CN113643409A - Method and device for representing vegetation production rate and storage medium - Google Patents
Method and device for representing vegetation production rate and storage medium Download PDFInfo
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Abstract
The application discloses a vegetation production rate characterization method and device and a storage medium. The method for representing the vegetation production rate based on the thermal infrared remote sensing data comprises the following steps: determining a ground surface temperature reference curve according to the ground surface temperature data and the ground surface reflectivity data, wherein the ground surface temperature data is obtained by inversion according to thermal infrared remote sensing data; determining a surface temperature curve to be detected according to the surface temperature data and the surface temperature reference curve; and determining the vegetation production rate index according to the ground surface temperature reference curve and the ground surface temperature curve to be detected.
Description
Technical Field
The application relates to the technical field of ecological remote sensing, in particular to a method and a device for representing vegetation production rate based on thermal infrared remote sensing data and a storage medium.
Background
The impact of climate change on global environmental degradation and economic activity has been a long-standing general concern for the public. Humans have had a significant impact on climate change through widespread land use changes and land management practices, which also means that land use management plays an important role in mitigating the sustainability of land systems. Implementing land use management practices to maintain or improve landscape conditions requires understanding of patterns of ecological potential of land systems and responses of key system element vegetation to management activities.
In the existing method system, the expression of the vegetation production rate based on remote sensing data lacks the quantitative characterization of the vegetation production rate from the dimension of energy and the establishment of a functional function. Partial research utilizes a surface temperature area inverted by thermal infrared data or empirically as an environmental factor to quantify the difference between the production rates of vegetation in different local areas, but cannot adapt to the evaluation of the annual growth period daily average production rate of vegetation with different space-time scales.
Aiming at the technical problem that the existing vegetation production rate method in the prior art cannot realize the evaluation of vegetation production rates with different space-time scales, an effective solution is not provided at present.
Disclosure of Invention
The embodiment of the disclosure provides a method and a device for characterizing vegetation production rate and a storage medium, which at least solve the technical problem that the existing vegetation production rate method in the prior art cannot realize the evaluation of vegetation production rate with different space-time scales.
According to an aspect of an embodiment of the present disclosure, there is provided a method for characterizing vegetation production rate, comprising: determining a ground surface temperature reference curve according to the ground surface temperature data and the ground surface reflectivity data, wherein the ground surface temperature data is obtained by inversion according to thermal infrared remote sensing data; determining a surface temperature curve to be detected according to the surface temperature data; and determining the vegetation production rate index according to the ground surface temperature reference curve and the ground surface temperature curve to be detected.
According to another aspect of the embodiments of the present disclosure, there is also provided a storage medium including a stored program, wherein the method of any one of the above is performed by a processor when the program is executed.
There is also provided, in accordance with another aspect of the disclosed embodiments, apparatus for characterizing vegetation production rate, including: the first determination module is used for determining a ground surface temperature reference curve according to the ground surface temperature data and the ground surface reflectivity data; the second determining module is used for determining a to-be-detected earth surface temperature curve according to the earth surface temperature data; and the third determining module is used for determining the vegetation production rate index according to the ground surface temperature reference curve and the ground surface temperature curve to be detected.
There is also provided, in accordance with another aspect of the disclosed embodiments, apparatus for characterizing vegetation production rate, including: a processor; and a memory coupled to the processor for providing instructions to the processor for processing the following processing steps: determining a ground surface temperature reference curve according to the ground surface temperature data and the ground surface reflectivity data; determining a surface temperature curve to be detected according to the surface temperature data; and determining the vegetation production rate index according to the ground surface temperature reference curve and the ground surface temperature curve to be detected.
Therefore, according to the embodiment, the vegetation production rate can be represented from the angle of energy utilization based on the surface temperature data inverted by the thermal infrared remote sensing data, and the technical effect of reflecting the vegetation production rate, ecological resistance and restoring force under the conditions of natural disturbance and artificial management more accurately is achieved. And the vegetation production rate is researched and analyzed from different hollow angles, so that the technical effect of accurately representing the yield of vegetation crops and the ecological potential of natural vegetation is achieved. Further solves the technical problem that the existing vegetation production rate method in the prior art can not realize the evaluation of vegetation production rates with different space-time scales.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the disclosure and together with the description serve to explain the disclosure and not to limit the disclosure. In the drawings:
fig. 1 is a hardware block diagram of a computing device for implementing the method according to embodiment 1 of the present disclosure;
figure 2 is a schematic flow diagram of a method of characterising a rate of vegetation production according to the first aspect of embodiment 1 of the present disclosure;
fig. 3 is a schematic diagram of surface temperature time-series data according to a first aspect of embodiment 1 of the present disclosure;
FIG. 4 is a schematic diagram of a surface temperature reference curve determined by averaging values according to a first aspect of embodiment 1 of the present disclosure;
FIG. 5 is a schematic diagram of determination of surface matrix pure pixels according to the first aspect of embodiment 1 of the present disclosure;
figure 6 is a schematic diagram of a vegetation production rate index according to the first aspect of embodiment 1 of the present disclosure;
fig. 7 is a schematic flow chart illustrating the calculation of a vegetation production rate index according to embodiment 1 of the present disclosure;
figure 8 is a schematic diagram of a characterization apparatus of vegetation production rate according to embodiment 2 of the present disclosure; and
fig. 9 is a schematic diagram of a characterization apparatus of vegetation production rate according to embodiment 3 of the present disclosure.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure. It is to be understood that the described embodiments are merely exemplary of some, and not all, of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
According to the present embodiment, there is provided an embodiment of a method for characterizing a rate of vegetation production, it being noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than presented herein.
The method embodiments provided by the present embodiment may be executed in a mobile terminal, a computer terminal, a server or a similar computing device. Fig. 1 illustrates a hardware architecture block diagram of a computing device for implementing a characterization method of vegetation production rate. As shown in fig. 1, the computing device may include one or more processors (which may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory for storing data, and a transmission device for communication functions. Besides, the method can also comprise the following steps: a display, an input/output interface (I/O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of the I/O interface), a network interface, a power source, and/or a camera. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the electronic device. For example, the computing device may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
It should be noted that the one or more processors and/or other data processing circuitry described above may be referred to generally herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part in software, hardware, firmware, or any combination thereof. Further, the data processing circuitry may be a single, stand-alone processing module, or incorporated in whole or in part into any of the other elements in the computing device. As referred to in the disclosed embodiments, the data processing circuit acts as a processor control (e.g., selection of a variable resistance termination path connected to the interface).
The memory can be used for storing software programs and modules of application software, such as program instructions/data storage devices corresponding to the characterization method of vegetation production rate in the embodiment of the disclosure, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, the above-mentioned characterization method of vegetation production rate of the application program is realized. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory located remotely from the processor, which may be connected to the computing device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device is used for receiving or transmitting data via a network. Specific examples of such networks may include wireless networks provided by communication providers of the computing devices. In one example, the transmission device includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computing device.
It should be noted here that in some alternative embodiments, the computing device shown in fig. 1 described above may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that FIG. 1 is only one example of a particular specific example and is intended to illustrate the types of components that may be present in a computing device as described above.
In the above operating environment, according to a first aspect of the present embodiment, a method for characterizing vegetation production rate is provided. Fig. 2 shows a flow diagram of the method, which, with reference to fig. 2, comprises:
s202: determining a ground surface temperature reference curve according to the ground surface temperature data and the ground surface reflectivity data, wherein the ground surface temperature data is obtained by inversion according to thermal infrared remote sensing data;
s204: determining a surface temperature curve to be detected according to the surface temperature data; and
s206: and determining the vegetation production rate index according to the ground surface temperature reference curve and the ground surface temperature curve to be detected.
As described in the background, in existing methodologies, the expression of vegetation production rate based on remote sensing data lacks quantitative characterization of vegetation production rate from the dimension of energy and the establishment of functional functions. Partial research utilizes a thermal infrared data area or empirically inverted ground surface temperature as an environmental factor to quantify the difference between the production rates of different local vegetation, but cannot adapt to the evaluation of the annual growth period daily average production rate of the vegetation with different space-time scales.
In view of the above, the present application provides a method for characterizing vegetation production rate, in which a computing device may determine a ground surface temperature reference curve according to ground surface temperature data and ground surface reflectivity data, wherein the ground surface temperature data is obtained by inversion according to thermal infrared remote sensing data (S202). The calculating equipment calculates and obtains the earth surface temperature reference curve through the earth surface temperature data and the earth surface reflectivity data, so that the earth surface temperature data obtained through the inversion of the thermal infrared remote sensing data represents the production rate of the vegetation, and the effect of representing the production rate of the vegetation from the energy perspective is achieved.
Further, the computing device determines a surface temperature curve to be detected according to the surface temperature data (S204). The computing equipment can calculate the earth surface temperature data according to a preset rule algorithm to obtain an earth surface temperature curve to be detected, wherein the earth surface temperature curve to be detected reflects the production rate of the vegetation more intuitively and accurately. Therefore, the temperature curve of the earth surface to be detected of the earth surface is obtained through the mode, and the relevant characteristics of the vegetation in the production period can be accurately reflected.
Further, the calculating device determines the vegetation production rate index according to the ground surface temperature reference curve and the ground surface temperature curve to be detected (S206). The vegetation production rate is characterized from the angle of the surface temperature, namely the method for quantitatively characterizing the vegetation production rate from the angle of energy utilization breaks through the traditional method based on morphological structure, can more quickly and accurately characterize the vegetation production rate, and is a brand new and easily updated technical method.
Therefore, by means of the mode, the vegetation production rate can be represented from the angle of energy utilization based on the earth surface temperature data inverted by the thermal infrared remote sensing data, and the technical effect of reflecting the vegetation production rate, ecological resistance and restoring force under the conditions of natural disturbance and artificial management more accurately is achieved. Further solves the technical problem that the existing vegetation production rate method in the prior art can not realize the evaluation of vegetation production rates with different space-time scales.
In addition, the surface temperature data and the surface reflectivity data are data for the same region during the calculation.
Optionally, the operation of determining a surface temperature reference curve based on the surface temperature data and the surface reflectivity data comprises: determining a plurality of surface temperature time series curves according to the surface temperature data, wherein the plurality of surface temperature time series curves correspond to pixels in the surface temperature data respectively; and determining a surface temperature reference curve according to the surface reflectivity data and the plurality of surface temperature time series curves.
Specifically, referring to fig. 3, for example, the surface temperature data includes data of a plurality of bands, each band corresponding to a temperature value, so that each pixel can form a surface temperature time series curve. The computing device may then determine a surface temperature reference curve based on the plurality of surface temperature time series curves and the surface reflectivity data. Therefore, the technical effect of determining the ground surface temperature reference curve is achieved through the mode.
In addition, the raster data image shown in fig. 3 is merely an example, and has no practical meaning. In addition, referring to fig. 4, a surface temperature time curve corresponding to the image elements is exemplarily shown, wherein the surface temperature data may include data of a plurality of months, for example, 12 data of 1-12 months, and the 12 surface temperature data image elements respectively correspond to each other.
In addition, for example, thermal infrared remote sensing data of each month in 1-12 months in Beijing area are collected, and then inversion is carried out on the thermal infrared remote sensing data to obtain single-band earth surface temperature data corresponding to each month.
Optionally, the operation of determining a plurality of surface temperature time series curves from the surface temperature data comprises: combining the single-waveband earth surface temperature data of a plurality of time nodes in the earth surface temperature data according to a time sequence to determine earth surface temperature time sequence data; and determining a plurality of surface temperature time series curves according to the surface temperature time series data.
Specifically, referring to fig. 3 and 4, although fig. 3 only shows three single-band ground temperature data, the number of the single-band ground temperature data is not limited to 3, for example, the ground temperature data may include data of each month of 12 months, and may also include data of each day of the year, and the time node is self-defined by the user and is not specifically limited herein.
Thus, the time node combines a plurality of pieces of single-band surface temperature data, and as shown in fig. 3, for example, data of 1 to 3 months are combined in the order of 1 month, 2 months, and 3 months to generate surface temperature time-series data. And then determining a plurality of surface temperature time series curves corresponding to each pixel respectively through the combined surface temperature time series data.
Further, for example, the surface temperature data may include 12 surface temperature data in 1-12 months, and then the 12 grid data are combined in time sequence by means of band combination, thereby generating surface temperature time-series data of 12 bands. And then extracting the earth surface temperature data information of each pixel of the earth surface temperature time series data to generate an earth surface temperature time series curve. Therefore, through the mode, the technical effect that the earth surface temperature data inverted by the thermal infrared remote sensing data determines a plurality of earth surface temperature time sequence curves corresponding to a plurality of pixels is achieved. And the vegetation production rate is researched and analyzed from different hollow angles, so that the technical effect of accurately representing the yield of vegetation crops and the ecological potential of natural vegetation is achieved.
Optionally, the operation of determining a plurality of surface temperature time series curves from the surface temperature time series data comprises: filtering the ground surface temperature time sequence data to determine ground surface temperature time sequence smooth data; and determining a plurality of surface temperature time series curves according to the surface temperature time series smooth data.
Specifically, the surface temperature time series data is smoothed and reconstructed, for example, with S-G filtering to obtain higher quality surface temperature time series data. In the parameter setting of the S-G filtering method, the window width of filtering and the order of polynomial fitting can be set according to the experimental effect and the requirement. Therefore, the determined multiple earth surface temperature time sequence curves are smoother by filtering the earth surface temperature data, and the technical effect of facilitating the determination of the earth surface temperature reference curve in the later period is achieved.
Furthermore, SG filtering can smooth multiple values in each pixel of the surface temperature time series data, for example, through a sliding window. The SG filtering is a common means in the field for denoising images, and is not described herein again.
Optionally, the operation of determining a surface temperature reference curve from the surface reflectivity data and the plurality of surface temperature time series curves comprises: performing data dimension reduction on the earth surface reflectivity data by using a principal component analysis algorithm, and determining wave band data of a preset dimension; extracting end members of a geometric vertex of the waveband data of a preset dimension to determine a plurality of surface matrix pure pixels; and determining a ground surface temperature reference curve according to the plurality of ground surface matrix pure pixels and the plurality of ground surface temperature time sequence curves.
In particular, principal component analysis is a method of removing unnecessary information between bands, compressing image information of multiple bands to a few converted bands more effective than the original bands. And the dimensionality reduction of the data is realized through principal component analysis. For example, more than 95% of the original total bands are compressed into three principal components (here, the predetermined dimension is not limited to 3, and may be another number of dimensions such as 2 or 4), and the transformed bands are very small in correlation, that is, the band including 95% of the data information may be used as the principal component band.
Further, a two-dimensional scatter diagram is formed by using image bands with small correlation, such as the first two bands of the principal component analysis transformation result, as an X, Y axis. In the ideal case, the scatter diagram is a triangle, and according to the mathematical description of the linear mixed model, the geometric positions of the pure end members are distributed at three vertexes of the triangle, and the points inside the triangle are linear combinations of the three vertexes, i.e., mixed pixels, as shown in fig. 5. Based on this principle, we can select end-member spectra on a two-dimensional scattergram. In the actual process of selecting end members, a convex area around the scatter diagram is often selected, and then an average spectrum on the original image corresponding to the area is obtained as an end member spectrum. Therefore, by the mode, the effect of obtaining the pure end members of the ground surface matrix by using the end member spectrum extraction technology based on the ground surface reflectivity data and calculating the ground surface temperature reference curve as the reference pixel is achieved.
In addition, for example, taking a water body endpoint as an example, taking the water body endpoint as a center of a circle, a pixel within a radius 1 (the radius 1 is merely illustrated, and may be other numerical values, and is defined according to a user requirement) is taken as a pure pixel, that is, the closer the pixel is to the water body endpoint, the purer the pixel is. The principle of sand and vegetation end points refers to the selection of pure pixels of water end points.
Optionally, the operation of determining the surface temperature reference curve according to the surface matrix pure pixels and the plurality of surface temperature time series curves comprises: carrying out average evaluation on the temperature data of the surface temperature time series curve corresponding to the plurality of surface matrix pure pixels, and determining a plurality of average values; and determining a surface temperature reference curve according to the plurality of average values.
Specifically, referring to fig. 3 and 4, for example, the surface temperature data includes 100 pixels, and the number of surface substrate pure pixels is 3 (here, the 3 surface substrate pure pixels are only illustrated by way of example, and the number of surface substrate pure pixels is based on actual calculation). And determining the image elements corresponding to the 3 surface matrix pure image elements in the surface temperature time sequence data, and then determining a surface temperature time sequence curve corresponding to the 3 image elements. Referring to fig. 4, the surface temperature corresponding to each time node of the surface temperature time series curve corresponding to 3 surface matrix pure pixels is averaged to obtain a surface temperature reference curve. Therefore, the technical effect of determining the ground surface temperature reference curve is achieved through the mode.
The sizes of the pixels of the earth surface temperature time sequence data and the earth surface reflectivity data are the same and are in one-to-one correspondence.
Optionally, the operation of determining the surface temperature curve to be detected according to the surface temperature data includes: determining the starting time, the ending time and the characteristic points of the growth period of the vegetation according to the plurality of surface temperature time series curves, wherein the characteristic points are used for dividing the growth period into a plurality of growth stages; and determining the surface temperature curve to be detected according to the starting time, the ending time and the characteristic points.
In particular, in a defined growth phase, a plurality of vegetation phenolics are included. If the energy balance difference of the vegetation is obviously different in different phenological periods, namely the change rule of the surface temperature curve has obvious stage characteristics in the whole growth period, the growth period needs to be segmented by means of the growth period characteristic points, the surface temperature curve of the growth period is subjected to segment fitting, and then the vegetation production rate index is calculated. Therefore, the starting time, the ending time and the characteristic points of the vegetation growth period are determined according to the multiple ground surface temperature time sequence curves, the multiple growth periods of the vegetation are determined, and the technical effect that the ground surface temperature curves to be detected are subjected to segmented fitting on different growth stages through the multiple growth periods of the vegetation is achieved.
Optionally, the operation of determining the start time, the end time and the characteristic point of the growth period according to a plurality of surface temperature time series curves comprises: performing first-order differentiation on the multiple surface temperature time series curves to determine a first-order differentiation curve; and determining the starting time, the ending time and the characteristic point according to the first-order differential curve.
Specifically, the steps of determining the start time, the end time and the characteristic points of the vegetation growth period through a plurality of surface temperature time series curves are as follows:
(1) determination of growth period start and end (start and end time):
ts= {𝑡∣D𝑡−2 > i and D𝑡−1 >i and D𝑡 <i formula (1)
te= {𝑡∣D𝑡 < j and D𝑡+1 > j and D𝑡+2 >j equation (2)
Wherein ts and te are the initial and ending time of the growing period preliminarily drawn up, D t represents the value of the first order differential curve of the earth surface temperature corresponding to the t moment, i and j are the first order differential value threshold values of the earth surface temperature at the initial and ending of the growing period.
Δt =te-tsFormula (3)
WhereinT s 、T e The time of the beginning and the end of the finally determined growth phase, respectively, k being the shortest growth phase duration.
(2) Determination of characteristic points
The characteristic point detection is carried out in the growth periodI.e. satisfyT s AndT e are all not equal to 0 and T1, T2∈（T s 、T e ) The calculation formula is as follows:
T1= {𝑡∣D𝑡 = 0 and D𝑡-1 > 0 and D𝑡-1 <0 equation (6)
T2= {𝑡∣D𝑡 = 0 and D𝑡-1 < 0 and D𝑡-1 >0 equation (6)
Wherein T1, T2 are the times of the characteristic points of the first and second growth phases, respectively.
Therefore, through the mode, the technical effect of determining the start point and the stop point of the vegetation and the characteristic point is achieved by carrying out first-order differentiation on the multiple surface temperature time series curves, and each growth stage of the vegetation in the production period is further determined.
Optionally, the operation of determining the surface temperature curve to be detected according to the start time, the end time and the feature point includes: determining a plurality of to-be-detected surface temperature sub-curves of a plurality of growth stages according to the starting time, the ending time and the characteristic points of the growth period; and determining the surface temperature curve to be detected according to the plurality of surface temperature sub-curves to be detected.
Specifically, referring to fig. 6, for example, if there are two characteristic points b and c (the growth stage is determined according to the characteristic points, and only two characteristic points are used for illustration), the start time a and the end time d of the growth stage, the growth cycle of the vegetation can be divided into three growth stages, i.e., a-b, b-c and c-d. As each growth stage is in a defined growth phase, it comprises a plurality of vegetation phenolics. If the energy balance difference of the vegetation is obviously different in different phenological periods, namely the change rule of the surface temperature curve has obvious stage characteristics in the whole growth period, the growth period needs to be segmented by means of the growth period characteristic points, a plurality of surface temperature time sequence curves in the growth period are subjected to segment fitting, and then the ecological state index is calculated.
For example, the surface temperature profile to be detected is determined by:
differentiating the surface temperature time series curves to obtain a first order differential function h (t)
: wherein T surface temperature, T time;
then h (t) is reduced, namely, a primitive function (a surface temperature curve to be detected) is worked out
F (t) the surface temperature curve to be detected, h (t) a first order differential function, and the values of points a, b, C and d are substituted to obtain the value of C. Thereby obtaining the surface temperature sub-curves to be detected in the three growth stages of a-b, b-c and c-d. And then determining the surface temperature curve to be detected according to the 3 surface temperature sub-curves to be detected. Therefore, the technical effect of accurately determining the earth surface temperature curve to be detected is achieved by respectively fitting the earth surface temperature curves to be detected in the growing periods of different vegetation stages, and the earth surface temperature time sequence curves to be detected corresponding to the pixel-by-pixel earth surface temperature time sequence curves can be obtained through the method.
Optionally, the operation of determining the vegetation production rate index according to the ground surface temperature reference curve and the ground surface temperature curve to be detected comprises: calculating the area of the earth surface temperature reference curve and the area of the earth surface temperature curve to be detected at the starting time and the ending time; and determining a vegetation production rate index according to the area of the region.
Specifically, referring to fig. 6, the vegetation production rate index is obtained by calculating the area of each stage of the growth period in stages. I.e., the area of the region m is the cumulative value of the vegetation production rate index VPI for each growth phase.
Wherein the vegetation production rate index VPI is calculated as follows:
Wherein VPI is a vegetation production rate index, Ts and Te are respectively the starting time and the ending time of a growing period, f (t) is a fitting function of a ground surface temperature curve to be detected, and g (t) is a fitting function of a ground surface temperature reference curve.
The defined growth phase includes a plurality of vegetation phenology phases. If the energy balance difference of the vegetation is obviously different in different phenological periods, namely the change rule of the surface temperature curve has obvious stage characteristics in the whole growth period, the growth period needs to be segmented by means of the growth period characteristic points, the surface temperature curve of the growth period is subjected to segment fitting, and then the vegetation production rate index is calculated. As in the case of fig. 6, the calculation of the vegetation production rate index VPI parameter can be converted into:
Wherein Ts and Ts are the time of beginning a and ending d of the growth period, T2At a time c characteristic of the second growth phase, f1 is (Ts, T)2) A fitted function of the surface temperature curve to be detected over a period of time, f2 being (T)2Te) a fitting function of the surface temperature profile to be detected over the time period, g (t) being a fitting function of the reference surface temperature profile.
Further, referring to fig. 1, according to a second aspect of the present embodiment, there is provided a storage medium. The storage medium comprises a stored program, wherein the method of any of the above is performed by a processor when the program is run.
Therefore, according to the method for representing the vegetation production rate, the vegetation production rate can be represented from the angle of energy utilization based on the earth surface temperature data inverted by the thermal infrared remote sensing data, and the technical effect of more accurately reflecting the vegetation production rate, ecological resistance and restoring force under the conditions of natural disturbance and artificial management is achieved. And the vegetation production rate is researched and analyzed from different hollow angles, so that the technical effect of accurately representing the yield of vegetation crops and the ecological potential of natural vegetation is achieved. Further solves the technical problem that the existing vegetation production rate method in the prior art can not realize the evaluation of vegetation production rates with different space-time scales.
In addition, referring to FIG. 7, a schematic diagram of the calculation process of the vegetation production rate index in the present invention is shown,
construction of time-series surface temperature (LST) curves (i.e., surface temperature time-series curves) over 5.1 years
According to the surface temperature product based on thermal infrared remote sensing data inversion, the daytime surface temperature remote sensing data of the time series in the year are subjected to wave band combination according to the time sequence, and are fused into multi-wave band data, and then S-G filtering is used for smoothing and reconstructing the data so as to obtain surface temperature time series data with higher quality. In the parameter setting of the S-G filtering method, the window width of filtering and the order of polynomial fitting can be set according to the experimental effect and the requirement.
5.2 determination of the ground surface temperature reference Curve
The method mainly comprises the steps of obtaining pure end members of ground surface matrixes by applying an end member spectrum extraction technology based on ground surface reflectivity data, calculating a ground surface temperature reference curve by taking the pure end members as reference pixels, and the method comprises the steps of Principal Component Analysis (PCA), end member extraction based on geometric vertexes and the like.
(1) Principal component analysis
Principal component analysis is a method of removing unnecessary information between bands and compressing image information of multiple bands to a few conversion bands more effective than the original bands. Through principal component analysis, the dimensionality reduction of data is realized, more than 95% of information content in all original wave bands is compressed in the first three principal components, and the correlation of each wave band after transformation is small.
(2) Geometric vertex-based end member extraction
And (4) constructing a two-dimensional scatter diagram by taking the image wave bands with small correlation, such as the first two wave bands of a transformation result of principal component analysis and the like, as X, Y axes. In the ideal case, the scatter plot is triangular in shape. According to the mathematical description of the linear hybrid model, the geometric positions of the clean end members are distributed at 3 vertices of the triangle, and the inside of the triangle is the linear combination of the vertices, i.e. the hybrid pixel, as shown in fig. 2. Therefore, the image element representing the ground substrate, namely the non-vegetation-coated image element, can be selected as the pure image element of the ground substrate.
(3) Calculating a surface temperature reference curve
And taking the average value curve of the selected surface pure pixels as a reference curve of the surface temperature.
5.3 detection of growth period Start and stop points and characteristic points
And (4) solving a first-order differential of the surface temperature curve, and detecting the start point, the end point and the characteristic point of the growing period on the first-order differential curve.
5.4 calculation of index parameters for production Rate of Vegetation
Because different ecological states of the earth surface have obvious energy balance difference characteristics in the vegetation growth period, the difference of the ecological states can be represented by the difference of the earth surface temperature in the growth period.
On one hand, the method is based on thermal infrared remote sensing data, and innovatively provides a method for quantitatively representing vegetation production rate from the perspective of energy utilization, breaks through the traditional method based on morphological structure, can more quickly and accurately represent vegetation production rate, and is a brand-new and easily-updated technical method.
On the other hand, a complete vegetation production rate characterization model with uniform professional concepts and physical implementation is constructed based on the land system surface ecological process and the energy balance. The unified method is standardized, and the universality and result comparability of the method applied under different space-time backgrounds are ensured.
The method is based on thermal infrared remote sensing data, can represent the vegetation production rate from the aspect of energy utilization, and more accurately reflects the production rate, ecological resistance and restoring force of the vegetation under the conditions of natural disturbance and artificial management. The method can be used for researching and analyzing the vegetation production rate in different time and space, finds out the crop yield and the ecological potential of natural vegetation, and serves the supervision and policy making of natural resources; the method can also be used as an important characteristic variable to assist in better cognition of the system evolution rule of the human and the ground, and more accurate simulation prediction is realized.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required by the invention.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
Example 2
Fig. 8 shows a device 800 for characterising the rate of vegetation production according to the present embodiment, which device 800 corresponds to the method according to the first aspect of embodiment 1. Referring to fig. 8, the apparatus 800 includes: the first determining module 810 is configured to determine a ground surface temperature reference curve according to ground surface temperature data and ground surface reflectivity data, where the ground surface temperature data is obtained by inversion according to thermal infrared remote sensing data; a second determining module 820, configured to determine a surface temperature curve to be detected according to the surface temperature data; and a third determining module 830, configured to determine a vegetation production rate index according to the ground surface temperature reference curve and the ground surface temperature curve to be detected.
Optionally, the first determining module 810 includes: the first determining submodule is used for determining a plurality of earth surface temperature time sequence curves according to the earth surface temperature data, wherein the earth surface temperature time sequence curves respectively correspond to pixels in the earth surface temperature data; and the second determining submodule is used for determining a surface temperature reference curve according to the surface reflectivity data and the plurality of surface temperature time series curves.
Optionally, the first determining sub-module includes: the first determining unit is used for combining the single-waveband earth surface temperature data of a plurality of time nodes in the earth surface temperature data according to the time sequence to determine earth surface temperature time sequence data; and a second determining unit for determining a plurality of surface temperature time series curves according to the surface temperature time series data.
Optionally, the second determining unit includes: the first determining subunit is used for filtering the ground surface temperature time series data and determining ground surface temperature time series smooth data; and the second determining subunit is used for determining a plurality of surface temperature time series curves according to the surface temperature time series smooth data.
Optionally, the second determining sub-module includes: the third determining unit is used for performing data dimension reduction on the earth surface reflectivity data by utilizing a principal component analysis algorithm and determining wave band data of a preset dimension; the fourth determining unit is used for extracting end members of geometric vertexes of the wave band data of the preset dimensionality and determining a plurality of surface matrix pure pixels; and the fifth determining unit is used for determining the ground surface temperature reference curve according to the plurality of ground surface matrix pure pixels and the plurality of ground surface temperature time sequence curves.
Optionally, the fifth determining unit includes: the third determining subunit is used for performing average evaluation on the temperature data of the surface temperature time series curve corresponding to the plurality of surface matrix pure pixels and determining a plurality of average values; and a fourth determining subunit, configured to determine a surface temperature reference curve according to the plurality of average values.
Optionally, the second determining module 820 includes: the third determining submodule is used for determining the starting time, the ending time and the characteristic points of the growth period of the vegetation according to the plurality of surface temperature time series curves, wherein the characteristic points are used for dividing the growth period into a plurality of growth stages; and the fourth determining submodule is used for determining the surface temperature curve to be detected according to the starting time, the ending time and the characteristic points.
Optionally, the third determining sub-module includes: the sixth determining unit is used for performing first-order differentiation on the multiple surface temperature time series curves to determine a first-order differential curve; and a seventh determining unit for determining the start time, the end time, and the feature point based on the first order differential curve.
Optionally, the fourth determining sub-module includes: the eighth determining unit is used for determining a plurality of to-be-detected surface temperature sub-curves of a plurality of growth stages according to the starting time, the ending time and the characteristic points of the growth period; and the ninth determining unit is used for determining the surface temperature curve to be detected according to the plurality of surface temperature sub-curves to be detected.
Optionally, the third determining module 830 includes: the calculation submodule is used for calculating the area of the earth surface temperature reference curve and the area of the earth surface temperature curve to be detected at the starting time and the ending time; and a fifth determining submodule for determining a vegetation production rate index according to the area.
Therefore, according to the embodiment, the vegetation production rate can be represented from the angle of energy utilization based on the surface temperature data inverted by the thermal infrared remote sensing data, and the technical effect of reflecting the vegetation production rate, ecological resistance and restoring force under the conditions of natural disturbance and artificial management more accurately is achieved. And the vegetation production rate is researched and analyzed from different hollow angles, so that the technical effect of accurately representing the yield of vegetation crops and the ecological potential of natural vegetation is achieved. Further solves the technical problem that the existing vegetation production rate method in the prior art can not realize the evaluation of vegetation production rates with different space-time scales.
Example 3
Figure 9 shows a device 900 for characterising the rate of vegetation production according to the present embodiment, which device 900 corresponds to the method according to the first aspect of embodiment 1. Referring to fig. 9, the apparatus 900 includes: a processor 910; and a memory 920 coupled to the processor 910 for providing instructions to the processor 910 to process the following steps: determining a ground surface temperature reference curve according to the ground surface temperature data and the ground surface reflectivity data, wherein the ground surface temperature data is obtained by inversion according to the thermal infrared remote sensing data; determining a surface temperature curve to be detected according to the surface temperature data; and determining the vegetation production rate index according to the ground surface temperature reference curve and the ground surface temperature curve to be detected.
Optionally, the operation of determining a surface temperature reference curve based on the surface temperature data and the surface reflectivity data comprises: determining a plurality of surface temperature time series curves according to the surface temperature data, wherein the plurality of surface temperature time series curves correspond to pixels in the surface temperature data respectively; and determining a surface temperature reference curve according to the surface reflectivity data and the plurality of surface temperature time series curves.
Optionally, the operation of determining a plurality of surface temperature time series curves from the surface temperature data comprises: combining the single-waveband earth surface temperature data of a plurality of time nodes in the earth surface temperature data according to a time sequence to determine earth surface temperature time sequence data; and determining a plurality of surface temperature time series curves according to the surface temperature time series data.
Optionally, the operation of determining a plurality of surface temperature time series curves from the surface temperature time series data comprises: filtering the ground surface temperature time sequence data to determine ground surface temperature time sequence smooth data; and determining a plurality of surface temperature time series curves according to the surface temperature time series smooth data.
Optionally, the operation of determining a surface temperature reference curve from the surface reflectivity data and the plurality of surface temperature time series curves comprises: performing data dimension reduction on the earth surface reflectivity data by using a principal component analysis algorithm, and determining wave band data of a preset dimension; extracting end members of a geometric vertex of the waveband data of a preset dimension to determine a plurality of surface matrix pure pixels; and determining a ground surface temperature reference curve according to the plurality of ground surface matrix pure pixels and the plurality of ground surface temperature time sequence curves.
Optionally, the operation of determining the surface temperature reference curve according to the surface matrix pure pixels and the plurality of surface temperature time series curves comprises: carrying out average evaluation on the temperature data of the surface temperature time series curve corresponding to the plurality of surface matrix pure pixels, and determining a plurality of average values; and determining a surface temperature reference curve according to the plurality of average values.
Optionally, the operation of determining the surface temperature curve to be detected according to the surface temperature data includes: determining the starting time, the ending time and the characteristic points of the growth period of the vegetation according to the plurality of surface temperature time series curves, wherein the characteristic points are used for dividing the growth period into a plurality of growth stages; and determining the surface temperature curve to be detected according to the starting time, the ending time and the characteristic points.
Optionally, the operation of determining the start time, the end time and the characteristic point of the growth period according to the ground surface temperature reference curve comprises: performing first-order differentiation on the multiple surface temperature time series curves to determine a first-order differentiation curve; and determining the starting time, the ending time and the characteristic point according to the first-order differential curve.
Optionally, the operation of determining the surface temperature curve to be detected according to the start time, the end time and the feature point includes: determining a plurality of to-be-detected surface temperature sub-curves of a plurality of growth stages according to the starting time, the ending time and the characteristic points of the growth period; and determining the surface temperature curve to be detected according to the plurality of surface temperature sub-curves to be detected.
Optionally, the operation of determining the vegetation production rate index according to the ground surface temperature reference curve and the ground surface temperature curve to be detected comprises: calculating the area of the earth surface temperature reference curve and the area of the earth surface temperature curve to be detected at the starting time and the ending time; and determining a vegetation production rate index according to the area of the region.
Therefore, according to the embodiment, the vegetation production rate can be represented from the angle of energy utilization based on the surface temperature data inverted by the thermal infrared remote sensing data, and the technical effect of reflecting the vegetation production rate, ecological resistance and restoring force under the conditions of natural disturbance and artificial management more accurately is achieved. And the vegetation production rate is researched and analyzed from different hollow angles, so that the technical effect of accurately representing the yield of vegetation crops and the ecological potential of natural vegetation is achieved. Further solves the technical problem that the existing vegetation production rate method in the prior art can not realize the evaluation of vegetation production rates with different space-time scales.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute 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 Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A vegetation production rate characterization method based on thermal infrared remote sensing data is characterized by comprising the following steps:
determining a ground surface temperature reference curve according to ground surface temperature data and ground surface reflectivity data, wherein the ground surface temperature data is obtained by inversion according to the thermal infrared remote sensing data;
determining a surface temperature curve to be detected according to the surface temperature data; and
and determining a vegetation production rate index according to the ground surface temperature reference curve and the to-be-detected ground surface temperature curve, wherein the vegetation production rate index is used for representing the production rate of vegetation.
2. The method of claim 1, wherein the operation of determining a surface temperature reference profile from the surface temperature data and the surface reflectivity data comprises:
determining a plurality of surface temperature time series curves according to the surface temperature data, wherein the surface temperature time series curves respectively correspond to pixels in the surface temperature data; and
and determining the surface temperature reference curve according to the surface reflectivity data and the plurality of surface temperature time series curves.
3. The method of claim 2, wherein the operation of determining a plurality of surface temperature time series profiles from the surface temperature data comprises:
combining the single-waveband earth surface temperature data of a plurality of time nodes in the earth surface temperature data according to a time sequence to determine the earth surface temperature time sequence data; and
and determining the plurality of surface temperature time series curves according to the surface temperature time series data.
4. The method of claim 3, wherein the operation of determining the plurality of surface temperature time series profiles from the surface temperature time series data comprises:
filtering the surface temperature time sequence data to determine surface temperature time sequence smooth data; and
and determining the plurality of surface temperature time series curves according to the surface temperature time series smooth data.
5. The method of claim 2, wherein the operation of determining the surface temperature reference profile from the surface reflectivity data and the plurality of surface temperature time series profiles comprises:
performing data dimension reduction on the earth surface reflectivity data by using a principal component analysis algorithm, and determining wave band data of a preset dimension;
extracting end members of a geometric vertex of the wave band data of the preset dimension to determine a plurality of surface matrix pure pixels; and
and determining the ground surface temperature reference curve according to the plurality of ground surface matrix pure pixels and the plurality of ground surface temperature time sequence curves.
6. The method of claim 5, wherein the operation of determining the surface temperature reference curve from the surface matrix clean pixels and the plurality of surface temperature time series curves comprises:
averaging the temperature data of the surface temperature time series curve corresponding to the plurality of surface matrix pure pixels to determine a plurality of average values; and
and determining the surface temperature reference curve according to the average values.
7. The method of claim 3, wherein the act of determining a surface temperature profile to be detected from the surface temperature data comprises:
determining a starting time, an ending time and characteristic points of a growth period of vegetation according to the plurality of surface temperature time series curves, wherein the characteristic points are used for dividing the growth period into a plurality of growth stages; and
and determining the earth surface temperature curve to be detected according to the starting time, the ending time and the characteristic points.
8. The method of claim 7, wherein the operation of determining a start time, an end time, and a characteristic point of a growth period from the surface temperature reference curve comprises:
performing first order differentiation on the surface temperature reference curve to determine a first order differentiation curve; and
determining the start time, the end time and the feature point from the first order differential curve, and
determining the operation of the earth surface temperature curve to be detected according to the starting time, the ending time and the characteristic points, wherein the operation comprises the following steps:
determining a plurality of to-be-detected surface temperature sub-curves of the plurality of growth stages according to the starting time, the ending time and the characteristic points of the growth period; and
and determining the earth surface temperature curve to be detected according to the earth surface temperature sub-curves to be detected.
9. The method of claim 8, wherein determining a vegetation production rate index from the surface temperature reference curve and the surface temperature to be measured curve comprises:
calculating the area of the surface temperature reference curve and the area of the surface temperature curve to be detected at the starting time and the ending time; and
and determining the vegetation production rate index according to the area.
10. A storage medium comprising a stored program, wherein the method of any one of claims 1 to 9 is performed by a processor when the program is run.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113962248A (en) * | 2021-12-01 | 2022-01-21 | 中国农业大学 | Method and device for determining biomass on grassland and storage medium |
| CN113962248B (en) * | 2021-12-01 | 2022-03-18 | 中国农业大学 | Method and device for determining biomass on grassland and storage medium |
| CN113963263A (en) * | 2021-12-23 | 2022-01-21 | 中国农业大学 | Method and device for determining growth attribute of perennial vegetation and storage medium |
| CN117689959A (en) * | 2024-01-30 | 2024-03-12 | 中交第二公路勘察设计研究院有限公司 | Remote sensing classification method for fusing vegetation life cycle features |
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