A Two-Way Detection Method for Spatial Conflicts Between Farmland and Forestry in County Areas

Abstract

This application relates to a method and system for bidirectional detection of spatial conflicts between cultivated land and forest land in a county. The method includes: acquiring a multi-source spatial dataset and a corresponding set of land use conflict rules to determine a first conflict area; calculating the spatial conflict intensity based on the forest land patch dataset to determine a second conflict area; then determining an initial conflict area; determining the range of conflict severity in the initial conflict area at the current survey time; determining the target adjustment range based on a preset spatial retention baseline and the range of conflict severity; and determining a first target rectification area based on the multi-source spatial dataset, the initial conflict area, the target adjustment range, and the set of land use conflict rules. This method can accurately identify spatial conflict areas between cultivated land and forest land in a county from both directions of cultivated land encroachment and forest land occupation, generate executable rectification plans with zoning and hierarchical adjustment amounts, and avoid generating new spatial conflicts after rectification through multi-scenario iterative verification.

Description

Technical Field

[0001] This invention relates to the technical field of smart justice and court trial data, and in particular to a two-way detection method for spatial conflicts between cultivated land and forest in a county. Background Technology

[0002] Farmland-forest space conflict refers to a spatial management contradiction within a county's territorial space where the uses of farmland and forest land overlap, intersect, or have boundaries disputed on the same plot, making it impossible to simultaneously meet the goals of farmland and forest land protection. This type of conflict is particularly prominent in mountainous and hilly counties, agroforestry ecotones, and concentrated areas of reforestation programs. It typically requires land classification and use adjustment to restore the uniformity and authority of territorial space use control. Traditionally, staff from the natural resources and forestry departments determine the scope of conflicting plots requiring rectification based on their respective departmental survey data and control boundaries, through manual comparison or experience-based judgment.

[0003] In traditional techniques, staff cannot pre-verify the dual impact of rectification plans on farmland and forest land protection before implementation. In practice, it is often necessary to first delineate a batch of conflict plots for rectification, and then make the next round of adjustments based on the rectification results and newly emerging conflicts. The rectification cycle and effectiveness are highly dependent on the staff's familiarity with land use policies and local land use history. Especially for complex conflict scenarios such as historical "multiple certificates for one piece of land," restoration of farmland after conversion to forest, and intersections between land occupation and compensation balance project areas and forestry land, accurately determining the boundaries, area, and rectification priority of conflicting plots is even more challenging. Summary of the Invention

[0004] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0005] In view of the problems existing in the prior art, the present invention is proposed.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: A two-way detection method for spatial conflict between cultivated land and forest in a county, the method comprising: Obtain a county-level multi-source spatial dataset and a corresponding set of land use conflict rules; the county-level multi-source spatial dataset includes cultivated land patch set and forest land patch set; the set of land use conflict rules includes cultivated land protection rules and forest land protection rules; The first conflict area is determined based on the cultivated land patch set and the land type conflict rule set; Based on the forest map patch set, spatial conflict intensity is calculated to determine the second conflict area; Based on the first conflict region and the second conflict region, determine the initial conflict region; Based on the multi-source spatial dataset, determine the range of conflict severity in the initial conflict area at the current investigation time. The target adjustment range is determined based on the preset space retention baseline and the range of conflict severity. The first target rectification area is determined based on the multi-source spatial dataset, the initial conflict area, the target adjustment range, and the land category conflict rule set.

[0007] As a preferred embodiment of the bidirectional detection method for spatial conflicts between cultivated land and forestry in a county according to the present invention, the step of obtaining the county-level multi-source spatial dataset and the corresponding land use conflict rule set includes: Acquire multi-period land survey data, forestry survey data, and first-space control boundary data for the target county; Based on the multi-period land survey data and forestry survey data, a county-level multi-source spatial dataset is constructed; the county-level multi-source spatial dataset includes cultivated land patch set and forest patch set. Based on the first spatial control boundary data and the multi-source spatial dataset, spatial overlay and rule matching processes are performed to determine the land category conflict rule set.

[0008] As a preferred embodiment of the two-way detection method for spatial conflicts between cultivated land and forestry in a county according to the present invention, the step of determining the first conflict area based on the cultivated land patch set and the land use conflict rule set includes: Based on the preset conflict rule intensity threshold, the cultivated land patch set, and the land type conflict rule set, the area of ​​cultivated land encroached upon is determined; Based on the spatial correspondence between cultivated land map patches and forest map patches in the multi-source spatial dataset and the area where cultivated land has been encroached upon, the first conflict area is determined.

[0009] As a preferred embodiment of the two-way detection method for spatial conflict between cultivated forests and farmlands in a county according to the present invention, wherein: the forest map patch set is divided into multiple spatial grid units, each spatial grid unit carrying multi-dimensional land use attributes; the step of calculating the spatial conflict intensity based on the forest map patch set to determine the second conflict area includes: Based on the forest map patch set, determine the land use attribute vector of the target spatial grid unit at the time of the basic survey and the land use attribute vector of the target spatial grid unit at the current time of the survey. Based on the land use attribute vector at the time of the basic survey, determine the basic spatial relationship matrix between the grid cell and its adjacent grid cells; Based on the land use attribute vector at the current survey time, determine the current spatial relationship matrix between the grid cell and its adjacent grid cells; Based on the basic spatial relationship matrix and the current spatial relationship matrix, construct the land use change gradient tensor of the grid cell and determine the conflict intensity index matrix corresponding to the target spatial grid cell; Based on the preset index transformation relationship and the conflict intensity index matrix corresponding to the target spatial grid cell, determine the conflict level matrix corresponding to the target spatial grid cell; Based on the conflict level matrix corresponding to each spatial grid cell and the preset conflict level threshold, the region formed by spatial grid cells with a conflict level higher than the preset conflict level threshold is designated as the second conflict region.

[0010] As a preferred embodiment of the two-way detection method for spatial conflicts between cultivated land and forestry in a county according to the present invention, wherein: determining the initial conflict area based on the first conflict area and the second conflict area includes: The union of the first conflict region and the second conflict region is taken as the initial conflict region.

[0011] As a preferred embodiment of the two-way detection method for spatial conflict between cultivated land and forestry in a county according to the present invention, the step of determining the target adjustment range based on a preset spatial retention baseline and the range of conflict severity includes: The target adjustment range is determined by subtracting the space retention base value from the upper and lower limits of the conflict severity range, respectively. The median of the target adjustment range is taken as the target adjustment magnitude.

[0012] As a preferred embodiment of the two-way detection method for spatial conflicts between cultivated land and forestry in a county according to the present invention, the step of determining the first target rectification area based on the multi-source spatial dataset, the initial conflict area, the target adjustment range, and the land use conflict rule set includes: The initial conflict area is projected onto a multi-source spatial dataset under the county standard map to determine the two-dimensional rectification area. Based on the area of ​​farmland encroachment and the set of land use conflict rules, determine the rule constraint strength corresponding to the area of ​​farmland encroachment; Based on the preset main axis direction, and according to the rule constraint intensity corresponding to the encroached farmland area, the adjustment coefficients of the two-dimensional rectification area corresponding to multiple two-dimensional sub-regions in the main axis direction are determined. Based on the multiple adjustment coefficients, target adjustment range, and two-dimensional rectification area, the first target rectification area is determined.

[0013] As a preferred embodiment of the two-way detection method for spatial conflict between cultivated land and forestry in a county according to the present invention, the step of determining the first target rectification area based on multiple adjustment coefficients, target adjustment range, and two-dimensional rectification area includes: By multiplying the multiple adjustment coefficients by the target adjustment range, the adjustment amount of multiple two-dimensional sub-regions of the two-dimensional rectification area in the direction of the main axis is determined; The first target rectification area is determined based on the adjustment amount of multiple two-dimensional sub-regions in the direction of the main axis and the two-dimensional rectification area.

[0014] As a preferred embodiment of the two-way detection method for spatial conflict between cultivated land and forestry in a county-level region according to the present invention, the method further includes: Based on the first target rectification area, the county-level multi-source spatial dataset is updated; Acquire second space control boundary data; Based on the second spatial control boundary data and the updated county-level multi-source spatial dataset, spatial overlay and rule matching processes are performed to determine the land use conflict status information corresponding to the updated county-level multi-source spatial dataset. If the land category conflict status information meets the preset conflict elimination conditions, then the first target rectification area will be taken as the final rectification area. If the land use conflict status information does not meet the preset conflict elimination conditions, then based on the updated county-level multi-source spatial dataset and the corresponding land use conflict status information, a second target rectification area is determined, and the county-level multi-source spatial dataset is updated based on the second target rectification area until the corresponding land use conflict status information meets the preset conflict elimination conditions. The first target rectification area and at least one second target rectification area are then taken as the final rectification area.

[0015] A two-way detection system for spatial conflicts between cultivated land and forestry in a county, applied to the aforementioned two-way detection method for spatial conflicts between cultivated land and forestry in a county, the system comprising: The data acquisition module is used to acquire a county-level multi-source spatial dataset and a corresponding set of land use conflict rules; the county-level multi-source spatial dataset includes cultivated land patch set and forest land patch set; the set of land use conflict rules includes cultivated land protection rules and forest land protection rules; the first detection module is used to determine the first conflict area based on the cultivated land patch set and the set of land use conflict rules. The second detection module is used to calculate the spatial conflict intensity based on the forest map patch set and determine the second conflict area; the initial conflict determination module is used to determine the initial conflict area based on the first conflict area and the second conflict area. The severity assessment module is used to determine the range of conflict severity of the initial conflict area at the current survey time based on the multi-source spatial dataset; the adjustment range determination module is used to determine the target adjustment range based on the preset spatial retention baseline and the range of conflict severity; the rectification area generation module is used to determine the first target rectification area based on the multi-source spatial dataset, the initial conflict area, the target adjustment range, and the land use conflict rule set.

[0016] The beneficial effects of this invention are as follows: The above-mentioned method and system for bidirectional detection of spatial conflicts between cultivated land and forest land in a county, after acquiring a multi-source spatial dataset and a corresponding set of land use conflict rules for the county, determines the first conflict area based on the cultivated land patch set and the set of land use conflict rules; and calculates the spatial conflict intensity based on the forest patch set to determine the second conflict area. Then, based on the first and second conflict areas, the initial conflict area is determined. Next, based on the multi-source spatial dataset, the range of conflict severity in the initial conflict area at the current survey time is determined. Finally, based on the preset spatial retention baseline and the range of conflict severity, the target adjustment range is determined. Based on the multi-source spatial dataset, the initial conflict area, the target adjustment range, and the set of land use conflict rules, the first target rectification area is determined, thereby accurately identifying the spatial conflict areas between cultivated land and forest land from both directions of cultivated land encroachment and forest land occupation, and generating an executable rectification plan with regional and hierarchical adjustment amounts. By introducing a spatial retention baseline value as a bottom-line constraint, it is ensured that the rectification plan does not break through the red lines for farmland protection and ecological protection while eliminating conflicts. By switching spatial control boundary conditions for multi-scenario iterative verification, the passive situation of new conflicts arising after rectification using traditional methods is avoided, and the adjustment and rework rate and administrative costs of the rectification plan in subsequent implementation are significantly reduced. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a flowchart illustrating a two-way detection method for spatial conflict between cultivated land and forestry in a county, as proposed in this invention. Figure 2 This is a schematic diagram of the iterative optimization process of a two-way detection method for spatial conflicts between cultivated land and forestry in a county, as proposed in this invention. Detailed Implementation

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0019] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0020] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0021] Farmland-forest space conflict refers to a spatial management contradiction within a county's land area where the protection uses of cultivated land and forest land overlap, intersect, or have boundaries disputed on the same plot, making it impossible to simultaneously meet the goals of cultivated land protection and forest land protection. This type of conflict is particularly prominent in mountainous and hilly counties, agroforestry ecotones, and concentrated areas of reforestation, not only creating dilemmas in the enforcement of land use control but also potentially triggering a series of chain problems such as breaches of the cultivated land red line, encroachment on ecological space, and damage to the rights and interests of land rights holders. Currently, grassroots natural resources and forestry authorities typically address farmland-forest space conflicts by separately reviewing national land survey results and forestry survey results, organizing on-site verification, and identifying land categories. However, these methods have revealed many shortcomings in practice.

[0022] Independent review by a single department and data comparison: The current practice is for the natural resources department to independently review the land use classification within its jurisdiction based on the annual land use change survey results, and the forestry department to independently review the results of the forestry survey. Staff compare the cultivated land map layer and the forest map layer through visual interpretation or simple layer overlay, marking overlapping areas as suspected conflict patches. Its limitations are: the spatial references, survey time points, land use classification systems, and minimum mapping area standards of the two sets of survey data are often inconsistent, and direct overlay comparison easily generates a large number of false conflict patches due to differences in data caliber. Furthermore, independent review by a single department can only identify one direction of conflict—whether cultivated land has been encroached upon for forestry purposes or whether forest land has been encroached upon for cultivated land purposes—and cannot simultaneously present conflict patterns in two directions within the same analytical framework, easily leading to a one-sided judgment of the overall conflict situation.

[0023] Planning Overlap and Control Boundary Comparison: In some pilot areas, spatial control boundary data, such as permanent basic farmland protection red line maps, ecological protection red line maps, and public welfare forest and natural forest protection scope maps, are overlaid and compared with current land use maps. Through spatial overlay analysis, plots located within the control boundary but whose current use is inconsistent with control requirements are identified, thus determining conflicts. Its limitation lies in the fact that although this method introduces rigid control boundaries as a criterion, it is essentially still a static comparison based on a single time segment. On the one hand, issues such as historical legacy issues like "multiple certificates for one plot of land," inconsistencies between current use and registered attributes caused by various policies such as returning farmland to forest and balancing land occupation and compensation cannot be effectively identified through static comparison at a single point in time.

[0024] On the other hand, the planning matching comparison can only output a binary judgment result of "conflict" or "no conflict", lacking the classification and quantification of the severity of the conflict, and also unable to provide actionable suggestions on rectification priorities and adjustment ranges.

[0025] Field verification and manual on-site identification: For plots of land that are difficult to determine through internal analysis, grassroots departments typically need to organize staff from both natural resources and forestry departments to conduct joint field verification. Staff bring tablets and paper maps to the site, observing actual planting conditions, soil conditions, topography, and other factors, combined with ownership certificates and historical records, to identify the attributes of each plot. Its limitations include: high labor and time costs for field verification; difficulty in achieving full coverage of the thousands of suspected conflict plots within a county; and a high dependence on the professional experience and policy familiarity of the verification personnel, with potential differences in identification standards among different personnel, making it difficult to guarantee consistency in the verification results. Furthermore, field verification can only confirm the current actual state of the plots, unable to trace historical changes, and lacks sufficient basis for judgment on complex conflict scenarios formed due to historical reasons.

[0026] Experience-driven rectification plan development: After conflict patches are identified, rectification plans are typically developed through consultation among county-level natural resources and forestry authorities. Based on the spatial distribution, area size, and rigidity of the control boundaries involved in the conflict patches, combined with local industrial development plans, ecological construction tasks, and arable land preservation gaps, staff empirically determine which patches require priority rectification, the direction of rectification, and the scale of rectification. Its limitation lies in the lack of systematic quantitative analysis tools to support this experience-based decision-making process.

[0027] Before the rectification plans are officially approved and implemented, those who formulate them cannot simulate and verify whether different rectification plans will induce new conflicts in cultivated land and forest space after switching planning conditions (such as stricter protection red lines and adjusted development boundaries). Therefore, in practice, there is a frequent cycle of "a new conflict arises as soon as a round of rectification is completed," which increases administrative costs and the workload at the grassroots level.

[0028] In summary, existing technologies suffer from problems such as insufficient accuracy in conflict area positioning, one-way detection leading to missed conflicts, and the lack of a pre-verification mechanism for rectification plans resulting in non-closed-loop results. There is an urgent need for a conflict detection scheme for cultivated forest space that can achieve two-way accurate detection, quantitative hierarchical evaluation, and closed-loop iterative verification.

[0029] To solve the above problems, refer to Figure 1 As an embodiment of the present invention, a two-way detection method for spatial conflict between cultivated land and forest in a county is provided, the method comprising the following steps: Step 1: Obtain the county-level multi-source spatial dataset and the corresponding land use conflict rule set; the county-level multi-source spatial dataset includes cultivated land patch set and forest land patch set; the land use conflict rule set includes cultivated land protection rules and forest land protection rules.

[0030] A county-level multi-source spatial dataset refers to a structured spatial data set formed by integrating land survey data and forestry survey data from at least two different survey points within a target county, after unified spatial reference integration, patch cleaning, and attribute association. The cultivated land patch set originates from the cultivated land layer in the land change survey results, recording attribute information such as the spatial boundaries, area, slope, grade, ownership, and permanent basic farmland delineation of each cultivated land plot within the county. The forest land patch set originates from the forestry map layer in the forestry survey results, recording attribute information such as the spatial boundaries, area, forest type, origin, forest category, and whether it is included in the public welfare forest or natural forest protection scope of each forest land plot within the county.

[0031] In this embodiment, the multi-source spatial dataset uses the currently widely used CGCS2000 national geodetic coordinate system as a unified spatial reference. The land use conflict rule set is a set of rules for determining conflicts among map patches in the multi-source spatial dataset, based on current national land spatial planning and control requirements and farmland protection policies. The farmland protection rules in the land use conflict rule set are used to determine whether farmland patches are actually encroached upon or covered by forest land use plans, and the forest land protection rules are used to determine whether forest land patches are actually encroached upon or covered by farmland use plans. Each rule contains at least three fields: conflict type, rule strength level, and handling priority. The conflict type includes three levels: "absolute prohibition," "relative restriction," and "risk warning."

[0032] Step 2: Determine the first conflict area based on the cultivated land patch set and the land type conflict rule set.

[0033] The first conflict zone refers to the spatial set of arable land parcels identified as having been encroached upon or at risk of being encroached upon from the perspective of arable land protection. The detection direction for the first conflict zone is "arable land → forest land," that is, determining whether forestry expansion or coverage has occurred on arable land parcels. Since arable land parcels and forest land parcels may be spatially adjacent, partially overlapping, or have intersecting boundaries due to historical reasons within a unified national land space, there is an objectively measurable spatial proximity and overlay relationship between arable land parcels and forest land parcels. When determining the first conflict zone, firstly, based on a preset conflict rule intensity threshold, a subset of arable land protection rules applicable to the arable land parcel set is selected from the land use conflict rule set; then, rule matching is performed on each parcel in the arable land parcel set, and parcels with rule intensity levels higher than the conflict intensity threshold are marked as arable land encroached parcels; finally, all marked parcels are spatially merged to form the first conflict zone.

[0034] The conflict rule intensity threshold is a pre-set benchmark value used to distinguish different degrees of land use conflict. In this embodiment, a dual-threshold determination mode is adopted: the determination of farmland encroachment requires that the farmland protection rule intensity level reaches "high" or above, and the corresponding forest land protection rule intensity level also reaches "medium" or above. In another optional implementation, a single-threshold determination mode can be adopted: as long as the farmland protection rule intensity level reaches "high" or above, or the corresponding forest land protection rule intensity level reaches "medium" or above, the map patch is determined to constitute farmland encroachment.

[0035] Step 3: Calculate the spatial conflict intensity based on the forest map patch set to determine the second conflict area.

[0036] The second conflict zone refers to the set of forest land parcels identified through spatial multi-temporal change analysis from a forest protection perspective as exhibiting abnormally drastic changes in land use, posing a risk of reverse encroachment by cultivated land, or already encroached upon. The detection direction for the second conflict zone is "forest → cultivated land," that is, determining whether there has been a substantial expansion of cultivated land use on the forest land parcels. Before calculating the spatial conflict intensity, the set of forest land parcels is discretized into regular spatial grid cells, each grid cell independently carrying the land use attribute vector for that location at multiple survey time points.

[0037] The basic process for calculating spatial conflict intensity is as follows: extract the land use attribute vectors of each grid cell at the basic survey time point and the current survey time point → construct the spatial relationship matrix of each of the two time points → perform differential analysis to obtain the land use change gradient tensor → calculate the conflict intensity index matrix → map it to the conflict level matrix → merge the areas formed by grid cells with conflict levels higher than the preset level threshold into the second conflict area.

[0038] Among them, the basic survey time point refers to the national land survey year used to establish the benchmark for land use change comparison, and the current survey time point refers to the most recent national land change survey year when the detection task was carried out; each element in the conflict intensity index matrix reflects the degree of drastic change in land use attributes of the grid cell between two time points; the conflict level matrix is ​​obtained by converting the conflict intensity index matrix through a preset piecewise mapping function.

[0039] Step 4: Determine the initial conflict area based on the first and second conflict areas.

[0040] The initial conflict area refers to the preliminary delineation of all conflicting farmland and forest land areas within a county, based on the combined detection results of both the direction of farmland encroachment and the direction of forest land occupation. The determination method is as follows: a GIS spatial overlay and merging operation is performed on the first and second conflict areas, and the geometric union of the two is taken as the initial conflict area.

[0041] Step 5: Based on the multi-source spatial dataset, determine the range of conflict severity in the initial conflict area at the current survey time.

[0042] The current survey point in time refers to the time point corresponding to the most recent round of land change surveys prior to the start date of the detection and analysis task. The range of conflict severity measures the span of differences in the number and spatial concentration of conflict patches within the initial conflict area. It is usually represented by two indicators: an upper limit and a lower limit. The upper limit corresponds to the area of ​​the core plots (groups) with the most concentrated conflict, while the lower limit corresponds to the area of ​​the scattered plots (groups) with the least conflict. In specific calculations, the spatial area and conflict level of each conflict patch within the initial conflict area are examined. The upper limit is obtained by accumulating the areas of patches with conflict levels of "extremely high" and "high," and the lower limit is obtained by accumulating the areas of patches with conflict levels of "medium" and "low." This yields the range of conflict severity in the initial conflict area of ​​the county.

[0043] Step 6: Determine the target adjustment range based on the preset space retention baseline and the range of conflict severity.

[0044] The spatial retention baseline refers to the minimum area of ​​arable land and forest land that must be maintained within a county, based on rigid control boundaries such as the permanent basic farmland protection red line, ecological protection red line, and the scope of public welfare forest and natural forest protection. The target adjustment range refers to the recommended scale indicator for land use adjustments or boundary optimization after removing the non-adjustable portion corresponding to the spatial retention baseline from the initial conflict area in this round of conflict rectification plans.

[0045] Step 7: Determine the first target rectification area based on the multi-source spatial dataset, the initial conflict area, the target adjustment range, and the land use conflict rule set.

[0046] The first target rectification area refers to the set of map features representing the final adjusted use scheme for cultivated land and forest land, based on the initial conflict area and after differentiated adjustment coefficient assignment and refined deconstruction, and bearing zone weight attributes. The specific process for determining the first target rectification area is as follows: First, the initial conflict area is projected onto a multi-source spatial dataset under the county's standard map sheet, forming a two-dimensional rectification area. Then, based on the rule constraint strength information corresponding to the encroached cultivated land area, the two-dimensional rectification area is divided into multiple two-dimensional sub-regions along a preset main axis direction. The rule constraint strength of each sub-region is normalized to obtain the corresponding adjustment coefficient. Next, the adjustment coefficient of each sub-region is multiplied by the target adjustment range to obtain the actual adjusted area of ​​each sub-region. Finally, the two-dimensional rectification area with adjustment annotations is back-projected back onto the three-dimensional multi-source spatial dataset to generate the first target rectification area. The main axis direction refers to the reference direction determined based on the main spatial orientation of the intermingled distribution of cultivated land and forest land within the county, generally parallel to the orientation of the county's main mountain ranges, river valleys, or the extension direction of the cultivated-forest transition zone. The adjustment coefficient is a weighting factor used to differentiate the total scale of target adjustment among sub-regions. Its value ranges from 0 to 1. The larger the coefficient, the greater the proposed rectification effort for that sub-region.

[0047] In summary, the aforementioned two-way detection method for spatial conflicts between cultivated land and forest land in counties, after acquiring a multi-source spatial dataset and a set of land use conflict rules, firstly determines the first conflict area from the perspective of cultivated land protection, and simultaneously determines the second conflict area from the perspective of forest land protection through spatial conflict intensity calculation. Then, the union of the detection results from the two directions is used to obtain the initial conflict area. Next, the range of conflict severity in the initial conflict area is measured, and a spatial retention baseline value is introduced as a baseline constraint to calculate the target adjustment range. Finally, combined with the rule constraint intensity information in the set of land use conflict rules, differentiated adjustment coefficients are generated along the main axis direction. After refining the initial conflict area into zones, the first target rectification area is formed, thereby achieving two-way, accurate, and quantifiable detection and rectification scheme generation for spatial conflicts between cultivated land and forest land in counties.

[0048] This solution integrates multi-source spatial data and land use conflict rule sets, incorporating heterogeneous data from multiple channels, standards, and time points, such as land surveys, forestry surveys, and spatial planning, into a unified analytical framework, thus breaking down data barriers between departments.

[0049] By simultaneously supporting independent detection and result fusion of both farmland encroachment and forest land occupation, this approach avoids the potential for overlooking conflicts that can occur with traditional one-way auditing methods that focus solely on either farmland or forest land. Through calculating the severity of the initial conflict area, determining the adjustment range under the constraint of spatial preservation baseline values, and implementing zoned adjustments based on the strength of rule constraints, the output primary target rectification area not only has a clearly defined boundary but also carries zonal and hierarchical adjustment information, directly guiding frontline rectification operations.

[0050] Based on this, the solution also supports multi-scenario iterative verification and adaptive optimization by switching spatial control boundary conditions, ensuring that the rectification plan has been fully verified before formal implementation, avoiding new conflicts between cultivated and forested spaces after rectification, thereby reducing the cost of repeated adjustments in administrative execution.

[0051] In one embodiment, obtaining the county-level multi-source spatial dataset and the corresponding land use conflict rule set includes: Obtain multiple phases of land survey data, forestry survey data, and first spatial control boundary data for the target county.

[0052] The target county is an administrative division that requires two-way detection of conflicts between cultivated and forestry spaces.

[0053] Multi-period land survey data refers to land use status data at at least two different survey points within the same county. Multi-period land survey data can be obtained through channels such as the annual land change survey results from the natural resources authorities and the results of the Third National Land Survey. First-space control boundary data refers to pre-delineated spatial scope data with mandatory binding force, including but not limited to permanent basic farmland protection red lines, ecological protection red lines, urban development boundaries, nature reserve areas, and public welfare forest and natural forest protection areas.

[0054] Based on multiple periods of national land survey data and forestry survey data, a county-level multi-source spatial dataset is constructed. This dataset includes cultivated land map patches and forest map patches. It is a structured spatial dataset obtained by integrating, cleaning, and associating attributes of the cultivated land map layer from the multiple periods of national land survey data and the forest map layer from the forestry survey data using a unified spatial reference coordinate system. The county-level multi-source spatial dataset includes at least two major categories: cultivated land map patches and forest map patches.

[0055] Based on the first spatial control boundary data and multi-source spatial datasets, spatial overlay and rule matching processes are performed to determine the set of land category conflict rules.

[0056] Spatial overlay and rule matching processing involves geometrically overlaying different spatial layers and determining the land use attributes of each spatial unit after overlay according to preset rule logic. The land use conflict rule set is a set of constraint rules corresponding to each map patch in the county's multi-source spatial dataset. The land use conflict rule set includes two main categories: cultivated land protection rules and forest land protection rules. Cultivated land protection rules are used to determine whether cultivated land patches are encroached upon by forest land use, and forest land protection rules are used to determine whether forest land patches are encroached upon by cultivated land use. Each rule in the land use conflict rule set includes at least a conflict type, rule strength level, and handling priority. Conflict types include "absolute prohibition," "relative restriction," and "risk warning."

[0057] Using the first spatial control boundary data as spatial constraints, spatial overlay analysis is performed on each map patch or grid in the county's multi-source spatial dataset to match the corresponding farmland protection rules or forest land protection rules, thereby obtaining a set of land use conflict rules corresponding to each map patch.

[0058] In one embodiment, determining the first conflict area based on the cultivated land patch atlas and the land use conflict rule set includes: Based on the preset conflict rule intensity threshold, cultivated land patch set, and land type conflict rule set, the area of ​​cultivated land encroached upon is determined. The conflict rule intensity threshold is a pre-set grading standard value used to distinguish the severity of conflicts between different land use categories. For example, in this embodiment, the land use conflict rule set includes farmland protection rules and forest land protection rules, so the conflict rule intensity threshold includes two categories: farmland encroachment threshold and forest land encroachment threshold. The area where farmland is encroached refers to the spatial range where the existing use of farmland plots is actually occupied or planned to be covered by forestry uses, which is usually manifested as an area where farmland plots overlap or the boundaries of forest plots are adjacent.

[0059] In a preferred implementation, the program automatically identifies plots within the cultivated land parcel set whose associated land use conflict rule strength is simultaneously higher than both the cultivated land encroachment threshold and the forest land encroachment threshold, and merges these plots to form a cultivated land encroachment area. In another optional implementation, as long as the land use conflict rule strength corresponding to a cultivated land parcel is higher than either the cultivated land encroachment threshold or the forest land encroachment threshold, the program classifies it as a cultivated land encroachment area.

[0060] During the execution process, the above identification and determination operations can either rely on visual interpretation by operators based on the land type survey results, or be automatically extracted based on preset spatial analysis algorithms. The specific choice can be flexibly made according to the standardization and accuracy requirements of the county data.

[0061] Based on the spatial correspondence between cultivated land map patches and forest map patches in the multi-source spatial dataset, as well as the areas where cultivated land has been encroached upon, the first conflict area is determined.

[0062] The spatial correspondence between cultivated land parcels and forest land parcels is a geometric intersection mapping relationship between the two types of parcels, pre-constructed through spatial overlay analysis. Under a unified national spatial planning system, land parcels with the same geographical coordinates often simultaneously carry multiple management attributes, such as cultivated land reserve resources and forest land reserves. Therefore, cultivated land parcels and forest land parcels have predetermined spatial overlap or adjacency relationships. The first conflict area refers to the set of specific spatially conflicting land parcels that can be mapped and demarcated within the forest land parcel set, under the guidance of the spatial correspondence. Based on the pre-established spatial correspondence between cultivated land parcels and forest land parcels in the multi-source spatial dataset, the range of parcels within the forest land parcel set that map to the encroached cultivated land area is identified, and this range is determined as the first conflict area.

[0063] In one embodiment, the forest map patch set is divided into multiple spatial grid cells, each carrying multidimensional land cover attributes; spatial conflict intensity is calculated based on the forest map patch set to determine the second conflict region, which includes: Step 1: Based on the forest map patch set, determine the land use attribute vector of the target spatial grid unit at the time of the basic survey and the land use attribute vector of the target spatial grid unit at the current time of the survey; The aforementioned grid partitioning process transforms forest map patches from continuous isometric patches into discretized regular analysis units, each capable of independently carrying and calculating land use attribute information. A spatial grid unit refers to the smallest analysis unit obtained by regularly dividing the county space according to preset side lengths (e.g., 30 meters, 50 meters, or 100 meters). The baseline survey time point refers to the historical survey year used to establish a comparative benchmark, while the current survey time point refers to the most recent land use change survey year since the analysis task was performed.

[0064] In practice, by using GIS spatial connection tools to assign the spatial extent and land use attributes of forest map patches to each spatial grid cell, the land use attribute vectors of the target spatial grid cell at two different time points can be extracted.

[0065] The target spatial grid cell can be any grid cell within the analysis range. Although this scheme will use a single target cell as an example to illustrate the calculation logic, in actual execution, all spatial grid cells covered by the forest map patch set will be traversed, and each cell will independently complete the following calculation steps: Step 2: Based on the land use attribute vectors at the time of the basic survey, determine the basic spatial relationship matrix between the grid cell and its adjacent grid cells; The basic spatial relationship matrix is ​​used to quantify the spatial proximity and attribute similarity between a target grid cell and its surrounding neighboring grid cells in terms of multidimensional land cover attributes at the time point of the basic survey.

[0066] In the specific construction process, the target grid cell is taken as the origin, and the set of adjacent grid cells is determined by the eight-neighbor method. The spatial weighted similarity between the land use attribute vector of the target grid cell and the land use attribute vector of each adjacent grid cell is calculated one by one. After arranging the similarity values ​​in order, the basic spatial relationship matrix of the target grid cell is obtained.

[0067] In this embodiment, the basic spatial relationship matrix is ​​an n×n matrix, where the value of n is equal to the number of attribute dimensions contained in the land category attribute vector.

[0068] Step 3: Continue as in Step 2 above, and determine the current spatial relationship matrix between the grid cell and its adjacent grid cells based on the land use attribute vector at the current survey time. The current spatial relationship matrix is ​​used to quantify the spatial proximity and attribute similarity between a target grid cell and its surrounding neighboring grid cells in terms of multidimensional land use attributes at the current survey point in time. The specific construction method is the same as in the second step: taking the target grid cell as the origin, the spatial weighted similarity of the land use attribute vectors between two points in time is calculated for each of the eight neighboring cells, and the similarity values ​​are arranged in the same order to obtain the current spatial relationship matrix. In this embodiment, the size of the current spatial relationship matrix is ​​consistent with the basic spatial relationship matrix, which is also an n×n matrix.

[0069] Step 4: Based on the basic spatial relationship matrix and the current spatial relationship matrix, construct the land use change gradient tensor of the grid cell and determine the conflict intensity index matrix corresponding to the target spatial grid cell; The conflict intensity index matrix is ​​used to measure the quantitative degree of cultivated-forest spatial conflict carried by the target spatial grid unit. The higher the index value, the more drastic the change in land use attributes at that location between two survey time points, and the more severe the conflict. In the calculation, the current spatial relationship matrix is ​​first subtracted element by element from the basic spatial relationship matrix to obtain the spatial relationship change matrix of the target grid unit. Then, based on the change amplitude and direction of each element in the spatial relationship change matrix, the rate of change distribution of land use attributes in the spatial neighborhood is approximately solved by the finite difference method to construct the land use change gradient tensor of the grid unit. Finally, the norm of the land use change gradient tensor is calculated, and the norm calculation result is used as the conflict intensity index of the target grid unit. After being organized in matrix form, the conflict intensity index matrix is ​​obtained.

[0070] Taking specific numerical values ​​as an example, if the basic spatial relationship matrix of a certain target grid cell is... The current spatial relationship matrix is If both are 3×3 in size, then the spatial relationship change matrix ΔM = – Using the central difference formula, partial derivatives of ΔM in the x and y directions are obtained, and combined, to obtain the land cover change gradient tensor G = The final conflict intensity index The Frobenius norm formula can be used to... We obtain, where Let be each specific value in the i-th row and j-th column of the gradient tensor G.

[0071] In this embodiment, the conflict intensity index matrix and the spatial relationship matrix maintain the same dimension.

[0072] Step 5: Determine the conflict level matrix corresponding to the target spatial grid cell based on the preset index conversion relationship and the conflict intensity index matrix corresponding to the target spatial grid cell; The exponential transformation mapping relationship is a pre-established rule used to map continuous conflict intensity indices to discrete conflict levels. The conflict level matrix is ​​a structured result recording the conflict severity level of the target grid cell in each analysis dimension. During calculation, each element in the conflict intensity index matrix is ​​mapped to its corresponding conflict level according to the exponential transformation mapping relationship, and the mapping result is filled into the corresponding matrix position to obtain the conflict level matrix.

[0073] As an example, the index transformation mapping relationship can be expressed as a piecewise function: set I as the conflict intensity index, when 0≤I<0.3, the conflict level is "low"; when 0.3≤I<0.6, the conflict level is "medium"; when 0.6≤I<0.8, the conflict level is "high"; when I≥0.8, the conflict level is "extremely high".

[0074] The threshold criteria mentioned above can be adjusted and optimized based on the historical situation of farmland-forest conflict in the target county, the intensity of land use planning and control, and the characteristics of local natural resources. It should be noted that this scheme sets differentiated conversion thresholds for encroachment on farmland and suppression of forest land, respectively, to reflect the different weights of the two conflict directions in terms of ecological and food security.

[0075] During execution, the program automatically traverses each element of the conflict intensity index matrix, substitutes its value into the preset piecewise mapping function, transforms it one by one, and fills it into the corresponding position in the conflict level matrix, thereby completing the transformation from continuous index to discrete level.

[0076] Step 6: Based on the conflict level matrix corresponding to each spatial grid cell and the preset conflict level threshold, the area formed by spatial grid cells with conflict levels higher than the preset conflict level threshold is designated as the second conflict area.

[0077] The conflict level threshold is a pre-set minimum standard used to determine whether a conflict meets the criteria for inclusion in the detection results. The second conflict area refers to the set of spatial areas within the forest map patch coverage that are identified as having serious spatial conflicts and need to be included in the subsequent rectification process due to drastic changes in land use at different times.

[0078] In the specific determination, the highest conflict level in the conflict level matrix of each spatial grid unit is extracted as the overall conflict level of that unit. Units with an overall conflict level higher than the preset conflict level threshold (e.g., "high" or "extremely high") are marked as conflict units. Then, all marked conflict units are merged in space, and the continuous area formed after merging is the second conflict area.

[0079] It should be noted that when merging conflicting units to form a continuous region, GIS spatial aggregation tools can be used to achieve this by performing neighborhood aggregation and external boundary extraction operations on the marked grid units.

[0080] In one embodiment, determining the initial conflict region based on the first conflict region and the second conflict region includes: The union of the first and second conflict regions is taken as the initial conflict region.

[0081] The initial conflict area refers to the set of all plots of land identified as having conflicts between cultivated land and forest land, preliminarily delineated within the county's spatial area after considering the detection results from two dimensions: the direction of encroachment on cultivated land and the direction of encroachment on forest land. This area forms the basis for subsequent quantification of conflict severity and detailed delineation of rectification scope.

[0082] By using spatial overlay and merging operations, two conflict regions from different detection paths are merged into a unified initial conflict region, enabling subsequent analysis to be conducted on the basis of fully reflecting the two-way conflict pattern.

[0083] In one embodiment, determining the target adjustment range based on a preset space retention baseline and a range of conflict severity includes: The target adjustment range is determined by subtracting the spatial preservation baseline value from the upper and lower limits of the conflict severity range, respectively. The spatial retention baseline is a minimum spatial scale limit for the amount of arable land or forest land that must be maintained within a county, determined in advance based on the requirements of national land spatial planning and control and the bottom line of food security. For example, in terms of arable land, the spatial retention baseline generally corresponds to the area of ​​permanent basic farmland protection; in terms of forest land, the spatial retention baseline generally corresponds to the minimum area of ​​ecological public welfare forests and natural forests. As an example, the upper limit of the range of conflict severity usually corresponds to the area of ​​the core plot where the conflict is most concentrated, while the lower limit usually corresponds to the area of ​​scattered plots on the edge of the conflict.

[0084] The target adjustment range refers to the area of ​​land parcels that can actually be used for land use adjustment or boundary optimization after deducting the basic space that must be retained from the conflict area.

[0085] Subtracting the spatial preservation baseline from the upper limit of conflict severity yields the upper limit of the adjustment range, and subtracting the spatial preservation baseline from the lower limit of conflict severity yields the lower limit of the adjustment range. The interval jointly defined by the upper and lower limits is the target adjustment range. The median of the target adjustment range is used as the target adjustment magnitude.

[0086] The target adjustment range refers to the scale indicator of the area of ​​land parcels that are recommended to be included in the land use adjustment or spatial boundary optimization in this round of conflict rectification. The arithmetic mean of the upper and lower limits of the target adjustment range is taken as the midpoint of the target adjustment range.

[0087] The purpose of adopting the above calculation method is to: by introducing the baseline constraint of the spatial retention base value, eliminate the unadjustable part from the range of conflict severity, and avoid the rectification plan from breaking through the red line of arable land or ecological red line; and then use the median of the target adjustment range as the adjustment range, which not only ensures the effective coverage of rectification measures, but also leaves a certain amount of flexibility for subsequent fine-tuning, and prevents excessive rectification from causing unnecessary impact on land rights holders.

[0088] In one embodiment, the first target rectification area is determined based on a multi-source spatial dataset, an initial conflict area, a target adjustment range, and a land use conflict rule set, including: The initial conflict area was projected onto a multi-source spatial dataset under the county standard map to determine the two-dimensional rectification area. A county-level standard map sheet is a map surface that uniformly divides and numbers the county's space according to the national standard topographic map sheet division rules. It is typically drawn at a scale of 1:10000 or 1:5000, and its map outline forms the spatial coordinate system reference for subsequent operations. The two-dimensional rectification area refers to the projected area of ​​the initial conflict zone onto the plane of the standard map sheet. It represents the area in planar representation, excluding three-dimensional information such as topographic relief, and is used to uniformly delineate the rectification work area on the map. Using GIS planar projection tools, the spatial data of the initial conflict zone is converted into the planar coordinate system of the standard map sheet; the resulting polygonal area is the two-dimensional rectification area.

[0089] Based on the area of ​​farmland encroachment and the set of land use conflict rules, determine the rule constraint strength corresponding to the area of ​​farmland encroachment; When constructing the land use conflict rule set, each map patch is assigned a corresponding rule constraint strength. Since the area where farmland is encroached is a subset composed of several map patches, the rule constraint strength level corresponding to each map patch in the area where farmland is encroached can be obtained one by one through spatial association query in the conflict rule set, forming the rule constraint strength distribution data exclusive to this area.

[0090] By using spatial attribute association operations, the constraint strength values ​​of all rules falling within the scope of the encroached farmland area are extracted from the land category conflict rule set, and the constraint strength information of the rules corresponding to the encroached farmland area is obtained by summarizing the data.

[0091] Based on the preset main axis direction, and according to the rule constraint intensity corresponding to the area of ​​farmland encroachment, the adjustment coefficients corresponding to multiple two-dimensional sub-regions of the two-dimensional rectification area in the main axis direction are determined. The preset main axis direction refers to the spatial reference direction determined in advance based on the main orientation of the intermingling of cultivated land and forest land in the county. In this embodiment, the main axis direction is generally taken as a direction parallel to the orientation of the main mountain range, river valley, or the extension direction of the cultivated-forest transition zone within the county. Multiple two-dimensional sub-regions are several strip-shaped sub-regions obtained by dividing the two-dimensional rectification area into parallel sections at equal intervals along the main axis direction. The projection width of each sub-region in the main axis direction is equal.

[0092] The adjustment coefficient is a weighting factor used to differentiate the adjustment amount for each two-dimensional sub-region. The value range is usually between 0 and 1. The higher the coefficient, the greater the suggested rectification effort for that sub-region.

[0093] During the calculation, the two-dimensional rectification area is first divided into a predetermined number of two-dimensional sub-regions along the main axis. Then, the rule constraint intensity level corresponding to the encroached farmland area within the coverage of each two-dimensional sub-region is determined through spatial overlay analysis. The rule constraint intensity level of each sub-region is then normalized, and the normalization result is used as the adjustment coefficient of that sub-region.

[0094] Regarding the normalization process, this embodiment provides two optional strategies: The first strategy is to select the highest level among the rule constraint strength levels for single-factor normalization, that is, to use the highest constraint strength in each sub-region as the representative value of that sub-region, and then divide it by the maximum representative value among all sub-regions to obtain the adjustment coefficient; The second strategy is to normalize the constraint strengths of the two major categories of rules—farmland protection rules and forest land protection rules—in each sub-region separately, and then perform a weighted summation according to preset weight coefficients (e.g., farmland protection weight 0.6, forest land protection weight 0.4) to obtain the comprehensive adjustment coefficient. In practical applications, an appropriate strategy can be selected based on the dominant direction of farmland-forest conflict in the county.

[0095] Taking the first strategy as an example, if the two-dimensional rectification area is divided into five sub-regions, and the highest rule constraint strength levels corresponding to each sub-region are "high", "very high", "medium", "high" and "low" respectively, converting the levels into numerical values ​​(such as low=1, medium=2, high=3, very high=4), then the representative value of each sub-region is {3,4,2,3,1}, with a maximum value of 4. The adjustment coefficients of each sub-region are 3 / 4=0.75, 4 / 4=1.00, 2 / 4=0.50, 3 / 4=0.75, and 1 / 4=0.25 respectively.

[0096] Based on multiple adjustment coefficients, target adjustment range, and two-dimensional rectification area, the first target rectification area is determined.

[0097] The first target rectification area refers to the set of spatial rectification plan patches that are finally determined based on the initial conflict area after differentiated value assignment and refined zoning, and can be directly used to guide front-line workers in adjusting land use and optimizing boundaries.

[0098] During execution, the adjustment coefficient of each two-dimensional sub-region is multiplied by the target adjustment range to obtain the actual adjustment area corresponding to the sub-region; the adjustment area of ​​each sub-region is then assigned to the corresponding spatial location of the two-dimensional rectification area; the two-dimensional rectification area with adjustment amount annotation is then restored to the three-dimensional multi-source spatial dataset through GIS back projection tools, ultimately forming the first target rectification area with clear boundaries, hierarchical levels, and zones in the county space.

[0099] In one embodiment, the first target rectification area is determined based on multiple adjustment coefficients, the target adjustment range, and the two-dimensional rectification area, including: By multiplying multiple adjustment coefficients by the target adjustment range, the adjustment amount of multiple two-dimensional sub-regions in the two-dimensional rectification area along the main axis direction is determined; Perform a multiplication operation on each two-dimensional sub-region, that is, multiply the adjustment coefficient of the sub-region by the target adjustment range determined in the above steps, and the product is the actual area of ​​the sub-region that is recommended to be included in the rectification.

[0100] Based on the adjustment amount of multiple two-dimensional sub-regions in the main axis direction of the two-dimensional rectification area and the two-dimensional rectification area, the first target rectification area is determined.

[0101] The first target rectification area refers to the spatial boundary result of the cultivated forest space use adjustment plan, which is finally formed after the differentiation coefficient adjustment and can be directly used as the basis for front-line rectification operations.

[0102] Based on the actual adjustment area of ​​each sub-region and the spatial base map of the two-dimensional rectification area, two-dimensional sub-regions with different adjustment levels are spatially spliced ​​and combined in an orderly manner to finally determine the first target rectification area. The first target rectification area is essentially a spatial scheme with zone weight attributes, defined by the basic plane range (two-dimensional rectification area) and the differentiated adjustment amounts of each sub-region, rather than a uniform single block area.

[0103] Reference Figure 2 In a more complete embodiment, after generating the first target rectification area, the above method further includes the following iterative verification and optimization steps.

[0104] S1: Update the county-level multi-source spatial dataset based on the first target rectification area; In the county-level multi-source spatial dataset, the land use attributes of the map patches covered by the first target rectification area are changed according to the usage adjustment direction specified in the rectification plan, thereby generating an updated county-level multi-source spatial dataset. S2: Obtain second space control boundary data; Secondary spatial control boundary data refers to an additional set of spatial control constraints, adjusted or selected based on the primary spatial control boundary data. This includes, but is not limited to, updated permanent basic farmland protection red lines, ecological protection red line adjustment plans, urban development boundary expansion plans, and various control boundaries added due to planning revisions. The purpose of setting secondary spatial control boundary data is to simulate and verify whether implemented rectification plans will trigger new spatial conflicts under stricter or adjusted planning conditions. For example, long-term (e.g., 2035) territorial spatial planning boundary data can be used as secondary spatial control boundary data to test the long-term adaptability of rectification plans.

[0105] S3: Based on the second spatial control boundary data and the updated county multi-source spatial dataset, perform spatial overlay and rule matching processing to determine the land use conflict status information corresponding to the updated county multi-source spatial dataset; Spatial overlay and rule matching processing is a technical method that performs geometric overlay analysis on multiple spatial layers and determines the overlay results patch by patch according to preset conflict determination rules. Land use conflict status information refers to the comprehensive detection results of conflict types, conflict levels, and spatial distribution of each patch, re-detected under the constraints of the updated county-level multi-source spatial dataset and the second spatial control boundary data. The detection dimensions of land use conflict status information include two directions: encroachment on cultivated land and occupation of forest land. Its output format is consistent with the initial detection, including fields such as conflict type, conflict level, and disposal priority, to facilitate comparative analysis with the initial detection results. Using the second spatial control boundary data as the updated spatial constraint, patch-by-patch spatial overlay analysis is performed on the updated county-level multi-source spatial dataset, and cultivated land protection rules and forest land protection rules are re-matched to generate updated land use conflict status information.

[0106] S4: If the land category conflict status information meets the preset conflict elimination conditions, then the first target rectification area will be taken as the final rectification area. The preset conflict resolution conditions refer to the pre-defined evaluation criteria for determining that conflicts in cultivated land and forest space have been effectively resolved. These typically include: zero new conflict patches, remaining conflict patch area below a preset threshold, and all remaining conflicts having a conflict level no higher than "low". The final rectification area refers to the set of land parcels that, after iterative verification, are confirmed to no longer generate new unacceptable conflicts and can be directly submitted for implementation as a formal rectification plan.

[0107] When the local conflict status information meets the above-mentioned conflict elimination conditions, it indicates that the implementation of the first target rectification area will not cause new problems under the second space control boundary conditions. At this time, the first target rectification area will be directly confirmed as the final rectification area.

[0108] S5: If the land use conflict status information does not meet the preset conflict elimination conditions, then the second target rectification area is determined based on the updated county multi-source spatial dataset and the corresponding land use conflict status information. The county multi-source spatial dataset is then updated based on the second target rectification area until the corresponding land use conflict status information meets the preset conflict elimination conditions. The first target rectification area and at least one second target rectification area are then taken as the final rectification area.

[0109] If the land use conflict status information does not meet the conflict elimination conditions, it indicates that the implementation of the first target rectification area has indeed triggered new farmland-forest conflicts under stricter planning conditions, or that the original conflicts, although alleviated, have not yet reached an acceptable standard. In this case, the system uses the updated county-level multi-source spatial dataset and land use conflict status information as input, and follows the complete process described above for determining the first target rectification area to automatically generate the second target rectification area; then, the second target rectification area is applied to the dataset update, and spatial overlay and rule matching detection are performed again; this process is repeated, and after each iteration, it is determined whether the land use conflict status information meets the conflict elimination conditions, until the iteration stops when it does; finally, all the first target rectification areas and second target rectification areas generated in all rounds are spatially merged and deduplicated to form the final rectification area.

[0110] In each iteration, the method for generating the second target rectification area is consistent with the process and algorithm for determining the first target rectification area mentioned above in this scheme. Both include steps such as determining the initial conflict area, measuring the severity of the conflict, calculating the target adjustment range, and making differentiated adjustments based on the strength of rule constraints. These steps will not be repeated here.

[0111] The aforementioned iterative verification and optimization process essentially forms a "pre-implementation simulation verification and adaptive optimization mechanism for the rectification plan of cultivated forest spatial conflict". This mechanism can, before the rectification plan is officially approved and implemented, verify in advance whether the rectification plan will induce new cultivated forest spatial conflicts by switching different spatial control boundary conditions (such as long-term planning conditions, stricter protection red line conditions, etc.); it avoids the passive situation of traditional conflict detection methods that only make a one-time judgment under the current conditions and need to be repeatedly adjusted after the implementation of the rectification plan when new conflicts arise.

[0112] Through multiple rounds of adaptive iteration, this scheme can gradually converge to generate a final rectification area that has been verified in multiple scenarios, while taking into account the dual rigid constraints of farmland protection and forest land protection goals. This significantly improves the scientific nature, robustness, and long-term adaptability of the rectification scheme, and reduces the adjustment and rework rate and administrative costs in the actual implementation of the rectification scheme.

[0113] Furthermore, this embodiment also discloses a two-way detection system for spatial conflicts between cultivated land and forestry in a county, applied to the aforementioned two-way detection method for spatial conflicts between cultivated land and forestry in a county. The system includes: The data acquisition module is used to acquire the county-level multi-source spatial dataset and the corresponding land use conflict rule set; the county-level multi-source spatial dataset includes cultivated land patch set and forest land patch set; the land use conflict rule set includes cultivated land protection rules and forest land protection rules; the first detection module is used to determine the first conflict area based on the cultivated land patch set and the land use conflict rule set. The second detection module is used to calculate the spatial conflict intensity based on the forest map patch set and determine the second conflict area; the initial conflict determination module is used to determine the initial conflict area based on the first conflict area and the second conflict area.

[0114] The severity assessment module is used to determine the range of conflict severity in the initial conflict area at the current survey time based on the multi-source spatial dataset; the adjustment range determination module is used to determine the target adjustment range based on the preset spatial retention baseline and the range of conflict severity; the rectification area generation module is used to determine the first target rectification area based on the multi-source spatial dataset, the initial conflict area, the target adjustment range, and the land use conflict rule set.

[0115] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A two-way detection method for spatial conflict between cultivated land and forest in a county, characterized in that, The method includes: Obtain a county-level multi-source spatial dataset and a corresponding set of land use conflict rules; the county-level multi-source spatial dataset includes cultivated land patch set and forest land patch set; the set of land use conflict rules includes cultivated land protection rules and forest land protection rules; The first conflict area is determined based on the cultivated land patch set and the land type conflict rule set; Based on the forest map patch set, spatial conflict intensity is calculated to determine the second conflict area; Based on the first conflict region and the second conflict region, determine the initial conflict region; Based on the multi-source spatial dataset, determine the range of conflict severity in the initial conflict area at the current investigation time. The target adjustment range is determined based on the preset space retention baseline and the range of conflict severity. The first target rectification area is determined based on the multi-source spatial dataset, the initial conflict area, the target adjustment range, and the land category conflict rule set.

2. The method for two-way detection of spatial conflict between cultivated land and forest in a county according to claim 1, characterized in that, The process of obtaining the county-level multi-source spatial dataset and the corresponding set of land use conflict rules includes: Acquire multi-period land survey data, forestry survey data, and first-space control boundary data for the target county; Based on the multi-period land survey data and forestry survey data, a county-level multi-source spatial dataset is constructed; the county-level multi-source spatial dataset includes cultivated land patch set and forest patch set. Based on the first spatial control boundary data and the multi-source spatial dataset, spatial overlay and rule matching processes are performed to determine the land category conflict rule set.

3. The method for two-way detection of spatial conflict between cultivated land and forestry in a county according to claim 2, characterized in that, The step of determining the first conflict area based on the cultivated land patch atlas and the land use conflict rule set includes: Based on the preset conflict rule intensity threshold, the cultivated land patch set, and the land type conflict rule set, the area of ​​cultivated land encroached upon is determined; Based on the spatial correspondence between cultivated land map patches and forest map patches in the multi-source spatial dataset and the area where cultivated land has been encroached upon, the first conflict area is determined.

4. The method for two-way detection of spatial conflict between cultivated land and forestry in a county according to claim 1, characterized in that, The forest map patch set is divided into multiple spatial grid units, and each spatial grid unit carries multi-dimensional land class attributes; The step of calculating the spatial conflict intensity based on the forest map patch set and determining the second conflict area includes: Based on the forest map patch set, determine the land use attribute vector of the target spatial grid unit at the time of the basic survey and the land use attribute vector of the target spatial grid unit at the current time of the survey. Based on the land use attribute vector at the time of the basic survey, determine the basic spatial relationship matrix between the grid cell and its adjacent grid cells; Based on the land use attribute vector at the current survey time, determine the current spatial relationship matrix between the grid cell and its adjacent grid cells; Based on the basic spatial relationship matrix and the current spatial relationship matrix, construct the land use change gradient tensor of the grid cell and determine the conflict intensity index matrix corresponding to the target spatial grid cell; Based on the preset index transformation relationship and the conflict intensity index matrix corresponding to the target spatial grid cell, determine the conflict level matrix corresponding to the target spatial grid cell; Based on the conflict level matrix corresponding to each spatial grid cell and the preset conflict level threshold, the region formed by spatial grid cells with a conflict level higher than the preset conflict level threshold is designated as the second conflict region.

5. The method for two-way detection of spatial conflict between cultivated land and forestry in a county according to claim 1, characterized in that, The step of determining the initial conflict region based on the first conflict region and the second conflict region includes: The union of the first conflict region and the second conflict region is taken as the initial conflict region.

6. The method for two-way detection of spatial conflicts between cultivated land and forestry in a county according to claim 1, characterized in that, The step of determining the target adjustment range based on the preset space retention baseline and the range of conflict severity includes: The target adjustment range is determined by subtracting the space retention base value from the upper and lower limits of the conflict severity range, respectively. The median of the target adjustment range is taken as the target adjustment magnitude.

7. The method for two-way detection of spatial conflicts between cultivated land and forestry in a county according to claim 3, characterized in that, The step of determining the first target rectification area based on the multi-source spatial dataset, the initial conflict area, the target adjustment range, and the land use conflict rule set includes: The initial conflict area is projected onto a multi-source spatial dataset under the county standard map to determine the two-dimensional rectification area. Based on the area of ​​farmland encroachment and the set of land use conflict rules, determine the rule constraint strength corresponding to the area of ​​farmland encroachment; Based on the preset main axis direction, and according to the rule constraint intensity corresponding to the encroached farmland area, the adjustment coefficients of the two-dimensional rectification area corresponding to multiple two-dimensional sub-regions in the main axis direction are determined. Based on the multiple adjustment coefficients, target adjustment range, and two-dimensional rectification area, the first target rectification area is determined.

8. The method for two-way detection of spatial conflicts between cultivated land and forestry in a county according to claim 7, characterized in that, The step of determining the first target rectification area based on multiple adjustment coefficients, the target adjustment range, and the two-dimensional rectification area includes: By multiplying the multiple adjustment coefficients by the target adjustment range, the adjustment amount of multiple two-dimensional sub-regions of the two-dimensional rectification area in the direction of the main axis is determined; The first target rectification area is determined based on the adjustment amount of multiple two-dimensional sub-regions in the direction of the main axis and the two-dimensional rectification area.

9. The method for two-way detection of spatial conflict between cultivated land and forestry in a county according to claim 2, characterized in that, The method further includes: Based on the first target rectification area, the county-level multi-source spatial dataset is updated; Acquire second space control boundary data; Based on the second spatial control boundary data and the updated county-level multi-source spatial dataset, spatial overlay and rule matching processes are performed to determine the land use conflict status information corresponding to the updated county-level multi-source spatial dataset. If the land category conflict status information meets the preset conflict elimination conditions, then the first target rectification area will be taken as the final rectification area. If the land use conflict status information does not meet the preset conflict elimination conditions, then based on the updated county-level multi-source spatial dataset and the corresponding land use conflict status information, a second target rectification area is determined, and the county-level multi-source spatial dataset is updated based on the second target rectification area until the corresponding land use conflict status information meets the preset conflict elimination conditions. The first target rectification area and at least one second target rectification area are then taken as the final rectification area.

10. A two-way detection system for spatial conflicts between cultivated land and forestry in a county, applied to the two-way detection method for spatial conflicts between cultivated land and forestry in a county as described in any one of claims 1-9, characterized in that, The system includes: The data acquisition module is used to acquire a county-level multi-source spatial dataset and a corresponding set of land use conflict rules; the county-level multi-source spatial dataset includes cultivated land patch sets and forest land patch sets; the set of land use conflict rules includes cultivated land protection rules and forest land protection rules. The first detection module is used to determine the first conflict area based on the cultivated land patch set and the land type conflict rule set; The second detection module is used to calculate the spatial conflict intensity based on the forest map patch set and determine the second conflict area. An initial conflict determination module is used to determine an initial conflict region based on the first conflict region and the second conflict region; The severity assessment module is used to determine the range of conflict severity in the initial conflict area at the current investigation time based on the multi-source spatial dataset. The adjustment range determination module is used to determine the target adjustment range based on the preset space retention base value and the range of conflict severity. The rectification area generation module is used to determine the first target rectification area based on the multi-source spatial dataset, the initial conflict area, the target adjustment range, and the land category conflict rule set.

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