GIS-based land space planning auxiliary compilation method

Abstract

A GIS-based territorial space planning auxiliary compilation method relates to the technical field of territorial space planning, and comprises the following steps: collecting multi-source heterogeneous data in a preset range, constructing a three-dimensional model in the preset range, and dividing the three-dimensional model into sub-regions of different land utilization types; according to the topographic features, the social economic data and the building adaptability coefficient of each sub-region in the three-dimensional model, obtaining the building fitness level of each sub-region, and obtaining the building fitness level of each sub-region; according to the topographic features, the soil detection data and the agricultural adaptability coefficient of each sub-region, obtaining the agricultural adaptability level of each sub-region; obtaining the ecological fitness level of each sub-region; the optimal land utilization type of each sub-region is obtained, the recommended compilation sub-region is obtained according to the optimal land utilization type of each sub-region and the space planning target, and the planning quality and efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of national land space planning, and in particular to a national land space planning auxiliary compilation method based on GIS. Background Art

[0002] CN115994685A "A method for assessing the status quo of national land space planning" includes the following steps: S1: pre-assessing the status quo of land space and clarifying the assessment indicators; S2: establishing a land space planning assessment plan and conducting risk assessment;

[0003] CN114239250A "A system and method for national land space planning and design" includes a land graphic acquisition module, a land sample acquisition module, a land graphic analysis module, a land sample analysis module, a sample and graphic fusion module and an analysis and planning module. The land graphic acquisition module is signal-connected to the land graphic analysis module, the land sample acquisition module is signal-connected to the land sample analysis module, and the land graphic analysis module and the land sample analysis module are both signal-connected to the sample and graphic fusion module.

[0004] With the acceleration of urbanization and the increasing resource and environmental constraints, national land space planning is crucial to achieving the rational use of land resources, ecological environmental protection, and sustainable social and economic development. Existing national land space planning methods often rely on manual experience and simple data statistical analysis, and there are problems such as difficulty in data integration, limited spatial analysis capabilities, and lack of scientificity and accuracy in planning schemes. For example, when determining the suitability of construction land, it is difficult to comprehensively consider multiple factors such as topography, geology, and ecology, which easily leads to a disconnect between planning and actual conditions. Summary of the invention

[0005] In order to solve the above technical problems, the purpose of the present invention is to provide a GIS-based land space planning auxiliary compilation method, comprising the following steps:

[0006] Step s1: collecting multi-source heterogeneous data within a preset range, wherein the multi-source heterogeneous data includes satellite remote sensing data, data elevation model data, land use classification data, soil testing data, ecological diversity data, and socio-economic data (including population density, traffic flow data, including the flow and direction of transportation modes such as roads, railways, and water transportation), constructing a three-dimensional model within the preset range, and dividing the three-dimensional model into sub-areas of different land use types;

[0007] Step s2: Obtain the building adaptability coefficient, agricultural adaptability coefficient and ecological adaptability coefficient of each sub-region, and obtain the building adaptability level of each sub-region according to the terrain characteristics, socio-economic data and building adaptability coefficient of each sub-region in the three-dimensional model, and simultaneously obtain the building adaptability level of each sub-region;

[0008] Step s3: According to the terrain characteristics, soil testing data and agricultural adaptability coefficient of each sub-region, the agricultural adaptability level of each sub-region is obtained; according to the terrain characteristics, satellite remote sensing data, ecological diversity data and ecological adaptability coefficient of each sub-region, the ecological adaptability level of each sub-region is obtained;

[0009] Step s4: Obtain the best land use type for each sub-region, and obtain the recommended sub-region according to the best land use type for each sub-region and the spatial planning objectives.

[0010] Furthermore, the process of collecting multi-source heterogeneous data within a preset range, constructing a three-dimensional model of the preset range, and dividing the three-dimensional model into sub-areas of different land use types includes:

[0011] Data preprocessing is performed on multi-source heterogeneous data. The data preprocessing process includes data coordinate system 1 (ensuring that all data are in the same geographic coordinate system for accurate spatial analysis) and data quality check (checking the integrity, accuracy and consistency of the data, such as eliminating erroneous land use classification records, etc.). A digital elevation model within a preset range is constructed based on the data elevation model data after data preprocessing. Feature extraction is performed on the satellite remote sensing data after data preprocessing to obtain texture features, obtain the geographic coordinate correspondence between texture features and the digital elevation model, and map the texture features to the surface of the digital elevation model according to the correspondence between texture features and geographic coordinates to generate a three-dimensional model within a preset range.

[0012] According to the land use classification data after data preprocessing, several land use types included in the preset range and the coverage positions of several land use types are obtained; according to the several land use types included in the preset range and the coverage positions of several land use types, the three-dimensional model is divided into sub-areas of different land use types (including agricultural land, construction land and ecological land).

[0013] Furthermore, the process of obtaining the building adaptability coefficient of each sub-area includes:

[0014] According to the socio-economic data after data preprocessing, the types of land features in the three-dimensional model (such as schools, hospitals, shopping malls, highway entrances and exits, railway freight stations, factories, and river water sources), the coverage positions of land feature types, and the population density of each sub-area are obtained, and the construction influence weights of different land feature types are preset. The construction influence weights of different land feature types are determined based on the experience of experts, with the aim of reducing the uncertainty in the fuzzy comprehensive evaluation process. For example, the weight of highway entrances and exits is determined to be 0.22, the weight of railway freight stations is 0.21, the weight of schools is 0.18, the weight of hospitals is 0.16, and the weight of shopping malls is 0.13. The geometric center of each sub-area in the three-dimensional model is obtained, and the Euclidean distance between the coverage position of each land feature type in the three-dimensional model and the geometric center of each sub-area is obtained. According to the Euclidean distance between the coverage position of each land feature type in the three-dimensional model and the geometric center of each sub-area and the construction influence weight of each land feature type, the building adaptability coefficient of each sub-area is obtained.

[0015] According to the Euclidean distance between the coverage position of each land feature type in the 3D model and the geometric center of each sub-area and the construction influence weight of each land feature type, the calculation formula for obtaining the building adaptability coefficient of each sub-area is:

[0016] Ui=δ∑ j∈M(i) (dist(ij)*β(j));

[0017] Among them, Ui represents the building adaptability coefficient of sub-area i, dist(ij) represents the Euclidean distance between the coverage position of feature type j and the geometric center of sub-area i, β(j) represents the construction influence weight of feature type j, and δ represents the conversion coefficient.

[0018] Furthermore, according to the terrain characteristics, socio-economic data and building adaptability coefficient of each sub-region in the three-dimensional model, the process of obtaining the building adaptability level of each sub-region includes:

[0019] The terrain characteristics (including terrain slope and geological type) of each sub-region in the three-dimensional model are obtained, and the terrain characteristics, population density and building adaptability coefficient of each sub-region are used as evaluation indicators. The weight coefficient of each evaluation indicator and several building adaptability levels are preset, and the membership matrix of each sub-region for different building adaptability levels is obtained through fuzzy comprehensive evaluation.

[0020] According to the membership matrix and the weight coefficients of each evaluation index, the building fitness level of each sub-area is obtained.

[0021] Furthermore, the process of obtaining the building fitness level of each sub-area according to the membership matrix and the weight coefficient includes:

[0022] The weight coefficients of the evaluation indicators and the membership matrix are integrated through a formula to obtain a fuzzy comprehensive evaluation matrix of the evaluation indicators, the membership of each sub-region to different building fitness levels is obtained according to the fuzzy comprehensive evaluation matrix, the building fitness level with the highest membership corresponding to each sub-region is screened out, and the building fitness level with the highest membership corresponding to each sub-region is used as the building fitness level of each sub-region;

[0023] Wherein, the formula is:

[0024] M = αM1 × βM2;

[0025] Among them, M is the fuzzy comprehensive evaluation matrix of the evaluation index, M1 is the weight coefficient of the evaluation index, M2 is the membership matrix, "×" represents the multiplication of the elements at the corresponding positions of the weight matrix of the evaluation index and the membership matrix, and α and β are weighting parameters for controlling the balance between the weight matrix and the membership matrix in the fuzzy comprehensive evaluation matrix of the evaluation index.

[0026] Furthermore, according to the terrain characteristics, soil test data and agricultural adaptability coefficient of each sub-region, the process of obtaining the agricultural adaptability level of each sub-region includes:

[0027] The agricultural impact weights of different landform types are preset, and the agricultural adaptability coefficient of each sub-region is obtained according to the Euclidean distance between the coverage position of each landform type in the three-dimensional model and the geometric center of each sub-region and the agricultural impact weight of each landform type. The soil detection data after data preprocessing is subjected to feature extraction to obtain the soil characteristics (including soil type and soil fertility) of each sub-region;

[0028] The topographic features, soil features and agricultural adaptability coefficients of each sub-region are used as evaluation indicators, the weight coefficients of each evaluation indicator and several agricultural adaptability levels are preset, and the membership matrix of each sub-region for different agricultural adaptability levels is obtained through fuzzy comprehensive evaluation;

[0029] According to the membership matrix and the weight coefficients of each evaluation index, the agricultural adaptability level of each sub-region is obtained.

[0030] Furthermore, according to the topographic features, satellite remote sensing data, ecological diversity data and ecological adaptability coefficient of each sub-region, the process of obtaining the ecological adaptability level of each sub-region includes:

[0031] Preset the ecological impact weights of different landform types, obtain the ecological adaptability coefficient of each sub-region according to the Euclidean distance between the coverage position of each landform type in the three-dimensional model and the geometric center of each sub-region and the ecological impact weight of each landform type, perform spectral feature extraction on the satellite remote sensing data after data preprocessing, obtain spectral reflectance data of different bands in the target area, and obtain the vegetation coverage of the target area according to the spectral reflectance data of different bands. For example, in the near-infrared band, the reflectance of vegetation is significantly higher than that of other landforms. By analyzing the reflectance ratio of vegetation in the near-infrared and red light bands (such as the normalized vegetation index NDVI), the vegetation coverage can be accurately estimated. The calculation formula is NDVI = (NIR-R) / (NIR+R), where NIR is the reflectance of the near-infrared band, and R is the reflectance of the red light band. When the NDVI value is close to 1, it indicates that the vegetation coverage is high; when the NDVI value is close to -1, it indicates a non-vegetation covered area. Perform feature extraction on the ecological diversity data after data preprocessing to obtain the diversity coefficient of each sub-region;

[0032] The calculation formula of the diversity coefficient of each sub-region is:

[0033]

[0034] Where D is the diversity coefficient, S is the number of species, and p is z is the proportion of the i-th species in the total number of individuals;

[0035] The topographic features, vegetation coverage, diversity coefficient and ecological adaptability coefficient of each sub-region are used as evaluation indicators, the weight coefficients of each evaluation indicator and several ecological adaptability levels are preset, and the membership matrix of each sub-region for different ecological adaptability levels is obtained through fuzzy comprehensive evaluation;

[0036] According to the membership matrix and the weight coefficients of each evaluation index, the ecological fitness level of each sub-region is obtained.

[0037] Furthermore, the process of obtaining the optimal land use type for each sub-region includes:

[0038] The ecological fitness level, agricultural fitness level and architectural fitness level of the sub-region are compared, and the optimal land use type of the sub-region is obtained according to the fitness level comparison results. For example, among the ecological fitness level, agricultural fitness level and architectural fitness level of the sub-region, if the ecological fitness level is greater than the agricultural fitness level and the architectural fitness level, then the optimal land use type of the sub-region is ecological land.

[0039] Furthermore, according to the optimal land use type and spatial planning objectives of each sub-region, the process of obtaining the recommended sub-regions includes:

[0040] Obtaining a spatial planning target within a preset range, wherein the spatial planning target includes a lower limit of the proportion of each land use type, and matching the land use type of each sub-area in the three-dimensional model with the optimal land use type for consistency. If the land use type of the sub-area is inconsistent with the optimal land use type, the sub-area is marked as a sub-area to be compiled;

[0041] Obtain the proportion of land use types, the lower limit of the land use proportion and the proportion of the sub-region to be compiled corresponding to the sub-region to be compiled; obtain the proportion of the second land use type corresponding to the sub-region to be compiled according to the proportion of the land use type corresponding to the sub-region to be compiled and the proportion of the sub-region to be compiled itself; compare the proportion of the second land use type with the lower limit of the land use proportion; if the proportion of the second land use type is greater than the lower limit of the land use proportion, mark the sub-region to be compiled as a recommended sub-region, and mark the best land use type of the recommended sub-region on the recommended sub-region in the three-dimensional model; and highlight the recommended sub-region, for example, using a line drawing function to define the boundary contour line of the sub-region to be compiled, setting the color, line width and other attributes of the drawn contour line to highlight it, and then using a rendering tool to highlight the model with the drawn contour line.

[0042] Compared with the prior art, the beneficial effects of the present invention are (I don't want the steps to be introduced, what I need is the detailed beneficial effects):

[0043] 1. Data integration and comprehensive analysis:

[0044] The present invention can comprehensively and integratedly reflect the natural, ecological and socio-economic characteristics of the national land space by collecting multi-source heterogeneous data such as satellite remote sensing data, data elevation model data, land use classification data, soil testing data, ecological diversity data and socio-economic data. Compared with a single data source, this multi-source data fusion method avoids the one-sidedness of information and provides a richer and more accurate basis for subsequent planning analysis. For example, when evaluating the construction suitability of an area, not only the terrain (data elevation model data) and existing land use (land use classification data) are considered, but also the impact of surrounding land features on construction (land feature types and distribution in socio-economic data) are combined to make the analysis results more in line with the actual situation.

[0045] By building a 3D model with a preset range based on multi-source data, planners can more clearly observe the spatial distribution of different regions, the relationship between terrain undulations and the current land use status in the 3D scene, which helps to more accurately grasp the overall spatial pattern and make more scientific planning decisions. Compared with traditional 2D map analysis, 3D models can show more three-dimensional and comprehensive spatial information, reducing planning errors caused by spatial cognitive limitations.

[0046] 2. Adaptability assessment and scientific planning:

[0047] The architectural adaptability coefficient, agricultural adaptability coefficient and ecological adaptability coefficient of each sub-region are obtained respectively, and on this basis, the architectural adaptability level, agricultural adaptability level and ecological adaptability level are further obtained. This multi-dimensional fitness assessment system can comprehensively consider multiple key factors for different land use types. For example, when evaluating the agricultural adaptability level, it not only considers the impact of terrain characteristics on agricultural production, but also combines the soil quality reflected by soil testing data, and the impact of surrounding landforms on agriculture (reflected by the agricultural adaptability coefficient), so that the judgment of which land use method is suitable for each sub-region is more scientific and accurate. This helps to rationally plan land use and avoid waste of resources or ecological damage caused by unreasonable land use decisions.

[0048] In the process of obtaining the fitness levels of buildings, agriculture and ecology, the fuzzy comprehensive evaluation method is used. This method can effectively deal with the uncertainty and ambiguity in the evaluation indicators. Since national land space planning involves many complex factors, many factors are difficult to accurately quantify. The fuzzy comprehensive evaluation uses the membership matrix to comprehensively evaluate the degree of membership of each sub-region to different levels by presetting the evaluation index weight coefficients and multiple adaptation levels, so as to obtain a fitness level that is more in line with the actual situation. Compared with the simple quantitative analysis method, this method can more comprehensively and reasonably consider the comprehensive impact of various factors and improve the scientificity and reliability of planning.

[0049] 3. Determination of the best land use type and planning optimization

[0050] By comparing the ecological, agricultural and architectural fitness levels of each sub-region, the optimal land use type for each sub-region is determined, which provides a clear direction for national land space planning. Ensuring that land resources can be developed and utilized according to their most appropriate uses will help achieve efficient allocation of land resources. For example, areas with high ecological fitness and relatively low architectural and agricultural fitness are identified as ecological protection areas, which not only protects the ecological environment but also avoids damage to the ecology caused by unreasonable construction or agricultural development; areas with high architectural fitness are planned as urban construction areas, which can give full play to the construction potential of the land and improve the rationality and sustainability of urban construction. At the same time, the recommended compilation sub-areas are marked and highlighted on the three-dimensional model, which is convenient for planners to view and analyze intuitively, greatly improving the visualization and decision-making efficiency of planning work. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a schematic diagram of a GIS-based land space planning auxiliary compilation method according to an embodiment of the present application. DETAILED DESCRIPTION

[0052] The following is a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0053] like Figure 1 As shown, a GIS-based land space planning auxiliary compilation method includes the following steps:

[0054] Step s1: collecting multi-source heterogeneous data within a preset range, wherein the multi-source heterogeneous data includes satellite remote sensing data, data elevation model data, land use classification data, soil testing data, ecological diversity data, and socio-economic data (including population density, traffic flow data, including the flow and direction of transportation modes such as roads, railways, and water transportation), constructing a three-dimensional model within the preset range, and dividing the three-dimensional model into sub-areas of different land use types;

[0055] Step s2: Obtain the building adaptability coefficient, agricultural adaptability coefficient and ecological adaptability coefficient of each sub-region, and obtain the building adaptability level of each sub-region according to the terrain characteristics, socio-economic data and building adaptability coefficient of each sub-region in the three-dimensional model, and simultaneously obtain the building adaptability level of each sub-region;

[0056] Step s3: According to the terrain characteristics, soil testing data and agricultural adaptability coefficient of each sub-region, the agricultural adaptability level of each sub-region is obtained; according to the terrain characteristics, satellite remote sensing data, ecological diversity data and ecological adaptability coefficient of each sub-region, the ecological adaptability level of each sub-region is obtained;

[0057] Step s4: Obtain the best land use type for each sub-region, and obtain the recommended sub-region according to the best land use type for each sub-region and the spatial planning objectives.

[0058] It should be further explained that, in the specific implementation process, the process of collecting multi-source heterogeneous data within a preset range, constructing a three-dimensional model within the preset range, and dividing the three-dimensional model into sub-areas of different land use types includes:

[0059] Data preprocessing is performed on multi-source heterogeneous data. The data preprocessing process includes data coordinate system 1 (ensuring that all data are in the same geographic coordinate system for accurate spatial analysis) and data quality check (checking the integrity, accuracy and consistency of the data, such as eliminating erroneous land use classification records, etc.). A digital elevation model within a preset range is constructed based on the data elevation model data after data preprocessing. Feature extraction is performed on the satellite remote sensing data after data preprocessing to obtain texture features, obtain the geographic coordinate correspondence between texture features and the digital elevation model, and map the texture features to the surface of the digital elevation model according to the correspondence between texture features and geographic coordinates to generate a three-dimensional model within a preset range.

[0060] According to the land use classification data after data preprocessing, several land use types included in the preset range and the coverage positions of several land use types are obtained; according to the several land use types included in the preset range and the coverage positions of several land use types, the three-dimensional model is divided into sub-areas of different land use types (including agricultural land, construction land and ecological land).

[0061] It should be further explained that, in the specific implementation process, the process of obtaining the building adaptability coefficient of each sub-area includes:

[0062] According to the socio-economic data after data preprocessing, the types of land features in the three-dimensional model (such as schools, hospitals, shopping malls, highway entrances and exits, railway freight stations, factories, and river water sources), the coverage positions of land feature types, and the population density of each sub-area are obtained, and the construction influence weights of different land feature types are preset. The construction influence weights of different land feature types are determined based on the experience of experts, with the aim of reducing the uncertainty in the fuzzy comprehensive evaluation process. For example, the weight of highway entrances and exits is determined to be 0.22, the weight of railway freight stations is 0.21, the weight of schools is 0.18, the weight of hospitals is 0.16, and the weight of shopping malls is 0.13. The geometric center of each sub-area in the three-dimensional model is obtained, and the Euclidean distance between the coverage position of each land feature type in the three-dimensional model and the geometric center of each sub-area is obtained. According to the Euclidean distance between the coverage position of each land feature type in the three-dimensional model and the geometric center of each sub-area and the construction influence weight of each land feature type, the building adaptability coefficient of each sub-area is obtained.

[0063] According to the Euclidean distance between the coverage position of each land feature type in the 3D model and the geometric center of each sub-area and the construction influence weight of each land feature type, the calculation formula for obtaining the building adaptability coefficient of each sub-area is:

[0064] Ui=δ∑ j∈M(i) (dist(ij)*β(j));

[0065] Among them, Ui represents the building adaptability coefficient of sub-area i, dist(ij) represents the Euclidean distance between the coverage position of feature type j and the geometric center of sub-area i, β(j) represents the construction influence weight of feature type j, and δ represents the conversion coefficient.

[0066] It should be further explained that, in the specific implementation process, according to the terrain characteristics, socio-economic data and building adaptability coefficient of each sub-region in the three-dimensional model, the process of obtaining the building adaptability level of each sub-region includes:

[0067] The terrain characteristics (including terrain slope and geological type) of each sub-region in the three-dimensional model are obtained, and the terrain characteristics, population density and building adaptability coefficient of each sub-region are used as evaluation indicators. The weight coefficient of each evaluation indicator and several building adaptability levels are preset, and the membership matrix of each sub-region for different building adaptability levels is obtained through fuzzy comprehensive evaluation.

[0068] According to the membership matrix and the weight coefficients of each evaluation index, the building fitness level of each sub-area is obtained.

[0069] It should be further explained that, in the specific implementation process, the process of obtaining the building fitness level of each sub-area according to the membership matrix and the weight coefficient includes:

[0070] The weight coefficients of the evaluation indicators and the membership matrix are integrated through a formula to obtain a fuzzy comprehensive evaluation matrix of the evaluation indicators, the membership of each sub-region to different building fitness levels is obtained according to the fuzzy comprehensive evaluation matrix, the building fitness level with the highest membership corresponding to each sub-region is screened out, and the building fitness level with the highest membership corresponding to each sub-region is used as the building fitness level of each sub-region;

[0071] Wherein, the formula is:

[0072] M = αM1 × βM2;

[0073] Among them, M is the fuzzy comprehensive evaluation matrix of the evaluation index, M1 is the weight coefficient of the evaluation index, M2 is the membership matrix, "×" represents the multiplication of the elements at corresponding positions of the weight matrix of the evaluation index and the membership matrix, and α and β are weighting parameters for controlling the balance between the weight matrix and the membership matrix in the fuzzy comprehensive evaluation matrix of the evaluation index.

[0074] It should be further explained that, in the specific implementation process, according to the terrain characteristics, soil test data and agricultural adaptability coefficient of each sub-region, the process of obtaining the agricultural adaptability level of each sub-region includes:

[0075] The agricultural impact weights of different landform types are preset, and the agricultural adaptability coefficient of each sub-region is obtained according to the Euclidean distance between the coverage position of each landform type in the three-dimensional model and the geometric center of each sub-region and the agricultural impact weight of each landform type. The soil detection data after data preprocessing is subjected to feature extraction to obtain the soil characteristics (including soil type and soil fertility) of each sub-region;

[0076] The topographic features, soil features and agricultural adaptability coefficients of each sub-region are used as evaluation indicators, the weight coefficients of each evaluation indicator and several agricultural adaptability levels are preset, and the membership matrix of each sub-region for different agricultural adaptability levels is obtained through fuzzy comprehensive evaluation;

[0077] According to the membership matrix and the weight coefficients of each evaluation index, the agricultural adaptability level of each sub-region is obtained.

[0078] It should be further explained that, in the specific implementation process, according to the terrain characteristics, satellite remote sensing data, ecological diversity data and ecological adaptability coefficient of each sub-region, the process of obtaining the ecological adaptability level of each sub-region includes:

[0079] Preset the ecological impact weights of different landform types, obtain the ecological adaptability coefficient of each sub-region according to the Euclidean distance between the coverage position of each landform type in the three-dimensional model and the geometric center of each sub-region and the ecological impact weight of each landform type, perform spectral feature extraction on the satellite remote sensing data after data preprocessing, obtain spectral reflectance data of different bands in the target area, and obtain the vegetation coverage of the target area according to the spectral reflectance data of different bands. For example, in the near-infrared band, the reflectance of vegetation is significantly higher than that of other landforms. By analyzing the reflectance ratio of vegetation in the near-infrared and red light bands (such as the normalized vegetation index NDVI), the vegetation coverage can be accurately estimated. The calculation formula is NDVI = (NIR-R) / (NIR+R), where NIR is the reflectance of the near-infrared band, and R is the reflectance of the red light band. When the NDVI value is close to 1, it indicates that the vegetation coverage is high; when the NDVI value is close to -1, it indicates a non-vegetation covered area. Perform feature extraction on the ecological diversity data after data preprocessing to obtain the diversity coefficient of each sub-region;

[0080] The calculation formula of the diversity coefficient of each sub-region is:

[0081]

[0082] Where D is the diversity coefficient, S is the number of species, and p is z is the proportion of the i-th species in the total number of individuals;

[0083] The topographic features, vegetation coverage, diversity coefficient and ecological adaptability coefficient of each sub-region are used as evaluation indicators, the weight coefficients of each evaluation indicator and several ecological adaptability levels are preset, and the membership matrix of each sub-region for different ecological adaptability levels is obtained through fuzzy comprehensive evaluation;

[0084] According to the membership matrix and the weight coefficients of each evaluation index, the ecological fitness level of each sub-region is obtained.

[0085] It should be further explained that, in the specific implementation process, the process of obtaining the best land use type for each sub-region includes:

[0086] The ecological fitness level, agricultural fitness level and architectural fitness level of the sub-region are compared, and the optimal land use type of the sub-region is obtained according to the fitness level comparison results. For example, among the ecological fitness level, agricultural fitness level and architectural fitness level of the sub-region, if the ecological fitness level is greater than the agricultural fitness level and the architectural fitness level, then the optimal land use type of the sub-region is ecological land.

[0087] It should be further explained that, in the specific implementation process, according to the optimal land use type and spatial planning objectives of each sub-region, the process of obtaining the recommended sub-region includes:

[0088] Obtaining a spatial planning target within a preset range, wherein the spatial planning target includes a lower limit of the proportion of each land use type, and matching the land use type of each sub-area in the three-dimensional model with the optimal land use type for consistency. If the land use type of the sub-area is inconsistent with the optimal land use type, the sub-area is marked as a sub-area to be compiled;

[0089] Obtain the proportion of land use types, the lower limit of the land use proportion and the proportion of the sub-region to be compiled corresponding to the sub-region to be compiled; obtain the proportion of the second land use type corresponding to the sub-region to be compiled according to the proportion of the land use type corresponding to the sub-region to be compiled and the proportion of the sub-region to be compiled itself; compare the proportion of the second land use type with the lower limit of the land use proportion; if the proportion of the second land use type is greater than the lower limit of the land use proportion, mark the sub-region to be compiled as a recommended sub-region, and mark the best land use type of the recommended sub-region on the recommended sub-region in the three-dimensional model; and highlight the recommended sub-region, for example, using a line drawing function to define the boundary contour line of the sub-region to be compiled, setting the color, line width and other attributes of the drawn contour line to highlight it, and then using a rendering tool to highlight the model with the drawn contour line.

[0090] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A GIS-based land space planning auxiliary compilation method, characterized in that: The following steps are involved: Step s1: Collect multi-source heterogeneous data within a preset range, wherein the multi-source heterogeneous data includes satellite remote sensing data, data elevation model data, land use classification data, soil testing data, ecological diversity data, and socio-economic data, construct a three-dimensional model within the preset range, and divide the three-dimensional model into sub-areas of different land use types; Step s2: Obtain the building adaptability coefficient, agricultural adaptability coefficient and ecological adaptability coefficient of each sub-region, and obtain the building adaptability level of each sub-region according to the terrain characteristics, socio-economic data and building adaptability coefficient of each sub-region in the three-dimensional model, and simultaneously obtain the building adaptability level of each sub-region; Step s3: According to the terrain characteristics, soil testing data and agricultural adaptability coefficient of each sub-region, the agricultural adaptability level of each sub-region is obtained; according to the terrain characteristics, satellite remote sensing data, ecological diversity data and ecological adaptability coefficient of each sub-region, the ecological adaptability level of each sub-region is obtained; Step s4: Obtain the best land use type for each sub-region, and obtain the recommended sub-region according to the best land use type for each sub-region and the spatial planning objectives.

2. The GIS-based land space planning auxiliary compilation method according to claim 1 is characterized in that: The process of constructing a 3D model of a preset range and dividing the 3D model into sub-areas of different land use types includes: Perform data preprocessing on multi-source heterogeneous data, build a digital elevation model within a preset range based on the preprocessed data elevation model data, perform feature extraction on the preprocessed satellite remote sensing data, obtain texture features, map the texture features to the surface of the digital elevation model, and generate a three-dimensional model within a preset range; According to the land use classification data after data preprocessing, several land use types included in the preset range and the coverage positions of several land use types are obtained; according to the several land use types included in the preset range and the coverage positions of several land use types, the three-dimensional model is divided into sub-areas of different land use types.

3. The GIS-based land space planning auxiliary compilation method according to claim 2 is characterized in that: The process of obtaining the building adaptability coefficient of each sub-area includes: The types of land features, the coverage positions of land feature types and the population density of each sub-region in the three-dimensional model are obtained based on the socio-economic data after data preprocessing. The construction influence weights of different land feature types are preset, the geometric center of each sub-region in the three-dimensional model is obtained, and the Euclidean distance between the coverage position of each land feature type in the three-dimensional model and the geometric center of each sub-region is obtained. Based on the Euclidean distance between the coverage position of each land feature type in the three-dimensional model and the geometric center of each sub-region and the construction influence weight of each land feature type, the building adaptability coefficient of each sub-region is obtained.

4. The GIS-based land space planning auxiliary compilation method according to claim 3 is characterized in that: According to the terrain characteristics, socio-economic data and building adaptability coefficient of each sub-region in the 3D model, the process of obtaining the building adaptability level of each sub-region includes: The terrain characteristics (including terrain slope and geological type) of each sub-region in the three-dimensional model are obtained, and the terrain characteristics, population density and building adaptability coefficient of each sub-region are used as evaluation indicators. The weight coefficient of each evaluation indicator and several building adaptability levels are preset, and the membership matrix of each sub-region for different building adaptability levels is obtained through fuzzy comprehensive evaluation. According to the membership matrix and the weight coefficients of each evaluation index, the building fitness level of each sub-area is obtained.

5. The GIS-based land space planning auxiliary compilation method according to claim 4 is characterized in that: According to the topographic characteristics, soil test data and agricultural adaptability coefficient of each sub-region, the process of obtaining the agricultural adaptability level of each sub-region includes: The agricultural impact weights of different landform types are preset, and the agricultural adaptability coefficient of each sub-region is obtained according to the Euclidean distance between the coverage position of each landform type in the three-dimensional model and the geometric center of each sub-region and the agricultural impact weight of each landform type. The soil detection data after data preprocessing is subjected to feature extraction to obtain the soil characteristics of each sub-region; The topographic features, soil features and agricultural adaptability coefficients of each sub-region are used as evaluation indicators, the weight coefficients of each evaluation indicator and several agricultural adaptability levels are preset, and the membership matrix of each sub-region for different agricultural adaptability levels is obtained through fuzzy comprehensive evaluation; According to the membership matrix and the weight coefficients of each evaluation index, the agricultural adaptability level of each sub-region is obtained.

6. The GIS-based land space planning auxiliary compilation method according to claim 5 is characterized in that: According to the topographic characteristics, satellite remote sensing data, ecological diversity data and ecological adaptability coefficient of each sub-region, the process of obtaining the ecological adaptability level of each sub-region includes: Preset the ecological impact weights of different landform types, obtain the ecological adaptability coefficient of each sub-region based on the Euclidean distance between the coverage position of each landform type in the three-dimensional model and the geometric center of each sub-region and the ecological impact weight of each landform type, perform spectral feature extraction on the satellite remote sensing data after data preprocessing, obtain spectral reflectance data of different bands in the target area, obtain the vegetation coverage of the target area based on the spectral reflectance data of different bands, perform feature extraction on the ecological diversity data after data preprocessing, and obtain the diversity coefficient of each sub-region; The topographic features, vegetation coverage, diversity coefficient and ecological adaptability coefficient of each sub-region are used as evaluation indicators, the weight coefficients of each evaluation indicator and several ecological adaptability levels are preset, and the membership matrix of each sub-region for different ecological adaptability levels is obtained through fuzzy comprehensive evaluation; According to the membership matrix and the weight coefficients of each evaluation index, the ecological fitness level of each sub-region is obtained.

7. The GIS-based land space planning auxiliary compilation method according to claim 6 is characterized in that: The process of obtaining the best land use type for each sub-region includes: The ecological fitness level, agricultural fitness level and building fitness level of the sub-region are compared, and the optimal land use type of the sub-region is obtained according to the fitness level comparison results.

8. The GIS-based land space planning auxiliary compilation method according to claim 7 is characterized in that: The process of obtaining recommendations for sub-regions based on the best land use type for each sub-region and the spatial planning objectives includes: Obtaining a spatial planning target within a preset range, wherein the spatial planning target includes a lower limit of the proportion of each land use type, and matching the land use type of each sub-area in the three-dimensional model with the optimal land use type for consistency. If the land use type of the sub-area is inconsistent with the optimal land use type, the sub-area is marked as a sub-area to be compiled; Obtain the proportion of land use types corresponding to the sub-area to be compiled, the lower limit of the land use proportion and the proportion of the sub-area to be compiled itself; obtain the proportion of the second land use type corresponding to the sub-area to be compiled according to the proportion of land use types corresponding to the sub-area to be compiled and the proportion of the sub-area to be compiled itself; compare the proportion of the second land use type with the lower limit of the land use proportion; if the proportion of the second land use type is greater than the lower limit of the land use proportion, mark the sub-area to be compiled as a recommended sub-area, mark the best land use type of the recommended sub-area in the three-dimensional model, and highlight the recommended sub-area.

Images