CN113793376B - Irrigation water body extraction method - Google Patents
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- 239000003621 irrigation water Substances 0.000 title claims abstract description 34
- 238000000605 extraction Methods 0.000 title claims description 16
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 68
- 238000009826 distribution Methods 0.000 claims abstract description 34
- 238000011160 research Methods 0.000 claims abstract description 24
- 238000000034 method Methods 0.000 claims abstract description 21
- 238000001228 spectrum Methods 0.000 claims abstract description 17
- 238000004220 aggregation Methods 0.000 claims abstract description 14
- 230000002776 aggregation Effects 0.000 claims abstract description 14
- 230000002159 abnormal effect Effects 0.000 claims abstract description 11
- 230000002262 irrigation Effects 0.000 claims abstract description 10
- 238000003973 irrigation Methods 0.000 claims abstract description 10
- 238000004458 analytical method Methods 0.000 claims abstract description 7
- 238000012545 processing Methods 0.000 claims description 11
- 230000035945 sensitivity Effects 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 8
- 238000007781 pre-processing Methods 0.000 claims description 8
- 238000012937 correction Methods 0.000 claims description 6
- 230000003595 spectral effect Effects 0.000 claims description 4
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000003809 water extraction Methods 0.000 claims description 3
- 238000003066 decision tree Methods 0.000 claims description 2
- 239000006185 dispersion Substances 0.000 claims description 2
- 230000005855 radiation Effects 0.000 claims description 2
- 238000012544 monitoring process Methods 0.000 description 5
- 239000002352 surface water Substances 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000002360 preparation method Methods 0.000 description 2
- 230000015556 catabolic process Effects 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
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Abstract
The invention discloses a method for extracting irrigation water, which comprises the following steps: (1) Selecting remote sensing image data according to irrigation time and geographical position requirements; (2) Calculating the sensitive spectrum index of the water body to obtain the spatial distribution of the sensitive spectrum index; (3) Carrying out space autocorrelation analysis on space information of the water body sensitive spectrum index; (4) Calculating a global Moran index (Moran's I), and judging whether aggregation or abnormal value occurs in a research area according to the value range of the I value; (5) If an aggregation or outlier occurs, calculating a local Moran's I index; (6) Calculating a Getis-Ord Gi index to obtain the spatial aggregation distribution of the water body; (7) And obtaining the planting structure of the research area by utilizing reasonable remote sensing images through land feature classification, and finally obtaining the irrigation water body area or the irrigation water body area of different crop types.
Description
Technical Field
The invention relates to the fields of water resource optimization, remote sensing image processing technology, space data processing and the like, in particular to a monitoring method for efficiently and accurately acquiring water body area.
Background
According to FAO reports, the global population will reach 97 billion by 2050 and 108 billion by 2080, and the demand for food is expected to increase greatly under the impetus of population factors. Irrigation is one of the effective measures for improving agricultural yield in approximately one third of the moderate or high degradation of farmland in the world, especially in dry land areas. With the development of socioeconomic performance, the contradiction between agricultural water and industrial municipal water is continuously aggravated, and the importance of irrigation in land management and the influence on regional and global climate are gradually recognized, and attention is paid to the scientific community.
Monitoring of irrigation area is most important to the extraction of the surface water. The existing extraction method of regional water body mainly utilizes the water body sensitivity index to obtain the water body area of the region by setting a threshold value, but the extraction of surface water body by utilizing the water body index faces two main problems: firstly, surface water bodies obtained by using different water body indexes are inconsistent and unreliable in results; second, the threshold is not fixed and may vary from environment to environment and from 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 difficult threshold value determination and large water body acquisition variability caused by different water body sensitivity indexes, further improves the accuracy of monitoring irrigation areas, and has a certain guiding significance on reasonable water preparation and high-utility water preparation of cultivated lands. The method comprises the following steps:
the invention realizes the aim by adopting the following technical scheme:
a method for extracting irrigation water body includes utilizing multi-phase multi-source remote sensing image as data source to calculate water body sensitivity index such as common normalized water difference index (NDWI), improved normalized water body index (MNDWI) and automatic extraction index (AWEI), calculating spatial distribution of water body sensitivity spectrum index, processing spatial distribution obtained by water body sensitivity index by space autocorrelation analysis method, extracting water body aggregation spatial distribution according to z score and p value in Getis-Ord Gi index, solving problems of difficult threshold determination and large difference of extracted water body caused by different selected water body sensitivity indexes, and further improving irrigation area extraction precision.
An irrigation water body extraction method comprises the following steps:
step 1, selecting remote sensing image data according to irrigation time and geographical position requirements;
step 2, calculating a water body sensitive spectrum index to obtain spatial distribution of the water body sensitive spectrum index;
step 3, carrying out space autocorrelation analysis on the space information of the sensitive spectrum index of the water body;
step 4, calculating a global Moran index (Moran's I), and judging whether aggregation or abnormal value occurs in a research area according to the value range of the I value;
step 5, if aggregation or abnormal value occurs, calculating a local Moran's I index;
step 6, calculating the Getis-Ord G i The index, obtain the space gathering distribution of the water;
step 7, obtaining a planting structure of a research area by utilizing reasonable remote sensing images through land feature classification, and finally obtaining an irrigation water body area or irrigation water body areas of different crop types;
further, the step 1 includes:
step 8, preprocessing is needed before the original image is applied, wherein preprocessing links comprise 3 links of geometric correction, radiation correction and atmospheric correction, and the preprocessing is selectively performed according to the type and the level of the downloaded remote sensing image;
step 9, preprocessing remote sensing images by using an existing module or a third party open source program on ENVI software;
further, the step 2 includes:
step 10, selecting MNCWI or AWEI water body sensitivity spectrum indexes;
step 11, calculating an improved normalized water index (MNDWI) with the following formula:
wherein ρ is the reflectance of the spectral band of the remote sensing image: BIUE is blue band, GREE is green band, NIR is near infrared band, SWIR1 is short wave infrared band, SWIR2 is short wave infrared band;
step 12, calculating an automatic extraction index (AWEI) including AWEI nsh And AWEI sh Two types, the formulas of which are 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 each meaning is the same as each meaning in the MNCWI calculation formula in the step 11;
further, the step 4 includes:
step 13, before calculating the global Morgan index, carrying out vectorization processing on spatial information data of the sensitive spectrum index of the water body, and converting the vectorized processing file into a file with the format of shp;
in step 14, a global Moran index (Moran's I) is calculated as follows:
wherein N is the number of data, W ij Is space weight, X i And X j The property values of the spatial object at i and j respectively,is the average value of X; z is a standardized statistical value, E (I) is an expected value of the autocorrelation of an observed variable, and var (I) is a variance;
the Moran's I index has a value range of [ -1,1], and I >0 represents that the attribute values show positive correlation in space and tend to gather the characteristics in space; 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 moland index calculation can be processed in ArcGIS or GeoDa related modules;
further, the step 5 includes:
step 16, judging according to Moran's I index I value, if no abnormal value appears, the method fails, the possible research area has no water body, and if aggregation or abnormal value appears, the analysis is carried out by utilizing local space autocorrelation;
step 17, calculating the 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 Morgan index in the step 4;
further, the step 6 includes:
step 18, calculating the Getis-Ord G i The index is given by the formula:
wherein x is j Is the attribute value of element j, w i,j Is the spatial weight between elements i and j, n is the total number of elements, and:
step 19, in the calculation of the Getis-Ord G i When the index is needed to be determined, a fixed distance model, a surface adjacent model, an inverse distance square model and a K nearest neighbor model can be selected to construct according to actual conditions when the weight matrix file of the space is constructed;
step 20, calculating a z score and a p value by using related modules of software such as ArcGIS or GeoDa;
step 21, selecting the spatial distribution of the Z score under the condition of being more 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 of the decision tree;
step 23, calculating the water body area 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 remote sensing images of proper time phases according to the characteristics of crops, calculating normalized vegetation indexes (NDVI), classifying the research area according to the numerical value of the NDVI, and obtaining the spatial distribution of cultivated land in the research area and the spatial distribution of crop planting structures;
step 25, carrying out registration processing on the spatial distribution of the cultivated land in the research area and the spatial distribution of the water body, and obtaining the spatial distribution of the irrigated water body of the cultivated land in the research area by using a mask method;
step 26, counting the pixel number of the irrigation water body of the cultivated land, and calculating the area of the irrigation water body;
step 27, carrying out registration processing on the spatial distribution of the crop planting structure in the research area and the spatial distribution of the water body, and obtaining the spatial distribution of irrigation water bodies of different crop types by using a mask method;
and 28, counting the pixel number of the farmland irrigation water bodies of different crop types, and calculating the area of the irrigation water bodies of different crop types.
The embodiment of the invention has the following beneficial effects:
in the scheme, the uncertainty of threshold selection often causes larger difference in extraction area when the water body of the research area is extracted by utilizing the water body sensitivity index, so that the accuracy of extracting the water body can be further improved, subjectivity in threshold selection is avoided, and the working intensity of ground monitoring is relieved; in the scheme, remote sensing image data of different time phases are utilized, so that the irrigation water body area and the spatial distribution of the irrigation water body area 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 workflow diagram of an irrigation water extraction method of the present invention;
FIG. 2 is a schematic diagram of monitoring results of an embodiment of an irrigation water extraction method of the present invention.
Detailed Description
The invention is further illustrated by the following examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
The embodiment of the invention provides an irrigation water body extraction method, which can rapidly, effectively and accurately monitor the irrigation water body area of a research area and has a certain guiding significance for further improving the water use efficiency of the area and improving the irrigation system.
As shown in fig. 1, the embodiment of the invention provides an irrigation water body extraction method, which comprises the following main steps:
step 1, selecting remote sensing image data according to time and geographic position requirements;
step 2, calculating a water body sensitive spectrum index to obtain spatial distribution of the water body sensitive spectrum index;
step 3, carrying out space autocorrelation analysis on the space information of the sensitive spectrum index of the water body;
step 4, calculating a global Moran index (Moran's I), and judging whether aggregation or abnormal value occurs in a research area according to the value range of the I value;
step 5, if aggregation or abnormal value occurs, calculating local Moran's I index:
step 6, calculating the Getis-Ord G i The index, obtain the space gathering distribution of the water;
and 7, obtaining a planting structure of the research area by utilizing reasonable remote sensing images through ground object classification, and finally obtaining the irrigation water body area or the irrigation water body areas of different crop types.
In the invention, MNCWI or AWI sensitive indexes or other sensitive water body spectral indexes are selected for the water body sensitive spectral indexes.
In the invention, when calculating the Getis-Ord Gi index, a fixed distance model, a surface adjacent 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 characteristics, technical scheme and beneficial effects of the invention are 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 characteristics 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.
Claims (7)
1. An irrigation water extraction method, comprising:
step 1, selecting remote sensing image data according to irrigation time and geographical position requirements;
step 2, calculating a water body sensitive spectrum index to obtain spatial distribution of the water body sensitive spectrum index;
step 3, carrying out space autocorrelation analysis on the space information of the sensitive spectrum index of the water body;
step 4, calculating a global Moran index (Moran's I), and judging whether aggregation or abnormal value occurs in a research area according to the value range of the I value;
step 5, if aggregation or abnormal value occurs, calculating a local Moran's I index;
step 6, calculating the Getis-Ord G i The index, obtain the space gathering distribution of the water;
and 7, obtaining a planting structure of the research area by utilizing reasonable remote sensing images through ground object classification, and finally obtaining the irrigation water body area or the irrigation water body areas of different crop types.
2. The method for extracting water from irrigation water according to claim 1, wherein the step 1 comprises:
step 8, preprocessing is needed before the original image is applied, wherein preprocessing links comprise 3 links of geometric correction, radiation correction and atmospheric correction, and the preprocessing is selectively performed according to the type and the level of the downloaded remote sensing image;
and 9, preprocessing the remote sensing image by using an existing module or a third-party open source program on ENVI software.
3. The method of claim 1, wherein step 2 comprises:
step 10, selecting MNCWI or AWEI water body sensitivity spectrum indexes;
step 11, calculating an improved normalized water index (MNDWI) with the following formula:
wherein ρ is the reflectance of the spectral band of the remote sensing image: BIUE is blue band, GREE is green band, NIR is near infrared band, SWIR1 is short wave infrared band, SWIR2 is short wave infrared band;
step 12, calculating an automatic extraction index (AWEI) including AWEI nsh And AWEI sh Two types, the formulas of which are 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 each meaning is the same as each meaning in the calculation formula of MNCWI in step 11.
4. The method of extracting irrigated water according to claim 1, wherein the step 4 includes:
step 13, before calculating the global Morgan index, carrying out vectorization processing on spatial information data of the sensitive spectrum index of the water body, and converting the vectorized processing file into a file with the format of shp;
in step 14, a global Moran index (Moran's I) is calculated as follows:
wherein N is the number of data, W ij Is space weight, X i And X j The property values of the spatial object at i and j respectively,is the average value of X; z is a standardized statistical value, E (I) is an expected value of the autocorrelation of an observed variable, and var (I) is a variance;
the Moran's I index has a value range of [ -1,1], and I >0 represents that the attribute values show positive correlation in space and tend to gather the characteristics in space; 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;
in step 15, the global moland index calculation may be processed in ArcGIS or GeoDa related modules.
5. The method of extracting irrigated water according to claim 1, wherein the step 5 includes:
step 16, judging according to Moran's I index I value, if no abnormal value appears, the method fails, the possible research area has no water body, and if aggregation or abnormal value appears, the analysis is carried out by utilizing local space autocorrelation;
step 17, calculating the 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 Morgan index in the step 4.
6. The method of extracting irrigated water according to claim 1, wherein the step 6 includes:
step 18, calculating the Getis-Ord G i The index is given by the formula:
wherein x is j Is the attribute value of element j, w i,j Is the spatial weight between elements i and j, n is the total number of elements, and:
step 19, in the calculation of the Getis-Ord G i When the index is needed to be determined, a fixed distance model, a surface adjacent model, an inverse distance square model and a K nearest neighbor model can be selected to construct according to actual conditions when the weight matrix file of the space is constructed;
step 20, calculating a z score and a p value by using a related module of ArcGIS or GeoDa software;
step 21, selecting the spatial distribution of the Z score under the condition of being more 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 utilizing a decision tree classification method;
and step 23, calculating the water body area of the region by using the spatial resolution of the pixels and the number of the pixels for water body statistics.
7. The method of claim 1, wherein the step 7 comprises:
step 24, selecting remote sensing images of proper time phases according to the characteristics of crops, calculating normalized vegetation indexes (NDVI), classifying the research area according to the numerical value of the NDVI, and obtaining the spatial distribution of cultivated land in the research area and the spatial distribution of crop planting structures;
step 25, carrying out registration processing on the spatial distribution of the cultivated land in the research area and the spatial distribution of the water body, and obtaining the spatial distribution of the irrigated water body of the cultivated land in the research area by using a mask method;
step 26, counting the pixel number of the irrigation water body of the cultivated land, and calculating the area of the irrigation water body;
step 27, carrying out registration processing on the spatial distribution of the crop planting structure in the research area and the spatial distribution of the water body, and obtaining the spatial distribution of irrigation water bodies of different crop types by using a mask method;
and 28, counting the pixel number of the farmland irrigation water bodies of different crop types, and calculating the area of the irrigation water bodies of different crop types.
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