CN117689224A - A method for identifying high-standard farmland construction potential areas for land planning - Google Patents

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

A method for identifying high-standard farmland construction potential areas for land planning

Technical field

The invention relates to the fields of agricultural geography and remote sensing science and technology, and in particular to a method for identifying high-standard farmland construction potential areas for land planning.

Background technique

High-standard farmland refers to fields that are flat, concentrated and contiguous, with complete facilities, supporting agricultural and electricity supplies, fertile soil, eco-friendly, strong disaster resistance, guaranteed harvests during droughts and floods, stable and high yields that are compatible with modern agricultural production and management methods, and are designated as basic farmland. Cultivated land focuses on the consolidation and restoration of existing farmland or potential farmland through engineering and technical means.

At present, the identification technology for high-standard farmland construction areas is basically divided into three categories:

First, start from the quality of cultivated land, obtain the existing cultivated land quality index, and compare it with the regional cultivated land standard quality plots, so as to obtain the farmland with the greatest potential for improving cultivated land quality as a high-standard farmland construction area; second, stay at the high-standard farmland construction area At the level of delineation of farmland, farmland attributes are divided solely based on qualitative indicators, and the establishment of the indicator system is relatively single, which cannot better meet the construction principles of high-standard farmland. The third is to select judgment indicators based on remote sensing images to use this to classify farmland. The data is numerically operated, and after normalization and weighting, a comprehensive index system for identifying high-standard farmland is obtained, and based on this, high-standard farmland construction areas are divided. The first two identification technologies cannot take into account the boundaries of farmland and land use zoning, and lack spatial control and guidance when facing land planning.

The third method makes up for the shortcomings of the first two technologies to a certain extent, but it still cannot take into account the spatial form of farmland and the quality of farmland, and there are limitations in the selection of judgment factors. It cannot fully meet the construction principles of high-standard farmland. There are great limitations when serving land spatial planning and subsequent high-standard farmland construction.

Contents of the invention

The present invention provides a method for identifying high-standard farmland construction potential areas for land planning. It combines qualitative analysis with quantitative analysis, and by integrating multi-source information data, it establishes evaluation indicators based on the delineation principles of high-standard farmland and constructs a method to identify high-standard farmland. A geographical mathematical model of the standard farmland construction area in order to realize regional identification, explore high-standard farmland construction potential areas and future regional development directions, and help coordinate and optimize the construction layout and rationally arrange the construction sequence.

Specifically, the present invention proposes a method for identifying high-standard farmland construction potential areas for land planning. The method for identifying high-standard farmland construction potential areas for land planning includes the following steps:

Step 1: Extract farmland areas based on survey data or remote sensing data;

Step 2: Construct a high-standard farmland evaluation index system containing multiple indicators, and unify all evaluation index data and farmland areas into raster data with the same spatial resolution;

Step 3: Based on the farmland area and evaluation index data, build an overall discrimination model for high-standard farmland potential area identification, and identify high-standard farmland construction potential areas based on this model;

Step 4: Construct a slope-vegetation coverage comprehensive evaluation index model;

Step 5: Determine the priority and priority of construction potential areas in farmland areas;

Step 6: Carry out mapping based on the construction potential priority of farmland areas and their spatial geographical location, and generate an annual high-standard farmland construction map.

Furthermore, in step 1, extracting farmland areas through remote sensing data also includes the following steps:

Step 11: Obtain 30m high-precision farmland distribution data in multiple typical years;

Step 12: Binarize the data of multiple typical years. The cultivated land raster area is marked as 1 and the non-arable land raster area is marked as 0, which is recorded as data set {C _year };

Step 13: Construct an information map of the data in order of years from near to far, and obtain farmland areas based on the new data set constructed from the information map.

Furthermore, in step 1, the information map is:

Among them, year represents the most recent typical year in the study period; n represents the time interval; i represents the serial number of N typical year data; A represents a new information map data set composed of cultivated land spatial distribution maps in all years.

Furthermore, in step 2, the evaluation indicators include slope, elevation DEM, precipitation P, shallow groundwater GW, surface available water AW, vegetation coverage fCV, evapotranspiration ET, soil bulk density BD, soil organic matter Carbon SOC, soil total nitrogen content TN, sand content Sand, clay content Clay, soil pH.

Furthermore, in step 2, the slope Slope is expressed as:

Among them, slope Slope is a function based on elevation, and/> are the change rates of the calculated slope pixels in the meridional and latitudinal directions respectively.

Furthermore, in step 2, the vegetation coverage fCV is expressed as:

Among them, fCV is the vegetation coverage, NDVI is the normalized vegetation index, NDVI _max and NDVI _min respectively represent the NDVI constant values under the most lush surface coverage and bare soil without surface coverage.

Furthermore, in step 3, the discriminant model is:

PR _wff =f(Slope,DEM,P,GW,AW,fCV,ET,BD,SPC,TN,Sand,Clay,pH)

Among them, PR _wff represents the identified high-standard farmland construction potential area; DEM is the elevation; Slope is the slope, which is calculated by the elevation through slope calculation formula; P is precipitation; GW is shallow groundwater; AW is surface available water; fCV is Vegetation coverage; ET is evapotranspiration; BD is soil bulk density; SOC is soil organic carbon; TN is soil total nitrogen content; Sand is sand content; Clay is clay content; pH is soil pH value.

Furthermore, in step 4, the constructed slope-vegetation coverage comprehensive evaluation index model is:

SF＝norS+norF

Among them, norS and norF are the normalized slope and vegetation coverage index respectively, Slope is the slope, Slope _max is the maximum slope constant, Slope _min is the minimum slope constant, fCV is the vegetation coverage, fCV _min is the minimum vegetation coverage constant, fCV _max is the maximum vegetation coverage constant.

Furthermore, in step 5, the high-standard farmland construction potential areas are arranged from large to small according to the slope-vegetation coverage comprehensive evaluation index to obtain the priority of the construction potential areas;

And according to the annual construction progress required by the upper-level planning, the construction potential areas are graded according to the priority order of the construction potential areas.

The beneficial effects achieved by the present invention are:

Compared with traditional statistical methods and existing conventional methods, the present invention has developed a method for identifying high-standard farmland construction potential areas for land planning. Practical experiments have proved that the method of the present invention can produce high-standard farmland construction principles. High-standard farmland construction potential areas, and data products can also directly provide data and method support for the delineation of cultivated land red lines in territorial spatial planning.

Description of the drawings

Figure 1 is a schematic flow chart of a method for identifying high-standard farmland construction potential areas for land planning provided by an embodiment of the present invention;

Figure 2 is a schematic diagram of an information map construction method in a method for identifying high-standard farmland construction potential areas for land planning provided by an embodiment of the present invention;

Figure 3 is a schematic diagram of the evaluation index system in a method for identifying high-standard farmland construction potential areas for land planning provided by an embodiment of the present invention;

Figure 4 is a schematic diagram of a high-standard farmland potential area in a method for identifying high-standard farmland construction potential areas for land planning provided by an embodiment of the present invention;

Figure 5 is a schematic diagram of the priority order and priority arrangement of construction potential areas in a method for identifying high-standard farmland construction potential areas for land planning provided by an embodiment of the present invention;

Figure 6 is a map of hierarchical annual construction potential areas in a method for identifying high-standard farmland construction potential areas for land planning provided by the embodiment of the present invention.

Detailed ways

The technical solution of the present invention will be described in more detail below with reference to the accompanying drawings. The present invention includes but is not limited to the following embodiments.

As shown in Figure 1, the present invention provides a method for identifying high-standard farmland construction potential areas for land planning. The method includes the following steps:

Step 1: Extract farmland areas based on survey data or remote sensing data.

If there is "Three Adjustments" (end of 2019) cultivated land data in the study area, stable cultivated land identification can also be directly replaced by the "Three Adjustments" cultivated land data.

However, due to restrictions on sharing mechanisms and other conditions, it is usually difficult to obtain a wide range of "three adjustments" data. The present invention also provides a farmland area extraction method based on shared data.

Step 11: Obtain 30m high-precision farmland (cultivated land) distribution data for N typical years (such as 2010, 2015, 2020, etc.);

Step 12, generate a cultivated land raster data set for each year;

As shown in Figure 2, the data of each year in the N typical years are binarized separately. The cultivated land raster area is marked as 1, and the non-cultivated land raster area is marked as 0, which is recorded as the data set {C _year } , C _year is a data set with spatial characteristics, including three dimensions of geographical location information and numerical information.

Step 13: Construct an information map of the data in the order of recent and distant years. The construction method is expressed by the following formula:.

Among them, year represents the most recent typical year in the study period; n represents the time interval, such as: every year (n=1) or every five years (n=5); i represents the serial number of N typical year data; A represents the cultivated land in all years A new information map data set composed of spatial distribution maps.

A new data set constructed based on the information map is used to obtain farmland areas.

Step 2: Construct a high-standard farmland evaluation index system that takes into account the three dimensions of natural conditions, ecological background and soil conditions, and process all data for spatial resolution consistency.

Obtain the normalized vegetation index (NDVI), elevation (DEM,), slope (Slope), precipitation data (P), land water storage information (GW), vegetation coverage (fCV), and actual evapotranspiration (ET) of the farmland area ), soil bulk density (BD), soil organic carbon (SOC), soil texture (Sand, Clay, Silt), soil total nitrogen content (Total Nitrogen, TN) and soil pH (pH), etc. Data is used to generate evaluation indicators.

The evaluation indicators of the present invention include slope 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, and sand particles Sand content, clay content, soil pH.

Among them, the elevation DEM, precipitation data P, land water storage information GW, actual evapotranspiration ET, soil bulk density BD, soil organic carbon SOC, sand content Sand, clay content Clay, total soil nitrogen content TN and soil pH can be Raster data obtained directly;

Surface available water AW is calculated from precipitation data P and actual evapotranspiration ET;

Slope is calculated by the elevation formula (2):

Among them, slope Slope is a function based on elevation, and/> are the change rates of the calculated slope pixels in the meridional and latitudinal directions respectively;

fCV is calculated by the Normalized Difference Vegetation Index (NDVI), referring to formula (3):

fCV is the vegetation coverage. In formula (3), NDVI _max and NDVI _min are constants, which represent the NDVI values under the most lush surface coverage and bare soil without surface coverage respectively. They are set to 0.95 and 0.05 respectively. The data corresponding to all evaluation indicators are unified into raster data that is the same and consistent with the spatial resolution of the cultivated land data using the bilinear interpolation method; in this implementation, the spatial resolution raster is 30m, so that subsequent steps can be performed in the 30m space Unified mathematical operations are performed on the resolution raster.

Step 3: Identify high-standard farmland potential areas.

As shown in Figure 3-4, based on the 30m spatial resolution raster data in steps 1 and 2, and with the help of the overlay analysis method, an overall discrimination model for high-standard farmland potential area identification is constructed, and high-standard farmland is identified based on this model. Areas of construction potential.

Select farmland areas that meet all evaluation indicators as high-standard farmland construction potential areas, and the mathematical expression of the discrimination model is as follows:

PR _wff =f(Slope,DEM,P,GW,AW,fCV,ET,BD,SOC,TN,Sand,Clay,pH) (4)

Among them, PR _wff represents the identified high-standard farmland construction potential area; DEM is the elevation; Slope is the slope; P is precipitation; GW is shallow groundwater; AW is surface available water; fCV is vegetation coverage; ET is evapotranspiration; BD is soil bulk density; SOC is soil organic carbon; TN is soil total nitrogen content; Sand is sand content; Clay is clay content; pH is soil pH.

So far, the potential areas for high-standard farmland construction in the study area have been delineated. However, in the actual land planning and utilization process, the construction of high-standard farmland is carried out in batches at different times according to the local government's plan and budget capacity, and is not all completed within the same period. Therefore, in order to meet the needs of high-standard farmland construction in real land planning, we further developed the following methods.

Step 4: Construct a slope-vegetation coverage comprehensive evaluation index model (SF).

Taking into account the issues of priority and priority in actual land planning work, it is necessary to prioritize and delineate all high-standard farmland construction potential areas in the study area. Simply put, this is a question of sorting potential construction grid areas. Therefore, we numerically rank the areas to be evaluated for construction potential by constructing a slope-vegetation coverage comprehensive evaluation index model. It is worth noting that the 13 indicators that were superimposed and analyzed at the beginning were reduced to 2 indicators (slope and vegetation coverage). The main reasons are as follows: 1. Slope is an important indicator for high-standard farmland construction. Developing farmland on too steep slopes can easily cause natural disasters such as water and soil erosion, and is inconvenient for farming. Therefore, the gentler the slope, the priority for high-standard farmland construction in the corresponding area. The higher the level; second, vegetation coverage is an index that comprehensively reflects the vegetation growth status and surface coverage degree obtained by numerical transformation of the normalized vegetation index. It comprehensively reflects the soil fertility and moisture conditions of a certain area to a certain extent. , temperature conditions and other comprehensive indicators of natural habitats. Therefore, the greater the vegetation coverage, the higher the priority for high-standard farmland construction in the corresponding area.

In view of the above considerations, a slope-vegetation coverage comprehensive evaluation index model is constructed, and the equation is as follows:

SF＝norS+norF (5)

Among them, norS and norF are the normalized slope and vegetation coverage index respectively, Slope is the slope, Slope _max is the maximum slope constant, taken as 25°, Slope _min is the minimum slope constant, taken as 0°, fCV is the vegetation coverage, fCV _min is the minimum vegetation coverage constant, which is taken as 0.4, and fCV _max is the maximum vegetation coverage constant, which is taken as 1.0. The larger the SF value, the greater the potential of the cultivated land to be developed into high-quality cultivated land.

Step 5: Determine the priority and priority of areas with construction potential.

Step 4 has already digitized all high-standard farmland construction potential areas in the study area, which lays a good foundation for the determination of priority and priority in this step.

As shown in Figure 5, in one embodiment, all farmland rasters in the research area space are sorted with the help of bubble sorting method. For example, the MATLAB software provides the sort() function to assist in realizing this sorting function. The sorting rule is defined as descending order (descend), so that the slope-vegetation coverage comprehensive evaluation index (SF) is arranged from large to small, and the large ones are ranked at the front of the team to determine the priority of the construction potential areas.

There are two main reasons for this consideration: first, priority is given to completing the quantitative goals proposed by the upper-level plan. Usually, in farmland development planning, priority will be given to development of relatively good quality to complete the quantitative goals proposed by the upper-level plan; second, it is most efficient but Based on the principle of lowest cost, when project funding is limited, priority is given to completing the upper-level planning quantity requirements.

The sorted farmland grids are graded according to the annual high-standard farmland construction progress required by the upper-level plan. In one embodiment, the overall proportion of progress construction area in the five-year plan in the upper-level plan is 35%, 25%, 20%, 10%, and 10% respectively. According to the construction area requirements, the sorted farmland grids can be graded and coded according to the proportion requirements to determine the priority of the construction potential areas.

Step 6: Carry out mapping based on the construction potential priority of farmland areas and their spatial geographical location, and generate an annual high-standard farmland construction map.

As shown in Figure 6, the coded information marking the priority is re-mapped according to its spatial geographical location (latitude and longitude information or row and column number position information). At this point, all the work of identifying high-standard farmland construction potential areas for land planning has been completed.

Compared with traditional statistical methods and existing conventional methods, the present invention has developed a method for identifying high-standard farmland construction potential areas for land planning. Practical experiments have proved that the method of the present invention can both demarcate high-standard farmland construction principles and Standard farmland construction potential areas, and data products can also directly provide data and method support for the delineation of cultivated land red lines in territorial spatial planning.

The present invention is not limited to the above-mentioned specific embodiments. Persons of ordinary skill in the art can adopt various other specific embodiments to implement the present invention based on the disclosure of the embodiments and drawings. Therefore, whoever adopts the design structure and ideas of the present invention, do some Simple transformation or modified design all fall within the scope of protection of the present invention.

Claims (9)

1. A method for identifying high-standard farmland construction potential areas for land planning, characterized in that the method for identifying high-standard farmland construction potential areas for land planning includes the following steps: Step 1: Extract farmland areas based on survey data or remote sensing data; Step 2: Construct a high-standard farmland evaluation index system containing multiple indicators, and unify all evaluation index data and farmland areas into raster data with the same spatial resolution; Step 3: Based on the farmland area and evaluation index data, build an overall discrimination model for high-standard farmland potential area identification, and identify high-standard farmland construction potential areas based on this model; Step 4: Construct a slope-vegetation coverage comprehensive evaluation index model; Step 5: Determine the priority and priority of construction potential areas in farmland areas; Step 6: Carry out mapping based on the construction potential priority of farmland areas and their spatial geographical location, and generate an annual high-standard farmland construction map. 2. The method for identifying high-standard farmland construction potential areas for land planning according to claim 1, characterized in that in step 1, extracting farmland areas based on remote sensing data also includes the following steps: Step 11: Obtain 30m high-precision farmland distribution data in multiple typical years; Step 12: Binarize the data of multiple typical years. The cultivated land raster area is marked as 1 and the non-arable land raster area is marked as 0, which is recorded as data set {C _year }; Step 13: Construct an information map of the data in order of years from near to far, and obtain farmland areas based on the new data set constructed from the information map. 3. The method for identifying high-standard farmland construction potential areas for land planning according to claim 2, characterized in that, in step 1, the information map is: Among them, year represents the most recent typical year in the study period; n represents the time interval; i represents the serial number of N typical year data; A represents a new information map data set composed of cultivated land spatial distribution maps in all years. 4. The method for identifying high-standard farmland construction potential areas for land planning according to claim 3, characterized in that in step 2, the evaluation indicators include slope 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, soil pH. 5. The method for identifying high-standard farmland construction potential areas for land planning according to claim 4, characterized in that, in step 2, the slope Slope is expressed as: Among them, slope Slope is a function based on elevation, and/> are the change rates of the calculated slope pixels in the meridional and latitudinal directions respectively. 6. The method for identifying high-standard farmland construction potential areas for land planning according to claim 5, characterized in that, in step 2, the vegetation coverage fCV is expressed as: Among them, fCV is the vegetation coverage, NDVI is the normalized vegetation index, NDVI _max and NDVI _min respectively represent the NDVI constant values under the most lush surface coverage and bare soil without surface coverage. 7. The method for identifying high-standard farmland construction potential areas for land planning according to claim 6, characterized in that, in step 3, the discrimination model is: PR _wff =f(Slope,DEM,P,GW,AW,fCV,ET,BD,SOC,TN,Sand,Clay,pH) Among them, PR _wff represents the identified high-standard farmland construction potential area; DEM is the elevation; Slope is the slope, which is calculated by the elevation through slope calculation formula; P is precipitation; GW is shallow groundwater; AW is surface available water; fCV is Vegetation coverage; ET is evapotranspiration; BD is soil bulk density; SOC is soil organic carbon; TN is soil total nitrogen content; Sand is sand content; Clay is clay content; pH is soil pH value. 8. The method for identifying high-standard farmland construction potential areas for land planning according to claim 7, characterized in that, in step 4, the constructed slope-vegetation coverage comprehensive evaluation index model is: SF＝norS+norF Among them, norS and norF are the normalized slope and vegetation coverage index respectively, Slope is the slope, Slope _max is the maximum slope constant, Slope _min is the minimum slope constant, fCV is the vegetation coverage, fCV _min is the minimum vegetation coverage constant, fCV _max is the maximum vegetation coverage constant. 9. The method for identifying high-standard farmland construction potential areas for land planning according to claim 8, characterized in that in step 5, the high-standard farmland construction potential areas are classified from large to small based on the slope-vegetation coverage comprehensive evaluation index. Arrange to obtain the priority order of areas with construction potential; And according to the annual construction progress required by the upper-level planning, the construction potential areas are graded according to the priority order of the construction potential areas.