CN112597948A - Urban land utilization change prediction method - Google Patents

Urban land utilization change prediction method Download PDF

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CN112597948A
CN112597948A CN202011601204.2A CN202011601204A CN112597948A CN 112597948 A CN112597948 A CN 112597948A CN 202011601204 A CN202011601204 A CN 202011601204A CN 112597948 A CN112597948 A CN 112597948A
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CN112597948B (en
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柳伟佳
汤焱
冯永玖
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Tongji University
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Abstract

The invention relates to a method for predicting urban land utilization change, which comprises the following steps: carrying out supervision and classification on the remote sensing image to obtain an urban land utilization classification image, and obtaining a driving factor image based on a driving factor; performing dimensionality reduction by using a principal component analysis method to obtain dimensionality reduction driving factor data and land utilization classification data; training an artificial neural network by using the dimension reduction driving factor data and the land utilization classification data training set, and taking the penultimate layer of the artificial neural network as extended characteristic data; according to the dimension reduction driving factor data and the expansion characteristic data, training a cellular automaton through a gradient lifting decision tree, and combining a limiting factor, probability enhancement, neighborhood scaling and neighborhood influence to obtain an urban land utilization change prediction model; and predicting the urban land use change according to the urban land use change prediction model. Compared with the prior art, the problem that characteristics cannot be fully used due to strong correlation among driving factors is solved, and the reliability and accuracy of results can be improved.

Description

Urban land utilization change prediction method
Technical Field
The invention relates to the field of urban land use change prediction, in particular to an urban land use change prediction method.
Background
The urban growth is a process of converting the land utilization type into urban land utilization, is the image embodiment of urbanization, and has important guiding significance for social production and development by predicting the urban growth. In urban growth prediction, a space driving factor is a factor considered by most methods, and mainly selects space variables such as GDP (graphics data processing), education degree, distance to a first-level highway and the like, but for complex urban development, a model can not have a good fitting effect only through simple and few feature establishment, and the actual feature number is further rare due to strong correlation among the features.
The specification of Chinese patent CN110826244A discloses a conjugate gradient cellular automata method for simulating rail transit to influence urban growth, which comprises the following steps: 1) carrying out supervision and classification on the remote sensing images to obtain city spatial pattern graphs of initial and final years; acquiring urban rail transit and various infrastructure data; 2) acquiring a space driving factor influencing the change of the urban pattern; 3) sampling the urban pattern diagram and the space variable to obtain effective sample points; 4) training effective sample points by using conjugate gradients to obtain a CA conversion rule; 5) acquiring the conversion probability of the land under different influences of urban rail transit based on the conversion rule; 6) establishing a geographic CA model based on CG by combining the model elements of the cellular automata and the CG rules; 7) simulating and predicting land utilization change by using a CACG model, and evaluating precision; 8) and outputting and storing the result. However, the method is mainly focused on urban rail transit characteristics, and has the problem that the characteristics cannot be fully used due to strong correlation among driving factors, and finally the result lacks reliability and accuracy.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for predicting urban land use change.
The purpose of the invention can be realized by the following technical scheme:
a method for predicting urban land use change, the method comprising the steps of:
s1: acquiring a remote sensing image;
s2: carrying out supervision and classification on the remote sensing image to obtain an urban land utilization classification image, and obtaining a driving factor image based on a driving factor;
s3: performing dimensionality reduction on the urban land utilization classified image and the driving factor image by using a principal component analysis method to obtain dimensionality reduction driving factor data and land utilization classified data;
s4: training an artificial neural network by using the dimension reduction driving factor data and the land utilization classification data as a training set, and performing feature expansion on the penultimate layer of the artificial neural network to obtain expanded feature data;
s5: according to the dimension reduction driving factor data and the expansion characteristic data, training a cellular automaton through a gradient lifting decision tree, and combining a limiting factor, probability enhancement, neighborhood scaling and neighborhood influence to obtain an urban land utilization change prediction model;
s6: and predicting the urban land use change according to the urban land use change prediction model.
In S2, the urban land use classification image includes an initial year urban land use classification image and an end year urban land use classification image, and the process of obtaining the initial year urban land use classification image and the end year urban land use classification image includes:
s11: acquiring an initial year satellite remote sensing image and an end year satellite remote sensing image;
s13: and acquiring an initial year urban land utilization classification image and an end year urban land utilization classification image by using a spectral angle supervision classification method based on the initial year satellite remote sensing image and the end year satellite remote sensing image.
In S11, after the initial year satellite remote sensing image and the end year satellite remote sensing image are acquired, spatial reference unification and geometric correction are performed on the initial year satellite remote sensing image and the end year satellite remote sensing image.
In S2, the driving factors include distances from land to highways, railways, subways, first-level highways, banks and universities, and driving factor images are calculated by using Euclidean distances based on remote sensing images.
And calculating to obtain a driving factor image by using the Euclidean distance in ArcGIS based on the remote sensing image.
In S3, before the principal component analysis method is used to perform dimensionality reduction on the urban land use classification image and the driver image, the urban land use classification image and the driver image are sampled by using random hierarchical sampling and systematic hierarchical sampling.
In S5, S5 includes the steps of:
s51: training the cellular automata through a gradient lifting decision tree according to the dimension reduction driving factor data and the expansion characteristic data to obtain a conversion rule of the cellular automata;
s52: obtaining urban land utilization conversion probability according to the conversion rule of the cellular automata;
s53: and establishing a land use change prediction model by combining the urban land use conversion probability, the limiting factor, the probability enhancement, the neighborhood scaling and the neighborhood influence.
In S6, the urban land use change prediction model is expressed as:
Figure BDA0002868852930000031
wherein the content of the first and second substances,
Figure BDA0002868852930000032
representing the state of the cell at time t +1,
Figure BDA0002868852930000033
representing the state of the cell at time t,
Figure BDA0002868852930000034
is a limiting factor, and takes a value of 0 or 1, 0 indicates that the cell can not be developed into a city cell, and 1 indicates that the cell can be developed into a city cellUrban cellular, PdRepresenting a land use conversion probability based on a driving factor,
Figure BDA0002868852930000035
representing the domain influence, f is the probability of total transformation PgA function of interest;
Pgexpressed as:
Figure BDA0002868852930000036
wherein, the value range of the time increment parameter TIP is 0.0-0.1, and the value range of the local adjustment parameter LAP is 0.5-1.0;
Pdexpressed as:
Figure BDA0002868852930000037
wherein f is0(x) Representing the initial learner, xiRepresenting the vector formed by the i-th driving factor, cmjRepresents the best fit value, I is the indicator function;
Figure BDA0002868852930000038
expressed as:
Figure BDA0002868852930000039
wherein, the central unit cell i does not participate in the calculation,
Figure BDA00028688529300000310
representing the total number of city cells within the m x m neighborhood.
The total transformation probability PgAnd when the current type of the cellular is larger than the set threshold, converting the cellular into the urban land type, otherwise, keeping the current type of the cellular unchanged.
In S5, after obtaining the transformation rule of the cellular automata, carrying out precision detection on the transformation rule, wherein the precision detection comprises a total precision index, a precision rate index, a recall rate index, a Kappa coefficient index, a figure goodness index and an F1 score.
Compared with the prior art, the invention has the following advantages:
(1) the problem that a plurality of driving factors cannot be fully utilized in model fitting is considered, principal component analysis and artificial neural network expansion are carried out, the plurality of driving factors can exert reasonable efficacy through feature extraction and enhancement, the problem that features cannot be fully used due to strong correlation among the driving factors is solved, and reliability and accuracy of results can be improved.
(2) By utilizing the cellular automata simulation framework, the geographic space variables can be conveniently extracted, the space sample data can be conveniently acquired, the preprocessing of other aspects can be carried out, and meanwhile, the framework can conveniently execute various cellular models and carry out precision evaluation on the simulation result.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a plot of a study area of an embodiment of the present invention;
FIG. 3 is a driver graph according to the present invention;
FIG. 4 shows real city land use change and CA of an embodiment of the inventionFE-GBDT、CALRComparing the transformation probability of the two models;
FIG. 5 shows real urban land distribution and CA in an embodiment of the present inventionFE-GBDT、CALRThe two models simulate the urban land distribution comparison diagram.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Examples
The embodiment provides a method for predicting urban land use change based on feature expansion and gradient boosting decision trees, as shown in fig. 1, including the following steps:
1) monitoring and classifying the satellite remote sensing images to obtain an initial year urban land utilization classified image and an end year urban land utilization classified image;
2) obtaining a driving factor image based on the driving factor; carrying out random layered sampling and system layered sampling on the city land utilization classified image in the initial year, the city land utilization classified image in the end year and the driving factor image;
3) performing data dimensionality reduction on the sampled urban land utilization classified image of the initial year, the sampled urban land utilization classified image of the final year and the driving factor image by using a Principal Component Analysis (PCA) method;
4) performing feature expansion on the data after dimensionality reduction through an Artificial Neural Network (ANN);
5) according to the dimension reduction driving factor data and the expansion characteristic data, combined with the limiting factor, the probability enhancement, the neighborhood scaling and the neighborhood influence, the Cellular Automata (CA) is trained through the gradient lifting decision tree to obtain the urban land utilization change prediction model (CA)FE-GBDTA model);
6) using CAFE-GBDTThe model carries out simulation application and verification analysis on urban land utilization, and assessment is realized through indexes such as Overall Accuracy (OA) and figure goodness (FOM);
7) and outputting and storing the simulation result.
Specifically, the method comprises the following steps:
the step 1) is specifically as follows:
after an initial year satellite remote sensing image and an end year satellite remote sensing image are obtained, carrying out space reference unification and geometric correction on the initial year satellite remote sensing image and the end year satellite remote sensing image;
and acquiring an initial year urban land utilization classification image and an end year urban land utilization classification image by using a spectral angle supervision classification method based on the initial year satellite remote sensing image and the end year satellite remote sensing image which are subjected to spatial reference unification and geometric correction.
The step 2) is specifically as follows:
the driving factors include the distance from land to highway, railway, subway, first-level highway, bank and institution, the distance from land to restaurant, city and city center, and elevation, GDP, area and education level;
based on the remote sensing image, calculating by using Euclidean distance in ArcGIS to obtain a driving factor image;
and carrying out random layered sampling and system layered sampling on the initial year remote sensing image, the end year remote sensing image and the driving factor image, and providing reliable effective sample data for establishing an urban land utilization change prediction model.
The step 3) is specifically as follows:
and performing data dimension reduction on effective sample data by using a Principal Component Analysis (PCA) method, reserving important features, and removing noise and unimportant features.
The step 4) is specifically as follows:
and training the effective sample data after dimensionality reduction by an Artificial Neural Network (ANN) method, and taking the penultimate layer of the artificial neural network as the feature after expansion.
The step 5) is specifically as follows:
s51: training the cellular automata through a gradient lifting decision tree according to the dimension reduction driving factor data and the expansion characteristic data to obtain a conversion rule of the cellular automata;
s52: obtaining urban land utilization conversion probability according to the conversion rule of the cellular automata;
s53: and establishing a land use change prediction model by combining the urban land use conversion probability, the limiting factor, the probability enhancement, the neighborhood scaling and the neighborhood influence.
The method for acquiring land transition probability data comprises the following steps:
assuming that s represents whether the cell state is converted, if the cell state is converted from Non-city (Non-Urban) to city (Urban) from time t to t +1, s is marked as 1; from time t to t +1, the cell state has not changed, and s is marked as 0.
The obtained urban land utilization change prediction model is expressed as follows:
Figure BDA0002868852930000061
wherein the content of the first and second substances,
Figure BDA0002868852930000062
representing the state of the cell at time t +1,
Figure BDA0002868852930000063
representing the state of the cell at time t,
Figure BDA0002868852930000064
is a limiting factor, takes the value 0 or 1, 0 indicates that the cell can not be developed into an urban cell, 1 indicates that the cell can be developed into an urban cell, PdRepresenting a land use conversion probability based on a driving factor,
Figure BDA0002868852930000065
representing the domain influence, f is the probability of total transformation PgA function of interest;
a Time Increment Parameter (TIP) is used to counteract the decay of the transition probability based on the drive factor, and a Local Adjustment Parameter (LAP) is used to counteract the increase in neighborhood impact. Therefore, land use conversion probability (P) based on driving factord) And total probability of transformation (P)g) This can be given by the following equation:
Figure BDA0002868852930000066
wherein, the TIP value range is 0.0-0.1, and the LAP value range is 0.5-1.0; f. of0(x) Represents an initial learner; x is the number ofiRepresents the vector formed by the ith driving factor; c. CmjRepresents the best fit value; i is an indicator function.
For the evaluation of neighborhood impact, CA more often adopts a regular neighborhood of squares or circles, e.g. the Moore neighborhood of m × m can be expressed as:
Figure BDA0002868852930000067
in the formula, the central unit cell i does not participate in calculation,
Figure BDA0002868852930000068
the total number of city cells in the m × m neighborhood range is represented, and Moore 5 × 5 is selected as the cell neighborhood in this embodiment.
The restriction factor Con indicates that the cells are not able to develop and transform into urban cells due to some restriction, including large water bodies, basic farmlands, ecological conservation areas, parks, greenbelts, etc. Con may be expressed as:
Con＝Bin(celli(t)～ava) (4)
in the formula, Con takes the value of 0 or 1, 0 indicates that the cell can not be developed into an urban cell, 1 indicates that the cell can be developed into an urban celli(t) denotes the i-th cell, and ava denotes the last cell.
The urban land utilization change prediction model can be added with a random factor R for simulating the state transition of the cells caused by uncertain factors, for example, a certain cell is converted from a non-urban state to an urban state by improving the development probability through the random factor under the condition that no urban cell is nearby. The random factor R is expressed as:
R＝1+(lnr)a (5)
in the formula, R is a random number between 0 and 1, alpha is a control parameter of a random factor R, and the value is an integer between 0 and 10.
Land use conversion probability P based on driving factordIs the core part of the transformation rule, which represents the influence of the driving factors on land utilization and influences the cell state at the next moment in a probability manner. The decision tree algorithm has the characteristics of strong interpretability, high calculation speed and the like, but the problem of overfitting is easily caused by singly using the decision tree algorithm, the fitting capacity of a single decision tree is restrained by reducing the complexity of the decision tree, and a plurality of decision trees are integrated by a gradient lifting method, so that the overfitting problem can be solved to a great extent. The training of the gradient boosting decision tree method is mainly divided into an initialization learner and an iterationTraining M trees, training the next tree by taking the residual error of the previous tree as a new true value of the sample, and continuously iterating the learner to finally obtain the learner.
Calculating the global transformation probability P of land utilization according to the formula (2)g. In actual calculation, the parameter calculation of the model is completed by utilizing Python language and CityAIModel software, and the calculation result is compared with a set threshold value PthdAnd comparing to judge whether the cells are transformed at one moment. When the global transformation probability of the cell i is larger than a set threshold value PthdAnd converting the cell into a city type, otherwise, keeping the state of the cell unchanged:
Figure BDA0002868852930000071
the step 6) is specifically as follows:
implementation of CA using Python language and CityAIModel softwareFE-GBDTThe simulation and prediction process of the model selects a land use pattern of a certain year as an initial state and utilizes CAFE-GBDTThe model runs for M times (the difference between the initial year and the final year) to obtain the simulation and prediction results of the land use change;
for CALRModel and CAFE-GBDTAnd evaluating the simulation precision of the land utilization result of the model simulation from multiple aspects.
And comparing the real land use classification image, and performing Precision calculation on the simulation result, wherein the main indexes are total Precision (OA), Precision (Precision), Recall (Recall), Kappa coefficient, figure goodness (FoM) and F1 Score (F1 Score). The precision rate is the proportion of the land which is actually the city and simulated as the city to the land which is simulated as the city, and the recall rate is the proportion of the land which is actually the city and simulated as the city to the land of the actual city.
The step 7) is specifically as follows:
and outputting and storing the prediction result in GIS software.
The following is a specific example:
takes 2010-2020 Suzhou city land utilization as a caseFor example, the position of the area in this case is shown in FIG. 2. To verify CAFE-GBDTEffectiveness of the model in land use Change simulation, in case of a CA model based on logistic regression (CA)LR) As a comparison object, the method simulates the change process of the land utilization of the same-period cities, and the result shows that CAFE-GBDTHas better simulation effect than CALRAnd (4) modeling. The process is as follows:
1) firstly, selecting satellite remote sensing images of Suzhou city in 2000, 2010 and 2020, and obtaining urban land utilization classification images by using a spectral angle method. Taking a remote sensing image and a vector map (including an administrative region map, a road traffic map and the like) as basic data, and calculating data such as distances and elevations to an expressway, a railway, a subway and a first-level highway by using Euclidean distances to obtain a driving factor image;
2) sampling the urban land utilization classified image and the driving factor image by using a random hierarchical sampling method;
3) the method utilizes Python language and CityAIModel software to realize the gradient lifting decision tree (FE-GBDT) and the Logistic Regression (LR) of feature expansion to obtain the land use conversion probability P based on the driving factordAnd CA conversion rules. FIG. 3 shows urban land transitions under two models;
4) utilizing the obtained land use conversion probability P based on the driving factordAnd CA conversion rule, establishing CAFE-GBDTModel and CALRA model;
TABLE 1 CALRAnd CAFE-GBDTAnd (4) analyzing the results.
Figure BDA0002868852930000081
5) With 2010 status as initial value, respectively running CAFE-GBDTAnd CALRModel 10 times, predicting 2020 land use change;
6) the results after the simulation prediction were compared with the real city growth, and variations in Overall Accuracy (OA), Precision (Precision), Recall (Recall), Kappa coefficient, figure goodness (FOM), and F1 score were analyzedAs shown in Table 1, it can be seen that CA of this exampleFE-GBDTThe method is obviously superior to CALRA method;
9) and outputting and storing the visualized result.

Claims (10)

1. A method for predicting urban land use change is characterized by comprising the following steps:
s1: acquiring a remote sensing image;
s2: carrying out supervision and classification on the remote sensing image to obtain an urban land utilization classification image, and obtaining a driving factor image based on a driving factor;
s3: performing dimensionality reduction on the urban land utilization classified image and the driving factor image by using a principal component analysis method to obtain dimensionality reduction driving factor data and land utilization classified data;
s4: training an artificial neural network by using the dimension reduction driving factor data and the land utilization classification data as a training set, and performing feature expansion on the penultimate layer of the artificial neural network to obtain expanded feature data;
s5: according to the dimension reduction driving factor data and the expansion characteristic data, training a cellular automaton through a gradient lifting decision tree, and combining a limiting factor, probability enhancement, neighborhood scaling and neighborhood influence to obtain an urban land utilization change prediction model;
s6: and predicting the urban land use change according to the urban land use change prediction model.
2. The method for predicting urban land use change according to claim 1, wherein in S2, said urban land use classification images comprise an initial year urban land use classification image and an end year urban land use classification image, and the process of obtaining the initial year urban land use classification image and the end year urban land use classification image comprises:
s11: acquiring an initial year satellite remote sensing image and an end year satellite remote sensing image;
s13: and acquiring an initial year urban land utilization classification image and an end year urban land utilization classification image by using a spectral angle supervision classification method based on the initial year satellite remote sensing image and the end year satellite remote sensing image.
3. The method for predicting urban land use changes according to claim 2, wherein in S11, after the initial year satellite remote sensing image and the end year satellite remote sensing image are acquired, spatial reference unification and geometric correction are performed on the initial year satellite remote sensing image and the end year satellite remote sensing image.
4. The method for predicting urban land use change according to claim 1, wherein in S2, the driving factors comprise distances from land to highways, railways, subways, first-level highways, banks and institutions, and driving factor images are obtained by Euclidean distance calculation based on remote sensing images.
5. The method for predicting urban land use changes according to claim 4, wherein the driving factor image is obtained by using Euclidean distance calculation in ArcGIS based on the remote sensing image.
6. The method for predicting urban land use changes according to claim 1, wherein in S3, the urban land use classification image and the driver image are sampled by random hierarchical sampling and systematic hierarchical sampling before the urban land use classification image and the driver image are subjected to dimensionality reduction by the principal component analysis method.
7. The method for predicting changes in urban land use according to claim 1, wherein in S5, S5 comprises the steps of:
s51: training the cellular automata through a gradient lifting decision tree according to the dimension reduction driving factor data and the expansion characteristic data to obtain a conversion rule of the cellular automata;
s52: obtaining urban land utilization conversion probability according to the conversion rule of the cellular automata;
s53: and establishing a land use change prediction model by combining the urban land use conversion probability, the limiting factor, the probability enhancement, the neighborhood scaling and the neighborhood influence.
8. The method for predicting urban land use change according to claim 1, wherein in S6, said model for predicting urban land use change is represented as:
Figure FDA0002868852920000021
wherein the content of the first and second substances,
Figure FDA0002868852920000022
representing the state of the cell at time t +1,
Figure FDA0002868852920000023
representing the state of the cell at time t,
Figure FDA0002868852920000024
is a limiting factor, takes the value 0 or 1, 0 indicates that the cell can not be developed into an urban cell, 1 indicates that the cell can be developed into an urban cell, PdRepresenting a land use conversion probability based on a driving factor,
Figure FDA0002868852920000025
representing the domain influence, f is the probability of total transformation PgA function of interest;
Pgexpressed as:
Figure FDA0002868852920000026
wherein, the value range of the time increment parameter TIP is 0.0-0.1, and the value range of the local adjustment parameter LAP is 0.5-1.0;
Pdexpressed as:
Figure FDA0002868852920000027
wherein f is0(x) Representing the initial learner, xiRepresenting the vector formed by the i-th driving factor, cmjRepresents the best fit value, I is the indicator function;
Figure FDA0002868852920000028
expressed as:
Figure FDA0002868852920000029
wherein, the central unit cell i does not participate in the calculation,
Figure FDA0002868852920000031
representing the total number of city cells within the m x m neighborhood.
9. The method as claimed in claim 8, wherein the total transformation probability P is a transformation probabilitygAnd when the current type of the cellular is larger than the set threshold, converting the cellular into the urban land type, otherwise, keeping the current type of the cellular unchanged.
10. The method for predicting urban land use changes according to claim 1, wherein in S5, after transformation rules of cellular automata are obtained, precision detection is performed on the transformation rules, and the precision detection includes an overall precision index, an accuracy rate index, a recall rate index, a Kappa coefficient index, a figure goodness index and an F1 score.
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