County scale cultivated land supplement resource potential tapping and effect evaluation method

Abstract

The invention belongs to the field of cultivated land protection and spatial information technology application, and particularly relates to a county scale cultivated land supplement resource potential tapping and effect evaluation method. The method comprises the steps of data acquisition, comprehensive ranking of renovatable resources, cultivated land growth probability analysis based on a random forest algorithm, suitability evaluation based on multi-target correction, cultivated land supplement simulation and implementation effect evaluation. And a cellular automaton module of the plaque generation land utilization simulation model simulates cultivated land change, and carries out quantitative evaluation of a cultivated land simulation result in combination with a landscape pattern index. The invention aims to provide the scientific and objective county scale cultivated land reserve resource potential tapping and improvement effect evaluation method, the problems of data distortion, strong subjectivity, scale mismatching, insufficient effect evaluation and the like in the prior art are solved, and technical support is provided for cultivated land occupation and supplement balance work.

Description

Technical Field

[0001] The present invention belongs to the field of farmland protection and spatial information technology application, and specifically relates to a method for tapping the potential and evaluating the effectiveness of supplementary farmland resources at a county scale. Background Art

[0002] Against the background of "balance between occupation and compensation" of cultivated land, the current implementation method for exploring the potential of cultivated land supplementary plots is mainly based on the farmers' own willingness to provide plots. It is not only difficult to ensure the goal of "coordinated optimization of quantity, quality, and layout" in the work of supplementing cultivated land, but also easy to lead to repeated compliance review and related work, consuming a lot of manpower and material resources. Although existing studies have proposed improved technologies for exploring potential plots, there are problems such as distortion of basic data, incomplete potential surveys, poor universality of evaluation methods for plot remediation suitability, and research scales that do not meet actual needs. In addition, there is currently a lack of technology for estimating the effectiveness of cultivated land supplementation.

[0003] The specific problems in the current potential land mining include: (1) It is difficult to fully identify the total potential of reserve resources for cultivated land replenishment. The identification of reserve resources for cultivated land is the primary step in carrying out cultivated land replenishment. However, the current method of relying on farmers' willingness to provide plots of land is subjective and blind. The selection of plots is likely to occupy the red line of national land space planning, resulting in repeated work and affecting the subsequent verification and storage of indicators. Although some current studies have attempted to use GIS technology to extract the distribution of reserve resources, there is a lack of unified standards for land selection, and the analysis results are often not comprehensive enough.

[0004] (2) It is difficult to accurately evaluate the suitability of reserve resources for supplementing cultivated land. After investigating the total potential of reserve resources for supplementing cultivated land, the key is to identify high-quality resources that can supplement cultivated land. There are two main types of existing solutions: evaluating the key factors that affect the suitability of land (such as soil properties, topography, climate change, etc.), or building a comprehensive system that affects the potential of cultivated land improvement (such as expert scoring method, hierarchical analysis method, entropy weight method, hot and cold spot analysis method, etc.). However, these methods are easily limited by subjective factors when constructing weight systems, so they are not universal. Secondly, related studies are mostly based on provincial and municipal scales, which cannot match the implementation model of cultivated land occupation and compensation based on county-level balance. Thirdly, the working base map of the occupation and compensation balance policy is land change survey data, while related studies mostly rely on land use / cover products under the remote sensing image classification system, which is inconsistent with the classification standards and resolution of the occupation and compensation balance working base map, resulting in model distortion and limited practical value.

[0005] (3) It is impossible to predict the effect of cultivated land replenishment. Before implementing land consolidation, simulating the layout of cultivated land resources after land consolidation and quantitatively estimating the effect can provide scientific support for auxiliary planning decisions. However, there is currently a lack of simulation methods for the layout of cultivated land after consolidation of suitable plots, and it is impossible to conduct quantitative assessments of the continuity, concentration, and regularity of cultivated land after consolidation, which makes it difficult to meet the actual needs of work. Summary of the invention

[0006] In response to the above-mentioned technical problems, the present invention provides a method for tapping the potential of supplementary cultivated land resources at the county scale, aiming to provide a scientific and objective method for tapping the potential of cultivated land reserve resources and evaluating the effectiveness of remediation at the county scale, to solve the problems of data distortion, strong subjectivity, scale mismatch, insufficient effectiveness evaluation, etc. in the prior art, and to provide technical support for the work of balancing the occupation and compensation of cultivated land.

[0007] The present invention is achieved through the following technical solutions: A method for tapping the potential and evaluating the effectiveness of supplementary cultivated land resources at the county level, the method comprising: (1) Data acquisition: obtaining basic data, data on the scope of situations where cultivated land replenishment is prohibited, and data on factors driving cultivated land growth; (2) Comprehensive survey of remediable resources: Based on the geographic information system platform, extract the secondary land type patches of various non-cultivated land types from the land type patch layer of the currently enabled land change survey database; based on the principle of overlay analysis, erase the land parcels within the red line of prohibited cultivated land replenishment from the secondary land type patches of various non-cultivated land types to obtain the total potential of cultivated land replenishment reserve resources; (3) Analysis of the probability of cultivated land growth based on the random forest algorithm: Based on the newly added cultivated land in the current year change data relative to the most recent national land survey data, the random forest algorithm is used to obtain the statistical relationship between the driving factors and the spatial expansion of cultivated land, and the spatial distribution of the probability of cultivated land growth is output in the form of raster data; (4) Suitability evaluation based on multi-objective correction: Correct the probability of cultivated land growth and identify the spatial suitability of cultivated land replenishment based on the corrected probability of cultivated land growth; (5) Simulation of cultivated land replenishment and evaluation of implementation effectiveness: Based on the cellular automation module of the patch-generated land use simulation model, the total potential of cultivated land replenishment reserve resources obtained in step (2), the corrected cultivated land growth probability obtained in step (4), and the demand for cultivated land replenishment quantity this year are input to simulate cultivated land changes, and a quantitative evaluation of the cultivated land simulation results is carried out in combination with the landscape pattern index.

[0008] Furthermore, in step (1): The methods for obtaining basic data include: obtaining the land type map layers of the most recent national land survey base period and the currently activated land change survey database as the basic data source for land type analysis; and obtaining cultivated land quality grade data to assist in the selection of potential plots.

[0009] Furthermore, in step (1): the scope data for the prohibition of implementing cultivated land supplementation includes urban development boundary data delineated by the national land space planning, ecological protection red line data, maximum water level line data, first-level water source protection zone data, three-survey slope map data, forest protection range data, and implemented land consolidation project data.

[0010] Furthermore, in step (1): The data on the driving factors of cultivated land growth include natural conditions data, socio-economic conditions data and accessibility data; the natural conditions data include elevation, slope, slope aspect, undulation, soil texture, soil organic matter content, soil pH, root soil oxygen utilization rate, precipitation, sunshine duration, average annual temperature, and crop maturity; the socio-economic conditions data include population distribution and gross domestic product distribution; the accessibility data include water source distribution, road network, and settlement distribution.

[0011] Furthermore, step (2) specifically includes: (2.1) Based on the GIS platform, 13 secondary land type patches are extracted from the land type patch layer of the currently used land change survey database, including arbor forest, bamboo forest, shrub forest, other forest, orchard, tea garden, other garden, other grassland, facility agricultural land, bare land, pond water surface, aquaculture pond, and ditch; (2.2) Based on the principle of overlay analysis, the land parcels within the red line of prohibited cultivated land supplementation were erased from the 13 secondary land classification patches to obtain the total potential of cultivated land supplementation reserve resources; The land parcels within the red line where the supplementation of cultivated land is prohibited include: those within the urban development boundary, within the ecological protection red line, within the highest water level line, within the first-level protection zone of the water source, at a location with a slope greater than 15°, within the forest protection area, within the scope of implemented land reclamation projects, and those with an area smaller than the minimum area shown in the above map.

[0012] Furthermore, step (3) includes: (3.1) Overlay and compare the land classification map of the most recent national land survey base period and the currently used land change survey to extract the newly added cultivated land in recent years; (3.2) The random forest algorithm was used to analyze the statistical relationship between driving factors and the spatial expansion of cultivated land. A 5% data sample was randomly extracted from the newly added cultivated land and 17 driving factors. Raster Data Unit The probability of cultivated land growth The calculation formula is as follows: ; In the formula, The value is 0 or 1, 1 means that other land use types are converted to cultivated land, and 0 means that other land use types are not converted to cultivated land; It is a vector composed of multiple driving factors; is the indicator function of the decision tree set; is a vector No. The prediction type of the decision tree; is the total number of decision trees; (3.3) Output result: The spatial distribution of the probability of cultivated land growth is output in the form of raster data; the output raster data includes the probability of cultivated land growth corresponding to all raster data units, and the value range of each cultivated land growth probability is (0,1).

[0013] (3.3) Output result: The spatial distribution of the probability of cultivated land growth is output in the form of raster data; the output raster data includes the probability of cultivated land growth corresponding to all raster data units, and the value range of each cultivated land growth probability is (0,1).

[0014] Furthermore, step (4) includes the following steps: (4.1) Extract the field of planting attribute name from the land type map layer of the currently enabled land change survey database, create a new field for scoring and assigning values, and classify and assign values ​​to the restoration attributes; assign l to the plots marked as ready for restoration, m to the plots marked as engineering restoration, and n to the plots of general restoration, and generate the correction layer R1 through Kriging interpolation; where l>m>n; (4.2) Create a new layer, extract the cultivated land quality grades of all cultivated land plots, process the potential plots based on the nearest neighbor comparison method, assign the quality grade of the cultivated land plot closest to the current potential plot to the corresponding potential plot, convert the result into a raster, perform normalization, and generate the correction layer R2; (4.3) Based on the cultivated land parcels in the currently enabled land change survey database, based on the ArcGIS pro platform, the center points of the cultivated land parcels are first extracted, point feature data is constructed, and the cultivated land density is calculated through the kernel density analysis tool. The output result is a raster layer, which is normalized to obtain the corrected layer R3; (4.4) Evaluate the regularity of cultivated land around the potential plots by calculating the compactness index; based on the ArcGIS Pro platform, create new perimeter, area and shape index fields in the cultivated land plot vector, use the Calculate Geometry tool to calculate the perimeter and area respectively, and use the Calculate Field tool to calculate the shape index field, and generate the correction layer R4 after normalization; (4.5) Fusing and superimposing the original probability layer with the R, R2, R3, and R4 correction layers to obtain the corrected probability of occurrence of cultivated land replenishment; (4.6) Based on the revised probability, the spatial suitability of cultivated land replenishment is accurately identified; the revised probability of cultivated land replenishment is superimposed on the total potential of cultivated land replenishment reserve resources to obtain the probability of cultivated land replenishment in the county. The spatial differences in the values ​​of the probability of cultivated land replenishment in the county represent the spatial differences in the suitability of cultivated land replenishment.

[0015] Furthermore, in step (4.4), the compactness index calculation formula is as follows: ; Among them, C is the compactness index, A is the area, and P is the perimeter.

[0016] Furthermore, in step (4.5), the modified probability of occurrence of cultivated land supplementation is expressed as a grid layer, and the grid layer is composed of a number of regularly arranged pixels; the calculation formula of the pixel value of the modified probability of occurrence of cultivated land supplementation at the position (x, y) of the layer is: ; in, is the pixel value of the corrected probability layer at position (x, y); is the pixel value of the original probability layer at position (x, y); is the pixel value of the kth correction layer at position (x, y), k=1,2,3,4.

[0017] Furthermore, step (5) specifically includes: (5.1) Use the cellular automaton model to simulate the results of cultivated land replenishment: The total potential of the reserve resources for supplementing cultivated land obtained in step (2), the modified probability of cultivated land growth obtained in step (4), and the demand for the amount of cultivated land supplemented this year are input into the cellular automation module CARS of the patch-generated land use simulation model, and the simulated results of cultivated land distribution in the next year are obtained through iterative calculation; (5.2) Estimation of remediation results based on landscape pattern index: The landscape index was calculated based on Fragstats 4.2 software to quantitatively evaluate the effectiveness of the simulation results under the current cultivated land supplement setting. At the type level, patch density was selected to evaluate the concentration of cultivated land remediation, the normalized shape index was selected to evaluate the regularity of cultivated land, and the separation index was selected to evaluate the continuity of cultivated land. The remediation results were quantitatively evaluated by comparing the landscape pattern characteristics before and after cultivated land supplementation.

[0018] Beneficial technical effects of the present invention: The method provided by the present invention is based on multi-source data integration, machine learning algorithms, land use simulation models and landscape pattern index analysis to tap the potential of cultivated land supplementary resources and evaluate their effectiveness at the county scale; it aims to assist in carrying out the work of balancing the occupation and compensation of cultivated land through scientific and intelligent technical means, and provide technical support for the rational development and protection of cultivated land resources at the county scale. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0020] On the contrary, the present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention as defined by the claims. Further, in order to make the public have a better understanding of the present invention, some specific details are described in detail in the detailed description of the present invention below. Those skilled in the art can fully understand the present invention without the description of these details.

[0021] Embodiment 1: The present invention provides an embodiment of a method for evaluating the effectiveness of tapping the potential of supplementary cultivated land resources at a county level, the method comprising: (1) Data acquisition: obtaining basic data, data on the scope of situations where cultivated land replenishment is prohibited, and data on factors driving cultivated land growth; (2) Comprehensive survey of remediable resources: Based on the geographic information system platform, extract the secondary land type patches of various non-cultivated land types from the land type patch layer of the currently enabled land change survey database; based on the principle of overlay analysis, erase the land parcels within the red line of prohibited cultivated land replenishment from the secondary land type patches of various non-cultivated land types to obtain the total potential of cultivated land replenishment reserve resources; (3) Analysis of the probability of cultivated land growth based on the random forest algorithm: Based on the newly added cultivated land in the current year of change data relative to the most recent national land survey data (e.g., the third national land survey data in 2025), the random forest algorithm is used to obtain the statistical relationship between the driving factors and the spatial expansion of cultivated land, and the spatial distribution of the probability of cultivated land growth is output in the form of raster data; (4) Suitability evaluation based on multi-objective correction: Correct the probability of cultivated land growth and identify the spatial suitability of cultivated land replenishment based on the corrected probability of cultivated land growth; (5) Simulation of cultivated land replenishment and evaluation of implementation effectiveness: Based on the cellular automation module of the patch-generated land use simulation model, the total potential of cultivated land replenishment reserve resources obtained in step (2), the corrected cultivated land growth probability obtained in step (4), and the demand for cultivated land replenishment quantity this year are input to simulate cultivated land changes, and a quantitative evaluation of the cultivated land simulation results is carried out in combination with the landscape pattern index.

[0022] In step (1): the method of obtaining basic data includes: obtaining the land classification map layer ("DLTB") of the most recent national land survey base period (such as the third national land survey base period) and the currently used land change survey database as the basic data source for land classification analysis; and obtaining cultivated land quality grade data to assist in the selection of potential plots; The scope data for the situations where the implementation of cultivated land supplementation is prohibited include the urban development boundary data delineated by the national land space planning (data name is "CZKFBJ"), the ecological protection red line data (data name is "STBHHX"), the highest water level line data, the first-level protection zone data of water sources, the third survey slope map data, the forest protection range data, and the data of implemented land reclamation projects; the scope data for the situations where the implementation of cultivated land supplementation is prohibited is vector data.

[0023] The data on the driving factors of cultivated land growth include natural conditions data, socio-economic conditions data and accessibility data; the natural conditions data include elevation, slope, slope aspect, undulation, soil texture, soil organic matter content, soil pH, root soil oxygen utilization rate, precipitation, sunshine duration, average annual temperature, and crop maturity; the socio-economic conditions data include population distribution and GDP distribution; the accessibility data include water source distribution, road network, and settlement distribution. The data on the driving factors of cultivated land growth include a total of 17 items of data covering three aspects: natural conditions, socio-economic conditions, and accessibility.

[0024] In this embodiment, step (2) specifically includes: (2.1) According to the new policy of balance between occupation and compensation, all types of non-cultivated land can be used as a source of supplementary cultivated land. Therefore, based on the geographic information system platform ArcGIS pro 3, 13 secondary land type patches were extracted from the land type patch layer ("DLTB") of the currently enabled land change survey database, including arbor forest land, bamboo forest land, shrub forest land, other forest land, orchard, tea garden, other garden land, other grassland, facility agricultural land, bare land, pond water surface, breeding pond, and ditch; (2.2) Based on the principle of overlay analysis, the “Erase” tool was used to erase the land parcels within the red line of the prohibition of implementing cultivated land supplementation from the 13 secondary land classification patches to obtain; The land parcels that are within the red line of prohibited land replenishment include: within the urban development boundary, within the ecological protection red line, within the highest water level, within the first-level water source protection area, within the slope greater than 15°, within the forest protection area, within the scope of the implemented land consolidation project, and the land parcel area is smaller than the minimum area on the map (400m 2 ) of the plots to obtain the total potential of arable land to supplement reserve resources.

[0025] In this example, in order to construct an objective comprehensive system that affects the potential of cultivated land improvement, the driving force mechanism of cultivated land expansion is first analyzed. Step (3) includes: (3.1) Overlay and compare the land classification map layer of the most recent national land survey base period (e.g., the third national land survey data in 2025) and the currently used land change survey to extract the newly added cultivated land in recent years; (3.2) The random forest algorithm was used to obtain the statistical relationship between the driving factors and the probability of cultivated land growth. A 5% data sample was extracted from the newly added cultivated land and 17 driving factors using random sampling. The number of training features was 17 (no more than the number of driving factors), and the number of decision trees was 20. The raster data unit The probability of cultivated land growth The calculation formula is as follows: ; In the formula, The value is 0 or 1, 1 means that other land use types are converted to cultivated land, and 0 means that other land use types are not converted to cultivated land; It is a vector composed of multiple driving factors; is the indicator function of the decision tree set; is a vector No. The prediction type of the decision tree; is the total number of decision trees; (3.3) Output result: The spatial distribution of the probability of cultivated land growth is output in the form of raster data; the output raster data includes the probability of cultivated land growth corresponding to all raster data units, and the value range of each cultivated land growth probability is (0,1).

[0026] In this embodiment, step (4) includes the following steps: This step analyzes the suitability of potential resources for supplementing cultivated land by correcting the probability of cultivated land growth. The core requirements of the new policy of balance between occupation and compensation are: ① The supplement of cultivated land is mainly to restore high-quality cultivated land, supplemented by new reclamation; ② The quality of supplemented cultivated land is not reduced; ③ The concentration of cultivated land borders is improved; ④ The regularity of the shape of cultivated land plots is enhanced. The following correction layers are set according to the core requirements: (4.1) Correction layer R1 (restoration priority): Corresponding to core requirement ①, extract the planting attribute name (ZZSXMC) field from the land type map layer of the currently enabled land change survey database, create a new field for scoring and assigning values, and classify and assign values ​​to the restoration attributes; assign l to the plots marked as ready for restoration, m to the plots marked as engineering restoration, and n to the plots of general restoration (i.e., not marked as "ready for restoration" or "engineering restoration"), and generate the correction layer R1 through kriging interpolation; where l>m is greater than n; specifically, l is 1.0, m is 0.7, and n is 0.5; (4.2) Correction layer R2 (cultivated land quality grade): corresponding to core requirement ②, create a new layer, extract the cultivated land quality grade of all cultivated land plots, process the potential plots based on the nearest neighbor comparison method, assign the quality grade of the cultivated land plot closest to the current potential plot to the corresponding potential plot, convert the result into a raster, perform normalization, and generate the correction layer R2; Specifically, the method of processing potential plots based on the nearest neighbor comparison method includes: when assigning values ​​to the plot quality grades, superimposing the potential plot data and the cultivated land quality grade data, and adopting different assignment methods for the three possible situations: a. If the land parcel to be assigned completely falls into a certain arable land quality grading unit or only overlaps with one arable land quality grading unit: directly assign the quality level of the grading unit to the land parcel to be assigned; b. When the land parcel to be assigned overlaps with one or more quality grading units: the quality grade information of the grading unit with the largest area is assigned to the land parcel to be assigned; c. If the land parcel to be assigned does not overlap with the quality grading unit at all: the value is assigned with reference to the adjacent grading unit; (4.3) Correction layer R3 (cultivated land concentration): Corresponding to core requirement ③, based on the cultivated land plots in the currently enabled land change survey database, based on the ArcGIS pro platform, first extract the center points of the cultivated land plots, construct point feature data, and calculate the cultivated land density through the Kernel Density tool. The output result is a raster layer, which is normalized to obtain the correction layer R3; (4.4) Correction layer R4 (regularity of cultivated land): Corresponding to core requirement ④, the regularity of cultivated land around potential plots is evaluated by calculating the compactness index. Based on the ArcGIS pro platform, create "Perimeter", "Area" and "Compactness_Index" fields in the cultivated land plot vector, use the Calculate Geometry tool to calculate the perimeter ("Perimeter" option) and area ("Area" option), respectively, use the Calculate Field tool to calculate the result of the "Shape Index" field, and generate the correction layer R4 after normalization; the calculation formula of the compactness index is as follows: ; Among them, C is the compactness index, A is the area, and P is the perimeter.

[0027] (4.5) The original probability layer is fused and superimposed with the R, R2, R3, and R4 correction layers to obtain the corrected probability of cultivated land replenishment; the corrected probability of cultivated land replenishment is expressed as a grid layer, which is composed of a number of regularly arranged pixels; the calculation formula for the pixel value of the corrected probability of cultivated land replenishment at the layer (x, y) is: ; in, is the pixel value of the corrected probability layer at position (x, y); is the pixel value of the original probability layer at position (x, y); is the pixel value of the kth correction layer at position (x, y), k=1,2,3,4.

[0028] in, is obtained by the formula in step (3.2) Obtained by calculation.

[0029] (4.6) Based on the revised probability, accurately identify the spatial suitability of cultivated land replenishment; superimpose the revised probability of cultivated land replenishment with the total potential of cultivated land replenishment reserve resources to obtain the probability of cultivated land replenishment in the county. The spatial difference in the probability of cultivated land replenishment in the county represents the spatial difference in the suitability of cultivated land replenishment; for example, analyze the overall trend of the suitability of cultivated land replenishment by analyzing the distribution at the county scale and the town scale; use the natural breakpoint method to divide the potential plots into high-quality restoration areas, general restoration areas, low-efficiency restoration areas and improved development areas according to the level of suitability for cultivated land replenishment; and optimize the potential of cultivated land replenishment resources.

[0030] In this embodiment, based on the cellular automation module (CARS) of patch-generating land use simulation (PLUS), the analysis results of step (2) and step (4) are integrated to simulate the change of cultivated land, and quantitative evaluation of the cultivated land simulation results is carried out in combination with the landscape pattern index. Step (5) specifically includes: (5.1) Use the cellular automaton model to simulate the results of cultivated land replenishment: The total potential of the reserve resources for supplementing cultivated land obtained in step (2), the modified probability of cultivated land growth obtained in step (4), and the demand for the amount of cultivated land supplemented this year are input into the cellular automation module CARS of the patch-generated land use simulation model, and the simulated results of cultivated land distribution in the next year are obtained through iterative calculation; CARS uses a threshold reduction mechanism and a roulette wheel mechanism based on random seeds to simulate cultivated land changes at the pixel and patch scales. Three parameters need to be set: "spatial constraint", "quantity demand", and "growth probability". The method is as follows: ① The spatial constraint is the total potential result of the cultivated land supplement reserve resources analyzed in step (2); ② The growth probability is the probability of cultivated land supplement after correction in step (4); ③ The quantity demand is determined according to the principle of "determining occupation based on supplement" ("determining occupation based on supplement" is an innovative control mechanism of the new policy of occupation and compensation balance. Decision makers should estimate the upper limit of the scale of cultivated land occupied by non-cultivated land construction in the next year based on actual conditions, and then coordinate the cultivated land supplement work in this year). In practice, it is necessary to set a surplus of cultivated land improvement scale (usually controlled at about 10%) to deal with the reduction of effective indicators caused by factors such as incomplete cultivation and slope coefficient calculation errors. For example, if a county estimates that the construction in the next year will occupy 100 mu of cultivated land, at least 100 mu of new cultivated land should be improved in this year, but in practice, there needs to be a surplus, usually 110 mu in this year is more appropriate.

[0031] (5.2) Estimation of remediation results based on landscape pattern index: Estimation of remediation results based on landscape pattern index: The landscape index was calculated based on Fragstats 4.2 software to quantitatively evaluate the implementation effectiveness of the simulation results under the current cultivated land supplement setting; at the type level, patch density was selected to evaluate the concentration of cultivated land remediation, the normalized shape index was selected to evaluate the regularity of cultivated land, and the separation index was selected to evaluate the continuity of cultivated land; by comparing the landscape pattern characteristics before and after cultivated land supplementation, the remediation results were quantitatively evaluated: specifically, based on the cultivated land type of the current year and the cultivated land simulated using cellular automata, the patch density, normalized shape index and separation index of the cultivated land before and after the remediation were calculated to evaluate the simulated remediation results.

[0032] In this step, the landscape index contains highly refined landscape pattern information and is an effective means to study land use configuration, distribution and change. The landscape index is calculated based on Fragstats 4.2 software to quantitatively evaluate the implementation effectiveness of the simulation results under the current settings; at the class level, patch density (PD) is selected to evaluate the concentration of cultivated land improvement, the normalized landscape shape index (NLSI) is selected to evaluate the regularity of cultivated land, and the splitting index (SPLIT) is selected to evaluate the continuity of cultivated land; by comparing the landscape pattern characteristics before and after cultivated land supplementation, the improvement effect is quantitatively evaluated.

[0033] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for tapping the potential and evaluating the effectiveness of supplementary cultivated land resources at the county level, characterized in that: The method comprises: (1) Data acquisition: obtaining basic data, data on the scope of situations where cultivated land replenishment is prohibited, and data on factors driving cultivated land growth; (2) Comprehensive survey of remediable resources: Based on the geographic information system platform, extract the secondary land type patches of various non-cultivated land types from the land type patch layer of the currently enabled land change survey database; based on the principle of overlay analysis, erase the land parcels within the red line of prohibited cultivated land replenishment from the secondary land type patches of various non-cultivated land types to obtain the total potential of cultivated land replenishment reserve resources; (3) Analysis of the probability of cultivated land growth based on the random forest algorithm: Based on the newly added cultivated land in the current year change data relative to the most recent national land survey data, the random forest algorithm is used to obtain the statistical relationship between the driving factors and the spatial expansion of cultivated land, and the spatial distribution of the probability of cultivated land growth is output in the form of raster data; (4) Suitability evaluation based on multi-objective correction: Correct the probability of cultivated land growth and identify the spatial suitability of cultivated land replenishment based on the corrected probability of cultivated land growth; (5) Simulation of cultivated land replenishment and evaluation of implementation effectiveness: Based on the cellular automation module of the patch-generated land use simulation model, the total potential of cultivated land replenishment reserve resources obtained in step (2), the corrected cultivated land growth probability obtained in step (4), and the demand for cultivated land replenishment quantity this year are input to simulate cultivated land changes, and a quantitative evaluation of the cultivated land simulation results is carried out in combination with the landscape pattern index.

2. According to claim 1, a method for tapping the potential and evaluating the effectiveness of supplementary cultivated land resources at the county level, characterized in that: In step (1): The methods for obtaining basic data include: obtaining the land type map layers of the most recent national land survey base period and the currently activated land change survey database as the basic data source for land type analysis; and obtaining cultivated land quality grade data to assist in the selection of potential plots.

3. According to claim 1, a method for tapping the potential and evaluating the effectiveness of supplementary cultivated land resources at the county level, characterized in that: In step (1): the scope data of the situation where the supplementation of cultivated land is prohibited includes the urban development boundary data delineated by the national land space planning, the ecological protection red line data, the highest water level line data, the first-level protection zone data of the water source, the slope map data of the third survey, the forest protection scope data, and the data of the implemented land consolidation projects.

4. According to claim 1, a method for tapping the potential and evaluating the effectiveness of supplementary cultivated land resources at the county level, characterized in that: In step (1): The data on the driving factors of cultivated land growth include natural conditions data, socio-economic conditions data and accessibility data; the natural conditions data include elevation, slope, slope aspect, undulation, soil texture, soil organic matter content, soil pH, root soil oxygen utilization rate, precipitation, sunshine duration, average annual temperature, and crop maturity; the socio-economic conditions data include population distribution and gross domestic product distribution; the accessibility data include water source distribution, road network, and settlement distribution.

5. According to claim 1, a method for tapping the potential and evaluating the effectiveness of supplementary cultivated land resources at the county level, characterized in that: Step (2) specifically includes: (2.1) Based on the GIS platform, 13 secondary land type patches are extracted from the land type patch layer of the currently used land change survey database, including arbor forest, bamboo forest, shrub forest, other forest, orchard, tea garden, other garden, other grassland, facility agricultural land, bare land, pond water surface, aquaculture pond, and ditch; (2.2) Based on the principle of overlay analysis, the land parcels within the red line of the prohibition of supplementary cultivated land were erased from the 13 secondary land classification patches; The land parcels within the red line where the implementation of cultivated land supplementation is prohibited include: those within the urban development boundary, within the ecological protection red line, within the highest water level line, within the first-level protection zone of the water source, at a location with a slope greater than 15°, within the forest protection area, within the scope of implemented land reclamation projects, and with an area smaller than the minimum area on the map, to obtain the total potential for cultivated land supplementation reserve resources.

6. According to claim 1, a method for tapping the potential and evaluating the effectiveness of supplementary cultivated land resources at the county level, characterized in that: Step (3) includes: (3.1) Overlay and compare the land classification map of the most recent national land survey base period and the currently used land change survey to extract the newly added cultivated land in recent years; (3.2) The random forest algorithm was used to analyze the statistical relationship between driving factors and the spatial expansion of cultivated land. A 5% data sample was randomly extracted from the newly added cultivated land and 17 driving factors. Raster Data Unit The probability of cultivated land growth The calculation formula is as follows: ； In the formula, The value is 0 or 1, 1 means that other land use types are converted to cultivated land, and 0 means that other land use types are not converted to cultivated land; It is a vector composed of multiple driving factors; is the indicator function of the decision tree set; is a vector No. The prediction type of the decision tree; is the total number of decision trees; (3.3) Output result: The spatial distribution of the probability of cultivated land growth is output in the form of raster data; the output raster data includes the probability of cultivated land growth corresponding to all raster data units, and the value range of each cultivated land growth probability is (0,1).

7. According to claim 1, a method for tapping the potential and evaluating the effectiveness of supplementary cultivated land resources at the county level, characterized in that: Step (4) includes the following steps: (4.1) Extract the field of planting attribute name from the land type map layer of the currently enabled land change survey database, create a new field for scoring and assigning values, and classify and assign values ​​to the restoration attributes; assign l to the plots marked as ready for restoration, m to the plots marked as engineering restoration, and n to the plots of general restoration, and generate the correction layer R1 through Kriging interpolation; where l>m>n; (4.2) Create a new layer, extract the cultivated land quality grades of all cultivated land plots, process the potential plots based on the nearest neighbor comparison method, assign the quality grade of the cultivated land plot closest to the current potential plot to the corresponding potential plot, convert the result into a raster, perform normalization, and generate the correction layer R2; (4.3) Based on the cultivated land parcels in the currently enabled land change survey database, based on the ArcGIS pro platform, the center points of the cultivated land parcels are first extracted, point feature data is constructed, and the cultivated land density is calculated through the kernel density analysis tool. The output result is a raster layer, which is normalized to obtain the corrected layer R3; (4.4) Evaluate the regularity of cultivated land around the potential plots by calculating the compactness index; based on the ArcGIS Pro platform, create new perimeter, area and shape index fields in the cultivated land plot vector, use the Calculate Geometry tool to calculate the perimeter and area respectively, and use the Calculate Field tool to calculate the shape index field, and generate the correction layer R4 after normalization; (4.5) Fusing and superimposing the original probability layer with the R, R2, R3, and R4 correction layers to obtain the corrected probability of occurrence of cultivated land replenishment; (4.6) Based on the revised probability, the spatial suitability of cultivated land replenishment is accurately identified; the revised probability of cultivated land replenishment is superimposed on the total potential of cultivated land replenishment reserve resources to obtain the probability of cultivated land replenishment in the county. The spatial differences in the values ​​of the probability of cultivated land replenishment in the county represent the spatial differences in the suitability of cultivated land replenishment.

8. According to claim 7, a method for tapping the potential and evaluating the effectiveness of supplementary cultivated land resources at the county level, characterized in that: In step (4.4), the compactness index calculation formula is as follows: ； Among them, C is the compactness index, A is the area, and P is the perimeter.

9. According to claim 7, a method for tapping the potential and evaluating the effectiveness of supplementary cultivated land resources at the county level, characterized in that: In step (4.5), the modified probability of occurrence of cultivated land supplementation is expressed as a grid layer, which is composed of a number of regularly arranged pixels; the calculation formula for the pixel value of the modified probability of occurrence of cultivated land supplementation at the layer (x, y) position is: ； in, is the pixel value of the corrected probability layer at position (x, y); is the pixel value of the original probability layer at position (x, y); is the pixel value of the kth correction layer at position (x, y), k=1,2,3,4.

10. The method for tapping the potential and evaluating the effectiveness of supplementary cultivated land resources at the county level according to claim 1, characterized in that: Step (5) specifically includes: (5.1) Use the cellular automaton model to simulate the results of cultivated land replenishment: The total potential of the reserve resources for supplementing cultivated land obtained in step (2), the modified probability of cultivated land growth obtained in step (4), and the demand for the amount of cultivated land supplemented this year are input into the cellular automation module CARS of the patch-generated land use simulation model, and the simulated results of cultivated land distribution in the next year are obtained through iterative calculation; (5.2) Estimation of remediation results based on landscape pattern index: The landscape index was calculated based on Fragstats 4.2 software to quantitatively evaluate the effectiveness of the simulation results under the current cultivated land supplement setting. At the type level, patch density was selected to evaluate the concentration of cultivated land remediation, the normalized shape index was selected to evaluate the regularity of cultivated land, and the separation index was selected to evaluate the continuity of cultivated land. The remediation results were quantitatively evaluated by comparing the landscape pattern characteristics before and after cultivated land supplementation.

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