CN117036986A - Method for predicting farmland concentrated continuous degree by current space distribution of cultivated land - Google Patents
Method for predicting farmland concentrated continuous degree by current space distribution of cultivated land Download PDFInfo
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Abstract
The invention relates to the technical field of farmland centralized planning and distribution, in particular to a method for predicting farmland centralized connectivity by current space distribution of cultivated land. According to the method, the appointed subareas are screened through the attributes of the land subareas in the target land area, and optimized trimming is carried out on the appointed subareas, so that the regularity of the land areas is improved, the phenomenon of fragmentation in the land areas is avoided, the farmland regularity and the concentration of the land areas are further guaranteed not to be affected, meanwhile, all the farmland subareas are collected, the adjacent data of all the farmland subareas and all the adjacent farmland subareas corresponding to the farmland subareas are collected, the farmland concentrated connection degree of the target land areas is analyzed, re-optimized trimming is carried out according to the farmland concentrated connection degree, the farmland concentrated connection degree of the land areas can be accurately predicted, analysis of space distribution change of the lands is realized, farmland centralization is promoted, farmland land layout is optimized, and farmland land resource centralized management is further realized.
Description
Technical Field
The invention relates to the technical field of farmland centralized planning and distribution, in particular to a method for predicting farmland centralized connectivity by current space distribution of cultivated land.
Background
Along with the continuous promotion of the urban process, reasonable planning and utilization of land space distribution are vital to the protection and efficient utilization of farmlands. The concentrated farmland tie degree is one of important indexes for evaluating the utilization efficiency of agriculture and the degree of agricultural standardization. In modern agricultural development, farmland centralized connection degree is improved, so that the agricultural mechanization degree can be improved, the improvement of agricultural production efficiency is promoted, and the agricultural sustainable development is promoted.
However, in the existing method, the concentrated farmland connection degree is often estimated based on historical data and a model, but the problem is that firstly, the model prediction accuracy is not high, the distribution attribute of the land area is difficult to accurately reflect, the layout cannot be optimized and adjusted pertinently according to the distribution attribute of the land area, meanwhile, irregular phenomenon occurs in part of the land area, farmland regularity and concentration of the land area are affected, the problem of fragmentation occurs in the land area, and further the later agricultural mechanization degree and agricultural production efficiency are affected.
Secondly, the data acquisition is difficult, the change of the space distribution of the land cannot be fully considered, the space data between the accurate lands is lacking, the space fault phenomenon can occur between the land areas and the adjacent land areas, the farmland centralization and connection degree of the land areas cannot be accurately predicted, farmland centralization cannot be promoted, farmland land layout cannot be optimized, farmland land resource centralization management is further affected, and agricultural production benefits and sustainable development are further reduced.
Disclosure of Invention
The invention aims to provide a method for predicting farmland centralized connectivity by using current farmland spatial distribution, which solves the problems in the background technology.
The technical scheme adopted for solving the technical problems is as follows: the invention provides a method for predicting farmland centralized connectivity by using current farmland spatial distribution, which comprises the following steps: the method comprises the steps of firstly, obtaining a remote sensing satellite image of a target land area through a remote sensing satellite technology, and obtaining a gray level image of the target land area through a gray level processing technology.
And secondly, recognizing the gray value of each pixel point in the gray image of the target land area, and dividing the gray value into each land area according to the distribution of the gray values.
And thirdly, comparing the gray value of each pixel point corresponding to each land subarea with a preset gray value range corresponding to each attribute land, and identifying the attribute of each land subarea.
And step four, collecting basic data of all the land subareas, wherein the basic data are areas, boundary line lengths and boundary outline shapes, screening all the land subareas of the non-applicable farmland land according to the attributes and the areas of all the land subareas, and recording the land subareas as all the appointed subareas.
Fifthly, overlapping and comparing the boundary contour shape of each appointed sub-region with the set reference contour shape, screening the reference contour shape of each appointed sub-region, and analyzing the regularity of each appointed sub-region by combining the area and the boundary line length of each appointed sub-region.
And step six, optimizing and trimming the designated subareas according to the regularity of the designated subareas, and collecting the trimmed area set and the rest of the land subareas except the designated subareas to obtain the cultivated land subareas.
And seventhly, counting each adjacent farmland subarea corresponding to each farmland subarea, and collecting adjacent data of each farmland subarea and each adjacent farmland subarea corresponding to each farmland subarea.
And eighth, analyzing the concentrated farmland connection degree of the target land area according to the adjacent data of each farmland subarea and each adjacent farmland subarea corresponding to each farmland subarea, comparing the concentrated farmland connection degree with the preset concentrated farmland connection degree, repeating the fifth step if the concentrated farmland connection degree is smaller than the preset concentrated farmland connection degree, and otherwise, uniformly coloring each farmland subarea and displaying.
In a preferred scheme, the land sub-areas are divided as follows: s1, extracting each pixel point from the gray level image of the target land area, and identifying the gray level value of each pixel point in the gray level image of the target land area.
S2, taking one pixel point as a target pixel point, comparing the gray value of the target pixel point with the gray value of each adjacent pixel point corresponding to the target pixel point to obtain a gray value difference value, counting each adjacent pixel point, of which the gray value difference value corresponding to the target pixel point is within a set gray value difference range, and marking the adjacent pixel point as each mark pixel point.
S3, counting the number of adjacent pixels with the gray value difference value within the set gray value difference range corresponding to each marked pixel, and marking the number as the number of the matched pixels of each marked pixel.
S4, if the number of the matched pixel points of each marked pixel point is zero, connecting the marked pixel points to form a closed area serving as a land sub-area; otherwise, taking all the conforming pixel points of all the summarized marked pixel points as all the appointed pixel points, and repeating S3 until a closed land subarea is formed.
S5, taking a certain pixel point outside the closed land subareas as a target pixel point, repeating the step S2, and further obtaining each land subarea.
In a preferred scheme, the attribute identification mode of each land subarea is as follows: extracting the gray value of each pixel point in each land subarea, comparing the gray value of each pixel point corresponding to each land subarea with a preset gray value range corresponding to each attribute land, and obtaining the gray value adaptation degree of each pixel point corresponding to each land subarea and each attribute landAnd screening the attribute land with the highest gray value adaptation degree as the attribute land corresponding to each pixel point of each land subarea, comparing the attribute lands corresponding to each pixel point of each land subarea, and screening the attribute land with the largest number of pixel points as the attribute of each land subarea.
In a preferred embodiment, the screening method of each specified sub-region is as follows: screening all the land subareas of the non-applicable farmland land according to the attribute of each land subarea, marking the land subareas as all the to-be-selected subareas, screening the areas of all the to-be-selected subareas according to the areas of all the land subareas, comparing the areas of all the to-be-selected subareas with the average value of the areas of all the to-be-selected subareas to obtain the area scale ratio coefficient of all the to-be-selected subareas, comparing the area scale ratio coefficient of all the to-be-selected subareas with the set area scale ratio coefficient threshold, and marking the areas to be-selected subareas with the statistical area scale ratio coefficient larger than the set area scale ratio coefficient threshold.
In a preferred scheme, the screening of the reference contour shape of each specified sub-region is performed by the following specific screening modes: scaling the area of each designated subarea according to the area of each set reference profile shape, performing multiple-angle superposition comparison of the boundary profile shape of each designated subarea after scaling and each set reference profile shape to obtain the superposition area of each boundary profile shape of each designated subarea and each reference profile shape corresponding to each angle, screening the maximum superposition area of the boundary profile shape of each designated subarea and each reference profile shape, and recording the maximum superposition area as，/>Is->The number of the sub-region is specified,，/>is->Number of the individual reference profile shapes, +.>Analyzing the coincidence degree of the boundary contour shape of each designated subarea and each reference contour shape>In the formula->Is->The area of each specified subarea is defined, and the reference contour shape with the highest contact ratio is taken as the reference contour shape of each specified subarea.
In a preferred embodiment, the regularity analysis method of each specified sub-region is as follows: amplifying the reference contour shape of each specified sub-region with equal area according to the area of the corresponding specified sub-region to obtain the reference contour shape region of each specified sub-region, extracting the boundary line length of the corresponding reference contour shape region of each specified sub-region, and recording the boundary line length asThe method comprises the steps of carrying out a first treatment on the surface of the Extracting the symmetrical line positions of the reference contour shape of each appointed subregion from the cloud database, and further carrying out symmetrical processing on each appointed subregion to obtain the area difference of the left and right regions of the symmetrical line corresponding to each appointed subregion, and marking the area difference as +.>。
The area and the boundary line length of each appointed subarea are extracted and respectively recorded asAnalyzing the regularity of each specified subregion>In the formula->Is natural constant (18)>。
In a preferred scheme, the optimizing and trimming are performed according to the regularity of each designated subarea, and specifically include: comparing the regularity of each designated subregion with a set regularity threshold, if the regularity of a designated subregion is smaller than the set regularity threshold, performing diagonal superposition comparison on the designated subregion and a corresponding reference contour shape region to obtain each non-superposition region of the designated subregion and the corresponding reference contour shape region, performing optimized trimming on each non-superposition region of the designated subregion and the corresponding reference contour shape region, counting each non-superposition region of each designated subregion and the corresponding reference contour shape region, and forming a trimmed region set.
In a preferred scheme, the adjacent data of each farmland subarea and each adjacent farmland subarea corresponding to each farmland subarea comprises the length of a boundary overlapping line and the space distance of each monitoring point in the boundary overlapping line.
In a preferred scheme, the farmland centralized continuous sheet analysis mode of the target land area is as follows: obtaining the boundary line length of each farmland subarea from the remote sensing satellite image of the target land area, and recording the boundary line length as，/>，/>Is->The serial numbers of the individual farmland subareas, and analyzing the concentrated continuous degree of the farmland of the target land area by combining the adjacent data of each farmland subarea and each adjacent farmland subarea>In the following、/>Respectively +.>The individual cultivated land subregion corresponds to +.>Boundary coincidence of adjacent tilling sub-areasLine length, boundary line of overlapping +.>Spatial distance of monitoring points->，/>，/>For the number of monitoring points in the boundary overlapping line, +.>Is natural constant (18)>Is the allowable space distance of the boundary line of the adjacent cultivated land area.
Compared with the prior art, the invention has the following beneficial effects: (1) According to the method, the distribution of gray values in the remote sensing satellite image of the target land area is divided into the land subareas, and the attribute of each land subarea is further identified, so that the situation that the layout can be optimized and adjusted in a targeted mode according to the distribution attribute of the land area in the later period is ensured, and the land utilization pattern and trend can be further analyzed.
(2) According to the method, each appointed subarea is screened according to the attribute and the area of each land subarea, and optimized and trimmed according to the regularity of each appointed subarea, so that the regularity of the land areas is improved, the phenomenon of fragmentation of the land areas is avoided, the farmland regularity and the concentration of the land areas are further ensured not to be influenced, and the later agricultural mechanization degree and the agricultural production efficiency are further promoted.
(3) According to the method, the farmland centralized connection degree of the target land area is analyzed by collecting the adjacent data of each land subarea and each adjacent land subarea corresponding to each land subarea, and re-optimizing and trimming are carried out according to the farmland centralized connection degree, so that the farmland centralized connection degree of the land area can be accurately predicted, analysis of land space distribution change is realized, the phenomenon of space faults between the land area and the adjacent land area is avoided, farmland centralization is promoted, farmland land layout is optimized, farmland land resource centralization management is further realized, and agricultural production benefit and sustainable development are improved.
(4) According to the invention, by uniformly coloring and displaying all farmland subareas, people can be helped to identify farmland centralized connection areas, and the distribution and utilization conditions of the farmland of the land can be more intuitively understood and analyzed, so that the efficiency and scientificity of land management and farmland planning are improved, and the method has important significance in promoting sustainable development of agriculture and reasonable utilization of land resources.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of the method of the present invention.
FIG. 2 is a schematic diagram of the optimized finishing of a target land area according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides a method for predicting farmland centralized connectivity by using current farmland spatial distribution, which comprises the following steps: the method comprises the steps of firstly, obtaining a remote sensing satellite image of a target land area through a remote sensing satellite technology, and obtaining a gray level image of the target land area through a gray level processing technology.
And secondly, recognizing the gray value of each pixel point in the gray image of the target land area, and dividing the gray value into each land area according to the distribution of the gray values.
On the basis of the above embodiment, the dividing manner of each land subarea is as follows: s1, extracting each pixel point from the gray level image of the target land area, and identifying the gray level value of each pixel point in the gray level image of the target land area.
S2, taking one pixel point as a target pixel point, comparing the gray value of the target pixel point with the gray value of each adjacent pixel point corresponding to the target pixel point to obtain a gray value difference value, counting each adjacent pixel point, of which the gray value difference value corresponding to the target pixel point is within a set gray value difference range, and marking the adjacent pixel point as each mark pixel point.
S3, counting the number of adjacent pixels with the gray value difference value within the set gray value difference range corresponding to each marked pixel, and marking the number as the number of the matched pixels of each marked pixel.
S4, if the number of the matched pixel points of each marked pixel point is zero, connecting the marked pixel points to form a closed area serving as a land sub-area; otherwise, taking all the conforming pixel points of all the summarized marked pixel points as all the appointed pixel points, and repeating S3 until a closed land subarea is formed.
S5, taking a certain pixel point outside the closed land subareas as a target pixel point, repeating the step S2, and further obtaining each land subarea.
And thirdly, comparing the gray value of each pixel point corresponding to each land subarea with a preset gray value range corresponding to each attribute land, and identifying the attribute of each land subarea.
On the basis of the above embodiment, the attribute identification manner of each land subarea is as follows: extracting the gray value of each pixel point in each land subarea, comparing the gray value of each pixel point corresponding to each land subarea with a preset gray value range corresponding to each attribute land, and obtaining the gray value adaptation degree of each pixel point corresponding to each land subarea and each attribute landAnd screening the attribute land with the highest gray value adaptation degree as the attribute land corresponding to each pixel point of each land subarea, comparing the attribute lands corresponding to each pixel point of each land subarea, and screening the attribute land with the largest number of pixel points as the attribute of each land subarea.
As a specific embodiment of the present invention, the property lands include, but are not limited to: the farming land, the garden land, the pasture land, the construction land and the residence land, and the gray value ranges of the gray images corresponding to the respective attribute lands are independent of each other, so that the attribute of each land subregion can be identified by comparing the gray value ranges corresponding to the respective attribute lands with the gray values of the respective pixel points in each land subregion.
Further, the agro-farming land, garden land and pasture land are applicable type farmland lands, and the construction land and residential land are non-applicable type farmland lands.
The method and the device divide the distribution of the gray values in the remote sensing satellite images of the target land areas into the land subareas, and further identify the attribute of each land subarea, so that the layout can be optimized and adjusted in a targeted manner according to the distribution attribute of the land areas in the later period, and the method and the device are beneficial to further analyzing the land utilization patterns and trends.
And step four, collecting basic data of all the land subareas, wherein the basic data are areas, boundary line lengths and boundary outline shapes, screening all the land subareas of the non-applicable farmland land according to the attributes and the areas of all the land subareas, and recording the land subareas as all the appointed subareas.
On the basis of the above embodiment, the screening manner of each specified sub-region is as follows: screening all the land subareas of the non-applicable farmland land according to the attribute of each land subarea, marking the land subareas as all the to-be-selected subareas, screening the areas of all the to-be-selected subareas according to the areas of all the land subareas, comparing the areas of all the to-be-selected subareas with the average value of the areas of all the to-be-selected subareas to obtain the area scale ratio coefficient of all the to-be-selected subareas, comparing the area scale ratio coefficient of all the to-be-selected subareas with the set area scale ratio coefficient threshold, and marking the areas to be-selected subareas with the statistical area scale ratio coefficient larger than the set area scale ratio coefficient threshold. Therefore, the land subareas with small partial areas are screened out through the area scale ratio coefficient of each subarea to be selected, and the land subareas with small partial areas are optimized and remedied into the land subareas suitable for farmland, so that the concentrated continuous degree of the farmland in the land areas is extracted, and the concentrated management of farmland land resources is realized.
As a specific embodiment of the present invention, the area scale ratio coefficient analysis formula of each sub-area to be selected is:wherein->Is->Area of the individual sub-regions to be selected, +.>，/>For the number of sub-areas to be selected, +.>For a set area-to-area-scale ratio, +.>，/>The maximum sub-area to be selected and the minimum sub-area to be selected are respectively.
Fifthly, overlapping and comparing the boundary contour shape of each appointed sub-region with the set reference contour shape, screening the reference contour shape of each appointed sub-region, and analyzing the regularity of each appointed sub-region by combining the area and the boundary line length of each appointed sub-region.
On the basis of the above embodiment, the screening of the reference contour shape of each specified sub-region is performed by the following specific screening method: scaling the area of each designated subarea according to the area of each set reference profile shape, performing multiple-angle superposition comparison of the boundary profile shape of each designated subarea after scaling and each set reference profile shape to obtain the superposition area of each boundary profile shape of each designated subarea and each reference profile shape corresponding to each angle, screening the maximum superposition area of the boundary profile shape of each designated subarea and each reference profile shape, and recording the maximum superposition area as，/>Is->Designating the number of the sub-region, ">，/>Is->Number of the individual reference profile shapes, +.>Analyzing the coincidence degree of the boundary contour shape of each designated subarea and each reference contour shape>In the formula->Is->Designating areas of the sub-regions, and taking the reference contour shape with the highest overlap ratio as each designated sub-regionReference contour shape of the domain.
It should be explained that the reference profile shapes include, but are not limited to: rectangular profile shape, square profile shape, circular profile shape, trapezoidal profile shape, and "L" profile shape.
As a specific embodiment of the present invention, the area of each designated sub-area is scaled, and the specific steps are as follows: extracting the area of each designated subarea, and analyzing the area scaling ratio of the boundary contour shape of each designated subarea and each reference contour shape according to the area of each set reference contour shape, whereinIf the area scaling ratio of the boundary contour shape of a specified subarea and the reference contour shape is smaller than 1, the area of the specified subarea is enlarged according to the corresponding area scaling ratio before the boundary contour shape of the specified subarea is overlapped and compared with the reference contour shape, and if the area scaling ratio of the boundary contour shape of the specified subarea and the reference contour shape is larger than 1, the area of the specified subarea is reduced according to the corresponding area scaling ratio before the boundary contour shape of the specified subarea is overlapped and compared with the reference contour shape. By scaling the area of each designated sub-region, the boundary contour shape of each designated sub-region can be fully overlapped with the set reference contour shape, and the accuracy and the integrity of data are further improved.
On the basis of the above embodiment, the regularity analysis method of each specified sub-region is as follows: amplifying the reference contour shape of each specified sub-region with equal area according to the area of the corresponding specified sub-region to obtain the reference contour shape region of each specified sub-region, extracting the boundary line length of the corresponding reference contour shape region of each specified sub-region, and recording the boundary line length asThe method comprises the steps of carrying out a first treatment on the surface of the And extracting the symmetrical line positions of the reference outline shape of each appointed subarea from the cloud databaseFurther, each specified sub-region is symmetrically processed to obtain the area difference of the left and right regions of the symmetry line corresponding to each specified sub-region, and the area difference is marked as +.>。
The area and the boundary line length of each appointed subarea are extracted and respectively recorded asAnalyzing the regularity of each specified subregion>In the formula->Is natural constant (18)>。
And step six, optimizing and trimming the designated subareas according to the regularity of the designated subareas, and collecting the trimmed area set and the rest of the land subareas except the designated subareas to obtain the cultivated land subareas.
On the basis of the above embodiment, the optimizing and trimming the specified subareas according to the regularity of the specified subareas specifically includes: comparing the regularity of each designated subregion with a set regularity threshold, if the regularity of a designated subregion is smaller than the set regularity threshold, performing diagonal coincidence comparison on the designated subregion and a corresponding reference contour shape region to obtain each non-coincident region of the designated subregion and the corresponding reference contour shape region, further performing optimized trimming on each non-coincident region of the designated subregion and the corresponding reference contour shape region, counting each non-coincident region of each designated subregion and the corresponding reference contour shape region, and forming a trimmed region set, as shown in fig. 2.
The method and the device screen each appointed subarea according to the attribute and the area of each land subarea, and optimally trim each appointed subarea according to the regularity of each appointed subarea, so that the regularity of the land areas is improved, the phenomenon of fragmentation of the land areas is avoided, the farmland regularity and the concentration of the land areas are further ensured not to be influenced, and further the later agricultural mechanization degree and the agricultural production efficiency are promoted.
And seventhly, counting each adjacent farmland subarea corresponding to each farmland subarea, and collecting adjacent data of each farmland subarea and each adjacent farmland subarea corresponding to each farmland subarea.
On the basis of the embodiment, the adjacent data of each farmland subarea and each adjacent farmland subarea corresponding to each farmland subarea comprises the length of a boundary overlapping line and the space distance of each monitoring point in the boundary overlapping line.
As a specific embodiment of the present invention, the adjacent data acquisition manner of each farmland subarea and each adjacent farmland subarea corresponding to each farmland subarea is: and defining boundary lines of all the farmland subareas in the remote sensing satellite image of the target land area according to the remote sensing satellite image of the target land area, acquiring boundary overlapping lines of all the farmland subareas and all the adjacent farmland subareas corresponding to the farmland subareas, and measuring to obtain the length of the boundary overlapping lines of all the farmland subareas and all the adjacent farmland subareas corresponding to the farmland subareas.
The method comprises the steps of carrying out three-dimensional remote sensing satellite scanning on a target land area through a remote sensing satellite technology, constructing a three-dimensional satellite model of the target land area, taking an endpoint of the target land area in the three-dimensional satellite model of the target land area as an origin, establishing a three-dimensional space coordinate system, setting up a plurality of monitoring points on boundary lines of all the farmland areas, screening all the monitoring points belonging to the corresponding farmland areas and all the monitoring points belonging to the corresponding adjacent farmland areas on boundary coincident lines of all the farmland areas and all the adjacent farmland areas, obtaining space position coordinates of all the monitoring points belonging to the corresponding farmland areas and all the monitoring points belonging to the corresponding adjacent farmland areas, calculating according to a space two-point coordinate distance formula to obtain space distances between all the monitoring points belonging to the corresponding farmland areas and all the monitoring points belonging to the corresponding adjacent farmland areas, screening to obtain shortest distances of all the monitoring points belonging to the corresponding farmland areas, and taking the shortest distances as space distances of all the monitoring points in boundary coincident lines of all the farmland areas and all the corresponding adjacent farmland areas.
And eighth, analyzing the concentrated farmland connection degree of the target land area according to the adjacent data of each farmland subarea and each adjacent farmland subarea corresponding to each farmland subarea, comparing the concentrated farmland connection degree with the preset concentrated farmland connection degree, repeating the fifth step if the concentrated farmland connection degree is smaller than the preset concentrated farmland connection degree, and otherwise, uniformly coloring each farmland subarea and displaying.
Based on the above embodiment, the farmland centralized connection degree analysis method of the target land area is as follows: obtaining the boundary line length of each farmland subarea from the remote sensing satellite image of the target land area, and recording the boundary line length as，/>，/>Is->The serial numbers of the individual farmland subareas, and analyzing the concentrated continuous degree of the farmland of the target land area by combining the adjacent data of each farmland subarea and each adjacent farmland subarea>In the formula->、/>Respectively +.>The individual cultivated land subregion corresponds to +.>The length of the boundary overlapping line of the adjacent plough subareas and the +.>Spatial distance of monitoring points->，/>，/>For the number of monitoring points in the boundary overlapping line, +.>Is natural constant (18)>Is the allowable space distance of the boundary line of the adjacent cultivated land area.
The method and the device can accurately predict the farmland centralized connection degree of the land areas by collecting the adjacent data of each farmland subarea and each adjacent farmland subarea corresponding to each farmland subarea, analyzing the farmland centralized connection degree of the target land areas and carrying out re-optimization and trimming according to the farmland centralized connection degree, so that analysis of land space distribution change is realized, the phenomenon of space fault between the land areas and the adjacent land areas is avoided, further farmland centralization is promoted, farmland land layout is optimized, farmland land resource centralization management is further realized, and agricultural production benefit and sustainable development are improved.
Meanwhile, the invention can help people to identify the farmland centralized connection area by uniformly coloring and displaying each farmland subarea, and more intuitively understand and analyze the land farmland distribution and utilization condition, thereby improving the efficiency and scientificity of land management and farmland planning, and having important significance for promoting sustainable development of agriculture and reasonable utilization of land resources.
The foregoing is merely illustrative and explanatory of the principles of this invention, as various modifications and additions may be made to the specific embodiments described, or similar arrangements may be substituted by those skilled in the art, without departing from the principles of this invention or beyond the scope of this invention as defined in the claims.
Claims (9)
1. The method for predicting the concentrated continuous cropping degree of the farmland by using the current space distribution of the cultivated land is characterized by comprising the following steps:
firstly, acquiring a remote sensing satellite image of a target land area through a remote sensing satellite technology, and acquiring a gray image of the target land area by adopting a gray processing technology;
secondly, recognizing the gray value of each pixel point in the gray image of the target land area, and dividing the gray value into land areas according to the distribution of the gray values;
thirdly, comparing the gray value of each pixel point corresponding to each land subarea with a preset gray value range corresponding to each attribute land, and identifying the attribute of each land subarea;
collecting basic data of all land subareas, wherein the basic data are areas, boundary line lengths and boundary outline shapes, screening all land subareas of non-applicable farmland lands according to the attributes and the areas of all land subareas, and recording the land subareas as all appointed subareas;
fifthly, overlapping and comparing the boundary contour shape of each appointed sub-region with the set reference contour shape, screening the reference contour shape of each appointed sub-region, and analyzing the regularity of each appointed sub-region by combining the area and the boundary line length of each appointed sub-region;
step six, optimizing and trimming the soil according to the regularity of each appointed subarea, and collecting the trimmed area set and the rest of each land subarea except each appointed subarea to obtain each cultivated land subarea;
seventh, counting each adjacent farmland subarea corresponding to each farmland subarea, and collecting adjacent data of each farmland subarea and each adjacent farmland subarea corresponding to each farmland subarea;
and eighth, analyzing the concentrated farmland connection degree of the target land area according to the adjacent data of each farmland subarea and each adjacent farmland subarea corresponding to each farmland subarea, comparing the concentrated farmland connection degree with the preset concentrated farmland connection degree, repeating the fifth step if the concentrated farmland connection degree is smaller than the preset concentrated farmland connection degree, and otherwise, uniformly coloring each farmland subarea and displaying.
2. The method for predicting farmland centralized connectivity according to the current spatial distribution of cultivated land according to claim 1, wherein: the dividing mode of each land subarea is as follows:
s1, extracting each pixel point from a gray level image of a target land area, and identifying the gray level value of each pixel point in the gray level image of the target land area;
s2, taking one pixel point as a target pixel point, comparing the gray value of the target pixel point with the gray value of each adjacent pixel point corresponding to the target pixel point to obtain a gray value difference value, counting each adjacent pixel point of which the gray value difference value corresponding to the target pixel point is within a set gray value difference value range, and marking the adjacent pixel point as each mark pixel point;
s3, counting the number of adjacent pixels with the gray value difference value within a set gray value difference range corresponding to each marked pixel, and marking the number as the number of the matched pixels of each marked pixel;
s4, if the number of the matched pixel points of each marked pixel point is zero, connecting the marked pixel points to form a closed area serving as a land sub-area; otherwise, taking all the conforming pixel points of all the summarized marked pixel points as all the appointed pixel points, and repeating the step S3 until a closed land subarea is formed;
s5, taking a certain pixel point outside the closed land subareas as a target pixel point, repeating the step S2, and further obtaining each land subarea.
3. The method for predicting farmland centralized connectivity according to the current spatial distribution of cultivated land according to claim 1, wherein: the attribute identification mode of each land subarea is as follows:
extracting gray values of all pixel points in all land subareas, and comparing the gray values of all pixel points corresponding to all land subareas with a preset gray value range corresponding to all attribute lands to obtain all landsGray value adaptation degree of each pixel point corresponding to sub-region and each attribute landAnd screening the attribute land with the highest gray value adaptation degree as the attribute land corresponding to each pixel point of each land subarea, comparing the attribute lands corresponding to each pixel point of each land subarea, and screening the attribute land with the largest number of pixel points as the attribute of each land subarea.
4. The method for predicting farmland centralized connectivity according to the current spatial distribution of cultivated land according to claim 1, wherein: the screening mode of each specified subarea is as follows:
screening all the land subareas of the non-applicable farmland land according to the attribute of each land subarea, marking the land subareas as all the to-be-selected subareas, screening the areas of all the to-be-selected subareas according to the areas of all the land subareas, comparing the areas of all the to-be-selected subareas with the average value of the areas of all the to-be-selected subareas to obtain the area scale ratio coefficient of all the to-be-selected subareas, comparing the area scale ratio coefficient of all the to-be-selected subareas with the set area scale ratio coefficient threshold, and marking the areas to be-selected subareas with the statistical area scale ratio coefficient larger than the set area scale ratio coefficient threshold.
5. The method for predicting farmland centralized connectivity according to the current spatial distribution of cultivated land according to claim 1, wherein: the specific screening mode of screening the reference contour shape of each appointed subregion is as follows:
scaling the area of each specified subarea according to the area of each set reference profile shape, performing multiple-angle superposition comparison on the boundary profile shape of each specified subarea after scaling and each set reference profile shape to obtain the superposition area of each boundary profile shape of each specified subarea and each reference profile shape corresponding to each angle, and screening the maximum superposition surface of the boundary profile shape of each specified subarea and each reference profile shapeThe product is recorded as，/>Is->Designating the number of the sub-region, ">，/>Is->Number of the individual reference profile shapes, +.>Analyzing the coincidence degree of the boundary contour shape of each designated subarea and each reference contour shape>In the formula->Is->The area of each specified subarea is defined, and the reference contour shape with the highest contact ratio is taken as the reference contour shape of each specified subarea.
6. The method for predicting farmland concentration and connectivity according to the current spatial distribution of cultivated land of claim 5, wherein: the regularity analysis mode of each appointed subarea is as follows:
the reference contour shape of each appointed subarea is subjected to equal-area discharge according to the area of the corresponding appointed subareaLarge processing to obtain reference contour shape regions of each specified sub-region, extracting boundary line length of the reference contour shape region corresponding to each specified sub-region, and recording the boundary line length asThe method comprises the steps of carrying out a first treatment on the surface of the Extracting the symmetrical line positions of the reference contour shape of each appointed subregion from the cloud database, and further carrying out symmetrical processing on each appointed subregion to obtain the area difference of the left and right regions of the symmetrical line corresponding to each appointed subregion, and marking the area difference as +.>；
The area and the boundary line length of each appointed subarea are extracted and respectively recorded asAnalyzing the regularity of each specified subregion>In the formula->Is natural constant (18)>。
7. The method for predicting farmland concentration and connectivity according to the current spatial distribution of cultivated land of claim 6, wherein: the optimizing and trimming are carried out on each appointed subarea according to the regularity, and the optimizing and trimming specifically comprise the following steps:
comparing the regularity of each designated subregion with a set regularity threshold, if the regularity of a designated subregion is smaller than the set regularity threshold, performing diagonal superposition comparison on the designated subregion and a corresponding reference contour shape region to obtain each non-superposition region of the designated subregion and the corresponding reference contour shape region, performing optimized trimming on each non-superposition region of the designated subregion and the corresponding reference contour shape region, counting each non-superposition region of each designated subregion and the corresponding reference contour shape region, and forming a trimmed region set.
8. The method for predicting farmland centralized connectivity according to the current spatial distribution of cultivated land according to claim 1, wherein: and the adjacent data of each farmland subarea and each adjacent farmland subarea corresponding to each farmland subarea comprises the length of a boundary overlapping line and the space distance of each monitoring point in the boundary overlapping line.
9. The method for predicting farmland concentration and connectivity according to the current spatial distribution of cultivated land of claim 8, wherein: the farmland centralized connection degree analysis mode of the target land area is as follows:
obtaining the boundary line length of each farmland subarea from the remote sensing satellite image of the target land area, and recording the boundary line length as，，/>Is->The serial numbers of the individual farmland subareas, and analyzing the concentrated continuous degree of the farmland of the target land area by combining the adjacent data of each farmland subarea and each adjacent farmland subareaIn the formula->、/>Respectively +.>The individual cultivated land subregion corresponds to +.>The length of the boundary overlapping line of the adjacent plough subareas and the +.>Spatial distance of monitoring points->，/>，/>For the number of monitoring points in the boundary overlapping line, +.>Is natural constant (18)>Is the allowable space distance of the boundary line of the adjacent cultivated land area.
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