Data reconstruction method and device for spatial distribution of cultivated land

Abstract

The present application relates to a method and device for data reconstruction of arable land spatial distribution, belonging to the technical field of arable land data processing, and solves the problem in the prior art that there is a large deviation between the arable land spatial distribution data and the actual situation, resulting in data distortion. For the method of data reconstruction of arable land spatial distribution, when the proportion of the reconstructed arable land area reaches the theoretical upper limit value, the historical reconstructed arable land statistical area is updated according to the difference between the historical reconstructed arable land statistical area and the sum of the area of the reconstructed arable land area proportion reaching the theoretical upper limit value, until the proportion of the reconstructed arable land area in each unit does not reach the theoretical upper limit value, and the reconstructed arable land spatial distribution of the study area is obtained. The present application can not only provide more refined and accurate spatial distribution characteristics and long-time series coverage, but also achieve the reliability consistent with the actual survey data.

Description

Technical Field

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

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

[0003] Existing data processing methods for quantifying the spatio-temporal changes of cultivated land include remote sensing inversion means and statistical inference means. Among these, the remote sensing inversion means provides a more refined spatial distribution of cultivated land, but there is a problem of insufficient coverage period in the cultivated land data reconstructed based on the remote sensing inversion means. At the same time, due to the problems of the spatial resolution of remote sensing data and the inversion accuracy of images in the remote sensing means, the cultivated land area reconstructed by it often has an obvious deviation from the results of ground surveys. For example, due to the problem of the spatial resolution of remote sensing images, it is difficult to accurately extract ridges, ditches, etc., resulting in their being included in the reconstructed cultivated land scope, causing an overestimation of the reconstructed cultivated land area.

[0004] On the contrary, the statistical inference means is based on historical statistics or materials such as official documents, local chronicles, and tax levies to conduct time series of cultivated land statistical data. The cultivated land data reconstructed by it can cover hundreds of years of history, but the spatial distribution characteristics provided by it come from spatial inference methods and have great uncertainties. However, the existing statistical inference methods only conduct spatial inference and prediction based on natural factors such as temperature, terrain, and soil suitability of cultivated land. Many factors such as historical population demand, skill ability, land annexation, and policy adjustment may lead to significant changes in the spatial distribution of cultivated land. Therefore, the cultivated land reconstruction method based on spatial statistical inference inevitably has a deviation from the actual spatial distribution of cultivated land; moreover, due to the differences in statistical means in different periods, some cultivated land survey data also have the situation of distortion and mutation.

[0005] In summary, the cultivated land spatial distribution data determined by the existing data processing methods of cultivated land spatial distribution data has a large deviation from the actual situation, resulting in data distortion. Summary of the Invention

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

[0007] Embodiments of the present disclosure provide a method for data reconstruction of the spatial distribution of cultivated land, including the steps of:

[0008] Determine the cultivated land distribution range of the research area and the average value of the proportion of cultivated land area within the cultivated land distribution range based on the land use data of the research area; wherein, the land use data is generated from remote sensing data including cultivated land distribution.

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

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

[0011] Sort out the statistical data of the cultivated land area in the research area to generate the historical reconstructed cultivated land statistical area.

[0012] Determine the correction coefficient of cultivated land spatial distribution according to the ratio of the initial reconstructed cultivated land spatial area to the historical reconstructed cultivated land statistical area.

[0013] Calculate 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 reconstructed cultivated land area reaches the theoretical upper limit value, update the historical reconstructed cultivated land statistical area according to the difference between the historical reconstructed cultivated land statistical area and the sum of the areas corresponding to the proportion of reconstructed cultivated land area reaching the theoretical upper limit value, until the proportion of each reconstructed cultivated land area does not reach the theoretical upper limit value, so as to obtain the reconstructed cultivated land spatial distribution of the research area.

[0015] The data reconstruction method for the cultivated land spatial distribution in the embodiments of the present disclosure determines land use data based on remote sensing data including cultivated land distribution, determines the cultivated land distribution range of the research area and the average value of the proportion of cultivated land area within the cultivated land distribution range according to the land use data of the research area, calculates the joint probability index of cultivated land spatial distribution according to natural factor data at the same time, further determines the correction coefficient and the proportion of reconstructed cultivated land area based on the historical 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 value, update the historical reconstructed cultivated land statistical area according to the difference between the historical reconstructed cultivated land statistical area and the sum of the areas corresponding to the proportion of reconstructed cultivated land area reaching the theoretical upper limit value, until the proportion of each reconstructed cultivated land area does not reach the theoretical upper limit value, so as to obtain the reconstructed cultivated land spatial distribution of the research area. The reconstructed cultivated land spatial distribution can not only provide more refined and accurate spatial distribution characteristics and long-term coverage, but 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 research area and the average value of the proportion of cultivated land area within the cultivated land distribution range according to the land use data of the research area includes the steps:

[0017] Perform reclassification of land use types and extraction of cultivated land types from remote sensing data, establish mapping units, and select the raster pixel with the lowest resolution among them as the reference of the mapping unit. According to the optimal area principle, unify the spatial resolution and calculate the proportion of cultivated land area in each mapping unit;

[0018] Screen the mapping units according to the proportion of cultivated land area, and use the pixel range of the screened mapping units as the cultivated land distribution range of the research area;

[0019] Overlay the cultivated land distribution ranges corresponding to all years as the maximum range of cultivated land distribution in the research area, and calculate the average value of the proportion of cultivated land area of all remote sensing data within the cultivated land distribution range of each year.

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

[0021] N = w i ×F i ;

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

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

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

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

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

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

[0030]

[0031] Among them, α y represents the correction coefficient in year y, represents the historical reconstructed arable land statistical area in year y, represents the initial reconstructed arable land spatial area.

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

[0033] A′ y =α y ×S y ;

[0034] Among them, A′ y is the proportion of reconstructed arable land area in year y, S y is the joint probability index of arable land spatial distribution in year y, and α y is the correction coefficient in year y.

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

[0036] Determine the maximum proportion of arable land area within the arable land distribution range according to the land use data of the study area, and use the maximum proportion of arable land area as the theoretical upper limit value.

[0037] The embodiments of the present disclosure also provide a data reconstruction device for arable land spatial distribution, including:

[0038] A distribution determination module, configured to determine the arable land distribution range of the study area and the average value of the proportion of arable land area within the arable land distribution range according to the land use data of the study area; wherein, the land use data is generated from remote sensing data including arable land distribution;

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

[0040] An area initialization module, configured to calculate the initial reconstructed cultivated land spatial area according to the joint probability index of cultivated land spatial distribution;

[0041] An area reconstruction module, configured to collate the statistical data of the cultivated land area in the research area and generate the historical reconstructed cultivated land statistical area;

[0042] A coefficient calculation module, configured to determine the correction coefficient of cultivated land spatial distribution 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, configured to calculate the proportion of the reconstructed cultivated land area according to the correction coefficient and the joint probability index of cultivated land spatial distribution;

[0044] A data reconstruction module, configured to, when the proportion of the reconstructed cultivated land area reaches the theoretical upper limit value, update the historical reconstructed cultivated land statistical area according to the difference between the historical reconstructed cultivated land statistical area and the sum of the areas of the reconstructed cultivated land area reaching the theoretical upper limit value, until the proportions of all the reconstructed cultivated land areas do not reach the theoretical upper limit value, so as to obtain the reconstructed cultivated land spatial distribution of the research area.

[0045] The data reconstruction device for the cultivated land spatial distribution according to the embodiments of the present disclosure determines land use data based on remote sensing data including cultivated land distribution, determines the cultivated land distribution range in the research area and the average value of the cultivated land area proportion within the cultivated land distribution range according to the land use data of the research area, calculates the joint probability index of cultivated land spatial distribution according to natural factor data at the same time, further determines the correction coefficient and the proportion of the reconstructed cultivated land area based on the historical reconstructed cultivated land statistical area and the initial reconstructed cultivated land spatial area, and when the proportion of the reconstructed cultivated land area reaches the theoretical upper limit value, updates the historical reconstructed cultivated land statistical area according to the difference between the historical reconstructed cultivated land statistical area and the sum of the areas of the reconstructed cultivated land area reaching the theoretical upper limit value, until the proportions of all the reconstructed cultivated land areas do not reach the theoretical upper limit value, so as to obtain the reconstructed cultivated land spatial distribution of the research area. The reconstructed cultivated land spatial distribution can not only provide finer and more accurate spatial distribution characteristics and long-time series coverage, but 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, storing computer-executable instructions non-transiently;

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

[0049] The above data control device determines land use data based on remote sensing data including arable land distribution, determines the arable land distribution range of the study area and the average value of the proportion of arable land area within the arable land distribution range according to the land use data of the study area, calculates the joint probability index of arable land spatial distribution according to natural factor data at the same time, further determines the correction coefficient and the proportion of reconstructed arable land area based on the historical reconstructed arable land statistical area and the initial reconstructed arable land spatial area, when the proportion of reconstructed arable land area reaches the theoretical upper limit value, updates the historical reconstructed arable land statistical area according to the difference between the historical reconstructed arable land statistical area and the sum of the areas of the proportion of reconstructed arable land area reaching the theoretical upper limit value, until the proportion of each reconstructed arable land area does not reach the theoretical upper limit value, and obtains the reconstructed arable land spatial distribution of the study area. The reconstructed arable land spatial distribution can not only provide more refined and accurate spatial distribution characteristics and long-time series coverage, but also achieve the reliability consistent with the 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 data reconstruction method for the arable land spatial distribution according to any embodiment of the present disclosure is implemented.

[0051] The above non-transitory computer-readable storage medium determines land use data based on remote sensing data including arable land distribution, determines the arable land distribution range of the study area and the average value of the proportion of arable land area within the arable land distribution range according to the land use data of the study area, calculates the joint probability index of arable land spatial distribution according to natural factor data at the same time, further determines the correction coefficient and the proportion of reconstructed arable land area based on the historical reconstructed arable land statistical area and the initial reconstructed arable land spatial area, when the proportion of reconstructed arable land area reaches the theoretical upper limit value, updates the historical reconstructed arable land statistical area according to the difference between the historical reconstructed arable land statistical area and the sum of the areas of the proportion of reconstructed arable land area reaching the theoretical upper limit value, until the proportion of each reconstructed arable land area does not reach the theoretical upper limit value, and obtains the reconstructed arable land spatial distribution of the study area. The reconstructed arable land spatial distribution can not only provide more refined and accurate spatial distribution characteristics and long-time series coverage, but also achieve the reliability consistent with the actual survey data. Description of the Drawings

[0052] Figure 1 It is a flowchart of the data reconstruction method for the arable land spatial distribution of an embodiment of the present disclosure;

[0053] Figure 2Flowchart of a data reconstruction method for the spatial distribution of cultivated land in a preferred embodiment;

[0054] Figure 3 Structural diagram of a data reconstruction device module for the spatial distribution of cultivated land in a disclosed embodiment;

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

[0056] Figure 5 Schematic diagram of a non-transitory computer-readable storage medium provided by at least one embodiment of the present disclosure. Detailed implementation manners

[0057] In order to make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present disclosure. Apparently, the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the described embodiments of the present disclosure without creative efforts shall fall within the scope of protection of the present disclosure.

[0058] Unless otherwise defined, the technical terms or scientific terms used in the present disclosure shall have the ordinary meanings understood by those of ordinary skill in the art to which the present disclosure pertains. The "first", "second", and similar terms used in the present disclosure do not denote any order, quantity, or importance, but are only used to distinguish different components. The terms such as "including" or "comprising" mean that the elements or objects appearing before this term cover the elements or objects listed after this term and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", and "right" are only used to represent relative positional relationships, and when the absolute position of the object being described 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, some details of known functions and known components are omitted in the present disclosure.

[0060] The embodiments of the present disclosure provide a data reconstruction method for the spatial distribution of cultivated land.

[0061] Figure 1 Flowchart of a data reconstruction method for the spatial distribution of cultivated land in a disclosed embodiment, as Figure 1 shown, the data reconstruction method for the spatial distribution of cultivated land in a disclosed embodiment includes steps S100 to S106:

[0062] S100. Determine the cultivated land distribution range of the study area and the average proportion of the cultivated land area within the cultivated land distribution range based on the land use data of the study area; wherein, the land use data is generated from remote sensing data including cultivated land distribution.

[0063] S101. Calculate the natural suitability distribution probability of cultivated land based on natural factor data, and calculate the joint probability index of cultivated land spatial distribution according to the average proportion of the cultivated land area and the natural suitability distribution probability of cultivated land.

[0064] S102. Calculate the initial reconstructed cultivated land spatial area according to the joint probability index of cultivated land spatial distribution.

[0065] S103. Sort out the statistical data of the cultivated land area in the study area to generate the historical reconstructed cultivated land statistical area.

[0066] S104. Determine the correction coefficient of cultivated land spatial distribution according to the ratio of the initial reconstructed cultivated land spatial area to the historical reconstructed cultivated land statistical area.

[0067] S105. Calculate the proportion of the 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, update the historical reconstructed cultivated land statistical area according to the difference between the historical reconstructed cultivated land statistical area and the sum of the areas of the reconstructed cultivated land area reaching the theoretical upper limit value until the proportion of each reconstructed cultivated land area does not reach the theoretical upper limit value, so as to obtain the reconstructed cultivated land spatial distribution of the study area.

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

[0070] Preferably, multi-source remote sensing data is used to determine the land use data, including land use data such as CULUCC, CCI-LC, CLCD, GlobeLand30, and MCD12Q1. A data set is established based on the multi-source remote sensing data, and land use type reclassification and extraction of cultivated land types are carried out to determine the land use data related to cultivated land data.

[0071] Preferably, preprocessing such as spatial registration and data format unification is carried out on the data set established based on multi-source remote sensing data to facilitate the data processing in step S100. Figure 2 It is a flowchart of a data reconstruction method for the spatial distribution of cultivated land in a preferred embodiment, as Figure 2As shown, the process of determining the cultivated land distribution range of the study area and the average value of the proportion of cultivated land area within the cultivated land distribution range 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. Screen the mapping units according to the proportion of cultivated land area, and use the pixel range of the screened mapping units as the cultivated land distribution range of the study area;

[0074] S202. Overlay the cultivated land distribution ranges corresponding to all years as the maximum range of the cultivated land distribution in the study area, and calculate the average value of the proportion of cultivated land area 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 used as the basic unit of the mapping unit for subsequent data processing. Preferably, the raster pixel with the lowest resolution among all land use data is selected as the reference of the mapping unit or the reference study for the demand of reconstructing forest data resolution, and the spatial resolution is unified according to the optimal area principle; for each land use data, calculate the proportion of cultivated land area of each mapping unit one by one. For example, select the resolution of the MCD12Q1 product as the reference to unify the spatial resolution of each land use data; at the same time, for non-yearly land use data, use the linear prediction method to supplement the missing data in the intermediate years for the cultivated land data related to each mapping unit.

[0076] Preferably, conduct a consistency test for each pixel of the preprocessed multi-source remote sensing data year by year, and eliminate the pixels with the proportion of cultivated land area > 0 in only 0 products or 1 product as the distribution range of the multi-source remote sensing data extracted as non-cultivated land, and the remaining pixel range is the cultivated land distribution range of the study area corresponding to the year; overlay the cultivated land distribution ranges of all years as the maximum range of the cultivated land distribution in the study area; in each year, calculate the average value of the proportion of cultivated land area of all remote sensing data within the cultivated land distribution range (marked as the cultivated land remote sensing distribution probability R) and the maximum value of the proportion of cultivated land area (marked as the maximum cultivated land remote sensing distribution probability R max )

[0077] In the embodiments 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 is selected. Preferably, in the embodiments of the present disclosure, elevation, slope, light and temperature production potential, soil organic matter, etc. are selected for normalization processing to determine the corresponding natural factor data. Among them, indicators such as altitude, slope, and soil organic matter use the maximum-minimum normalization function, and the light and temperature production potential uses the maximum normalization function. The formulas are 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] Soil organic matter normalization function: O′ = (O max - O) / (O max - O min );

[0081] Light and temperature production potential maximum normalization function: P′ = P / P max ;

[0082] Among them, E′, S′, O′, P′ are the normalized elevation, slope, soil organic matter, and light and temperature production potential values respectively, E, S, O, P are the original elevation, slope, soil organic matter, and light and temperature production potential values respectively, E max , S max , O max , P max are the maximum values of elevation, slope, soil organic matter, and light and temperature production potential respectively, 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 natural suitability distribution probability of cultivated land according to the natural factor data is as follows:

[0084] N = w i × F i ;

[0085] Among them, the natural factor data includes multiple natural suitability factors of cultivated land, N is the natural suitability distribution probability 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 value of the proportion of cultivated land area and the natural suitability distribution probability of cultivated land is as follows:

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

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

[0089] Preferably, in the embodiments of the present disclosure, in the years with remote sensing data (such as after 1985), the cultivated land spatial distribution is only based on the remote sensing probability distribution; before 1985, limited by the availability of remote sensing data, S y needs to be calculated by combining the remote sensing distribution probability R of cultivated land and the natural suitability distribution probability N of cultivated land, and the calculation formula is as follows:

[0090]

[0091] Wherein, y s is the starting year of cultivated land historical reconstruction.

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

[0093]

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

[0095] Among them, the historical reconstructed cultivated land statistical area is determined based on various historical statistical data. Data research and induction are carried out for the study area, such as collecting the cultivated land area record data in the local chronicles in the historical period of the study area and the agricultural census, land survey data, and statistical yearbooks in the modern period, as well as the cultivated land area inferred based on materials such as taxes in historical times, and establishing a dataset of the historical initial cultivated land statistical area of the study area at each time point; at the same time, collect the grain output of the study area after 1949.

[0096] For the historical initial cultivated land statistical area dataset, set rules for different time periods to conduct inspections, corrections, and interpolations respectively to obtain the cultivated land area dataset of the study area under unified standards. For example, for the cultivated land data before 1949, linear extrapolation is carried out using the cultivated land area at key time nodes; for the cultivated land statistical data after 1949, considering the problem of statistical survey data deviation in some time periods, the cultivated land area is corrected by time period. For the cultivated land area from 1960 to 1980, it is corrected using grain output. For the data after 1980, the cultivated land area from the national land surveys in 2009 and 2019 is used as the benchmark and the cultivated land change data for the remaining years is used for correction.

[0097] Specifically, for the cultivated land data from 1949 to 1960, the statistical survey results are directly adopted; a linear regression relationship is established using the cultivated land area and grain output data from 1949 to 1960 to reconstruct the cultivated land area from 1960 to 1980; calculate the annual cultivated land change area from 1980 to 2009, and based on the cultivated land area from the second national land survey in 2009, calculate the annual cultivated land area from 1980 to 2009; calculate the cultivated land area change rate from 2009 to 2019, and based on the cultivated land areas from the two national land surveys in 2009 and 2019, calculate the annual cultivated land area from 2009 to 2019. After 2020, the cultivated land data is subject to the land survey results.

[0098] The processing of the historical initial cultivated land statistical area dataset adjusts the distortion and deviation existing in the statistics. Based on the years with accurate data and combined with the data changing year by year, the above rules are set for correction.

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

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

[0101]

[0102] where, α y represents the correction coefficient in year y, represents the historical reconstructed cultivated land statistical area in year y, represents the initial reconstructed cultivated land spatial 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:

[0104] Determine the maximum proportion of cultivated land area within the range of cultivated land distribution based on the land use data of the study area, and use the maximum proportion of cultivated land area as the theoretical upper limit value.

[0105] That is, calculate the upper limit value of cultivated land area for each mapping unit in year y. Specifically, for years with remote sensing data (after 1985 in the embodiments of the present disclosure), use the maximum proportion of cultivated land area (the maximum probability R of cultivated land remote sensing distribution max ) as the theoretical upper limit value for each mapping unit.

[0106] In the embodiments of the present disclosure, for years without remote sensing data, use the proportion of non-desert land area in the mapping unit as a constraint, and set the upper limit of the proportion of cultivated land area within the mapping unit to 0.95. Take the minimum value of the two as the theoretical upper limit value for each mapping unit. Among them, 0.95 is a preferred value and does not represent the only 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 cultivated land spatial distribution is as follows:

[0108] A′ y =α y ×S y ;

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

[0110] When the proportion of reconstructed cultivated land area reaches the theoretical upper limit value, update the historical statistical area of reconstructed cultivated land according to the difference between the historical statistical area of reconstructed cultivated land and the sum of the areas of the proportion of reconstructed cultivated land area reaching the theoretical upper limit value until the proportion of each reconstructed cultivated land area does not reach the theoretical upper limit value, and obtain the reconstructed cultivated land spatial distribution of the study area.

[0111] Among them, the cultivated land spatial distribution is determined by the proportion of reconstructed cultivated land area of each mapping unit in each year. If A′ y exceeds the theoretical upper limit value, assign it to the theoretical upper limit value in the corresponding year. On this basis, divide the maximum range of cultivated land distribution in the study area into two parts: A′ y reaching the upper limit and A′ y not reaching the upper limit. The mapping units reaching the upper limit no longer participate in the calculation. The historical statistical area of reconstructed cultivated land subtracts the sum of the cultivated land areas of all mapping units reaching the upper limit, and serves as the historical statistical area of reconstructed cultivated land for the next correction process, that is, the corrected The mapping units that do not reach the upper limit enter the next process, repeat the steps of the embodiments of the present disclosure, and perform the above-mentioned repeated calculation steps of the correction coefficient of the cultivated land spatial distribution and the proportion of cultivated land area in each mapping unit. When the proportion of the reconstructed cultivated land area A' of all mapping units y do not exceed the theoretical upper limit value, the repetition step ends, that is, the reconstructed cultivated land spatial distribution of the study area in year y is obtained. Perform the above operations to complete the reconstructed cultivated land spatial distribution data sets for all years.

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

[0113] The embodiments of the present disclosure also provide a data reconstruction device for the cultivated land spatial distribution.

[0114] Figure 3 It is a module structure diagram of the data reconstruction device for the cultivated land spatial distribution of an embodiment of the disclosure, as Figure 3 shown. A data reconstruction device for the cultivated land spatial distribution in one embodiment includes:

[0115] A distribution determination module 100, configured to determine the cultivated land distribution range of the study area and the average value of the proportion of cultivated land area within the cultivated land distribution range according to the land use data of the study area; wherein, the land use data is generated from remote sensing data including cultivated land distribution;

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

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

[0118] An area reconstruction module 103, configured to sort out the statistical data of the cultivated land area of the study area and generate the historical reconstructed cultivated land statistical area;

[0119] The coefficient calculation module 104 is configured to determine a correction coefficient for 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] The proportion calculation module 105 is configured 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 configured to update the historical reconstructed cultivated land statistical area according to the difference between the historical reconstructed cultivated land statistical area and the sum of the areas of the reconstructed cultivated land areas reaching the theoretical upper limit value when the proportion of the reconstructed cultivated land area reaches the theoretical upper limit value, until the proportions of all the reconstructed cultivated land areas do not reach the theoretical upper limit value, so as to obtain the reconstructed cultivated land spatial distribution of the study area.

[0122] The data reconstruction device for the cultivated land spatial distribution according to the embodiments of the present disclosure 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 value of the proportion of the cultivated land area within the cultivated land distribution range according to the land use data of the study area, calculates the joint probability index of the cultivated land spatial distribution according to the natural factor data at the same time, further determines the correction coefficient and the proportion of the reconstructed cultivated land area based on the historical reconstructed cultivated land statistical area and the initial reconstructed cultivated land spatial area, and updates the historical reconstructed cultivated land statistical area according to the difference between the historical reconstructed cultivated land statistical area and the sum of the areas of the reconstructed cultivated land areas reaching the theoretical upper limit value when the proportion of the reconstructed cultivated land area reaches the theoretical upper limit value, until the proportions of all the reconstructed cultivated land areas do not reach the theoretical upper limit value, so as to obtain the reconstructed cultivated land spatial distribution of the study area. The reconstructed cultivated land spatial distribution can not only provide more refined and accurate spatial distribution characteristics and long-time series coverage, but also achieve the reliability consistent with the actual survey data.

[0123] At least one embodiment of the present disclosure further provides a data control device. Figure 4 It is a schematic block diagram of a data control device provided by at least one embodiment of the present disclosure. For example, as Figure 4 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 be made to execute one or more steps in the data reconstruction method for the cultivated land spatial distribution according to any embodiment of the present disclosure.

[0124] For the specific implementation of each step of the data reconstruction method for the cultivated land spatial distribution and the related explanatory content, reference may be made to the relevant content in the embodiments of the data reconstruction method for the cultivated land spatial distribution described above, which will not be elaborated here. It should be noted that Figure 4The components of the data control device 20 shown are exemplary and not restrictive. According to actual application requirements, the data control device 20 may also have other components.

[0125] In one embodiment, the processor 201 and the memory 200 may communicate directly or indirectly with each other. For example, the processor 201 and the memory 200 may communicate through 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. The present disclosure does not limit the type and function of the network herein. For another example, the processor 201 and the memory 200 may also communicate through a bus connection. The bus may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. For example, the processor 201 and the memory 200 may be disposed at a remote data server side (cloud) or a distributed energy system side (local side), or may also be disposed at a client side (such as a mobile device such as a mobile phone). For example, the processor 201 may be a Central Processing Unit (CPU), a Tensor Processing Unit (TPU), or a Graphics Processing Unit (GPU), etc., which has data processing capabilities and / or instruction execution capabilities, and may control other components in the data control device 20 to perform desired functions. The Central Processing Unit (CPU) may be of an X86 or ARM architecture, etc.

[0126] In one embodiment, the memory 200 may include any combination of one or more computer program products. The computer program products 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, etc. Non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, Erasable Programmable Read Only Memory (EPROM), Portable Compact Disc Read Only Memory (CD-ROM), USB memory, flash memory, etc. One or more computer-executable instructions may be stored on the computer-readable storage media. The processor 201 may run the computer-executable instructions to implement various functions of the data control device 20. Various application programs and various data may also be stored in the memory 200, as well as various data used and / or generated by the application programs, etc.

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

[0128] At least one embodiment of the present disclosure also provides a non-transitory computer-readable storage medium. Figure 5 It is a schematic diagram of a non-transitory computer-readable storage medium provided by at least one embodiment of the present disclosure. For example, as Figure 5As shown, one or more computer-executable instructions 301 can be non-transiently 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 can be caused to execute one or more steps in the data reconstruction method for the arable land spatial distribution according to any embodiment of the present disclosure.

[0129] In one embodiment, the non-transitory computer-readable storage medium 30 can be applied to the above data control device 20. For example, it can 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 can refer to the description of the memory 200 in the embodiment of the data control device 20, and the repeated parts will not be elaborated.

[0131] It should be noted that when the memory 200 stores different non-transitory computer-executable instructions, the data control device 20 correspondingly serves as a firmware upgrade device. When the computer-executable instructions are run by the processor 201, the processor 201 can be caused to execute one or more steps in the data reconstruction method for the arable land spatial distribution according to any embodiment of the present disclosure.

[0132] For the present disclosure, there are also the following points to note:

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

[0134] (2) For the sake of clarity, in the accompanying drawings used to describe the embodiments of the present invention, the thickness and dimensions of layers or structures are enlarged. It can be 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 can be "directly" on or under the other element, or there can be intermediate elements.

[0135] (3) Without conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other to obtain new embodiments. The above are only the specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. The protection scope of the present disclosure should be subject to the protection scope of the claims.

[0136] The technical features of the above embodiments can be combined arbitrarily. For the sake of brief description, 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, it should be considered as the scope recorded in this specification.

[0137] The above embodiments only represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the scope of the patented application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended 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 natural suitability distribution probability of cultivated land, R is the remote sensing distribution probability of cultivated land; the average value of the cultivated land area ratio is marked as the remote sensing distribution probability 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: Among them, α y represents the correction factor 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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