization processing on evaluation indexes of each marsh wetland priority recovery area by using fuzzy membership calculation; and converting the normalized value of the evaluation index of each marsh wetland priority recovery area into raster data, and calculating the marsh wetland recovery priority index of each raster in the research area. According to the method, the accuracy and the effectiveness of determining the marsh wetland priority recovery area can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of determining priority restoration areas of ecosystems, and more specifically to a method for determining priority restoration areas of swamp wetlands. Background Art

[0002] Swamp wetlands refer to low-lying areas that are flooded all year round or temporarily, and the soil is rich in water. Swamp wetlands play an extremely important role in the ecosystem, with multiple ecological functions such as conserving water resources, purifying water quality, storing flood water and preventing drought, and maintaining biodiversity. At the same time, swamp wetlands are also irreplaceable foraging and breeding grounds for many wetland birds, playing a pivotal role in the material cycle, energy flow and maintenance of the stability of the ecosystem. Moreover, swamp wetlands are particularly sensitive to climate change and human activities. Swamp wetlands have become one of the most threatened ecosystems in the world and are continuing to degrade.

[0003] At present, there is no evaluation technology and method for determining priority restoration areas for marsh wetlands. Related research mainly focuses on the identification of wetland or biodiversity priority protection areas, which mainly focuses on determining the protection priority sequence after evaluating the protection priority of existing wetland areas. The goals are different from those of determining priority restoration areas for marsh wetlands, and the methods are also very different. Priority protection area identification is mainly evaluated from the aspects of the irreplaceability and threat level of geographical units, the typicality and endangerment of ecosystems, and the diversity and endangerment of species, including biodiversity hotspots and key biodiversity areas, which are mainly used for the rational layout and site selection of nature reserves. At present, a variety of analysis software such as C-PlanSites, Spots, and Marxan have been derived, but it is difficult to comprehensively investigate and accurately determine the problem of priority restoration areas for marsh wetlands.

[0004] Therefore, how to provide a method for determining priority restoration areas of marsh wetlands that can solve the above-mentioned problems is an issue that technical personnel in this field urgently need to solve. Summary of the invention

[0005] In view of this, the present invention provides a method for determining priority restoration areas of swamp wetlands, so as to improve the accuracy and effectiveness of determining priority restoration areas of swamp wetlands.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A method for determining a priority restoration area for a marsh wetland comprises the following steps:

[0008] Obtain multi-period data from the study area and construct an evaluation index system for priority restoration areas of marsh wetlands;

[0009] The entropy weight method and analytic hierarchy process were used to determine the weights of the evaluation indicators of the priority restoration areas of swamp wetlands in the evaluation indicator system of the priority restoration areas of swamp wetlands.

[0010] Calculate the evaluation index values ​​of each marsh wetland priority restoration area;

[0011] The fuzzy membership calculation was used to normalize the evaluation indicators of each priority restoration area of ​​marsh wetlands;

[0012] The normalized values ​​of the assessment indicators of each marsh wetland priority restoration area were converted into raster data, and the marsh wetland restoration priority index of each grid in the study area was calculated.

[0013] Furthermore, the entropy weight method and analytic hierarchy process were used to determine the weights of the evaluation indicators of the priority restoration areas of swamp wetlands in the evaluation indicator system of the priority restoration areas of swamp wetlands, including:

[0014] Construct a normalized matrix:

[0015]

[0016] Among them, X ij is the jth sample of the i-th evaluation index object, y ij The samples are processed after standardization;

[0017] Calculate information entropy:

[0018]

[0019]

[0020] Among them, P ij is the proportion of the jth sample value of the i-th evaluation index object; e is the information entropy value;

[0021] Calculate the information utility value:

[0022] d i =1-e i

[0023] Among them, d is the information utility value;

[0024] Calculate the weight coefficient:

[0025]

[0026] Among them, β i is the weight coefficient;

[0027] Calculate the weights of assessment indicators for priority restoration areas of marsh wetlands:

[0028]

[0029] In the formula, w i is the evaluation index weight of the priority restoration area of ​​swamp wetlands, α i is the weight coefficient based on AHP, β i is the weight coefficient obtained based on the entropy weight method.

[0030] Furthermore, the assessment indicators for each of the priority restoration areas of the marsh wetlands include:

[0031] Climate dryness and wetness, habitat quality, terrain humidity index, distance to the river, water network density, population density, road density, GDP value, land use type, and swamp wetland evolution trend.

[0032] The normalized values ​​of the assessment indicators of each priority restoration area of ​​swamp wetlands are converted into raster data, including

[0033] Furthermore, the fuzzy membership calculation was used to normalize the evaluation indicators of each priority restoration area of ​​swamp wetlands. The calculation formula is:

[0034]

[0035] In the formula, Fuzzy Linear(x) is the normalized value of evaluation index i, between 0 and 1; x is the calculated value of evaluation index i; x min is the minimum value of evaluation index i; x max is the highest value of the evaluation index i.

[0036] Furthermore, the wetland restoration priority index of each grid in the study area was calculated, including:

[0037]

[0038] Where W is the priority index for wetland restoration, α i is the weight obtained by AHP method, β i is the weight obtained by the entropy weight method; x i Assign a score to each indicator.

[0039] Specifically, the indicator weight in the present invention refers to the indicator weight obtained by the entropy weight method and the AHP method, and the priority index refers to the result obtained by combining the weight with the normalized indicator value, and the priority restoration area is determined by this result. The score of each indicator comes from the original grid data, and the score of some indicators comes from the expert scoring.

[0040] It can be seen from the above technical solution that, compared with the prior art, the present invention discloses a method for determining priority restoration areas for swamp wetlands, which can objectively and comprehensively determine priority restoration areas for swamp wetlands, effectively provide target areas for swamp wetland restoration, and improve the accuracy and effectiveness of swamp wetland restoration projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0042] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] See also Figure 1 The embodiment of the present invention discloses a method for determining a priority restoration area of ​​a wetland, comprising the following steps:

[0045] Obtain multi-period data from the study area and construct an evaluation index system for priority restoration areas of marsh wetlands;

[0046] The entropy weight method and analytic hierarchy process were used to determine the weights of the evaluation indicators of the priority restoration areas of swamp wetlands in the evaluation indicator system of the priority restoration areas of swamp wetlands.

[0047] Calculate the evaluation index values ​​of each marsh wetland priority restoration area;

[0048] The fuzzy membership calculation was used to normalize the evaluation indicators of each priority restoration area of ​​marsh wetlands;

[0049] The normalized values ​​of the assessment indicators of each marsh wetland priority restoration area were converted into raster data, and the marsh wetland restoration priority index of each grid in the study area was calculated.

[0050] In a specific embodiment, an evaluation index system for priority restoration areas of marsh wetlands is constructed based on restoration potentials such as marsh-river / lake connectivity and marsh habitat quality, as well as restoration costs such as population density and land use type. The evaluation index system for priority restoration areas of marsh wetlands includes 10 indicators, including climate dryness and wetness, habitat quality, terrain humidity index, shortest distance to rivers, water network density, population density, road density, GDP, land use type, and marsh wetland evolution trend, as shown in Table 1-1.

[0051] Table 1-1 Evaluation index system for priority restoration areas of marsh wetlands

[0052]

[0053]

[0054] In a specific embodiment, the weight calculation of different evaluation indicators includes the following:

[0055] The weights of the evaluation indicators of the priority restoration area of ​​swamp wetlands are determined by the entropy weight method and the hierarchical analysis method. The weight of each indicator is calculated by the entropy weight method and the hierarchical analysis method, and then the average value is taken as the final weight of the evaluation indicator.

[0056] AHP is a hierarchical weight decision analysis method that can qualitatively and quantitatively solve multi-objective problems. In order to determine the priority restoration area of ​​swamp wetlands and clarify the weights of each selected indicator, the AHP analytic hierarchy process is used to calculate the weights of each evaluation indicator. First, the "AHP analytic hierarchy process questionnaire on the selection of priority restoration areas for swamp wetlands in Songnen Plain" was distributed using Questionnaire Star. A total of 22 questionnaire results were collected, of which 86% of the respondents were majored in wetlands, and all respondents were intermediate or above. The collected questionnaire results were tested for consistency, and the proportion correction method in the "AHP2" software was used to adjust the inconsistent questionnaire results to ensure that the adjusted matrix passed the consistency test and had a similarity of >85% with the original matrix. The weight calculation results of the evaluation indicators of the priority restoration area of ​​swamp wetlands based on the AHP analytic hierarchy process are shown in Table 1-2.

[0057] Table 1-2 Weights of evaluation indicators for priority restoration areas of wetlands based on AHP

[0058]

[0059]

[0060] The entropy weight method is an objective value assignment method based on the concept of information entropy, which measures the degree of dispersion of indicators by calculating the entropy value of each indicator, and then determines the weight of the indicator. In this embodiment, the entropy weight method is used to calculate the raster data of each evaluation indicator of the priority restoration area of ​​swamp wetlands, and the calculation formula is as follows.

[0061] (1) Constructing a standardized matrix

[0062]

[0063] Among them, X ij is the jth sample of the i-th evaluation index object, y ij The samples are processed after standardization;

[0064] (2) Information entropy

[0065]

[0066] Among them, P ij is the proportion of the jth sample value of the i-th evaluation index object; e is the information entropy value;

[0067] (3) Information utility value

[0068] d i =1-e i

[0069] Among them, d is the information utility value.

[0070] (4) Weight coefficient

[0071]

[0072] Among them, β i is the weight coefficient.

[0073] The calculation results of the weights of the evaluation indicators for the priority restoration areas of wetlands based on the entropy weight method are shown in Table 1-3

[0074] Table 1-3 Weights of evaluation indicators for priority restoration areas of wetlands based on entropy weight method

[0075] index Weight Climate dryness and humidity 1.5185％ Habitat quality 9.9259％ Topographic Wetness Index (TWI) 1.7432％ The closest distance to the river 0.1000％ Water network density 41.1289％ Population Density 0.0058％ Road density 0.0054％ GDP 0.0041％ Land use type 0.8155％ Current status of wetland evolution 44.7527％

[0076] Finally, the weights calculated based on the AHP hierarchical analysis method and the entropy weight method were averaged to obtain the weights of the evaluation indicators for the priority restoration area of ​​swamp wetlands, as shown in Table 1-4. The calculation formula for the weights of the evaluation indicators for the priority restoration area of ​​swamp wetlands is as follows:

[0077]

[0078] In the formula, w i is the evaluation index weight of the priority restoration area of ​​swamp wetlands, α i is the weight obtained based on AHP, β i is the weight obtained based on the entropy weight method.

[0079] Table 1-4 Final weights of evaluation indicators for priority restoration areas of wetlands

[0080]

[0081] In a specific embodiment, the acquisition and calculation of each evaluation index includes the following contents:

[0082] (1) Climate dryness and wetness:

[0083] Obtain meteorological data for the study area and use the difference between annual average precipitation and evaporation to calculate the degree of dryness and wetness of the climate. The larger the difference, the more conducive it is to replenishing atmospheric moisture in the wetland area. The calculation formula is as follows:

[0084]

[0085] In the formula, p i is the average annual precipitation; e i is the average annual evaporation; n is the number of years.

[0086] (2) Habitat quality:

[0087] A landscape type distribution map of the study area was prepared. Based on the landscape type distribution map, the habitat quality evaluation module of the InVEST model and the geographic information system software were used to evaluate the habitat quality, with the marsh wetland as the target habitat. The higher the habitat quality, the less affected it is by other landscape types and the greater its restoration potential. Human disturbance landscapes such as cultivated land, urban land, rural settlements, other construction land and unused land were used as sources of stress, and the parameter indicators of each landscape type in the study area were determined, as shown in Tables 1-5 and 1-6. The habitat quality calculation formula is as follows:

[0088]

[0089] In the formula, Q xj represents the habitat quality of grid x in land use type j; H j represents the habitat suitability of land use type j; D xj represents the stress level of grid x in land use type j; Z represents the normalization constant; k is the scaling constant.

[0090]

[0091] In the formula, R is the number of stress factors; r is the stress factor; y is the grid number of stress factor r; Y r is the number of grids occupied by the stress factor; W r is the stress factor weight, ranging from 0 to 1; i rxy is the impact of the stress factor r on each grid of the habitat (exponential or linear); β x is the habitat disturbance resistance level; S jr is the relative sensitivity of different habitats to each stress factor.

[0092] Among them, the formulas of linear attenuation function and exponential attenuation function are as follows:

[0093]

[0094] Where, d xy is the linear distance between grids x and y; drmax It is the maximum range of the threat factor.

[0095] Table 1-5 Stress sources and their weight parameters

[0096] Source of stress Maximum impact distance / km Weight Decay function arable land 4 0.9 Linear Urban land 9 0.8 index Rural settlements 5 0.6 index Other construction land 6 0.7 index Unused land 3 0.3 Linear

[0097] Table 1-6 Sensitivity parameters of different landscape types

[0098]

[0099] (3) Shape wetland index:

[0100] Obtain the terrain DEM data of the study area, use geographic information system software to fill the terrain DEM data, calculate flow direction and flow, calculate slope, etc., and obtain the terrain moisture index. The higher the terrain moisture index, the greater the restoration potential. The terrain moisture index calculation formula is as follows:

[0101]

[0102] Where TWI is the terrain wetness index; SCA is the runoff per unit area; slope is the slope.

[0103] (4) Shortest distance to the river:

[0104] Calculate the closest distance between the evaluation area and the river water body. The closer the distance, the easier it is for the wetland to replenish water and restore. The closest distance between the evaluation area and the river is calculated using the Euclidean distance analysis tool of the geographic information system. The calculation formula is as follows:

[0105]

[0106] Where d is the distance from the river; (x1, y1) and (x2, y2) represent the coordinates of two points respectively.

[0107] (5) Water network density:

[0108] Water network density refers to the ratio of the total length of the water system in a certain area to its area. The denser the water network, the easier it is for the wetland to be replenished and restored. The study area is divided into 1km grids to calculate the water network density. The water network density calculation formula is as follows:

[0109]

[0110] Where D μ is the water network density; L is the length of the water system in the grid; S is the grid area.

[0111] (6) Population density:

[0112] The population density of the study area was extracted using the “China Population Spatial Distribution Kilometer Grid Dataset”; the denser the population, the higher the cost of wetland restoration.

[0113] (7) Road density:

[0114] Road density refers to the ratio of the total length of roads in a certain area to the area; the denser the roads, the higher the cost of wetland restoration. The road density was calculated by dividing the study area into 1km grids. The formula for calculating road density is as follows:

[0115]

[0116] Where A is the road density; l i is the length of the road in the grid; S is the area of ​​the grid.

[0117] (8) GDP:

[0118] The GDP data of the study area was extracted using the “China GDP Spatial Distribution Kilometer Grid Dataset”, and GDP was used to reflect the level of regional economic development; the higher the GDP, the higher the cost of wetland restoration.

[0119] (9) Land use type:

[0120] Differences in land use types directly affect the cost of wetland restoration. The expert scoring method is used to determine the valuation standards for different land use types. See Table 1-7.

[0121] Table 1-7 Value assignment standards for different land use types

[0122] Land use type Assignment arable land 6.20 woodland 5.45 grassland 6.50 Waters 7.90 Urban and rural industrial and mining residential land 3.20 Unused land 5.95

[0123] (10) Evolution trend of swamp wetlands:

[0124] The degradation types of marsh wetlands in different regions were determined through multi-period land use data. The areas that have been marsh wetlands throughout the study period are stable; the areas that have changed from non-marsh wetlands to marsh wetlands are recovery types; the areas that have repeatedly changed between marsh wetlands and non-marsh wetlands are fluctuating types; and the areas that have changed from marsh wetlands to non-marsh wetlands are degraded types. The determination of the priority areas for marsh wetland restoration mainly analyzes the fluctuating type and degraded type, and uses the expert scoring method to determine the value standards for the fluctuating type and degraded type marsh wetlands, see Table 1-8.

[0125] Table 1-8 Assignment criteria for the evolution trend of swamp wetlands

[0126] Current status of wetland evolution Assignment Fluctuation 7.14 Degenerate 4.57

[0127] In a specific embodiment, the normalization process of the evaluation index assignment includes the following contents:

[0128] First, the data of each evaluation index of the priority restoration area of ​​swamp wetlands is normalized. The fuzzy membership calculation is used to normalize the existing grid data of each index. The calculation formula is as follows:

[0129]

[0130] In the formula, Fuzzy Linear(x) is the normalized value of evaluation index i, between 0 and 1; x is the calculated value of evaluation index i; x min is the minimum value of evaluation index i; x max is the highest value of the evaluation index i.

[0131] Specifically, the present invention first performs normalization processing, and then constructs a standardized matrix based on the values, and eliminates the influence of zero values.

[0132] In a specific embodiment, the calculation of the wetland restoration priority index includes the following:

[0133] Calculate the priority index of wetland restoration for each grid in the study area. Determine the priority level of wetland restoration according to the size of the wetland restoration priority index. The calculation formula of the wetland restoration priority index is as follows:

[0134]

[0135] Where W is the priority index for wetland restoration, w i Weights of indicators evaluated for priority restoration areas of marsh wetlands; x i Assign a score to each indicator.

[0136] Embodiment 2:

[0137] Taking the Songnen Plain as an example to carry out the priority restoration of marsh wetlands in the study area, various public data of the study area were collected through the data website.

[0138] Source of land use data: The current status and evolution of land use types are analyzed using 1km precision data from 2000, 2005, 2010, 2015 and 2020 in the China Multi-period Land Use Remote Sensing Monitoring Dataset (CNLUCC). The data comes from the Resource and Environmental Science Data Registration and Publishing System.

[0139] Meteorological data source: The precipitation and evaporation data of 2010s are collected from the spatial interpolation dataset of China's average meteorological elements. The data accuracy is 1km and the data comes from the Resource and Environmental Science Data Registration and Publication System.

[0140] DEM digital elevation data: DEM digital elevation data comes from the geospatial data cloud DEM digital elevation data, SRTMDEM 90M resolution original elevation data.

[0141] Road data, water system data: The data comes from OpenStreetMap (OSM) maps.

[0142] GDP data: The 2019 China GDP spatial distribution kilometer grid dataset is used, with a data accuracy of 1km. The data comes from the Resource and Environmental Science Data Registration and Publishing System.

[0143] Population data: The 2019 China population spatial distribution kilometer grid dataset is used, with a data accuracy of 1km. The data comes from the Resource and Environmental Science Data Registration and Publishing System.

[0144] In a specific embodiment, data preprocessing includes:

[0145] Calculation of different evaluation indicators:

[0146] (1) Climate dryness and humidity:

[0147] The difference between precipitation and evaporation in the Songnen Plain in the past ten years was calculated to reflect the climate dryness and wetness in different regions.

[0148] (2) Habitat quality:

[0149] Habitat quality The habitat quality analysis module in the InVEST model was used to calculate the habitat quality of the Songnen Plain. Based on the 2020 China land use remote sensing data, swamp wetlands were used as habitats. Through literature review, land use analysis results, field investigations, and the InVEST model user manual, cultivated land, urban land, rural settlements, other construction land, and unused land were identified as sources of stress, and parameter indicators for each type were determined [10-15], see Tables 1-9 and 1-10.

[0150] Table 1-9 Stress sources and parameters

[0151]

[0152]

[0153] Table 1-10 Sensitivity parameters of various types of land

[0154]

[0155] (3) Topographic humidity index:

[0156] ArcGIS software is used to fill depressions, calculate flow direction and flow, calculate slope, and calculate terrain humidity index on DEM data.

[0157] (4) Shortest distance to the river:

[0158] The shortest distance to the river was calculated using the Euclidean distance analysis in the spatial analysis toolbox of ArcGIS software.

[0159] (5) Water network density:

[0160] The study area was divided into 1 km grids and the water network density was calculated.

[0161] (6) Population density:

[0162] The population density of the study area was extracted using the “2019 China Population Spatial Distribution Kilometer Grid Dataset”.

[0163] (7) Road density:

[0164] Since road grade has no effect on road density, various roads in the study area were merged and the road density was calculated after the study area was divided into 1 km grids.

[0165] (8) Road density:

[0166] The GDP data of the study area was extracted using the “2019 China GDP Spatial Distribution Kilometer Grid Dataset”.

[0167] (9) Land use type:

[0168] First, based on the "China Multi-period Land Use Remote Sensing Monitoring Dataset (CNLUCC)", the land use types were reclassified into 6 types: cultivated land, forest land, grassland, water area, urban and rural industrial and mining residential land, and unused land. Then, the expert scoring method was used to assign values ​​and scores to the swamp wetland restoration costs of the 6 land types and the average value was taken to obtain the assignment standards for each type, as shown in Table 1-12. The assigned value is between 0 and 10. The higher the assigned value, the lower the swamp wetland restoration cost.

[0169] Table 1-11 Land use type assignment standards using the AHP method

[0170] Land use type Assignment arable land 6.20 woodland 5.45 grassland 6.50 Waters 7.90 Urban and rural industrial and mining residential land 3.20 Unused land 5.95

[0171] (10) Evolution trend of swamp wetlands:

[0172] The land use data from 2000 to 2020 were used in a 5-year cycle to analyze the evolution trend of marsh wetlands. The determination of the priority restoration area was mainly based on the analysis of the fluctuation type and degradation type. The expert scoring method was used to assign values ​​and scores to the restoration costs of the two degradation trends of marsh wetlands and the average value was taken to obtain the assignment standards for the fluctuation type and degradation type marsh wetlands, as shown in Table 1-12. The assigned value is between 0 and 10. The higher the assigned value, the lower the marsh wetland restoration cost.

[0173] Table 1-12 Evaluation criteria for the evolution trend of swamp wetlands

[0174] Evolution trend of wetlands Assignment Fluctuation 7.14 Degenerate 4.57

[0175] In a specific embodiment, the normalization process of the evaluation index assignment includes:

[0176] The fuzzy membership calculation in ArcGIS software is used to normalize the existing raster data of each indicator. The calculation formula is as follows:

[0177]

[0178] In the formula, Fuzzy Linear(x) is the normalized value of evaluation index i, between 0 and 1; x is the calculated value of evaluation index i; x min is the minimum value of evaluation index i; x max is the highest value of the evaluation index i.

[0179] In a specific embodiment, data preprocessing is performed for 10 wetland restoration priority area assessment indicators.

[0180] Specifically, the situation of conversion from swamp wetland to other land use types and from other land use types to swamp wetland types within 20 years; the area of ​​swamp land conversion is automatically calculated by ArcGIS and then classified and calculated. The specific area data are shown in Table 1-13 and Table 1-14. The most converted type of swamp is cultivated land, which means that the largest area of ​​swamp land has been developed into cultivated land within 20 years, which is the first major factor in swamp degradation. The most converted type of other types of swamp is cultivated land, followed by water area.

[0181] Table 11-13 Swamp wetland transfer area

[0182]

[0183] Table 1-14 Area converted to swampland

[0184]

[0185] Specifically, in order to obtain the distribution area of ​​stable wetlands in the past 20 years, the land use type data from 2000-2005, 2005-2010, 2010-2015, and 2015-2020 were processed respectively to obtain the area where the wetlands were stable and unchanged within five years, and then the four sets of data were intersected year by year to obtain the distribution area of ​​stable wetlands in the past 20 years. Stable wetlands are mainly distributed in Qiqihar and Daqing, and are more dispersed in Heihe and Baicheng, and are concentrated in the southeast of Zhalong Nature Reserve and the northwest of Yueliangpao.

[0186] Specifically, by processing the land use data from 2000 to 2020, the areas that have been transformed from non-swampland to swampland and have always remained swampland were obtained as the distribution areas of restored swamps. The restored swamps are mainly concentrated along the Nenjiang River and the Songhua River, and are relatively scattered in the northern part of the Songnen Plain and less distributed in the southeast.

[0187] Specifically, by processing the land use data from 2000 to 2020, it is found that the areas where the marshes have been fluctuating are called fluctuating marshes. The area of ​​fluctuating marshes is small and scattered, and is relatively concentrated in the southern part of Zhalong Nature Reserve, Shengli Reservoir and near Yueliangpao, and is mainly distributed in Qiqihar City, Daqing City and Baicheng City.

[0188] Specifically, by processing the land use data from 2000 to 2020, it was found that the areas that have been converted from swamps to non-swamps and have remained non-swamps are defined as degraded types. The degraded swamps are widely distributed, mainly in the southwest of Zhalong Nature Reserve, the west of Yueliangpao, along the Songhua River and Nenjiang River, and more in the north, northeast and west of the Songnen Plain.

[0189] From the results of the calculation of the swamp wetland restoration priority index, it can be seen that the index is higher near Yueliangpao, Zhalong Wetland and along the rivers in the north and northeast of the Songnen Plain, and the distribution is more concentrated and continuous. It is the priority restoration area for swamp wetlands. Its distribution is consistent with the stable and restorative swamp wetland distribution area, and after restoration, it can form a relatively continuous swamp wetland landscape; followed by the forest land, unused land, water area and grassland area in the northeast, north, central and western Songnen Plain and western Xing'an League; the lowest priority restoration level is urban and rural, industrial and mining and residential land.

[0190] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0191] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for determining priority restoration areas of marsh wetlands, characterized in that: The following steps are involved: Obtain multi-period data from the study area and construct an evaluation index system for priority restoration areas of marsh wetlands; The entropy weight method and analytic hierarchy process were used to determine the weights of the evaluation indicators of the priority restoration areas of swamp wetlands in the evaluation indicator system of the priority restoration areas of swamp wetlands. Calculate the evaluation index values ​​of each marsh wetland priority restoration area; The fuzzy membership calculation was used to normalize the evaluation indicators of each priority restoration area of ​​marsh wetlands; The normalized values ​​of the assessment indicators of each marsh wetland priority restoration area were converted into raster data, and the marsh wetland restoration priority index of each grid in the study area was calculated.

2. A method for determining priority restoration areas of marsh wetlands according to claim 1, characterized in that: The entropy weight method and analytic hierarchy process were used to determine the weights of the evaluation indicators of the priority restoration areas of swamp wetlands in the evaluation indicator system of priority restoration areas of swamp wetlands, including: Construct a normalized matrix: Among them, X ij is the jth sample of the i-th evaluation index object, y ij The samples are processed after standardization; Calculate information entropy: Among them, P ij is the proportion of the jth sample value of the i-th evaluation index object; e is the information entropy value; Calculate the information utility value: d i =1-e i Among them, d is the information utility value; Calculate the weight coefficient: Among them, β i is the weight coefficient; Calculate the weights of assessment indicators for priority restoration areas of marsh wetlands: In the formula, w i is the evaluation index weight of the priority restoration area of ​​swamp wetlands, α i is the weight coefficient based on AHP, β i is the weight coefficient obtained based on the entropy weight method.

3. The method for determining priority restoration areas of marsh wetlands according to claim 1, characterized in that: The assessment indicators for each of the priority restoration areas of the marsh wetlands include: Climate dryness and wetness, habitat quality, terrain humidity index, distance to the river, water network density, population density, road density, GDP value, land use type, and swamp wetland evolution trend.

4. A method for determining priority restoration areas of wetlands according to claim 1, characterized in that: The normalized values ​​of the assessment indicators of each priority restoration area of ​​swamp wetlands are converted into raster data, including The fuzzy membership calculation was used to normalize the evaluation indicators of each priority restoration area of ​​swamp wetlands. The calculation formula is: In the formula, Fuzzy Linear(x) is the normalized value of evaluation index i, between 0 and 1; x is the calculated value of evaluation index i; x min is the minimum value of evaluation index i; x max is the highest value of the evaluation index i.

5. The method for determining priority restoration areas of marsh wetlands according to claim 1, characterized in that: Calculate the marsh wetland restoration priority index for each grid in the study area, including: Where W is the priority index for wetland restoration, α i is the weight obtained by AHP, β i is the weight obtained by the entropy weight method; x i Assign a score to each indicator.

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