CN117036981B - Grassland biomass remote sensing monitoring method and system - Google Patents

Grassland biomass remote sensing monitoring method and system Download PDF

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CN117036981B
CN117036981B CN202311168274.7A CN202311168274A CN117036981B CN 117036981 B CN117036981 B CN 117036981B CN 202311168274 A CN202311168274 A CN 202311168274A CN 117036981 B CN117036981 B CN 117036981B
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除多
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Abstract

The embodiment of the invention provides a grassland biomass remote sensing monitoring method and system, belonging to the technical field of satellite remote sensing, wherein the method comprises the following steps: setting a plurality of sampling points on the grassland within a preset time period T, and carrying out recursion statistics on biological samples obtained from the sampling points; converting the remote sensing data at different geographic positions to obtain a conversion matrix U corresponding to the grassland; for any element U in the conversion matrix U ij Cutting; and carrying out data calculation on the grassland biomass to obtain remote sensing prediction data of the grassland biomass. By adopting the scheme, the grassland degradation degree can be quantitatively known through accurate grassland biomass estimation, and the ecological environment problem caused by grassland degradation is reduced.

Description

Grassland biomass remote sensing monitoring method and system
Technical Field
The invention relates to the technical field of satellite remote sensing, in particular to a grassland biomass remote sensing monitoring method and system.
Background
Grasslands are the most widely distributed ecosystem type of Tibet, account for more than 70% of the total land area of Tibet, and are the main body of the Qinghai-Tibet high-primordial ecological safety protection and barrier. However, grassland degradation has become a major obstacle for the social, economic, ecological sustainable development and ecological safety barrier construction of the Qinghai-Tibet plateau. Therefore, how to calculate the grassland biomass more accurately has become a key to understanding the degree of grassland degradation and to calculate the degradation area. However, the findings differ greatly from the area of degradation alone.
The degradation degree of the grasslands can be quantitatively known through accurate grassland biomass estimation, so that a decision-making department can take corresponding countermeasures and management measures, and ecological environment problems caused by the grassland degradation are reduced to the greatest extent.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a grassland biomass remote sensing monitoring method and a grassland biomass remote sensing monitoring system, which at least partially solve the problems existing in the prior art.
In a first aspect, an embodiment of the present invention provides a method for remotely sensing and monitoring biomass on a grassland, including:
setting a plurality of sampling points on a grass in a preset time period T, and dividing the preset time period T into n continuous time period integration sets T= { T1, T2, … Tn } by recursively counting biological samples obtained from the sampling points, and simultaneously obtaining biological type integration sets P= { P1, P2, … Pm } in the preset time period T, wherein m represents the number of different biological species in the biological type integration set;
obtaining remote sensing data corresponding to the grasslands, and converting the remote sensing data at different geographic positions to obtain a conversion matrix U corresponding to the grasslands, wherein elements in the conversion matrix U are positive integers smaller than delta;
according to For splitting length, for any element U in the transformation matrix U ij Cutting to obtain element U ij Attribute value F of (2) ij ，/>And based on the attribute value F ij Determining element U ij The specific biological type Pk, k is less than or equal to m and mod () corresponding to the biological type collection P is a rounding function;
based on the first calculation function set F= { F1, F2, … fn } corresponding to the time period integration set T= { T1, T2, … Tn }, the second calculation function set G= { G1, G2, … gm } corresponding to the different types of biological type integration set P= { P1, P2, … Pm }, and U in the conversion matrix U ij Attribute value F of (2) ij And carrying out data calculation on the grassland biomass to obtain remote sensing prediction data of the grassland biomass.
According to a specific implementation manner of the embodiments of the present disclosure, the setting a plurality of sampling points on a grass field in a preset time period T includes:
three small sample sides of 50cm multiplied by 50cm are sampled each time by adopting a harvesting sample Fang Chenchong method, and meanwhile GPS data, elevation and land utilization type data of sampling points are recorded.
According to a specific implementation manner of the embodiment of the present disclosure, the dividing the preset time period T into n consecutive time period aggregate sets t= { T1, T2, … Tn } by performing recursive statistics on the biological samples obtained in the sampling points, and obtaining the biological type aggregate sets p= { P1, P2, … Pm } in the preset time period T simultaneously includes:
Weighing the biological samples obtained by sampling according to the sequence of sampling time to obtain a weight curve of the biological samples in a preset time period;
performing curvature calculation on the weight curve, and dividing the weight curve by taking a point with a curvature value larger than a preset value in the weight curve as a dividing point to obtain n dividing line segments;
and taking the time periods corresponding to the n segmentation segments as the time period aggregate T= { T1, T2, … Tn }.
According to a specific implementation manner of the embodiment of the present disclosure, the dividing the preset time period T into n consecutive time period aggregate sets t= { T1, T2, … Tn } by performing recursive statistics on the biological samples obtained in the sampling points, and obtaining the biological type aggregate sets p= { P1, P2, … Pm } in the preset time period T simultaneously further includes:
performing species identification on the biological sample collected by the sampling point;
based on the result of the species identification, a set of biological types within a preset time period T is determined.
According to a specific implementation manner of the embodiment of the present disclosure, the obtaining remote sensing data corresponding to the grassland, and performing conversion processing on the remote sensing data at different geographic positions to obtain a conversion matrix U corresponding to the grassland includes:
Converting the remote sensing data from data in an HDF format into an image in a TIFF format;
graying processing is carried out on the image, so that a gray image of the grassland is obtained;
determining the segmentation size of the minimum segmentation unit of the gray image based on the acquired sampling size of the sampling point;
based on the dividing size, dividing the gray scale graph, calculating the characteristic values of the images of the plurality of minimum dividing units after dividing to obtain a plurality of dividing characteristic values, and taking the dividing characteristic values as the constituent elements in the matrix to further obtain a conversion matrix U corresponding to the grassland.
According to a specific implementation manner of the embodiment of the present disclosure, the obtaining remote sensing data corresponding to the grassland, performing conversion processing on the remote sensing data at different geographic positions to obtain a conversion matrix U corresponding to the grassland, further includes:
and spatially ordering the minimum segmentation unit according to the actual geographic position so as to arrange the element values in the conversion matrix U according to the spatially ordered result.
According to a specific implementation manner of the embodiment of the disclosure, the determining, based on the attribute value Fij, a specific biological type Pk corresponding to the element Uij in the biological type set P includes:
Pre-calculating gray image values of different biological types in the biological type collection P;
based on gray image values of different biological types, gray average value calculation is carried out on different biological types in the biological type collection P, so as to obtain characteristic average values of the different biological types;
attribute value F ij Comparing with characteristic average values of different biological types to determine element U ij And the specific biological type Pk corresponding to the biological type collection P.
According to a specific implementation manner of the embodiment of the disclosure, the calculating the biomass on the lawn based on the first set of calculation functions f= { F1, F2, … fn } corresponding to the time period aggregate t= { T1, T2, … Tn }, the second set of calculation functions g= { G1, G2, … gm }, corresponding to the different types of biological type aggregate p= { P1, P2, … Pm }, and the attribute value Fij of Uij in the transformation matrix U includes:
based on the current time point T0, determining a coefficient vector v= [ β1, β2, … βn ] corresponding to the first calculation function set f= { F1, F2, … fn };
u in the pair conversion matrix U ij Attribute value F of (2) ij Carrying out similar statistics to obtain occurrence frequency vectors Z= [ Z1, Z2, … Zm of different biological types in different biological type collection P= { P1, P2, … Pm } ]；
Biomass Y on grass is calculated as: y=v×f×z T *G。
According to a specific implementation manner of the embodiment of the disclosure, the method further includes, before performing data calculation on biomass on the lawn based on the first calculation function set f= { F1, F2, … fn } corresponding to the time period aggregate t= { T1, T2, … Tn }, the second calculation function set g= { G1, G2, … gm } corresponding to the different type of biological type aggregate p= { P1, P2, … Pm }, and the attribute value Fij of Uij in the conversion matrix U:
from the history data, a first calculation function set f= { F1, F2, … fn } and a second calculation function set g= { G1, G2, … gm } are calculated in advance.
In a second aspect, an embodiment of the present invention provides a grassland biomass remote sensing monitoring system, comprising:
the collection module is used for setting a plurality of sampling points on a grassland within a preset time period T, dividing the preset time period T into n continuous time period integration sets T= { T1, T2, … Tn } by recursively counting biological samples obtained from the sampling points, and simultaneously obtaining biological type integration sets P= { P1, P2, … Pm } within the preset time period T, wherein m represents the number of types of different organisms in the biological type integration set;
the conversion module is used for acquiring remote sensing data corresponding to the grasslands, converting the remote sensing data at different geographic positions to obtain a conversion matrix U corresponding to the grasslands, wherein elements in the conversion matrix U are positive integers smaller than delta;
Segmentation moduleFor followingFor splitting length, for any element U in the transformation matrix U ij Cutting to obtain element U ij Attribute value F of (2) ij ，/>And based on the attribute value F ij Determining element U ij The specific biological type Pk, k is less than or equal to m and mod () corresponding to the biological type collection P is a rounding function;
a calculation module, configured to base on a first set of calculation functions f= { F1, F2, … fn } corresponding to the time period aggregate t= { T1, T2, … Tn }, a second set of calculation functions g= { G1, G2, … gm }, corresponding to different types of biological type aggregate p= { P1, P2, … Pm }, and U in the transformation matrix U ij Attribute value F of (2) ij And carrying out data calculation on the grassland biomass to obtain remote sensing prediction data of the grassland biomass.
In a third aspect, an embodiment of the present invention further provides an electronic device, including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of remote sensing of grassland biomass in any of the implementations of the first aspect or Ren Di described above.
In a fourth aspect, embodiments of the present invention also provide a non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of grassland biomass remote sensing monitoring of the first aspect or any implementation of the first aspect.
In a fifth aspect, embodiments of the present invention also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the method of remote sensing of grassland biomass in any of the preceding aspects or implementations of the first aspect.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings are also obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for remotely sensing and monitoring biomass on grasslands, which is provided by the embodiment of the invention;
FIG. 2 is a schematic flow chart of another method for remotely sensing and monitoring biomass on grasslands according to an embodiment of the invention;
FIG. 3 is a schematic flow chart of another method for remotely sensing and monitoring biomass on grasslands according to an embodiment of the invention;
FIG. 4 is a schematic flow chart of another method for remotely sensing and monitoring biomass on grasslands according to an embodiment of the invention;
FIG. 5 is a schematic structural diagram of a remote sensing monitoring system for biomass on grasslands, which is provided by the embodiment of the invention;
fig. 6 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Other advantages and effects of the present disclosure will become readily apparent to those skilled in the art from the following disclosure, which describes embodiments of the present disclosure by way of specific examples. It will be apparent that the described embodiments are merely some, but not all embodiments of the present disclosure. The disclosure is also to be embodied or carried out in other and different embodiments, and the details in this specification are to be understood as being a function of various other adaptations and modifications without departing from the spirit of the disclosure. The following embodiments and features in the embodiments are combined with each other without conflict. All other embodiments, which can be made by one of ordinary skill in the art without inventive effort, based on the embodiments in this disclosure are intended to be within the scope of this disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the following claims. It should be apparent that the aspects described in this disclosure may be embodied in a wide variety of forms and that any specific structure and/or function described in this disclosure is illustrative only. Based on the present disclosure, one skilled in the art will appreciate that one aspect described in this disclosure may be implemented independently of any other aspects, and that various ways of combining two or more of these aspects. For example, apparatus may be implemented and/or methods practiced using any number of the aspects set forth in this disclosure. In addition, such apparatus may be implemented and/or such method practiced using other structure and/or functionality in addition to one or more of the aspects set forth in the disclosure.
It should also be noted that the illustrations provided in the following embodiments merely illustrate the basic concepts of the disclosure by way of illustration, and only the components related to the disclosure are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
In addition, in the following description, specific details are provided in order to provide a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.
The embodiment of the disclosure provides a grassland biomass remote sensing monitoring method. The grassland biomass remote sensing monitoring method provided by the embodiment is executed by a computing device which is implemented as software or as a combination of software and hardware, and is integrally provided in a server, a terminal device, or the like.
Referring to fig. 1, 2, 3 and 4, an embodiment of the present disclosure provides a method for remotely sensing and monitoring biomass on a grassland, including:
s101, setting a plurality of sampling points on a grass land in a preset time period T, and dividing the preset time period T into n continuous time period integration sets T= { T1, T2, … Tn } by recursively counting biological samples obtained from the sampling points, wherein P= { P1, P2, … Pm } of the biological type integration sets in the preset time period T are obtained, and m represents the number of different types of organisms in the biological type integration sets.
Specifically, in order to realize the matching with the MODIS satellite remote sensing product on the spatial scale, 11 sampling points are arranged in the region with relatively gentle terrain and uniform grassland vegetation type and spatial distribution in the research region. The sampling work of the grassland biomass (AbovegroundBiomass, AGB) was performed twice within 15 days a month and 3 days before and after 30 days for these 11 points from 1 month to 12 months a year. The sampling method adopts a harvesting sample Fang Chenchong method, three small sample sides of 50cm multiplied by 50cm are sampled each time, and meanwhile GPS data, elevation, land utilization type and the like of an observation point are recorded. The AGB observation steps are: firstly, square coils with the area of 50cm multiplied by 50cm are randomly thrown out at grassland sampling points, 3 grassland parts in the sample squares with the area of 50cm multiplied by 50cm are all mowed on the ground by a sharp blade, then, sundries such as adhered soil, gravel and the like are removed, all the grassland parts are put into paper bags and are brought back to a laboratory, green fresh grasses and dry (including wilting matters and withering matters) parts of all the samples are respectively sorted in the laboratory, and then, the grassland parts are dried to constant weight in an oven at 85 ℃ in the grassland laboratory and then weighed. The weights in the last three samples were averaged. The dried weight of the green fresh grass part is the dry matter weight (drymatter content offresh grass) of the fresh grass in the grassland, or the biomass on the grassland, namely the dry matter weight (g/m 2) of the fresh grass in unit area.
Weighing the sampled biological sample according to the sequence of sampling time to obtain a weight curve of the biological sample in a preset time period; performing curvature calculation on the weight curve, and dividing the weight curve by taking a point with a curvature value larger than a preset value in the weight curve as a dividing point to obtain n dividing line segments; by setting the time zone sets t= { T1, T2, … Tn } as the time zone sets corresponding to the n segments, it is possible to conduct targeted study on grassland biomass based on different seasonal differences.
S102, acquiring remote sensing data corresponding to the grasslands, and converting the remote sensing data at different geographic positions to obtain a conversion matrix U corresponding to the grasslands, wherein elements in the conversion matrix U are positive integers smaller than delta.
The MODIS/Terra sensor has wide application in monitoring vegetation from area to the world due to the characteristics of high time resolution, high spectrum resolution, moderate space resolution and the like. The MODIS wave amplitude is narrower, a plurality of atmosphere absorption bands are avoided, and a stricter cloud removal algorithm and more thorough atmosphere correction are realized when the vegetation index is calculated. Therefore, the MODIS vegetation index can better reflect the space-time variation characteristics of vegetation, and becomes the main remote sensing data for developing remote sensing dynamic monitoring and research of large-scale grassland vegetation.
The MOD13Q1 product of the nasamoodis land product distribution center may be used. The MODIS 13Q1 product belongs to MODIS land thematic data, is an MODIS vegetation index product developed by NASAMODIS land product group according to a unified algorithm, and has been subjected to cloud removal, radiation correction, atmosphere correction and the like. The global MOD13Q1 data is a three-level grid data product adopting a sinusidal projection mode, the spatial resolution is 250m×250m, and vegetation index data is provided every 16 days.
The remote sensing image processing process is that firstly, the downloaded MOD13Q1 data is converted into a TIFF format from an HDF format by utilizing MRT (MODIS ReprojectionTools) software, the SIN projection system is converted into a Geographic projection system, a plurality of images are spliced at the same time, then corresponding NDVI and EVI values are read in ENVI image processing software according to GPS data of 11 ground sampling points, and finally, the vegetation index value stored as integer is converted into a value between 0 and delta.
As a specific implementation manner, the remote sensing data can be converted from data in the HDF format into an image in the TIFF format; graying processing is carried out on the image, so that a gray image of the grassland is obtained; determining the dividing size of the minimum dividing unit of the gray image based on the acquired sampling size of the sampling point, so that the dividing size corresponds to the sampling size one by one; based on the dividing size, dividing the gray scale pattern to obtain a minimum dividing unit image corresponding to the sampling size, calculating the characteristic values of the divided images of the minimum dividing units to obtain a plurality of dividing characteristic values, and taking the dividing characteristic values as constituent elements in a matrix to obtain a conversion matrix U corresponding to the grassland. The calculation of the segmentation feature values may employ various feature value calculation methods, which are not described herein.
S103, according toFor splitting length, for any element U in the transformation matrix U ij Cutting to obtain element U ij Attribute value F of (2) ij ，/>And based on the attribute value F ij Determining element U ij And in the specific biological types Pk, k is less than or equal to m and mod () corresponding to the biological type collection P is a rounding function.
Gray image values of different biological types in the biological type collection P can be calculated in advance, and gray average value calculation is carried out on the different biological types in the biological type collection P based on the gray image values of the different biological types to obtain characteristic average values of the different biological types; attribute value F ij Comparing with characteristic average values of different biological types to determine element U ij And the specific biological type Pk corresponding to the biological type collection P. In this way, a specific biological type Pk can be calculated relatively quickly.
S104, based on the first calculation function set f= { F1, F2, … fn } corresponding to the time period aggregate t= { T1, T2, … Tn }, and the second calculation function corresponding to the different type biological type aggregate p= { P1, P2, … Pm }Set g= { G1, G2, … gm }, and U in the transformation matrix U ij Attribute value F of (2) ij And carrying out data calculation on the grassland biomass to obtain remote sensing prediction data of the grassland biomass.
Specifically, the coefficient vector v= [ β1, β2, … βn ] corresponding to the first calculation function set f= { F1, F2, … fn } may be determined based on the current time point T0, and coefficient elements in the coefficient vector are used for dynamic adjustment according to the current time point T0, that is, βi=e (T0), e () is an association function, so that the calculation functions fi are set to have different calculation weights at different time points in one year.
By converting U in matrix U ij Attribute value F of (2) ij The similar statistics are carried out, the occurrence times of different biological types in the different types of biological type collection P= { P1, P2, … Pm } can be obtained, the occurrence times of the different biological types are combined, and the occurrence times vector Z= [ Z1, Z2, … Zm of the different biological types is obtained]Thus the biomass Y on the grass can be calculated as: y=v×f×z T *G。
As one way, the first calculation function set f= { F1, F2, … fn } and the second calculation function set g= { G1, G2, … gm } can be calculated in advance by history data, thereby facilitating the use of the first calculation function and the second calculation function.
According to a specific implementation manner of the embodiments of the present disclosure, the setting a plurality of sampling points on a grass field in a preset time period T includes:
Three small sample sides of 50cm multiplied by 50cm are sampled each time by adopting a harvesting sample Fang Chenchong method, and meanwhile GPS data, elevation and land utilization type data of sampling points are recorded.
Referring to fig. 2, according to a specific implementation manner of the embodiment of the present disclosure, the performing recursion statistics on the biological samples obtained in the sampling points, dividing the preset time period T into n consecutive time period aggregate t= { T1, T2, … Tn }, and simultaneously obtaining the biological type aggregate p= { P1, P2, … Pm }, where the preset time period T includes:
s201, weighing the sampled biological sample according to the sequence of sampling time to obtain a weight curve of the biological sample in a preset time period;
s202, performing curvature calculation on the weight curve, and dividing the weight curve by taking a point with a curvature value larger than a preset value in the weight curve as a dividing point to obtain n dividing line segments;
s203, regarding the time periods corresponding to the n segments as the time period aggregate t= { T1, T2, … Tn }.
According to a specific implementation manner of the embodiment of the present disclosure, the dividing the preset time period T into n consecutive time period aggregate sets t= { T1, T2, … Tn } by performing recursive statistics on the biological samples obtained in the sampling points, and obtaining the biological type aggregate sets p= { P1, P2, … Pm } in the preset time period T simultaneously further includes:
Performing species identification on the biological sample collected by the sampling point;
based on the result of the species identification, a set of biological types within a preset time period T is determined.
Referring to fig. 3, according to a specific implementation manner of the embodiment of the present disclosure, the obtaining remote sensing data corresponding to the grassland, and performing conversion processing on the remote sensing data at different geographic positions to obtain a conversion matrix U corresponding to the grassland includes:
s301, converting the remote sensing data from data in an HDF format into an image in a TIFF format;
s302, performing graying processing on the image to obtain a gray image of the grassland;
s303, determining the segmentation size of the minimum segmentation unit of the gray image based on the acquired sampling size of the sampling point;
s304, based on the dividing size, dividing the gray scale pattern, calculating the characteristic value of the images of the plurality of minimum dividing units after dividing to obtain a plurality of dividing characteristic values, and taking the dividing characteristic values as the constituent elements in the matrix to further obtain a conversion matrix U corresponding to the grassland.
According to a specific implementation manner of the embodiment of the present disclosure, the obtaining remote sensing data corresponding to the grassland, performing conversion processing on the remote sensing data at different geographic positions to obtain a conversion matrix U corresponding to the grassland, further includes:
And spatially ordering the minimum segmentation unit according to the actual geographic position so as to arrange the element values in the conversion matrix U according to the spatially ordered result.
Referring to fig. 4, according to a specific implementation manner of the embodiment of the disclosure, the determining, based on the attribute value Fij, a specific biological type Pk corresponding to the element Uij in the biological type set P includes:
s401, pre-calculating gray image values of different biological types in the biological type collection P;
s402, gray level average value calculation is carried out on different biological types in the biological type collection P based on gray level image values of the different biological types, so that characteristic average values of the different biological types are obtained;
s403, attribute value F ij Comparing with characteristic average values of different biological types to determine element U ij And the specific biological type Pk corresponding to the biological type collection P.
According to a specific implementation manner of the embodiment of the disclosure, the calculating the biomass on the lawn based on the first set of calculation functions f= { F1, F2, … fn } corresponding to the time period aggregate t= { T1, T2, … Tn }, the second set of calculation functions g= { G1, G2, … gm }, corresponding to the different types of biological type aggregate p= { P1, P2, … Pm }, and the attribute value Fij of Uij in the transformation matrix U includes:
Based on the current time point T0, determining a coefficient vector v= [ β1, β2, … βn ] corresponding to the first calculation function set f= { F1, F2, … fn };
u in the pair conversion matrix U ij Attribute value F of (2) ij Carrying out similar statistics to obtain occurrence frequency vectors Z= [ Z1, Z2, … Zm of different biological types in different biological type collection P= { P1, P2, … Pm }]；
Biomass Y on grass is calculated as: y=v×f×z T *G。
According to a specific implementation manner of the embodiment of the disclosure, the method further includes, before performing data calculation on biomass on the lawn based on the first calculation function set f= { F1, F2, … fn } corresponding to the time period aggregate t= { T1, T2, … Tn }, the second calculation function set g= { G1, G2, … gm } corresponding to the different type of biological type aggregate p= { P1, P2, … Pm }, and the attribute value Fij of Uij in the conversion matrix U:
from the history data, a first calculation function set f= { F1, F2, … fn } and a second calculation function set g= { G1, G2, … gm } are calculated in advance.
Referring to fig. 5, an embodiment of the present invention also discloses a grassland biomass remote sensing monitoring system 50, comprising:
the collection module 501 is configured to set a plurality of sampling points on a lawn in a preset time period T, divide the preset time period T into n continuous time period sets t= { T1, T2, … Tn }, and obtain a biological type set p= { P1, P2, … Pm }, where m represents the number of types of different organisms in the biological type set by performing recursive statistics on biological samples obtained from the sampling points;
The conversion module 502 is configured to obtain remote sensing data corresponding to the grassland, and perform conversion processing on the remote sensing data at different geographic positions to obtain a conversion matrix U corresponding to the grassland, where elements in the conversion matrix U are positive integers less than δ;
a segmentation module 503, configured toFor splitting length, for any element U in the transformation matrix U ij Cutting to obtain element U ij Attribute value F of (2) ij ，/>And based on the attribute value F ij Determining element U ij The specific biological type Pk, k is less than or equal to m and mod () corresponding to the biological type collection P is a rounding function;
calculation module 504 for calculating a first set of calculation functions f= { F1, F2, … fn } corresponding to the time period aggregate t= { T1, T2, … Tn }, a second set of calculation functions g= { G1, G2, … gm }, corresponding to different types of biological type aggregate p= { P1, P2, … Pm }, and U in the transformation matrix U based on the time period aggregate t= { T1, T2, … Tn } ij Attribute value F of (2) ij And carrying out data calculation on the grassland biomass to obtain remote sensing prediction data of the grassland biomass.
Referring to fig. 6, an embodiment of the present invention also provides an electronic device 60, including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of remote sensing of grassland biomass in the method embodiment described above.
Embodiments of the present invention also provide a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the foregoing method embodiments.
Embodiments of the present invention also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the method of remote sensing of grassland biomass in the method embodiments described above.
Referring now to fig. 6, a schematic diagram of an electronic device 60 suitable for use in implementing embodiments of the present disclosure is shown. The electronic devices in the embodiments of the present disclosure include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 6 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.
As shown in fig. 6, the electronic device 60 includes a processing means (e.g., a central processing unit, a graphics processor, etc.) 601 that performs various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the electronic device 60 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Typically, the following devices are connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, magnetic tape, hard disk, etc.; and a communication device 609. The communication means 609 allows the electronic device 60 to communicate with other devices wirelessly or by wire to exchange data. While fig. 6 shows an electronic device 60 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. Alternatively, more or fewer devices may be implemented or provided.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts are implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program is downloaded and installed from a network via communication means 609, or from storage means 608, or from ROM 602. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by the processing device 601.
It should be noted that the computer readable medium described in the present disclosure is a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium is, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer-readable storage medium is any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium includes a data signal that propagates in baseband or as part of a carrier wave, in which computer-readable program code is carried. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium is transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The computer readable medium is contained in the electronic device; but also alone without being assembled into the electronic device.
Computer program code for carrying out operations of the present disclosure is written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code executes entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer is connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams represents 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.
It is to be understood that portions of the present invention are implemented in hardware, software, firmware, or a combination thereof.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present invention should be included in the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.

Claims (6)

1. A method for remotely sensing and monitoring biomass on a grassland, comprising:
setting a plurality of sampling points on a grass in a preset time period T, and dividing the preset time period T into n continuous time period integration sets T= { T1, T2, … Tn } by recursively counting biological samples obtained from the sampling points, and simultaneously obtaining biological type integration sets P= { P1, P2, … Pm } in the preset time period T, wherein m represents the number of different biological species in the biological type integration set;
obtaining remote sensing data corresponding to the grasslands, and converting the remote sensing data at different geographic positions to obtain a conversion matrix U corresponding to the grasslands, wherein elements in the conversion matrix U are positive integers smaller than delta;
According toFor splitting length, for any element U in the transformation matrix U ij Cutting to obtain element U ij Attribute value F of (2) ij ，And based on the attribute value F ij Determining element U ij The specific biological type Pk, k is less than or equal to m and mod () corresponding to the biological type collection P is a rounding function;
based on the first calculation function set F= { F1, F2, … fn } corresponding to the time period integration set T= { T1, T2, … Tn }, the second calculation function set G= { G1, G2, … gm } corresponding to the different types of biological type integration set P= { P1, P2, … Pm }, and U in the conversion matrix U ij Attribute value F of (2) ij Performing data calculation on the grassland biomass to obtain remote sensing prediction data of the grassland biomass; wherein the method comprises the steps of
The obtaining the remote sensing data corresponding to the grassland, and converting the remote sensing data at different geographic positions to obtain a conversion matrix U corresponding to the grassland comprises the following steps:
converting the remote sensing data from data in an HDF format into an image in a TIFF format; graying processing is carried out on the image, so that a gray image of the grassland is obtained; determining the segmentation size of the minimum segmentation unit of the gray image based on the acquired sampling size of the sampling point; based on the segmentation size, carrying out segmentation processing on the gray level image, carrying out eigenvalue calculation on the images of a plurality of segmented minimum segmentation units to obtain a plurality of segmentation eigenvalues, and taking the segmentation eigenvalues as constituent elements in a matrix to further obtain a conversion matrix U corresponding to the grassland; the minimum segmentation unit is spatially ordered according to the actual geographic position, so that element values in the conversion matrix U are arranged according to the spatially ordered result;
Said attribute value F based ij Determining element U ij The specific biological type Pk corresponding to the biological type collection P includes:
pre-calculating gray image values of different biological types in the biological type collection P; based on gray image values of different biological types, gray average value calculation is carried out on different biological types in the biological type collection P, so as to obtain characteristic average values of the different biological types; attribute value F ij Comparing with characteristic average values of different biological types to determine element U ij The specific biological type Pk corresponding to the biological type collection P;
the first set of computation functions F= { F1, F2, … fn } corresponding to the time period aggregate T= { T1, T2, … Tn }, the second set of computation functions G= { G1, G2, … gm }, corresponding to different types of biological type aggregate P= { P1, P2, … Pm }, and U in the transformation matrix U ij Attribute value F of (2) ij Performing data calculations on biomass on the grass, comprising:
based on the current time point T0, a coefficient vector V= [ beta 1, beta 2, … beta n ] corresponding to the first calculation function set F= { F1, F2, … fn } is determined]The method comprises the steps of carrying out a first treatment on the surface of the U in the pair conversion matrix U ij Attribute value F of (2) ij Carrying out similar statistics to obtain occurrence frequency vectors Z= [ Z1, Z2, … Zm of different biological types in different biological type collection P= { P1, P2, … Pm } ]The method comprises the steps of carrying out a first treatment on the surface of the Biomass Y on grass is calculated as: y=v×f×z T *G。
2. The method according to claim 1, wherein the setting a plurality of sampling points on the grass for a preset period of time T comprises:
three small sample sides of 50cm multiplied by 50cm are sampled each time by adopting a harvesting sample Fang Chenchong method, and meanwhile GPS data, elevation and land utilization type data of sampling points are recorded.
3. The method according to claim 2, wherein said dividing the preset time period T into n consecutive time period aggregate sets t= { T1, T2, … Tn } by recursively counting the biological samples obtained in the sampling points while obtaining the biological type aggregate sets p= { P1, P2, … Pm } within the preset time period T includes:
weighing the biological samples obtained by sampling according to the sequence of sampling time to obtain a weight curve of the biological samples in a preset time period;
performing curvature calculation on the weight curve, and dividing the weight curve by taking a point with a curvature value larger than a preset value in the weight curve as a dividing point to obtain n dividing line segments;
and taking the time periods corresponding to the n segmentation segments as the time period aggregate T= { T1, T2, … Tn }.
4. A method according to claim 3, wherein said dividing the preset time period T into n consecutive time period aggregate sets t= { T1, T2, … Tn } by recursively counting the biological samples obtained in the sampling points while obtaining the biological type aggregate sets p= { P1, P2, … Pm } within the preset time period T further comprises:
performing species identification on the biological sample collected by the sampling point;
based on the result of the species identification, a set of biological types within a preset time period T is determined.
5. The method according to claim 1, wherein the first set of computing functions f= { F1, F2, … fn } corresponding to the time period aggregate t= { T1, T2, … Tn } is based on different types of raw materialsA second set of computation functions G= { G1, G2, … gm } corresponding to the object type set P= { P1, P2, … Pm }, and U in the transformation matrix U ij Attribute value F of (2) ij Before performing data calculation on the biomass on the lawn, the method further comprises:
from the history data, a first calculation function set f= { F1, F2, … fn } and a second calculation function set g= { G1, G2, … gm } are calculated in advance.
6. A system for remote sensing of biomass on grass land, comprising:
The collection module is used for setting a plurality of sampling points on a grassland within a preset time period T, dividing the preset time period T into n continuous time period integration sets T= { T1, T2, … Tn } by recursively counting biological samples obtained from the sampling points, and simultaneously obtaining biological type integration sets P= { P1, P2, … Pm } within the preset time period T, wherein m represents the number of types of different organisms in the biological type integration set;
the conversion module is used for acquiring remote sensing data corresponding to the grasslands, converting the remote sensing data at different geographic positions to obtain a conversion matrix U corresponding to the grasslands, wherein elements in the conversion matrix U are positive integers smaller than delta;
a segmentation module for followingFor splitting length, for any element U in the transformation matrix U ij Cutting to obtain element U ij Attribute value F of (2) ij ，/>And based on the attribute value F ij Determining element U ij The specific biological type Pk, k is less than or equal to m and mod () corresponding to the biological type collection P is a rounding function;
a calculation module, configured to calculate a first set of calculation functions f= { F1, F2, … fn } corresponding to the time period aggregate t= { T1, T2, … Tn }, and different types of biological typesSecond set of computation functions corresponding to the aggregate set p= { P1, P2, … Pm }, g= { G1, G2, … gm }, and U in the transformation matrix U ij Attribute value F of (2) ij Performing data calculation on the grassland biomass to obtain remote sensing prediction data of the grassland biomass; wherein the method comprises the steps of
The obtaining the remote sensing data corresponding to the grassland, and converting the remote sensing data at different geographic positions to obtain a conversion matrix U corresponding to the grassland comprises the following steps:
converting the remote sensing data from data in an HDF format into an image in a TIFF format; graying processing is carried out on the image, so that a gray image of the grassland is obtained; determining the segmentation size of the minimum segmentation unit of the gray image based on the acquired sampling size of the sampling point; based on the segmentation size, carrying out segmentation processing on the gray level image, carrying out eigenvalue calculation on the images of a plurality of segmented minimum segmentation units to obtain a plurality of segmentation eigenvalues, and taking the segmentation eigenvalues as constituent elements in a matrix to further obtain a conversion matrix U corresponding to the grassland; the minimum segmentation unit is spatially ordered according to the actual geographic position, so that element values in the conversion matrix U are arranged according to the spatially ordered result;
said attribute value F based ij Determining element U ij The specific biological type Pk corresponding to the biological type collection P includes:
pre-calculating gray image values of different biological types in the biological type collection P; based on gray image values of different biological types, gray average value calculation is carried out on different biological types in the biological type collection P, so as to obtain characteristic average values of the different biological types; attribute value F ij Comparing with characteristic average values of different biological types to determine element U ij The specific biological type Pk corresponding to the biological type collection P;
the first set of computation functions f= { F1, F2, … fn } corresponding to the time period aggregate set t= { T1, T2, … Tn }, and the second set of computation functions g=corresponding to the different types of biological type aggregate sets p= { P1, P2, … Pm }{ g1, g2, … gm }, and U in the transformation matrix U ij Attribute value F of (2) ij Performing data calculations on biomass on the grass, comprising:
based on the current time point T0, a coefficient vector V= [ beta 1, beta 2, … beta n ] corresponding to the first calculation function set F= { F1, F2, … fn } is determined]The method comprises the steps of carrying out a first treatment on the surface of the U in the pair conversion matrix U ij Attribute value F of (2) ij Carrying out similar statistics to obtain occurrence frequency vectors Z= [ Z1, Z2, … Zm of different biological types in different biological type collection P= { P1, P2, … Pm } ]The method comprises the steps of carrying out a first treatment on the surface of the Biomass Y on grass is calculated as: y=v×f×z T *G。
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