CN112148785B - Crop distribution charting method - Google Patents

Crop distribution charting method Download PDF

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CN112148785B
CN112148785B CN202011016570.1A CN202011016570A CN112148785B CN 112148785 B CN112148785 B CN 112148785B CN 202011016570 A CN202011016570 A CN 202011016570A CN 112148785 B CN112148785 B CN 112148785B
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左丽君
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Aerospace Information Research Institute of CAS
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Abstract

本发明公开了一种作物分布制图，依据遥感数据提取的栅格尺度作物种植模式信息以及相应行政单元该种植模式上各类作物的面积比例，计算每个栅格上各类作物的面积，从而制作作物空间分布图。本发明将遥感识别的作物物候特征与作物实际物候结合，以确定作物空间分布状况，能够通过遥感宏观、快速获取、多尺度等特征，实现大范围、多尺度、且具有较高空间异质性的作物空间分布制图。

Figure 202011016570

The invention discloses a crop distribution map. According to grid-scale crop planting pattern information extracted from remote sensing data and the area ratio of various crops in the planting pattern of a corresponding administrative unit, the area of various crops on each grid is calculated, thereby Make a crop spatial distribution map. The invention combines the crop phenology characteristics identified by remote sensing with the actual crop phenology to determine the spatial distribution of crops, and can realize large-scale, multi-scale, and high spatial heterogeneity through remote sensing macroscopic, fast acquisition, multi-scale and other characteristics. Mapping of the spatial distribution of crops.

Figure 202011016570

Description

Crop distribution charting method
Technical Field
The invention relates to the field of data analysis, in particular to a crop distribution chart.
Background
The crop space-time distribution reflects the situation that human beings use land to engage in agricultural production in space at different periods, and is important basic data for developing researches on the patterns and functions of farmland ecosystems, the circulation of the land ecosystems, global changes and the like. The understanding of the crop space-time distribution has important significance for guaranteeing national food safety and sustainable development of resource environment.
At present, the crop space-time distribution information acquisition approach mainly comprises administrative statistics and remote sensing monitoring. The statistical method can only reflect the quantity change of the crops in a certain administrative unit, and the spatial difference of the crops in the statistical unit is difficult to reflect; moreover, the statistical data acquisition consumes a large amount of manpower, material resources and financial resources, and is also interfered by human factors. The simple remote sensing technology has the influence of factors such as atmospheric interference, scale conversion, mixed pixels and the like, and is difficult to acquire the spatial distribution information of the large-area-scale full crop series. Therefore, the statistical data and the remote sensing data are deeply fused, and the development of the spatial distribution mapping of multiple crops and even the whole crop type is particularly necessary.
Disclosure of Invention
The invention aims to provide a crop distribution mapping method,
in order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the invention comprises the following steps
A, acquiring remote sensing data information, constructing a vegetation index time sequence curve of a pixel scale, extracting multiple species information through the number of wave crests of the time sequence curve of the pixel scale, and extracting, classifying and refining planting season information reflected by the pixel scale according to the occurrence time of the wave crests to obtain planting mode information of each raster crop;
b, acquiring the seeding area data of the administrative unit crops, merging the crops in different seasons of the administrative unit, and obtaining the seeding area information of the crops in different seasons of various planting modes;
and C, calculating the areas of various crops on each grid according to the grid scale crop planting mode extracted by remote sensing, the corresponding cultivated area distribution of the grid scale crop planting mode and the area information of the seasonal crops on different planting modes, and manufacturing a crop space distribution map.
Further, the characteristic parameters comprise the number of time sequence curve peaks and time points of the time sequence curve peaks, and the grid planting mode information is obtained by presumption according to the characteristic parameters:
specifically, the preprocessing includes denoising the data by using a time series harmonic analysis method to smooth a time series curve.
Further, the specific flow of pixel scale extraction is as follows:
let each time phase pixel value of pixel i be a1，a2…a23，
Comparing EVI magnitude relation between two adjacent pixels, and using delta aiThe sequence data represents:
Figure BDA0002699267670000021
② calculating adjacent delta aiThe difference between:
ΔΔai＝Δai+1-Δai (3-8)
wherein, if Δ Δ ai<0, then i-1 is the time point of the local peak, ai-1For local peak EVIA value; if Δ Δ ai>0, then i-1 is the time point of the local trough, ai-1Is the local trough EVI value.
And thirdly, because of the existence of the winter peak of the crop and the peak information of some interference, the real peak point of the growing period of the crop needs to be further extracted. If the peak point satisfies that the EVI value of two continuous time points increases before and the EVI value of two continuous time points decreases after, the point belongs to the EVI peak point of the growing period of the crops. The sum of the effective peak points is the multimaturity information of the pixel, namely the peak number is 1, and the pixel is mature for one year; the number of wave peaks is 2, and the product is twice cooked in one year; the number of wave peaks is more than 2, the temperature is more mature in one year, the temperature of the secondary maturation zone is required to be more than 10 ℃ and more than 3500 ℃, and the temperature of the secondary maturation zone is required to be more than 10 ℃ and more than 5000 ℃.
Specifically, the administrative unit divides crops into winter crops, spring crops and summer crops according to the lunar calendar information of the crop types, and the crops are corresponding to various planting mode refinements existing in the administrative unit, and the planting mode refinement method comprises the following steps
a, for the area with triple cropping potential, if the extracted multiple cropping result is triple cropping, dividing the plot into three-season crop multiple cropping in winter-spring-summer; if the extracted multiple cropping result is double cropping, extracting the time point of occurrence of the peak value of the extracted time series curve, determining whether the peak value of crop growth reflected by the curve occurs in spring, summer or autumn, and further dividing the plot into a winter-summer multiple cropping mode or a spring-summer multiple cropping mode; and if the extracted multiple cropping result is one-time cropping, extracting a peak occurrence point of the time series curve, and determining that the planting mode is a winter mode, a spring mode or a summer mode.
b, for the area with double cropping potential, if the double cropping result extracted in the step 2 is double cropping, dividing the plot into a winter-summer double cropping mode; and if the extracted multiple cropping result is one-time cropping, extracting a peak occurrence point of the time series curve, and determining that the planting mode is a winter mode, a spring mode or a summer mode.
And c, directly extracting peak occurrence points for the region which can only realize one-time harvest, and determining whether the peak occurrence points are in a spring mode or a summer mode.
Further, the crop space distribution map is based on the following formula, the grid scale crop area is calculated, and spatialization is carried out to form the crop space distribution map.
Ail＝CAjl×HIiik
Wherein A isijRepresents the area of crop i in grid l; CAikRepresenting the area of the planting pattern j in the grid l; HI (high-intensity)iikRepresents the area proportion of the plot of the crop i in the planting pattern j in the grid l, wherein the grid 1 is extracted by the administrative unit.
In particular, the area ratio of various crops in a unit area of cultivation on a plot with different planting patterns (HI)ijk) The formula is as follows:
HIijk＝HAikj/CAjk
wherein, HAilIndicating the area of sowing of the crop i in the administration unit k, CAklThe area of the manifold pattern j in the administration unit k is shown.
Further, in the step A, curve fitting is carried out according to different multiple cropping potential areas by adopting corresponding frequency numbers, a second-maturing area adopts 3, and a third-maturing area adopts 4.
Compared with the prior art, the invention has the following beneficial effects:
the invention combines the crop phenology characteristics identified by remote sensing with the actual phenology of the crops to determine the spatial distribution condition of the crops, realizes the crop spatial distribution mapping with large range, multiple scales and higher spatial heterogeneity by the characteristics of macro, quick acquisition, multiple scales and the like of remote sensing, and can acquire the spatial distribution information of the crops which have both spatial heterogeneity information and full crop information by the combination of deep mining of remote sensing data and statistical data.
Drawings
FIG. 1 is a schematic diagram of a crop distribution charting process provided by the present invention;
Detailed Description
The present invention will be further illustrated by the following examples and the accompanying drawings, which include, but are not limited to, the following examples.
Referring to FIG. 1, in the present embodiment, the winter wheat is used as an example to perform a crop distribution chart
Step 1: data are denoised by a time series harmonic analysis method (harmonic analysis of time series, HANTS) to obtain a smooth time series curve. And (3) performing curve fitting in different multi-cropping potential areas by adopting different frequencies (Numberoff sequences), wherein the second-maturing area adopts 3, and the third-maturing area adopts 4.
Step 2: after curve denoising, extracting multiple species information in pixel scale, wherein the specific flow is as follows:
let each time phase pixel value of pixel i be a1，a2…a23，
Comparing EVI magnitude relation between two adjacent pixels, and using delta aiThe sequence data represents:
Figure BDA0002699267670000051
② calculating adjacent delta aiThe difference between:
ΔΔai＝Δai+1-Δai (3-8)
wherein, if Δ Δ ai<0, then i-1 is the time point of the local peak, ai-1Is the local peak EVI value; if Δ Δ ai>0, then i-1 is the time point of the local trough, ai-1Is the local trough EVI value.
And thirdly, because the information of the winter peak of winter wheat and some interfering wave peaks exists, the real peak point of the crop in the growing period needs to be further extracted. If the peak point satisfies that the EVI value of two continuous time points increases before and the EVI value of two continuous time points decreases after, the point belongs to the EVI peak point of the growing period of the crops. The sum of the effective peak points is the multimaturity information of the pixel, namely the peak number is 1, and the pixel is mature for one year; the number of wave peaks is 2, and the product is twice cooked in one year; if the number of peaks is greater than 2, the product is more mature than one year.
The temperature of the secondary ripening area is required to be higher than 3500 ℃ when the temperature is more than or equal to 10 ℃, and the temperature of the secondary ripening area is required to be higher than 5000 ℃ when the temperature is more than or equal to 10 ℃.
And step 3: on the basis of extracting the multiple cropping information, the planting season information reflected by the pixel is extracted, and then the planting mode is extracted.
1 for the area with triple cropping potential, if the triple cropping result extracted in the step 2 is triple cropping, dividing the plot into winter-spring-summer triple crop multiple cropping; if the extracted multiple cropping result is double cropping, extracting the time point of occurrence of the peak value of the time series curve extracted in the step 2, determining whether the peak value of the crop growth reflected by the curve occurs in spring, summer or autumn, and further dividing the plot into a winter-summer multiple cropping mode or a spring-summer multiple cropping mode; and if the extracted multiple cropping result is one-time cropping, extracting a peak occurrence point of the time series curve, and determining that the planting mode is a winter mode, a spring mode or a summer mode.
2 for the area with double cropping potential, if the double cropping result extracted in the step 2 is double cropping, dividing the plot into a winter-summer double cropping mode; and if the extracted multiple cropping result is one-time cropping, extracting a peak occurrence point of the time series curve, and determining that the planting mode is a winter mode, a spring mode or a summer mode.
And 3, directly extracting peak occurrence points for the region which can only realize the first-maturing, and determining the mode to be a spring mode or a summer mode.
And 4, step 4: and calculating the cultivated land area with the winter crop planting mode in each administrative unit by taking the administrative unit with the obtained statistical data of the winter wheat seeding area as a boundary.
And 5: according to the farming season information of the crop types contained in each administrative unit, the crops are divided into winter crops, spring crops and summer crops, and the winter crops correspond to the planting modes of the winter crops existing in the administrative unit.
Step 6: and calculating the area proportion of the winter wheat in each administrative statistical unit in the land plots of various planting modes. According to the planting mode corresponding to the winter crops in each administrative unit determined in the step 5, combining the area of the planting mode land parcel in the administrative unit obtained in the step 4, calculating the unit cultivated land area on the land parcels with different planting modesWheat area ratio (HI)ijk) The formula is as follows:
HIjk＝HAkj/CAjk (1)
wherein, HAilIndicates the sowing area of winter wheat in administrative Unit k, CAklRepresents the area of the planting pattern j containing winter crops in the administration k.
And 7: and calculating the areas of the winter wheat on the grids in different planting modes according to the grid planting mode information and the corresponding farmland distribution and combining the areas of the winter wheat crops on the planting modes of the administrative units, thereby manufacturing a winter wheat space distribution map. Specifically, the area of the winter wheat is calculated in a grid scale based on the following formula, and spatialization is performed to form a crop space distribution map.
Al＝CAjl×HIjl
Wherein A islRepresents the area of winter wheat in grid l; CAljRepresenting the area of the planting pattern j in the grid l; HI (high-intensity)ljAnd (3) representing the area proportion of the winter wheat in the plot of the planting mode j in the grid l, wherein the grid l belongs to the administrative unit k and is obtained in the step 6.
It will be clear to a person skilled in the art that the scope of the present invention is not limited to the examples discussed in the foregoing, but that several amendments and modifications thereof are possible without deviating from the scope of the present invention as defined in the attached claims. While the invention has been illustrated and described in detail in the drawings and the description, such illustration and description are to be considered illustrative or exemplary and not restrictive. The invention is not limited to the disclosed embodiments.
Variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims. In the claims, the term "comprising" does not exclude other steps or elements. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. Any reference signs in the claims shall not be construed as limiting the scope of the invention.
The above-mentioned embodiment is only one of the preferred embodiments of the present invention, and should not be used to limit the scope of the present invention, but all the insubstantial modifications or changes made within the spirit and scope of the main design of the present invention, which still solve the technical problems consistent with the present invention, should be included in the scope of the present invention.

Claims (6)

1.一种作物分布制图方法 ，其特征在于，包括以下步骤1. a crop distribution mapping method, is characterized in that, comprises the following steps A获取遥感数据信息，构建像元尺度的植被指数时间序列曲线，通过所述像元尺度时间序列曲线波峰数量的提取复种信息，依据波峰发生时间，对所述像元尺度反映的种植季节信息进行提取和归类细化，得到各个栅格作物种植模式信息，所述像元尺度提取复种信息的具体流程如下：A obtains remote sensing data information, constructs a time series curve of vegetation index at the pixel scale, extracts the multiple cropping information by the number of peaks of the time series curve at the pixel scale, and performs the planting season information reflected at the pixel scale according to the time when the peak occurs. Extraction and classification are refined to obtain crop planting pattern information of each grid. The specific process of extracting multiple cropping information at the pixel scale is as follows: 设像元i各时相像素值为a1，a2… a23，Let the pixel values of each phase of pixel i be a 1 , a 2 ... a 23 , ①比较两相邻像素之间的EVI的大小关系，并用
Figure RE-79601DEST_PATH_IMAGE002
序列数据来表示：①Compare the size relationship of EVI between two adjacent pixels, and use
Figure RE-79601DEST_PATH_IMAGE002
sequence data to represent:
Figure RE-59059DEST_PATH_IMAGE004
Figure RE-59059DEST_PATH_IMAGE004
 ②计算相邻
Figure RE-917424DEST_PATH_IMAGE002
之差：②Calculate adjacent
Figure RE-917424DEST_PATH_IMAGE002
Difference:
Figure RE-515896DEST_PATH_IMAGE006
Figure RE-515896DEST_PATH_IMAGE006
 其中，若
Figure RE-392585DEST_PATH_IMAGE008
<0，则i+1为局部波峰所在时间点，a i+1为局部波峰EVI值；若
Figure RE-DEST_PATH_IMAGE009
>0，则 i+1为局部波谷所在时间点， a i+1为局部波谷EVI值；Among them, if
Figure RE-392585DEST_PATH_IMAGE008
<0, then i +1 is the time point where the local peak is located, and a i +1 is the EVI value of the local peak; if
Figure RE-DEST_PATH_IMAGE009
>0, then i +1 is the time point where the local trough is located, and a i +1 is the EVI value of the local trough; ③由于存在作物的冬前峰以及一些干扰的波峰信息，因此，还需要进一步提取作物生长期的真实峰值点，若波峰点满足之前有连续两个时间点EVI值增加并且之后连续两个时间点EVI值减小，则该波峰点属于作物生长期EVI峰值点，有效的波峰点数之和即为该像元的多熟信息，即波峰数为1，则为一年一熟；波峰数为2，则为一年二熟；波峰数大于2的，则为一年多熟；③Because of the existence of the pre-winter peak of crops and some interfering peak information, it is necessary to further extract the true peak point of the crop growth period, if the peak point satisfies the EVI value increases for two consecutive time points before and two consecutive time points after that. If the EVI value decreases, the peak point belongs to the EVI peak point in the crop growth period, and the sum of the valid peak points is the multi-crop information of the pixel, that is, if the number of peaks is 1, it is one crop per year; the number of peaks is 2 , it is twice a year; if the number of crests is more than 2, it is more than a year; B获取行政单元作物播种面积数据，将所述行政单元不同季相作物进行归并，求得各种植模式不同季相作物播种面积信息,行政单元依据作物种类的农时历信息，将作物划分为冬季作物、春季作物和夏季作物，并将其与行政单元内存在的各类种植模式细化对应，所述种植模式细化的方法包括：B obtains the crop sown area data of the administrative unit, merges the crops of different seasons in the administrative unit, and obtains the sown area information of the crops of different seasons in each planting mode. The administrative unit divides the crops into winter crops according to the agricultural calendar information of the crop types. , spring crops and summer crops, and map them to the refinement of various planting patterns existing in the administrative unit. The methods for refinement of planting patterns include: a对于复种潜力为三熟的地区，如果提取的复种结果是三熟地块，则将该三熟地块划为冬-春-夏三季作物复种；如果提取的复种结果是两熟地块，则对该两熟地块的时间序列曲线峰值发生时间点进行提取，确定曲线反映的作物生长峰值是发生在春季、夏季或是秋季，进而将该两熟地块划为冬-夏复种模式或春-夏复种模式；如果提取的复种结果是一熟地块，则对该一熟地块时间序列曲线的峰值发生点进行提取，确定种植模式为冬季模式、春季模式或夏季模式；a For the area with three cropping potential, if the extracted multiple cropping result is a three-cropping plot, the three-cropping plot shall be classified as a winter-spring-summer three-cropping crop; if the extracted multiple-cropping result is a two-cropping plot, Then extract the peak occurrence time point of the time series curve of the two-crop plot, determine whether the peak of crop growth reflected by the curve occurs in spring, summer or autumn, and then classify the two-crop plot as a winter-summer replanting mode or Spring-summer multiple cropping mode; if the extracted multiple cropping result is a single-cropping plot, extract the peak occurrence point of the time series curve of the single-cropping plot, and determine the planting mode as winter mode, spring mode or summer mode; b对于复种潜力为两熟的地区，如果提取的复种结果是两熟地块，则将该地块划为冬-夏复种模式；如果提取的复种结果是一熟地块，则对时间序列曲线的峰值发生点进行提取，确定种植模式为冬季模式、春季模式或夏季模式；b For the area with double cropping potential, if the extracted multiple cropping result is a double cropping plot, the plot is classified as a winter-summer multiple cropping pattern; if the extracted multiple cropping result is a single cropping plot, the time series curve Extract the peak occurrence point of the planting mode, and determine whether the planting mode is winter mode, spring mode or summer mode; c对于只能实行一熟的地区，直接提取峰值发生点，确定是春季模式或夏季模式；c For areas where only one crop can be implemented, directly extract the peak occurrence point and determine whether it is the spring mode or summer mode; C依据遥感提取的栅格尺度作物种植模式及其对应的耕地面积分布以及不同种植模式上各季相作物的面积信息，计算每个栅格上各类作物的面积，从而制作作物空间分布图。C According to the grid-scale crop planting patterns and their corresponding arable land area distributions extracted by remote sensing, as well as the area information of crops of each season on different planting patterns, the area of various crops on each grid is calculated to produce a crop spatial distribution map. 2.根据权利要求1所述的一种作物分布制图方法 ，其特征在于，特征参数包括时序曲线波峰数和时序曲线波峰时间点，根据所述特征参数推定得到栅格种植模式信息。2 . The method for mapping crop distribution according to claim 1 , wherein the characteristic parameters include the number of peaks of the time series curve and the time point of the time series curve peaks, and the grid planting pattern information is estimated and obtained according to the characteristic parameters. 3 . 3.根据权利要求1所述的一种作物分布制图方法 ，其特征在于，预处理包括采用时间序列谐波分析法对数据进行去噪处理使时间序列曲线平滑。3. A crop distribution mapping method according to claim 1, characterized in that the preprocessing comprises using a time series harmonic analysis method to denoise the data to smooth the time series curve. 4.根据权利要求1所述一种作物分布制图方法 ，其特征在于，所述作物空间分布图基于下述公式，计算栅格尺度作物面积，并进行空间化，形成作物空间分布图：4. a kind of crop distribution mapping method according to claim 1, is characterized in that, described crop space distribution map is based on following formula, calculates grid scale crop area, and carries out spatialization, forms crop space distribution map: A il = CA jl ×HI ijk A il = CA jl ×HI ijk 其中，A il 表示栅格l中作物i的面积；CA jl 表示栅格l中种植模式j的面积；HI ijk 表示栅格l中作物i在种植模式j地块的面积比例，栅格l 由行政单元k提取。Among them, A il represents the area of crop i in grid l ; CA jl represents the area of planting pattern j in grid l ; HI ijk represents the area ratio of crop i in the plot of planting pattern j in grid l , and grid l is composed of Administrative unit k extraction. 5.根据权利要求1所述一种作物分布制图方法 ，其特征在于，不同种植模式地块上单位耕地面积各类作物的面积占比，公式如下：5. a kind of crop distribution mapping method according to claim 1 is characterized in that, the area ratio of various crops per unit arable land area on different planting mode plots, the formula is as follows: HI ijk = HA ik /CA jk HI ijk = HA ik /CA jk 其中，HA ik 表示作物i在行政单元k的播种面积，CA jk 表示种植模式j在行政单元k的面积。Among them, HA ik represents the sown area of crop i in administrative unit k , and CA jk represents the area of planting pattern j in administrative unit k . 6.根据权利要求1所述一种作物分布制图方法 ，其特征在于，在步骤A中，根据不同的复种潜力区采用对应的频数进行曲线拟合，二熟区采用3，三熟区采用4。6. a kind of crop distribution mapping method according to claim 1, is characterized in that, in step A, adopts corresponding frequency to carry out curve fitting according to different multiple cropping potential districts, and second-cropping district adopts 3, and three-cropping district adopts 4. .
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