Method and device for identifying priority order of urban ecological restoration, and electronic equipment

Abstract

The application provides a kind of urban ecological restoration priority order identification method, device and electronic equipment, the method comprises: obtaining the spatial distribution data of multiple ecosystem service indicators in different years in target urban area and carrying out standardization processing;According to the spatial distribution data of multiple ecosystem service indicators in different years after standardization processing, determine the ecosystem service cluster value ESB of each pixel corresponding to the preset grid scale in different years in target urban area;According to the ESB of each pixel in different years and impervious surface index value ISA, determine the ecological restoration target year period;For any target pixel, according to its ISA change rate in the ecological restoration target year period, and the ESB change rate of its neighborhood pixel in the ecological restoration target year period, determine its ecological restoration priority order. Prior area of urban ecological restoration area can be divided, improve the efficiency and economy of urban area ecological restoration.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ecological restoration, and in particular to a method and device for identifying the priority order of urban ecological restoration, and an electronic device. BACKGROUND

[0002] Ecological restoration is considered as an important way to effectively alleviate the negative impact of urbanization on the ecosystem. In urban areas, ecological restoration needs to determine priority areas through spatial analysis techniques, establish planning layout measures to ensure the target space of ecological restoration, accurately divide ecological restoration areas, establish importance evaluation of ecological restoration, and determine the priority level of restoration.

[0003] Currently, ecological restoration is mainly applied to key ecological areas such as natural landscapes, habitats, and nature reserves. The division of urban ecological restoration areas often uses statistical data of a single year to divide ecological restoration areas, or divides uniform ecological restoration areas for fixed research ranges. Therefore, the potential impact of urban development on ecosystem services is ignored, and the spatial heterogeneity of ecosystem services due to social development and natural conditions within the city is also ignored. SUMMARY

[0004] In view of the problems in the prior art, the present application provides a method and device for identifying the priority order of urban ecological restoration, and an electronic device.

[0005] In a first aspect, the present application provides a method for identifying the priority order of urban ecological restoration, comprising:

[0006] obtaining spatial distribution data of a plurality of ecosystem service indicators in different years in a target urban area and performing standardization processing;

[0007] determining the ecosystem service cluster value ESB of each pixel corresponding to a preset grid scale in the target urban area in different years according to the spatial distribution data of the plurality of ecosystem service indicators in different years after standardization processing;

[0008] determining an ecological restoration target year period according to the ESB of each pixel in different years and the impervious surface index value ISA of each pixel in different years;

[0009] for any target pixel in each pixel, determining the ecological restoration priority order of the target pixel according to the ISA change rate of the target pixel in the ecological restoration target year period and the ESB change rate of the neighborhood pixels of the target pixel in the ecological restoration target year period.

[0010] Optionally, the plurality of ecosystem service indicators comprises habitat quality, carbon storage, soil retention, water production, food supply, fruit supply, and park service capacity.

[0011] Optionally, the determining the target period of ecological restoration according to the ESB of each pixel in different years and the impervious surface index ISA of each pixel in different years comprises:

[0012] According to the ESB of each pixel in different years and the impervious surface index ISA of each pixel in different years, a linear regression model is established to determine the slope value of the change of ESB relative to ISA in different years;

[0013] According to the years corresponding to the maximum and the second maximum of the slope values, the target period of ecological restoration is determined.

[0014] Optionally, the determining the ecological restoration priority order of the target pixel according to the ISA change rate of the target pixel in the target period of ecological restoration and the ESB change rate of the neighborhood pixels of the target pixel in the target period of ecological restoration comprises:

[0015] According to the ISA change rate of the target pixel in the target period of ecological restoration and the ESB change rate of the neighborhood pixels of the target pixel in the target period of ecological restoration, a bivariate Moran index corresponding to the target pixel is determined, and the bivariate Moran index is used to represent the spatial correlation between ESB and ISA;

[0016] According to the bivariate Moran index corresponding to the target pixel and the significance test result, the ecological restoration priority order of the target pixel is determined.

[0017] Optionally, the determining the ecological restoration priority order of the target pixel according to the bivariate Moran index corresponding to the target pixel and the significance test result comprises:

[0018] According to the bivariate Moran index corresponding to the target pixel and the significance test result, a spatial cluster area type corresponding to the target pixel is determined.

[0019] According to the spatial cluster area type, the ecological restoration priority order of the target pixel is determined.

[0020] The spatial cluster area type comprises the following five types:

[0021] A high-high cluster area, which is used to represent a cluster area with high urbanization intensity and high loss of surrounding ecosystem services;

[0022] A high-low agglomeration zone, which is used to represent an agglomeration zone with high urbanization intensity but relatively small loss of surrounding ecosystem services;

[0023] A low-high agglomeration zone, which is used to represent an agglomeration zone with relatively small changes in urbanization intensity but relatively high loss of surrounding ecosystem services;

[0024] A low-low agglomeration zone, which is used to represent an agglomeration zone with relatively small changes in urbanization intensity and relatively small loss of surrounding ecosystem services;

[0025] A non-significant impact zone, which is used to represent an agglomeration zone with balanced changes in urbanization intensity and ecosystem services, good ecosystem services, or good effects of post-urbanization ecosystem restoration, or low degree of urbanization impact.

[0026] Optionally, the method further comprises:

[0027] Performing ecosystem service index cluster analysis of a preset grid scale according to the spatial distribution data of the plurality of ecosystem service indexes in different years after standardization processing, to determine the ecosystem service cluster type of each pixel corresponding to the preset grid scale in the target urban area in different years.

[0028] In a second aspect, the present application further provides a device for identifying urban ecological restoration priority order, comprising:

[0029] A standardization processing module, configured to acquire spatial distribution data of a plurality of ecosystem service indexes in different years in a target urban area and perform standardization processing;

[0030] A first determination module, configured to determine an ecosystem service cluster value ESB of each pixel corresponding to a preset grid scale in the target urban area in different years according to the spatial distribution data of the plurality of ecosystem service indexes in different years after standardization processing;

[0031] A second determination module, configured to determine an ecological restoration target year period according to the ESB of each pixel in different years and an impervious surface index value ISA of each pixel in different years;

[0032] A third determination module, configured to determine an ecological restoration priority order of any target pixel in the each pixel according to an ISA change rate of the target pixel in the ecological restoration target year period and an ESB change rate of a neighborhood pixel of the target pixel in the ecological restoration target year period.

[0033] In a third aspect, the present application also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the urban ecological restoration priority order identification method according to the first aspect.

[0034] In a fourth aspect, the present application also provides a non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the urban ecological restoration priority order identification method according to the first aspect.

[0035] In a fifth aspect, the present application also provides a computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements the urban ecological restoration priority order identification method according to any one of the above aspects.

[0036] The urban ecological restoration priority order identification method, device and electronic device provided by the present application can determine the ecological restoration target year period by obtaining the spatial distribution data of a plurality of ecosystem service indicators in different years in the target urban area and standardizing the data, and determining the ecosystem service cluster value ESB and the impervious surface index value ISA of each pixel corresponding to a preset grid scale in the target urban area in different years. Then, the ecological restoration priority order of any target pixel in the ecological restoration target year period is determined according to the ISA change rate of the target pixel and the ESB change rate of the field pixel of the target pixel in the period. Furthermore, the priority area of the urban ecological restoration area can be divided, and the efficiency and economy of urban area ecological restoration can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort.

[0038] Figure 1 A flowchart of the urban ecological restoration priority order identification method provided by the present application;

[0039] Figure 2 A flowchart of the urban ecological restoration priority area division provided by the present application;

[0040] Figure 3 A structural diagram of the urban ecological restoration priority order identification device provided by the present application;

[0041] Figure 4 A structural diagram of the electronic device provided by the present application. DETAILED DESCRIPTION

[0042] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below in conjunction with the accompanying drawings in the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0043] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below in conjunction with the accompanying drawings in the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0044] Ecological restoration is considered as an important way to effectively alleviate the negative impact of urbanization on the ecological system. In the management of urban ecological systems, ecological restoration for the improvement of urban ecological system service capacity has not been fully integrated into the national spatial ecological restoration system, making it difficult to be effectively implemented in national spatial planning. Ecological restoration in urban areas requires systematic coordination of urbanization level, ecological restoration demand and ecological system service improvement planning layout.

[0045] Therefore, through spatial analysis technology to determine the priority area, establish planning layout measures to ensure the target space of ecological restoration, accurately divide the ecological restoration area, establish the importance evaluation of ecological restoration and establish the priority of restoration, are the key technical problems currently faced by urban ecological planning and management.

[0046] Landscape optimization is an important technical method for determining the priority area of ecological restoration. Based on the theory of landscape ecology, it adjusts and combines the spatial elements of landscape pattern and ecological process feedback relationship to achieve the maximum ecological benefit and regional sustainable development. Landscape optimization adjusts the components and spatial configuration characteristics of landscape patches to achieve the goals of biodiversity protection, ecosystem management and landscape sustainability. Coupling the supply of ecosystem services and the demand of urban residents for ecosystem services, facing the pattern and function feedback of ecological system, carrying out landscape optimization, improving and adjusting the landscape structure and function layout, restoring and rebuilding ecological landscape, and improving regional ecosystem services, are important goals of landscape optimization, which provide technical support for urban ecological planning and national spatial ecological restoration layout.

[0047] In the past, ecological restoration technology is mainly applied to key ecological areas such as natural landscape, habitat and nature reserve. In high-intensity urbanization areas, there are complex human-nature interactions, and the demand, frequency and effect of restoration are much greater than those of key ecological land, but the restoration layout and restoration measures are relatively difficult to implement space, and the reason is that there is a large land use contradiction, and the value of ecological land is often ignored. Urbanization changes the landscape pattern and also changes the ecosystem services. With the decrease of ecosystem service supply and the increase of demand, the social and economic development and human activities in urban areas are usually higher than those in other natural spaces, and cities are facing various challenges of imbalance between supply and demand of ecosystem services.

[0048] Therefore, developing an ecological restoration layout technology based on the coupling of urbanization effect and urbanization development level can fully utilize the multi-functional properties of the ecosystem and the various needs of urban residents for ecosystem services, and thus provide a layout scheme for the reconstruction, restoration and restoration of fragmented ecological land, which helps to improve the management level of urban ecosystems.

[0049] The urban ecological restoration priority order identification method, device and electronic equipment provided by the application can determine the Ecosystem Service Bundles (ESB) and Impervious Surface Area (ISA) of each pixel corresponding to a preset grid scale in the target urban area in different years by obtaining the spatial distribution data of multiple ecosystem service indicators in different years and standardizing the data, and can determine the ecological restoration target year period. Then, according to the ISA change rate of any target pixel in the ecological restoration target year period and the ESB change rate of the field pixel of the target pixel in the period, the ecological restoration priority order of the target pixel is determined. Further, the priority area of the urban ecological restoration area can be divided, and the efficiency and economy of urban area ecological restoration can be improved.

[0050] Figure 1 A flowchart of a city ecological restoration priority order identification method provided by the application is shown in FIG. Figure 1 As shown in the figure, the method comprises:

[0051] Step 100, obtaining spatial distribution data of multiple ecosystem service indicators in different years in the target urban area and performing standardization processing.

[0052] Specifically, first, determine multiple ecosystem service indicators for evaluating the ecosystem services of the target urban area. After determining the ecosystem service indicators, obtain the spatial distribution data of the multiple ecosystem service indicators in the target urban area in different years.

[0053] Optionally, the plurality of ecosystem service indicators can include:

[0054] (1) Habitat quality.

[0055] According to the existing land use type data, different landscape types in the target urban area can be divided into three categories according to the natural degree, i.e., natural landscape (such as forest land, grassland, water area, tidal flat, and bare land, etc.), semi-natural landscape (such as garden land and cultivated land, etc.), and artificial landscape (such as urban construction land, etc.).

[0056] The habitat quality of different landscape types decreases with the decrease of the natural degree, and is affected by the stress factors such as urban construction land, bare land, garden land, cultivated land, and road.

[0057] In an embodiment, the Habitat Quality module in the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model can be used to evaluate the habitat quality, and the evaluation result is a dimensionless habitat quality index, which ranges from 0 to 1, and the higher the value, the better the habitat quality, so as to obtain the spatial distribution data of the habitat quality in different years.

[0058] (2) Carbon storage.

[0059] In an embodiment, based on the land use type data and / or land cover type data, and the data of carbon density, the Carbon module in the InVEST model can be used to calculate the aboveground vegetation carbon storage, underground vegetation carbon storage, dead organic matter carbon storage, and soil carbon storage, so as to obtain the total carbon storage in the specified area, and thus obtain the spatial distribution data of the carbon storage in different years.

[0060] (3) Soil conservation amount.

[0061] In an embodiment, the Revised Universal Soil Loss Equation (RUSLE) model can be used to simulate the annual average soil erosion amount, and the soil conservation amount can be estimated based on the soil erosion amount, so as to obtain the spatial distribution data of the soil conservation amount in different years.

[0062] (4) Water yield.

[0063] In an embodiment, the Water Yield module in the InVEST model can be used to calculate and evaluate the water yield in the specified area according to the water balance equation, so as to obtain the spatial distribution data of the water yield in different years.

[0064] (5) Grain supply.

[0065] In an embodiment, the distribution of arable land can be known according to existing land use type data, so as to calculate the area of arable land, and the total grain yield data counted each year is allocated to each region, so as to obtain the spatial distribution data of grain supply in different years.

[0066] (6) Fruit supply.

[0067] In an embodiment, the distribution of garden land can be known according to existing land use type data, so as to calculate the area of garden land, and the total fruit yield data counted each year is allocated to each region, so as to obtain the spatial distribution data of fruit supply in different years.

[0068] (7) Park service capacity.

[0069] In an embodiment, the park service capacity can be evaluated by selecting four indexes of park area, park type, natural degree and water body based on the attribute data of the parks with an area greater than 1 hectare and open to the public in all parks in the city, and establishing a park service capacity weight evaluation formula:

[0070] P = a1AR + a2TY + a3NA + a4WB

[0071] Wherein, P is the service capacity of the park; AR is the park area index; TY is the park type index; NA is the natural degree index; WB is the water body index; a1, a2, a3 and a4 are the weights of each factor, which can be determined by entropy weight method. Based on the park type, a park radiation index based on 15-minute walking and 30-minute bus can be established, the park service capacity index is spatialized, and the spatial distribution data of the park service capacity in different years is obtained.

[0072] After obtaining the spatial distribution data of the plurality of ecosystem service indexes in different years in the target city region, the data of the plurality of ecosystem service indexes is standardized.

[0073] In an embodiment, the standardization processing can be standard deviation normalization processing, that is, the following formula is used:

[0074]

[0075]

[0076]

[0077] Wherein x i is the original value of the data of a certain ecosystem service index, The mean of the data of the ecosystem service index, n is the number of pixels in the spatial distribution (one pixel corresponds to one data of the ecosystem service index), S is the standard deviation of the data of the ecosystem service index corresponding to all pixels, Z i The standardized value of the data of the ecosystem service index.

[0078] The spatial distribution data of the ecosystem service index are standardized, which is beneficial to analyze the spatio-temporal evolution law of the ecosystem service, and makes the multiple ecosystem service types have comparability and operability.

[0079] In step 101, according to the spatial distribution data of the multiple ecosystem service indexes in different years after standardization, the ecosystem service cluster value ESB of each pixel corresponding to a preset grid scale in different years in the target urban area is determined.

[0080] Specifically, after obtaining the standardized values of the spatial distribution data of the multiple ecosystem service indexes in different years, a grid scale is preset, for example, 1km is taken as the grid scale, the pixels are divided, and the ESB of each pixel divided in the target urban area in different years can be determined according to the standardized values.

[0081] In one embodiment, since one pixel corresponds to the standardized values of the spatial distribution data of the multiple ecosystem service indexes in different years, the mean of the standardized values of the spatial distribution data of each ecosystem service index in a year can be taken as the ESB of the pixel in the year.

[0082] In step 102, according to the ESB of each pixel in different years and the impervious surface index value ISA of each pixel in different years, the target year period of ecological restoration is determined.

[0083] Specifically, ISA can be used to symbolize the intensity and level of urbanization.

[0084] In one embodiment, medium-resolution remote sensing images such as Landsat Thematic Mapper (Landsat TM) can be used as data sources, a linear spectral separation technology model is used to extract ISA based on a vegetation-impervious surface-soil (Vegetation-Impervious surface-Soil, V-I-S) remote sensing information end-member theoretical model. The main remote sensing image processing process includes: image cutting, minimum noise separation transformation, pure pixel index processing, N-dimensional visualizer collection end-member, linear spectral separation, result verification correction and other steps, so as to obtain the percentage of impervious surface (0-100%) in urban areas, which is used to represent the spatial distribution of urbanization.

[0085] Since the ESB represents the ecosystem service, and the ISA represents the urbanization intensity and level, the period of the greatest impact of urbanization on the ecosystem service can be determined by the relationship between the ESB of each pixel in different years and the ISA of each pixel in different years, so as to determine the target period of the ecological restoration.

[0086] In step 103, for any target pixel in each pixel, the ecological restoration priority of the target pixel is determined according to the ISA change rate of the target pixel in the target period of the ecological restoration and the ESB change rate of the neighborhood pixels of the target pixel in the target period of the ecological restoration.

[0087] Specifically, after the target period of the ecological restoration is determined, the ISA change rate of any target pixel in each pixel in the target period of the ecological restoration can be determined, and the ESB change rate of the neighborhood pixels of the target pixel in the target period of the ecological restoration can also be determined, according to which the impact of urbanization on the ecosystem service of the neighborhood pixels of the target pixel can be determined, so as to determine the ecological restoration priority of the target pixel.

[0088] The method for identifying the ecological restoration priority of the urban area provided by the application can determine the ESB and the ISA of each pixel corresponding to a preset grid scale in different years in a target urban area by obtaining the spatial distribution data of a plurality of ecosystem service indexes in different years in the target urban area and performing standardization processing, determine the target period of the ecological restoration, and then determine the ecological restoration priority of any target pixel in the target period of the ecological restoration according to the ISA change rate of the target pixel and the ESB change rate of the neighborhood pixels of the target pixel in the target period, so as to determine the ecological restoration priority of the target pixel. Furthermore, the priority area of the urban ecological restoration area can be divided, and the efficiency and economy of the ecological restoration of the urban area can be improved.

[0089] Optionally, the target period of the ecological restoration is determined according to the ESB of each pixel in different years and the impervious surface index ISA of each pixel in different years, and the method comprises the following steps.

[0090] A linear regression model is established according to the ESB and the ISA of each pixel in different years to determine the slope value of the change of the ESB corresponding to the ISA in different years.

[0091] The target period of the ecological restoration is determined according to the years corresponding to the maximum value and the second maximum value in the slope values.

[0092] Specifically, the ecological restoration priority can be determined by establishing a linear regression model. By sampling the corresponding relationship between ESB and ISA of each pixel, a linear regression model can be established to determine the influence of urbanization intensity on the ecosystem service cluster. The formula of the linear regression model is as follows:

[0093] ESB = a + b ISA ESB ESB

[0094] Wherein, ESB and ISA represent the ecosystem service cluster value and the impervious surface index value of a certain year (for example: 1980), a is the intercept, and b is the slope. ESB ESB

[0095] Therefore, different years correspond to different slope values, which can be used to compare the reaction of the ecosystem service of a certain pixel in different years to the urbanization intensity. The two years corresponding to the maximum and second maximum slope values in these different years can be selected as the starting time and ending time of the ecological restoration target year period, so as to obtain the ecological restoration target year period.

[0096] Alternatively, a linear regression model can be established according to the ESB change value and the ISA change value of each pixel in the ecological restoration target year period, and the formula is as follows:

[0097] ΔESB = a + b ΔISA ΔESB ΔESB

[0098] ΔESB and ΔISA represent the change rate of the ecosystem service cluster and the impervious surface index between a certain year period (for example: 1980-1990), a is the intercept, and b is the slope. ΔESB ΔESB

[0099] The regression relationship of the ESB change rate and the ISA change rate of each pixel in the formula is investigated, and the strength of the regression relationship is used as a criterion to determine whether the obtained year period is the ecological restoration target year period.

[0100] Alternatively, according to the ISA change rate of the target pixel in the ecological restoration target year period and the ESB change rate of the neighbor pixels of the target pixel in the ecological restoration target year period, the ecological restoration priority of the target pixel is determined, including:

[0101] ​​​​​​​​According to the ISA change rate of the target pixel in the ecological restoration target year period and the ESB change rate of the neighborhood pixels of the target pixel in the ecological restoration target year period, a bivariate Moran index corresponding to the target pixel is determined, and the bivariate Moran index is used to represent the spatial correlation between the ESB and the ISA.

[0102] According to the bivariate Moran index corresponding to the target pixel and the significance test result, the ecological restoration priority order of the target pixel is determined.

[0103] Specifically, after the ecological restoration target year period is determined, the spatial correlation between the ISA change rate of the target pixel in the ecological restoration target year period and the ESB change rate of the neighborhood pixels of the target pixel in the ecological restoration target year period can be obtained by using a bivariate local Moran model.

[0104] The formula of the bivariate local Moran model is as follows:

[0105]

[0106]

[0107] Wherein, x is the ΔISA, y is the ΔESB, and Z is the bivariate Moran index. x,i is the standardized value of the ΔISA of the target pixel i, Z y,j is the standardized value of the ΔESB of the neighborhood pixel j of the target pixel, I B,i is the bivariate Moran index in space. i,j is the spatial adjacency weight matrix between adjacent pixels, and when the value is 1, it indicates that the two are adjacent, and when the value is 0, it indicates that the two are not adjacent. is the ΔISA value of the pixel i, is the mean value of the ΔISA of the adjacent pixels of the pixel i, δ x is the variance value of the ΔISA of the adjacent pixels of the pixel i; is the ΔESB value of the pixel j, is the mean value of the ΔESB of the adjacent pixels of the pixel j, δ y is the variance value of the ΔESB of the adjacent pixels of the pixel j.

[0108] The relationship between the ISA change rate of the target pixel in the ecological restoration target year period and the ESB change rate of the neighborhood pixels of the target pixel in the ecological restoration target year period can be known through the bivariate Moran index, and then the significance test is performed on the obtained bivariate Moran index result. According to the bivariate Moran index of the target pixel and the significance test result, the ecological restoration priority order of the target pixel can be determined.

[0109] Optionally, the ecological restoration priority order of the target pixel is determined according to the bivariate Moran index corresponding to the target pixel and the significance test result, and the ecological restoration priority order of the target pixel comprises:

[0110] According to the bivariate Moran index corresponding to the target pixel and the significance test result, the type of the spatial cluster area corresponding to the target pixel is determined.

[0111] According to the type of the spatial cluster area, the ecological restoration priority order of the target pixel is determined.

[0112] Specifically, after obtaining the bivariate Moran index corresponding to the target pixel and the significance test result, the strength of the correlation between the ISA change rate of the target pixel in the ecological restoration target year period and the ESB change rate of the neighborhood pixels of the target pixel in the ecological restoration target year period can be known according to the bivariate Moran index. The value range of the bivariate Moran index is between-1 and 1, and the greater the absolute value is, the stronger the correlation is.

[0113] Then, the significance test is performed on the result, and the type of the spatial cluster area corresponding to the target pixel can be determined according to the p value obtained by the significance test.

[0114] The type of the spatial cluster area comprises the following five types:

[0115] (1) High-high (H-H) cluster area, the high-high cluster area is used to represent a cluster area with high urbanization intensity and high loss of surrounding ecosystem services.

[0116] Specifically, when p < 0.001, the target pixel can be corresponded to the H-H cluster area.

[0117] (2) High-low (H-L) cluster area, the high-low cluster area is used to represent a cluster area with high urbanization intensity but relatively small loss of surrounding ecosystem services.

[0118] Specifically, when 0.001≤p<0.005, the target pixel can be corresponded to the H-L cluster area.

[0119] (3) Low-high (L-H) cluster area, the low-high cluster area is used to represent a cluster area with small change of urbanization intensity but relatively high loss of surrounding ecosystem services.

[0120] Specifically, when 0.005≤p<0.01, the target pixel can be corresponded to the L-H cluster area.

[0121] (4) Low-low (L-L) cluster area, the low-low cluster area is used to represent a cluster area with small change of urbanization intensity and relatively small loss of surrounding ecosystem services.

[0122] Specifically, when 0.01≤p<0.05, the target pixel can be corresponded to the L-L cluster area.

[0123] (5) The non-significant influence area is used to represent that the change relationship between the urbanization intensity and the ecosystem service tends to be balanced, the ecosystem service is good, or the ecosystem restoration has a good effect in the later stage of urbanization, or the cluster area has a low degree of urbanization influence.

[0124] Specifically, when p≥0.05, the target pixel can be corresponded to the L-L cluster area.

[0125] After obtaining the above five types of cluster areas, the ecological restoration priority of the target pixel can be determined.

[0126] For example, the high-high (H-H) cluster area can be divided into a key ecological restoration area, indicating that the city has the potential to provide higher quality ecosystem services, and the key layout of ecological restoration projects, the reconstruction of natural-like ecosystems, such as country parks, etc. The high-low (H-L) cluster area can be divided into an important ecological restoration area, which limits the influence of urbanization on ecosystem services by guiding artificial ecological restoration measures, restores the function of the ecosystem, and has not yet carried out ecological construction in the area of rapid urbanization. The low-high (L-H) cluster area indicates that the urbanization has a difficult-to-reverse impact on the ecosystem service, and needs to restore the ecosystem function through artificial reconstruction of the ecosystem, such as the river ecosystem and riparian zone flowing through the urbanized area. The low-low (L-L) cluster area indicates that the urbanization intensity has a small impact on the natural ecosystem service, and can be repaired by a near-natural solution, while this area can improve the human well-being of residents by improving the accessibility of transportation. The non-significant influence area will not be arranged with significant ecological restoration measures and projects in the partition.

[0127] Optionally, the method can further include:

[0128] According to the spatial distribution data of the multiple ecosystem service indicators in different years after standardization processing, the cluster analysis of the ecosystem service indicators of the preset grid scale is performed to determine the ecosystem service cluster type of each pixel corresponding to the preset grid scale in the target city area in different years.

[0129] Specifically, after obtaining the standardized values of the spatial distribution data of the multiple ecosystem service indicators in different years, a grid scale is preset, for example, 1km as the grid scale, and the cluster analysis of the ecosystem service indicators of the preset grid scale can be performed.

[0130] The standard values of the preset grid scale ecosystem service indicators of multiple years can be taken as clustering samples, each sample containing standard values of multiple ecosystem service indicators.

[0131] For example, there are 7 ecosystem service indicators: (1) habitat quality, (2) carbon storage, (3) soil retention, (4) water yield, (5) food supply, (6) fruit supply, and (7) park service capacity. The standard values of the ecosystem service indicators can be divided into three ranges: high value range (value 0.6-1), medium value range (value 0.2-0.6), and low value range (value 0-0.2). The classification result can be:

[0132] The samples in a category have standard values of three ecosystem service indicators (1), (2) and (4) in the high value range, and standard values of four ecosystem service indicators (3), (5), (6) and (7) in the low value range.

[0133] In an embodiment, the K-means algorithm can be used for clustering analysis of the preset grid scale ecosystem service indicators.

[0134] In an embodiment, the Calinsky criterion can be used to determine the optimal number of clusters, and the number of clusters corresponding to the maximum Calinsky index is the optimal number of clusters. The specific formula for calculating the Calinsky index is as follows:

[0135]

[0136] Wherein, C k is the Calinsky index when the number of clusters is k, SS B is the between-group sum of squares, SS W is the within-group sum of squares, and n is the number of samples.

[0137] The clustering analysis of the preset grid scale ecosystem service indicators can make the different time periods more comparable. The changes in the specific ecosystem service indicators of the region can be known through the changes in the ecological service cluster types.

[0138] The following describes the method provided by the above embodiments of the present application through a specific embodiment.

[0139] Figure 2 A process schematic diagram for dividing the urban ecological restoration priority area provided by the present application is shown in FIG. 1. Figure 2 As shown in FIG. 1, the process includes the following steps:

[0140] S1, data acquisition and preprocessing. Select the area that needs to be carried out urban ecological restoration planning as the research area, obtain the corresponding remote sensing image data, air temperature and precipitation data, soil type data, road network data, digital elevation model, crop yield, park green space (range and opening year, etc.) and urban administrative division data. Use ENVI software for image correction and other preprocessing, supervised classification and reclassification work.

[0141] S2, urban ecosystem service assessment. In the specified urban study area, the Habitat Quality module in the InVEST model is used to assess habitat quality, the Carbon module is used to calculate the spatial distribution and dynamic changes of urban multi-year carbon storage; according to the water balance equation, the water yield in the evaluation period is calculated; using the cultivated land and garden distribution in the urban land use / cover type data, the cultivated land area and garden area are calculated, combined with the per mu yield of grain and fruit to calculate the grain supply service and fruit supply service; according to the park area, type, natural degree and water body four indexes, the park service ability is comprehensively evaluated by the assignment method; and the above seven kinds of ecosystem service indexes are standardized.

[0142] S3, identification of ecosystem service cluster. The identification of ecosystem service cluster is based on cluster analysis, and K-means algorithm is used to cluster urban ecosystem services. The main process is divided into S3.1 and S3.2.

[0143] S3.1, determination of the number of ecosystem service cluster categories. The best cluster number is determined by Calinsky criterion, which is defined as follows:

[0144]

[0145] Wherein, C k is the Calinsky index when the cluster number is k, SS B is the between-group error sum of squares, SS W is the within-group error sum of squares, and n is the sample size.

[0146] S3.2, identification of ecosystem service cluster. According to the kilometer grid scale ecosystem service standard value obtained in the above, all kilometer grids in different years are used as clustering samples, each sample contains seven ecosystem service data, and the sample is input into K-means algorithm for clustering. According to the clustering results, different ecosystem service clusters are obtained.

[0147] S4, quantifying the urbanization process indicators. Based on the V-I-S theory model, the linear spectral separation technology model is used to interpret the surface cover characteristics of the specified region and time period, and the spatial pattern of the impervious surface index is extracted. The main remote sensing processing process includes: image cutting, minimum noise separation transformation, pure pixel index processing, N-dimensional visualizer collection end element, linear spectral separation, result verification correction and other steps.

[0148] S5, quantifying the impact of urbanization on the ecosystem service cluster

[0149] ESB = a ESB + b ESB ISA

[0150] ΔESB = a ΔESB + b ΔESB ΔISA

[0151] ESB and ISA represent the ecosystem service cluster and the impervious surface index, respectively, and ΔESB and ΔISA represent the changes in the ecosystem service cluster and the impervious surface index, respectively, b ESB and b ΔESB are the slopes, representing the response of the ecosystem service cluster to the intensity of urbanization (change), a ESB and a ΔESB are the intercepts.

[0152] S6, spatial identification of ecological restoration priority area. Using the spatial statistical method of bi-Local Indicators of Spatial Association (bi-LISA), such as the bi-Local Moran's Index method, the spatial correlation between the change rate of ESB and the change rate of ISA is explored, so as to divide the urban ecological restoration priority order and determine the urban ecological restoration priority area.

[0153] This embodiment selects seven ecosystem service indicators, including support services (habitat quality), supply services (food supply and fruit supply), regulation services (carbon storage, soil conservation and water yield) and cultural services (park service capacity), based on the urbanization process and ecosystem service problems of the urban study area, to carry out long-term ecosystem service evaluation; based on the correlation between the changes in the impervious surface index and the ecosystem service cluster, the ecological restoration priority order is identified.

[0154] The urban ecological restoration priority order identification device provided by the present application is described below, and the urban ecological restoration priority order identification device described below can be correspondingly referred to the urban ecological restoration priority order identification method described above.

[0155] Figure 3A structural schematic diagram of the urban ecological restoration priority order identification device provided by the present application is shown in Figure 3 The device comprises:

[0156] A standardization processing module 300 is configured to acquire spatial distribution data of a plurality of ecosystem service indicators in different years in a target urban area and perform standardization processing.

[0157] A first determination module 310 is configured to determine, according to the spatial distribution data of the plurality of ecosystem service indicators in different years after standardization processing, an ecosystem service cluster value ESB of each pixel corresponding to a preset grid scale in the target urban area in different years.

[0158] A second determination module 320 is configured to determine an ecological restoration target year period according to the ESB of each pixel in different years and an impervious surface index value ISA of each pixel in different years.

[0159] A third determination module 330 is configured to determine, for any target pixel in each pixel, an ecological restoration priority order of the target pixel according to an ISA change rate of the target pixel in the ecological restoration target year period and an ESB change rate of a neighborhood pixel of the target pixel in the ecological restoration target year period.

[0160] Optionally, the plurality of ecosystem service indicators can include habitat quality, carbon storage, soil retention, water production, food supply, fruit supply, and park service capacity.

[0161] Optionally, the determination of the ecological restoration target year period according to the ESB of each pixel in different years and the impervious surface index value ISA of each pixel in different years comprises:

[0162] Establishing a linear regression model according to the ESB and the ISA of each pixel in different years to determine a slope value of the ESB relative to the ISA change in different years;

[0163] Determining the ecological restoration target year period according to years corresponding to a maximum value and a second maximum value in the slope values.

[0164] Optionally, the determination of the ecological restoration priority order of the target pixel according to the ISA change rate of the target pixel in the ecological restoration target year period and the ESB change rate of the neighborhood pixel of the target pixel in the ecological restoration target year period comprises:

[0165] Determining a bivariate Moran index corresponding to the target pixel according to the ISA change rate of the target pixel in the ecological restoration target year period and the ESB change rate of the neighborhood pixel of the target pixel in the ecological restoration target year period, the bivariate Moran index being used to represent spatial correlation between the ESB and the ISA.

[0166] According to the bivariate Moran index corresponding to the target pixel and the significance test result, the ecological restoration priority order of the target pixel is determined.

[0167] Optionally, according to the bivariate Moran index corresponding to the target pixel and the significance test result, the ecological restoration priority order of the target pixel is determined, including:

[0168] According to the bivariate Moran index corresponding to the target pixel and the significance test result, the spatial cluster area type corresponding to the target pixel is determined.

[0169] According to the spatial cluster area type, the ecological restoration priority order of the target pixel is determined.

[0170] The spatial cluster area type includes the following five types:

[0171] The high-high cluster area is used to represent a cluster area with high urbanization intensity and high loss of surrounding ecosystem services.

[0172] The high-low cluster area is used to represent a cluster area with high urbanization intensity, but relatively small loss of surrounding ecosystem services.

[0173] The low-high cluster area is used to represent a cluster area with small change in urbanization intensity, but relatively high loss of surrounding ecosystem services.

[0174] The low-low cluster area is used to represent a cluster area with small change in urbanization intensity, and relatively small loss of surrounding ecosystem services.

[0175] The non-significant impact area is used to represent a cluster area with balanced change relationship between urbanization intensity and ecosystem services, good ecosystem services, or good effect of ecosystem restoration after urbanization, or low degree of urbanization impact.

[0176] Optionally, the first determination module 310 can also be used for:

[0177] According to the spatial distribution data of the plurality of ecosystem service indexes in different years after standardization processing, the clustering analysis of the ecosystem service indexes in the preset grid scale is performed, and the ecosystem service cluster type of each pixel corresponding to the preset grid scale in the target urban area in different years is determined.

[0178] It should be noted that the above device provided by the present application can realize all method steps realized by the above method embodiment, and can achieve the same technical effects. Therefore, the same parts and beneficial effects of the method embodiment will not be described in detail.

[0179] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 4 As shown, the electronic device may include a processor 410, a communications interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communications interface 420, and the memory 430 communicate with each other via the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute any of the urban ecological restoration priority identification methods provided in the above embodiments.

[0180] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0181] It should be noted that the electronic device provided by the present invention can implement all the method steps implemented in the above method embodiments and can achieve the same technical effect. Therefore, the parts and beneficial effects that are the same as those in the method embodiments will not be described in detail here.

[0182] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute any of the urban ecological restoration priority identification methods provided in the above embodiments.

[0183] It should be noted that the computer program product provided by the present invention can implement all the method steps implemented in the above method embodiments and can achieve the same technical effect. Therefore, the parts and beneficial effects that are the same as those in the method embodiments will not be described in detail here.

[0184] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform any of the urban ecological restoration priority identification methods provided in the above embodiments.

[0185] It should be noted that the non-transitory computer-readable storage medium provided by the present invention can implement all the method steps implemented in the above method embodiments and can achieve the same technical effect. Here, the parts that are the same as those in the method embodiments and the beneficial effects will not be described in detail.

[0186] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0187] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0188] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying the priority order of urban ecological restoration, characterized in that, include: Acquire spatial distribution data of multiple ecosystem service indicators within the target city area in different years and perform standardization processing; Based on the spatial distribution data of the multiple ecosystem service indicators after standardization in different years, the ecosystem service cluster value (ESB) of each pixel corresponding to the preset grid scale in the target city area is determined in different years. Based on the ESB of each pixel in different years and the ISA value of each pixel in different years, the target year and period for ecological restoration are determined. For any target pixel in each pixel, the ecological restoration priority order of the target pixel is determined based on the ISA change rate of the target pixel during the target ecological restoration year period and the ESB change rate of the neighboring pixels of the target pixel during the target ecological restoration year period. The process of determining the target year and time period for ecological restoration based on the ESB of each pixel in different years and the ISA value of each pixel in different years includes: Based on the ESB and ISA of each pixel in different years, a univariate linear regression model is established to determine the slope value of the change of ESB relative to ISA in different years. The target year period for ecological restoration is determined based on the years corresponding to the maximum and second-largest slope values.

2. The method for identifying the priority order of urban ecological restoration according to claim 1, characterized in that, The various ecosystem service indicators include: habitat quality, carbon storage, soil retention, water production, food supply, fruit supply, and park service capacity.

3. The method for identifying the priority order of urban ecological restoration according to claim 1, characterized in that, The step of determining the ecological restoration priority order of the target pixel based on the ISA change rate of the target pixel during the target ecological restoration year period and the ESB change rate of the neighboring pixels of the target pixel during the target ecological restoration year period includes: Based on the ISA change rate of the target pixel during the target year of ecological restoration and the ESB change rate of the neighboring pixels of the target pixel during the target year of ecological restoration, the bivariate Moran index corresponding to the target pixel is determined. The bivariate Moran index is used to characterize the spatial correlation between ESB and ISA. Based on the bivariate Moran index and significance test results corresponding to the target pixels, the priority order for ecological restoration of the target pixels is determined.

4. The method for identifying the priority order of urban ecological restoration according to claim 3, characterized in that, The step of determining the ecological restoration priority order of the target pixels based on the bivariate Moran index and significance test results includes: Based on the bivariate Moran index and significance test results corresponding to the target pixel, the spatial clustering type corresponding to the target pixel is determined; Based on the type of spatial cluster, determine the priority order for ecological restoration of the target pixels; The spatial cluster types include the following five: High-high agglomeration area, which is used to represent an agglomeration area with high urbanization intensity and high loss of surrounding ecosystem services; High-low agglomeration zone, which is used to represent agglomeration zone with high urbanization intensity but relatively small loss of surrounding ecosystem services; Low-high agglomeration area, which is used to represent agglomeration area with relatively small changes in urbanization intensity but relatively high loss of surrounding ecosystem services; Low-low agglomeration zone, which is used to represent agglomeration zone with relatively small changes in urbanization intensity and relatively small loss of surrounding ecosystem services; The insignificant impact area is used to represent an agglomeration area where the relationship between urbanization intensity and ecosystem services tends to be balanced, the ecosystem services are relatively good, or the ecosystem restoration in the later stage of urbanization has achieved good results, or the degree of urbanization impact is low.

5. The method for identifying the priority order of urban ecological restoration according to claim 1, characterized in that, The method further includes: Based on the spatial distribution data of the multiple ecosystem service indicators after standardization in different years, a cluster analysis of ecosystem service indicators at a preset grid scale is performed to determine the ecosystem service cluster type of each pixel at the preset grid scale within the target city area in different years.

6. A device for identifying the priority order of urban ecological restoration, characterized in that, include: The standardization processing module is used to acquire spatial distribution data of multiple ecosystem service indicators in different years within the target city area and perform standardization processing. The first determining module is used to determine the ecosystem service cluster value (ESB) of each pixel corresponding to a preset grid scale within the target urban area in different years based on the spatial distribution data of the multiple ecosystem service indicators after standardization in different years. The second determining module is used to determine the target year and time period for ecological restoration based on the ESB of each pixel in different years and the ISA value of each pixel in different years. The third determining module is used to determine the ecological restoration priority order of any target pixel in each pixel based on the ISA change rate of the target pixel during the ecological restoration target year period and the ESB change rate of the neighboring pixels of the target pixel during the ecological restoration target year period. The second determining module is used to establish a univariate linear regression model based on the ESB and ISA of each pixel in different years to determine the slope value of the change of ESB relative to ISA in different years; and to determine the target year period for ecological restoration based on the years corresponding to the maximum and second largest values ​​of the slope values.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the urban ecological restoration priority identification method as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the urban ecological restoration priority identification method as described in any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the urban ecological restoration priority identification method as described in any one of claims 1 to 5.

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