CN109933901B - MCR city expansion simulation method for optimizing service value of ecosystem - Google Patents
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Abstract
The invention relates to an MCR city expansion simulation method for optimizing ecosystem service value, which applies an MCR minimum accumulated resistance model to city expansion simulation, and realizes city expansion simulation guided by ecosystem service value optimization through the optimization of a traditional weight fixed value and scenario simulation method. The expansion model can simulate the urban expansion simulation situation under the optimization of the service value of the ecological system according to objective data and a statistical method, so that the simulation result of the model can be reproduced, and meanwhile, the quantitative evaluation of the value optimization level can be carried out.
Description
Technical Field
The invention relates to a MCR city expansion simulation method for optimizing the service value of an ecosystem.
Background
Due to the unreasonable urban expansion, the severe problems of weakened environmental bearing level, reduced cultivated land reserve resources, reduced ecological environment quality and the like, the sustainability of the urban ecological system faces huge examination. In order to estimate the urban expansion and the influence thereof, a scholars adopts a spatial expansion model to simulate the urban expansion process. Such as a CA model, a multi-agent model, an MCR minimum cumulative resistance model, a CLUS-S model, etc., and there is a research trend of shifting from a monomer model to an integrated model.
The following two main problems are present in the operation of the conventional city expansion simulation model. On one hand, the determination of the weight coefficient has an important influence on the construction of a reasonably usable urban expansion simulation model. In the weight determination link of the existing urban expansion simulation model, most scholars adopt a relatively qualitative or subjective definition method such as an expert scoring method, an analytic hierarchy process, a fuzzy analysis method and the like. The methods cannot realize relative visualization of model construction, so that the result of model simulation is difficult to avoid being influenced by human participation to a certain degree. On the other hand, in the design link of the scenario scheme of the spatial expansion model, the few scholars consider that the important constraint condition of the service value of the ecosystem is embedded into the urban expansion scenario, so that the model simulation scheme is difficult to express through an actual quantitative result.
In addition, the scene simulation of city expansion is an effective tool for understanding urbanization and supporting city planning construction and management, and can purposefully realize the optimization and promotion of city space patterns. Although the students set multi-guide development targets in the urban expansion scene, the targets are often independent, and the correlation analysis and the comprehensive consideration of the target scenes such as land, economy, ecology and the like are still insufficient. City expansion is performed under the multi-factor combined action, and the independent development of a certain target is considered to be usually disjointed from the actual development condition of a city, so that the city planning decision is difficult to support.
Disclosure of Invention
In view of the above, an object of the present invention is to provide an MCR city expansion simulation method for optimizing ecosystem service value, which can simulate a city expansion simulation scenario under the ecosystem service value optimization according to objective data and a statistical method, so that a model simulation result can be reproduced, and meanwhile, quantitative evaluation of a value optimization level can be performed.
In order to achieve the purpose, the invention adopts the following technical scheme:
an MCR city expansion simulation method for optimizing ecosystem service value comprises the following steps:
step S1, acquiring space influence factors of city expansion and constructing an index system of city expansion resistance;
step S2, calculating the urban expansion resistance index weight by adopting a probability transformation method;
step S3, obtaining a city expansion resistance value distribution diagram of the research area through the weighted summation of the city expansion resistance indexes;
step S4, sequencing the grids of the non-construction land according to the resistance values from small to large according to the obtained urban expansion resistance value distribution map, and constructing an MCR minimum accumulated resistance model;
step S5, screening a resistance value grid of the MCR minimum accumulated resistance model until the area meets the city expansion scale of the research year, and outputting a simulation scene;
step S6, combining urban expansion scale prediction, setting urban expansion constraint conditions for improving the service value of the ecological system, and combining the MCR minimum accumulated resistance model to obtain the urban expansion simulation situation under the optimization of the service value of the ecological system
Further, the step S1 is specifically:
step S11, extracting spatial influence factors related to city expansion by combining the remote sensing image map, and dividing attribute intervals according to index parameter characteristics;
and step S12, vectorizing the space influence factors, drawing a space vector data set, and constructing an urban expansion resistance index system.
Further, the step S2 is specifically:
step S21, carrying out land use change analysis according to the land use current situation diagram, and constructing a land use transfer matrix;
step S22: calculating the probability of converting the cultivated land, the forest land and the water area into the land for urban construction according to the land utilization transfer matrix, and calculating by combining the divided resistance index intervals, wherein the calculation formula is as follows:
wherein: p is l Probability of changing the type of land use of class I to a land for urban construction within a certain interval, C l The area of the first class land in a certain interval is changed into the urban construction land C a Is the total area of the interval;
step S23: the resistance index data is subjected to standardization processing, a reverse index is converted into a forward index through formula transformation, and the calculation formula is as follows:
R i ＝(1-P l )×100；
wherein R is i Is the resistance value after change;
step S24: and (3) combining the difference coefficients to carry out normalization processing on the indexes to obtain the influence weights of different indexes on city expansion, wherein the calculation formula is as follows:
wherein: w is a group of n Is the weight of the nth index, V n A difference coefficient value for the nth index; the difference coefficient is based on the maximum value P of the interval transition probability max And a minimum value P min The calculation formula is as follows:
wherein: v n Is the coefficient of variation of the nth index, S n For interval transition probability standard deviation, M n Is the average of the interval transition probabilities.
Further, the step S6 is specifically:
step S61: combining urban expansion scale prediction, simulating an urban expansion benchmark scene of a research year, and constructing a multiple regression model of urban construction land scale prediction by adopting population scale, total production value and total fixed investment data of all the years:
Y＝M+a(P)+b(G)+c(L)；
wherein: y represents the research year of the dependent variable, M represents a constant, P represents population scale, G represents a total production value, L represents a total investment of the fixed asset, and abc represents corresponding variable weight values respectively;
step S62: and setting intensive land utilization, ecological bottom line protection and ecological system service value measurement as constraint conditions to perform optimization scene simulation by adjusting parameters of the simulation model, and obtaining an urban expansion simulation scene under the optimization of the ecological system service value by combining the MCR minimum cumulative resistance model.
Further, the step S62 is specifically:
step S621, combining the humanity construction land standard, reducing the space expansion scale of the urban construction land through the constraint of the humanity construction land index, and simulating to obtain an urban expansion simulation scene of intensive land utilization;
step S622, on the basis of the urban expansion simulation scene of intensive land utilization, bringing the control and protection ranges in environmental protection planning, water resource planning and land utilization planning into an ecological protection bottom line area, adjusting the resistance value of the area to 100, and simulating to obtain an urban expansion simulation scene of ecological bottom line protection;
step S623, combining the ecological system service of land utilization type on the basis of the urban expansion simulation scene of ecological bottom line protection
Further, the resistance factors include elevation, gradient, water body, land utilization type, major road, minor road, expansion of construction land, town center, city development boundary.
Compared with the prior art, the invention has the following beneficial effects:
the MCR minimum accumulated resistance model is applied to urban expansion simulation, and the urban expansion simulation guided by the optimization of the service value of the ecosystem is realized through the optimization of the traditional weight setting value and scene simulation method. The method can simulate the city expansion situation under the optimization of the service value of the ecological system according to objective data and a statistical method, so that the simulation result of the model can be reproduced, and the quantitative evaluation of the value optimization level can be carried out.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a current state diagram of land use classification at different periods in the example of the present invention, (a) is a current state diagram of land use in 2000, (b) is a current state diagram of land use in 2015;
FIG. 3 is a graph of space vector data for a resistance metric associated with an example of the present invention;
FIG. 4 is a graph of the final urban expansion resistance values in an example of the present invention;
FIG. 5 is a graph of an independent regression analysis of a simulation of the total amount of urban distension in accordance with an example of the invention. (a) Population regression analysis, (b) total production value regression analysis, (c) fixed investment total regression analysis;
FIG. 6 is a graph of 2049 year results from a simulation using the MCR model in an example of the present invention;
FIG. 7 is a 2049-year land use intensive simulation scenario diagram under the ecosystem service value optimization goal in the embodiment of the invention,
FIG. 8 is a 2049 year ecological baseline protection simulation scenario diagram under the ecosystem service value optimization goal in the embodiment of the invention,
fig. 9 is a simulation scenario diagram of 2049 year ecosystem service value improvement under the ecosystem service value optimization goal in the embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings and examples.
Referring to fig. 1, the MCR city expansion simulation method for optimizing ecosystem service value provided by the present invention includes the following steps:
step 1: based on 2000 and 2015 remote sensing image maps of building cities and land utilization status maps of Fujian provinces, the method provided by the invention is adopted to simulate 2049-year service value optimization scenarios of urban ecosystems of the building cities. Extracting relevant spatial influence factors of city expansion, and constructing a city expansion resistance index system, wherein the concrete implementation comprises the following substeps:
step 1.1: using an Arcgis tool, extracting relevant spatial influence factors of city expansion in Arcgis based on a remote sensing image map which is subjected to geometric correction and radiation correction in 2000 and 2015 of a mansion city, such as fig. 2(a and b), wherein the relevant spatial influence factors comprise elevation, gradient, water body, land utilization type, main road, secondary road, construction land expansion, town center and city development boundary, and dividing radiation intervals according to experience by combining a multi-ring buffer zone tool in Arcgis;
the division mode of the buffer zone interval is not unique, and is determined according to the index attribute, such as: r belongs to { < 25m, 25-50m, 50-80m, > 80m }; r belongs to { < 8 °, 8-15 °, 15-24 °, > 24 ° } and the like;
step 1.2: performing spatial vectorization extraction on resistance indexes of city expansion in mansion cities by visual observation by using an Arcgis tool, and drawing a spatial vector data set, as shown in FIG. 3;
step 2: the method for calculating the urban expansion resistance index weight of the Xiamen city specifically comprises the following sub-steps of:
step 2.1: calculating the land use change area according to the land use current state diagrams of two time phases in Xiamen city by using 'fusion' and 'intersection' tools of Arcgis, dragging different land use types to construct a transfer matrix by combining the 'data perspective table' function of an EXCEL tool, and quantitatively calculating the land use change condition;
step 2.2: according to the land utilization transfer matrix calculation result in the step 2.1, calculating the probability that the cultivated land, the forest land and the water land in the Xiamen city are converted into the urban construction land in the two time phase processes by using an EXCEL tool, and calculating by combining the divided index intervals, wherein the specific calculation formula is as follows:
wherein: p l Probability of converting the first type of land utilization type into urban construction land within a certain interval, C l The area of the first class land in a certain interval is changed into the urban construction land C a Is the total area of the interval;
step 2.3: the resistance index data is subjected to standardization processing by using an EXCEL tool, a reverse index is converted into a forward index through formula transformation, and the calculation formula is as follows:
R i ＝(1-P l )×100
wherein R is i To a changed resistance value, P l The probability that the l type land utilization type is converted into the town construction land within a certain interval calculated in the step 2.2 is obtained;
step 2.4: using EXCEL tool, based on the maximum P of the interval transition probability max And minimum value P min Calculating the difference coefficient by comparing the differences, wherein the calculation formula is as follows:
wherein: v n Is the n-thCoefficient of variation of individual indices, S n For interval transition probability standard deviation, M n Is the average of the interval transition probabilities.
And (3) normalizing the indexes by using an EXCEL tool to obtain the influence weights of different indexes on the expansion of the city in Xiamen, wherein the calculation formula is as follows:
wherein: w is a group of n Is the weight of the nth index, V n Is the coefficient of difference value of the nth index.
TABLE 1 index system of urban expansion resistance in Xiamen city and weight summary table
And step 3: weighting and summing the city expansion resistance indexes of the Xiamen city calculated in the step 2 by using a 'grid calculator' tool of Arcgis to obtain a city expansion resistance value distribution map of the research area, wherein 1689564 grids are counted, and the result is shown in figure 4; (ii) a
And 4, step 4: sequencing all non-construction land grids on the Xiamen city expansion resistance value distribution diagram obtained in the step 3 according to the resistance values from small to large by using an Arcgis tool;
step 6: the method comprises the following steps of adjusting model parameters by using an Arcgis tool, setting a series of city expansion constraint conditions with the improvement of the ecosystem service value as the guide, and realizing the situation optimization simulation of city expansion of Xiamen city under the optimization of the ecosystem service value, wherein the concrete realization comprises the following substeps:
step 6.1: forecasting the urban expansion scale of the Xiamen city by combining Arcgis and SPSS tools, and carrying out 2049-year urban expansion reference scene simulation of the Xiamen city;
constructing a multiple regression model for predicting the urban construction land scale by adopting a time sequence method and combining the population scale, annual GDP (gross production price) and fixed investment total statistical data of the Xiamen city, wherein the regression fitting result is shown in a figure 5;
Y(area)＝-50.883+0.516(P)+0.028(G)+0.037(I)
wherein: y represents the research year of the dependent variable, M represents a constant, P represents the population scale, G represents the total production value, L represents the total investment of the fixed asset, and abc is the corresponding variable weight value respectively;
forecasting 2049-year urban construction land scale of 901km of Xiamen city by using multivariate regression model 2 Compared with the 2000-year increase of the scale of urban construction land, the scale of the urban construction land is increased by 901-252.9-648.1 km 2 ；
Distribution of resistance values along the urban dilatation calculated with Arcigis (FIG. 4), in order of resistance values from small to large, 648.1km 2 Distributing the images into space, namely extracting 720111 grids to obtain 2049-year urban expansion reference scenes, wherein the simulation result is shown in fig. 6;
based on the reference scene simulation, the progressive city expansion optimization scene scheme simulation is realized through the adjustment of model parameters:
step 6.2: through the adjustment of simulation model parameters, a series of city expansion constraint conditions which are guided by the improvement of the service value of the ecosystem are set for scene optimization simulation:
step 6.2.1: optimizing and simulating the intensive land utilization situation, and reducing the space expansion scale of the urban construction land by the constraint of the anthropomorphic construction land index to simulate;
according to the existing situation development, the construction land for mansion city can reach 111.9m in 2049 years 2 People, considering that the future developable construction land of mansion is relatively limited, and considering that the index of the construction land for planning per capita is controlled to be 80m by 2049 years 2 A person;
according to 'village planning guide in Fujian province', villages with lower areas are considered to be merged with peripheral central villages, and village land is used for returning ploughs;
urban land intensive utilizationThe simulated scene is expanded. At the moment, the town construction land is increased by 390.9km 2 I.e. 434333 grids. At this time, 257.7km is saved in utilization compared with the reference scene scheme 2 In use, the simulation results are shown in FIG. 7;
step 6.2.2: : optimizing and simulating the ecological bottom line protection scene, bringing the management and control and protection requirements in the related planning into an ecological protection bottom line area, adjusting the resistance value of the area to the highest value and simulating;
the following ecological baseline ranges were protected using the "erase" tool in Arcgis with the control development zone in the mansion city related protection program as the city expansion constraint:
primary and secondary protection areas of drinking water sources, core areas of scenic spots, forest parks and suburb parks and natural protection areas;
rivers, lakes, reservoirs, wetlands, important urban open channels and their protection ranges;
mountain bodies with the gradient larger than 15 degrees and other geological disaster prone areas;
important municipal traffic corridors;
other areas such as basic farmlands, woodlands, ecological green wedge core areas, ecological galleries and the like which need to be strictly protected for maintaining the integrity of the ecological system;
and at the moment, the scale increment of the town construction land is used for intensively utilizing the land, so that the city expansion simulation scene of ecological bottom line protection is obtained. The simulation results are shown in FIG. 8;
step 6.2.3: simulating the ecological system service value optimization scene, combining the land utilization type ecological system service value measurement and calculation results, and simulating according to the ascending order of the service value;
combining the ecosystem service value with a China ecosystem unit area service value equivalent table provided by the Thailand and the like, calculating by using an EXCEL tool, and sequencing to be village construction land, cultivated land, grassland, forest land and water area;
and (3) carrying out urban expansion preferential space distribution by taking the land utilization types of the ecosystem service value from low to high as an order, and obtaining an urban expansion simulation scene under the ecological system service value optimization view according to a simulation result shown in a graph 9.
The above description is only a preferred embodiment of the present invention, and all the changes and modifications made according to the claims should be covered by the present invention.
Claims (5)
1. An MCR city expansion simulation method for optimizing ecosystem service value is characterized by comprising the following steps:
step S1, acquiring space influence factors of city expansion and constructing an index system of city expansion resistance;
step S2, calculating the urban expansion resistance index weight by adopting a probability transformation method;
the step S2 specifically includes:
s21, carrying out land use change analysis according to the land use current situation diagram to construct a land use transfer matrix;
step S22: calculating the probability of converting the cultivated land, the forest land and the water area into the urban construction land according to the land utilization transfer matrix, and calculating by combining the divided resistance index intervals, wherein the calculation formula is as follows:
wherein: p l Probability of converting the first type of land utilization type into urban construction land within a certain interval, C l The area of the first class land in a certain interval is changed into the urban construction land C a Is the total area of the interval;
step S23: the resistance index data is subjected to standardization processing, a reverse index is converted into a forward index through formula transformation, and the calculation formula is as follows:
R i ＝(1-P l )×100；
wherein R is i Is the resistance value after change;
step S24: the resistance indexes are normalized by combining the difference coefficients to obtain the influence weight coefficients of the different resistance indexes on urban expansion, and the calculation formula is as follows:
wherein: w n Is the weight coefficient of the nth index, V n A difference coefficient value for the nth index; the difference coefficient is based on the maximum value P of the interval transition probability max And a minimum value P min The calculation is carried out according to the following formula:
wherein: v n Is the coefficient of variation of the nth index, S n For interval transition probability standard deviation, M n Is the average of interval transition probabilities;
step S3, obtaining a city expansion resistance value distribution map of a research area through the weighted summation of the city expansion resistance indexes;
step S4, sequencing the grids of the non-construction land from small to large according to the resistance values according to the obtained urban expansion resistance value distribution map, and constructing an MCR minimum accumulated resistance model;
step S5, screening a resistance value grid of the MCR minimum accumulated resistance model until the area meets the urban expansion scale of the research year, and outputting a simulation scene;
and step S6, setting urban expansion constraint conditions for improving the service value of the ecological system by combining urban expansion scale prediction, and obtaining an urban expansion simulation situation under the optimization of the service value of the ecological system by combining an MCR minimum accumulated resistance model.
2. The method for simulating urban expansion of an MCR optimized by ecosystem service value according to claim 1, wherein the method comprises the following steps: the step S1 specifically includes:
step S11, extracting spatial influence factors related to city expansion by combining a remote sensing image map, and dividing attribute intervals according to index parameter characteristics;
and step S12, vectorizing the space influence factors, drawing a space vector data set, and constructing an urban expansion resistance index system.
3. The method for simulating urban MCR expansion based on ecosystem service value optimization according to claim 1, wherein the method comprises the following steps: the step S6 specifically includes:
step S61: combining urban expansion scale prediction, simulating an urban expansion benchmark scene of a research year, and constructing a multiple regression model of urban construction land scale prediction by adopting population scale, total production value and total fixed investment data of all the years:
Y＝M+a(P)+b(G)+c(L)；
wherein: y represents the research year of the dependent variable, M represents a constant, P represents the population scale, G represents the total production value, L represents the total investment of the urban fixed assets, and abc is corresponding variable weight coefficients respectively;
step S62: and (3) setting intensive land utilization, ecological bottom line protection and ecological system service value measurement as constraint conditions to perform optimization scene simulation by adjusting parameters of the simulation model, and obtaining the city expansion simulation scene under the optimization of the ecological system service value by combining the MCR minimum accumulated resistance model.
4. The method of claim 3, wherein the simulation method for urban expansion of the MCR with optimized ecosystem service value comprises the following steps: the step S62 specifically includes:
step S621, combining the standard of the human-average construction land, reducing the space expansion scale of the land for urban construction through the constraint of the indexes of the human-average construction land, and simulating to obtain an urban expansion simulation scene of intensive utilization of land;
step S622, on the basis of the urban expansion simulation scenario of intensive land utilization, controlling and protecting ranges in environmental protection planning, water resource planning and land utilization planning are brought into an ecological protection bottom line area, and the resistance value of the area is adjusted to 100 for simulation to obtain an urban expansion simulation scenario of ecological bottom line protection;
and S623, simulating according to the ascending order of service values by combining the measurement and calculation results of the service values of the ecological system of the land utilization type on the basis of the urban expansion simulation scene of ecological bottom line protection to obtain the urban expansion simulation scene of the optimized service values of the ecological system.
5. The method for simulating urban MCR expansion based on ecosystem service value optimization according to claim 1, wherein the method comprises the following steps: the resistance factors include elevation, gradient, water body, land utilization type, major road, minor road, construction land expansion, town center, and city development boundary.
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