CN115204547A - Method for determining suitability of farmland in hilly and mountainous areas based on high-resolution remote sensing image - Google Patents
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Abstract
The invention discloses a method for determining the arable land suitability in hilly and mountainous areas based on high-resolution remote sensing images, which comprises the following steps: calculating four values of a land area A, a land shape S, a land length-width ratio LW and a land height difference H of the target area; comparing the four values with a threshold value respectively to obtain four index values of a plot area index AI, a plot shape index SI, a plot length-width ratio index LWI and a plot height difference index HI; if all the index values are 1, the optimization is determined. The method comprises the steps of selecting land suitability machine evaluation indexes from 4 dimensions closely related to agricultural machinery operation in land shape, area, length-width ratio and height difference, drawing land boundaries of minimum units of a sample area based on ArcGIS by utilizing a high-resolution remote sensing image and high-precision DEM data of the sample area, calculating land relevant evaluation index values, and comprehensively determining land suitability machine of the sample area by utilizing a Boolean type assignment mode and a short plate principle.
Description
Technical Field
The invention belongs to the field of intelligent planting, and particularly relates to a method for determining land suitability in hilly and mountainous areas based on high-resolution remote sensing images.
Background
At present, the research of farmland transformation and evaluation in the academic world mainly focuses on the high-standard farmland construction aspect, including pre-construction evaluation and post-construction evaluation. In the aspect of assessment before high-standard farmland construction, farmland improvement target area development parameters are mainly collected from land utilization structures, farmland spatial patterns, improvement of production capacity, soil environment quality, population social factors, terrain conditions and the like, high-standard farmland construction conditions are comprehensively analyzed, farmland improvement potentials such as newly added farmland quantity, farmland quality improvement, grain productivity improvement and the like are measured and calculated, the difficulty degree of improvement of a target area is comprehensively evaluated, and a decision basis is provided for site selection and priority arrangement of farmland improvement projects. In the aspect of evaluation after high-standard farmland construction, the ecological environment effect after farmland renovation is more concerned, including soil physicochemical property change, soil nutrient change, farmland ecosystem change and the like, and partial scholars think that the soil renovation has changed greatly, especially can aggravate the regional unbalanced problem of farmland soil physicochemical property, but the unbalanced phenomenon migrates to diminish gradually along with time. The heterogeneity of soil nutrients can be increased in farmland improvement, spatial variation of different degrees such as soil pH, salinity, nitrogen, phosphorus, potassium and the like can be caused, an originally stable farmland ecosystem can be destroyed, but the farmland facility level after farmland improvement is greatly improved, and the farmland facility level comprises basic facilities such as water conservancy facilities, ecological protection conditions and machine ploughing channels. The literature for systematically evaluating the land rationalization level is not inquired, the literature for evaluating the farmland facility level is mainly focused on the aspect of water conservancy facilities, only relates to the accessibility of tractor-ploughed channels related to rationalization, and has no explanation on the land operation rationalization. Farmland computerized reconstruction has begun to test points in part of hilly and mountainous areas, but due to the lack of related evaluation methods and standards, popularization and application of test point project achievements are not facilitated.
Disclosure of Invention
The invention provides a hilly and mountainous area cultivated land informatization determination method based on a high-resolution remote sensing image, aiming at the problems in the background art.
The technical scheme is as follows:
a method for determining the arable land suitability in hilly and mountainous areas based on high-resolution remote sensing images comprises the following steps:
calculating four values of a land area A, a land shape S, a land length-width ratio LW and a land height difference H of the target area;
comparing the four values with a threshold value respectively to obtain four index values of a plot area index AI, a plot shape index SI, a plot length-width ratio index LWI and a plot height difference index HI;
if all the index values are 1, the optimization is determined.
Specifically, the method comprises the following steps:
s1, acquiring aerial photography data of a target region;
s2, using data processing software to draw aerial photography data into space figures of the land parcel closed boundary of the target region and other terrain elements, filling relevant attributes, and obtaining a vector attribute table;
s3, acquiring four numerical values of a plot area A, a plot shape S, a plot length-width ratio LW and a plot height difference H of the target area based on the vector attribute table;
s4, comparing the four values with a threshold value respectively to obtain four index values of a plot area index AI, a plot shape index SI, a plot length-width ratio index LWI and a plot height difference index HI;
and S5, outputting a result of the computerization appropriateness judgment of the target area based on the four index values.
Specifically, in S1, the aerial photography data is high-precision DEM data generated by LIDAR airborne laser radar data.
Specifically, in S2, the data processing software is ArcGIS.
Specifically, in S2, an ArcGIS space working platform and a space analysis tool are used, and spatial patterns of the land parcel closed boundary of the target region and other terrain elements are drawn through technical means of space reading, space superposition and space analysis, and relevant attributes are filled in, so as to obtain a vector attribute table.
Specifically, in S3:
land area a: obtaining a closed vector boundary of each land parcel based on a vector attribute table, and calculating the area in each closed vector boundary by using an ArcGIS space analysis method, namely the land parcel area A;
land shape S: fitting to obtain the minimum external rectangle of the enclosed boundary of the land parcel by utilizing the spatial analysis means of ArcGIS, and calculating to obtain the area A of the minimum external rectangle MER Then, the area A of the land parcel and the minimum circumscribed rectangle area A thereof are calculated MER The ratio is called the rectangularity of the land parcel, namely the land parcel shape S;
plot aspect ratio LW: obtaining the length L and the width W of the minimum external rectangle of the land parcel by utilizing an ArcGIS space analysis means, and calculating the length-width ratio of the minimum external rectangle, namely the length-width ratio LW of the land parcel;
land block height difference H: and (3) providing the area of each plot in the vector attribute table, acquiring the minimum circumscribed rectangle of each plot through a spatial attribute calculation tool, nesting DEM data of corresponding regions, calculating the average elevation of each plot, calculating the elevation difference between adjacent plots, and acquiring a plot elevation difference index H.
Specifically, in S4, the index value that satisfies the threshold requirement is encoded as 1, and the index value that does not satisfy the threshold requirement is encoded as 0.
Specifically, in S5, when all the index values are 1, the optimization is determined.
The invention has the advantages of
The method comprises the steps of selecting land suitability machine evaluation indexes from 4 dimensions closely related to agricultural machinery operation in land shape, area, length-width ratio and height difference, drawing land boundaries of minimum units of a sample area based on ArcGIS by utilizing a high-resolution remote sensing image and high-precision DEM data of the sample area, calculating land relevant evaluation index values, and comprehensively determining land suitability machine of the sample area by utilizing a Boolean type assignment mode and a short plate principle.
The invention takes the sampled video data of the sample area as the basis to carry out digital analysis and objectively judge whether the sample area is suitable for organization. Compared with the traditional subjective judgment, the method has higher efficiency, scientificity and accuracy. The method can be used for evaluation of high-standard farmland construction projects and farmland computerization transformation projects, the traditional manual surveying and mapping method is large in workload and high in cost due to the wide farmland distribution, the field operation workload can be effectively reduced and the early-stage investment cost of the project can be reduced by adopting the method for evaluation, meanwhile, the scientific design of the evaluation index guarantees the scientificity of the evaluation method, and the application of ArcGIS technology and high-resolution remote sensing images also fully guarantees the accuracy of the evaluation result.
Drawings
FIG. 1 is a distribution diagram of sampling villages in the example
FIG. 2 is a diagram of the distribution of a part of sampling village plots in the example
Detailed Description
The method of the present invention is further illustrated by the following examples, but the scope of the present invention is not limited thereto:
nanjing is located along the bank of the lower reaches of Yangtze river, north Lianjiang Huai plain, east China Yangtze river delta, nanjing territorial area 658,231.3 hectare, agricultural land 438039.0 hectare, and occupies 66.5% of the territorial area. Wherein, cultivated land 24593.1 hectare, garden 9404.1 hectare, forest land 73927.9 hectare, other agricultural land 109063.1 hectare account for 37.31%, 1.43%, 11.23%,16.57% of the territorial area respectively. The basic farmland is mainly distributed in six-Hei, jiangning, lishui, high pure and Pukou regions, and the total is 220389.9hm 2 Accounts for 96.92 percent of the total area of the basic farmlands in the whole city, wherein the distribution proportion of the basic farmlands in the Liuhe and Jiangning areas is the largest,
data source and processing method
The study data of the examples mainly derive from 0.3 meter aerial photography data covering the entire world of Nanjing in 2018 (scale, 1. The data processing software is ArcKis 10.2, and the software running computer is configured to be core i7CPU and 8G memory. 2 natural villages are randomly extracted from 5 agroing areas, namely a Liuhe area, a Pugong area, a Jiangning area, a high pure area and a Lishui area in Nanjing, 10 sampling villages are counted, the distribution of the specific sampling villages is shown in figure 1, and the general condition of the farmland in the sampling villages is shown in table 1.
The method comprises the steps of respectively drawing space graphs of the enclosed boundaries of plots of the sampling village and other topographic elements by using an ArcGIS space working platform and a space analysis tool through technical means such as space reading, space superposition, space analysis and the like, filling related attributes, providing the area of each drawn plot in a vector attribute table, calculating the minimum circumscribed rectangle of each drawn plot through related space attribute calculation tools and means such as area, length, width and the like, nesting DEM data of corresponding areas, and calculating the average elevation of each plot through technical means such as correction, adjustment and the like.
TABLE 1 sampling village farmland general conditions
(II) evaluation index
The agricultural rural part issues 'land and mountain area farmland should organize work guide', and the standard of should organizing transformation of hilly mountain area farmland plots and machine-ploughed channels is proposed: 1) The shape of the land is as follows: taking a rectangle as a principle; 2) The land size is as follows: the length of the short side is more than 5m, the length-width ratio is 3-5. 3) Maximum cut and fill height: less than or equal to 2m. 4) The direct access rate of the finished land production road is 100%. In view of this, the following indexes for the computerization evaluation of cultivated land in Nanjing are proposed:
1. plot Area Index (AI). In recent years, agricultural machinery in China is developing towards large and medium-sized machines, the operation width of the agricultural machinery is increased, the land area is too small, the turning flexibility of the agricultural machinery is affected, and the efficiency advantage of large and medium-sized machines cannot be fully exerted. The larger the land area is, the higher the operating efficiency of the agricultural machine is. Therefore, the measured value of the land area A is used to represent the land area computability degree. According to the requirement of 'suitable machine work guide for farmland in hilly and mountainous areas' that the cultivated land area is more than 0.07 hectare and the area in the sampled land is more than 667m 2 The number of the blocks is 910 blocks, which accounts for the total number of the sampled blocks87.5 percent of the total weight. Because the 'work guide' issued by the agricultural rural ministry is more oriented to areas with poorer terrain conditions such as southwest hilly and mountainous areas, and the land occupation ratio in the Nanjing terrain is higher, the area limit value of the Nanjing plot is larger, the operation width (3 m) of the combine harvester with the largest Nanjing reserve is taken as a reference, the optimized cultivated land parameters can meet the requirement that the harvester has 5 round-trip harvesting swaths, and the turning performance is not limited. The area parameter of the Nanjing plot is determined according to the maximum recommended by the guide, the short side is 30m, the long side is 150m, the area is 0.45 hectare, and the AI value of the plot area index can be calculated by the formula (1).
2. Parcel Shape Index (SI). When the harvester walks linearly, the operation is simple, the operation efficiency is high, the motion track and the cutting width form a rectangle, and therefore, the shape of the land is closer to the rectangle, and the operation efficiency of the agricultural machine is higher. The degree of rectangularity is used for representing the degree of computability of the land shape, and the degree of rectangularity R is represented by land area A and minimum circumscribed rectangle area A thereof MER The ratio of the two is expressed by the formula (2).
Wherein R represents the rectangularity of the land shape; a represents the actual area of the land, m 2 ；A MER Represents the minimum circumscribed rectangular area, m, of the land mass 2 . The rectangular degree value range is 0<R is less than or equal to 1, and when the shape of the ground block is rectangular, R =1; the more irregular the shape of the block, the smaller the value of R. The more similar the cultivation rectangle degree is, the better, but as the difficulty of transforming partial terrains into rectangular plots is higher, the Suwenjie (2017) samples and surveys the agricultural land in Jiangsu region, and the better the rectangle degree of the Jiangsu plot is not less than 0.75 [31] . The land shape index SI value can be calculated by equation (3).
3. Plot aspect ratio index (LWI). The ploughing width must not be less than the working width of the agricultural machine, and the agricultural machine can work in the field. The times of turning around and turning the agricultural machine is positively correlated with the width of the cultivated land. Because effective operation cannot be formed when the agricultural machinery turns around and turns, the loss link of the operating efficiency of the agricultural machinery is mainly in the turning and turning stage. When the length-width ratio of the ground blocks is larger, the proportion of the turning time to the total operation time is smaller, and the operation efficiency loss of the agricultural machine is smaller. Because the farmland is complicated in shape and the length and the width of part of the land are more unavailable, the aspect ratio LW of the minimum circumscribed rectangle of the land is used for representing the optimization degree of the land aspect ratio for measurement. The guideline states that the aspect ratio of the plot should not be less than 3, and that the LWI value of the plot aspect ratio index can be calculated from equation (4).
4. Plot height difference index (HI). Uneven terrain is a typical characteristic of hilly and mountainous areas, paddy fields distributed under the terrain conditions show that different land parcels are not at the same altitude, and the adjacent land parcels have height difference, so that the field transfer efficiency of agricultural machinery is influenced, and even the life and property safety of farmers is threatened. Thus, the plot average height difference h is used herein to characterize the plot height difference, as in equation (5).
In the formula, h i Representing the average height difference, m, of the ith sampling region land; al imax Representing the maximum elevation, m, of the plot in the ith sampling zone; al im.n Representing the minimum elevation, m, of the plot in the ith sampling zone; m is i Representing the number of plots spaced between the maximum elevation and minimum elevation plots for the ith sample area. Under the condition that the field height difference is less than 0.5m, the old clever harvester and the like (2015) can smoothly complete field transfer of all machine type crawler-type combine harvesters [34] . Therefore, the block height difference index HI value can be calculated by equation (6).
(III) Boolean assignment comprehensive evaluation system
The land parcel is taken as an evaluation basic unit, 4 indexes such as the land parcel shape, the land parcel area, the land parcel length-width ratio, the land parcel height difference and the like are sequenced one by one, and the 4-bit coding mode is adopted to assign the evaluation indexes of the land parcel one by one from left to right, wherein each bit of code represents the index value of the corresponding evaluation index. Carrying out comprehensive dimension evaluation by using a short plate principle, and representing that a target land block meets the requirement of optimization if and only if 4-bit coding values of an evaluation field block are all 1; on the other hand, the evaluation plots are insufficient in one or more aspects such as area and shape, and do not satisfy all the computerization appropriateness requirements. Based on the above, a coding value system for arable land rationalization evaluation is established, and the coding value system is used for analyzing the rationalization degree of the arable land from multiple dimensions (see table 2 for details).
TABLE 2 coding values and meanings of comprehensive evaluation indexes suitable for mechanized farming
(IV) the assessment results of suitable cultivation
(4-1) sampling total distribution of farmland data
And loading the image data of the sampling villages into ArcGIS, drawing a certain number of land parcel boundaries for each village, identifying land parcel elevations by the ArcGIS, and calculating the cultivated land area, wherein the detailed table is shown in figure 2.
FIG. 2 shows the distribution of the sample village plots p1, p2, and p3, where a single closed graphic unit is a plot, the data marked in the plot is the elevation data, and the higher the elevation change rule in the plot is, the darker the color of the area is.
TABLE 3 sample village plot materialization general situation
As can be seen from table 3, the sampled village farmland as a whole does not meet the rationalization standards in terms of plot area, shape and aspect ratio, and the elevation difference can meet the rationalization requirements.
(4-2) evaluation results of suitability of cultivated land
And calculating the evaluation value of each land block of the sampling village by a Boolean assignment comprehensive evaluation method, and details are shown in Table 4.
TABLE 4 land optimization evaluation results of sampling village
From the plot area, 168 plots in 1040 plots have an area not less than 0.45hm2 and account for 16.15% of the total plots, are mainly distributed in Jiang Jia dun and oil squeezing village near the southern Maoshan, and are distributed in the middle northeast village in a small amount. From the shape of the land, the rectangle degree of 148 land is not less than 0.75, accounts for 14.23% of the total land, and is mainly distributed in the small Bao village and the West King village in the six-in-one area. From the aspect ratio of the land, the aspect ratio of 46 lands is not less than 3, accounts for 4.42 percent of the total land, and is mainly distributed in south kiln village of Jiangning district. From the plot height difference, 602 plots have height difference smaller than 0.5m and account for 57.88% of the total plot.
From the comprehensive evaluation, the number of the fully realized 'computerized' (coded into 1111) plots in 1040 plots is 0; the number of plots that are completely out of order (coded as 0000) is 288, accounting for 27.69% of the total plots; the number of the plot blocks with 25% of the optimization level (coding value of 0001/0100/1000) is 551, and the total plot block accounts for 52.98%; the number of the land blocks with the optimization level of 50% (coding values of 0011/0101/0110/1001/1100) is 174, accounting for 16.73% of the total land blocks; the number of plots for the optimum level of organization 75% (code value 0111/1101) was 27, accounting for 2.60% of the total plot.
By utilizing the technology described by the invention, the computerization-applicable transformation scale and distribution condition of the grain fields in hills and mountainous areas of Nanjing are measured and calculated, computerization-applicable transformation guide of the grain fields in hills and mountainous areas of Nanjing is formulated according to the measurement and calculation result, and the grain fields are published by agricultural rural bureaus of Nanjing.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments, or alternatives may be employed, by those skilled in the art, without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (8)
1. A method for determining land suitability in hilly and mountainous areas based on high-resolution remote sensing images is characterized by comprising the following steps of
Calculating four values of a land area A, a land shape S, a land length-width ratio LW and a land height difference H of the target area;
comparing the four values with a threshold value respectively to obtain four index values of a plot area index AI, a plot shape index SI, a plot length-width ratio index LWI and a plot height difference index HI;
if all the index values are 1, the optimization is determined.
2. Method according to claim 1, characterized in that it comprises the following steps:
s1, acquiring aerial photography data of a target region;
s2, using data processing software to draw aerial photography data into space graphs of the land parcel closed boundary of the target region and other terrain elements, filling related attributes, and obtaining a vector attribute table;
s3, acquiring four numerical values of a plot area A, a plot shape S, a plot length-width ratio LW and a plot height difference H of the target area based on the vector attribute table;
s4, comparing the four values with a threshold value respectively to obtain four index values of a plot area index AI, a plot shape index SI, a plot length-width ratio index LWI and a plot height difference index HI;
and S5, outputting the optimization judgment result of the target area based on the four index values.
3. The method according to claim 2, wherein in S1 the aerial photography data is high precision DEM data generated from LIDAR airborne LIDAR data.
4. The method according to claim 2, wherein in S2, the data processing software is ArcGIS.
5. The method of claim 4, wherein in S2, an ArcGIS space working platform and a space analysis tool are used to draw a space pattern of the land parcel closed boundary of the target region and other topographic features through technical means of space reading, space superposition and space analysis, and fill in related attributes to obtain a vector attribute table.
6. The method according to claim 2, characterized in that in S3:
land area a: obtaining a closed vector boundary of each land parcel based on a vector attribute table, and calculating the area in each closed vector boundary by using an ArcGIS space analysis method, namely the land parcel area A;
land shape S: fitting to obtain the minimum external rectangle of the enclosed boundary of the land parcel by utilizing the spatial analysis means of ArcGIS, and calculating to obtain the area A of the minimum external rectangle MER Then, the area A of the land parcel and the minimum circumscribed rectangle area A thereof are calculated MER The ratio of (a) is called the rectangularity of the land, namely the land shape S;
plot aspect ratio LW: obtaining the length L and the width W of the minimum external rectangle of the land parcel by utilizing an ArcGIS space analysis means, and calculating the length-width ratio of the minimum external rectangle, namely the length-width ratio LW of the land parcel;
land block height difference H: and (3) providing the area of each plot in the vector attribute table, acquiring the minimum circumscribed rectangle of each plot through a spatial attribute calculation tool, nesting DEM data of corresponding regions, calculating the average elevation of each plot, calculating the elevation difference between adjacent plots, and acquiring a plot elevation difference index H.
7. The method according to claim 2, wherein in S4, the index value satisfying the threshold requirement is encoded to be 1, and the index value not satisfying the threshold requirement is encoded to be 0.
8. The method according to claim 7, wherein in S5, the optimization is determined when all the index values are 1.
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