CN116090674A

CN116090674A - Early warning method, device and system for grassland degradation

Info

Publication number: CN116090674A
Application number: CN202310364305.XA
Authority: CN
Inventors: 温庆可; 袁艺馨; 任月娟
Original assignee: Aerospace Information Research Institute of CAS
Current assignee: Aerospace Information Research Institute of CAS
Priority date: 2023-04-07
Filing date: 2023-04-07
Publication date: 2023-05-09
Anticipated expiration: 2043-04-07
Also published as: CN116090674B

Abstract

The invention provides a grassland degradation early warning method, device and system, and belongs to the technical field of remote sensing treatment. The method comprises the following steps: receiving grass identification information of a grass distribution area to be predicted; utilizing grassland identification information of a grassland distribution area to be predicted as retrieval information, and carrying out query matching in a rule base; under the condition that grassland degradation early warning rules corresponding to grassland identification information are matched from the rule base by utilizing grassland identification information, a degradation prediction result of a grassland distribution area to be predicted is obtained based on the matched grassland degradation early warning rules and current grassland remote sensing data of the grassland distribution area to be predicted. The early warning method, the early warning device and the early warning system for grassland degradation provided by the invention realize that the grassland degradation characteristic which appears before the strong season can be mined through the rule that the grassland NDVI changes with seasons in the history period, so that the grassland growth problem in the current year of the area can be found out in time and early warning can be realized as soon as possible, and a regional grassland degradation quasi-real-time assessment scheme can be formed.

Description

Early warning method, device and system for grassland degradation

Technical Field

The invention relates to the technical field of remote sensing treatment, in particular to a grassland degradation early warning method, device and system.

Background

Traditional grassland degradation monitoring is mainly completed through field investigation means, and a great deal of manpower and time are consumed. With the continuous development of remote sensing technology, the spatial resolution, the time resolution and the spectral resolution of images are continuously improved, the history or the current situation analysis of grassland degradation by using the remote sensing technology can effectively and reliably reflect the grassland degradation problem, the analysis of the change trend of grassland distribution for many years in an area is carried out, and grassland distribution area information to be recovered is provided, but research is only carried out after 8 months of grassland growth season, and is limited by the time delay of receiving, preprocessing and releasing images by a satellite remote sensing ground station, so that information support can not be provided for grassland remediation work with poor current year growth condition in time.

Therefore, the current grassland degradation remote sensing monitoring research is mainly based on the historical and current analysis of grassland degradation, less research is performed on grassland degradation prediction, early warning of grassland degradation before the arrival of a growing season is difficult, and the provision of specific position information for grassland restoration projects of related departments in time is hindered.

Disclosure of Invention

The invention provides a method, a device and a system for early warning of grassland degradation, which are used for solving the defect that early warning of grassland degradation is difficult to be carried out before a growing season arrives in the prior art.

The invention provides a grassland degradation early warning method, which comprises the following steps:

receiving grass identification information of a grass distribution area to be predicted;

utilizing the grassland identification information of the grassland distribution area to be predicted as search information, and carrying out query matching in a rule base; the grassland degradation early warning device is used for detecting grassland degradation of the grassland distribution areas of the grassland degradation early warning device, wherein the grassland degradation early warning rules correspond to the grassland distribution areas of the grassland degradation early warning device; the grassland degradation early warning rule is determined by using the different degradation grades of NDVI converted by the remote sensing data corresponding to the grassland distribution area after the grassland degradation early warning is finished along with the seasonal variation trend;

and under the condition that the grassland degradation early warning rule corresponding to the grassland identification information is matched from the rule base by utilizing the grassland identification information, acquiring a degradation prediction result of the grassland distribution area to be predicted based on the matched grassland degradation early warning rule and the current grassland remote sensing data of the grassland distribution area to be predicted.

According to the early warning method for grassland degradation provided by the invention, after receiving grassland identification information of the grassland distribution area to be predicted, the early warning method further comprises the following steps:

Under the condition that the grassland identification information of the grassland distribution area to be predicted is determined to be failed to be matched with a rule base, historical grassland remote sensing data of the grassland distribution area to be predicted are obtained;

determining a target grassland degradation early warning rule according to the trend of the NDVI value corresponding to each pixel point in the historical grassland remote sensing data along with the seasonal variation;

and acquiring a degradation prediction result of the grassland distribution area to be predicted based on the constructed target grassland degradation early warning rule and the current grassland remote sensing data of the grassland distribution area to be predicted.

According to the early warning method for grassland degradation provided by the invention, the method for determining the early warning rule for grassland degradation according to the trend of the NDVI value corresponding to each pixel point in the historical grassland remote sensing data along with the seasonal variation comprises the following steps:

based on the historical grassland remote sensing data, the NDVI values converted by any pixel point are arranged according to seasonal changes, so that an NDVI time sequence corresponding to any pixel point is obtained;

extracting a first degradation characteristic and a second degradation characteristic based on NDVI time sequences corresponding to all pixel points;

determining the target grassland degradation early warning rule based on the first degradation feature and the second degradation feature;

The first degradation characteristic is determined according to a certain low value level of the whole NVDI value corresponding to the same time point in the NDVI time sequence corresponding to different pixel points; the second degradation characteristic is determined according to a difference relation between the NVDI values corresponding to two adjacent time points in the NDVI time series corresponding to the same pixel point.

According to the early warning method for grassland degradation provided by the invention, based on the historical grassland remote sensing data, the NDVI values converted by any pixel point are arranged according to the seasonal variation, so as to obtain an NDVI time sequence corresponding to any pixel point, which comprises the following steps:

preprocessing the historical grassland remote sensing data to obtain corrected reflectivity data;

determining NVDI values of each pixel point at each time point based on the corrected reflectivity data;

according to the ascending order of the target time points, the NVDI values of the pixel points at the time points are screened out from the NVDI values belonging to the maximum NVDI values of the target time points, and an NDVI time sequence corresponding to any pixel point is formed;

the target time points are time points contained in each month from 1 month to 7 months of each year, and the order of arrangement of the target time points is that the priority of the years is greater than the priority of the months.

According to the early warning method for grassland degradation provided by the invention, before the historical grassland remote sensing data of the grassland distribution area to be predicted is obtained, the early warning method further comprises the following steps:

determining a grassland degradation area and a degradation grade based on the historical area remote sensing data;

dividing the grassland degradation area into first grassland distribution areas according to grassland identification characteristics, and counting NDVI mean time sequences of different degradation grades in each first grassland distribution area;

clustering analysis is carried out on NDVI mean time sequences of different degradation grades in all the first grassland distribution areas, and after a second grassland distribution area is obtained;

wherein the grassland distribution area to be predicted is any one of the second grassland distribution areas.

According to the early warning method for grassland degradation provided by the invention, after the current grassland remote sensing data of the grassland distribution area to be predicted and based on the constructed target grassland degradation warning rule, the degradation prediction result of the grassland distribution area to be predicted is obtained, the early warning method further comprises the following steps:

obtaining a degradation prediction result of the grassland distribution area to be predicted and grassland remote sensing data of the next observation period, and obtaining a degradation verification result of the grassland distribution area to be predicted;

And after the degradation verification result is confirmed to pass verification, storing the constructed target grassland degradation early warning rule into the rule base.

The invention also provides a grassland degradation early warning device, which comprises:

the grass area confirming module is used for receiving grass identification information of a grass area distribution area to be predicted;

the rule matching module is used for inquiring and matching in the rule base by using the grassland identification information of the grassland distribution area to be predicted as search information; the grassland degradation early warning device is used for detecting grassland degradation of the grassland distribution areas of the grassland degradation early warning device, wherein the grassland degradation early warning rules correspond to the grassland distribution areas of the grassland degradation early warning device; the grassland degradation early warning rule is determined by using the different degradation grades of NDVI converted by the remote sensing data corresponding to the grassland distribution area after the grassland degradation early warning is finished along with the seasonal variation trend;

the first prediction module is used for acquiring a degradation prediction result of the grassland distribution area to be predicted based on the matched grassland degradation early warning rule and the current grassland remote sensing data of the grassland distribution area to be predicted under the condition that the grassland degradation early warning rule corresponding to the grassland identification information is matched from the rule base by utilizing the grassland identification information.

The invention also provides a grassland degradation early warning system, which comprises a server node at a remote place and a grassland management node in communication connection with the server node;

the server node is configured to execute any one of the early warning methods for grassland degradation, and send the obtained degradation prediction result of the grassland distribution area to be predicted to the grassland management node;

the grassland management node is used for formulating a control strategy matched with the degradation prediction result according to the degradation prediction result of the grassland distribution area to be predicted.

The invention also provides electronic equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the early warning method for grassland degradation is realized by the processor when the program is executed.

The invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of early warning of grassland degradation as described in any of the above.

The invention also provides a computer program product comprising a computer program which when executed by a processor implements a method of early warning of grassland degradation as described in any of the above.

According to the grassland degradation early warning method, the grassland degradation early warning device and the grassland degradation early warning system, when grassland identification information of the grassland distribution area to be predicted is matched with the grassland degradation early warning rule from the rule base, a decision is made to directly predict current grassland remote sensing data observed by the grassland distribution area to be predicted by using the grassland degradation early warning rule corresponding to the grassland distribution area to be predicted, and a degradation prediction result of whether the grassland distribution area to be predicted is degraded or not in a future time is obtained. The method realizes the characteristic of degradation evolution process by the rule of normalized vegetation index (NDVI) of the grassland distribution area in a history period along with seasonal variation, and can excavate grassland degradation characteristics before the grassland growth season, thereby timely early warning is performed by finding the grassland growth condition problem of the area in the current year as soon as possible, and a regional grassland degradation quasi-real-time evaluation scheme is formed.

Drawings

In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic flow chart of a grassland degradation early warning method provided by the invention;

FIG. 2 is a second flow chart of the early warning method for grassland degradation according to the present invention;

FIG. 3 is a schematic structural diagram of the early warning device for grassland degradation, which is provided by the invention;

FIG. 4 is a schematic diagram of the early warning system for grassland degradation provided by the invention;

fig. 5 is a schematic structural diagram of an electronic device provided by the present invention.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

The terms "first," "second," and the like in this application are used for distinguishing between similar objects and not for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged, as appropriate, such that embodiments of the present application may be implemented in sequences other than those illustrated or described herein, and that the objects identified by "first," "second," etc. are generally of a type and not limited to the number of objects, e.g., the first object may be one or more.

It is to be understood that the terminology used in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this disclosure, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

The terms "comprises" and "comprising" indicate the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

Fig. 1 is a schematic flow chart of the early warning method for grassland degradation. As shown in fig. 1, the early warning method for grassland degradation provided by the embodiment of the invention includes: step 101, receiving grass identification information of a grass distribution area to be predicted.

It should be noted that, the execution main body of the early warning method for grassland degradation provided by the embodiment of the invention is the early warning device for grassland degradation. The early warning device for grassland degradation can be a central processing unit (Central Processing Unit, CPU) or a development board integrated based on the CPU for information processing and program running.

The service object of the grassland early-stage early-warning device is a grassland or land management party provided with the grassland early-stage early-warning device for monitoring and early-warning the growing situation of grasslands in jurisdiction along with the seasonal variation.

It should be noted that, before step 101, after the manager and/or the user of the early warning device for grassland degradation need to log in, the relevant feature value of the grassland distribution area to be predicted needs to be input in the text box corresponding to the identification feature of the grassland in the objective environment in the front page according to the actual prediction task requirement, so as to form the grassland identification information unique to other grasslands.

Among other things, grassland identification features including, but not limited to, elevation, grade, slope, grassland coverage, etc., may be supplemented or replaced with factor data affecting the condition of grassland growth, such as air temperature, precipitation, etc. The embodiment of the present invention is not particularly limited thereto.

Specifically, in step 101, the early warning device for grassland degradation may receive grassland identification information input by the user for the grassland distribution area to be predicted at the front end display interface according to the actual prediction requirement, and the defined prediction time.

The predicted time may be default to the current time corresponding to the grass identification information input by the user, or may be set to any time after the current time.

For example, according to the growth law of grass, it is known that grass that remains alive after dormancy in winter enters the growing period from 1 month to 7 months each year, and that grass that comes into coming into growing season is waiting to be in August, and then goes into dormancy in winter again, and so on. The defined prediction time can be set at the growing season of grass, i.e. 8 months.

And 102, utilizing the grassland identification information of the grassland distribution area to be predicted as retrieval information, and carrying out query matching in a rule base.

The grassland degradation early warning device is used for detecting the grassland degradation of the grassland distribution area, wherein the grassland degradation early warning rules correspond to the grassland distribution area, and the grassland degradation early warning device is used for detecting the grassland degradation of the grassland distribution area. The grassland degradation early warning rule is determined by changing NDVI of different degradation grades along with the seasonal variation trend by using remote sensing data corresponding to grassland distribution areas with grassland degradation early warning.

It should be noted that, the rule base is a grassland degradation early warning rule corresponding to a grassland which includes a plurality of grassland degradation early warning devices which have completed grassland degradation early warning.

The grassland degradation early warning rule refers to a constraint condition obtained after converting remote sensing data correspondingly collected in a grassland distribution area in a history period into a normalized vegetation index (NDVI) to quantify grassland supply and leaf area indexes and counting the variation trend of the grassland supply and leaf area indexes along with seasons.

Among them, NDVI is a good indicator of drought. When water limits vegetation growth, it is relatively low in NDVI and vegetation density.

Specifically, in step 102, the early warning device for grassland degradation uses the grassland identification information of the grassland distribution area to be predicted obtained in step 101 as search information, and performs query matching in a rule base, and the result is divided into two types: match success and match failure.

The matching is successful, the condition that the breeding data of the current grassland degradation early warning can be matched with the information stored in the locally stored rule base, which indicates that the grassland degradation early warning device executes the same or similar early warning task as the current grassland degradation early warning task, and in the corresponding historical early warning task, the corresponding grassland degradation early warning rule is obtained after the corresponding grassland degradation early warning rule is processed on the historical remote sensing data observed by the grassland, so that the corresponding grassland degradation early warning rule corresponding to the historical early warning task can be extracted from the rule base.

The failure of matching is the condition that the grassland identification information to be subjected to grassland degradation early warning at present is not matched with any information in a locally stored rule base, and the condition that the grassland degradation early warning device does not monitor similar grasslands yet, a corresponding rule construction process is required to be started to obtain corresponding grassland degradation early warning rules, and grassland degradation early warning work is carried out according to the obtained grassland degradation early warning rules.

And 103, under the condition that the grassland degradation early warning rule corresponding to the grassland identification information is matched from the rule base by utilizing the grassland identification information, acquiring a degradation prediction result of the grassland distribution area to be predicted based on the matched grassland degradation early warning rule and the current grassland remote sensing data of the grassland distribution area to be predicted.

Specifically, in step 103, the early warning device for grassland degradation uses the grassland degradation warning rule matched in step 102 to constrain the current grassland remote sensing data observed in the grassland distribution area to be predicted, so as to obtain a degradation prediction result which can represent the growth situation of the grassland distribution area to be predicted at the future prediction time.

The degradation prediction result may be a binarized result, that is, only the grassland is predicted to degrade at a future prediction time, or the grassland is not predicted to degrade at a future prediction time.

The degradation prediction result may be a multi-labeled result, that is, only different degradation levels that occur at a future prediction time are shown, for example, degradation conditions that occur in the future may be classified into light degradation, moderate degradation, high degradation, and the like according to plaque size conditions of the prediction result.

When the grassland identification information of the grassland distribution area to be predicted is matched with the grassland degradation early warning rule from the rule base, the embodiment of the invention makes a decision to directly predict the current grassland remote sensing data observed by the grassland distribution area to be predicted by using the grassland degradation early warning rule corresponding to the grassland distribution area to be predicted, so as to obtain a degradation prediction result of whether the grassland distribution area to be predicted is degraded in a future time. The method realizes the characteristic of degradation evolution process by the rule of normalized vegetation index (NDVI) of the grassland distribution area in a history period along with seasonal variation, and can excavate grassland degradation characteristics before the grassland growth season, thereby timely early warning is performed by finding the grassland growth condition problem of the area in the current year as soon as possible, and a regional grassland degradation quasi-real-time evaluation scheme is formed.

On the basis of any one of the above embodiments, after receiving the grassland identification information of the grassland distribution area to be predicted, the method further includes: and under the condition that the grassland identification information of the grassland distribution area to be predicted is determined to be failed to be matched with the rule base, acquiring historical grassland remote sensing data of the grassland distribution area to be predicted.

Specifically, after step 101, the early warning device for grassland degradation uses the grassland identification information of the grassland distribution area to be predicted obtained in step 101 as search information, performs query matching in a rule base, and when it is determined that the query result is a matching failure, needs to obtain historical grassland remote sensing data observed in all historical periods of the grassland distribution area to be predicted, so as to construct a corresponding grassland degradation warning rule.

If the limited prediction time is 8 months of the current year, the history period corresponding to the historical grassland remote sensing data may be 1 month to 7 months of the two years before the current year.

And determining a target grassland degradation early warning rule according to the trend of the NDVI value corresponding to each pixel point in the historical grassland remote sensing data along with the seasonal variation.

Specifically, after the grassland degradation early warning device converts the historical grassland remote sensing data into the NDVI value, the target grassland degradation early warning rule which is not included in the rule base is generalized aiming at the rule that the NDVI value of any pixel point changes along with time in the historical period.

It can be appreciated that the above-described procedure of establishing the target grassland degradation early warning rule may also be used to update and maintain the grassland degradation early warning rule existing in the rule base.

Specifically, the grassland degradation early warning device utilizes a newly established target grassland degradation early warning rule to restrict current grassland remote sensing data observed by a grassland distribution area to be predicted, and obtains degradation prediction results which can represent growth situations of the grassland distribution area to be predicted at future prediction time.

According to the method, when the grassland identification information of the grassland distribution area to be predicted is not matched with the grassland degradation early warning rule from the rule base, the current grassland remote sensing data observed by the grassland distribution area to be predicted is decided to be mined according to the degradation rule, and then whether the grassland distribution area to be predicted is degraded or not is predicted at a certain time in the future according to the obtained target grassland degradation early warning rule. The method realizes the characteristic of degradation evolution process by the rule that the NDVI of the grassland distribution area changes along with seasons in the history period, and can excavate grassland degradation characteristics before the grassland growth season, so that the grassland growth condition problem of the current year of the area can be found as early as possible to carry out timely early warning, and a regional grassland degradation quasi-real-time evaluation scheme is formed.

On the basis of any one of the above embodiments, determining a target grassland degradation early warning rule according to a trend of NDVI values corresponding to pixels in the historical grassland remote sensing data along with seasonal variation, includes: based on the historical grassland remote sensing data, the NDVI values converted by any pixel point are arranged according to the seasonal variation, and an NDVI time sequence corresponding to any pixel point is obtained.

Specifically, the early warning device for grassland degradation performs the following processing on any pixel point in the historical grassland remote sensing data, namely, sequentially calculating the corresponding NDVI value of the reflectivity stored in the pixel point by month of the image in the historical grassland remote sensing data to obtain the time-varying NDVI time sequence of the vegetation coverage condition of the pixel point until the time-varying NDVI time sequences corresponding to all the pixel points are integrated.

And extracting a first degradation characteristic and a second degradation characteristic based on the NDVI time sequences corresponding to all the pixel points.

The first degradation characteristic is determined according to a certain overall low value level between NVDI values corresponding to the same time point in NDVI time sequences corresponding to different pixel points; the second degradation characteristic is determined according to a difference relation between the NVDI values corresponding to two adjacent time points in the NDVI time series corresponding to the same pixel point.

Specifically, the early warning device for grassland degradation performs statistical processing on the NDVI time sequences corresponding to all the pixel points, and for the same sampling time point, takes a part of the NDVI time sequences of different pixel points, which is lower than a certain low value level of the whole, corresponding to the time point, as a first degradation feature.

And regarding the pixel points of the same sample, taking the difference degree between NVDI values corresponding to any two adjacent time points of the pixel points in the NDVI time sequence as a second degradation characteristic.

The target lawn degradation pre-warning rule is determined based on the first degradation feature and the second degradation feature.

Specifically, the early warning device for grassland degradation uses the first degradation characteristic and the second degradation characteristic to generalize a group of constraint conditions as a target grassland degradation warning rule output.

By taking 1 month to 7 months of the history period as the first two years as an example, the embodiment of the invention provides a construction mode of a target grassland degradation early warning rule:

(1) First degradation characteristics corresponding to the same month in NDVI time series corresponding to different pixels:

wherein,,

、/>

、/>

and->

Means that the pixel point of the grassland distribution area to be predicted has a maximum value of NDVI (New data display) within 4-6 months, >

、/>

、/>

And->

The method is that in an NDVI time sequence formed by the ascending arrangement of the NDVI values in 4-6 months of pixels of a predicted grassland distribution area, the n-th bit of the NDVI value is selected as a threshold value according to subregions of different applications, and the value range of n is generally between 0 and 30.

(2) Second degradation characteristics corresponding to adjacent months in the NDVI time sequence corresponding to the same pixel point:

wherein,,

、/>

and->

The method is that the pixel point of the grassland distribution area to be predicted is the maximum NDVI value in the month of 5-7 months, m can select an adaptive threshold value according to different grassland distribution areas to be predicted, and the value range of m is generally optimal between 0.1 and 0.4 according to a large amount of priori data tests.

According to the embodiment of the invention, NDVI values of all pixels are integrated from historical grassland remote sensing data, the obtained NDVI time sequence is utilized to excavate degradation characteristics according to a change rule of season alternation, and the degradation characteristics are used as constraint conditions corresponding to a generalized target grassland degradation early warning rule. The construction of the NDVI time sequence of the grassland distribution area is realized, and the historical grassland degradation evolution process in the reconstruction area is restored.

Based on any one of the above embodiments, based on the historical grassland remote sensing data, the NDVI values converted by any one pixel are arranged according to the seasonal variation, so as to obtain an NDVI time sequence corresponding to any one pixel, which includes: and preprocessing the historical grassland remote sensing data to obtain corrected reflectivity data.

Specifically, the early warning device for grassland degradation needs to pre-process historical grassland remote sensing data under a certain specified spatial resolution from the remote sensing big data analysis platform in advance so as to obtain corrected reflectivity data products.

For example, the early warning device for grassland degradation can directly call the form of geometric fine-correction sentinel series 2A/B in the Google Earth Engine cloud computing platform through codes, and then uniformly perform time screening and cloud removal processing on the image set of the historical grassland remote sensing data.

And determining NVDI values of each pixel point at each time point based on the corrected reflectivity data.

Specifically, the early warning device for grassland degradation converts the reflectivity data corresponding to the same pixel point of each image in the historical grassland remote sensing data into NVDI values at different corresponding time points in sequence.

And screening the NVDI values of the pixel points at each time point according to the ascending sequence of the target time points, wherein the NVDI values belong to the maximum NVDI values of the target time points, and forming an NDVI time sequence corresponding to any pixel point.

It should be noted that the target time point may be determined according to the predicted time. If the predicted time is 8 months of the growing season of the grass of the current year, the target time point is 1-7 months in the first two years of the current year.

Specifically, the early warning device for grassland degradation screens out the largest NVDI value from target time points which are arranged in a month-by-month ascending order aiming at the NVDI value of any pixel point at each time point, and synthesizes the images with the same number as the target time points into a final set, thereby acquiring an NDVI time sequence corresponding to any pixel point.

After the original historical grassland remote sensing data is correspondingly preprocessed, the corresponding NVDI value is converted through the corrected reflectivity data, so that the corresponding NDVI time sequence is synthesized by utilizing the month-by-month NVDI maximum value. The method realizes that the NDVI time sequence is constructed by counting the maximum NDVI value month by month when the grassland distribution area is subjected to the grassland growing period in the history period, so that the history grassland degradation evolution process in the area has sharp contrast.

On the basis of any one of the above embodiments, before the acquiring the historical grassland remote sensing data of the grassland distribution area to be predicted, the method further includes: and determining a grassland degradation area and a degradation level based on the historical area remote sensing data.

Specifically, if more than one type of grassland distribution exists in the observation area, the grassland degradation early warning device can collect the remote sensing data of the historical area corresponding to the observation area, and can calculate the grassland coverage in the historical period to determine the grassland degradation area and the degradation level.

Dividing the grassland degradation area into first grassland distribution areas according to grassland identification characteristics, and counting NDVI mean time sequences of different degradation grades in each first grassland distribution area.

Specifically, the early warning device for grassland degradation reclassifies the geographic area corresponding to the remote sensing data of the historical area through the characteristic values corresponding to grassland identification characteristics (such as elevation, gradient, slope direction, grassland coverage and the like), and divides a plurality of first grassland distribution areas from a plurality of characteristic dimensions. And classifying the NDVI values of each first grassland distribution area according to different degradation grades, and carrying out mean statistics on each class to obtain an NDVI mean time sequence stored month by month in the first grassland distribution area according to different degradation grades.

And performing cluster analysis on the NDVI mean time sequences with different degradation levels in all the first grassland distribution areas to obtain a second grassland distribution area.

Specifically, the early warning device for grassland degradation carries out clustering and partitioning on NDVI average value time sequences corresponding to all first grassland distribution areas belonging to one degradation level, and combines the first grassland distribution areas with small vegetation coverage difference degree into one partition to obtain second grassland distribution areas with the number of the partitions being less than that of the first grassland distribution areas but the area of the second grassland distribution areas being increased.

The clustering analysis algorithm can perform corresponding analysis according to the curve similarity, and can also use an open source algorithm such as a K-means algorithm, a dynamic time planning algorithm and the like, and the embodiment of the invention is not particularly limited.

Then, the early warning device for grassland degradation selects a grassland distribution area to be predicted from the second grassland distribution areas.

According to the embodiment of the invention, different degradation grades are divided for degradation degrees of all grassland degradation areas in the preprocessed historical grassland remote sensing data, a first grassland distribution area is divided in detail for the grassland degradation areas through grassland identification features, and the first grassland distribution areas with little degradation difference under the same degradation grade are combined into a second grassland distribution area, so that an NDVI average time sequence with progressive degradation grades and progressive areas is obtained. By identifying and analyzing different types of grasslands in the unified observation area, errors caused by the fact that the unified grassland coverage conversion is carried out by using the whole remote sensing image can be avoided.

On the basis of any one of the above embodiments, after obtaining the degradation prediction result of the grassland distribution area to be predicted based on the constructed target grassland degradation early warning rule and the current grassland remote sensing data of the grassland distribution area to be predicted, the method further includes: and obtaining a degradation prediction result of the grassland distribution area to be predicted and grassland remote sensing data of the next observation period.

Specifically, after the grassland degradation early warning device constructs the target grassland degradation early warning rule for the grassland area which is not included in the rule base, grassland remote sensing data of the grassland distribution area to be predicted in the next observation period is required to be obtained to compare the grassland coverage level, and the degradation verification result of the grassland distribution area to be predicted is calculated by utilizing the difference degree of the grassland coverage level between the grassland remote sensing data and the grassland remote sensing data.

Specifically, the grassland degradation early warning device stores the newly constructed target grassland degradation early warning rule under a field of grassland identification information about a grassland distribution area to be predicted in a rule base after determining that the degradation verification result indicates that the degradation verification result is passed.

Fig. 2 is a schematic flow chart of a grassland degradation early warning method according to the second embodiment of the present invention. As shown in fig. 2, the embodiment of the invention provides a concrete implementation of a grassland degradation early warning method:

(1) The construction step of the NDVI time sequence remote sensing image data set firstly adopts a form that codes directly call the geometric fine correction sentinel series 2A/B in the Google Earth Engine cloud computing platform. And then uniformly carrying out time screening, cloud removal processing, NDVI calculation and image synthesis (month-to-month data for representing seasonal variation characteristics) on the image set according to the maximum value of the NDVI. And finally, an NDVI time sequence remote sensing image data set is constructed efficiently.

(2) And under each degradation level, carrying out statistics and drawing on NDVI mean time series curves of different grassland distribution areas, and clustering and partitioning according to the similarity of the curves. First, the degradation area and degradation level are determined by calculating the grass coverage for two years before and after. Dividing the grassland distribution area according to the data of the elevation, the gradient, the slope direction and the grassland coverage after reclassification, and counting and drawing NDVI mean time series curves with different degradation grades in each area. Finally, a K-shape algorithm in a Python tslearn package is called to cluster the regions according to the curve similarity, and the final grassland distribution region partition is determined. The method comprises the steps of calculating a monthly NDVI mean value of different degradation grades of each partition, adding a circulating structure into codes, calculating the grade of each partition region by region, month by month and step by utilizing a reduced region function in a cloud computing platform, outputting a result in a background according to a CSV format, reading and integrating a plurality of CSV format files by utilizing a Python module glob, and further drawing an NDVI mean value time sequence curve of each region, month by step and step by step.

(3) Clustering partition degradation characteristic mining and pixel-level grassland degradation early warning. The grassland degradation early warning rules can be constructed by the degradation characteristics reflected by the NDVI time sequence curves of each partition, and the pixel-level grassland degradation early warning is completed. The lawn degradation early warning rule needs to be constructed by utilizing the NDVI change characteristics before 8 months of the growing season of the lawn so as to play a role in early warning the lawn degradation in quasi-real time.

According to the embodiment of the invention, the basic rule of the grassland degradation development process of the area is excavated from the normalized vegetation index time sequence curve, and the basic rule is applied to the degradation early warning of each grassland pixel of the area after verification, so that a ten-meter grassland degradation early warning result can be generated. The early warning result of the grassland degradation at the pixel level is beneficial to statistics and analysis of the grassland degradation degree in the area, development of evaluation of the urgency of grassland restoration, provision of specific position information and urgency information of the grassland restoration for timely and effective development of grassland restoration engineering, and provision of quasi-real-time data and information support for related departments' decisions.

Fig. 3 is a schematic structural diagram of the early warning device for grassland degradation provided by the invention. On the basis of any of the above embodiments, as shown in fig. 3, the apparatus includes: a grass validation module 310, a rule matching module 320, and a first prediction module 330, wherein:

The grassland identification module 310 is configured to receive grassland identification information of a grassland distribution area to be predicted.

And the rule matching module 320 is configured to perform query matching in a rule base by using the grassland identification information of the grassland distribution area to be predicted as search information. The grassland degradation early warning device is used for detecting the grassland degradation of the grassland distribution area, wherein the grassland degradation early warning rules correspond to the grassland distribution area, and the grassland degradation early warning device is used for detecting the grassland degradation of the grassland distribution area. The grassland degradation early warning rule is determined by changing NDVI of different degradation grades along with the seasonal variation trend by using remote sensing data corresponding to grassland distribution areas with grassland degradation early warning.

The first prediction module 330 is configured to obtain a degradation prediction result of the grassland distribution area to be predicted based on the grassland degradation pre-warning rule and the current grassland remote sensing data of the grassland distribution area to be predicted, where the grassland degradation pre-warning rule corresponding to the grassland identification information is matched from the rule base by using the grassland identification information.

Specifically, the grass area confirmation module 310, the rule matching module 320, and the first prediction module 330 are electrically connected in sequence.

The grass verification module 310 may receive grass identification information entered by a user at the front display interface for a grass distribution area to be predicted, and a defined prediction time, according to actual prediction needs.

The rule matching module 320 uses the grassland identification information of the grassland distribution area to be predicted obtained by the grassland identification module 310 as search information, and performs query matching in a rule base, and the results thereof are divided into two types: match success and match failure.

The first prediction module 330 uses the grassland degradation early warning rule matched by the rule matching module 320 to constrain the current grassland remote sensing data obtained by observing the grassland distribution area to be predicted, so as to obtain a degradation prediction result which can represent the growth situation of the grassland distribution area to be predicted at the future prediction time.

Optionally, the device further comprises a grassland remote sensing acquisition module, a rule construction module and a second prediction module, wherein:

the grassland remote sensing acquisition module is used for acquiring historical grassland remote sensing data of the grassland distribution area to be predicted under the condition that the grassland identification information of the grassland distribution area to be predicted is determined to be failed to be matched with the rule base.

And the rule construction module is used for determining a target grassland degradation early warning rule according to the trend of the NDVI value corresponding to each pixel point in the historical grassland remote sensing data along with the seasonal variation.

The second prediction module is used for acquiring a degradation prediction result of the grassland distribution area to be predicted based on the constructed target grassland degradation early warning rule and the current grassland remote sensing data of the grassland distribution area to be predicted.

Optionally, the rule building module includes a time sequence acquisition unit, a degradation feature extraction unit, and a rule building unit, wherein:

and the time sequence acquisition unit is used for arranging the NDVI values converted by any pixel point according to the seasonal change based on the historical grassland remote sensing data to obtain an NDVI time sequence corresponding to any pixel point.

And the degradation characteristic extraction unit is used for extracting a first degradation characteristic and a second degradation characteristic based on the NDVI time sequences corresponding to all the pixel points.

And the rule construction unit is used for determining the target grassland degradation early warning rule based on the first degradation characteristic and the second degradation characteristic.

The first degradation characteristic is determined according to a certain low value level of the whole NVDI value corresponding to the same time point in the NDVI time sequence corresponding to different pixel points. The second degradation characteristic is determined according to a difference relation between the NVDI values corresponding to two adjacent time points in the NDVI time series corresponding to the same pixel point.

Optionally, the time series acquisition unit includes a reflectivity data acquisition subunit, a time series replacement subunit, and a time series acquisition subunit, wherein:

And the reflectivity data acquisition subunit is used for preprocessing the historical grassland remote sensing data to obtain corrected reflectivity data.

And the time sequence conversion subunit is used for determining NVDI values of each pixel point at each time point based on the corrected reflectivity data.

And the time sequence acquisition subunit is used for screening the NVDI value belonging to the target time point pair maximum NVDI value from the NVDI values of each pixel point at each time point according to the ascending order of the target time point to form an NDVI time sequence corresponding to any pixel point.

Optionally, the apparatus further comprises a degradation ranking module, a first partitioning module, and a second partitioning module, wherein:

and the degradation grading module is used for determining a grassland degradation area and a degradation grade based on the history area remote sensing data.

The first partitioning module is used for dividing the grassland degradation area into first grassland distribution areas according to grassland identification characteristics, and counting NDVI average time sequences of different degradation levels in each first grassland distribution area.

And the second partitioning module is used for carrying out cluster analysis on the NDVI mean time sequences with different degradation levels in all the first grassland distribution areas to obtain a second grassland distribution area.

Optionally, the apparatus further comprises a prediction verification module and a rule base update module, wherein:

and the prediction verification module is used for obtaining the degradation prediction result of the grassland distribution area to be predicted and grassland remote sensing data of the next observation period.

And the rule base updating module is used for storing the constructed target grassland degradation early warning rule into the rule base after the degradation verification result is confirmed to pass verification.

The early warning device for grassland degradation provided by the embodiment of the invention is used for executing the early warning method for grassland degradation, the implementation mode of the early warning device for grassland degradation is consistent with that of the early warning method for grassland degradation provided by the invention, and the same beneficial effects can be achieved, and the description is omitted here.

When the grassland identification information of the grassland distribution area to be predicted is matched with the grassland degradation early warning rule from the rule base, the embodiment of the invention makes a decision to directly predict the current grassland remote sensing data observed by the grassland distribution area to be predicted by using the grassland degradation early warning rule corresponding to the grassland distribution area to be predicted, so as to obtain a degradation prediction result of whether the grassland distribution area to be predicted is degraded in a future time. The method realizes the characteristic of degradation evolution process by the rule that the NDVI of the grassland distribution area changes along with seasons in the history period, and can excavate grassland degradation characteristics before the grassland growth season, so that the grassland growth condition problem of the current year of the area can be found as early as possible to carry out timely early warning, and a regional grassland degradation quasi-real-time evaluation scheme is formed.

Fig. 4 is a schematic structural diagram of the early warning system for grassland degradation provided by the invention. On the basis of any of the above embodiments, as shown in fig. 4, the system includes: a remotely located server node 410, and a meadow management node 420 communicatively coupled to the server node 410.

The server node 410 is configured to execute any one of the early warning methods for grassland degradation, and send the obtained degradation prediction result of the grassland distribution area to be predicted to the grassland management node 420.

The grassland management node 420 is configured to formulate a control strategy matched with the degradation prediction result according to the degradation prediction result of the grassland distribution area to be predicted.

Specifically, the early warning system for grassland degradation may have a server node 410 at a monitoring center, and a plurality of grassland management nodes 420 for data transmission with the server node 410 through wireless communication technology.

Among other wireless communication technologies, but not limited to, WIFI wireless cellular signals (2G, 3G, 4G, 5G).

The server node 410 receives current grassland remote sensing data transmitted by the satellite in real time, and periodically executes the flow steps of the grassland degradation early warning method, so as to pre-judge the future growth of grasslands according to the current grassland coverage condition, and send the degradation prediction result pre-judged for a certain designated grassland distribution area to the grassland management node 420 (or a local management node) corresponding to the grassland distribution area.

The grassland management node 420 may specify a corresponding policy based on the administered grassland distribution area degradation prediction to address future severe conditions.

Fig. 5 illustrates a physical schematic diagram of an electronic device, as shown in fig. 5, which may include: processor 510, communication interface (Communications Interface) 520, memory 530, and communication bus 540, wherein processor 510, communication interface 520, memory 530 complete communication with each other through communication bus 540. Processor 510 may invoke logic instructions in memory 530 to perform a method for early warning of grassland degradation, the method comprising: receiving grass identification information of a grass distribution area to be predicted; utilizing the grassland identification information of the grassland distribution area to be predicted as search information, and carrying out query matching in a rule base; the grassland degradation early warning device is used for detecting grassland degradation of the grassland distribution areas of the grassland degradation early warning device, wherein the grassland degradation early warning rules correspond to the grassland distribution areas of the grassland degradation early warning device; the grassland degradation early warning rule is determined by using the different degradation grades of NDVI converted by the remote sensing data corresponding to the grassland distribution area after the grassland degradation early warning is finished along with the seasonal variation trend; and under the condition that the grassland degradation early warning rule corresponding to the grassland identification information is matched from the rule base by utilizing the grassland identification information, acquiring a degradation prediction result of the grassland distribution area to be predicted based on the matched grassland degradation early warning rule and the current grassland remote sensing data of the grassland distribution area to be predicted.

Further, the logic instructions in the memory 530 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) 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, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

In another aspect, the present invention also provides a computer program product, the computer program product comprising a computer program, the computer program being storable on a non-transitory computer readable storage medium, the computer program, when executed by a processor, being capable of executing the early warning method of grassland degradation provided by the methods described above, the method comprising: receiving grass identification information of a grass distribution area to be predicted; utilizing the grassland identification information of the grassland distribution area to be predicted as search information, and carrying out query matching in a rule base; the grassland degradation early warning device is used for detecting grassland degradation of the grassland distribution areas of the grassland degradation early warning device, wherein the grassland degradation early warning rules correspond to the grassland distribution areas of the grassland degradation early warning device; the grassland degradation early warning rule is determined by using the different degradation grades of NDVI converted by the remote sensing data corresponding to the grassland distribution area after the grassland degradation early warning is finished along with the seasonal variation trend; and under the condition that the grassland degradation early warning rule corresponding to the grassland identification information is matched from the rule base by utilizing the grassland identification information, acquiring a degradation prediction result of the grassland distribution area to be predicted based on the matched grassland degradation early warning rule and the current grassland remote sensing data of the grassland distribution area to be predicted.

In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the method for early warning of grassland degradation provided by the above methods, the method comprising: receiving grass identification information of a grass distribution area to be predicted; utilizing the grassland identification information of the grassland distribution area to be predicted as search information, and carrying out query matching in a rule base; the grassland degradation early warning device is used for detecting grassland degradation of the grassland distribution areas of the grassland degradation early warning device, wherein the grassland degradation early warning rules correspond to the grassland distribution areas of the grassland degradation early warning device; the grassland degradation early warning rule is determined by using the different degradation grades of NDVI converted by the remote sensing data corresponding to the grassland distribution area after the grassland degradation early warning is finished along with the seasonal variation trend; and under the condition that the grassland degradation early warning rule corresponding to the grassland identification information is matched from the rule base by utilizing the grassland identification information, acquiring a degradation prediction result of the grassland distribution area to be predicted based on the matched grassland degradation early warning rule and the current grassland remote sensing data of the grassland distribution area to be predicted.

The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.

From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. The early warning method for grassland degradation is characterized by comprising the following steps:

2. The early warning method for grassland degradation according to claim 1, further comprising, after the receiving grassland identification information of the grassland distribution area to be predicted:

3. The early warning method for grassland degradation according to claim 2, wherein the determining the target grassland degradation warning rule according to the trend of NDVI values corresponding to the pixels in the historical grassland remote sensing data along with the seasonal variation comprises:

4. The early warning method for grassland degradation according to claim 3, wherein the step of arranging the NDVI values converted from any pixel according to the seasonal variation based on the historical grassland remote sensing data to obtain the NDVI time sequence corresponding to any pixel comprises:

5. The early warning method of grassland degradation according to claim 2, further comprising, prior to the acquiring the historical grassland remote sensing data of the grassland distribution area to be predicted:

6. The early warning method for grassland degradation according to claim 2, further comprising, after the obtaining of the degradation prediction result of the grassland distribution area to be predicted based on the constructed target grassland degradation early warning rule and the current grassland remote sensing data of the grassland distribution area to be predicted:

7. Early warning device of grassland degradation, characterized by, include:

8. The early warning system for grassland degradation is characterized by comprising a server node at a remote place and a grassland management node in communication connection with the server node;

the server node is configured to execute the early warning method for grassland degradation according to any one of claims 1 to 6, and issue the obtained degradation prediction result of the grassland distribution area to be predicted to the grassland management node;

9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the early warning method of grassland degradation of any one of claims 1 to 6 when the program is executed by the processor.

10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the early warning method of grassland degradation according to any one of claims 1 to 6.