Ecological restoration priority area identification method and device and computer equipment

Abstract

The present application relates to a kind of ecological restoration priority area identification method, device and computer equipment.Wherein, the ecological restoration priority area identification method uses the importance result of multiple time points of ecosystem service, avoids the limitation of traditional static snapshot, significantly improves the technical limitation of traditional method in priority area identification accuracy and practical applicability.Meanwhile, by the spatial superposition of the importance of ecosystem service, the stability of ecosystem service and the determination result of ecological restoration suitability, generate ecological restoration priority area, avoid the evaluation deviation of single index, improve the scientificity and system of priority area selection.In addition, focus on " high value-low stability-low cost" area for repair resources, improve the unit area repair benefit.

Description

Technical Field

[0001] This invention relates to the field of ecological restoration technology, and more specifically, to a method, apparatus, and computer equipment for identifying priority areas for ecological restoration. Background Technology

[0002] Ecosystem degradation and inappropriate transformation have severely impacted the sustainable development of human society. Ecological restoration is a crucial measure to address global ecosystem degradation and enhance ecosystem service functions. Currently, ecological restoration has shifted from large-scale governance to the systematic restoration of mountains, rivers, forests, fields, lakes, grasslands, and deserts, urgently requiring the scientific identification of priority restoration areas to optimize their spatial and temporal distribution. Existing technologies primarily identify priority areas through four methods: multi-indicator comprehensive assessment, ecological network construction, multi-scenario simulation, and multi-criteria decision analysis. Among these, the multi-criteria decision method offers greater practical flexibility due to its ability to balance ecological benefits with socio-economic constraints.

[0003] However, the inventors found that traditional identification methods still have limitations in terms of time stability and comprehensiveness of indicators, resulting in insufficient accuracy and unclear direction of the identified priority areas, which restricts the practical guiding value of priority area delineation. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, device, and computer equipment for identifying priority areas for ecological restoration.

[0005] To achieve the above objectives, in one aspect, embodiments of this application provide a method for identifying priority areas for ecological restoration, including:

[0006] Obtain the ecosystem service importance results for each partition at each time point; where each partition is obtained by dividing the target area.

[0007] Based on the results of the ecosystem service importance of each region at each time point, the service importance areas are obtained;

[0008] Calculate the ecosystem service stability index for each region within the important service region, and determine the stability level of each region within the important service region based on the ecosystem service stability index;

[0009] Highly suitable areas for ecological restoration were extracted from the target area;

[0010] Based on the stability levels of each area in the important service areas and the highly suitable areas for ecological restoration, priority areas for ecological restoration are identified.

[0011] In one embodiment, the step of determining the service-important region based on the ecosystem service importance results of each partition at each time point includes:

[0012] The importance of ecosystem services in each region at each time point was processed using frequency statistics to obtain the first region and the second region.

[0013] The regression model was used to process the ecosystem service importance results for the second region, and the regression results were obtained.

[0014] The first region and the second region corresponding to the regression results that meet the preset conditions are identified as important service regions; the preset conditions are that the slope is greater than 0 and the significance level is less than a set value.

[0015] In one embodiment, the step of processing the ecosystem service importance results of each partition at each time point using frequency statistics to obtain the first region and the second region includes:

[0016] Based on the ecosystem service importance results for each region at each time point, the probability of each region's ecosystem service importance result being important is calculated.

[0017] Determine the first probability and the second probability; wherein the first probability is greater than the second probability;

[0018] The partitions whose probability of occurrence is greater than or equal to the first probability are designated as the first region;

[0019] The partitions with a probability less than the first probability but greater than the second probability are designated as the second region.

[0020] In one embodiment, the step of extracting a highly suitable area for ecological restoration from the target area includes:

[0021] Calculate the ecological restoration potential index and the ecological restoration socio-economic cost index for each zone;

[0022] Based on the ecological restoration potential index and the ecological restoration socio-economic cost index, the restoration potential level and cost level of each area in the target region are obtained;

[0023] Based on restoration potential and cost levels, highly suitable areas for ecological restoration were identified within the target region.

[0024] In one embodiment, the step of calculating the socio-economic cost index of ecological restoration for each zone is based on the following formula:

[0025] SECI = Max{HAI B HAI C Accessibility}

[0026] HAI B =0.5×PD i +0.5×NTL i

[0027] HAI C =Cropland_NDVI i

[0028] Accessibility = 0.5 × Slope i +0.5×DisToRoads i

[0029] Among them, SECI is the socioeconomic cost index of ecological restoration in region i; HAI B HAI represents the intensity of daily human and economic activities in region i; C The intensity of agricultural activities carried by region i is represented by PD; Accessibility is the transportation accessibility of region i; i and NTL i These represent the population density and nighttime light index for region i, respectively; Cropland_NDVI i For the food production capacity of region i; Slope i The slope of region i; DisToRoads i Let be the distance from region i to the main road.

[0030] In one embodiment, the step of obtaining the ecosystem service importance results for each partition at each time point includes:

[0031] Select key ecosystem services based on the ecosystem characteristics of the target area;

[0032] Process key ecosystem services to obtain integrated ecosystem service data;

[0033] Based on integrated ecosystem service data, the importance of ecosystem services in each region at each time point was determined.

[0034] In one embodiment, the step of calculating the ecosystem service stability index for each region within a service-critical region is based on the following formula:

[0035]

[0036] Wherein, CV is the ecosystem service stability index; σ is the standard deviation of the integrated ecosystem service data; and μ is the arithmetic mean of the integrated ecosystem service data.

[0037] In one embodiment, the step of determining the stability level of each region within a service-critical region based on an ecosystem service stability index includes:

[0038] Determine a first grading threshold and a second grading threshold; wherein the first grading threshold is less than the second grading threshold;

[0039] The comparison results were obtained by comparing the ecosystem service stability index, the first-level threshold, and the second-level threshold of each region.

[0040] Based on the comparison results, the stability level of each area in the important service areas is determined.

[0041] On one hand, embodiments of the present invention provide a device for identifying priority areas for ecological restoration, comprising:

[0042] The acquisition module is used to obtain the ecosystem service importance results for each partition at each time point; wherein, each partition is obtained by dividing the target area.

[0043] The Service Importance Area Confirmation Module is used to determine the service importance area based on the ecosystem service importance results of each partition at each time point;

[0044] The stability grading module is used to calculate the ecosystem service stability index of each region in the important service area, and to determine the stability level of each region in the important service area based on the ecosystem service stability index.

[0045] The module for identifying highly suitable areas for ecological restoration is used to extract highly suitable areas for ecological restoration from the target area.

[0046] The ecological restoration priority zone confirmation module is used to determine ecological restoration priority zones based on the stability level of each area in the important service area and the highly suitable areas for ecological restoration.

[0047] On the other hand, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the computer program is configured to execute the steps of the above-described method for identifying priority ecological restoration areas when it is run.

[0048] Compared with the prior art, one of the technical solutions in this application has the following advantages and beneficial effects:

[0049] The aforementioned priority ecological restoration areas utilize ecosystem service importance results from multiple time points, avoiding the limitations of traditional static snapshots. Simultaneously, incorporating the ecosystem service stability index into the identification of important ecosystem service areas enables hierarchical classification of these areas, facilitating more precise identification and protection of key ecosystem service areas to enhance biodiversity conservation effectiveness and ensure the sustainable and stable supply of ecosystem services. By spatially overlaying the results of ecosystem service importance, ecosystem service stability, and ecological restoration suitability assessments, priority ecological restoration areas are generated, avoiding assessment biases from single indicators and improving the scientific rigor and systematic nature of priority area selection. Furthermore, focusing restoration resources on areas with "high value, low stability, and low cost" improves the restoration efficiency per unit area. The above-mentioned methods significantly improve upon the technical limitations of traditional methods in terms of the accuracy of priority area identification and practical applicability. They can effectively guide ecological restoration practitioners to prioritize the allocation of limited resources to areas with important ecosystem services, low ecosystem service stability, and high ecological restoration suitability, based on the restoration sequence. This avoids simply treating important ecosystem service areas with high ecological degradation risks as ecological protection objects, prevents the failure of measures and waste of funds that may be caused by traditional practices such as homogenized ecological governance measures, ensures the supply capacity of important ecosystem service areas, and maximizes the marginal benefits of ecological investment. Attached Figure Description

[0050] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0051] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0052] Figure 1 This is a schematic flowchart illustrating a method for identifying priority ecological restoration areas in one embodiment;

[0053] Figure 2 This is a schematic flowchart illustrating the steps for obtaining important service areas based on the ecosystem service importance results of each partition at each time point in one embodiment.

[0054] Figure 3 This is a schematic flowchart illustrating the steps for obtaining a spatial distribution map of ecological restoration levels in one embodiment;

[0055] Figure 4This is a schematic flowchart illustrating the steps of processing the ecosystem service importance results of each partition at each time point using frequency statistics in one embodiment to obtain the first region and the second region.

[0056] Figure 5 This is a schematic flowchart illustrating the steps of extracting a highly suitable ecological restoration zone from a target area in one embodiment;

[0057] Figure 6 This is a schematic flowchart illustrating the steps for obtaining the importance of ecosystem services in each partition at each time point in one embodiment. Detailed Implementation

[0058] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings, which illustrate embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of this application will be thorough and complete.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0060] In the following description, the use of suffixes such as "module," "part," or "unit" to denote elements is solely for the purpose of illustration and has no specific meaning in itself. Therefore, "module" and "part" may be used interchangeably.

[0061] It is understood that the term "connection" in the following embodiments should be understood as "electrical connection," "communication connection," etc., if the connected circuits, modules, units, etc., have electrical signal or data transmission with each other.

[0062] When used herein, the singular forms of “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising,” “including,” or “having,” etc., specify the presence of the stated feature, whole, step, operation, component, part, or combination thereof, but do not preclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof.

[0063] In one embodiment, such as Figure 1 As shown, a method for identifying priority areas for ecological restoration is provided, including:

[0064] S110, Obtain the results of ecosystem service importance for each partition at each time point; wherein, each partition is obtained by dividing the target area;

[0065] Among them, the importance of ecosystem services refers to the strength of the functions of ecosystems in providing services to humans (such as water conservation, carbon sequestration, soil conservation, and biodiversity maintenance).

[0066] Specifically, the importance of ecosystem services is categorized as important or general; this importance can be derived from ecosystem service data. Ecosystem service data can be obtained through methods such as field measurements (e.g., carbon storage, water purification capacity), indicator systems (e.g., water conservation capacity, biodiversity index), or model simulations. Areas with ecosystem service data ranking in the top 10%, 20%, or 30% can be assessed as important, while other areas can be assessed as general. The ecosystem service importance results at each of the aforementioned time points refer to the importance results over multiple consecutive or evenly spaced years. To mitigate the impact of short-term fluctuations and meet the needs of statistical modeling, the data series is generally longer than 10 years; in a specific example, 21 consecutive years could be selected.

[0067] Furthermore, the process of dividing the target area into several sub-regions aims to refine the analysis units. This division can be achieved through grid partitioning or by using ecological functional zones (such as watersheds or vegetation type zones).

[0068] S120, based on the results of the importance of ecosystem services in each region at each time point, the service-important areas are obtained;

[0069] Specifically, within the entire time series, if the importance result of a certain partition is high in frequency or significantly increases in frequency, then that partition is considered an important service area. High frequency or significantly increased frequency can be determined using any means within this field.

[0070] In a specific example, such as Figure 2As shown, the steps for determining the important ecosystem service regions based on the ecosystem service importance results of each partition at each time point include: S210, processing the ecosystem service importance results of each partition at each time point using frequency statistics to obtain the first region and the second region; S220, processing the ecosystem service importance results of the second region using a regression model to obtain the regression results; S230, confirming the first region and the second region corresponding to the regression results that meet preset conditions as important service regions; wherein, the preset conditions are a slope greater than 0 and a significance level less than a set value. Specifically, frequency statistics refers to calculating the frequency of occurrence of ecosystem service importance throughout the entire time series. For example, based on quartiles, regions with an overall occurrence probability greater than 75% and less than 25% can be extracted as high-frequency stable regions and low-frequency stable regions with important importance results, respectively. Specifically, regions with an overall occurrence probability greater than 75% are the first region. Regions with an overall occurrence probability between 25% and 75% are the second region. Further, the regression model is used to test the trend and significance of the overall occurrence probability within the [25%, 75%] interval. The regression model can be a binary logistic regression model. The importance result is assigned a value of 1 for cases of importance and 0 for cases of moderate importance. Specifically, time is used as the independent variable, and the importance value (0 or 1) of the region in the corresponding year is used as the dependent variable. A binary logistic regression is performed. Regions with a positive slope and a significance level of P < 0.05 in the regression results are identified as regions with significantly increased frequencies and confirmed as important service regions. Through dual verification of frequency statistics and regression models (such as logistic regression), short-term fluctuations are effectively filtered out, and important service regions are accurately identified.

[0071] S130, calculate the ecosystem service stability index of each region in the important service area, and determine the stability level of each region in the important service area based on the ecosystem service stability index.

[0072] Specifically, the ecosystem service stability index reflects the fluctuation range of integrated ecosystem services in different regions over the entire time series, and is divided into different stability levels based on the fluctuation range. In one example, the step of calculating the ecosystem service stability index of each region in a service-important region is based on the following formula:

[0073]

[0074] Wherein, CV is the ecosystem service stability index; σ is the standard deviation of the integrated ecosystem service data; and μ is the arithmetic mean of the integrated ecosystem service data. The larger the CV value, the greater the fluctuation of the integrated ecosystem service in the region over the entire time series.

[0075] Specifically, the ecosystem service stability index for each region can be calculated using the formula described above. Taking the calculation using 21 years of data as an example, for each region, σ is the standard deviation of the integrated ecosystem service data for that region over 21 years, and μ is the arithmetic mean of the integrated ecosystem service data for that region over 21 years.

[0076] In one example, the steps of determining the stability level of each region in an important service region based on the ecosystem service stability index include: determining a first grading threshold and a second grading threshold; wherein the first grading threshold is less than the second grading threshold; comparing the ecosystem service stability index, the first grading threshold, and the second grading threshold of each region to obtain a comparison result; and determining the stability level of each region in the important service region based on the comparison result.

[0077] Specifically, based on the ecosystem service stability index, 0.05 and 0.15 can be used as the first and second classification thresholds, respectively. By comparing the results, the important service areas can be subdivided into high stability areas (CV < 0.05), medium stability areas (0.05 ≤ CV < 0.15), and low stability areas (CV ≥ 0.15).

[0078] S140, extracting highly suitable areas for ecological restoration from the target area;

[0079] Among them, highly suitable areas for ecological restoration refer to areas with low restoration costs and high restoration potential. Restoration potential can refer to the maximum degree of improvement that regional ecosystem services can potentially achieve, specifically the difference between the current state and the reference state of ecosystem services.

[0080] Specifically, areas with low repair costs and high repair potential can be extracted using any means in this field.

[0081] S150, based on the stability level of each area in the important service area and the highly suitable area for ecological restoration, is used to determine the priority area for ecological restoration.

[0082] Specifically, stability levels can be categorized as low stability, medium stability, and high stability. By overlaying low-stability areas within the important service region with highly suitable areas for ecological restoration, the intersecting area becomes the priority zone for ecological restoration.

[0083] Furthermore, a spatial distribution map of ecological restoration levels can be obtained, such as... Figure 3As shown, firstly, a distinction is made between protected areas and restoration areas within important ecosystem service areas. Areas where important ecosystem service areas intersect with highly suitable areas for ecological restoration are designated as ecological restoration areas, while non-intersecting areas are designated as ecological protected areas. In other words, if an area belongs to both a highly suitable area for ecological restoration and an important ecosystem service area, it is an ecological restoration area; if an area does not belong to a highly suitable area for ecological restoration but belongs to an important ecosystem service area, it is an ecological protected area; and if an area does not belong to an important ecosystem service area, it is a generally important area.

[0084] Secondly, the ecological restoration levels of the areas where important ecosystem service zones of low stability, medium stability, and high stability intersect with highly suitable ecological restoration zones are set as P1, P2, and P3, respectively.

[0085] Finally, for areas where the general importance zone of ecosystem services intersects with the high suitability zone for ecological restoration, the ecological restoration level is set as P4.

[0086] It is understandable that ecological protection zones are areas with important ecosystem services and good ecological conditions. The stability of ecosystem services in these areas is mainly affected by climate factors and lacks significant potential for human restoration. Therefore, conservation and protection methods should be adopted, focusing on maintaining the authenticity of the ecosystem and enhancing its stability. Areas corresponding to the P1 level have important ecosystem services and high suitability for ecological restoration, but low ecosystem service stability, indicating that these types of areas are susceptible to both climate change and human activities. Therefore, assisted regeneration methods should be adopted. In addition to eliminating stress factors and restoring degraded areas, measures such as improving the physical environment and introducing suitable species are needed to enhance the resilience of the ecosystem to variable factors such as climate. Areas corresponding to the P1 level are also the aforementioned priority areas for ecological restoration. Areas corresponding to the P2 level have important ecosystem services and high suitability for ecological restoration, but their ecosystem services exhibit moderate fluctuations, indicating that the degree of external factor fluctuations in this area is generally low or that the ecosystem has a certain degree of resistance to interannual fluctuations in external factors. Therefore, a combination of assisted regeneration and natural restoration methods should be adopted, with targeted and appropriate artificial assistance to leverage the ecosystem's self-restoration capacity. Regions at level P3 are characterized by important ecosystem services, high suitability for ecological restoration, and high stability of ecosystem services. This means that the interannual fluctuations of external factors in these regions are small, or the ecosystems are less sensitive to such fluctuations, exhibiting good self-recovery capabilities. Therefore, natural restoration is recommended to remove major stressors and ensure positive succession and continued stability of the ecosystem. Regions at level P4, while highly suitable for ecological restoration, currently have weak ecosystem service supply capacity, and most areas have been converted into artificial ecosystems such as farmland and construction land. Therefore, ecological reconstruction is recommended to restructure the ecosystem and enable it to enter a virtuous cycle. Different reconstruction pathways can be further established based on proximity and connectivity with important ecosystem service areas.

[0087] The aforementioned priority ecological restoration areas utilize ecosystem service importance results from multiple time points, avoiding the limitations of traditional static snapshots. Simultaneously, incorporating the ecosystem service stability index into the identification of important ecosystem service areas enables hierarchical classification of these areas, facilitating more precise identification and protection of key ecosystem service areas to enhance biodiversity conservation effectiveness and ensure the sustainable and stable supply of ecosystem services. By spatially overlaying the results of ecosystem service importance, ecosystem service stability, and ecological restoration suitability assessments, priority ecological restoration areas are generated, avoiding assessment biases from single indicators and improving the scientific rigor and systematic nature of priority area selection. Furthermore, focusing restoration resources on areas with "high value, low stability, and low cost" improves the restoration efficiency per unit area. The above-mentioned methods significantly improve upon the technical limitations of traditional methods in terms of the accuracy of priority area identification and practical applicability. They can effectively guide ecological restoration practitioners to prioritize the allocation of limited resources to areas with important ecosystem services, low ecosystem service stability, and high ecological restoration suitability, based on the restoration sequence. This avoids simply treating important ecosystem service areas with high ecological degradation risks as ecological protection objects, prevents the failure of measures and waste of funds that may be caused by traditional practices such as homogenized ecological governance measures, ensures the supply capacity of important ecosystem service areas, and maximizes the marginal benefits of ecological investment.

[0088] In one embodiment, such as Figure 4 As shown, the steps for processing the ecosystem service importance results of each region at each time point using the frequency statistics method to obtain the first and second regions include:

[0089] S410, Based on the results of the importance of ecosystem services in each region at each time point, calculate the probability that the importance of ecosystem services in each region is important;

[0090] Specifically, the importance of ecosystem services is categorized as important or moderate. For ease of calculation, the importance results can be assigned values, for example, important is 1 and moderate is 0. The probability of an important ecosystem service importance result occurring in each region is calculated as the number of times the important result occurs, divided by the total number of time points (e.g., 21 years), thus yielding the probability of an important ecosystem service importance result occurring.

[0091] S420, determine the first probability and the second probability; wherein the first probability is greater than the second probability;

[0092] Specifically, the thresholds can be determined using the quartile method, setting the first probability at the 75th quantile and the second probability at the 25th quantile. It should be noted that other methods can also be used to determine the first and second probabilities, but these are not specified here.

[0093] S430, the partitions with a probability greater than or equal to the first probability are identified as the first region;

[0094] S440, the partitions with a probability less than the first probability but greater than the second probability are identified as the second region.

[0095] Specifically, partitions with an occurrence probability greater than 75% are designated as the first region. Partitions with an occurrence probability between 25% and 75% are designated as the second region.

[0096] In one embodiment, such as Figure 5 As shown, the steps for extracting highly suitable areas for ecological restoration from the target area include:

[0097] S510, calculate the ecological restoration potential index and the ecological restoration socio-economic cost index for each zone.

[0098] The ecological restoration potential index represents the maximum possible improvement in regional ecosystem services under the combined effects of human restoration measures and natural ecological processes. It is defined as the difference between the current state of ecosystem services and a reference state. Given the diversity of ecosystems, the historical best state of a regional ecosystem is generally used as a reference target. Furthermore, to eliminate or mitigate the bias caused by extreme weather conditions in determining the reference state, smoothing treatment of relevant climatic factors is also necessary.

[0099] The socioeconomic cost index for ecological restoration measures the potential socioeconomic inputs involved in implementing ecological restoration projects in a region. Understandably, in densely populated and economically vibrant areas, ecological restoration incurs not only direct engineering costs but also compensation costs for resettlement, social costs, and opportunity costs associated with abandoning industrial and economic activities. Furthermore, accessibility is a crucial factor; areas with steep terrain or remote locations often face greater difficulty in obtaining restoration resources. Therefore, the socioeconomic feasibility of ecological restoration needs to be comprehensively considered from these perspectives.

[0100] Specifically, the formula for calculating the ecological restoration potential index is as follows:

[0101]

[0102] ERP i =ERC i -CES i,p ;

[0103] In the formula, ERC i Represents the integrated ecosystem services data under the reference state of region i; Max(ES i,v ) represents the maximum time-series value of ecosystem service v in region i; u represents the number of selected key ecosystem services; ERPi This indicates the potential for enhancing ecosystem services through regional i-ecological restoration; CES i,p This represents the integrated ecosystem services data for region i under its current state.

[0104] The integrated ecosystem services under both the reference and current states were calculated after smoothing for climate factors. The climate factors involved were rainfall, temperature, and potential evapotranspiration. The smoothing method used was to take the multi-year average of these factors over the study period as the input parameters for climate variables in the ecosystem service assessment model.

[0105] Furthermore, integrated ecosystem service data can be characterized by four key ecosystem services: water conservation, water purification, soil and water conservation, and habitat quality. Among these, the vegetation cover factor involved in soil and water conservation calculations is highly sensitive to fluctuations in rainfall and temperature; therefore, it is necessary to adjust the vegetation index using rainfall and temperature data under average climatic conditions.

[0106]

[0107]

[0108]

[0109] In the formula, Based on the annual rainfall (PRE) of region i during the study period i ), temperature (TEM) i The data, including NDVI and precipitation, were used to establish a multiple linear fitting relationship between NDVI and the two variables of precipitation and temperature; m i n i and c i These are the regression coefficients for rainfall and temperature, and the constant term, respectively; NDVI i,j Let represent the true NDVI value for region i in year j. The NDVI climate prediction value is obtained by substituting the rainfall and temperature data of region i in year j into the multivariate linear fitting equation; e i,j The residual of the NDVI climate prediction results for region i in year j is generally considered to be the impact of human activities; The NDVI replacement value for region i in year j under average climatic conditions; avePRE i and aveTEM i These represent the multi-year average rainfall and multi-year average temperature for the study period, respectively.

[0110] Furthermore, the socio-economic cost index of ecological restoration is obtained based on the following formula:

[0111] SECI = Max{HAIB HAI C Accessibility}

[0112] HAI B =0.5×PD i +0.5×NTL i

[0113] HAI C =Cropland_NDVI i

[0114] Accessibility = 0.5 × Slope i +0.5×DisToRoads i

[0115] Among them, SECI is the socioeconomic cost index of ecological restoration in region i; HAI B HAI represents the intensity of daily human and economic activities in region i; C The intensity of agricultural activities carried by region i is represented by PD; Accessibility is the transportation accessibility of region i; i and NTL i These represent the population density and nighttime light index for region i, respectively; Cropland_NDVI i For the food production capacity of region i; Slope i The slope of region i; DisToRoads i Let be the distance from region i to the main road, used to reflect potential transportation costs. Main roads are those of significant importance, connecting key nodes, and handling high traffic volumes, such as national highways, provincial highways, and urban arterial roads. Due to PD... i and NTL i It primarily reflects the intensity of human activity on construction land, but cannot reflect the intensity of agricultural activity on arable land. Therefore, based on the significant correlation between NDVI and crop yield, the NDVI index of arable land, Cropland_NDVI, is adopted. i Characterizing the grain production capacity of region i

[0116] It should be noted that PD i NTL i Slope i and DisToRoads i Before calculation, normalization is required to eliminate the influence of different units. This application example uses the range standardization method. Meanwhile, because the PD in this application example area... i and NTL iThe data distribution is extremely uneven. To avoid the influence of extreme values ​​on the result distribution, the minimum and maximum values ​​are set to the 5th and 95th percentiles, respectively. B HAI C After calculating the results with Accessibility, the results are divided into three levels: low cost, medium cost, and high cost using the natural breakpoint method, and assigned values ​​of 1, 2, and 3 respectively. The SECI is then obtained by synthesizing the maximum value.

[0117] S520, based on the ecological restoration potential index and the ecological restoration socio-economic cost index, obtains the restoration potential level and cost level of each area in the target region;

[0118] Specifically, after obtaining the ecological restoration potential index for each region, the results were divided into low-potential, medium-potential, and high-potential levels using the natural breakpoint method. The SECI value represents the low-cost, medium-cost, and high-cost levels, respectively.

[0119] S530 identifies highly suitable areas for ecological restoration within the target region based on restoration potential and cost levels.

[0120] Specifically, referring to the table below, based on the ecological restoration potential level and the ecological restoration socioeconomic cost level, an ecological restoration suitability judgment matrix is ​​constructed. Different combinations of ecological restoration suitability levels are set, thereby extracting highly suitable areas for ecological restoration. Areas with low cost and high potential, low cost and medium potential, and medium cost and high potential are identified as highly suitable areas for ecological restoration.

[0121]

[0122] The above method reflects the suitability of ecological restoration from two aspects: ecological restoration potential and socio-economic costs of ecological restoration. It provides an objective quantification of the expected benefits and potential costs of ecological restoration, so that the suitable ecological restoration areas identified by this method have both high potential for enhancing ecosystem services and good practical operability. This helps to clarify the target level of ecological restoration and improve the cost-benefit ratio of project implementation.

[0123] In one embodiment, such as Figure 6 As shown, the steps for obtaining the importance of ecosystem services in each partition at each time point include:

[0124] S610, Select key ecosystem services based on the ecosystem characteristics of the target area;

[0125] Key ecosystem services can be selected based on the ecosystem characteristics of the target area. In one example, the target area is a watershed ecosystem with numerous rivers and lakes, so four key ecosystem services—water conservation, water purification, soil and water conservation, and habitat quality—were selected to represent the comprehensive ecosystem services. It should be noted that other key ecosystem services can also be used to represent the comprehensive ecosystem services. Among them, water conservation is first assessed by the annual water yield module of InVEST (Integrated Valuation of Ecosystem Services and Trade-offs), and then corrected by soil saturated hydraulic conductivity, topographic index and velocity coefficient; water purification is first assessed by the nutrient transport ratio module of InVEST model to evaluate the nitrogen and phosphorus output of the target area, and then the reciprocal of the sum of nitrogen and phosphorus output is taken; soil and water conservation is based on the modified general soil loss equation, and the rainfall erosivity factor, soil erodibility factor, slope length factor, vegetation cover factor and soil and water conservation factor of the target area are calculated separately, and then substituted into the equation to calculate; habitat quality is based on the habitat quality module of InVEST model, and is assessed based on the habitat suitability of land use / cover type and the degree of threat to the habitat.

[0126] Furthermore, the modified formula used in this application example to convert water production into water conservation is as follows:

[0127]

[0128] In the formula, WC represents water conservation, Kast represents soil saturated hydraulic conductivity, TI represents topographic index, Velocity represents flow velocity coefficient, and WY represents water yield.

[0129] S620 processes key ecosystem services to obtain integrated ecosystem service data;

[0130] Specifically, integrated ecosystem service data can be obtained by normalizing and weighting multiple key ecosystem services separately. The above calculation requires the acquisition of basic data, which specifically includes meteorological, soil, remote sensing, socioeconomic, and vector boundary data. Meteorological data includes rainfall, temperature, and potential evapotranspiration; soil data includes soil texture, soil properties, and bedrock depth; remote sensing data includes topography, normalized vegetation index, and land use / cover; socioeconomic data includes population density, nighttime light index, and road network; and vector boundary data includes target area boundaries and sub-basin boundaries.

[0131] S630, based on integrated ecosystem service data, determines the importance of ecosystem services in each region at each time point.

[0132] Specifically, the importance of ecosystem services is extracted using a percentile threshold method, with thresholds of 10%, 20%, 30%, etc. In one example, the top 30% of regions in terms of overall ecosystem service level at different time points are designated as having important ecosystem services and assigned a value of 1; other regions are designated as having average ecosystem services and assigned a value of 0.

[0133] In one embodiment, a device for identifying ecological restoration priority areas is provided, comprising:

[0134] The acquisition module is used to obtain the ecosystem service importance results for each partition at each time point; wherein, each partition is obtained by dividing the target area.

[0135] The Service Importance Area Confirmation Module is used to determine the service importance area based on the ecosystem service importance results of each partition at each time point;

[0136] The stability grading module is used to calculate the ecosystem service stability index of each region in the important service area, and to determine the stability level of each region in the important service area based on the ecosystem service stability index.

[0137] The module for identifying highly suitable areas for ecological restoration is used to extract highly suitable areas for ecological restoration from the target area.

[0138] The ecological restoration priority zone confirmation module is used to determine ecological restoration priority zones based on the stability level of each area in the important service area and the highly suitable areas for ecological restoration.

[0139] Specific limitations regarding the identification device for priority ecological restoration areas can be found in the limitations of the identification method for priority ecological restoration areas described above, and will not be repeated here. Each module in the aforementioned identification device for priority ecological restoration areas can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module. It should be noted that the module division in this embodiment is illustrative and only represents a logical functional division; other division methods may be used in actual implementation.

[0140] In one embodiment, a computer device is also provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0141] Obtain the ecosystem service importance results for each partition at each time point; where each partition is obtained by dividing the target area.

[0142] Based on the results of the ecosystem service importance of each region at each time point, the service importance areas are obtained;

[0143] Calculate the ecosystem service stability index for each region within the important service region, and determine the stability level of each region within the important service region based on the ecosystem service stability index;

[0144] Highly suitable areas for ecological restoration were extracted from the target area;

[0145] Based on the stability levels of each area in the important service areas and the highly suitable areas for ecological restoration, priority areas for ecological restoration are identified.

[0146] In one embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, performs the following steps:

[0147] Obtain the ecosystem service importance results for each partition at each time point; where each partition is obtained by dividing the target area.

[0148] Based on the results of the ecosystem service importance of each region at each time point, the service importance areas are obtained;

[0149] Calculate the ecosystem service stability index for each region within the important service region, and determine the stability level of each region within the important service region based on the ecosystem service stability index;

[0150] Highly suitable areas for ecological restoration were extracted from the target area;

[0151] Based on the stability levels of each area in the important service areas and the highly suitable areas for ecological restoration, priority areas for ecological restoration are identified.

[0152] It is understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described herein, or combinations thereof.

[0153] For software implementation, the techniques described herein can be implemented by units that perform the functions described herein. The software code can be stored in memory and executed by a processor. The memory can be implemented in the processor or external to the processor.

[0154] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0155] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0156] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0157] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0158] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0159] If the aforementioned function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, 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 this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks. It should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. In the absence of further restrictions, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0160] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for identifying priority areas for ecological restoration, characterized in that, include: Obtain the ecosystem service importance results for each partition at each time point; wherein each partition is obtained by dividing the target area; the ecosystem service importance results include important and general. Based on the results of the ecosystem service importance of each of the aforementioned partitions at each time point, service-important regions are obtained; The ecosystem service stability index is calculated for each region within the important service region, and the stability level of each region within the important service region is determined based on the ecosystem service stability index. The ecosystem service stability index reflects the fluctuation range of integrated ecosystem services in different regions over the entire time series, and is classified into different stability levels based on the fluctuation range. The ecosystem service stability index is obtained from the standard deviation and arithmetic mean of the integrated ecosystem service data. The integrated ecosystem service data is characterized by four key ecosystem services: water conservation, water purification, soil and water conservation, and habitat quality. The water conservation aspect is first assessed using the annual water yield module of the integrated assessment model of ecosystem services and trade-offs to evaluate the water yield of the target area. The quantity is then corrected by soil saturated hydraulic conductivity, topographic index, and flow velocity coefficient; the water purification first uses the nutrient transport ratio module in the integrated assessment model of ecosystem services and trade-offs to assess the nitrogen and phosphorus output of the target area, and then takes the reciprocal of the sum of nitrogen and phosphorus output; the soil and water conservation is based on the modified general soil loss equation, first calculating the rainfall erosivity factor, soil erodibility factor, slope and slope length factor, vegetation cover factor, and soil and water conservation factor of the target area, and then substituting them into the modified general soil loss equation to calculate the result; the habitat quality is based on the habitat quality module of the integrated assessment model of ecosystem services and trade-offs, based on the habitat suitability of land use / cover type, and the threat level of the land use / cover type to the habitat. Highly suitable areas for ecological restoration are extracted from the target area; wherein, the highly suitable areas for ecological restoration are related to the ecological restoration potential index and the ecological restoration socio-economic cost index; Based on the stability level of each area in the important service area and the highly suitable ecological restoration area, an ecological restoration priority area is obtained; wherein, the low-stability area in the important service area and the highly suitable ecological restoration area are superimposed, and the intersecting area obtained by superimposing is the ecological restoration priority area.

2. The method for identifying priority ecological restoration areas according to claim 1, characterized in that, The steps for determining service-important regions based on the ecosystem service importance results of each of the aforementioned partitions at each time point include: The importance of ecosystem services in each of the aforementioned regions at each time point was processed using frequency statistics to obtain the first region and the second region. The results of the importance of ecosystem services in the second region were processed using a regression model to obtain the regression results; The first region and the second region corresponding to the regression results that meet the preset conditions are identified as the important service regions; wherein, the preset conditions are that the slope is greater than 0 and the significance level is less than a set value.

3. The method for identifying priority ecological restoration areas according to claim 2, characterized in that, The steps for processing the ecosystem service importance results of each of the aforementioned partitions at each time point using frequency statistics to obtain the first region and the second region include: Based on the ecosystem service importance results of each partition at each time point, calculate the probability that the ecosystem service importance result of each partition is important; Determine a first probability and a second probability; wherein the first probability is greater than the second probability; The partitions whose probability of occurrence is greater than or equal to the first probability are determined as the first region; The partitions whose probability of occurrence is less than the first probability but greater than the second probability are identified as the second region.

4. The method for identifying priority ecological restoration areas according to claim 1, characterized in that, The step of extracting highly suitable areas for ecological restoration from the target area includes: Calculate the ecological restoration potential index and the ecological restoration socioeconomic cost index for each of the aforementioned zones; Based on the ecological restoration potential index and the ecological restoration socioeconomic cost index, the restoration potential level and cost level of each area in the target region are obtained; Based on the restoration potential level and the cost level, a highly suitable area for ecological restoration is identified in the target area.

5. The method for identifying priority ecological restoration areas according to claim 1, characterized in that, In the step of calculating the socio-economic cost index of ecological restoration for each of the aforementioned zones, the socio-economic cost index of ecological restoration is obtained based on the following formula: ； ； ； ； in, For the region The socio-economic cost index of ecological restoration; Indicates the region The intensity of daily human and economic activities carried on it; Indicates the region The intensity of agricultural activities carried on the land; For the region Accessibility of transportation; and They are respectively regions Population density and nighttime light index; For the region Grain production capacity; For the region The slope; For the region Distance to the main road.

6. The method for identifying priority ecological restoration areas according to claim 1, characterized in that, The steps to obtain the importance of ecosystem services for each partition at each time point include: Based on the ecosystem characteristics of the target area, key ecosystem services are selected; Process the key ecosystem services to obtain integrated ecosystem service data; Based on the comprehensive ecosystem service data, the importance of ecosystem services in each of the aforementioned regions at each time point is determined.

7. The method for identifying priority ecological restoration areas according to claim 6, characterized in that, In the step of calculating the ecosystem service stability index of each region in the important service region, the ecosystem service stability index is obtained based on the following formula: ； in, The stability index of the ecosystem services; The standard deviation of integrated ecosystem service data, The arithmetic mean of integrated ecosystem service data.

8. The method for identifying priority ecological restoration areas according to claim 7, characterized in that, The steps for determining the stability level of each region within the important service regions based on the ecosystem service stability index include: Determine a first grading threshold and a second grading threshold; wherein the first grading threshold is less than the second grading threshold; The comparison results are obtained by comparing the ecosystem service stability index of each region with the first classification threshold and the second classification threshold. Based on the comparison results, the stability level of each region in the important service areas is determined.

9. A device for identifying priority areas for ecological restoration, characterized in that, include: The acquisition module is used to acquire the ecosystem service importance results of each partition at each time point; wherein, each partition is obtained by dividing the target area; the ecosystem service importance results include important and general. The service importance area confirmation module is used to obtain the service importance area based on the ecosystem service importance results of each partition at each time point; The stability grading module is used to calculate the ecosystem service stability index of each region within the important service area, and to determine the stability level of each region within the important service area based on the ecosystem service stability index. The ecosystem service stability index reflects the fluctuation range of integrated ecosystem services in different regions over the entire time series, and is divided into different stability levels based on the fluctuation range. The ecosystem service stability index is obtained from the standard deviation and arithmetic mean of the integrated ecosystem service data. The integrated ecosystem service data is characterized by four key ecosystem services: water conservation, water purification, soil and water conservation, and habitat quality. The water conservation target is first assessed by the annual water production module of the integrated assessment model of ecosystem services and trade-offs. The regional water yield is then corrected using soil saturated hydraulic conductivity, topographic index, and flow velocity coefficient. Water purification first uses the nutrient transport ratio module in the integrated assessment model of ecosystem services and trade-offs to assess the nitrogen and phosphorus output of the target area, then takes the reciprocal of the sum of nitrogen and phosphorus outputs. Soil and water conservation is based on the modified general soil loss equation. The rainfall erosivity factor, soil erodibility factor, slope length factor, vegetation cover factor, and soil and water conservation factor of the target area are calculated separately, then substituted into the modified general soil loss equation. Habitat quality is based on the habitat quality module of the integrated assessment model of ecosystem services and trade-offs, assessed based on the habitat suitability of land use / cover types and the threat level of the land use / cover types. The ecological restoration high suitability zone identification module is used to extract ecological restoration high suitability zones from the target area; wherein, the ecological restoration high suitability zones are related to the ecological restoration potential index and the ecological restoration socio-economic cost index; The ecological restoration priority area confirmation module is used to obtain the ecological restoration priority area based on the stability level of each area in the important service area and the ecological restoration highly suitable area; wherein, the low stability area in the important service area and the ecological restoration highly suitable area are superimposed, and the intersection area obtained by superposition is the ecological restoration priority area.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is configured to execute the method for identifying ecological restoration priority areas as described in any one of claims 1 to 8 when it is run.

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