Cultivated land space distribution data reconstruction method and device

Abstract

The invention relates to a data reconstruction method and device for cultivated land space distribution, belongs to the technical field of cultivated land data processing, and solves the problem of data distortion caused by large deviation between cultivated land space distribution data and real conditions in the prior art. According to the data reconstruction method of cultivated land space distribution, when a reconstructed cultivated land area proportion reaches a theoretical upper limit value, a historical reconstructed cultivated land statistical area is updated according to a difference value between the historical reconstructed cultivated land statistical area and the area sum of the reconstructed cultivated land area proportion reaching the theoretical upper limit value; and obtaining the reconstructed cultivated land space distribution of the research area until the reconstructed cultivated land area proportion of each unit does not reach the theoretical upper limit value. According to the method, finer and more accurate spatial distribution characteristics and long-time sequence coverage can be provided, and the reliability consistent with actual survey data can be realized.

Description

Technical Field

[0001] The present application relates to the technical field of cultivated land data processing, and in particular to a method and device for reconstructing cultivated land spatial distribution data. Background Art

[0002] Reconstructing long-term spatial distribution data of cultivated land not only provides indispensable basic information for agricultural development, ecological protection and climate change research, but also has important significance in promoting sustainable land management, improving food production efficiency and maintaining ecosystem balance. Therefore, the accuracy of spatial distribution data of cultivated land is an important technical data for agricultural production development and has obvious technical significance.

[0003] The existing data processing methods for quantifying the spatiotemporal changes of cultivated land include remote sensing inversion and statistical inference. Among them, remote sensing inversion provides a more detailed spatial distribution of cultivated land, but the cultivated land data reconstructed based on remote sensing inversion has the problem of insufficient coverage period. At the same time, due to the problems of spatial resolution of remote sensing data and image inversion accuracy, the reconstructed cultivated land area based on remote sensing often has obvious deviations from the results of ground surveys. For example, field ridges and ditches are difficult to be accurately extracted due to the spatial resolution of remote sensing images, resulting in their inclusion in the scope of reconstructed cultivated land, resulting in an overestimation of the reconstructed cultivated land area.

[0004] On the contrary, statistical inference methods are based on historical statistics or official documents, local chronicles, taxes and other materials to reconstruct cultivated land statistical data time series. The reconstructed cultivated land data can cover hundreds of years of history, but the spatial distribution characteristics of cultivated land provided by them come from spatial inference methods, which have great uncertainty. However, the existing statistical inference methods only make spatial inferences and predictions based on natural factors such as temperature, topography, soil and other crop suitability factors of cultivated land. Many factors such as historical population demand, skill ability, land annexation, policy adjustments, etc. may cause significant changes in the spatial distribution of cultivated land. Therefore, the cultivated land reconstruction method based on spatial statistical inference inevitably deviates from the actual spatial distribution of cultivated land; and due to the differences in statistical methods in different time periods, some cultivated land survey data are distorted and mutated.

[0005] In summary, the cultivated land spatial distribution data determined by the existing data processing methods have large deviations from the actual situation, resulting in data distortion. Summary of the invention

[0006] In view of the above analysis, the embodiments of the present invention aim to provide a method and device for reconstructing data of spatial distribution of cultivated land, so as to solve one or more of the above problems existing in the prior art.

[0007] The present disclosure provides a method for reconstructing data of spatial distribution of cultivated land, comprising the steps of:

[0008] Determine the cultivated land distribution range of the study area and the average value of the cultivated land area ratio within the cultivated land distribution range according to the land use data of the study area; wherein the land use data is generated by remote sensing data including cultivated land distribution;

[0009] Calculate the probability of natural suitability distribution of cultivated land according to the natural factor data, and calculate the joint probability index of cultivated land spatial distribution according to the average value of cultivated land area proportion and the probability of natural suitability distribution of cultivated land;

[0010] Calculate the initial reconstructed cultivated land spatial area according to the joint probability index of cultivated land spatial distribution;

[0011] Arrange the statistical data of cultivated land area in the study area to generate historically reconstructed cultivated land statistical area;

[0012] Determine a correction coefficient for spatial distribution of cultivated land according to the ratio of the spatial area of ​​the initially reconstructed cultivated land to the statistical area of ​​the historically reconstructed cultivated land;

[0013] Calculating the proportion of reconstructed cultivated land area according to the correction coefficient and the joint probability index of cultivated land spatial distribution;

[0014] When the proportion of the reconstructed cultivated land area reaches the theoretical upper limit, the historical reconstructed cultivated land statistical area is updated according to the difference between the historical reconstructed cultivated land statistical area and the sum of the areas of the reconstructed cultivated land areas that reach the theoretical upper limit, until the proportions of the reconstructed cultivated land areas do not reach the theoretical upper limit, so as to obtain the spatial distribution of the reconstructed cultivated land in the study area.

[0015] The data reconstruction method of the spatial distribution of cultivated land in the disclosed embodiment determines land use data based on remote sensing data including cultivated land distribution, determines the cultivated land distribution range of the study area and the average proportion of cultivated land area within the cultivated land distribution range according to the land use data of the study area, and calculates the joint probability index of the spatial distribution of cultivated land according to the natural factor data, further determines the correction coefficient and the proportion of reconstructed cultivated land area based on the historically reconstructed cultivated land statistical area and the initial reconstructed cultivated land spatial area, and when the proportion of reconstructed cultivated land area reaches the theoretical upper limit, the historically reconstructed cultivated land statistical area is updated according to the difference between the sum of the historically reconstructed cultivated land statistical area and the proportion of reconstructed cultivated land area that reaches the theoretical upper limit, until each reconstructed cultivated land area proportion does not reach the theoretical upper limit, and the reconstructed cultivated land spatial distribution of the study area is obtained. The reconstructed spatial distribution of cultivated land can provide more refined and accurate spatial distribution characteristics and long-term series coverage, and can also achieve reliability consistent with actual survey data.

[0016] As one of the optional embodiments, the process of determining the cultivated land distribution range of the study area and the average value of the cultivated land area ratio within the cultivated land distribution range according to the land use data of the study area comprises the steps of:

[0017] Reclassify the land use types of remote sensing data and extract the cultivated land types, establish mapping units, and select the grid pixels with the lowest resolution as the reference of the mapping units. According to the principle of optimal area, unify the spatial resolution and calculate the proportion of cultivated land area in each mapping unit.

[0018] The mapping unit is selected according to the proportion of the cultivated land area, and the pixel range of the selected mapping unit is used as the cultivated land distribution range of the study area;

[0019] The cultivated land distribution ranges corresponding to all years were superimposed as the maximum range of cultivated land distribution in the study area, and the average cultivated land area proportion of all remote sensing data was calculated within the cultivated land distribution range of each year.

[0020] As one of the optional embodiments, the process of calculating the probability of natural suitability distribution of cultivated land according to natural factor data is as follows:

[0021] N=w i ×F i ;

[0022] Among them, the natural factor data include multiple natural suitability factors of cultivated land, N is the distribution probability of natural suitability of cultivated land, w i is the weight of the natural suitability factor of cultivated land, w i =1 / n, n is the number of natural suitability factors of cultivated land, F i is the normalized value of the natural suitability factor of cultivated land;

[0023] The process of calculating the joint probability index of cultivated land spatial distribution according to the average cultivated land area ratio and the probability of natural suitability distribution of cultivated land is as follows:

[0024] S y =w y *N+(1-w y )*R;

[0025] Among them, S y is the joint probability index of cultivated land spatial distribution in year y, w y is the joint probability coefficient of year y, N is the probability of natural suitability distribution of cultivated land, and R is the probability of remote sensing distribution of cultivated land.

[0026] As one of the optional embodiments, the process of calculating the initial reconstructed cultivated land spatial area according to the cultivated land spatial distribution joint probability index is as follows:

[0027]

[0028] in, is the initial reconstructed cultivated land area, S y is the joint probability index of spatial distribution of cultivated land in year y, and A is the area of ​​the corresponding mapping unit.

[0029] As one of the optional embodiments, the correction coefficient of the spatial distribution of cultivated land is determined according to the ratio of the initial reconstructed cultivated land spatial area to the historical reconstructed cultivated land statistical area, as follows:

[0030]

[0031] Where, represents the correction coefficient for year y, represents the historical reconstructed cultivated land statistical area in year y, Represents the initial reconstructed cultivated land space area.

[0032] As one of the optional embodiments, the process of calculating the proportion of reconstructed cultivated land area according to the correction coefficient and the joint probability index of the cultivated land spatial distribution is as follows:

[0033] A′ y =α y ×S y ;

[0034] Among them, A′ y is the proportion of rebuilt cultivated land area in year y, S y is the joint probability index of cultivated land spatial distribution in year y, α y is the correction factor for year y.

[0035] As one of the optional embodiments, the process of determining the theoretical upper limit value includes the steps of:

[0036] The maximum value of the proportion of cultivated land area within the cultivated land distribution range is determined according to the land use data of the study area, and the maximum value of the proportion of cultivated land area is taken as the theoretical upper limit.

[0037] The present disclosure also provides a device for reconstructing data of spatial distribution of cultivated land, including:

[0038] A distribution determination module, used to determine the cultivated land distribution range of the study area and the average value of the cultivated land area ratio within the cultivated land distribution range according to the land use data of the study area; wherein the land use data is generated by remote sensing data including cultivated land distribution;

[0039] An index calculation module, used to calculate the probability of natural suitability distribution of cultivated land according to natural factor data, and to calculate the joint probability index of spatial distribution of cultivated land according to the average value of cultivated land area proportion and the probability of natural suitability distribution of cultivated land;

[0040] An area initialization module, used for calculating the initial reconstructed cultivated land spatial area according to the cultivated land spatial distribution joint probability index;

[0041] An area reconstruction module is used to sort out the statistical data of the cultivated land area in the study area and generate historically reconstructed cultivated land statistical areas;

[0042] A coefficient calculation module, used to determine the correction coefficient of the spatial distribution of cultivated land according to the ratio of the initial reconstructed cultivated land spatial area to the historical reconstructed cultivated land statistical area;

[0043] A proportion calculation module, used to calculate the proportion of reconstructed cultivated land area according to the correction coefficient and the joint probability index of the cultivated land spatial distribution;

[0044] A data reconstruction module is used to update the historical reconstructed cultivated land statistical area according to the difference between the historical reconstructed cultivated land statistical area and the area ratio of the reconstructed cultivated land area that reaches the theoretical upper limit when the proportion of the reconstructed cultivated land area reaches the theoretical upper limit, until the proportion of each reconstructed cultivated land area does not reach the theoretical upper limit, so as to obtain the spatial distribution of the reconstructed cultivated land in the study area.

[0045] The data reconstruction device of the spatial distribution of cultivated land of the disclosed embodiment determines land use data based on remote sensing data including cultivated land distribution, determines the cultivated land distribution range of the study area and the average proportion of cultivated land area within the cultivated land distribution range according to the land use data of the study area, and calculates the joint probability index of the spatial distribution of cultivated land according to the natural factor data, further determines the correction coefficient and the proportion of reconstructed cultivated land area based on the historically reconstructed cultivated land statistical area and the initial reconstructed cultivated land spatial area, and when the proportion of reconstructed cultivated land area reaches the theoretical upper limit, updates the historically reconstructed cultivated land statistical area according to the difference between the sum of the historically reconstructed cultivated land statistical area and the proportion of reconstructed cultivated land area that reaches the theoretical upper limit, until each proportion of reconstructed cultivated land area does not reach the theoretical upper limit, and obtains the reconstructed spatial distribution of cultivated land in the study area. The reconstructed spatial distribution of cultivated land can provide more refined and accurate spatial distribution characteristics and long-term series coverage, and can also achieve reliability consistent with actual survey data.

[0046] At least one embodiment of the present disclosure further provides a data control device, including:

[0047] one or more memories non-transitorily storing computer-executable instructions;

[0048] One or more processors are configured to run computer executable instructions, wherein the computer executable instructions, when executed by the one or more processors, implement the data reconstruction method for the spatial distribution of cultivated land according to any embodiment of the present disclosure.

[0049] The above-mentioned data control device determines land use data based on remote sensing data including cultivated land distribution, determines the cultivated land distribution range of the study area and the average proportion of cultivated land area within the cultivated land distribution range according to the land use data of the study area, and calculates the joint probability index of cultivated land spatial distribution according to natural factor data, and further determines the correction coefficient and the proportion of reconstructed cultivated land area based on the historically reconstructed cultivated land statistical area and the initial reconstructed cultivated land spatial area. When the proportion of reconstructed cultivated land area reaches the theoretical upper limit, the historically reconstructed cultivated land statistical area is updated according to the difference between the sum of the historically reconstructed cultivated land statistical area and the proportion of reconstructed cultivated land area that reaches the theoretical upper limit, until the proportion of each reconstructed cultivated land area does not reach the theoretical upper limit, and the reconstructed cultivated land spatial distribution of the study area is obtained. The reconstructed cultivated land spatial distribution can provide more refined and accurate spatial distribution characteristics and long-term series coverage, and can also achieve reliability consistent with actual survey data.

[0050] At least one embodiment of the present disclosure further provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, a method for reconstructing the spatial distribution of cultivated land according to any embodiment of the present disclosure is implemented.

[0051] The above-mentioned non-transient computer-readable storage medium determines land use data based on remote sensing data including cultivated land distribution, determines the cultivated land distribution range of the study area and the average cultivated land area ratio within the cultivated land distribution range based on the land use data of the study area, and calculates the joint probability index of cultivated land spatial distribution based on natural factor data, further determines the correction coefficient and the reconstructed cultivated land area ratio based on the historically reconstructed cultivated land statistical area and the initial reconstructed cultivated land spatial area, and when the reconstructed cultivated land area ratio reaches the theoretical upper limit, updates the historically reconstructed cultivated land statistical area based on the difference between the sum of the historically reconstructed cultivated land statistical area and the reconstructed cultivated land area ratio that reaches the theoretical upper limit, until each reconstructed cultivated land area ratio does not reach the theoretical upper limit, and obtains the reconstructed cultivated land spatial distribution of the study area. The reconstructed cultivated land spatial distribution can provide more refined and accurate spatial distribution characteristics and long-term series coverage, and can also achieve reliability consistent with actual survey data. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A flow chart of a method for reconstructing data on spatial distribution of cultivated land according to a disclosed embodiment;

[0053] Figure 2A flow chart of a method for reconstructing data on spatial distribution of cultivated land according to a preferred embodiment;

[0054] Figure 3 A module structure diagram of a data reconstruction device for spatial distribution of cultivated land according to a disclosed embodiment;

[0055] Figure 4 A schematic block diagram of a data control device provided for at least one embodiment of the present disclosure;

[0056] Figure 5 A schematic diagram of a non-transitory computer-readable storage medium provided for at least one embodiment of the present disclosure. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution of the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings of the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the described embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0058] Unless otherwise defined, the technical terms or scientific terms used in the present disclosure should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0059] In order to keep the following description of the embodiments of the present disclosure clear and concise, the present disclosure omits detailed descriptions of some known functions and known components.

[0060] The embodiment of the present disclosure provides a method for reconstructing data on spatial distribution of cultivated land.

[0061] Figure 1 FIG. 1 is a flow chart of a method for reconstructing data of spatial distribution of cultivated land according to a disclosed embodiment. Figure 1 As shown, a method for reconstructing data of spatial distribution of cultivated land in a disclosed embodiment includes steps S100 to S106:

[0062] S100, determining the cultivated land distribution range of the study area and the average value of the cultivated land area ratio within the cultivated land distribution range according to the land use data of the study area; wherein the land use data is generated by remote sensing data including cultivated land distribution;

[0063] S101, calculating the probability of natural suitability distribution of cultivated land according to natural factor data, and calculating the joint probability index of cultivated land spatial distribution according to the average value of cultivated land area proportion and the probability of natural suitability distribution of cultivated land;

[0064] S102, calculating the initial reconstructed cultivated land spatial area according to the cultivated land spatial distribution joint probability index;

[0065] S103, collating the statistical data of the cultivated land area in the study area to generate the historical reconstructed cultivated land statistical area;

[0066] S104, determining a correction coefficient for spatial distribution of cultivated land according to a ratio of the initial reconstructed cultivated land spatial area to the historical reconstructed cultivated land statistical area;

[0067] S105, calculating the proportion of reconstructed cultivated land area according to the correction coefficient and the joint probability index of cultivated land spatial distribution;

[0068] S106, when the proportion of the reconstructed cultivated land area reaches the theoretical upper limit value, the historical reconstructed cultivated land statistical area is updated according to the difference between the historical reconstructed cultivated land statistical area and the sum of the areas of the proportions of the reconstructed cultivated land areas that reach the theoretical upper limit value, until the proportions of the reconstructed cultivated land areas do not reach the theoretical upper limit value, thereby obtaining the spatial distribution of the reconstructed cultivated land in the study area.

[0069] The study area is a pre-defined geographical scope, which can be reflected in the remote sensing data in the form of data. The pre-collected remote sensing data corresponds to the geographical scope including the study area and the area outside the study area. The study area can be divided into the cultivated land distribution range and the non-cultivated land distribution range.

[0070] Preferably, multi-source remote sensing data is used to determine land use data, including CULUCC, CCI-LC, CLCD, GlobeLand30, MCD12Q1, etc. A data set is established based on the multi-source remote sensing data, and land use types are reclassified and cultivated land types are extracted to determine land use data related to cultivated land data.

[0071] Preferably, the data set established based on multi-source remote sensing data is pre-processed by spatial registration, data format unification, etc., to facilitate the data processing in step S100. Figure 2 FIG. 1 is a flow chart of a method for reconstructing data of spatial distribution of cultivated land according to a preferred embodiment. Figure 2As shown, the process of determining the cultivated land distribution range of the study area and the average proportion of cultivated land area within the cultivated land distribution range according to the land use data of the study area in step S100 includes steps S200 to S202:

[0072] S200, reclassify the land use types of remote sensing data and extract the cultivated land types, establish mapping units, unify the spatial resolution according to the optimal area principle, and calculate the proportion of cultivated land area in each mapping unit;

[0073] S201, screening the mapping unit according to the proportion of the cultivated land area, and using the pixel range of the screened mapping unit as the cultivated land distribution range of the study area;

[0074] S202, superimposing the cultivated land distribution ranges corresponding to all years as the maximum range of cultivated land distribution in the study area, and calculating the average cultivated land area proportion of all remote sensing data within the cultivated land distribution range of each year.

[0075] As one of the preferred embodiments, the land use data is processed subsequently with the mapping unit as the basic unit. Preferably, the grid pixel with the lowest resolution among all the land use data is selected as the reference of the mapping unit or the requirement of the resolution of the reconstructed forest data is referred to, and the spatial resolution is unified according to the principle of optimal area; for each land use data, the proportion of cultivated land area of ​​each mapping unit is calculated. For example, the resolution of the MCD12Q1 product is selected as a reference to unify the spatial resolution of each land use data; at the same time, for non-annual land use data, the linear prediction method is used for the cultivated land data related to each mapping unit to supplement the missing data in the middle years.

[0076] Preferably, the preprocessed multi-source remote sensing data is subjected to a consistency check for each year and pixel, and pixels with only 0 products or 1 product with a cultivated land area ratio greater than 0 are removed as the distribution range of the multi-source remote sensing data extracted for non-cultivated land, and the remaining pixel range is the cultivated land distribution range of the study area in the corresponding year; the cultivated land distribution ranges of all years are superimposed as the maximum range of cultivated land distribution in the study area; in each year, the average cultivated land area ratio (marked as the cultivated land remote sensing distribution probability R) and the maximum cultivated land area ratio (marked as the maximum cultivated land remote sensing distribution probability R) of all remote sensing data are calculated within the cultivated land distribution range. max ).

[0077] In the embodiment of the present disclosure, the natural factor data is related to the suitability of cultivated land reclamation, and the natural factor data affecting the suitability of cultivated land reclamation are selected. Preferably, the embodiment of the present disclosure selects elevation, slope, light temperature production potential and soil organic matter for normalization processing to determine the corresponding natural factor data, wherein the indicators such as elevation, slope and soil organic matter use the maximum and minimum value normalization function, and the light temperature production potential uses the maximum value normalization function, and the formula is as follows:

[0078] Altitude normalization function: E′=(E max -E) / (E max -E min );

[0079] Slope normalization function: S′=(S max -S) / (S max -S min );

[0080] Normalized function of soil organic matter: O′=(O max -O) / (O max -O min );

[0081] Normalized function of maximum light-temperature production potential: P′=P / P max ;

[0082] Among them, E′, S′, O′, and P′ are the normalized values ​​of elevation, slope, soil organic matter, and light and temperature production potential, respectively; E, S, O, and P are the original values ​​of elevation, slope, soil organic matter, and light and temperature production potential, respectively; E max , S max , O max , P max are the maximum values ​​of elevation, slope, soil organic matter, light and temperature production potential, E min , S min , O min are the minimum values ​​of elevation, slope, and soil organic matter, respectively.

[0083] As one of the preferred embodiments, the process of calculating the probability of natural suitability distribution of cultivated land based on natural factor data is as follows:

[0084] N=w i ×F i ;

[0085] Among them, the natural factor data include multiple natural suitability factors of cultivated land, N is the distribution probability of natural suitability of cultivated land, w i is the weight of the natural suitability factor of cultivated land, w i =1 / n, n is the number of natural suitability factors of cultivated land, F iis the normalized value of the natural suitability factor of cultivated land;

[0086] The process of calculating the joint probability index of cultivated land spatial distribution according to the average cultivated land area ratio and the probability of natural suitability distribution of cultivated land is as follows:

[0087] S y =w y *N+(1-w y )*R;

[0088] Among them, S y is the joint probability index of cultivated land spatial distribution in year y, w y is the joint probability coefficient of year y, N is the probability of natural suitability distribution of cultivated land, and R is the probability of remote sensing distribution of cultivated land.

[0089] Preferably, in the disclosed embodiment, for years with remote sensing data (e.g., after 1985), the spatial distribution of cultivated land is based only on remote sensing probability distribution; before 1985, due to the availability of remote sensing data, S y It is necessary to combine the remote sensing distribution probability R of cultivated land and the natural suitability distribution probability N of cultivated land for calculation. The calculation formula is as follows:

[0090]

[0091] Among them, y s The starting year for the reconstruction of cultivated land history.

[0092] As one of the preferred embodiments, the process of calculating the initial reconstructed cultivated land spatial area according to the cultivated land spatial distribution joint probability index is as follows:

[0093]

[0094] in, is the initial reconstructed cultivated land area, S y is the joint probability index of spatial distribution of cultivated land in year y, and A is the area of ​​the corresponding mapping unit.

[0095] Among them, the historically reconstructed cultivated land statistical area is determined based on various historical statistical data, and data investigation and data summary are carried out for the study area. For example, data on cultivated land area recorded in local chronicles of the historical period and agricultural census, land survey data, and statistical yearbooks of the modern period are collected, as well as cultivated land area inferred based on taxation and other data in historical times, to establish the historical initial cultivated land statistical area data set for the study area at each time point; at the same time, the grain output of the study area after 1949 is collected.

[0096] For the historical initial cultivated land statistical area dataset, the rules are set for different time periods to be tested, corrected and interpolated respectively to obtain the cultivated land area dataset of the study area under a unified standard. For example, the cultivated land data before 1949 are linearly estimated using the cultivated land area at key time nodes; the cultivated land statistical data after 1949 are corrected by different time periods in consideration of the problem of statistical survey data deviation in some time periods. The cultivated land area from 1960 to 1980 is corrected by grain output. The data after 1980 are corrected by using the cultivated land area of ​​the land survey in 2009 and 2019 as the benchmark and the cultivated land change data of other years.

[0097] Specifically, the cultivated land data from 1949 to 1960 directly adopts the statistical survey results; the linear regression relationship is established using the data such as the cultivated land area and grain output from 1949 to 1960 to reconstruct the cultivated land area from 1960 to 1980; the annual change in cultivated land area from 1980 to 2009 is calculated, and the annual cultivated land area from 1980 to 2009 is calculated based on the cultivated land area of ​​the second national land survey in 2009; the change rate of cultivated land area from 2009 to 2019 is calculated, and the annual cultivated land area from 2009 to 2019 is calculated based on the cultivated land area of ​​the two national land surveys in 2009 and 2019. After 2020, the cultivated land data shall be based on the land survey results.

[0098] The processing of the historical initial cultivated land statistical area data set adjusted the distortion and deviation in the statistics. Based on the year of accurate data and combined with the information of year-on-year changes, the above rules were set for correction.

[0099] Based on this, the historical reconstruction of cultivated land statistical area is generated according to the statistical data of cultivated land area in the long time series of the study area. where y represents the year of historical reconstruction.

[0100] As one of the preferred embodiments, the correction coefficient of the spatial distribution of cultivated land is determined according to the ratio of the spatial area of ​​the initially reconstructed cultivated land to the statistical area of ​​the historically reconstructed cultivated land, as follows:

[0101]

[0102] Where, represents the correction coefficient for year y, represents the historical reconstructed cultivated land statistical area in year y, Represents the initial reconstructed cultivated land space area.

[0103] As one of the embodiments, in the years with remote sensing data, the process of determining the theoretical upper limit value includes the steps of:

[0104] The maximum value of the proportion of cultivated land area within the cultivated land distribution range is determined according to the land use data of the study area, and the maximum value of the proportion of cultivated land area is taken as the theoretical upper limit.

[0105] That is, the upper limit of the cultivated land area of ​​each mapping unit in year y is calculated. Specifically, for the years with remote sensing data (after 1985 in the disclosed embodiment), the maximum cultivated land area ratio (maximum probability of cultivated land remote sensing distribution R max ) as the theoretical upper limit value of each mapping unit.

[0106] In the disclosed embodiment, in the years without remote sensing data, the non-desert land area ratio of the mapping unit is used as a constraint, and the upper limit of the cultivated land area ratio in the mapping unit is set to 0.95. The minimum value of the two is taken as the theoretical upper limit value of each mapping unit. Among them, 0.95 is a preferred value and does not represent a unique limitation.

[0107] As one of the preferred embodiments, the process of calculating the proportion of reconstructed cultivated land area according to the correction coefficient and the joint probability index of the cultivated land spatial distribution is as follows:

[0108] A′ y =α y ×S y ;

[0109] Among them, A′ y is the proportion of rebuilt cultivated land area in year y, S y is the joint probability index of cultivated land spatial distribution in year y, α y is the correction factor for year y.

[0110] When the proportion of reconstructed cultivated land area reaches the theoretical upper limit, the historical reconstructed cultivated land statistical area is updated according to the difference between the historical reconstructed cultivated land statistical area and the sum of the areas of the reconstructed cultivated land areas that reach the theoretical upper limit, until the proportions of the reconstructed cultivated land areas do not reach the theoretical upper limit, so as to obtain the spatial distribution of the reconstructed cultivated land in the study area.

[0111] The spatial distribution of cultivated land is determined by the proportion of reconstructed cultivated land area in the mapping unit of each year. y If the value exceeds the theoretical upper limit, it is assigned the theoretical upper limit value in the corresponding year. On this basis, the maximum range of cultivated land distribution in the study area is divided into A′ y Reach the upper limit and A′ y The two parts that do not reach the upper limit, the mapping units that reach the upper limit will no longer participate in the calculation, and the historical reconstruction of the cultivated land statistical area Subtract the sum of the cultivated land areas of all mapping units that have reached the upper limit, and use it as the historical reconstructed cultivated land statistical area in the next step of the correction process, that is, the corrected The mapping units that do not reach the upper limit enter the next step of the process, repeat the steps of the embodiment of the present disclosure, and perform the above-mentioned repeated calculation steps of the correction coefficient of the spatial distribution of cultivated land and the proportion of cultivated land area in each mapping unit. y When all of them do not exceed the theoretical upper limit, the steps are repeated to end, and the reconstructed spatial distribution of cultivated land in the study area in year y is obtained. Perform the above operations to complete the reconstructed spatial distribution dataset of cultivated land in all years.

[0112] The disclosed embodiment extracts the spatial distribution information provided by remote sensing inversion means and the time series change information of ground surveys, and integrates the above information to effectively overcome the problems of spatial deviation and insufficient time coverage of existing methods. At the same time, based on the key step of time series correction of multi-source cultivated land statistical survey data, the cultivated land statistical survey data of key nodes and the cultivated land change survey data of different survey sources are effectively integrated, thereby establishing a unified long-term series data of cultivated land statistical area. In summary, the spatial distribution basis of cultivated land, the statistical area allocation rules and the calculation process are established, and the reconstructed spatial distribution data of cultivated land can not only provide more refined and accurate spatial distribution characteristics and long-term series coverage, but also achieve reliability consistent with ground survey data.

[0113] The disclosed embodiment also provides a device for reconstructing data of spatial distribution of cultivated land.

[0114] Figure 3 FIG. 1 is a module structure diagram of a data reconstruction device for spatial distribution of cultivated land according to a disclosed embodiment. Figure 3 As shown, a data reconstruction device for spatial distribution of cultivated land according to one embodiment includes:

[0115] The distribution determination module 100 is used to determine the cultivated land distribution range of the study area and the average value of the cultivated land area ratio within the cultivated land distribution range according to the land use data of the study area; wherein the land use data is generated by remote sensing data including cultivated land distribution;

[0116] The index calculation module 101 is used to calculate the probability of natural suitability distribution of cultivated land according to the natural factor data, and calculate the joint probability index of cultivated land spatial distribution according to the average value of cultivated land area proportion and the probability of natural suitability distribution of cultivated land;

[0117] An area initialization module 102 is used to calculate the initial reconstructed cultivated land spatial area according to the cultivated land spatial distribution joint probability index;

[0118] The area reconstruction module 103 is used to sort out the statistical data of the cultivated land area in the study area and generate the historical reconstructed cultivated land statistical area;

[0119] A coefficient calculation module 104, used to determine a correction coefficient of the spatial distribution of cultivated land according to the ratio of the initial reconstructed cultivated land spatial area to the historical reconstructed cultivated land statistical area;

[0120] A proportion calculation module 105 is used to calculate the proportion of the reconstructed cultivated land area according to the correction coefficient and the joint probability index of the cultivated land spatial distribution;

[0121] The data reconstruction module 106 is used to update the historical reconstructed cultivated land statistical area according to the difference between the historical reconstructed cultivated land statistical area and the area ratio of the reconstructed cultivated land area that reaches the theoretical upper limit value when the proportion of the reconstructed cultivated land area reaches the theoretical upper limit value, until the proportion of each reconstructed cultivated land area does not reach the theoretical upper limit value, thereby obtaining the reconstructed cultivated land spatial distribution of the study area.

[0122] The data reconstruction device of the spatial distribution of cultivated land of the disclosed embodiment determines land use data based on remote sensing data including cultivated land distribution, determines the cultivated land distribution range of the study area and the average proportion of cultivated land area within the cultivated land distribution range according to the land use data of the study area, and calculates the joint probability index of the spatial distribution of cultivated land according to the natural factor data, further determines the correction coefficient and the proportion of reconstructed cultivated land area based on the historically reconstructed cultivated land statistical area and the initial reconstructed cultivated land spatial area, and when the proportion of reconstructed cultivated land area reaches the theoretical upper limit, updates the historically reconstructed cultivated land statistical area according to the difference between the sum of the historically reconstructed cultivated land statistical area and the proportion of reconstructed cultivated land area that reaches the theoretical upper limit, until each proportion of reconstructed cultivated land area does not reach the theoretical upper limit, and obtains the reconstructed spatial distribution of cultivated land in the study area. The reconstructed spatial distribution of cultivated land can provide more refined and accurate spatial distribution characteristics and long-term series coverage, and can also achieve reliability consistent with actual survey data.

[0123] At least one embodiment of the present disclosure further provides a data control device. Figure 4 A schematic block diagram of a data control device provided by at least one embodiment of the present disclosure. Figure 4 As shown, the data control device 20 may include one or more memories 200 and one or more processors 201. The memory 200 is used to store computer executable instructions non-transiently; the processor 201 is used to run the computer executable instructions, and when the computer executable instructions are run by the processor 201, the processor 201 can execute one or more steps in the data reconstruction method of the spatial distribution of cultivated land according to any embodiment of the present disclosure.

[0124] The specific implementation and related explanation of each step of the method for reconstructing the spatial distribution of cultivated land can be found in the above-mentioned embodiment of the method for reconstructing the spatial distribution of cultivated land, and will not be elaborated here. Figure 4The components of the data control device 20 shown are merely exemplary and non-limiting. The data control device 20 may also have other components according to actual application requirements.

[0125] In one embodiment, the processor 201 and the memory 200 can communicate with each other directly or indirectly. For example, the processor 201 and the memory 200 can communicate via a network connection. The network may include a wireless network, a wired network, and / or any combination of a wireless network and a wired network, and the present disclosure does not limit the type and function of the network. For another example, the processor 201 and the memory 200 can also communicate via a bus connection. The bus may be a peripheral component interconnect standard (PCI) bus or an extended industrial standard architecture (EISA) bus, etc. For example, the processor 201 and the memory 200 may be arranged at a remote data server end (cloud) or a distributed energy system end (local end), or may be arranged at a client end (e.g., a mobile device such as a mobile phone). For example, the processor 201 may be a device having data processing capability and / or instruction execution capability such as a central processing unit (CPU), a tensor processor (TPU), or a graphics processor GPU, and may control other components in the data prediction device 20 to perform desired functions. The central processing unit (CPU) may be an X86 or ARM architecture, etc.

[0126] In one embodiment, the memory 200 may include any combination of one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, erasable programmable read-only memory (EPROM), portable compact disk read-only memory (CD-ROM), USB memory, flash memory, etc. One or more computer-executable instructions may be stored on the computer-readable storage medium, and the processor 201 may execute the computer-executable instructions to implement various functions of the data prediction device 20. Various applications and various data, as well as various data used and / or generated by the application, etc. may also be stored in the memory 200.

[0127] It should be noted that the data control device 20 can achieve technical effects similar to those of the aforementioned method for reconstructing spatial distribution of cultivated land, and the repeated parts will not be repeated.

[0128] At least one embodiment of the present disclosure also provides a non-transitory computer-readable storage medium. Figure 5 A schematic diagram of a non-transitory computer-readable storage medium provided for at least one embodiment of the present disclosure. Figure 5As shown, one or more computer executable instructions 301 may be non-transitory stored on a non-transitory computer readable storage medium 30. For example, when the computer executable instructions 301 are executed by a computer, the computer may execute one or more steps in the method for reconstructing the spatial distribution of cultivated land according to any embodiment of the present disclosure.

[0129] In one embodiment, the non-transitory computer-readable storage medium 30 may be applied to the above-mentioned data control device 20 , for example, it may be the memory 200 in the data control device 20 .

[0130] In one embodiment, the description of the non-transitory computer-readable storage medium 30 may refer to the description of the memory 200 in the embodiment of the data control device 20, and the repeated parts will not be repeated.

[0131] It should be noted that the memory 200 stores different non-transiently stored computer executable instructions, and the data control device 20 corresponds to a firmware upgrade device. When the computer executable instructions are executed by the processor 201, the processor 201 can execute one or more steps in the data reconstruction method of the spatial distribution of cultivated land according to any embodiment of the present disclosure.

[0132] There are a few points to note about this disclosure:

[0133] (1) The drawings of the embodiments of the present disclosure only relate to the structures related to the embodiments of the present disclosure, and other structures may refer to the general design.

[0134] (2) For the sake of clarity, the thickness and size of layers or structures are exaggerated in the drawings used to describe the embodiments of the present invention. It is understood that when an element such as a layer, film, region, or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element, or there may be intervening elements.

[0135] (3) In the absence of conflict, the embodiments of the present disclosure and the features in the embodiments may be combined with each other to obtain new embodiments. The above are only specific implementations of the present disclosure, but the protection scope of the present disclosure is not limited thereto. The protection scope of the present disclosure shall be subject to the protection scope of the claims.

[0136] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0137] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent application shall be subject to the attached claims.

Claims

1. A method for reconstructing spatial distribution data of cultivated land, characterized in that: Includes steps: Determine the cultivated land distribution range of the study area and the average value of the cultivated land area ratio within the cultivated land distribution range according to the land use data of the study area; wherein the land use data is generated by remote sensing data including cultivated land distribution; Calculate the probability of natural suitability distribution of cultivated land according to the natural factor data, and calculate the joint probability index of cultivated land spatial distribution according to the average value of cultivated land area proportion and the probability of natural suitability distribution of cultivated land; Calculate the initial reconstructed cultivated land spatial area according to the joint probability index of cultivated land spatial distribution; Arrange the statistical data of cultivated land area in the study area to generate historically reconstructed cultivated land statistical area; Determine a correction coefficient for spatial distribution of cultivated land according to the ratio of the spatial area of ​​the initially reconstructed cultivated land to the statistical area of ​​the historically reconstructed cultivated land; Calculating the proportion of reconstructed cultivated land area according to the correction coefficient and the joint probability index of cultivated land spatial distribution; When the proportion of the reconstructed cultivated land area reaches the theoretical upper limit, the historical reconstructed cultivated land statistical area is updated according to the difference between the historical reconstructed cultivated land statistical area and the sum of the areas of the reconstructed cultivated land areas that reach the theoretical upper limit, until the proportions of the reconstructed cultivated land areas do not reach the theoretical upper limit, so as to obtain the spatial distribution of the reconstructed cultivated land in the study area.

2. The method for reconstructing the spatial distribution of cultivated land according to claim 1, characterized in that: The process of determining the cultivated land distribution range of the study area and the average proportion of cultivated land area within the cultivated land distribution range according to the land use data of the study area comprises the steps of: Reclassify the land use types of remote sensing data and extract the types of cultivated land, establish mapping units, unify the spatial resolution according to the optimal area principle, and calculate the proportion of cultivated land area in each mapping unit; The mapping unit is selected according to the proportion of the cultivated land area, and the pixel range of the selected mapping unit is used as the cultivated land distribution range of the study area; The cultivated land distribution ranges corresponding to all years were superimposed as the maximum range of cultivated land distribution in the study area, and the average cultivated land area proportion of all remote sensing data was calculated within the cultivated land distribution range of each year.

3. The method for reconstructing the spatial distribution of cultivated land according to claim 1, characterized in that: The process of calculating the probability of natural suitability distribution of cultivated land based on natural factor data is as follows: N=w i ×F i ; Among them, the natural factor data include multiple natural suitability factors of cultivated land, N is the distribution probability of natural suitability of cultivated land, w i is the weight of the natural suitability factor of cultivated land, w i =1 / n, n is the number of natural suitability factors of cultivated land, F i is the normalized value of the natural suitability factor of cultivated land; The process of calculating the joint probability index of cultivated land spatial distribution according to the average cultivated land area ratio and the probability of natural suitability distribution of cultivated land is as follows: S y =w y *N+(1-w y )*R; Among them, S y is the joint probability index of cultivated land spatial distribution in year y, w y is the joint probability coefficient of year y, N is the probability of natural suitability distribution of cultivated land, and R is the probability of remote sensing distribution of cultivated land.

4. The method for reconstructing the spatial distribution of cultivated land according to claim 1, characterized in that: The process of calculating the initial reconstructed cultivated land spatial area according to the cultivated land spatial distribution joint probability index is as follows: in, is the initial reconstructed cultivated land area, S y is the joint probability index of spatial distribution of cultivated land in year y, and A is the area of ​​the corresponding mapping unit.

5. The method for reconstructing the spatial distribution of cultivated land according to claim 1, characterized in that: The correction coefficient of the spatial distribution of cultivated land is determined according to the ratio of the spatial area of ​​the initial reconstructed cultivated land to the statistical area of ​​the historical reconstructed cultivated land, as follows: Where, represents the correction coefficient for year y, represents the historical reconstructed cultivated land statistical area in year y, Represents the initial reconstructed cultivated land space area.

6. The method for reconstructing the spatial distribution of cultivated land according to claim 1, characterized in that: The process of calculating the proportion of reconstructed cultivated land area of ​​each mapping unit according to the correction coefficient and the joint probability index of cultivated land spatial distribution is as follows: A′ y =a y ×S y ; Among them, A′ y is the proportion of rebuilt cultivated land area in year y, S y is the joint probability index of cultivated land spatial distribution in year y, α y is the correction factor for year y.

7. The method for reconstructing the spatial distribution of cultivated land according to claim 1, characterized in that: The process of determining the theoretical upper limit value comprises the steps of: The maximum value of the proportion of cultivated land area within the cultivated land distribution range is determined according to the land use data of the study area, and the maximum value of the proportion of cultivated land area is taken as the theoretical upper limit.

8. A data reconstruction device for spatial distribution of cultivated land, characterized in that: include: A distribution determination module, used to determine the cultivated land distribution range of the study area and the average value of the cultivated land area ratio within the cultivated land distribution range according to the land use data of the study area; wherein the land use data is generated by remote sensing data including cultivated land distribution; An index calculation module, used to calculate the probability of natural suitability distribution of cultivated land according to natural factor data, and to calculate the joint probability index of spatial distribution of cultivated land according to the average value of cultivated land area proportion and the probability of natural suitability distribution of cultivated land; An area initialization module, used for calculating the initial reconstructed cultivated land spatial area according to the cultivated land spatial distribution joint probability index; An area reconstruction module is used to sort out the statistical data of the cultivated land area in the study area and generate historically reconstructed cultivated land statistical areas; A coefficient calculation module, used to determine the correction coefficient of the spatial distribution of cultivated land according to the ratio of the initial reconstructed cultivated land spatial area to the historical reconstructed cultivated land statistical area; A proportion calculation module, used to calculate the proportion of reconstructed cultivated land area according to the correction coefficient and the joint probability index of the cultivated land spatial distribution; A data reconstruction module is used to update the historical reconstructed cultivated land statistical area according to the difference between the historical reconstructed cultivated land statistical area and the area ratio of the reconstructed cultivated land area that reaches the theoretical upper limit when the proportion of the reconstructed cultivated land area reaches the theoretical upper limit, until the proportion of each reconstructed cultivated land area does not reach the theoretical upper limit, so as to obtain the spatial distribution of the reconstructed cultivated land in the study area.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the method for reconstructing the spatial distribution of cultivated land as described in any one of claims 1 to 7 is implemented.

10. A data control device, characterized in that: include: one or more memories non-transitorily storing computer-executable instructions; One or more processors are configured to run computer executable instructions, wherein the computer executable instructions, when executed by the one or more processors, implement the method for reconstructing the spatial distribution of cultivated land as described in any one of claims 1 to 7.

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