Method and system for analyzing potential of land parcels for comprehensively renovating global land

Abstract

The invention relates to the technical field of land resource management, and discloses a global land comprehensive renovation plot potential analysis method and system, and the method comprises the steps: obtaining land utilization current situation data and multi-temporal remote sensing image data of a project area to be renovated; extracting cultivated land resource pattern spots based on land utilization current situation data to form an initial evaluation object set; identifying and rejecting the initial evaluation object set based on the multi-temporal remote sensing image data to obtain a to-be-verified pattern spot set; removing the pattern spot set to be verified through field verification to obtain a primary potential pattern spot set; constructing an evaluation index system to carry out quantitative comprehensive evaluation, and determining a target area; performing laboratory analysis on the soil of the target area to obtain key soil quality parameters; and grading the soil quality based on the parameters, and outputting an evaluation result. The system corresponds to the method. According to the method, accurate screening and scientific evaluation of the potential land parcels are realized, and the accuracy, comprehensiveness and engineering guidance of evaluation work are improved.

Description

Technical Field

[0001] This application relates to the field of land resource management technology, specifically a method and system for analyzing the potential of land parcels in comprehensive land consolidation. Background Technology

[0002] In the comprehensive land consolidation work, effectively stripping and reusing the valuable resource of topsoil is an important part of improving the quality of newly added arable land and implementing arable land protection.

[0003] However, existing technologies often face numerous challenges in identifying and assessing plots with soil stripping potential within remediation areas. On the one hand, some plots within the remediation area may have suffered irreversible engineering disturbances during previous construction, rendering their topsoil practically worthless for stripping. Traditional methods struggle to efficiently and accurately exclude such plots from a large pool of arable land, leading to inaccurate subsequent work and wasted exploration resources. On the other hand, the assessment of potentially strippable plots often relies on a single area or subjective experience, lacking a systematic and quantitative comprehensive evaluation system that integrates spatial attributes, environmental risks, and soil baseline quality. This can easily result in one-sided potential assessments, failing to scientifically reflect the plot's comprehensive potential level across multiple dimensions such as resource scale, spatial clustering, pollution risk, and actual soil fertility, thus affecting the scientific rigor and economic viability of subsequent soil stripping, transportation, storage, and utilization plans.

[0004] Therefore, there is an urgent need for a land potential analysis method that can automatically screen, comprehensively evaluate from multiple dimensions, and output graded results. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for analyzing the potential of land parcels in comprehensive land consolidation, in order to solve the technical problems mentioned in the background.

[0006] To achieve the above objectives, this application discloses the following technical solutions: Firstly, this application discloses a method for analyzing the potential of land parcels undergoing comprehensive land consolidation, which includes the following steps: S1: Obtain multi-source spatial data of the project area to be remediated, wherein the multi-source spatial data includes at least land use status data and multi-temporal remote sensing image data; S2: Based on the land use status data, extract all cultivated land resource patches in the project area to form an initial evaluation object set; S3: Based on the multi-temporal remote sensing image data, analyze the initial evaluation object set, identify and remove the patches whose surface cover has been irreversibly disturbed by engineering, and obtain the set of patches to be verified; S4: Conduct on-site verification of the set of patches to be verified, and remove patches that do not meet the conditions for protection of arable layer resources based on the on-site surface conditions to obtain a primary potential set of patches; S5: Construct an evaluation index system with spatial attribute factors characterizing the scale and concentration of topsoil resources and risk factors characterizing the potential risk of soil pollution, conduct a quantitative comprehensive evaluation of the primary potential patch set, and screen out target areas with topsoil stripping potential based on the evaluation results. S6: Collect soil samples within the target area for laboratory analysis to obtain key soil quality parameters; S7: Based on the key soil quality parameters, classify the soil quality level of the target area, integrate its spatial and attribute information, and output the evaluation results of the soil resource potential of the topsoil of the plot.

[0007] Optionally, in step S3, the analysis and identification of irreversible engineering disturbance patches based on the multi-temporal remote sensing image data includes: For each patch in the initial evaluation object set, the mean normalized building index and the mean normalized vegetation index are calculated on the pre-construction and post-construction time-phase remote sensing images, respectively. If the increase in the mean normalized building index of a patch is greater than the preset building index threshold, and the decrease in the mean normalized vegetation index is greater than the preset vegetation index threshold, then the patch is determined to be a patch whose surface cover has undergone irreversible engineering disturbance.

[0008] Optionally, the spatial attribute factors include at least an area factor reflecting the size of the patch itself and a proximity factor reflecting the degree of spatial clustering of the patches; the risk factors include at least a soil pollution risk factor based on an assessment of the surrounding environment.

[0009] Optionally, the quantification method of the proximity factor includes: Calculate the set of shortest distances between the target patch and the boundaries of all other patches in the primary potential patch set, and take the minimum value in the set of shortest distances as the quantized value of the proximity factor.

[0010] Optionally, the methods for quantifying the soil pollution risk factors include: Based on the spatial distance between the target patch and various preset potential pollution sources, the cumulative value of the pollution source impact is calculated inversely proportionally to the distance and used as the quantitative value of the soil pollution risk factor.

[0011] Optionally, in step S5, the quantitative comprehensive evaluation determines the weights of each factor in the evaluation index system using a combined weighting method, wherein the combined weighting method includes: S51: Using the analytic hierarchy process (AHP), a judgment matrix is ​​constructed based on expert experience, and the subjective weight vector of each factor is calculated. ; S52: Based on the quantified value data matrix of the primary potential patch set for each factor, the objective weight vector of each factor is calculated using the entropy weight method. ; S53: Calculate the spatial heterogeneity index of each factor Inter-factor conflict index The spatial heterogeneity index mentioned above The coefficient of variation of the quantified values ​​of the i-th factor across all primary potential patches; the inter-factor conflict index The correlation between the i-th factor and other factors is represented by the sum of the absolute values ​​of the Pearson correlation coefficients between the i-th factor and the quantified values ​​of all other factors. S54: Based on the aforementioned spatial heterogeneity index Conflict index between the factors Calculate the overall adjustment coefficient for each factor. The calculation formula is: ,in and These are the maximum values ​​of the spatial heterogeneity index and the inter-factor conflict index, respectively, among all factors; S55: Calculate the final combined weight of each factor using a nonlinear combination model. The calculation formula is: ,in Let be the subjective weight vector of the i-th factor. Let be the objective weight vector of the i-th factor. The total number of factors.

[0012] Optionally, in step S7, the soil quality grade classification includes: Principal component analysis was performed on the key soil quality parameters to extract the top k principal components whose cumulative variance contribution rate met the preset conditions. The score of each target area on the top k principal components was calculated to form a principal component score matrix. Based on the principal component score matrix, the K-means clustering algorithm is used to cluster all target regions; The clusters obtained from clustering are defined as different soil quality grades.

[0013] Optionally, in step S7, the output of the topsoil resource potential assessment results includes: Based on the spatial boundary of the target region, a potential area map with spatial topological relationships is generated; An attribute data table is associated with each patch unit in the potential area map. The attribute data table records at least the soil quality grade, spatial area, and recommended soil stripping thickness calculated based on the soil quality grade and surrounding road network data for the patch unit.

[0014] Optionally, the method for calculating the recommended soil stripping thickness includes: Based on the soil quality grade, query the preset base thickness value corresponding to each grade; Starting from the spatial centroid of the patch unit in the potential area map, and based on the road network data of the project area, calculate the shortest network travel time to at least one preset soil centralized storage area or utilization area. The base thickness value is adjusted by reducing it based on the shortest network travel time. The longer the shortest network travel time, the greater the reduction ratio applied. Secondly, this application discloses a comprehensive land consolidation potential analysis system, which includes: The multi-source spatial data acquisition module is configured to acquire multi-source spatial data of the project area to be remediated, wherein the multi-source spatial data includes at least land use status data and multi-temporal remote sensing image data. The initial evaluation object set generation module is configured to extract all cultivated land resource patches in the project area based on the land use status data to form an initial evaluation object set. The verifiable patch set generation module is configured to analyze the initial evaluation object set based on the multi-temporal remote sensing image data, identify and remove patches whose land cover has undergone irreversible engineering disturbance, and obtain the verifiable patch set. The primary potential map patch generation module is configured to conduct on-site verification of the map patch set to be verified, and remove map patches that do not meet the conditions for protection of arable layer resources based on the on-site surface conditions, thereby obtaining the primary potential map patch set; The quantitative comprehensive evaluation module is configured to construct an evaluation index system with spatial attribute factors characterizing the scale and concentration of topsoil resources and risk factors characterizing the potential risk of soil pollution, to conduct a quantitative comprehensive evaluation of the primary potential patch set, and to screen out target areas with topsoil stripping potential based on the evaluation results. The soil sample data analysis module is configured to collect soil samples in the target area for laboratory analysis to obtain key soil quality parameters. The potential evaluation result output module is configured to classify the soil quality level of the target area based on the key soil quality parameters, integrate its spatial and attribute information, and output the evaluation result of the soil resource potential of the topsoil of the plot. The multi-source spatial data acquisition module, the initial evaluation object set generation module, the unverified patch set generation module, the primary potential patch set generation module, the quantitative comprehensive evaluation module, the soil sample data analysis module, and the potential evaluation result output module are connected in sequence.

[0015] Beneficial Effects: The proposed method and system for analyzing the potential of land parcels in comprehensive land consolidation firstly integrates multi-source spatial data with on-site verification to efficiently exclude cultivated land parcels that have been disturbed by engineering projects and lack conservation value, thus accurately focusing on the objects to be evaluated. Secondly, by constructing an evaluation index system that integrates spatial attribute factors and risk factors for quantitative comprehensive evaluation, it can scientifically and comprehensively screen target areas with the potential for topsoil stripping. Finally, based on soil quality parameters obtained from on-site sampling, it classifies the soil into grades and outputs concrete evaluation results by linking spatial and attribute information. This overcomes the problems of impure objects, single indicators, and strong subjectivity in traditional assessments, improving the accuracy of potential parcel identification, the comprehensiveness of assessment dimensions, and the practical applicability of the results in guiding practice, providing reliable technical support for the scientific protection and efficient utilization of topsoil resources in comprehensive land consolidation. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating the method for analyzing the potential of land parcels in comprehensive land consolidation provided in this application embodiment; Figure 2 A structural block diagram of the land potential analysis system for comprehensive land consolidation provided in this application embodiment. Detailed Implementation

[0018] To facilitate understanding of the technical solutions provided in the embodiments of this application, the background technology involved in the embodiments of this application will be described below.

[0019] Comprehensive land consolidation is an important means to optimize the spatial pattern of the national territory, promote farmland protection, and promote the economical and intensive use of land. In this process, stripping and storing high-quality topsoil (i.e., arable soil) from farmland occupied by construction and using it for new arable land or improvement of substandard land is a key link in improving farmland quality and achieving resource recycling. Therefore, scientifically and accurately identifying plots within the consolidation area with the potential for topsoil stripping and comprehensively evaluating their resource value is fundamental to determining the effectiveness of subsequent work.

[0020] However, current practices in this field still face several technical bottlenecks throughout the entire process from potential identification to evaluation output, affecting the efficiency and scientific rigor of the work. Firstly, in the initial screening of potential land parcels, the cultivated land resource patches within the remediation area often include some parcels that have been irreversibly damaged by previous construction (such as temporary sheds, material storage yards, compaction, etc.), whose topsoil structure has been damaged or severely polluted, rendering them worthless. Traditional methods mainly rely on manual field verification or single-phase image interpretation to identify such parcels, which is not only inefficient and costly but also prone to omissions due to subjective judgment criteria or information lag, resulting in the inclusion of a large number of invalid parcels in subsequent evaluations and wasting exploration and evaluation resources.

[0021] Secondly, existing methods for evaluating the potential of land parcels after initial screening are often one-sided. Common practices include simply ranking parcels by size or relying on expert experience for qualitative scoring, lacking a systematic and quantitative comprehensive evaluation system. This approach ignores other important attributes of topsoil as a spatial resource, such as the spatial clustering of parcels (affecting the efficiency of centralized stripping and transportation) and potential pollution risks to the surrounding environment (affecting the safety of soil reuse). Furthermore, the determination of evaluation indicator weights is often subjective and arbitrary, failing to organically integrate industry experience (subjective weights) with the objective data characteristics of the set of parcels to be evaluated (objective weights). It also fails to consider the spatial differentiation of the evaluation indicators themselves and the information conflicts between indicators, resulting in final evaluation results that fail to truly and evenly reflect the comprehensive potential of the parcels across multiple dimensions, thus lacking reliability.

[0022] Finally, regarding the output and application of evaluation results, traditional methods typically only provide a list of potential sites or a simple distribution map, lacking in-depth, structured information integration. The results often lack baseline soil quality data for different potential levels, recommended stripping thicknesses based on spatial location, and transportation cost analysis considering road networks. This results in a lack of direct and detailed data support for subsequent stripping operations, soil transportation, and utilization planning, reducing the practical value and guiding significance of the evaluation results.

[0023] Based on this, this embodiment provides a method and system for analyzing the potential of land consolidation plots across the entire region, which can automatically and efficiently exclude invalid plots, conduct scientific and comprehensive evaluation using multi-dimensional quantitative indicators, and output deeply integrated spatial and attribute information.

[0024] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application. Secondly, in this document, the term "comprising" is 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.

[0025] Firstly, this embodiment provides a method for analyzing the potential of land parcels undergoing comprehensive land consolidation, such as... Figure 1 As shown, the method includes the following steps in sequence: S1: Obtain multi-source spatial data of the project area to be remediated. The multi-source spatial data includes at least land use status data and multi-temporal remote sensing image data.

[0026] In implementation, the project area to be remediated refers to the complete geographical area planned for land remediation (such as reclamation of construction land, construction of high-standard farmland, etc.). Multi-source spatial data is integrated through a Geographic Information System (GIS) platform. Land use status data comes from the latest annual change results of the national land survey, in vector polygon layers (such as Shapefile or FileGeodatabase Feature Class), whose attribute tables include a "land type name" or "land type code" field for identifying cultivated land. Multi-temporal remote sensing image data includes at least two key temporal phases: the phase before large-scale construction in the project area and the current phase after construction. Image data can be sourced from Landsat, Sentinel-2, or higher resolution commercial satellites, and has undergone preprocessing such as radiometric calibration, atmospheric correction, and geometric fine correction to ensure comparability between different temporal phases. In practical applications, remote sensing image data can also be selected from other satellite or aerial image sources; this application embodiment does not limit this.

[0027] S2: Based on the current land use data, extract all cultivated land resource patches in the project area to form an initial evaluation object set.

[0028] During implementation, based on the land use status vector layer obtained from S1, the spatial query function of GIS software is used to filter out the land parcels according to their land use codes (for example, parcels coded as "0101" (paddy field), "0102" (irrigated land), and "0103" (dry land). These filtered arable land resource parcels are then copied to a new vector layer, which constitutes the initial evaluation object set. Each parcel is an independent evaluation object, possessing its own inherent geometric shape, area, and location attributes.

[0029] S3: Based on multi-temporal remote sensing image data, the initial evaluation object set is analyzed to identify and remove the patches whose land cover has been irreversibly disturbed by engineering, thus obtaining the set of patches to be verified.

[0030] In practice, irreversible engineering disturbances refer to engineering construction activities that cause permanent damage or severe pollution to the physical structure of the topsoil, such as hardening (buildings, roads), deep excavation, compaction, and use as solid waste dumps. By identifying and removing land cover features from a large pool of initial topsoil features, those that exhibit significant engineering changes in surface cover characteristics before and after construction can be marked and removed from the initial evaluation set. The remaining set of land cover features that have not undergone such significant changes constitutes the set of land cover features to be verified. This step narrows down the scope requiring on-site verification.

[0031] S4: Conduct on-site verification of the map patch set to be verified, and remove map patches that do not meet the conditions for protection of arable layer resources based on the on-site surface conditions to obtain the primary potential map patch set.

[0032] During implementation, professional personnel conducted field surveys of the plots to be verified obtained from S3. The verification of surface conditions included, but was not limited to: whether the soil was severely compacted, gravelly, or salinized; whether it had been buried or severely polluted by construction waste, industrial waste, etc.; and whether the topsoil thickness had been severely damaged due to soil extraction (e.g., less than 15 cm). Plots exhibiting any of these conditions were deemed unsuitable for topsoil resource protection and were removed. Through on-site verification, plots that, although not showing obvious engineering features in remote sensing imagery, had actually lost their topsoil stripping value could be excluded, ensuring that every plot in the initial potential plot set physically met the basic prerequisites for soil stripping.

[0033] S5: Construct an evaluation index system with spatial attribute factors characterizing the scale and concentration of topsoil resources and risk factors characterizing the potential risk of soil pollution, conduct a quantitative comprehensive evaluation of the primary potential patch set, and select target areas with topsoil stripping potential based on the evaluation results.

[0034] During implementation, an evaluation index system comprising multiple factors is constructed for each patch in the initial potential patch set. This system includes at least two types of factors: first, spatial attribute factors, used to quantify the endowment of topsoil as a spatial resource, such as the "area factor" reflecting resource quantity and the "proximity factor" reflecting the efficiency of resource spatial distribution; second, risk factors, used to quantify the threats that the external environment may pose to soil quality, such as the "soil pollution risk factor." Subsequently, a quantitative comprehensive evaluation model (such as a weighted summation model) is used to calculate the quantitative values ​​of all factors for each patch, resulting in a comprehensive potential score. All patches are ranked according to their comprehensive potential scores, and a threshold (such as the top 30% of patches) or a natural breakpoint in the score can be set to select areas with higher comprehensive potential as target areas.

[0035] S6: Collect soil samples within the target area for laboratory analysis to obtain key soil quality parameters.

[0036] During implementation, within the target area selected by S5, soil samples are laid out and collected according to relevant standards such as the "Technical Specification for Soil Environmental Monitoring" (HJ / T 166). Sampling points are typically laid out using a grid method or a representative plot method. At each sampling point, soil samples from the 0-30cm topsoil layer are collected using a stainless steel soil drill or shovel and mixed to form a composite sample. The soil samples are sent to a qualified laboratory for analysis. Key soil quality parameters obtained include at least: soil texture (sand, silt, and clay content), organic matter content, pH value, total nitrogen, available phosphorus, available potassium, cation exchange capacity (CEC), and heavy metal content (such as cadmium, mercury, arsenic, lead, and chromium). These parameters are core indicators for assessing soil fertility and environmental safety.

[0037] S7: Based on key soil quality parameters, classify the soil quality level of the target area, integrate its spatial and attribute information, and output the evaluation results of the topsoil resource potential of the plot.

[0038] In implementation, the first step is to use laboratory analysis data obtained from S6 to classify the soil quality of each target area (which can be each patch or a combined area of ​​multiple adjacent patches). Then, the classified soil quality grades, patch area, geometry, and other spatial information, along with other key attributes (such as the recommended stripping thickness calculated based on the grade and transportation conditions), are integrated into a unified GIS output layer. The output can be a potential area distribution map with attribute tables, accompanied by a corresponding analysis report.

[0039] Based on the above, this method establishes a complete evaluation object base through S1-S2; through S3-S4, it constructs a two-level filtering mechanism of remote sensing initial screening + on-site verification, which efficiently and accurately removes map patches that do not have stripping value, improving the purity and efficiency of subsequent evaluation work; through the evaluation index system in S5, it scientifically and comprehensively ranks the screened plots based on their overall potential, overcoming the one-sidedness of single-index evaluation; finally, through S6-S7, it deeply integrates the baseline data from laboratory analysis with spatial information, outputting practical results with clear grades and recommended parameters that can be directly used to guide engineering planning (such as determining stripping areas, stripping amounts, and transportation routes), forming a closed-loop chain from identification and evaluation to application.

[0040] Because traditional visual interpretation of land parcels subject to irreversible engineering disturbances is inefficient and highly subjective, it is crucial to automatically and objectively identify such disturbances based on remote sensing imagery. Therefore, as an optional implementation method in this embodiment, in S3, the identification of land parcels with irreversible engineering disturbances based on multi-temporal remote sensing image data analysis includes: For each patch in the initial evaluation object set, calculate the mean normalized building index and mean normalized vegetation index on the pre-construction and post-construction time-phase remote sensing images respectively. If the increase in the mean normalized building index of a patch is greater than the preset building index threshold, and the decrease in the mean normalized vegetation index is greater than the preset vegetation index threshold, then the patch is determined to be a patch whose surface cover has undergone irreversible engineering disturbance.

[0041] In implementation, the Normalized Difference Building Index (NDBI) and Normalized Difference Vegetation Index (NDVI) are commonly used indices in remote sensing for extracting building and vegetation information, and their calculation methods are existing technologies. For each cultivated land patch in the initial evaluation object set, the vector boundaries of the patch are used for zonal statistical analysis on both pre-construction and post-construction imagery to calculate its average NDBI and NDVI values. Then, the increment of NDBI (post-construction minus pre-construction) and the decrease of NDVI (pre-construction minus post-construction) are calculated. Building index thresholds and vegetation index thresholds are set. When a patch simultaneously satisfies an increment greater than the building index threshold and a decrease greater than the vegetation index threshold, it is determined that irreversible engineering disturbance has occurred. The building index threshold and vegetation index threshold can be determined based on the actual conditions of the project area through experience or sample analysis.

[0042] Based on the above, by jointly analyzing the changes in artificial building features and vegetation features across two key time phases, and by setting dual thresholds for joint judgment, permanent cover changes caused by engineering construction can be effectively distinguished from seasonal changes. This method enables rapid, batch, and automated identification of engineering-disturbed plots in large-area cultivated land parcels.

[0043] To conduct an accurate comprehensive evaluation, as an optional implementation method in this embodiment, the spatial attribute factors include at least an area factor reflecting the size of the patch itself and a proximity factor reflecting the degree of spatial aggregation of the patches; the risk factors include at least a soil pollution risk factor based on the assessment of the surrounding environment.

[0044] In other words, during implementation, the evaluation indicator system should include at least three core factors: ①Area factor: Directly represents the physical quantity of topsoil resources. For a patch, its area factor quantification value is its planar area, in hectares. The larger the area, the more soil resources can usually be stripped.

[0045] ② Proximity factor: Characterizes the degree of spatial clustering of the patch with other potential strippable patches. A high degree of clustering is conducive to organizing concentrated and contiguous stripping operations, reducing mechanical transfer costs, and improving construction efficiency.

[0046] ③ Soil pollution risk factor: This indicates the possibility that a plot of land may be contaminated due to potential pollution sources in the surrounding area. This is a preventive indicator. Even if the current soil quality of a plot of land is acceptable, if it is in a high-risk environment, the safety of its future soil quality is questionable, and its stripping value will be reduced accordingly.

[0047] Therefore, the area factor ensures that the evaluation considers the scale of the resource base; the proximity factor incorporates spatial allocation efficiency into the evaluation system, guiding the selection of potential areas that are convenient for centralized construction; and the soil pollution risk factor uses environmental safety as a prerequisite evaluation condition, avoiding secondary pollution problems that may arise from using soil from high-risk areas for land reclamation. These three types of factors together constitute a multi-dimensional evaluation framework from resource background to spatial efficiency to environmental safety, providing a more comprehensive information foundation for scientific decision-making. Of course, in practical applications, the evaluation index system can also add other factors according to the characteristics of the project area, such as the slope factor (affecting the difficulty of mechanized stripping) and the soil type factor (for preliminary judgment of soil texture), etc., which are not limited in this embodiment.

[0048] Specifically, the quantification of proximity factor can accurately reflect the spatial clustering degree of patches. Therefore, as a further optional implementation method of this embodiment, the quantification method of proximity factor includes: Calculate the set of shortest distances between the target patch and the boundaries of all other patches in the primary potential patch set, and take the minimum value in this set of shortest distances as the quantized value of the proximity factor.

[0049] During implementation, the set of shortest distances between the target patch and the boundaries of all other patches in the primary potential patch set is calculated, and the minimum value in this set of shortest distances is taken as the quantified value of the proximity factor.

[0050] During implementation, for any target patch in the primary potential patch set... It is necessary to calculate its relationship with every other polygon in the set except itself. The shortest spatial Euclidean distance between the boundaries This can be done in batches using spatial analysis tools in GIS software (such as "nearest neighbor analysis"). This yields a set of distances. Then, take the minimum value. As a pattern The proximity factor quantification value. The smaller the value, the stronger the pattern. The closer to other strippable land parcels, the better the spatial clustering; if A large value indicates that the patch... It is relatively isolated. To facilitate weighted calculations with other factors, it is usually necessary to... Perform normalization, for example, using minimax normalization: ,in and These are all the image patches. The maximum and minimum values. Normalized. The larger the value, the better the proximity.

[0051] Therefore, proximity is quantified by using the shortest distance to the nearest neighbor's boundary, which intuitively and accurately reflects the spatial isolation of a single patch. This indicator directly relates to whether construction machinery needs to travel long distances to work on the next plot. This quantification method is simple to calculate, the results are easy to interpret, and it can effectively distinguish spatial clustering differences at the patch level, thus assigning a reasonable weight to spatial allocation efficiency in the comprehensive evaluation.

[0052] Secondly, quantifying soil pollution risk factors can comprehensively assess the combined impact of multiple potential pollution sources. Therefore, as another further optional implementation method in this embodiment, the method for quantifying soil pollution risk factors includes: Based on the spatial distance between the target patch and various preset potential pollution sources, the cumulative value of the pollution source impact is calculated inversely proportionally to the distance and used as the quantitative value of the soil pollution risk factor.

[0053] During implementation, the first step is to identify and digitize all potential pollution sources around the project area. (Such as industrial and mining enterprises, farms, etc.). Each type of pollution source can be assigned a relative source intensity coefficient. (For example, chemical enterprises are set at 1.0, and major transportation routes at 0.3), to reflect the differences in their potential pollution capacity. For any target patch in the primary potential patch set... Calculate its geometric center (or centroid) to each pollution source. Euclidean distance Then, the cumulative impact value of all pollution sources on the patch is calculated using a distance-inverse weighted model, i.e., the quantified value of the pollution risk factor. : In order to deal with when Approaching 0 When the distance approaches infinity, a minimum distance can be set. (e.g., 10 meters), when season .same, The higher the value, the higher the potential pollution risk faced by the patch. Before a comprehensive evaluation, it is usually necessary to also... Normalization is performed.

[0054] Therefore, this method simulates the physical law that pollution impact decreases with increasing distance by using inverse distance weighting. Simultaneously, by assigning different source strength coefficients to different types of pollution sources, it distinguishes the degree of harm from different pollution sources. This quantitative approach can more precisely and scientifically assess the comprehensive potential risk of a site in a complex pollution source environment. Compared to simple buffer zone analysis, its results are more discriminative and physically meaningful, and can more effectively identify high-risk environmental areas in potential assessments.

[0055] Since purely subjective weighting (such as AHP) may be influenced by expert preferences, and purely objective weighting (such as entropy weighting) may rely entirely on data while ignoring practical significance, and since conventional combined weighting is often just a simple linear weighting, failing to consider the impact of the statistical characteristics of the factor data itself on weight allocation, in this embodiment, as an optional implementation method, in S5, the quantitative comprehensive evaluation determines the weight of each factor in the evaluation index system through a combined weighting method, wherein the combined weighting method includes: S51: Using the analytic hierarchy process (AHP), a judgment matrix is ​​constructed based on expert experience, and the subjective weight vector of each factor is calculated. During implementation, multiple domain experts were invited to conduct pairwise comparisons of the relative importance of each evaluation factor, construct a judgment matrix, and calculate subjective weights after passing a consistency test. The Analytic Hierarchy Process (AHP) is a mature existing technology.

[0056] S52: Based on the quantified value data matrix of the primary potential map patch set for each factor, the objective weight vector of each factor is calculated using the entropy weight method. During implementation, it is set up with A preliminary potential map, Each evaluation factor is used. The normalized quantized values ​​of each patch on each factor are combined to form a data matrix. ,in , First calculate the... Under the factor, the first The proportion of index values ​​for each map patch Then calculate the first... Information entropy of each factor ,in Next, calculate the... Information utility value of each factor Finally, the first Objective weights of each factor This yields the objective weight vector. , This indicates transpose.

[0057] S53: Calculate the spatial heterogeneity index of each factor Inter-factor conflict index Spatial heterogeneity index The coefficient of variation of the quantified values ​​of the i-th factor across all primary potential patches; inter-factor conflict index. The correlation between the i-th factor and other factors is represented by the sum of the absolute values ​​of the Pearson correlation coefficients between this factor and the quantified values ​​of all other factors. In implementation, for the i-th factor... One factor: Calculate its spatial heterogeneity index ,in and These are the standard deviation and mean of the factor's quantified values ​​across all patches; calculate its inter-factor conflict index. ,in It is a factor With factors The Pearson correlation coefficient.

[0058] S54: Based on Spatial Differentiation Index Inter-factor conflict index Calculate the overall adjustment coefficient for each factor. The calculation formula is: ,in and These are the maximum values ​​of the spatial heterogeneity index and the inter-factor conflict index, respectively, among all factors. In implementation, each factor's... and Divide each by the maximum value of its respective sequence to eliminate dimensions, obtaining normalized heterogeneity and conflict. Then multiply the two to obtain the adjustment coefficient. . The magnitude of the factor reflects its "strong discriminative power" in the objective dataset. Large) but "weak information independence" (Large) comprehensive characteristics. The larger the value, the more one should rely on the patterns revealed by objective data (entropy weight method) when combining weights, because the data of this factor itself varies significantly but overlaps with the information of other factors, and subjective judgment may not be able to capture such complex data relationships.

[0059] S55: Calculate the final combined weight of each factor using a nonlinear combination model. The calculation formula is: ,in Let be the subjective weight vector of the i-th factor. Let be the objective weight vector of the i-th factor. For the total number of factors. In implementation, for the ... Each factor, and its final composite weight Based on adjustment coefficient The geometric mean model (non-linear) for subjective weight values and objective weight value To integrate. Specifically, and Proportional. When When the value is close to 0, it indicates that the data for that factor has low discriminative power or strong information independence; in this case, the final weight is closer to the subjective weight. ;when When the value is close to 1, it indicates that the factor data has high discriminative power but also high information overlap. In this case, the final weight is closer to the objective weight. Finally, for all factors... Normalize the summation to ensure This yields the final comprehensive weight vector.

[0060] Based on the above, this combined weighting method uses the spatial heterogeneity index and the inter-factor conflict index as the basis for dynamically adjusting the fusion ratio of subjective and objective weights, overcoming the rigidity problem of the fixed ratio of subjective and objective weights in the traditional combined weighting method. This allows the weight allocation to adapt to the characteristics of specific project datasets, thereby making the comprehensive evaluation results more robust and scientific.

[0061] When classifying soil quality based on multiple key soil quality parameters, avoiding subjectively set thresholds and effectively handling the correlation between parameters are crucial to obtaining objective and reasonable classification results. Therefore, as an optional implementation method in this embodiment, in S7, the soil quality classification includes: Principal component analysis was performed on key soil quality parameters to extract the top k principal components whose cumulative variance contribution rate met the preset conditions. The scores of each target area on the top k principal components were calculated to form a principal component score matrix. Based on the principal component score matrix, the K-means clustering algorithm is used to cluster all target regions; The clusters obtained from clustering are defined as different soil quality grades.

[0062] In implementation, it is assumed that there are Key soil quality parameters (such as organic matter, pH, total nitrogen, etc.) were measured for each target area (plot or sampling point). The parameters form the original data matrix. First, the original data is standardized (e.g., Z-score standardization) to eliminate the influence of dimensions. Then, principal component analysis (PCA) is performed to calculate the eigenvalues ​​and eigenvectors of the covariance matrix. The cumulative variance contribution rate (e.g., ...) is selected. (before) Principal components ( ).this The principal components are the original A linear combination of several uncorrelated or highly correlated parameters can represent most of the information in the original data and is orthogonal to each other (uncorrelated). Next, the parameters for each target region are calculated in this... The scores on each principal component yield a... Principal component score matrix ( (This refers to the number of target regions). Finally, using this score matrix as input, unsupervised classification is performed using the K-means clustering algorithm. The number of clusters needs to be pre-defined (i.e., the expected number of soil quality levels, e.g., 3 levels: excellent, medium, and poor). The K-means algorithm will iteratively calculate and classify... The target area is divided into a specified number of... Clusters are created such that objects within the same cluster are as close as possible to each other in the principal component score space, while the distance between different clusters is maximized. After the clustering process converges, each cluster corresponds to a soil quality grade. The specific label for the grade (e.g., Grade I, Grade II, Grade III) can be assigned based on the average performance of samples within the cluster on the original soil parameters.

[0063] Based on the above, principal component analysis (PCA) is used to reduce the dimensionality of multiple soil parameters that may exhibit multicollinearity and decouple them into a few independent principal components. This simplifies the data structure and eliminates mutual interference between parameters. Then, K-means clustering is used for objective classification in the principal component space. The classification is based on the natural distribution structure of data points in the multidimensional space, rather than artificially set fixed thresholds. The soil quality grading results obtained by this method are entirely driven by the data itself, possessing strong objectivity and accurately reflecting the potential natural grouping of soil quality within the project area. This provides a scientific basis for differentiated soil stripping and utilization strategies.

[0064] To effectively integrate soil quality assessment results with spatial information and output digital results that can be directly used for engineering planning, as an optional implementation method in this embodiment, in S7, the assessment results of the topsoil resource potential of the plot are output, including: Based on the spatial boundaries of the target area, a potential area map with spatial topological relationships is generated; For each patch unit in the potential area map, an attribute data table is associated. The attribute data table records at least the soil quality grade, spatial area, and recommended soil stripping thickness calculated based on the soil quality grade and surrounding road network data for that patch unit.

[0065] During implementation, the potential area map is a GIS vector polygon layer, whose geometric boundaries are the boundaries of the target areas selected by S5. Spatial topology means that there is no overlap or gap between polygons, completely covering all potential areas. In the GIS software, multiple fields are added to the attribute table of this layer, including but not limited to: "Soil Quality Grade" (e.g., "Grade I", "Grade II", etc.), "Spatial Area" (unit: hectares, automatically calculated by the system), and "Recommended Stripping Thickness" (unit: centimeters). The value of the Recommended Stripping Thickness field is obtained through a specific calculation model. The final output can be a GIS project file (e.g., ".aprx", ".mxd") or a geodatabase containing the vector layer and its complete attribute table, along with a metadata description document in the corresponding format. Users can open this result in the GIS platform for visualization, spatial querying, filtering by attribute, area summary statistics, and other operations.

[0066] Based on the above, by generating a structured potential area map and linking it with a data table containing key decision-making attributes, the abstract soil quality assessment results and calculation models are output and solidified into a machine-readable, analyzable, and shareable digital outcome that integrates graphics and attributes. This facilitates use by professionals in different stages such as planning and construction. They can directly obtain information such as the location, extent, grade, and estimated strippable soil volume (area × recommended thickness) of each potential plot from the outcome, providing a direct and accurate data foundation for developing detailed stripping operation plans, soil allocation schemes, and cost estimates, thereby ensuring the reliability and practicality of plot potential analysis.

[0067] Determining the recommended soil stripping thickness is a matter of balancing soil resource conservation and engineering economics. A simple, uniform thickness standard cannot reflect the impact of soil quality differences and transportation costs on practical operability. Therefore, as a further optional implementation method in this embodiment, the recommended soil stripping thickness calculation method includes: Based on the soil quality grade, query the preset base thickness value corresponding to each grade; Starting from the spatial centroid of the patch unit in the potential area map, and based on the road network data of the project area, calculate the shortest travel time from it to at least one preset soil centralized storage area or utilization area. The base thickness value obtained from the query is reduced and corrected based on the shortest network travel time. The longer the shortest network travel time, the greater the reduction ratio applied.

[0068] During implementation, the first step is to establish a mapping table between soil quality grades and baseline stripping thickness values. For example, a baseline thickness can be preset for Grade I (high-quality soil). Grade II (Medium Soil) Grade III (Poor Soil) These baseline values ​​reflect the principle of protecting high-quality soil resources by stripping away as much as possible. Then, by constraining transportation costs, a road network dataset (vector line layer, including road grade, speed limits, and other attributes) is obtained for the project area, and the locations of one or more centralized soil storage or utilization areas (such as planned spoil heaps or plots to be reclaimed) are defined. For each plot unit in the potential area map, using its geometric centroid as the starting point, GIS network analysis tools (such as shortest path analysis) are used to calculate the shortest travel time along the actual road network to the nearest (or specified) storage / utilization area. (minutes). Define a reduction function. To adjust the base thickness. For example, using segmented reduction: if (like (30 minutes), no reduction; if (like (For 120 minutes), the reduction ratio increases linearly. ,in This is the maximum reduction factor (e.g., 0.5); if , Therefore, the final recommended peel thickness is... Thus, even for Class I soils, the recommended stripping thickness for distant patches will be lower than that for closer patches.

[0069] Therefore, the recommended stripping thickness is determined by combining the intrinsic quality value of soil resources (reflected by the graded base thickness) with external economic feasibility (reflected by transportation time reduction). Through this dynamic calculation, the output recommended thickness is no longer a static value, but a personalized suggestion based on the specific spatial location and transportation conditions of each plot. This makes the final evaluation results closer to engineering practice, providing project managers with a more scientific and economical decision-making reference on how much soil to strip, thereby ensuring the reliability and practicality of plot potential analysis.

[0070] Secondly, this embodiment provides a system for analyzing the potential of land parcels undergoing comprehensive land consolidation, which corresponds to the aforementioned method for analyzing the potential of land parcels undergoing comprehensive land consolidation, such as... Figure 2 As shown, the system includes: The multi-source spatial data acquisition module is configured to acquire multi-source spatial data of the project area to be remediated. The multi-source spatial data includes at least land use status data and multi-temporal remote sensing image data. The initial evaluation object set generation module is configured to extract all cultivated land resource patches in the project area based on land use status data to form the initial evaluation object set. The module for generating the set of patches to be verified is configured to analyze the initial evaluation object set based on multi-temporal remote sensing image data, identify and remove patches whose land cover has been irreversibly disturbed by engineering, and obtain the set of patches to be verified. The primary potential patch set generation module is configured to conduct on-site verification of the patch set to be verified, and remove patches that do not meet the conditions for protection of arable layer resources based on the on-site surface conditions, thereby obtaining the primary potential patch set; The quantitative comprehensive evaluation module is configured to construct an evaluation index system with spatial attribute factors characterizing the scale and concentration of topsoil resources and risk factors characterizing the potential risk of soil pollution, to conduct a quantitative comprehensive evaluation of the primary potential patch set, and to screen out target areas with topsoil stripping potential based on the evaluation results. The soil sample data analysis module is configured to collect soil samples in the target area for laboratory analysis to obtain key soil quality parameters. The potential assessment results output module is configured to classify the soil quality level of the target area based on key soil quality parameters, integrate its spatial and attribute information, and output the assessment results of the topsoil resource potential of the plot. The module consists of a multi-source spatial data acquisition module, an initial evaluation object set generation module, a set of patches to be verified generation module, a primary potential patch set generation module, a quantitative comprehensive evaluation module, a soil sample data analysis module, and a potential evaluation result output module, which are connected in sequence.

[0071] It should be noted that the land consolidation potential analysis system of this embodiment corresponds to the aforementioned land consolidation potential analysis method. Therefore, the parts of the land consolidation potential analysis system that are not described in detail (including but not limited to specific technical means and effects) can be referred to the relevant descriptions in the aforementioned land consolidation potential analysis method, and will not be repeated here.

[0072] In the embodiments provided in this application, it should be understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code, or any suitable combination thereof. For hardware implementation, the processor may be implemented in one or more of the following: application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, other electronic units designed to implement the functions described herein, or combinations thereof. For software implementation, some or all of the processes of the embodiments may be performed by a computer program instructing the associated hardware. During implementation, the program may be stored in a computer-readable storage medium or transmitted as one or more instructions or code on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of a computer program from one place to another. Storage media may be any available medium accessible to a computer. Computer-readable storage media may include, but are not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code having the form of instructions or data structures and accessible to a computer.

[0073] Finally, it should be noted that the above description is only a preferred embodiment of this application and is not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for analyzing the potential of land parcels in a comprehensive land consolidation project, characterized in that, The method includes the following steps: S1: Obtain multi-source spatial data of the project area to be remediated, wherein the multi-source spatial data includes at least land use status data and multi-temporal remote sensing image data; S2: Based on the land use status data, extract all cultivated land resource patches in the project area to form an initial evaluation object set; S3: Based on the multi-temporal remote sensing image data, analyze the initial evaluation object set, identify and remove the patches whose surface cover has been irreversibly disturbed by engineering, and obtain the set of patches to be verified; S4: Conduct on-site verification of the set of patches to be verified, and remove patches that do not meet the conditions for protection of arable layer resources based on the on-site surface conditions to obtain a primary potential set of patches; S5: Construct an evaluation index system with spatial attribute factors characterizing the scale and concentration of topsoil resources and risk factors characterizing the potential risk of soil pollution, conduct a quantitative comprehensive evaluation of the primary potential patch set, and screen out target areas with topsoil stripping potential based on the evaluation results. S6: Collect soil samples within the target area for laboratory analysis to obtain key soil quality parameters; S7: Based on the key soil quality parameters, classify the soil quality level of the target area, integrate its spatial and attribute information, and output the evaluation results of the soil resource potential of the topsoil of the plot.

2. The method for analyzing the potential of land parcels in comprehensive land consolidation according to claim 1, characterized in that, In step S3, the analysis and identification of irreversible engineering disturbance patches based on the multi-temporal remote sensing image data includes: For each patch in the initial evaluation object set, the mean normalized building index and the mean normalized vegetation index are calculated on the pre-construction and post-construction time-phase remote sensing images, respectively. If the increase in the mean normalized building index of a patch is greater than the preset building index threshold, and the decrease in the mean normalized vegetation index is greater than the preset vegetation index threshold, then the patch is determined to be a patch whose surface cover has undergone irreversible engineering disturbance.

3. The method for analyzing the potential of land parcels in comprehensive land consolidation according to claim 1, characterized in that, The spatial attribute factors include at least an area factor reflecting the size of the patch itself and a proximity factor reflecting the degree of spatial clustering of the patches; The risk factors include at least soil pollution risk factors based on assessments of the surrounding environment.

4. The method for analyzing the potential of land parcels in comprehensive land consolidation according to claim 3, characterized in that, The quantification method of the proximity factor includes: Calculate the set of shortest distances between the target patch and the boundaries of all other patches in the primary potential patch set, and take the minimum value in the set of shortest distances as the quantized value of the proximity factor.

5. The method for analyzing the potential of land parcels in comprehensive land consolidation according to claim 3, characterized in that, The methods for quantifying the soil pollution risk factors include: Based on the spatial distance between the target patch and various preset potential pollution sources, the cumulative value of the pollution source impact is calculated inversely proportionally to the distance and used as the quantitative value of the soil pollution risk factor.

6. The method for analyzing the potential of land parcels in comprehensive land consolidation according to claim 1, characterized in that, In step S5, the quantitative comprehensive evaluation determines the weights of each factor in the evaluation index system using a combined weighting method, wherein the combined weighting method includes: S51: Using the analytic hierarchy process (AHP), a judgment matrix is ​​constructed based on expert experience, and the subjective weight vector of each factor is calculated. ; S52: Based on the quantified value data matrix of the primary potential patch set for each factor, the objective weight vector of each factor is calculated using the entropy weight method. ; S53: Calculate the spatial heterogeneity index of each factor Inter-factor conflict index The spatial heterogeneity index mentioned above The coefficient of variation of the quantified values ​​of the i-th factor across all primary potential patches; the inter-factor conflict index The correlation between the i-th factor and other factors is represented by the sum of the absolute values ​​of the Pearson correlation coefficients between the i-th factor and the quantified values ​​of all other factors. S54: Based on the aforementioned spatial heterogeneity index Conflict index between the factors Calculate the overall adjustment coefficient for each factor. The calculation formula is: ,in and These are the maximum values ​​of the spatial heterogeneity index and the inter-factor conflict index, respectively, among all factors; S55: Calculate the final combined weight of each factor using a nonlinear combination model. The calculation formula is: ,in Let be the subjective weight vector of the i-th factor. Let be the objective weight vector of the i-th factor. The total number of factors.

7. The method for analyzing the potential of land parcels in comprehensive land consolidation according to claim 1, characterized in that, In S7, the soil quality grade classification includes: Principal component analysis was performed on the key soil quality parameters to extract the top k principal components whose cumulative variance contribution rate met the preset conditions. The score of each target area on the top k principal components was calculated to form a principal component score matrix. Based on the principal component score matrix, the K-means clustering algorithm is used to cluster all target regions; The clusters obtained from clustering are defined as different soil quality grades.

8. The method for analyzing the potential of land parcels in comprehensive land consolidation according to claim 1, characterized in that, In S7, the output of the topsoil resource potential assessment results includes: Based on the spatial boundary of the target region, a potential area map with spatial topological relationships is generated; An attribute data table is associated with each patch unit in the potential area map. The attribute data table records at least the soil quality grade, spatial area, and recommended soil stripping thickness calculated based on the soil quality grade and surrounding road network data for the patch unit.

9. The method for analyzing the potential of land parcels in comprehensive land consolidation according to claim 8, characterized in that, The recommended method for calculating soil stripping thickness includes: Based on the soil quality grade, query the preset base thickness value corresponding to each grade; Starting from the spatial centroid of the patch unit in the potential area map, and based on the road network data of the project area, calculate the shortest network travel time to at least one preset soil centralized storage area or utilization area. The base thickness value obtained is reduced and corrected based on the shortest network travel time. The longer the shortest network travel time, the greater the reduction ratio applied.

10. A comprehensive land consolidation potential analysis system, characterized in that, The system includes: The multi-source spatial data acquisition module is configured to acquire multi-source spatial data of the project area to be remediated, wherein the multi-source spatial data includes at least land use status data and multi-temporal remote sensing image data. The initial evaluation object set generation module is configured to extract all cultivated land resource patches in the project area based on the land use status data to form an initial evaluation object set. The verifiable patch set generation module is configured to analyze the initial evaluation object set based on the multi-temporal remote sensing image data, identify and remove patches whose land cover has undergone irreversible engineering disturbance, and obtain the verifiable patch set. The primary potential map patch generation module is configured to conduct on-site verification of the map patch set to be verified, and remove map patches that do not meet the conditions for protection of arable layer resources based on the on-site surface conditions, thereby obtaining the primary potential map patch set; The quantitative comprehensive evaluation module is configured to construct an evaluation index system with spatial attribute factors characterizing the scale and concentration of topsoil resources and risk factors characterizing the potential risk of soil pollution, to conduct a quantitative comprehensive evaluation of the primary potential patch set, and to screen out target areas with topsoil stripping potential based on the evaluation results. The soil sample data analysis module is configured to collect soil samples in the target area for laboratory analysis to obtain key soil quality parameters. The potential evaluation result output module is configured to classify the soil quality level of the target area based on the key soil quality parameters, integrate its spatial and attribute information, and output the evaluation result of the soil resource potential of the topsoil of the plot. The multi-source spatial data acquisition module, the initial evaluation object set generation module, the unverified patch set generation module, the primary potential patch set generation module, the quantitative comprehensive evaluation module, the soil sample data analysis module, and the potential evaluation result output module are connected in sequence.

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