阅读说明：本技术 一种灌溉水体提取方法 (Irrigation water body extraction method ) 是由 苏涛 崔杏园 王建 崔灵芝 于 2021-09-14 设计创作，主要内容包括：本发明公开了一种灌溉水体提取的方法,包括以下步骤：(1)根据灌溉时间和地理位置要求选择遥感影像数据；(2)计算水体敏感光谱指数,获得其空间分布；(3)对水体敏感光谱指数的空间信息进行空间自相关分析；(4)计算全局莫兰指数(Moran’s I),根据I值的取值范围判断研究区域是否出现聚集或异常值；(5)若出现聚集或异常值,则计算局部Moran’s I指数；(6)计算Getis-Ord Gi指数,获得水体空间聚集分布；(7)利用合理的遥感影像通过地物分类,获得研究区的种植结构,进而最终获得灌溉水体面积或不同作物种类的灌溉水体面积。(The invention discloses an irrigation water body extraction method, which comprises the following steps: (1) selecting remote sensing image data according to the requirements of irrigation time and geographic position; (2) calculating the sensitive spectral index of the water body to obtain the spatial distribution of the sensitive spectral index; (3) performing spatial autocorrelation analysis on the spatial information of the water body sensitive spectral index; (4) calculating a global Moran index (Moran's I), and judging whether aggregation or abnormal values occur in the research area according to the value range of the I value; (5) if an aggregate or outlier occurs, then calculate the local Moran's I index; (6) calculating a Getis-Ord Gi index to obtain the water space aggregation distribution; (7) and reasonable remote sensing images are utilized to obtain the planting structure of the research area through ground object classification, and further the irrigation water body area or the irrigation water body areas of different crop types are finally obtained.)

1. An extraction method for an irrigation water body, comprising:

step 1, selecting remote sensing image data according to irrigation time and geographical position requirements;

step 2, calculating the water sensitive spectral index to obtain the spatial distribution of the water sensitive spectral index;

step 3, carrying out spatial autocorrelation analysis on the spatial information of the water body sensitive spectral index;

step 4, calculating a global Moran index (Moran' sI), and judging whether an aggregation or an abnormal value occurs in a research area according to the value range of the I value;

step 5, if an aggregation or abnormal value occurs, calculating a local Moran' sI index;

step 6, calculating Getis-OrdG_iIndexing to obtain water space aggregation distribution;

and 7, obtaining the planting structure of the research area by utilizing the reasonable remote sensing image through ground object classification, and further finally obtaining the irrigation water body area or the irrigation water body areas of different crop types.

2. The method of claim 1, wherein the step 1 comprises:

step 8, the original image needs to be preprocessed before being applied, wherein preprocessing links comprise 3 links such as geometric correction, radiation correction and atmospheric correction, and selective preprocessing can be performed according to the type and the grade of the downloaded remote sensing image;

and 9, preprocessing the remote sensing image by utilizing an existing module on the ENVI software or a third-party open source program.

3. The method of claim 1, wherein the step 2 comprises:

step 10, the water body sensitive spectral indexes are more, MNDWI or AWEI and other water body sensitive spectral indexes are selected, but the water body sensitive spectral indexes are not limited to use;

step 11, calculating an improved normalized water body index (MNDWI) according to the following formula:

wherein ρ is the reflectivity of the spectral band of the remote sensing image: BIUE is a blue band (0.45-0.51 μm), GRE is a green band (0.53-0.59 μm), NIR is a near infrared band (0.85-0.88 μm), SWIR1 is a short wave infrared band (1.57-1.65 μm), and SWIR2 is a short wave infrared band (2.21-2.29 μm).

Step 12, calculate the auto extraction index (AWEI), including AWEI_nshAnd AWEI_shTwo types, the formula of which is as follows:

AWEI_nsh＝4×(ρ_GREEN-ρ_SWIR1)-(0.25×ρ_NIR+2.75×ρ_SWIR1)

AWEI_sh＝ρ_BIUE+2.5×ρ_GREEN-1.5×(ρ_NIR+ρ_SWIR1)-0.25×ρ_SWIR2)

the meaning of each item is the same as that of the calculation formula of MNDWI in step 11.

4. The method of claim 1, wherein the step 4 comprises:

step 13, before calculating the global Morland index, vectorizing the water sensitive spectrum index spatial information data, wherein the vectorizing can be processed in ArcGIS, and the file is converted into a file with a format of shp;

step 14, calculate the global Moran index (Moran' sI), which is expressed as follows:

where N is the number of data, W_ijIs a spatial weight, X_iAnd X_jThe property values of the spatial object at the ith and jth positions respectively,is the average value of X; z is the normalized statistical value, E (I) is the expected value of the autocorrelation of the observed variable, and var (I) is the variance.

The value range of the Moran' sI index is [ -1,1], and I >0 represents that the attribute value presents spatial positive correlation and tends to spatial aggregation characteristics; i <0 represents that the attribute value presents space negative correlation and tends to space dispersion characteristics; i-0 represents that the attribute values tend to be spatially randomly distributed.

The global Morland index calculation can be processed in ArcGIS or GeoDa related modules, step 15.

5. The method of claim 1, wherein the step 5 comprises:

step 16, judging according to the Moran' sI index I value, if no abnormal value occurs, the method is invalid, no water body appears in a possibly researched area, and if aggregation or abnormal value occurs, analyzing by using local space autocorrelation;

step 17, calculating a local Moran' sI index, wherein the formula is as follows:

wherein the meanings of all the items are the same as the meanings of all the items in the calculation formula of the global Moran index in the step 4.

6. The method of claim 1, wherein the step 6 comprises:

step 18, calculating Getis-OrdG_iThe formula of the index is as follows:

wherein x_jIs the attribute value of element j, w_i,jIs the spatial weight between elements i and j, n is the total number of elements, and:

step 19, in calculating Getis-OrdG_iDuring indexing, the weight needs to be determined, and a fixed distance model, a surface adjacency model (shared edge and intersection), a surface adjacency model (shared edge, intersection and adjacent point), an inverse distance model, an inverse distance square model, a K nearest neighbor model and the like can be selected for construction according to actual conditions when a weight matrix file of a space is constructed;

step 20, calculating a z score and a p value by using relevant modules of ArcGIS or GeoDa software and the like;

step 21, selecting the spatial distribution with the Z score larger than 1.96 as the spatial aggregation distribution of the water body, wherein the confidence coefficient is 95%;

step 22, obtaining the spatial distribution of the water body by using a classification method such as a decision tree;

and step 23, calculating the area of the water body of the region by using the spatial resolution of the pixels and the number of the pixels counted by the water body.

7. The method of claim 1, wherein the step 7 comprises:

step 24, selecting a remote sensing image with a proper time phase according to the characteristics of crops, calculating a normalized vegetation index (NDVI), classifying the research area according to the numerical value of the NDVI, and obtaining the spatial distribution of cultivated land and the spatial distribution of a crop planting structure of the research area;

step 25, registering spatial distribution of cultivated land and spatial distribution of water body in the research area, and obtaining spatial distribution of irrigation water body of cultivated land in the research area by using a mask method;

step 26, counting the number of pixels of the irrigation water body of the farmland, and calculating the area of the irrigation water body;

step 27, carrying out registration treatment on the spatial distribution of the crop planting structure and the spatial distribution of the water body in the research area, and obtaining the spatial distribution of the irrigation water body of different crop species by using a mask method;

and step 28, counting the pixel numbers of the farmland irrigation water bodies of different crop types, and calculating the irrigation water body area sizes of different crop types.

Technical Field

The invention relates to the fields of water resource optimization, remote sensing image processing technology, spatial data processing and the like, in particular to a monitoring method for efficiently and accurately acquiring the water body area.

Background

According to FAO reports, the global population reaches 97 billion by 2050, and 108 billion by 2080, and the food demand is expected to increase greatly under the push of population factors. In order to increase agricultural yields, irrigation is one of the effective measures in about one third of the world's farmlands, particularly dry land areas, to moderate or high deterioration. With the development of socio-economic, the contradiction between agricultural water and industrial urban water is growing, and the importance of irrigation in land management, as well as the impact on regional and global climate, is gradually recognized and brought to the scientific community's attention.

The most important thing to monitor the irrigation area is the extraction of the ground water. At present, the extraction method of regional water mainly utilizes a water sensitivity index to obtain the water area of a region by setting a threshold, but the extraction of surface water by utilizing the water sensitivity index faces two main problems: the first is that the results of surface water bodies obtained by using different water body indexes are inconsistent and unreliable; the second is that the threshold is not fixed and may vary from environment to environment and location to location. Therefore, how to improve the extraction accuracy of the surface water body is one of the hot spots of the current research.

In summary, in order to solve the above problems, the present invention provides an extraction method for irrigation water.

Disclosure of Invention

The invention provides an irrigation water body extraction method, which can better solve the problems of difficulty in threshold value determination and large difference of obtained water bodies due to different water body sensitivity indexes, further improve the irrigation area monitoring precision and have certain guiding significance for reasonable water quantity preparation and high-efficiency water use of cultivated land. The method comprises the following steps:

the invention adopts the following technical scheme for realizing the purpose:

an irrigation water body extraction method utilizes multi-temporal multi-source remote sensing images as data sources to calculate water body sensitivity indexes such as common normalized water difference index (NDWI), improved normalized water body index (MNDWI), automatic extraction index (AWEI) and the like, but not limited to the enumerated water body sensitivity indexes, after spatial distribution of water body sensitivity spectral indexes is calculated, spatial distribution obtained by the water body sensitivity indexes is processed by utilizing a spatial autocorrelation analysis method, water body gathering spatial distribution is extracted according to z score and p value in a Getis-Ord Gi index, the problems that threshold value determination is difficult, extraction of water body difference is large due to different water body sensitivity indexes are solved, and irrigation area extraction accuracy can be further improved.

An extraction method of irrigation water body comprises the following steps:

step 1, selecting remote sensing image data according to irrigation time and geographical position requirements;

step 2, calculating the water sensitive spectral index to obtain the spatial distribution of the water sensitive spectral index;

step 3, carrying out spatial autocorrelation analysis on the spatial information of the water body sensitive spectral index;

step 4, calculating a global Moran index (Moran's I), and judging whether aggregation or abnormal values occur in the research area according to the value range of the value I;

step 5, if the aggregation or abnormal value occurs, calculating a local Moran's I index;

step 6, calculating Getis-Ord G_iIndexing to obtain water space aggregation distribution;

step 7, obtaining a planting structure of a research area by classifying ground objects by using a reasonable remote sensing image, and further finally obtaining the area of an irrigation water body or the areas of irrigation water bodies of different crop types;

further, the step 1 comprises:

step 8, the original image needs to be preprocessed before being applied, wherein preprocessing links comprise 3 links such as geometric correction, radiation correction and atmospheric correction, and selective preprocessing can be performed according to the type and the grade of the downloaded remote sensing image;

step 9, preprocessing the remote sensing image by utilizing an existing module on the ENVI software or a third-party open source program;

further, the step 2 comprises:

step 10, the water body sensitive spectral indexes are more, MNDWI or AWEI and other water body sensitive spectral indexes are selected, but the water body sensitive spectral indexes are not limited to use;

step 11, calculating an improved normalized water body index (MNDWI) according to the following formula:

wherein ρ is the reflectivity of the spectral band of the remote sensing image: BIUE is a blue band (0.45-0.51 μm), GRE is a green band (0.53-0.59 μm), NIR is a near infrared band (0.85-0.88 μm), SWIR1 is a short wave infrared band (1.57-1.65 μm), and SWIR2 is a short wave infrared band (2.21-2.29 μm).

Step 12, calculate the auto extraction index (AWEI), including AWEI_nshAnd AWEI_shTwo types, the formula of which is as follows:

AWEI_nsh＝4×(ρ_GREEN-ρ_SWIR1)-(0.25×ρ_NIR+2.75×ρ_SWIR1)

AWEI_sh＝ρ_BIUE+2.5×ρ_GREEN-1.5×(ρ_NIR+ρ_SWIR1)-0.25×ρ_SWIR2)

wherein the meanings of the items are the same as the meanings of the items in the MNDWI calculation formula in the step 11;

further, the step 4 comprises:

step 13, before calculating the global Morland index, vectorizing the water sensitive spectrum index spatial information data, wherein the vectorizing can be processed in ArcGIS, and the file is converted into a file with a format of shp;

step 14, calculate the global Moran index (Moran's I), which is given by the formula:

where N is the number of data, W_ijIs a spatial weight, X_iAnd X_jThe property values of the spatial object at the ith and jth positions respectively,is the average value of X; z is a normalized statistical quantity, E (I) is an expected value of the autocorrelation of the observed variable, var (I) is a squareAnd (4) poor.

The value range of the Moran's I index is [ -1,1], and I >0 represents that the attribute values present spatial positive correlation and tend to spatial aggregation characteristics; i <0 represents that the attribute value presents space negative correlation and tends to space dispersion characteristics; i-0 represents that the attribute values tend to be spatially randomly distributed.

Step 15, the global Morland index calculation can be processed in ArcGIS or GeoDa related modules;

further, the step 5 comprises:

step 16, judging according to the Moran's I index I value, if no abnormal value occurs, the method is invalid, no water body appears in a possibly researched area, and if aggregation or an abnormal value occurs, analyzing by using local space autocorrelation;

step 17, calculating a local Moran's I index, wherein the formula is as follows:

wherein each meaning is the same as each meaning in the calculation formula of the global Moran index in the step 4;

further, the step 6 comprises:

step 18, calculating Getis-Ord G_iThe formula of the index is as follows:

wherein x_jIs the attribute value of element j, w_i,jIs the spatial weight between elements i and j, n is the total number of elements, and:

step 19, calculating Getis-Ord G_iDuring indexing, the weight needs to be determined, and a fixed distance model, a surface adjacency model (shared edge and intersection), a surface adjacency model (shared edge, intersection and adjacent point), an inverse distance model, an inverse distance square model, a K nearest neighbor model and the like can be selected for construction according to actual conditions when a weight matrix file of a space is constructed;

step 20, calculating a z score and a p value by using relevant modules of ArcGIS or GeoDa software and the like;

step 21, selecting the spatial distribution with the Z score larger than 1.96 as the spatial aggregation distribution of the water body, wherein the confidence coefficient is 95%;

step 22, obtaining the spatial distribution of the water body by using a classification method such as a decision tree;

step 23, calculating the area of the water body of the region by using the spatial resolution of the pixels and the number of the pixels counted by the water body;

further, the step 7 includes:

step 24, selecting a remote sensing image with a proper time phase according to the characteristics of crops, calculating a normalized vegetation index (NDVI), classifying the research area according to the numerical value of the NDVI, and obtaining the spatial distribution of cultivated land and the spatial distribution of a crop planting structure of the research area;

step 25, registering spatial distribution of cultivated land and spatial distribution of water body in the research area, and obtaining spatial distribution of irrigation water body of cultivated land in the research area by using a mask method;

step 26, counting the number of pixels of the irrigation water body of the farmland, and calculating the area of the irrigation water body;

step 27, carrying out registration treatment on the spatial distribution of the crop planting structure and the spatial distribution of the water body in the research area, and obtaining the spatial distribution of the irrigation water body of different crop species by using a mask method;

and step 28, counting the pixel numbers of the farmland irrigation water bodies of different crop types, and calculating the irrigation water body area sizes of different crop types.

The embodiment of the invention has the following beneficial effects:

in the scheme, when the water body sensitivity index is used for extracting the water body in the research area, the uncertainty of threshold selection often causes larger difference of extraction areas, the water body extraction precision can be further improved by using the scheme, the subjectivity of threshold selection is avoided, and the working intensity of ground monitoring is reduced; in the scheme, the remote sensing image data of different time phases are utilized, the area and the spatial distribution of the irrigation water body in different time periods can be monitored, and a basis is provided for perfecting the irrigation system of the research area.

Drawings

FIG. 1 is a schematic view of the operational flow of an irrigation water extraction method of the present invention;

FIG. 2 is a schematic view of the monitoring results of an embodiment of the method for extracting irrigation water according to the present invention.

Detailed Description

The invention is further illustrated by the following specific examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.

The embodiment of the invention provides an irrigation water body extraction method, which can quickly, effectively and accurately monitor the irrigation water body area of a research area and has certain guiding significance for further improving the regional water utilization efficiency and perfecting an irrigation system.

As shown in fig. 1, an embodiment of the present invention provides an irrigation water body extraction method, including the following main steps:

step 1, selecting remote sensing image data according to time and geographic position requirements;

step 2, calculating the water sensitive spectral index to obtain the spatial distribution of the water sensitive spectral index;

step 3, carrying out spatial autocorrelation analysis on the spatial information of the water body sensitive spectral index;

step 4, calculating a global Moran index (Moran's I), and judging whether aggregation or abnormal values occur in the research area according to the value range of the value I;

step 5, if aggregation or outliers occur, calculate the local Moran's I index:

step 6, calculating Getis-Ord G_iIndexing to obtain water space aggregation distribution;

step 7, obtaining a planting structure of a research area by classifying ground objects by using a reasonable remote sensing image, and further finally obtaining the area of an irrigation water body or the areas of irrigation water bodies of different crop types;

in the invention, the selection of the water body sensitive spectral index suggests to select the MNDWI or AWEI and other sensitive spectral indexes of sensitive water bodies, and other sensitive spectral indexes of sensitive water bodies can also be selected.

In the invention, when the Getis-Ord Gi index is calculated, a fixed distance model, a surface adjacency model (shared edge and intersection), a surface adjacency model (shared edge, intersection and adjacency point), an inverse distance model, an inverse distance square model, a K nearest neighbor model and the like are selected according to actual conditions to construct a weight matrix.

The basic features, technical solutions and advantageous effects of the present invention have been described above. It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.