CN115294460B - Method for determining degradation degree of phyllostachys praecox forest, medium and electronic device - Google Patents
Method for determining degradation degree of phyllostachys praecox forest, medium and electronic device Download PDFInfo
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Abstract
The invention provides a method, medium and electronic equipment for determining the degradation degree of a phyllostachys praecox forest, wherein the method comprises the following steps: acquiring a current remote sensing image of a target area, wherein the wave bands of the current remote sensing image at least comprise a near infrared wave band, a middle infrared wave band, a red light wave band and a blue light wave band; determining a horizontal vegetation index and overground biomass of each sample in a target area according to the current remote sensing image; inputting the range vegetation index and the overground biomass into a preset degradation threshold judgment model to determine the value of a pixel point corresponding to a sample party in the current remote sensing image; and determining the phyllostachys praecox forest degradation index of each phyllostachys praecox pattern spot in the current remote sensing image according to the values of the pixel points in the current remote sensing image, wherein the phyllostachys praecox pattern spots comprise a plurality of pixel points, and the phyllostachys praecox forest degradation index of the phyllostachys praecox pattern spots is determined according to the values of all the pixel points in the phyllostachys praecox pattern spots. Therefore, the growth condition of the phyllostachys praecox can be conveniently, quickly and comprehensively acquired through the satellite remote sensing image.
Description
Technical Field
The invention relates to the field of computers, in particular to a method, medium and electronic equipment for determining the degradation degree of a phyllostachys praecox forest.
Background
The phyllostachys praecox is a native bamboo species in a certain city and is famous excellent bamboo for bamboo shoots in China. In the last 20 years, the phyllostachys pracecox shoot industry has been the first major industry of rural economy in a certain city, and in the important bamboo shoot producing areas, 60% of income of bamboo farmers comes from bamboo shoots. The industry of the phyllostachys pracecox shoots is still in the leading position by promoting industrialized operation in a certain market from aspects of scientific and technological popularization, market construction and propaganda and promotion.
With the application and popularization of the early covering technology of the phyllostachys praecox forest, the yield and the economic benefit of bamboo shoots of the phyllostachys praecox forest are obviously improved, but the phenomenon of degradation of the phyllostachys praecox forest in different degrees is caused by the increase of the covering age, and the phyllostachys praecox forest has a trend of becoming more and more serious. Due to excessive management, in recent years, the bamboo forest begins to degenerate due to the fact that a large amount of chemical fertilizer is applied to the bamboo forest, soil is acidified, and the bamboo forest is degraded; due to insufficient labor force, the practical benefit of the bamboo shoots is reduced, and the problems of pipe loss and waste of bamboo forests occur.
Therefore, evaluating and diagnosing the growth vigor of the phyllostachys praecox forest so as to facilitate the business departments in agricultural and rural bureaus to take intervention measures in time is an effective means for promoting the sustainable development of the phyllostachys praecox industry.
Disclosure of Invention
The invention aims to at least solve the problem that the growth vigor of the phyllostachys praecox forest is difficult to diagnose accurately in the prior art.
In view of the above, an object of the present invention is to provide a method for determining the degree of degradation of a phyllostachys praecox forest.
It is another object of the invention to provide a computer medium.
It is a further object of the present invention to provide an electronic device.
In order to achieve the above object, a technical solution of a first aspect of the present invention provides a method for determining a bamboo forest degradation degree, including:
acquiring a current remote sensing image of a target area, wherein the wave bands of the current remote sensing image at least comprise a near infrared wave band, a middle infrared wave band, a red light wave band and a blue light wave band;
determining a horizontal vegetation index and overground biomass of each sample in the target area according to the current remote sensing image;
inputting the range vegetation index and the aboveground biomass into a preset degradation threshold judgment model to determine the value of a pixel point corresponding to the sample in the current remote sensing image;
and determining a phyllostachys praecox forest degradation index of each phyllostachys praecox pattern spot in the current remote sensing image according to the values of the pixel points in the current remote sensing image, wherein the phyllostachys praecox pattern spot comprises a plurality of the pixel points, and the phyllostachys praecox forest degradation index of the phyllostachys praecox pattern spot is determined according to the values of all the pixel points in the phyllostachys praecox pattern spot.
Optionally, the method further comprises:
determining the degradation grade of the phyllostachys praecox forest of the phyllostachys praecox pattern spots according to the phyllostachys praecox forest degradation index of the phyllostachys praecox pattern spots;
and according to the bamboo forest degradation grade, giving a color attribute to the bamboo pattern spots in the current remote sensing image.
Optionally, the talar vegetation index is calculated by the following formula:
A=(EVIi-EVIavg)/EVIavg （1）
EVI =2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1)) （2）
in the formula (1) and the formula (2), A is a true vegetation index, and NIR is a near infrared band; RED is the RED light band; BLUE band; EVI is an enhanced vegetation index; EVIi is the current year enhanced vegetation index value; EVIavg is the mean value of the enhanced vegetation indexes in the last three years.
Optionally, the aboveground biomass is calculated by the following formula:
B = -34.58 - 105.16 * IIVI + 54.44 * EVI + 71.75 * TM437 （3）
EVI = 2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1)) （4）
IIVI = (NIR - SWIR1) / (NIR + SWIR1) （5）
TM437 = NIR * RED / SWIR2 （6）
wherein, in the formulas (3) to (6), B is aboveground biomass, and NIR is a near infrared band; RED is the RED band; BLUE is BLUE light band; the SWIR1 and the SWIR2 are intermediate infrared bands with different wavelengths; EVI is an enhanced vegetation index; IIVI is the near-infrared vegetation index; TM437 is a name for the expression TM4 TM3/TM 7.
Optionally, the degradation threshold determination model is:
A<C AND B<D
wherein, A represents the distance vegetation index, B represents the aboveground biomass, C represents the annual distance vegetation index threshold value, and D represents the thunderbamboo degradation ratio threshold value.
Optionally, inputting the talar vegetation index and the above-ground biomass into a preset degradation threshold judgment model to determine a value of a pixel point corresponding to the sample in the current remote sensing image, including:
segmenting the current remote sensing image according to a digital image P value segmentation method, wherein the P value is the same as the phyllostachys praecox degradation ratio threshold;
if the aboveground biomass of the pixel point is determined to be smaller than the thunderbamboo degradation ratio threshold value after the division, and the distance vegetation index of the pixel point is determined to be smaller than the annual distance vegetation index threshold value, determining the value of the pixel point to be 0; otherwise, determining the value of the pixel point to be 1.
Optionally, before the inputting the talar vegetation index and the above-ground biomass into a preset degradation threshold judgment model to determine a value of a pixel point corresponding to the sample in the current remote sensing image, the method further includes:
acquiring a historical remote sensing image of a detection area, and determining a historical flat vegetation index and a historical aboveground biomass corresponding to the historical remote sensing image;
inputting the historical distance vegetation index and the biomass on the historical ground into a degradation threshold judgment model before verification so as to determine the value of a pixel point corresponding to a sample in the historical remote sensing image;
acquiring the degradation degree of a sample of the detection area;
and verifying a degradation threshold judgment model before verification according to the degradation degree of the sample and the value of the pixel point.
Optionally, the verifying a degradation threshold determination model before verification according to the degradation degree of the sample and the value of the pixel point includes:
determining the accuracy of a degradation threshold judgment model before verification according to the degradation degree of the sample and the value of the pixel point;
and when the accuracy exceeds a preset threshold, determining that the model passes verification by using a degradation threshold.
An aspect of the second aspect of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the method provided by the first aspect of the present invention.
A technical solution of a third aspect of the present invention provides an electronic device, including:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to implement the method provided by the first aspect of the present invention.
Through the technical scheme, the growth condition of the phyllostachys praecox can be conveniently, quickly and comprehensively obtained through the satellite remote sensing image, the difficulty and the cost of bamboo forest growth condition field study and judgment of phyllostachys praecox technical experts are greatly reduced, and a theoretical basis is provided for the decision of degraded phyllostachys praecox recovery of agricultural rural bureaus.
Specifically, determining a threshold value by combining a talar vegetation index to judge the growth trend; and (3) carrying out pixel point mean value statistics on the phyllostachys praecox planting vector pattern on the degradation judgment grid map, judging the growth condition of the phyllostachys praecox forest, and realizing the graded extraction of the growth vigor of the phyllostachys praecox forest.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a flow diagram of a method for determining a degree of degradation of a phyllostachys praecox forest according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a method for determining a degree of degradation of a phyllostachys praecox forest according to an embodiment of the present invention;
FIG. 3 is a schematic illustration of a remote sensing image after color attributes have been assigned to bamboo patches in accordance with an embodiment of the present invention;
FIG. 4 is a block diagram of an electronic device according to one embodiment of the invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments thereof which are illustrated in the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
Some embodiments according to the invention are described below with reference to fig. 1 to 4.
Referring to fig. 1, the method for determining the degradation degree of a phyllostachys edulis forest according to an embodiment of the present invention may include steps S11 to S14.
In step S11, a current remote sensing image of the target area is obtained, where the bands of the current remote sensing image at least include a near-infrared band, a mid-infrared band, a red light band, and a blue light band.
For example, the multispectral data come from Sentinel-2 Sentinel images, are high-resolution multispectral imaging satellites, are 786 kilometers in height, carry a multispectral imager for land monitoring, and can provide images of vegetation, soil and water coverage, inland waterways and coastal areas and the like. The multispectral imager can cover 13 spectral bands, the width reaches 290 kilometers, two satellites are complementary, and the revisit period is 5 days.
Multispectral data of the scheme is from a geographic monitoring cloud platform Google Earth Engine, the data type selects an index required by 2A data calculation which is subjected to radiation correction, and the time phase selects medium value synthetic data of 7~8 in 2021.
In step S12, determining a range vegetation index and aboveground biomass of each sample in a target area according to the current remote sensing image;
in step S13, inputting the range vegetation index and the aboveground biomass into a preset degradation threshold judgment model to determine the value of a pixel point corresponding to a sample in the current remote sensing image;
in step S14, determining a phyllostachys praecox forest degradation index of each phyllostachys praecox pattern spot in the current remote sensing image according to the values of the pixel points in the current remote sensing image, wherein the phyllostachys praecox pattern spot comprises a plurality of pixel points, and the phyllostachys praecox forest degradation index of the phyllostachys praecox pattern spot is determined according to the values of all the pixel points in the phyllostachys praecox pattern spot
Thus, determining a threshold value by combining the vegetation index to judge the growth trend; and (4) carrying out pixel point mean value statistics by using the phyllostachys praecox planting vector pattern spots on the degradation judgment grid pattern, and judging the growth condition of the phyllostachys praecox forest. Therefore, the growth condition of the phyllostachys praecox can be conveniently, quickly and comprehensively obtained through the satellite remote sensing image, the difficulty and the cost of bamboo forest growth condition field study and judgment of phyllostachys praecox technical experts are greatly reduced, and a theoretical basis is provided for the decision of degraded phyllostachys praecox recovery of agricultural rural bureaus.
In one possible embodiment, the method further comprises:
determining the degradation grade of the phyllostachys praecox forest of the phyllostachys praecox map spots according to the phyllostachys praecox forest degradation index of the phyllostachys praecox map spots;
and according to the degradation level of the phyllostachys praecox forest, giving color attributes to the phyllostachys praecox pattern spots in the current remote sensing image.
The range-flat vegetation index may reflect the inter-annual variation of vegetation, and is typically used to compare the current growth conditions, with lower values giving poorer growth. The method is used for comparing the growth change situation between the years and analyzing the degeneration trend of the phyllostachys praecox. The vegetation index is selected from an enhanced vegetation index which is improved from a normalized vegetation index, and the problem that the traditional vegetation index is easy to saturate and lacks of linear coverage with actual vegetation coverage can be solved. Thus, in one possible embodiment, the talar vegetation index is calculated by the following formula:
A=(EVIi-EVIavg)/EVIavg （1）
EVI =2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1)) （2）
in the formula (1) and the formula (2), A is a true vegetation index, and NIR is a near infrared band; RED is the RED band; BLUE is BLUE light band; EVI is an enhanced vegetation index; EVIi is the current year enhanced vegetation index value; EVIavg is the mean value of the enhanced vegetation indexes in the last three years.
In one possible embodiment, the aboveground biomass can be obtained by quantitative inversion formula according to the article "estimation of aboveground biomass of Phyllostachys based on Landsat TM data", wherein the related TM image bands can all find the alternative bands in the Sentinel-2 image. Above-ground biomass is calculated by the following formula:
B = -34.58 - 105.16 * IIVI + 54.44 * EVI + 71.75 * TM437 （3）
EVI = 2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1)) （4）
IIVI = (NIR - SWIR1) / (NIR + SWIR1) （5）
TM437 = NIR * RED / SWIR2 （6）
wherein, in the formulas (3) to (6), B is aboveground biomass, and NIR is a near infrared band; RED is the RED band; BLUE is BLUE light band; the SWIR1 and the SWIR2 are middle infrared wave bands with different wavelengths; EVI is an enhanced vegetation index; IIVI is the near-infrared vegetation index; TM437 is the expression TM4 TM3/TM 7.
In one possible embodiment, the degradation threshold decision model is:
A<C AND B<D
wherein, A represents the index of the vegetation at the distance level, B represents the aboveground biomass, C represents the threshold of the index of the vegetation at the distance level between the years, and D represents the threshold of the degradation ratio of the phyllostachys praecox.
In a possible implementation manner, inputting the true-to-flat vegetation index and the aboveground biomass into a preset degradation threshold judgment model to determine values of pixel points corresponding to a sample in a current remote sensing image, including:
segmenting the current remote sensing image according to a digital image P value segmentation method, wherein the P value is the same as the phyllostachys praecox degradation ratio threshold;
if the aboveground biomass of the pixel point is determined to be smaller than the thunderbamboo degradation ratio threshold value after the division, and the distance vegetation index of the pixel point is smaller than the annual distance vegetation index threshold value, determining the value of the pixel point to be 0; otherwise, determining the value of the pixel point to be 1.
That is to say, in the scheme, the degraded image is a binary image of the pixels on a grid map, and a digital image P-value segmentation method is adopted as a binary method.
The degeneration ratio of the phyllostachys praecox is 69.6 according to the investigation report on the transformation of the degenerated bamboo forest of phyllostachys praecox in a certain market. And calculating the aboveground biomass B of the phyllostachys praecox growing region, performing statistical analysis, and taking the quantile value of the aboveground biomass B at 69.6 as the P value. Calculated to be 19.38, then the P value used in the image segmentation method would be determined to be 19.38. Meanwhile, the degradation determination needs to be assisted by using an annual interval flat vegetation index in addition to the P value based on the aboveground biomass. It is believed that when the bamboo is not well grown, its value is less than 0.03 (i.e., the annual spacing average vegetation index threshold may be 0.03).
In a possible implementation manner, before inputting the indexes of the planar vegetation and the terrestrial biomass into a preset degradation threshold judgment model to determine values of pixel points corresponding to a sample in a current remote sensing image, the method may further include:
acquiring a historical remote sensing image of a detection area, and determining a historical flat vegetation index and a historical aboveground biomass corresponding to the historical remote sensing image;
inputting the historical distance vegetation index and the biomass on the historical land into a degradation threshold judgment model before verification so as to determine the value of a pixel point corresponding to a sample party in a historical remote sensing image;
acquiring the degradation degree of a sample in a detection area;
and verifying the degradation threshold judgment model before verification according to the degradation degree of the sample and the value of the pixel point.
Therefore, the degradation threshold judgment model can be verified to improve the accuracy of determining the degradation degree of the phyllostachys praecox forest.
In a possible implementation manner, verifying a degradation threshold determination model before verification according to the degradation degree of a sample and the value of a pixel point includes:
determining the accuracy of a degradation threshold judgment model before verification according to the degradation degree of the sample and the values of the pixels;
and when the accuracy exceeds a preset threshold, determining that the degradation threshold judges that the model passes verification.
In this scheme, the sample space is to be arranged in the thunderbamboo growing region with excellent or poor growth vigor, and the area of the sample space is set to be 10m × 10m, so as to maintain the size consistency with the remote sensing image data. For the prototype, the following items of information need to be recorded:
a. numbering the sample parties; b. a base name; c. the number of vertical bamboo plants; d. the number of new bamboo plants; e. latitude and longitude; f. the degree of degradation.
And after the data of the sample is compiled, collected and arranged, the corresponding relation between the position of the sample and whether the sample is degraded is obtained. And (4) carrying out formula correctness verification by combining the degradation threshold judgment model, and trusting the degradation threshold judgment model when the accuracy is higher than 80%.
Referring to fig. 2, when the accuracy of determining the model according to the degradation threshold is higher than the preset threshold, it may be determined to trust the degradation threshold determination model, i.e., corresponding to the "accuracy determination" step in fig. 2.
The "bamboo deterioration grid" in fig. 2 may be a grid processing of the current remote sensing image. The degradation threshold decision model is then used to determine the phyllostachys praecox degradation index for each phyllostachys praecox patch, e.g., for a single phyllostachys praecox patch, the patch will include a plurality of pixels in the phyllostachys praecox degradation grid map, and the mean of the pixel values is calculated, which is the phyllostachys praecox degradation index (corresponding to the mean statistics in fig. 2).
According to the aforementioned references, the severe degradation ratio is 51.79%, and the total degradation ratio is 69.67%, so that for the phyllostachys edulis degradation index, 0 to 0.5179 is considered to be extremely poor in growth, 0.5179 to 0.6967 is considered to be poor in growth, and 0.6967 to 1 is considered to be good in growth. And (3) endowing each phyllostachys praecox pattern with color attributes according to the rules, and making a thematic map (refer to fig. 3) to finish the growth grading extraction of the phyllostachys praecox.
Fig. 4 is a block diagram illustrating one type of electronic device 20 according to an example embodiment. For example, the electronic device 20 may be provided as a server. Referring to fig. 4, the electronic device 20 includes a processor 22, which may be one or more in number, and a memory 23 for storing computer programs executable by the processor 22. The computer program stored in memory 23 may include one or more modules that each correspond to a set of instructions. Further, the processor 22 may be configured to execute the computer program to perform the above-described method of determining the degree of degradation of a phyllostachys praecox forest.
Additionally, the electronic device 20 may also include a power component 21 and a communication component 24, the power component 21 may be configured to perform power management of the electronic device 20, and the communication component 24 may be configured to enable communication, e.g., wired or wireless communication, of the electronic device 20. The electronic device 20 may further include an input/output (I/O) interface 25. The electronic device 20 may operate based on an operating system, such as Windows Server, stored in the memory 23 TM ，Mac OS X TM ，Unix TM ，Linux TM And so on.
In another exemplary embodiment, a computer readable storage medium including program instructions which, when executed by a processor, implement the steps of the above-described method for determining a degree of degradation of a phyllostachys praecox forest is also provided. For example, the non-transitory computer readable storage medium may be the memory 23 described above including program instructions executable by the processor 22 of the electronic device 20 to perform the method for determining the extent of degradation of a phyllostachys forest described above.
In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-described method of determining a degree of degradation of a phyllostachys forest when executed by the programmable apparatus.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means can be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (8)
1. A method for determining the degradation degree of a phyllostachys praecox based on an image is characterized by comprising the following steps:
acquiring a current remote sensing image of a target area, wherein the wave bands of the current remote sensing image at least comprise a near infrared wave band, a middle infrared wave band, a red light wave band and a blue light wave band;
determining a horizontal vegetation index and overground biomass of each sample in the target area according to the current remote sensing image;
inputting the range vegetation index and the aboveground biomass into a preset degradation threshold judgment model to determine the value of a pixel point corresponding to the sample in the current remote sensing image;
determining a phyllostachys praecox forest degradation index of each phyllostachys praecox pattern spot in the current remote sensing image according to the values of the pixel points in the current remote sensing image, wherein the phyllostachys praecox pattern spot comprises a plurality of the pixel points, and the phyllostachys praecox forest degradation index of the phyllostachys praecox pattern spot is determined according to the values of all the pixel points in the phyllostachys praecox pattern spot;
inputting the range vegetation index and the aboveground biomass into a preset degradation threshold judgment model to determine the value of a pixel point corresponding to the sample in the current remote sensing image, wherein the method comprises the following steps:
segmenting the current remote sensing image according to a digital image P value segmentation method, wherein the P value is the same as the phyllostachys praecox degradation ratio threshold;
if the aboveground biomass of the pixel point is determined to be smaller than the thunderbamboo degradation ratio threshold value after the division, and the distance vegetation index of the pixel point is determined to be smaller than the annual distance vegetation index threshold value, determining the value of the pixel point to be 0; otherwise, determining the value of the pixel point to be 1;
the degradation threshold determination model is as follows:
A<C AND B<D
wherein, A represents the distance vegetation index, B represents the aboveground biomass, C represents the annual distance vegetation index threshold value, and D represents the thunderbamboo degradation ratio threshold value.
2. The method of determining the degree of bamboo degradation of claim 1, further comprising:
determining the degradation grade of the phyllostachys praecox forest of the phyllostachys praecox map spots according to the phyllostachys praecox forest degradation index of the phyllostachys praecox map spots;
and according to the bamboo forest degradation grade, giving a color attribute to the bamboo pattern spots in the current remote sensing image.
3. The method of claim 1, wherein the talus vegetation index is calculated by the following formula:
A=(EVIi-EVIavg)/EVIavg （1）
EVI =2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1)) （2）
in the formula (1) and the formula (2), A is a true vegetation index, and NIR is a near infrared band; RED is the RED band; BLUE is BLUE light band; EVI is an enhanced vegetation index; EVIi is the current year enhanced vegetation index value; EVIavg is the mean value of the enhanced vegetation indexes in the last three years.
4. The method of determining the degree of bamboo degradation of claim 1, wherein the above-ground biomass is calculated by the following formula:
B = -34.58 - 105.16 * IIVI + 54.44 * EVI + 71.75 * TM437 （3）
EVI = 2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1)) （4）
IIVI = (NIR - SWIR1) / (NIR + SWIR1) （5）
TM437 = NIR * RED / SWIR2 （6）
wherein, in the formulas (3) to (6), B is aboveground biomass, and NIR is a near infrared band; RED is the RED band; BLUE is BLUE light band; the SWIR1 and the SWIR2 are intermediate infrared bands with different wavelengths; EVI is an enhanced vegetation index; IIVI is the near-infrared vegetation index; TM437 is the expression TM4 TM3/TM 7.
5. The method for determining the degradation degree of bamboo reeds according to any one of claims 1 to 4, wherein before inputting the talar vegetation index and the aboveground biomass into a preset degradation threshold judgment model to determine the value of a pixel point corresponding to the sample in the current remote sensing image, the method further comprises:
acquiring a historical remote sensing image of a detection area, and determining a historical flat vegetation index and a historical aboveground biomass corresponding to the historical remote sensing image;
inputting the historical distance vegetation index and the biomass on the historical ground into a degradation threshold judgment model before verification so as to determine the value of a pixel point corresponding to a sample in the historical remote sensing image;
acquiring the degradation degree of a sample of the detection area;
and verifying a degradation threshold judgment model before verification according to the degradation degree of the sample and the value of the pixel point.
6. The method for determining the degradation degree of bamboo as claimed in claim 5, wherein the verifying the degradation threshold determination model before verification according to the degradation degree of the sample and the value of the pixel point comprises:
determining the accuracy of a degradation threshold judgment model before verification according to the degradation degree of the sample and the value of the pixel point;
and when the accuracy exceeds a preset threshold, determining that the model passes verification by using a degradation threshold.
7. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
8. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to carry out the steps of the method of any one of claims 1 to 6.
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