CN115438855A - Mountain city land utilization optimal configuration method - Google Patents
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Abstract
The invention discloses a mountain city land utilization optimal configuration method, which comprises the following steps of S1, data preprocessing: remote sensing data of the land of a research area are obtained through a GEE platform, wherein the remote sensing data comprise two types of spatial data and statistical data; s2, land utilization structure optimization: establishing a multi-objective planning model for the land utilization layout optimization problem of the selected research area; s3, land utilization space layout optimization: and on the basis of the land utilization structure optimization result, constructing a grid-based plaque generation land utilization simulation PLUS model for the land utilization layout optimization problem. In the invention, a PLUS model is constructed and adopted, the situations of ecology, economy and consideration of the ecology and the economy are comprehensively considered, and the regional spatial layout is reasonably optimized according to conversion rules, factor weights and the like on the basis of obtaining the change situation of a land utilization structure, so that the reasonability of land resource spatial configuration is improved, and an auxiliary decision basis of land utilization configuration is provided for different development targets of mountain cities.
Description
Technical Field
The invention relates to the technical field of land space layout optimization, in particular to a land utilization optimization configuration method for mountain cities.
Background
The land utilization layout optimization is an important way for realizing sustainable land utilization, various types are reasonably arranged in space according to a land utilization structure so as to achieve the purpose of improving the land utilization coupling benefit, and the method is a typical multi-objective space optimization problem. The early research aiming at the land use layout optimization mainly utilizes GIS (geographic information system) combined with a multi-criterion evaluation technology to allocate the most suitable land use type to each land parcel on the basis of evaluating natural, social and economic conditions so as to realize the land use layout optimization.
Most of the existing land utilization optimization methods adopt a method combining land utilization structure optimization and spatial layout optimization, reasonable change of various land types is quantitatively controlled by establishing an objective function, and different models are coupled for optimizing the spatial layout, however, most researches only consider a single situation, if an ecological benefit target is achieved, the consideration on regional ecological characteristics is not enough, and if constraint conditions which need to be considered for special cities (such as mountain cities) generally have regional characteristics.
Therefore, an optimal configuration method for land utilization of mountain cities is provided.
Disclosure of Invention
The invention aims to provide a mountain city land utilization optimal configuration method to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: an optimal configuration method for land utilization of mountain cities comprises the following steps:
s1, data preprocessing: the remote sensing data of the research area land is obtained through the GEE platform, and the remote sensing data comprises two types of spatial data and statistical data: the spatial data comprises land utilization data and basic geographic data; the statistical data comprises driving factor data, and original data of the driving factor in the research area is screened;
s2, land utilization structure optimization: establishing a multi-objective planning model for the land utilization layout optimization problem of the selected research area, wherein the model is expressed as:
in the formula, fn (x) represents an objective function, n represents the number of objective functions, ei represents a correlation coefficient of the objective function, xi represents a decision variable, xi ≧ 0, s.t represents a constraint condition, C represents a constraint coefficient, B represents a limit quantity:
the multi-target planning model is formed by an economic benefit function and an ecological benefit target function according to a plurality of decision variables classified by land use, and a plurality of targets reach the maximum value under the constraint of a plurality of conditions;
s3, land utilization space layout optimization: on the basis of the land utilization structure optimization result, a grid-based plaque generation land utilization simulation PLUS model is constructed for a land utilization layout optimization problem, the land utilization simulation PLUS model comprises a conversion rule mining module based on a land expansion analysis strategy and a CA simulation module based on a multi-type random plaque seed mechanism, and conversion rules and field factor weights are set, so that the land utilization layout optimization problem is constructed to describe and simulate the mapping relation between human activities and natural factors, the PLUS model is used for solving to obtain land utilization space layout optimization data, and the land utilization space optimization distribution result is partitioned and counted.
Preferably, in step S1, the driving factor data includes, but is not limited to, data such as traffic network, high-speed rail stations, population density, DEM, and yearbook.
Preferably, the economic benefit function in step S2 is:
the ecological benefit objective function is:
in the formula, f1 (x) and f2 (x) represent the maximum economic benefit and the maximum ecological benefit of the land resources in the research area, e1i and e2i represent an economic benefit coefficient and an ecological benefit coefficient respectively, xi represents the area of each land utilization type, and i represents different classes.
Preferably, the decision variables in step S2 include, but are not limited to, construction land (x 1), forest land (x 2), arable land (x 3), grassland (x 4), garden land (x 5), water body (x 6), and bare land (x 7).
Preferably, the constraint conditions in step S2 include, but are not limited to: the method comprises the following steps of land total area constraint, general population constraint, ecological environment constraint, policy constraint, land development utilization constraint and decision variable non-negative constraint.
Preferably, the PLUS model in the step S3 is based on a transformation rule mining module (LEAS) of a land expansion analysis strategy and a CA simulation module (CARS) of a multi-type random plaque seed mechanism, and can be used for simulating land utilization change caused by interaction of human activities and natural factors and predicting a land utilization development situation at a certain time in the future;
random sampling is carried out on each land class by adopting a random forest method, and the random forest method can be used for outputting the growth probability of the land utilization type k of the i grid, wherein the formula is as follows:
preferably, in the formula, d can only be 1 or 0, d =1 indicates that there is conversion from other terrestrial types to k types, and the rest is represented by d = 0; x represents a drive factor vector; the I (-) function is about a set of decision variables in a random forest; hn (x) represents the prediction type of the vector x under the nth decision variable; m is the number of all decision variables.
Compared with the prior art, the invention has the beneficial effects that:
in the invention, ecological benefits, economic benefits and the consideration of the ecological benefits and the economic benefits are respectively considered, an objective function and constraint conditions are established to obtain the change condition of the regional land utilization structure, a land utilization change simulation model is used for optimizing the regional spatial layout on the basis of the change condition of the regional land utilization structure, a PLUS model is constructed and adopted, the ecological benefits, the economic benefits and the consideration of the ecological benefits and the economic benefits are comprehensively considered, and the regional spatial layout is reasonably optimized according to conversion rules, factor weights and the like on the basis of obtaining the change condition of the land utilization structure, so that the rationality of land resource spatial configuration is improved, and an auxiliary decision basis for land utilization configuration is provided for different development targets of mountain cities.
Drawings
FIG. 1 is an overall technical flow diagram of the present invention;
FIG. 2 is a 2018 land utilization status diagram of a Chongqing city Chua family group according to the present invention;
FIG. 3 is a city expansion space layout under the Chongqing city Chua family group economy priority of the present invention;
FIG. 4 is a city expansion space layout under the Chongqing city Chua family group ecology priority of the present invention;
fig. 5 is an expanded spatial layout of a city of the Chongqing city Chua family group with economy and ecology.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1, the present invention provides a technical solution of an optimal configuration method for land utilization in mountain cities:
s1, data preprocessing: the remote sensing data of the land of the research area are obtained through the GEE platform, wherein the remote sensing data comprise two types of spatial data and statistical data: the spatial data comprises land utilization data and basic geographic data; the statistical data comprises driving factor data, and original data of the driving factor in the research area is screened;
specifically, remote sensing images are carried out based on a GEE platform, remote sensing data such as geography of a research area are obtained, land utilization classification is carried out by combining local land utilization current state features and national standards of 'land utilization current state classification' (GB/T21010-2017), and land utilization classification data of the research area in three years are obtained;
then, selecting original data of driving factors in a research area according to the obtained data, and performing related operations according to data such as a traffic network, high-speed rail stations, population density, DEM (dynamic effect model), a statistical yearbook and the like to obtain the driving factors influencing the land utilization spatial layout, wherein specific related basic data are shown in the following table:
s2, land utilization structure optimization: establishing a multi-objective planning model for the land utilization layout optimization problem of the selected research area, wherein the model is expressed as follows:
in the formula, fn (x) represents an objective function, n represents the number of objective functions, ei represents a correlation coefficient of the objective function, xi represents a decision variable, xi ≧ 0, s.t represents a constraint condition, C represents a constraint coefficient, B represents a limit quantity:
the multi-target planning model is formed by an economic benefit function and an ecological benefit objective function according to a plurality of decision variables of land utilization classification, and a plurality of targets reach the maximum value under the constraint of a plurality of conditions;
specifically, the method comprises the following steps:
1. setting decision variables and explicit constraint conditions:
in order to establish a multi-target planning model, seven decision variables are set according to land use classification, wherein the decision variables are respectively as follows: construction land (x 1), forest land (x 2), cultivated land (x 3), grassland (x 4), garden land (x 5), water body (x 6) and bare land (x 7);
defining the constraint conditions of the multi-target model as follows:
1.1, total land area constraint: the sum of all land use type areas in the research area is equal to the total area, and the functional expression is as follows:
in the formula, xi is the area of each land utilization type, and S is the total area of the land utilization in the research area;
1.2, general population constraints: the land bearing population number of the research area should not exceed the annual prediction population number of the current city planning, and the functional expression is as follows:
a 1 ×x 1 +a 2 ×(x 2 +x 3 +x 4 +x 5 )≤b 1
in the formula, a1 and a2 are average population densities of construction land and agricultural land respectively, b1 is the total planning of urban land and soil space, the population number is predicted, and x1, x2, x3, x4 and x5 are areas of construction land, forest land, cultivated land, garden land and grassland. Predicting by a grey prediction model by adopting population density of urban land and agricultural land and population of general population in a research district in the last three years to obtain data map values of a1, a2 and b1, and reserving two positions after decimal point as result
1.3, ecological environment constraint: the functional expression is as follows:
x 2 +x 3 +x 4 +x 5 ≥5718.28×θ
x2, x3, x4 and x5 are areas of forest land, cultivated land, garden land and grassland, theta is an elastic coefficient, the length of the prediction time is considered, and the value range of theta is set as 0.75,1.25 by referring to relevant documents of a research area;
1.4, policy constraints; according to the preliminary requirements of general planning of land utilization in a research area (recent ten years) and general planning of homeland space in the research area (recent fifteen years), the areas of the garden land and the grassland are stably increased, and the interval ranges of X1 and X5 are determined;
1.5, land development and utilization rate constraint: the method ensures that the land development utilization rate of the research area is not less than that of the current year, and the functional expression of the method is as follows:
in the formula, S is the total area of a research area, r is the land development utilization rate of the current year, and x7 is a numerical value smaller than a decision variable bare land;
1.6, decision variable non-negative constraint: each land utilization type should satisfy a non-negative condition: namely, it is
x i ≥0 (i＝1，2，……，7)；
2. According to the specific conditions and specific data of the research area, using an economic benefit objective function and an ecological benefit objective function to obtain data, and optimizing the land utilization spatial layout:
economic benefit objective function:
in the formula, f1 (x) represents economic benefit, e1i represents economic benefit coefficient, xi represents decision variable;
ecological benefit maximization objective function:
in the formula, f2 (x) represents ecological benefit, e2i represents ecological benefit coefficient, and xi represents decision variable. And selecting the ecological service value as an ecological benefit coefficient.
S3, optimizing the land utilization spatial layout: on the basis of the land utilization structure optimization result, a land utilization layout optimization problem is established, a grid-based patch generation land utilization simulation PLUS model is established, a conversion rule mining module based on a land expansion analysis strategy and a CA simulation module based on a multi-type random patch seed mechanism are determined, and a conversion rule and field factor weight are set, so that the land utilization layout optimization problem is established, the mapping relation between human activities and natural factors is simulated, the PLUS model is used for solving to obtain land utilization space layout optimization data, and the land utilization space optimization distribution result is subjected to zoning statistics.
In particular
1. Optimizing the spatial layout under economic priority, maximizing a land utilization structure optimization result according to an economic benefit target, simulating urban spatial layout by using a PLUS model for 2035 years to ensure economic city spatial layout, and correspondingly adjusting conversion rules and field factor weight setting according to the economic benefit target;
2. optimizing spatial layout under ecological priority, according to the optimization result of the ecological benefit target maximization land utilization structure, simulating urban spatial layout under the condition of protecting ecology for 2035 years by using a PLUS model, and correspondingly adjusting conversion rules and field factor weight setting according to the ecological benefit target;
3. the economic and ecological consideration space layout optimization is shown in table 6 according to the land utilization optimization results considering both economic and ecological benefits. And simulating 2035 years by using a PLUS model, considering both the spatial layout of the Chua family group, and correspondingly adjusting the conversion rule and the field factor weight setting according to both the considering targets.
The embodiment is as follows:
the invention is specifically implemented in a Chua family group in Chongqing city, and the implementation steps are as follows:
the method comprises the following steps: referring to fig. 2, the cai family group data is preprocessed:
(1) Land utilization data: a Chua family group remote sensing image in 2015-2018 years is acquired on a Google Earth Engine platform, the image is classified by a random forest classification method through visual interpretation in combination with a Google Earth high-definition image, land utilization data in 2015-2018 years are acquired, and the interpretation result meets the precision requirement.
(2) Basic geographic data: basic geographic data are obtained on platforms such as an OpenStreetMap platform, a Chinese academy resource environment science and data center, a Chongqing city geographic information center and a geospatial data cloud, and the spatial distance from the grid point of the Chua family group to each element is calculated by using ArcGIS
(3) And (3) counting yearbook data: unit economic benefits of output values of 2015-2018 urban population and agricultural population, forestry, tea gardens, orchards and the like, GDP data and the like need to be obtained from statistical yearbooks.
Step two: constructing a multi-target linear programming model:
seven decision variables are set according to the classification of land utilization of Chua family group in Chongqing city, and a multi-target planning model is established according to the following table:
table 1 land utilization structure of 2018 years of cai family group in chongqing city
| Variables of | Type of land use | Current area (hectare) |
| x1 | Land for construction | 2136.72 |
| X2 | Woodlands | 856.36 |
| X3 | Cultivation of land | 4601.00 |
| X4 | Grass land | 87.66 |
| X5 | Garden ground | 173.26 |
| X6 | Water body | 605.84 |
| X7 | Bare land | 309.85 |
Obtaining a specific numerical value according to the decision variable and the function expression of the definite constraint condition:
1. and (3) restraining the total land area: the sum of all land use type areas of the cai family group is equal to its total area, i.e. S =8770.69 hectare;
2. general population constraints: the land bearing population of the Chuia group should not exceed the predicted population of 2035 years, namely a1=75.36, a2=4.36 and b1=487787.02 are predicted by a gray prediction model by adopting population density and total population of Chongqing city town land population and agricultural land population in 2015-2018;
3. ecological environment constraint: considering the length of the prediction time and referring to relevant documents, setting the value interval of theta as 0.75, 1.25;
4. policy constraints are as follows: according to the preliminary requirements of general plan of land utilization in Chongqing City (2006-2020) and general plan of homeland space in Chongqing City (2019-2035), the stable increase of the area of garden land and grassland is ensured, the range of land for urban and rural construction in 2035 is increased by 17.64 percent, namely, x1 is not less than 2136.72 and not more than 2513.63, and x5 is not less than 173.26;
5. land development utilization rate constraint: ensuring that the land development utilization rate of the Chua family group is not less than the land development utilization rate of 2018, namely r is the land development utilization rate of 2018, and x7 is not more than 309.85 hectare;
6. decision variables are not negative constraints: each land use type should satisfy a non-negative condition.
7. The objective function is maximized through economic benefit, and economic benefit coefficients in 2035 years are predicted through a grey prediction model by using economic benefit data in 2015-2018, namely:
f 1 (x)＝1538.75x 1 +4.61x 2 +6.16x 3 +86.41x 4 +234.98x 5 +162.69x 6 +0.0001x 7 ；
the results of solving the economic benefit objective function are as follows:
TABLE 2 Cai family team with economic benefits goal of 2035 years and structural optimization results for soil utilization
8. Ecological benefit maximization objective function:
in the formula, f2 (x) represents ecological benefit, e2i represents ecological benefit coefficient, and xi represents decision variable. And selecting the ecological service value as an ecological benefit coefficient. By referring to the ecological service value equivalent table of the unit area of the Chinese land ecosystem and the ecological service value table of the unit area of the Chinese land ecosystem, which are provided in Xiagao, and combining the characteristics of the Chua family group, the equivalent factor method and the value coefficient adjusting method of the Chinese land ecosystem are adopted to adjust the coefficients, and the formula is as follows:
and (3) modifying the service value coefficient of the ecosystem:
in the formula, NPP i 、f i Net primary productivity and vegetation coverage, NPP, of the i-th pixel, respectively mean 、f mean The method comprises the steps that the average value of net primary productivity of vegetation and the average value of vegetation coverage in a research area are respectively, and NPP data can be directly obtained from an environmental resource data center;
vegetation coverage:
in the formula, NDVI min 、NDVI max The minimum and maximum values of the NDVI of the study area, respectively.
The ecological benefit objective function obtained by the formula is as follows:
f 2 (x)＝0.0001x 1 +19436.47x 2 +6102.68x 3 +6703.76x 4 +12739.42x 5 +40708.94x 6 +388.34x 7
the result of solving the ecological benefit objective function is as follows:
TABLE 3 optimization results of ecological benefit target soil utilization structure of Cai family group in 2035 years
Step three: PLUS model simulation
1. Referring to fig. 3, the space layout optimization under economic priority,
according to the economic benefit target maximization land utilization structure optimization result, the PLUS model is used for simulating urban space layout guaranteed economically in 2035 years, the conversion rules and the field factor weight setting are correspondingly adjusted according to the economic benefit target, and the output result is shown in the following table
Table 4, area statistics table for construction land of each division under economic benefit target of 2035 years for cai family group:
| administrative division | 2018 Current situation (hectare) | Optimization results (hectare) | Difference (hectare) |
| Shijia beam town | 217.53 | 264.96 | 47.43 |
| Cai house street | 1408.14 | 1624.68 | 216.54 |
| Herb of Chijiaxi | 511.05 | 623.99 | 112.94 |
The construction land of the Chua family group is continuously expanded on the basis of the original 2018, the influence of the terrain and the topography of the region is considered, although the city gradually develops, the city is greatly influenced by the slope direction, and the city only develops in the region with relatively flat topography. Meanwhile, three typical areas A, B and C are selected for analysis in the streams town of children, the streams street of the Chua, the posts street and the beam town of the Dongjia respectively: in general, three regional construction sites are expanded to different extents, and bare land, garden land and forest land areas are reduced to different extents.
According to the statistical result of administrative district division, the construction land of two towns and one street of the Chua family group is in an expansion state, and the area is respectively increased by 47.43 hectares, 216.54 hectares and 112.94 hectares. The Chua family group city has larger expansion scale under the condition of ensuring economic benefit, the whole city is expanded from the west of a research area along the east side of the edge area of an original forest land, but the condition of scattered disorder expansion occurs, and the ecological environment is influenced to a certain extent.
2. Referring to fig. 4, spatial layout optimization under ecological priority:
according to the ecological benefit target maximization land utilization structure optimization result, simulating urban space layout under the condition of protecting ecology for 2035 years by using a PLUS model, and correspondingly adjusting conversion rules and field factor weight setting according to the ecological benefit target, wherein the output result is as follows:
TABLE 5 Cai family group 2035 years ecological benefit target area statistical table for construction land of each division
| Administrative division | 2018 Current situation (hectare) | Optimization results (hectare) | Difference (hectare) |
| Shijia beam town | 217.53 | 289.98 | 72.45 |
| Cai house street | 1408.14 | 1428.75 | 20.61 |
| Herb of Chijiaxi | 511.05 | 484.08 | -26.97 |
Under ecological benefit, cities broken in 2018 in west and south areas of the Chua family group are degraded and gradually gathered towards the center to form a small village and town gathering area, original construction land is gradually wasted to form cultivated land and forest land, and the cities are gradually expanded towards areas with relatively low terrains in north and east areas. Meanwhile, three typical areas D, E and F are selected for analysis in the streams town of children, the streams street of the Chua, the posts street and the beam town of the Dongjia respectively: the urban construction land in three areas is reduced in different degrees under the condition of maximizing ecological benefits, part of scattered and broken rural construction land is gradually developed into cultivated land, original residents gradually gather to adjacent larger villages and towns, and the construction land of the adjacent villages and towns at the periphery is slightly increased.
According to the statistical result of administrative district division, the construction land of the beam town of the Xia family and the street of the Chua's post is in an expanded state, and the area is respectively increased by 72.45 hectares and 20.61 hectares. The optimization result of the construction land for the children xi town is reduced by 26.97 hectares in 2018. On the basis of taking ecological protection as a target, the urban expansion scale is obviously reduced, the blind disordered expansion of construction land is avoided, and the ecological environment is effectively protected.
3. Referring to FIG. 5, the optimization of the spatial layout for economic and ecological considerations
The results of land use optimization considering both economic and ecological benefits are shown in table 6. Utilizing a PLUS model to simulate space layout of both the following Chua family group in 2035 years, and correspondingly adjusting conversion rules and field factor weight setting according to both the following targets, wherein output results are as follows:
TABLE 6 Cai family group in 2035 years considering economic and ecological benefit targets and optimization results of soil utilization structure
TABLE 7 Cai family group, 2035 years of economic and ecological statistics table for land area for construction of each district
And G, H and I typical areas are selected for analysis in the Chixi town, the Chi-Jian street and the Shijia beam town respectively: compared with the 2018 current land utilization distribution situation, the construction land of the three areas is in an expansion state and continues to expand outwards on the basis of the original city.
The construction land of two towns and one street of the Chua family group under both conditions is in an expansion state, and the area is respectively increased by 60.39 hectares, 114.39 hectares and 90.33 hectares. Compared with the spatial distribution of the results of the ecological benefit maximization and the economic benefit maximization optimization, the spatial distribution of the construction land is concentrated towards the north of the research area and the streets of the Cai's house, the spatial distribution of the construction land tends to be concentrated, the scattered distribution of the city under the large-order expansion and the blind occupation of the cultivated land, the garden land and the forest land are avoided, the economic growth is ensured, the ecological balance is maintained, and the long-term development of the city is facilitated.
In summary, the following steps: in the invention, through field investigation of the Chua family group in Chongqing city, and implementation, ecological benefit, economic benefit and the consideration of both are considered respectively, an objective function and a constraint condition are established, so that the change condition of the regional land utilization structure is obtained, on the basis, a land utilization change simulation model is used for optimizing the spatial layout of the region, a PLUS model is established and adopted, the ecological and economic conditions and the consideration of both are considered comprehensively, and on the basis of obtaining the change condition of the land utilization structure, the spatial layout of the region is optimized reasonably according to conversion rules, factor weights and the like, so that the rationality of the spatial configuration of land resources is improved, and an auxiliary decision basis for land utilization configuration is provided for different development targets of mountain cities.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. An optimal configuration method for land utilization in mountain cities is characterized by comprising the following steps:
s1, data preprocessing: the remote sensing data of the land of the research area are obtained through the GEE platform, wherein the remote sensing data comprise two types of spatial data and statistical data: the spatial data comprises land utilization data and basic geographic data; the statistical data comprises driving factor data, and original data of the driving factor in the research area is screened;
s2, land utilization structure optimization: establishing a multi-objective planning model for the land utilization layout optimization problem of the selected research area, wherein the model is expressed as follows:
in the formula, fn (x) represents an objective function, n represents the number of objective functions, ei represents a correlation coefficient of the objective function, xi represents a decision variable, xi ≧ 0, s.t represents a constraint condition, C represents a constraint coefficient, B represents a limit quantity:
the multi-target planning model is formed by an economic benefit function and an ecological benefit target function according to a plurality of decision variables classified by land use, and a plurality of targets reach the maximum value under the constraint of a plurality of conditions;
s3, land utilization space layout optimization: on the basis of the land utilization structure optimization result, a land utilization layout optimization problem is established, a grid-based patch generation land utilization simulation PLUS model is established, a conversion rule mining module based on a land expansion analysis strategy and a CA simulation module based on a multi-type random patch seed mechanism are determined, and a conversion rule and field factor weight are set, so that the land utilization layout optimization problem is established, the mapping relation between human activities and natural factors is simulated, the PLUS model is used for solving to obtain land utilization space layout optimization data, and the land utilization space optimization distribution result is subjected to zoning statistics.
2. The mountain city land utilization optimal configuration method of claim 1, wherein: in step S1, the driving factor data includes, but is not limited to, data such as traffic network, high-speed rail stations, population density, DEM, and yearbook.
3. The mountain city land use optimal configuration method according to claim 1, wherein: the economic benefit function in step S2 is:
the ecological benefit objective function is:
in the formula, f1 (x) and f2 (x) represent the maximum economic benefit and the maximum ecological benefit of the land resources in the research area, e1i and e2i represent an economic benefit coefficient and an ecological benefit coefficient respectively, xi represents the land utilization type area, and i represents different classes.
4. The mountain city land use optimal configuration method according to claim 1, wherein: the decision variables in the step S2 include, but are not limited to, construction land (x 1), forest land (x 2), farmland (x 3), grassland (x 4), garden land (x 5), water body (x 6), and bare land (x 7).
5. The mountain city land utilization optimal configuration method of claim 1, wherein: the constraint conditions in step S2 include, but are not limited to: the method comprises the following steps of land total area constraint, general population constraint, ecological environment constraint, policy constraint, land development utilization constraint and decision variable non-negative constraint.
6. The mountain city land utilization optimal configuration method of claim 1, wherein: the PLUS model in the step S3 is based on a transformation rule mining module (LEAS) of a land expansion analysis strategy and a CA simulation module (CARS) of a multi-type random plaque seed mechanism, and can be used for simulating land utilization change of interaction between human activities and natural factors and predicting land utilization development scenes in a certain time in the future;
random sampling is carried out on each land class by adopting a random forest method, and the random forest method can be used for outputting the growth probability of the land utilization type k of the i grid, wherein the formula is as follows:
in the formula, d can only be 1 or 0, d =1 indicates that there is conversion from other right-of-land type to k type, and the rest is represented by d = 0; x represents a drive factor vector; the I (-) function is about a set of decision variables in a random forest; hn (x) represents the prediction type of the vector x under the nth decision variable; m is the number of all decision variables.
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