CN110991332A - Vegetation index early warning method - Google Patents
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Abstract
The application discloses vegetation index early warning method specifically includes: acquiring remote sensing image data of a target area, preprocessing the remote sensing image data, and calculating to obtain a vegetation index NDVI value; projecting the obtained vegetation index NDVI value to obtain vegetation index grid maps of different years; calculating the mean value of the vegetation indexes from the initial year to the target year according to the vegetation index grid map, generating a annual vegetation index mean grid map, and calculating the standard deviation of the vegetation indexes according to the mean value of the vegetation indexes; calculating the difference value of the mean value of the target period year and the annual vegetation index to obtain a difference value grid map; and outputting a vegetation index difference grid map. The early warning or normal condition of the vegetation index NDVI of the region can be directly revealed through the differential grid map, and the region needing ecological restoration is further accurately identified; based on a geographic information system, the weather data is seamlessly linked with other weather index data such as vegetation indexes, accumulated temperature and the like when correlation analysis is carried out.
Description
Technical Field
The application relates to the technical field of ecological environment protection, in particular to a geographic information system calculation method for evaluating vegetation index conditions.
Background
The vegetation index (NDVI) is used for detecting the vegetation growth state, vegetation coverage, eliminating partial radiation errors and the like. NDVI reflects the background effects of plant canopy, such as soil, wet ground, snow, dry leaves, thickness, etc., and is related to vegetation coverage. A vegetation index (NDVI) product inverted by various satellite remote sensing data is one of ecological environment series data products derived from a geographical national condition monitoring cloud platform.
At the present stage, people can only know the current data of the vegetation index NDVI, namely, the remote sensing image data is used for generating the vegetation index NDVI distribution data of the region through GIS or ENVI waveband operation processing. Some scholars use the combination of the vegetation index NDVI and other ecological factors to evaluate the comprehensive condition of the ecological environment, and some scholars classify the NDVI and then perform classified numerical operation.
However, the current data of the vegetation index NDVI only reveal the spatial distribution value of the NDVI in a certain period, and cannot reveal whether the NDVI is high or low, that is, a model for evaluating the NDVI condition is not formed. Many other basic ecological data are needed for evaluating the comprehensive condition of the ecological environment, and the data are difficult to obtain; the NDVI classification and the classification numerical operation are only to reveal the change between two stages of NDVI, but the change of NDVI itself in a long-time sequence is not considered, so the prior art has the above problems.
Disclosure of Invention
The application provides a vegetation index condition evaluation method, which aims to solve the problem that the current data of a vegetation index in the prior art only discloses the spatial distribution value of the vegetation index at a certain period and cannot disclose whether the vegetation index is higher or lower than the average value of the annual vegetation index.
In order to solve the technical problem, the vegetation index early warning method comprises the following steps of obtaining remote sensing image data of a target area, preprocessing the remote sensing image data, and calculating to obtain a vegetation index NDVI value; projecting the obtained vegetation index NDVI value to obtain vegetation index grid maps of different years; calculating the mean value of the vegetation indexes from the initial year to the target year according to the vegetation index grid map, generating a annual vegetation index mean grid map, and calculating the standard deviation of the vegetation indexes according to the mean value of the vegetation indexes; calculating the difference value of the mean value of the target period year and the annual vegetation index to obtain a difference value grid map; and outputting a vegetation index difference grid map.
Further, the acquiring remote sensing image data of the target area and preprocessing the remote sensing image data includes: radiation correction, atmospheric correction, and outlier processing.
Further, the specific method of the radiation correction is as follows: opening the original image, and selecting Basic Tools-pregrooving-Calibration Utilities-Landsat. The Lansat Type is selected according to the sensor Type, Data Acquisition time (Data Acquisition) and Sun elevation (Sun level) are acquired from a head file of the remote sensing image, and the Calibration Type is selected as Radiance (Radiance).
Further, the vegetation index NDVI value is calculated, and the method specifically includes the following steps: preprocessing the remote sensing image data; carrying out geometric fine correction on the remote sensing image by utilizing a field actual measurement control point; determining the optimal segmentation scale of the remote sensing image according to the land type condition of the target area to finish image segmentation; extracting image features; and calculating the normalized vegetation index NDVI.
Further, the calculating the mean value of the vegetation index from the initial year to the target year according to the vegetation index grid map to generate a annual vegetation index mean grid map specifically includes the following steps: using a grid calculator in ArcGIS, the vegetation index of each year in the grid is summed, and the summed sum is used to remove the total year to calculate the vegetation index average of the annual grid data for each period.
And further, calculating the standard deviation of the vegetation index according to the mean value of the vegetation index after the adult vegetation index mean grid map is generated, and generating a standard deviation grid map.
Further, the calculating the vegetation index standard deviation according to the vegetation index average value and generating the standard deviation grid map includes: firstly, calculating the sum of the mean value of the vegetation index and the standard deviation of the vegetation index; and subtracting the sum of the mean value of the vegetation indexes and the standard deviation of the vegetation indexes from the vegetation indexes of the target year to obtain difference data of the target year, and drawing a difference grid map according to the obtained difference data of the target year.
Further, the difference value in the difference value grid graph is smaller than a preset threshold value and is recorded as early warning; if the value is larger than the preset threshold value, the value is recorded as normal, and the preset value is-0.15.
Further, the mean grid map varies with the change of year.
Has the advantages that: different from the prior art, the beneficial effects of this application are: according to the method disclosed by the embodiment, the early warning or normal condition of the vegetation index NDVI of the area can be directly revealed through the differential grid map, and the area needing ecological restoration is further accurately identified; based on a geographic information system, the weather data is seamlessly linked with other weather index data such as vegetation indexes, accumulated temperature and the like when correlation analysis is carried out.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a graph of the vegetation index NDVI for month 7 of a certain region as provided herein;
fig. 2 is another flowchart of the vegetation index status evaluation method provided in the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described clearly and completely with reference to fig. 1-2 in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The vegetation index (NDVI) is used for detecting the vegetation growth state, vegetation coverage, eliminating partial radiation errors and the like. NDVI reflects the background effects of plant canopy, such as soil, wet ground, snow, dry leaves, thickness, etc., and is related to vegetation coverage. A vegetation index (NDVI) product inverted by various satellite remote sensing data is one of ecological environment series data products derived from a geographical national condition monitoring cloud platform.
However, the current NDVI calculation only calculates the current NDVI status data at a certain time point, such as a certain year or a certain month, as shown in fig. 1, which shows the vegetation index NDVI of a certain region in 7 months, and cannot show whether an NDVI is high or low compared to a standard value or an average value, and cannot evaluate the change of the NDVI in a time sequence.
In order to solve the limitation that only the current vegetation index can be obtained in the prior art, the application provides a vegetation index early warning method, and specifically please refer to fig. 2, where fig. 2 is a flowchart of the vegetation index early warning method provided by the application.
As shown in fig. 2, the vegetation index early warning method of the embodiment includes the following steps:
step S100: and acquiring remote sensing image data of a target area, preprocessing the remote sensing image data, and calculating to obtain an NDVI value.
In an implementation manner of this embodiment, a satellite sensor with the following resolution is selected to obtain remote sensing data, and the county/flag remote sensing data is as follows: high resolution number 1-pms (spatial resolution 16km, about 4 days), Sentinel number 2 (Sentinel-2A/2B spatial resolution 10m, about 5 days) environment minisatellite A/B CCD camera (spatial resolution 30m, 31 days) Landsat series satellite (spatial resolution 30m, time resolution 16 days) resource 02C (10 m, time resolution 55 days). It should be noted that images of the SPOT5 satellite, the rapideye satellite, the landsat5 satellite, or the landsat7 satellite can be selected according to the land type of the target area, and the invention is not limited in particular.
In the embodiment, S101, remote sensing image data is preprocessed
In this embodiment, the remote sensing data preprocessing includes: radiation correction, atmospheric correction and outlier processing; basic data used when the remote sensing image is subjected to radiation correction, atmospheric correction and abnormal value processing, such as the basic data of experimental area elevation, meteorological parameters and the like, can be acquired by a target area official website and a meteorological data website, and RSD atmospheric correction, USGSLaSRC atmospheric correction and the like can be selected, and the invention is not particularly limited.
Specifically, in this embodiment, the radiation correction is specifically implemented by: opening the original image, and selecting basic tools-pregrooving-Calibration Utilities-Landsat. The Lansat Type is selected according to the sensor Type, Data Acquisition time (Data Acquisition) and Sun elevation (Sun level) are acquired from a head file of the remote sensing image, and the Calibration Type is selected as Radiance (Radiance).
In this embodiment, the specific method of atmospheric correction is as follows:
entering ENVI-spectrum-flash or Basic tools-pregrooving-Calirectionalities-flash. Inputting a radiance image, wherein the input Data must be Data after radiation correction, and converting the radiation correction Data into a BIL or BIP format (Convert Data); and editing a header file of the input data, mainly editing the wavelength (wavelength) and the wavelength width, and if the input data is not a hyperspectral data, marking the wavelength width.
After data is input, a conversion factor (Single scale factor) is set, and in this embodiment, the conversion factor is set to 0.1, and different conversion factors affect different bands for input.
Setting output parameters and inputting imaging: the File name and the position of an Output File are set in an Output reflection File and an Output retrieval for flash files, sensor parameters and the longitude and latitude (Scene Center Location) of the image Center point are set according to a head File of a remote sensing image, the image can be opened, and the longitude and latitude of the Center point can be checked. Flight Date, Flight Time GMT.
The aerosol model provides four standards, namely four choices of (country) Rural, (city) Urban, (sea) Maritime and Tropospheric, and the standards are selected according to actual conditions.
S102, carrying out geometric fine correction on the remote sensing image by utilizing a field actual measurement control point;
in this embodiment, the preprocessed remote sensing image needs to be geometrically and precisely corrected. Specifically, a plurality of ground control points can be collected in the target area according to needs, and the geometric correction module of commercial remote sensing software such as ENVI, ERDAS and PCI is adopted for implementation.
S103, determining the optimal segmentation scale of the remote sensing image according to the land type condition of the target area, and completing image segmentation;
s104, extracting image features
In this embodiment, the image features are spectral and geometric features, including band brightness, band ratio, maximum difference, aspect ratio, circularity, wavelet domain fractal texture, and the like. Optionally, the image feature may also be other spectral derivative features or texture features, such as a normalized difference vegetation index, a normalized difference water body index, a GLCM texture feature, and the like, and the invention is not particularly limited S105: calculating a normalized vegetation index NDVI
The reflection coefficient is calculated by the ratio of the difference of the values of the near infrared band and the visible RED band to the sum of the values of the two bands, namely NDVI = (NIR-RED)/(NIR + RED), NIR represents the reflection coefficient of the near infrared band, and RED represents the reflection coefficient of the visible RED band.
Step S200: projecting the obtained NDVI values to obtain vegetation index raster graphs of different years; calculating the mean value of the vegetation indexes from the initial year to the target year according to the vegetation index grid map, generating a annual vegetation index mean grid map, and calculating the standard deviation of the vegetation indexes according to the mean value of the vegetation indexes;
s201: the vegetation index average of the annual grid data of each period is calculated by adding the vegetation index of each year in the grid and removing the total year by the added sum, using a rater Calculator (grid Calculator) in ArcGIS. A time series of annual vegetation indices is formed to analyze the variation characteristics of the vegetation indices over the target area over the years.
Such as: the mean of the grid data of the annual vegetation index of the year 2000 to the present is calculated to generate an annual vegetation index mean grid map, and the process is to open a grid calculator page, select (the '2000 vegetation index' + '2001 vegetation index' + '2002 vegetation index' + '2003 vegetation index' …)/total number of years, and obtain the mean of the grid. And obtaining a mean grid image according to the mean value projection. The mean is automatically updated as the year is updated.
The above: the annual vegetation index mean grid map is automatically updated with the updating of the year. Such as: if the current year is 2019, then the vegetation index of the year 2019 is calculated to be the mean value, because the vegetation index of the year is unknown at the time of 2019, and the calculation is 18 years ago; the mean value includes the mean value from the starting year to the current year, and if the current year is updated, the mean value is updated.
S202: calculating the standard deviation of the vegetation index according to the mean value of the vegetation index, and generating a standard deviation grid diagram:
the standard deviation of the vegetation index can reflect the discrete degree of the annual vegetation index, and provides a standard for calculating the difference of the annual vegetation index subsequently.
Step S300: calculating the difference between the target period year and the NDVI mean value to obtain a difference grid diagram; and outputting the vegetation index early warning grid map.
In this embodiment, the calculating a vegetation index standard deviation according to the vegetation index average value and generating a standard deviation grid map includes:
using a Raster Calculator tool in ArcGIS, firstly calculating the sum of the mean value of the vegetation index and the standard deviation of the vegetation index; and subtracting the sum of the mean value of the vegetation indexes and the standard deviation of the vegetation indexes from the vegetation indexes of the target year to obtain difference data of the target year, and drawing a difference grid map according to the obtained difference data of the target year.
And (3) comparing the difference value of the overground biomass of the target period year in the raster data with a preset threshold value by using a reclassification tool (reclassification) in ArcGIS, and marking differently according to the comparison result.
The difference value in the difference value grid graph is smaller than a preset threshold value and is recorded as early warning; if the value is larger than the preset threshold value, the value is recorded as normal, and the preset value is-0.15.
In this embodiment, the threshold is generally-0.15, and may be manually adjusted or preset in the system; for example, if the difference is greater than-0.15, it indicates that the vegetation index NDVI of the current year is increased from the average value of the last N years, and is marked as normal; if the difference is less than-0.15, the vegetation index in the current year reduces the average value of the last N years, and the average value is marked as early warning.
In this embodiment, the vegetation index of the current year marked in the difference grid map is early warning or normal. Further, the warning or normal is marked with a different color or transparency or hatching.
According to the method disclosed by the embodiment, the early warning or normal condition of the vegetation index NDVI of the area can be directly revealed through the differential grid map, and the area needing ecological restoration is further accurately identified; based on a geographic information system, the weather data is seamlessly linked with other weather index data such as vegetation indexes, accumulated temperature and the like when correlation analysis is carried out.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part. The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is only for the purpose of illustrating embodiments of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application or are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.
Claims (9)
1. A vegetation index early warning method is characterized by comprising the following steps:
acquiring remote sensing image data of a target area, preprocessing the remote sensing image data, and calculating to obtain a vegetation index NDVI value;
projecting the obtained vegetation index NDVI value to obtain vegetation index grid maps of different years; calculating the mean value of the vegetation indexes from the initial year to the target year according to the vegetation index grid map, generating a annual vegetation index mean grid map, and calculating the standard deviation of the vegetation indexes according to the mean value of the vegetation indexes;
calculating the difference value of the mean value of the target period year and the annual vegetation index to obtain a difference value grid map; and outputting a vegetation index difference grid map.
2. The vegetation index early warning method according to claim 1, wherein the acquiring remote sensing image data of a target area and preprocessing the remote sensing image data comprises: radiation correction, atmospheric correction, and outlier processing.
3. The vegetation index warning method according to claim 2, wherein the radiation correction is performed by the following specific method: opening an original image, and selecting Basic Tools-pregrooving-Calibration Utilities-Landsat; the Lansat Type is selected according to the sensor Type, Data Acquisition time (Data Acquisition) and Sun elevation (Sun level) are acquired from a head file of the remote sensing image, and the Calibration Type is selected as Radiance (Radiance).
4. The vegetation index early warning method according to claim 1, wherein the vegetation index NDVI value is obtained through calculation, and the vegetation index early warning method specifically comprises the following steps: preprocessing the remote sensing image data; carrying out geometric fine correction on the remote sensing image by utilizing a field actual measurement control point; determining the optimal segmentation scale of the remote sensing image according to the land type condition of the target area to finish image segmentation; extracting image features; and calculating the normalized vegetation index NDVI.
5. The vegetation index early warning method according to claim 1, wherein the vegetation index mean value grid map is generated by calculating the vegetation index mean value from an initial year to a target year according to the vegetation index grid map, and specifically comprises the following steps: using a grid calculator in ArcGIS, the vegetation index of each year in the grid is summed, and the summed sum is used to remove the total year to calculate the vegetation index average of the annual grid data for each period.
6. The vegetation index warning method of claim 5, wherein a standard deviation grid map is generated by calculating a vegetation index standard deviation according to the vegetation index mean after the adult vegetation index mean grid map is generated.
7. The vegetation index condition assessment method of claim 6, wherein said calculating vegetation index standard deviations from said vegetation index mean and generating a grid of standard deviation plots comprises:
firstly, calculating the sum of the mean value of the vegetation index and the standard deviation of the vegetation index; and subtracting the sum of the mean value of the vegetation indexes and the standard deviation of the vegetation indexes from the vegetation indexes of the target year to obtain difference data of the target year, and drawing a difference grid map according to the obtained difference data of the target year.
8. The vegetation index pre-warning method according to claim 7, wherein the difference in the grid map of differences is less than a preset threshold and is recorded as a pre-warning; if the value is larger than the preset threshold value, the value is recorded as normal, and the preset value is-0.15.
9. The vegetation index condition assessment method of claim 5, wherein said mean grid map varies with year.
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