CN117689224A - High-standard farmland construction potential area identification method oriented to homeland planning - Google Patents
High-standard farmland construction potential area identification method oriented to homeland planning Download PDFInfo
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Abstract
The invention provides a method for identifying a high-standard farmland construction potential area facing homeland planning, which comprises the following steps: step 1, extracting farmland areas based on investigation data or remote sensing data; step 2, constructing a high-standard farmland evaluation index system, and carrying out spatial resolution consistency processing on all evaluation index data; step 3, constructing a high-standard farmland potential area identification overall discrimination model based on the farmland area and the evaluation index data, and identifying a high-standard farmland construction potential area based on the model; step 4, constructing a gradient-vegetation coverage comprehensive evaluation index model; step 5, determining the priority and the priority of the construction potential area of the farmland area; and 6, drawing according to the construction potential priority and the spatial geographic position of the farmland area, and generating a high-standard annual farmland construction map. The method calculates the high-standard farmland construction potential area which accords with the high-standard farmland construction principle, and provides data and method support for planning the red line of the cultivated land in the homeland space.
Description
Technical Field
The invention relates to the technical fields of agricultural geography and remote sensing science, in particular to a method for identifying a high-standard farmland construction potential area facing to homeland planning.
Background
The high-standard farmland is a farmland which is flat in field blocks, centralized and continuous, perfect in facilities, matched with agricultural electricity, rich in soil, eco-friendly, strong in disaster resistance, suitable for modern agricultural production and operation modes, capable of guaranteeing the harvest of drought and waterlogging, stable in yield and high in yield and defined as basic farmland, and the existing farmland or potential farmland is arranged and repaired by the means of engineering technology in a focusing manner.
At present, the identification technology of high-standard farmland construction areas is basically divided into three types:
firstly, from the cultivation land quality, acquiring the current cultivation land quality index, and comparing with the regional cultivation land standard quality land mass to obtain a farmland with maximum cultivation land quality lifting potential as a high-standard farmland construction area; secondly, the method stays at a demarcation level of a high-standard farmland, namely, the farmland attributes are singly divided according to qualitative indexes, the establishment of an index system is single, and the construction principle of the high-standard farmland cannot be well met; thirdly, a judgment index is selected based on the remote sensing image, so that numerical operation is carried out on the cultivated land data, a comprehensive index system for identifying the high-standard farmland is obtained after normalization and weighting, and the high-standard farmland construction area is divided. The first two recognition technologies cannot consider the boundary of farmland and land utilization partition, and lack space regulation and guiding functions when planning for homeland.
The third method makes up the deficiencies of the former two technologies to a certain extent, but still cannot consider the space form and the cultivation quality of farmlands, has limitation in selection of judging factors, cannot comprehensively meet the construction principle of high-standard farmlands, and has larger limitation in serving the space planning of China and the subsequent construction of high-standard farmlands.
Disclosure of Invention
The invention provides a method for identifying a high-standard farmland construction potential area facing homeland planning, which combines qualitative analysis and quantitative analysis, establishes evaluation indexes based on a demarcation principle of a high-standard farmland by fusing multisource information data, and constructs a geographic mathematical model for identifying the high-standard farmland construction area so as to realize area identification, explore the development directions of the high-standard farmland construction potential area and future areas, assist in overall optimization of construction layout and reasonable arrangement of construction time sequence.
Specifically, the invention provides a method for identifying a high-standard farmland construction potential area facing to homeland planning, which comprises the following steps:
step 1, extracting farmland areas based on investigation data or remote sensing data;
step 2, constructing a high-standard farmland evaluation index system comprising a plurality of indexes, and unifying all evaluation index data and farmland areas into grid data with the same spatial resolution;
step 3, constructing a high-standard farmland potential area identification overall discrimination model based on the farmland area and the evaluation index data, and identifying a high-standard farmland construction potential area based on the model;
step 4, constructing a gradient-vegetation coverage comprehensive evaluation index model;
step 5, determining the priority and the priority of the construction potential area of the farmland area;
and 6, drawing according to the construction potential priority and the spatial geographic position of the farmland area, and generating a high-standard annual farmland construction map.
Further, in step 1, the method for extracting farmland areas by remote sensing data further comprises the following steps:
step 11, obtaining high-precision farmland distribution data of 30m of a plurality of typical years;
step 12, binarizing the data of a plurality of typical years, marking the cultivated land grid area as 1, marking the uncultivated land grid area as 0, and marking the cut land grid area as a data set { C } year }；
And 13, constructing an information map of the data according to the order from the near to the far of the year, and acquiring a farmland area based on a new data set constructed by the information map.
Further, in step 1, the information map is:
wherein year represents the last typical year in the study period; n represents a time interval; i represents the serial number of N typical year data; a represents a new information map data set consisting of spatial distribution maps of all the annual farmland.
Further, in step 2, the evaluation index includes Slope, elevation DEM, precipitation P, shallow groundwater GW, surface available water AW, vegetation coverage fCV, evapotranspiration ET, soil bulk density BD, soil organic carbon SOC, soil total nitrogen content TN, sand content Sand, clay content Clay, and soil pH.
Further, in step 2, the Slope is expressed as:
wherein the Slope is a function based on elevation,and->The rate of change of the calculated gradient pixels in the warp and weft directions, respectively.
Further, in step 2, the vegetation coverage fCV is expressed as:
wherein fCV is vegetation coverage, NDVI is normalized vegetation index, NDVI max And NDVI min Respectively represent the most luxuriant groundThe values of NDVI constants for the surface coverage and bare soil conditions without surface coverage.
Further, in step 3, the discrimination model is:
PR wff ＝f(Slope,DEM,P,GW,AW,fCV,ET,BD,SPC,TN,Sand,Clay,pH)
wherein PR is PR wff Representing the identified high-standard farmland construction potential area; DEM is elevation; slope is a gradient calculated by a Gao Chengjing gradient calculation formula; p is precipitation; GW is shallow groundwater; AW is surface available water; fCV is vegetation coverage; ET is the amount of evapotranspiration; BD is soil volume weight; SOC is soil organic carbon; TN is the total nitrogen content of the soil; sand is Sand content; clay is the cosmid content; the pH is the pH of the soil.
Further, in step 4, the slope-vegetation coverage comprehensive evaluation index model is constructed as follows:
SF＝norS+norF
wherein norS and norF are normalized Slope and vegetation coverage index, slope is Slope, respectively max Is the maximum Slope constant, slope min Is the minimum slope constant, fCV is vegetation coverage, fCV min Is the minimum vegetation coverage constant, fCV max Is the maximum vegetation coverage constant.
In step 5, acquiring the priority of the construction potential area from large to small according to the gradient-vegetation coverage comprehensive evaluation index of the high-standard farmland construction potential area;
and the construction progress is developed every year according to the upper planning requirement, and the construction potential areas are classified according to the construction potential area priority.
The beneficial effects achieved by the invention are as follows:
compared with the traditional statistical method and the conventional method, the invention develops a high-standard farmland construction potential area identification method for homeland planning, and practical experiments prove that the method can generate the high-standard farmland construction potential area conforming to the high-standard farmland construction principle, and meanwhile, data products can also directly provide data and method support for the red line planning of cultivated land in the homeland space planning.
Drawings
Fig. 1 is a schematic flow chart of a method for identifying a high-standard farmland construction potential area facing homeland planning provided by the embodiment of the invention;
fig. 2 is a schematic diagram of an information map construction method in a high-standard farmland construction potential area identification method facing homeland planning provided by the embodiment of the invention;
FIG. 3 is a schematic diagram of an evaluation index system in a method for identifying a high-standard farmland construction potential area facing to homeland planning, which is provided by the embodiment of the invention;
fig. 4 is a schematic diagram of a high-standard farmland potential area in a method for identifying a high-standard farmland construction potential area facing homeland planning provided by an embodiment of the present invention;
fig. 5 is a schematic diagram of a construction potential area priority and priority arrangement scheme in a high-standard farmland construction potential area identification method facing homeland planning provided by the embodiment of the invention;
fig. 6 is a hierarchical annual construction potential area drawing in the method for identifying a high-standard farmland construction potential area facing homeland planning provided by the embodiment of the invention.
Detailed Description
The technical scheme of the present invention will be described in more detail with reference to the accompanying drawings, and the present invention includes, but is not limited to, the following examples.
As shown in figure 1, the invention provides a method for identifying a high-standard farmland construction potential area facing to homeland planning, which comprises the following steps:
and step 1, extracting farmland areas based on investigation data or remote sensing data.
If there is "three-tone" (2019, end) tilling data for the study area, stable tilling identification can also be directly replaced with "three-tone" tilling data.
However, it is generally difficult to obtain a wide range of "tri-tone" data due to the limitations of the sharing mechanism. The invention also provides a farmland area extraction method based on the shared data.
Step 11, acquiring 30m high-precision farmland (cultivated land) distribution data of N typical years (such as 2010, 2015, 2020 and the like);
step 12, generating a cultivated land grid data set of each year;
as shown in fig. 2, binarization processing is performed on each of the N typical year data, the cultivated land grid area is marked with 1, the uncut land grid area is marked with 0, and the data is marked as a data set { C } year }，C year Is a data set with spatial characteristics, and comprises three dimensions of geographic position information and numerical information.
And 13, constructing an information map of the data according to the order of the year, wherein the construction mode is expressed as the following formula: .
Wherein year represents the last typical year in the study period; n represents a time interval such as: each year (n=1) or five years (n=5); i represents the serial number of N typical year data; a represents a new information map data set consisting of spatial distribution maps of all the annual farmland.
And acquiring a farmland area based on the new data set constructed by the information map.
And 2, constructing a high-standard farmland evaluation index system considering three dimensions of natural conditions, ecological background and soil conditions, and carrying out spatial resolution consistency processing on all data.
And acquiring data such as normalized vegetation index (NDVI), elevation (DEM), gradient (Slope), precipitation data (P), land water reserve information (GW), vegetation coverage (fCV), actual Evaporation (ET), soil volume weight (BD), soil organic carbon (Soil organic carbon, SOC), soil texture (Sand, clay, silt), total Nitrogen content (Total Nitrogen, TN) and soil pH value (pH) of the farmland area and the like for generating an evaluation index.
The evaluation indexes of the invention comprise Slope, elevation DEM, precipitation P, shallow groundwater GW, surface available water AW, vegetation coverage fCV, evapotranspiration ET, soil volume weight BD, soil organic carbon SOC, soil total nitrogen content TN, sand content Sand, clay content Clay and soil pH value pH.
The elevation DEM, the precipitation data P, the land water reserves information GW, the actual evaporation ET, the soil volume weight BD, the soil organic carbon SOC, the Sand content Sand, the Clay content Clay, the soil total nitrogen content TN and the soil pH value pH are grid data which can be directly obtained;
the surface available water AW is obtained by calculation through precipitation data P and actual evaporation ET;
slope is calculated by elevation equation (2):
wherein the Slope is a function based on elevation,and->The change rates of the calculated gradient pixels in the warp direction and the weft direction are respectively;
fCV is calculated from normalized vegetation index (NDVI), referring to formula (3):
fCV is vegetation coverage, NDVI in equation (3) max And NDVI min Is constant and represents the most luxuriant surface coverage and no surfaceNDVI values for the covered bare soil conditions were set at 0.95 and 0.05, respectively. Unifying the data corresponding to all the evaluation indexes into grid data which are identical and consistent with the spatial resolution of the cultivated land data by a bilinear interpolation method; in this embodiment, the spatial resolution grid is 30m, so that the subsequent steps perform a unified mathematical operation on the 30m spatial resolution grid.
And 3, identifying a high-standard farmland potential area.
As shown in fig. 3-4, based on the 30m spatial resolution raster data in the step 1 and the step 2, a high-standard farmland potential area identification overall discrimination model is constructed by means of a superposition analysis method, and the high-standard farmland construction potential area is identified based on the model.
And selecting a farmland area meeting all evaluation indexes simultaneously as a high-standard farmland construction potential area, and judging a mathematical expression of a model as follows:
PR wff ＝f(Slope,DEM,P,GW,AW,fCV,ET,BD,SOC,TN,Sand,Clay,pH) (4)
wherein PR is PR wff Representing the identified high-standard farmland construction potential area; DEM is elevation; slope is grade; p is precipitation; GW is shallow groundwater; AW is surface available water; fCV is vegetation coverage; ET is the amount of evapotranspiration; BD is soil volume weight; SOC is soil organic carbon; TN is the total nitrogen content of the soil; sand is Sand content; clay is the cosmid content; the pH is the pH value of the soil.
To this end, areas of high standard farmland construction potential have been delineated for areas of investigation. However, in the actual homeland planning and utilization process, the construction of the high-standard farmland is carried out in a time-sharing and batch-by-batch manner according to the local government planning and budgeting capability, and is not completely completed in the same time. Therefore, in order to meet the high-standard farmland construction requirements in real national and soil planning, the following method is further developed.
And 4, constructing a gradient-vegetation coverage comprehensive evaluation index model (SF).
Considering the problem of priority and priority in the actual work of homeland planning, the priority and priority of all high-standard farmland construction potential areas in the research area need to be defined. Briefly, this is a problem of ordering potential construction grid areas. Therefore, the construction potential areas to be evaluated are subjected to numerical ranking by constructing a gradient-vegetation coverage comprehensive evaluation index model. Notably, here 13 indices from the initial overlay analysis are reduced to 2 indices (slope and vegetation coverage). The main reasons are as follows: firstly, the gradient is an important index for high-standard farmland construction, natural disasters such as water and soil loss are easily caused by over-steep slope development farmland, and cultivation is inconvenient, so that the slower the gradient is, the higher the priority of the high-standard farmland construction is in a corresponding area; and secondly, vegetation coverage is an index which comprehensively reflects vegetation growth conditions and surface coverage degrees and is obtained by numerical conversion of normalized vegetation indexes, and the index is a comprehensive index which comprehensively reflects natural habitats such as soil fertility, water conditions, temperature conditions and the like of a certain area to a certain extent, so that the higher the vegetation coverage is, the higher the priority of farmland construction with high standards is in corresponding areas.
In view of the above, a slope-vegetation coverage comprehensive evaluation index model is constructed, and the equation is as follows:
SF＝norS+norF (5)
wherein norS and norF are normalized Slope and vegetation coverage index, slope is Slope, respectively max Is the maximum gradient constant, 25 DEG Slope is taken min Is the minimum slope constant, 0 degrees is taken, fCV is vegetation coverage, fCV min Is the minimum vegetation coverage constant, 0.4 is taken, fCV max Is the maximum vegetation coverage constant, taking 1.0. The larger the SF number, the greater the potential for the farmland to develop into a good quality farmland.
And 5, determining the priority and the priority of the construction potential area.
And step 4, all high-standard farmland construction potential areas in the research area are digitalized, so that a good foundation is laid for the determination of the priority and the priority in the step.
In one embodiment, as shown in FIG. 5, all of the field grids in the investigation region space are ranked by bubbling ranking or the like, for example: MATLAB software provides a sort () function to assist in this sort function, defining the sort rules as descending order (descend), and allowing the grade-vegetation coverage comprehensive evaluation index (SF) to be arranged from large to small, with the large being arranged in the front of the team to determine the construction potential area priority.
This consideration is mainly for two reasons: firstly, the quantity target proposed by the upper planning is preferably completed, and the relatively good quality is preferably developed in farmland development planning to complete the quantity target proposed by the upper planning; secondly, the principle of maximum efficiency but lowest cost is adopted, and the upper planning quantity requirement is completed preferentially under the condition of limited project funding.
And grading the ordered farmland grids according to the high-standard farmland construction progress developed annually required by upper planning. In one embodiment, the overall proportion of the progress construction area in the five-year plan in the upper level plan is 35%,25%,20%,10%, respectively. The ordered farmland grids can be graded coded according to the construction area requirements and the proportion requirements, and the priority of the construction potential areas is determined.
And 6, drawing according to the construction potential priority and the spatial geographic position of the farmland area, and generating a high-standard annual farmland construction map.
As shown in fig. 6, the coded information of the mark priority is mapped again according to the spatial geographic position (longitude and latitude information or row and column position information). Thus, all the work of identifying the high-standard farmland construction potential area facing the homeland planning is completed.
Compared with the traditional statistical method and the conventional method, the invention develops a high-standard farmland construction potential area identification method for homeland planning, and practical experiments prove that the method can not only define the high-standard farmland construction potential area conforming to the high-standard farmland construction principle, but also directly provide data and method support for planning the red line of cultivated land in the homeland space.
The present invention is not limited to the above embodiments, and those skilled in the art can implement the present invention in various other embodiments according to the examples and the disclosure of the drawings, so that the design of the present invention is simply changed or modified while adopting the design structure and concept of the present invention, and the present invention falls within the scope of protection.
Claims (9)
1. The method for identifying the high-standard farmland construction potential area facing the homeland planning is characterized by comprising the following steps of:
step 1, extracting farmland areas based on investigation data or remote sensing data;
step 2, constructing a high-standard farmland evaluation index system comprising a plurality of indexes, and unifying all evaluation index data and farmland areas into grid data with the same spatial resolution;
step 3, constructing a high-standard farmland potential area identification overall discrimination model based on the farmland area and the evaluation index data, and identifying a high-standard farmland construction potential area based on the model;
step 4, constructing a gradient-vegetation coverage comprehensive evaluation index model;
step 5, determining the priority and the priority of the construction potential area of the farmland area;
and 6, drawing according to the construction potential priority and the spatial geographic position of the farmland area, and generating a high-standard annual farmland construction map.
2. The method for identifying a high-standard farmland construction potential area facing homeland planning according to claim 1, wherein in step 1, extracting a farmland area based on remote sensing data further comprises the steps of:
step 11, obtaining high-precision farmland distribution data of 30m of a plurality of typical years;
step 12, binarizing the data of a plurality of typical years, marking the cultivated land grid area as 1, marking the uncultivated land grid area as 0, and marking the cut land grid area as a data set { C } year }；
And 13, constructing an information map of the data according to the order from the near to the far of the year, and acquiring a farmland area based on a new data set constructed by the information map.
3. The method for identifying a high-standard farmland construction potential area facing homeland planning according to claim 2, wherein in step 1, the information map is:
wherein year represents the last typical year in the study period; n represents a time interval; i represents the serial number of N typical year data; a represents a new information map data set consisting of spatial distribution maps of all the annual farmland.
4. The method for identifying a high-standard farmland construction potential area facing homeland planning according to claim 3, wherein in step 2, the evaluation index comprises Slope, elevation DEM, precipitation P, shallow groundwater GW, surface available water AW, vegetation coverage fCV, evapotranspiration ET, soil volume weight BD, soil organic carbon SOC, soil total nitrogen content TN, sand content Sand, clay content Clay, and soil pH.
5. The method for identifying a high-standard farmland construction potential area for homeland planning according to claim 4, wherein in step 2, the Slope is expressed as:
wherein the Slope is a function based on elevation,and->The rate of change of the calculated gradient pixels in the warp and weft directions, respectively.
6. The method for identifying a high-standard farmland construction potential area for homeland planning according to claim 5, wherein in step 2, the vegetation coverage fCV is expressed as:
wherein fCV is vegetation coverage, NDVI is normalized vegetation index, NDVI max And NDVI min The NDVI constant values for the most exuberant surface coverage and bare earth conditions without surface coverage, respectively.
7. The method for identifying a high-standard farmland construction potential area facing homeland planning of claim 6, wherein in step 3, the discrimination model is:
PR wff ＝f(Slope,DEM,P,GW,AW,fCV,ET,BD,SOC,TN,Sand,Clay,pH)
wherein PR is PR wff Representing the identified high-standard farmland construction potential area; DEM is elevation; slope is a gradient calculated by a Gao Chengjing gradient calculation formula; p is precipitation; GW is shallow groundwater; AW is surface available water; fCV is vegetation coverage; ET is the amount of evapotranspiration; BD is soil volume weight; SOC is soil organic carbon; TN is the total nitrogen content of the soil; sand is Sand content; clay is the cosmid content; the pH is the pH of the soil.
8. The method for identifying a high-standard farmland construction potential area for homeland planning according to claim 7, wherein in step 4, the constructed gradient-vegetation coverage comprehensive evaluation index model is:
SF＝norS+norF
wherein norS and norF are normalized Slope and vegetation coverage index, slope is Slope, respectively max Is the maximum Slope constant, slope min Is the minimum slope constant, fCV is vegetation coverage, fCV min Is the minimum vegetation coverage constant, fCV max Is the maximum vegetation coverage constant.
9. The method for identifying a high-standard farmland construction potential area facing homeland planning of claim 8, wherein in step 5, the high-standard farmland construction potential areas are arranged from large to small according to gradient-vegetation coverage comprehensive evaluation indexes to obtain construction potential area priorities;
and the construction progress is developed every year according to the upper planning requirement, and the construction potential areas are classified according to the construction potential area priority.
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