Optimal configuration method for land utilization of cultivated wasteland in salinized irrigated area

Abstract

The invention discloses an optimal configuration method for land utilization of a cultivated wasteland in a salinization irrigation area, which mainly comprises two parts of land utilization quantity demand and cultivated wasteland spatial layout, and utilizes a method of coupling a water-salt equilibrium model SALTMOD and a land utilization CLUE-S model. The method comprises the following steps: combining an appropriate tillage and waste land ratio based on irrigation and drainage regulation and control and cultivated land salinity control with land utilization space distribution, and systematically exploring an appropriate salt wasteland space dynamic regulation and control scheme suggestion considering land utilization development and regional salinity income and expenditure balance from quantity constraint and space configuration; certain technical and data support is provided for saline-alkali soil treatment in arid and semi-arid irrigated areas.

Description

Technical Field

[0001] The invention belongs to the technical field of land use landscape pattern optimization in saline-alkali land management, and specifically relates to a method for optimizing the spatial configuration of land use of cultivated land in salinized irrigation areas. Background Art

[0002] There is a close interaction between the spatial pattern of land use and the spatiotemporal evolution of water resources and the redistribution of salt. With the continuous deepening of research on the spatial distribution characteristics, patterns and change processes of land use, the importance of land use landscape and spatial optimization configuration has become increasingly prominent. This optimization configuration not only involves the allocation of land use quantity and spatial layout, but also requires comprehensive consideration of ecological processes and land use unit configuration. In arid and semi-arid irrigation areas where cultivated land and wasteland are interlaced, salt wasteland, as a special type of land use, has a reasonable spatial layout and comprehensive consideration of its ecological salt absorption function, which is of great significance for soil salinization control and the sustainable development of land resources.

[0003] The evolution of the spatial pattern of cultivated wasteland land use is a complex dynamic process, which is affected by a variety of factors. At present, there are few technical methods for optimizing the spatial configuration of cultivated wasteland under the conditions of water and salt migration. How to combine the special land use development of "salt wasteland" with the control of cultivated land salinity, and how to optimize the land use structure of salt wasteland in space by quantitative methods, and how to truly "see and calculate" the method technology is not yet mature. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the method for optimizing land use configuration of salinized cultivated land in irrigation areas provided by the present invention overcomes the above-mentioned deficiencies in the prior art. It can start from the salt control of cultivated land in irrigation areas and the salt absorption dynamics and spatial pattern development of salt wasteland, and put forward suggestions for the spatial dynamic development of salt wasteland suitable for the region in terms of the number of salt wasteland (cultivated wasteland ratio), spatial location (spatial pattern), and salt dynamics (water-salt balance), thereby providing certain technical and data support for the management of saline-alkali land in arid and semi-arid irrigation areas.

[0005] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a method for optimizing the land use of cultivated land in salinized irrigation areas, comprising the following steps:

[0006] S1. In the non-spatial analysis module of the CLUE-S land use model, the water-salt balance SALTMOD model was constructed to simulate and calculate the land use quantity demand in the study area. The area and quantity demand of land use types with an annual step length were calculated by determining the most suitable cultivated land ratio in the study area.

[0007] S2. In the spatial analysis module of the CLUE-S land use model, find the influencing factors of the spatial distribution of different land use types, and combine the calculated land use type area and quantity requirements to obtain the most suitable spatial layout of different land use grid cells by running the CLUE-S land use model simulation;

[0008] S3. Compare and analyze the simulation results of the CLUE-S land use model with the actual results to verify the simulation accuracy of the CLUE-S land use model, and optimize the CLUE-S land use model based on the test results;

[0009] S4. The calculated most suitable cultivated-wasteland ratio is used as input data for optimizing the CLUE-S land use model to predict the future long-term spatial pattern of land use, analyze the spatial distribution pattern of suitable salt-wasteland under the control of cultivated land salinity, and achieve optimal allocation of cultivated-wasteland land use.

[0010] Furthermore, in step S1, in the constructed water-salt balance SALTMOD model, the cultivated land and salt wasteland in the study area are taken as the research objects, and the total control area only includes the cultivated land and salt wasteland, and its regional division is expressed as:

[0011] A+B=cultivated land in the study area

[0012] U = Salt wasteland

[0013] A+B+U=1

[0014] Among them, A represents irrigated land with crops or vegetation growing, B represents fallow or rain-fed irrigated land, and U represents non-irrigated salt wasteland;

[0015] In the water-salt balance SALTMOD model, the rotation mode of agricultural land Kr=1, the irrigated land areas A and B are all rotated, the salt wasteland area U remains unchanged, and a year is divided into three simulation periods: growth period, autumn irrigation period and non-growth period.

[0016] Furthermore, in step S1, the method for simulating and calculating the land use quantity demand in the study area is specifically as follows:

[0017] S11. Process and analyze the basic data input from the outside through the water-salt balance SALTMOD model and output the corresponding data;

[0018] The input basic data include agricultural data, hydrological data, meteorological data and soil data of the study area; the output data include soil salinity, groundwater depth, drainage volume and drainage mineralization;

[0019] S12. Calibrate and verify the water-salt balance SALTMOD model using the specific parameters of the study area;

[0020] S13. Different scenarios were set based on the current annual irrigation and drainage conditions and different cropland-waste ratios in the study area, and the water-salt balance SALTMOD model after calibration was used to predict and simulate the dynamic changes of salinity in cultivated land under different scenarios.

[0021] S14, using the predicted salinity of cultivated land as a control constraint, determine the cultivated land ratio in the study area;

[0022] S15. Based on the determined cultivated land to wasteland ratio of the study area, determine the area and quantity requirements of land use types in annual increments.

[0023] Furthermore, the step S2 is specifically as follows:

[0024] S21. Based on remote sensing technology, the land use types in the study area are classified according to the spectral characteristics and spatial information of different land use types, and the reclassified land use data and driving factor data are uniformly represented;

[0025] S22. Conduct land use driving factor regression analysis based on the uniformly represented land use data and driving factor data;

[0026] S23. Configuration model file, including:

[0027] The land use type area and quantity requirements are used as input data, and the main parameter files of the CLUE-S land use model are configured based on the grid-type spatial data of the prediction simulation base period;

[0028] According to the results of the regression analysis of land use driving factors, the regression parameter file and driving force file of the CLUE-S land use model are configured;

[0029] Configure the land use type file for the simulation start year of the CLUE-S land use model and the land use demand file from the forecast simulation base period to the end of the simulation;

[0030] S24. Set land use type distribution rules, including:

[0031] Set land use transfer rules, land use type transfer elasticity coefficients, and regional constraints for land use optimization configuration;

[0032] S25. According to the configured model file and the set land use type distribution rules, the CLUE-S land use model is run, and the operation is continuously iterated to simulate the most suitable spatial layout of different land use grid units.

[0033] Furthermore, in step S21, the reclassified land use data and driving factor data are represented using a unified coordinate system, projection, spatial range and spatial resolution, and all raster data are uniformly converted into floating-point data.

[0034] Furthermore, the step S22 is specifically as follows:

[0035] S22-1, determining the driving factor according to the uniformly represented land use data and driving factor data, and performing rasterization processing on the driving factor data;

[0036] The driving factors include elevation, slope, NDVI, annual rainfall, annual average temperature, distance from the town center, distance from major railways, highways, national, provincial, county and township roads, distance from canal systems, distance from drainage ditches, multi-year average groundwater depth, population density and gross domestic product (GDP);

[0037] S22-2, encoding various land use types, and reclassifying each land use type map to obtain a binary map of each land type;

[0038] S22-3. Based on the binary maps of each land type and the driving factor data of the same period, binary logistic regression analysis was performed on the spatial pattern of land use and its driving factors in different years to obtain the suitability value of each land use grid unit for transformation into a certain land use type.

[0039] Furthermore, in step 22-3, the suitability value of converting the land use grid unit into a certain land use type is expressed as:

[0040]

[0041] Where P i is the probability of the land use type appearing in each grid cell in the region; x0, x1, …, x n-1 are driving factors; β0, β1, β n-1 are the coefficients of the explanatory variables in the regression equation; n is the number of driving factors.

[0042] Furthermore, in step S25, the CLUE-S land use model is run to simulate the most suitable spatial layout of different land use grid cells as:

[0043] TPROP u,i =ELAS i +ITER i +P u,i

[0044] Where, TPROP u,irepresents the overall suitability of grid u for land use type i, ELAS i represents the transfer elasticity coefficient of land type i, representing the conversion cost of land use type, ITER i represents the competition factor of land type i, which is automatically set during the iteration of model simulation, P u,i It represents the distribution probability of grid u to i, which is the regression result of land use status on different driving factors.

[0045] Furthermore, the method to verify the simulation accuracy of the CLUE-S land use model is as follows:

[0046] The real land use type and the simulated land use type are spatially superimposed, and the remote sensing classification of land use types in different years and the simulated land use spatial classification map are superimposed and subtracted through the raster calculator, and the number of 0-value grids is extracted as the correct number of grids for simulation, and its proportion to the total number of grids is analyzed;

[0047] The remote sensing data of land use types in different years were spatially compared with the simulated data of land use types obtained by the corresponding simulation, and the Kappa coefficient was calculated;

[0048] The simulation accuracy of the CLUE-S land use model was tested based on the proportion of correctly simulated grids to the total number of grids and the Kappa coefficient.

[0049] The beneficial effects of the present invention are:

[0050] The present invention uses a method of coupling the water-salt balance model SALTMOD and the land use CLUE-S model to combine the suitable cultivated-waste ratio based on irrigation and drainage regulation and cultivated land salinity control with the spatial distribution of land use, and proposes a method for optimizing the spatial configuration of cultivated-waste land use in salinized irrigation areas. From the perspectives of quantitative constraints and spatial configuration, a systematic exploration is made of suggestions for the dynamic spatial control of suitable salt-waste land that considers land use development and regional salt balance, providing certain technical support for the management of saline-alkali land in arid and semi-arid irrigation areas and maintaining the healthy and sustainable development of the ecological environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A flow chart of the land utilization optimization configuration method for salinized irrigated wasteland provided by the present invention.

[0052] Figure 2 The present invention provides the changing trend of the cultivated land-waste ratio in the study area from 1996 to 2020.

[0053] Figure 3 The present invention provides the dynamic changes of water and salt in the root layer soil under different cultivated land-wasteland ratio conditions in the research area.

[0054] Figure 4 The land use driving factor grid map provided by the present invention.

[0055] Figure 5 The present invention provides a comparison chart of land use remote sensing interpretation and CLUE-S spatial pattern simulation in 2006 and 2016.

[0056] Figure 6 The present invention provides a comparison chart of land use remote sensing interpretation and CLUE-S spatial pattern simulation in 2006 and 2016.

[0057] Figure 7 The present invention provides a comparison chart of land use remote sensing interpretation and CLUE-S spatial pattern simulation in 2006 and 2016. DETAILED DESCRIPTION

[0058] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0059] The embodiment of the present invention provides a method for optimizing the use of cultivated land in salinized irrigation areas. Figure 1 As shown, the following steps are included:

[0060] S1. In the non-spatial analysis module of the CLUE-S land use model, the water-salt balance SALTMOD model was constructed to simulate and calculate the land use quantity demand in the study area. The area and quantity demand of land use types with an annual step length were calculated by determining the most suitable cultivated land ratio in the study area.

[0061] S2. In the spatial analysis module of the CLUE-S land use model, find the influencing factors of the spatial distribution of different land use types, and combine the calculated land use type area and quantity requirements to obtain the most suitable spatial layout of different land use grid cells by running the CLUE-S land use model simulation;

[0062] S3. Compare and analyze the simulation results of the CLUE-S land use model with the actual results to verify the simulation accuracy of the CLUE-S land use model, and optimize the CLUE-S land use model based on the test results;

[0063] S4. The calculated most suitable cultivated-wasteland ratio is used as input data for optimizing the CLUE-S land use model to predict the future long-term spatial pattern of land use, analyze the spatial distribution pattern of suitable salt-wasteland under the control of cultivated land salinity, and achieve optimal allocation of cultivated-wasteland land use.

[0064] In step S1 of the embodiment of the present invention, the non-spatial analysis module of the CLUE-S land use model is the predicted value of the land demand in the study area, which needs to be directly input from the outside world, so other models or methods need to be embedded in the non-spatial module for calculation. The non-spatial analysis module mainly predicts the changes in the area and quantity of land use types based on the comprehensive driving factors of land use changes in the study area. This embodiment mainly takes the control of salinity in the root layer of cultivated land as the main constraint, and takes the cultivated wasteland ratio and various land use areas in which the salinity of cultivated land can be well controlled during the forecast period as constraints. The water-salt balance model SALTMOD is used to simulate and calculate the land use quantity demand in the study area, and the land use type area and quantity sequence data with a time step of one year are determined by the cultivated wasteland ratio in the study area, which is used as the input data of the spatial module of the CLUE-S land use model.

[0065] In step S1 of this embodiment, in the constructed water-salt balance SALTMOD model, the cultivated land and salt wasteland in the study area are taken as the research objects, and the total control area only includes the cultivated land and salt wasteland, and its regional division is expressed as:

[0066] A+B=cultivated land in the study area

[0067] U = Salt wasteland

[0068] A+B+U=1

[0069] Among them, A represents irrigated land with crops or vegetation growing, B represents fallow or rain-fed irrigated land, and U represents non-irrigated salt wasteland. Specifically, the total control area of ​​the study area is deducted from water areas, construction land, unused land, and non-irrigated forest and grassland, and only includes cultivated land (irrigated farmland and grassland and non-irrigated farmland) and salt wasteland.

[0070] In the water-salt balance SALTMOD model, the rotation mode of agricultural land Kr=1, the irrigated land areas A and B are all rotated, the salt wasteland area U is fixed, and the year is divided into three simulation periods: the growth period, the autumn irrigation period, and the non-growth period. For example, the growth period is from April to September, the autumn irrigation period is from October to November, and the non-growth period is from December to March of the following year.

[0071] In step S1 of this embodiment, the method for simulating and calculating the land use quantity demand in the study area is specifically as follows:

[0072] S11. Process and analyze the basic data input from the outside through the water-salt balance SALTMOD model and output the corresponding data;

[0073] Among them, the input basic data include agricultural data, hydrological data, meteorological data and soil data of the study area; the output data include soil salinity, groundwater depth, drainage volume and drainage mineralization; illustratively, the input data can include regional soil salinity, groundwater mineralization, groundwater depth, surface water hydrological data (precipitation, potential evapotranspiration, irrigation volume, surface runoff) and groundwater hydrological data (groundwater extraction volume, drainage reuse volume), etc.

[0074] S12. Calibrate and verify the water-salt balance SALTMOD model using the specific parameters of the study area;

[0075] Specifically, some input parameters in the model can be measured or estimated, while some parameters such as natural drainage Gn, root layer leaching rate Flr, transition layer leaching rate Flx, and natural groundwater drainage rate Gn through aquifers are difficult to obtain directly through experimental measurements. Appropriate parameter values ​​are determined by fitting model simulation values ​​with measured values.

[0076] S13. Different scenarios were set based on the current annual irrigation and drainage conditions and different cropland-waste ratios in the study area, and the water-salt balance SALTMOD model after calibration was used to predict and simulate the dynamic changes of salinity in cultivated land under different scenarios.

[0077] S14, using the predicted salinity of cultivated land as a control constraint, determine the cultivated land ratio in the study area;

[0078] S15. Based on the determined cultivated land to wasteland ratio of the study area, determine the area and quantity requirements of land use types in annual increments.

[0079] In step S13 of this embodiment, based on the model verified by regional specific parameter calibration, scenario simulation prediction is carried out; taking the current situation in 2016 as an example, based on the current annual irrigation and drainage conditions and different scenarios of cultivated land ratio, the future long-term trend of soil salinity change in cultivated land is analyzed, as shown in Table 1, and scenario setting is performed;

[0080] Table 1: Scenario settings

[0081] Solution Settings Cultivated land ratio Current Status in 2016 6.185 Solution 1 5.500 Solution 2 6.000 Solution 3 6.500 Solution 4 7.000 Solution 5 7.500 Solution 6 8.000 Solution 7 8.500 Solution 8 9.000 Solution 9 9.500 Solution 10 10.000

[0082] like Figure 2 As shown, the ratio of cultivated land to wasteland in the study area showed a slow increasing trend from 1996 to 2020. In 2016, the ratio of cultivated land to wasteland in the study area was about 6.185. Under the current irrigation and drainage conditions, the salt content in the 1m deep soil layer of the cultivated land in the study area showed a slight decreasing trend. After 10 years, the salt content in the root layer of the cultivated land can be reduced by about 1.03%.

[0083] like Figure 3 As shown in the figure, for the above scheme, assuming that the cultivated land area will continue to expand in the future, the reclamation area of ​​salt wasteland will increase, the area of ​​salt wasteland will continue to decrease, and the cultivated wasteland ratio will increase, the simulation results of the SALTMOD water-salt balance model show that with the increase of cultivated wasteland ratio, the salinity of the root layer soil of cultivated land will gradually increase. When the cultivated wasteland ratio increases to 8, the salinity of the regional root layer soil will slightly decrease. When the cultivated wasteland ratio increases to 8.5, the salinity of the regional root layer soil will increase. Assuming that the irrigation and drainage conditions of the irrigation area remain unchanged in the future, considering the factors of salt control in the root layer soil of the irrigation area, it is proposed that under the background of land use development in the irrigation area, the regional cultivated wasteland ratio is more suitable between 8.0 and 8.5. After the cultivated wasteland ratio exceeds 8.5, in the long run, the accumulation of salt in the root layer of the irrigation area cultivated land will show an obvious increasing trend. The continuous and cyclical changes in the distribution pattern of salt wasteland can extend the life of dry salt drainage and make the salt wasteland a permanent salt reservoir. When the irrigation and drainage conditions remain unchanged, the salt wasteland is reduced to a certain extent, and the drainage space of the irrigation area is reduced, which is not conducive to the drainage of cultivated land. As shown in Table 2, the change rate of root layer soil salinity in different wasteland tillage ratio scenarios in 2016 compared with the current conditions is shown.

[0084] Table 2: Change rate of root layer soil salinity in different scenarios of wasteland cultivation ratio in 2016 compared with the current conditions

[0085]

[0086] In step S2 of the embodiment of the present invention, the spatial analysis module of the CLUE-S land use model is mainly based on the CLUE-S land use model. By finding the main influencing factors of the spatial distribution of different land use types, extracting their distribution rules based on Logistic regression, and using continuous iterative operations, the most suitable spatial layout of different land use grid units is found.

[0087] Specifically, step S2 of the embodiment of the present invention is as follows:

[0088] S21. Based on remote sensing technology, the land use types in the study area are classified according to the spectral characteristics and spatial information of different land use types, and the reclassified land use data and driving factor data are uniformly represented;

[0089] S22. Conduct land use driving factor regression analysis based on the uniformly represented land use data and driving factor data;

[0090] S23. Configuration model file, including:

[0091] The land use type area and quantity requirements are used as input data, and the main parameter files of the CLUE-S land use model are configured based on the grid-type spatial data of the prediction simulation base period;

[0092] According to the results of the regression analysis of land use driving factors, the regression parameter file and driving force file of the CLUE-S land use model are configured;

[0093] Configure the land use type file for the simulation start year of the CLUE-S land use model and the land use demand file from the forecast simulation base period to the end of the simulation;

[0094] S24. Set land use type distribution rules, including:

[0095] Set land use transfer rules, land use type transfer elasticity coefficients, and regional constraints for land use optimization configuration;

[0096] S25. According to the configured model file and the set land use type distribution rules, the CLUE-S land use model is run, and the operation is continuously iterated to simulate the most suitable spatial layout of different land use grid units.

[0097] In step S21 of this embodiment, the reclassified land use data and driving factor data are represented using a unified coordinate system, projection, spatial range and spatial resolution, and all raster data are uniformly converted into floating-point data.

[0098] Specifically, based on remote sensing technology, the land use types in the study area were classified according to the spectral characteristics and spatial information of different land use types. After reclassification, the land use data, driving factor data, etc. adopted a unified coordinate system, projection, spatial range and spatial resolution, and all raster data were uniformly converted into floating-point data.

[0099] Step S22 of this embodiment is specifically as follows:

[0100] S22-1, determining the driving factor according to the uniformly represented land use data and driving factor data, and performing rasterization processing on the driving factor data;

[0101] The driving factors include elevation, slope, NDVI, annual rainfall, annual average temperature, distance from the town center, distance from major railways, highways, national, provincial, county and township roads, distance from canal systems, distance from drainage ditches, multi-year average groundwater depth, population density and gross domestic product (GDP);

[0102] S22-2, encoding various land use types, and reclassifying each land use type map to obtain a binary map of each land type;

[0103] S22-3. Based on the binary maps of each land type and the driving factor data of the same period, binary logistic regression analysis was performed on the spatial pattern of land use and its driving factors in different years to obtain the suitability value of each land use grid unit for transformation into a certain land use type.

[0104] In step S22-1 of the present embodiment, combined with the characteristics of regional land use changes and the main driving forces, and taking into account the scientific nature and representativeness of the selected driving factors, 12 driving factors in four categories, namely, natural, socio-economic, locational and water resources, are screened to determine the above-mentioned driving factors. Based on the ArcGIS platform, the data of each driving factor are rasterized, and the raster resampling unit size is 100m×100m. The study area is divided into 799*891 (columns*rows), with a total of 246465 raster units. All driving factor raster data are converted to ASCII format through the ArcGIS platform and named sc1gr*.fil respectively. * represents the code of the driving factor, and * takes a value of 0-11. The above-mentioned land use driving factor rasters are as follows: Figure 4 shown.

[0105] In step S22-2 of the present embodiment, based on the above-mentioned land use type remote sensing interpretation results, various types of land use types are coded, and cultivated land, forest land, grassland, water area, construction land, unused land, and salt wasteland are coded as 0, 1, 2, 3, 4, 5, and 6 respectively. Then, the land use type map is reclassified on the ArcGIS platform to obtain the binary map of each land type, and the value of the binary map is set to 0 and 1, 1 represents a certain land type (the ID number of this land type is set to 1 in the attribute table of each vector file), and 0 represents the remaining land use types except this land type (other land type IDs are assigned to 0). Under the premise of ensuring that all data dimensions are consistent, the binary map of each land type is converted into an ASCII format file, named cov*.asc, * represents the code of each land type, and * takes a value of 0-6. See Table 3 below for specific land use type and driving factor file descriptions.

[0106] Table 3: Land use type and driving factor files

[0107] file name Land use type / driving factors cov0.asc arable land cov1.asc woodland cov2.asc grassland cov3.asc Waters cov4.asc Construction land cov5.asc Unused land cov6.asc Salt wasteland Sclgr0.fil Elevation Sclgr1.fil Slope Sclgr2.fil Vegetation Index NDVI Sclgr3.fil Annual rainfall Sclgr4.fil Average annual temperature Sclgr5.fil Distance from the town center Sclgr6.fil Distance to major railways, highways, national, provincial, county and township roads Sclgr7.fil Distance from canal system Sclgr8.fil Distance from drain Sclgr9.fil Average groundwater depth over the years (1990-2020) Sclgr10.fil Population Density Sclgr11.fil Gross Domestic Product (GDP)

[0108] In step S22-3 of this embodiment, the suitability value of converting the land use grid unit into a certain land use type is expressed as:

[0109]

[0110] Where P i is the probability of the land use type appearing in each grid cell in the region; x0, x1, …, x n-1are driving factors; β0, β1, β n-1 are the coefficients of the explanatory variables in the regression equation; n is the number of driving factors.

[0111] Specifically, in this embodiment, based on the above land use types and driving factor data of the same period, Binary Logistic regression analysis was performed on the land use spatial pattern and its driving factors in 1996 and 2016. The ASCII code data files Cov*.0~6.asc and driving factor data files Sclgr0~11.fil of each land use type were converted into Stat.txt files based on the File Convert tool provided by the CLUE-S model. Then Stat.txt was imported into SPSS software, where 12 driving factors were placed in the independent variables and various land types were placed in the dependent variables. Binary Logistic regression analysis was performed in turn, and the analysis results are shown in Tables 4 and 5.

[0112] Table 4: Logistic regression analysis and test results in 1996

[0113]

[0114] Table 5: Logistic regression analysis and test results in 2016

[0115]

[0116]

[0117] In step S23 of this embodiment, during the process of configuring the main parameter file:

[0118] ① Main parameter file configuration

[0119] According to the relevant results of spatial analysis, the main parameters of the simulation operation are set and saved as a "txt" document. The specific parameter meanings and settings are shown in Tables 6 and 7 below.

[0120] Table 6: Input files of CLUE-S model

[0121] file name illustrate mian.1 Edit the main settings of the model Cov_all.0 Land use map for the simulation start year demand.in* Land demand documents under different scenarios (* represents different land use types) ELAS Stability parameters of land use types allow.txt Editing the Land-Use Conversion Matrix region*.fil Regional constraint file (* represents different constraint files) Sclgr*.fil Simulation driving force file (land use change impact factor) (* represents the serial number of the driving force) alloc.reg Editing the regression equation

[0122] Table 7: Main parameter settings of CLUE-S model

[0123]

[0124]

[0125] ②Regression parameter file configuration

[0126] When configuring the regression parameter file, the regression coefficients of each driving factor are calculated from the previous driving factors, regression return coefficients and other parameters, and the regression equations of each land use type are obtained by bringing them into the regression model. The regression parameter file is renamed (allocate.reg) and saved to the installation directory as the input variable. Based on this, the regression parameter file configurations for 1996 and 2016 are shown in Tables 8 and 9;

[0127] Table 8: Regression parameter file settings for 1996

[0128]

[0129]

[0130]

[0131]

[0132] Table 9: 2016 regression parameter file settings

[0133]

[0134]

[0135]

[0136] ③ Driving force file configuration

[0137] The ASCII code files converted from each driving factor file are placed under the installation file of CLUE-S according to the model requirements.

[0138] ④Land use type file configuration

[0139] The attribute values ​​of cultivated land, forest land, grassland, water area, construction land, unused land and salt wasteland in the land use status map of the study area in the initial years of simulation in 1996 and 2016 were set to 0, 1, 2, 3, 4, 5, 6 respectively, and then converted into a 100m×100m raster file. The raster map was then reclassified according to the codes of each land type. Finally, the reclassification results were converted into ASCII format through the Conversion Tools / Raster to ASCII function of ArcGIS10.5 software, named cov_all.0, and entered into the CLUE-S model.

[0140] ⑤Land use demand document

[0141] From the base period of the forecast simulation to the end of the simulation, the quantity of each land use type in each year is calculated on a yearly basis and input into the model in Notepad txt format as the quantity control end of the land use optimization configuration.

[0142] In step S25 of this embodiment, the spatial analysis module uses the calculation results of the non-spatial analysis module as input data, and based on the grid-type spatial data of the prediction simulation base period, calculates the spatial distribution probability of each land use type according to the driving factors of land use change, the constraints for obtaining land use change, the land use conversion rules, etc., and uses continuous iterative operations to find the most suitable spatial layout of different grid units, thereby realizing the spatial simulation of landscape changes.

[0143] In step S24 of this embodiment, the land use transfer rule is set as follows:

[0144] The land use type transfer matrix file is a matrix that sets the parameters of whether each land use type can be converted into each other. The conversion rules between different land use types are named allow.txt file in the model, and the file is copied to the installation directory of the CLUE-S software. The matrix includes two parameters, 0 and 1. The row represents the current land use type, and the column represents the possible land type in the future. 1 indicates that it can be converted between now and the future, and 0 indicates that it cannot be converted. This study is based on the long-term series of regional land use remote sensing results, combined with historical conditions and previous knowledge and experience, and initially determined by referring to relevant literature. The conversion rule file. All land use in the study area can be transformed, so the land use type transfer matrix file is 1. Set the allow.txt file in the form of a 7×7 matrix.

[0145] For land use type transfer elasticity coefficient setting:

[0146] When optimizing land use, the CLUE-S model needs to set the conversion rules between different land types under different scenarios in the future, which are mainly reflected by two parameters: land use type conversion elasticity coefficient (ELAS) and land use type conversion file. ELAS generally reflects the ability or difficulty of converting one land use type to another. The ELAS parameter value is between 0 and 1. The larger the ELAS value, the more difficult it is to convert to other land use types, and the higher the stability of the land type. Conversely, the lower the stability, the easier it is to convert.

[0147] The transfer elasticity of land types is mainly set based on the actual situation of land use type transformation in the region from 1991 to 2020. After repeated simulation, screening and adjustment, the coefficient with better model effect is selected as the final parameter. The ELAS values ​​of the seven land types of cultivated land, forest land, grassland, water area, construction land, unused land and salt wasteland are 0.85, 0.70, 0.55, 0.40, 0.90, 0.45 and 0.10 respectively.

[0148] Regional constraint settings for land use optimization configuration:

[0149] The region files include unrestricted region files (region_nopark.fil) and restricted region files (region_park*.fil). This file is converted from raster data into ASCII code files by assigning corresponding values ​​to restricted / unrestricted regions. Assuming that no restricted region is set, the study area restriction file is a raster file with all zeros, and the file is converted into an ASCII file, named region_nopark.fil, and copied to the installation directory.

[0150] In this embodiment, based on the above-mentioned model file configuration and land use type distribution rule setting, the spatial analysis process module uses the calculation results of the non-spatial analysis module as input data, and is based on the raster spatial data of the prediction simulation base period. According to the driving factors of land use changes, the constraints for obtaining land use changes, land use conversion rules, etc., the spatial distribution probability of each land use type is calculated, and continuous iterative operations are used to find the most suitable spatial layout of different grid units, thereby realizing the spatial simulation of landscape changes.

[0151] Therefore, in step S25 of this embodiment, the CLUE-S land use model is run to simulate the most suitable spatial layout of different land use grid cells, which is expressed as:

[0152] TPROP u,i =ELAS i +ITER i +P u,i

[0153] Where, TPROP u,i represents the overall suitability of grid u for land use type i, ELAS i represents the transfer elasticity coefficient of land type i, representing the conversion cost of land use type, ITER i represents the competition factor of land type i, which is automatically set during the iteration of model simulation, P u,i It represents the distribution probability of grid u to i, which is the regression result of land use status on different driving factors.

[0154] In step S3 of the embodiment of the present invention, the method for verifying the simulation accuracy of the CLUE-S land use model is specifically as follows:

[0155] The real land use type and the simulated land use type are spatially superimposed, and the remote sensing classification of land use types in different years and the simulated land use spatial classification map are superimposed and subtracted through the raster calculator, and the number of 0-value grids is extracted as the correct number of grids for simulation, and its proportion to the total number of grids is analyzed;

[0156] The remote sensing data of land use types in different years were spatially compared with the simulated data of land use types obtained by the corresponding simulation, and the Kappa coefficient was calculated;

[0157] The simulation accuracy of the CLUE-S land use model was tested based on the proportion of correctly simulated grids to the total number of grids and the Kappa coefficient.

[0158] Specifically, in this embodiment, in order to verify the accuracy of the CLUE-S model operation results, the land use in 1996 of the study area was used as the starting point to simulate the land use in 2006 and 2016. The simulation results were compared and analyzed with the actual results. The Kappa coefficient was used to verify the simulation results of the CLUE-S model. The Kappa coefficient formula is as follows:

[0159]

[0160] Where P o is to correctly simulate the grid cell scale.

[0161] In this example, land use types are divided into 7 categories, so P c =1 / 7; P p The ideal state is the correct simulation grid unit ratio, that is, 100%. The Kappa coefficient range is [-1,1]. It is generally believed that Kappa>0.6 has significant consistency, while Kappa>0.8 indicates a good simulation effect.

[0162] Table 10: Model accuracy test evaluation table

[0163] 1996-2006 1996-2016 Correct simulation grid number 186979 182777 Accuracy % 75.86％ 74.16％ Kappa coefficient 0.72 0.70

[0164] The model accuracy test results obtained based on the above method are shown in Table 10 and Figure 5As shown in the figure, the land use types interpreted by remote sensing and the land use types simulated by CLUE-S were spatially superimposed. The 2006 and 2016 remote sensing classifications were superimposed and subtracted from the CLUE-S simulated land use spatial classification maps through the grid calculator to extract the number of 0-value grids, that is, the number of simulated correct grids. The number of 0-value phase elements in 2006 and 2016 was 186979 and 182777, respectively, accounting for 75.86% and 74.16% of the total grids, respectively. The land use simulation data of 2006 and 2016 were selected for spatial comparison analysis with the remote sensing interpretation data. The kappa coefficients were 0.72 and 0.70, respectively, indicating that the simulation data was consistent with the current land use data interpreted by remote sensing.

[0165] In step S4 of the embodiment of the present invention, based on the suitable cultivated-waste ratio proposed by the above non-spatial model simulation, it is assumed that salt wasteland and cultivated land will be transformed in the next 10 years, and the area of ​​other land use types remains unchanged, and the spatial distribution pattern of land use in the next 10 years is simulated. The prediction result of the spatial distribution pattern of land use in the irrigation area in 2026 is obtained (such as Figure 6 ), and the spatial pattern of salt wasteland considering salinity control of cultivated land in irrigated areas (as shown in Figure 7 As shown in the data, in the next 10 years from 2016 to 2026, the salt wasteland transfer rate is 23.28%, mainly transferred to cultivated land and forest land, with transfer rates of 18.68% and 1.56% respectively. The transfer rate is 6.65%, mainly transferred from cultivated land and grassland, with transfer rates of 2.42% and 1.34% respectively.

[0166] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0167] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for optimizing the use of cultivated land in salinized irrigation areas, characterized in that: The following steps are involved: S1. In the non-spatial analysis module of the CLUE-S land use model, the water-salt balance SALTMOD model was constructed to simulate and calculate the land use quantity demand in the study area. The area and quantity demand of land use types with an annual step length were calculated by determining the most suitable cultivated land ratio in the study area. S2. In the spatial analysis module of the CLUE-S land use model, find the influencing factors of the spatial distribution of different land use types, and combine the calculated land use type area and quantity requirements to obtain the most suitable spatial layout of different land use grid cells by running the CLUE-S land use model simulation; S3. Compare and analyze the simulation results of the CLUE-S land use model with the actual results to verify the simulation accuracy of the CLUE-S land use model, and optimize the CLUE-S land use model based on the test results; S4. The calculated most suitable cultivated-wasteland ratio is used as input data for optimizing the CLUE-S land use model to predict the future long-term spatial pattern of land use, analyze the spatial distribution pattern of suitable salt-wasteland under the control of cultivated land salinity, and achieve optimal allocation of cultivated-wasteland land use.

2. The method for optimizing land use of salinized irrigated land according to claim 1, characterized in that: In step S1, in the constructed water-salt balance SALTMOD model, the cultivated land and salt wasteland in the study area are taken as the research objects, and the total control area only includes the cultivated land and salt wasteland, and its regional division is expressed as: A+B=cultivated land in the study area U = Salt wasteland A+B+U=1 Among them, A represents irrigated land with crops or vegetation growing, B represents fallow or rain-fed irrigated land, and U represents non-irrigated salt wasteland; In the water-salt balance SALTMOD model, the rotation mode of agricultural land Kr=1, the irrigated land areas A and B are all rotated, the salt wasteland area U remains unchanged, and a year is divided into three simulation periods: growth period, autumn irrigation period and non-growth period.

3. The method for optimizing land use of salinized irrigated land according to claim 1, characterized in that: In step S1, the method for simulating and calculating the land use quantity demand in the study area is specifically as follows: S11. Process and analyze the basic data input from the outside through the water-salt balance SALTMOD model and output the corresponding data; The input basic data include agricultural data, hydrological data, meteorological data and soil data of the study area; the output data include soil salinity, groundwater depth, drainage volume and drainage mineralization; S12. Calibrate and verify the water-salt balance SALTMOD model using the specific parameters of the study area; S13. Different scenarios were set based on the current annual irrigation and drainage conditions and different cropland-waste ratios in the study area, and the water-salt balance SALTMOD model after calibration was used to predict and simulate the dynamic changes of salinity in cultivated land under different scenarios. S14, using the predicted salinity of cultivated land as a control constraint, determine the cultivated land ratio in the study area; S15. Based on the determined cultivated land to wasteland ratio of the study area, determine the area and quantity requirements of land use types in annual increments.

4. The method for optimizing land use of salinized irrigated land according to claim 1, characterized in that: The step S2 is specifically as follows: S21. Based on remote sensing technology, the land use types in the study area are classified according to the spectral characteristics and spatial information of different land use types, and the reclassified land use data and driving factor data are uniformly represented; S22. Conduct land use driving factor regression analysis based on the uniformly represented land use data and driving factor data; S23. Configuration model file, including: The land use type area and quantity requirements are used as input data, and the main parameter files of the CLUE-S land use model are configured based on the grid-type spatial data of the prediction simulation base period; According to the results of the regression analysis of land use driving factors, the regression parameter file and driving force file of the CLUE-S land use model are configured; Configure the land use type file for the simulation start year of the CLUE-S land use model and the land use demand file from the forecast simulation base period to the end of the simulation; S24. Set land use type distribution rules, including: Set land use transfer rules, land use type transfer elasticity coefficients, and regional constraints for land use optimization configuration; S25. According to the configured model file and the set land use type distribution rules, the CLUE-S land use model is run, and the operation is continuously iterated to simulate the most suitable spatial layout of different land use grid units.

5. The method for optimizing land use of salinized irrigated land according to claim 4, characterized in that: In the step S21, the reclassified land use data and driving factor data are represented by a unified coordinate system, projection, spatial range and spatial resolution, and all raster data are uniformly converted into floating point data.

6. The method for optimizing land use of salinized irrigated land according to claim 4, characterized in that: The step S22 is specifically as follows: S22-1, determining the driving factor according to the uniformly represented land use data and driving factor data, and performing rasterization processing on the driving factor data; The driving factors include elevation, slope, NDVI, annual rainfall, annual average temperature, distance from the town center, distance from major railways, highways, national, provincial, county and township roads, distance from canal systems, distance from drainage ditches, multi-year average groundwater depth, population density and gross domestic product (GDP); S22-2, encoding various land use types, and reclassifying each land use type map to obtain a binary map of each land type; S22-3. Based on the binary maps of each land type and the driving factor data of the same period, binary logistic regression analysis was performed on the spatial pattern of land use and its driving factors in different years to obtain the suitability value of each land use grid unit for transformation into a certain land use type.

7. The method for optimizing land use of salinized irrigated land according to claim 6, characterized in that: In step 22-3, the suitability value of converting the land use grid unit into a certain land use type is expressed as: Where P i is the probability of the land use type appearing in each grid cell in the region; x0, x1, …, x n-1 are driving factors; β0, β1, β n-1 are the coefficients of the explanatory variables in the regression equation; n is the number of driving factors.

8. The method for optimizing land use of salinized irrigated land according to claim 4, characterized in that: In step S25, the CLUE-S land use model is run to simulate the most suitable spatial layout of different land use grid cells, which is expressed as: TPROP u,i =THEY i +ITER i +P u,i Where, TPROP u,i represents the overall suitability of grid u for land use type i, ELAS i represents the transfer elasticity coefficient of land type i, representing the conversion cost of land use type, ITER i represents the competition factor of land type i, which is automatically set during the iteration of model simulation, P u,i It represents the distribution probability of grid u to i, which is the regression result of land use status to different driving factors.

9. The method for optimizing land use in salinized irrigation areas according to claim 1, characterized in that: The specific method for testing the simulation accuracy of the CLUE-S land use model is as follows: The real land use type and the simulated land use type are spatially superimposed, and the remote sensing classification of land use types in different years and the simulated land use spatial classification map are superimposed and subtracted through the raster calculator, and the number of 0-value grids is extracted as the correct number of grids for simulation, and its proportion to the total number of grids is analyzed; The remote sensing data of land use types in different years were spatially compared with the simulated data of land use types obtained by the corresponding simulation, and the Kappa coefficient was calculated; The simulation accuracy of the CLUE-S land use model was tested based on the proportion of correctly simulated grids to the total number of grids and the Kappa coefficient.

Images