CN114882374A - Method for predicting degradation degree of phyllostachys praecox forest and electronic device - Google Patents
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Abstract
The invention provides a method for predicting the degradation degree of a phyllostachys praecox forest based on physiological parameters of phyllostachys praecox and remote sensing data products and electronic equipment, wherein the method comprises the following steps: determining the degradation degree of the sample area according to the parameter information of the sample area; acquiring remote sensing information of a sample area according to longitude and latitude information of the sample area; training an initial phyllostachys pracecox forest degradation degree prediction model according to the degradation degrees of a plurality of sample areas and remote sensing information to obtain a phyllostachys pracecox forest degradation degree prediction model; and inputting the remote sensing information of the target area into the Redball degradation degree prediction model to obtain the predicted degradation degree of the target area output by the Redball degradation degree prediction model. Therefore, through the trained phyllostachys praecox forest degradation degree prediction model, the prediction degradation degrees of a plurality of target areas can be obtained, and then the macro analysis can be quickly formed on a large area range.
Description
Technical Field
The invention relates to the technical field of remote sensing, in particular to a phyllostachys praecox forest degradation degree prediction method based on phyllostachys praecox physiological parameters and remote sensing data products.
Background
The phyllostachys praecox has the characteristics of early bamboo shoot emergence, long bamboo shoot period, delicious bamboo shoot taste, high yield, good economic benefit and the like, and is one of main bamboo species planted in bamboo production areas for bamboo shoots in Zhejiang province. Since the 90 s of the 20 th century, the technology of covering bamboo shoots on woodland has been applied in a large scale in some phyllostachys praecox production areas, and the technology can produce out-of-season bamboo shoots and has good economic benefit. In the initial stage of covering cultivation, the yield of the phyllostachys pracecox shoots is obviously improved, and the underground penis and the upper side buds of the penis can keep good structural conditions. However, with the increase of the covering cultivation period, a large amount of covering materials which are difficult to rot and decompose are reserved in the forest land, and chemical fertilizers are excessively applied, so that the phyllostachys praecox forests in many phyllostachys praecox producing areas are degraded to different degrees, and the sustainable development of regional bamboo industry and the economic income of vast bamboo farmhouses are seriously influenced.
Forest land degeneration is one of the hot problems in current forestry and ecological research, and diagnosis and evaluation of the degeneration degree are the first problems to be solved in the process of researching the degeneration of the ecological system. At present, farmers mainly judge whether the bamboo forest is degraded according to the acre yield of the bamboo forest. According to survey, the undegraded yield of the covered bamboo shoots is 2500-4000 jin per mu, and the yield of the natural bamboo shoots can reach about 1500 jin; for degraded bamboo forests, the yield of the covered bamboo shoots is about 1000 jin per mu, and the yield of the natural bamboo shoots is 200-300 jin, even no yield. In the examination of the bamboo forest degradation in the scientific research institutions, the bamboo forest degradation condition is indirectly judged through information such as overground biomass, bamboo forest stand parameters, bamboo physiological parameters and the like besides the yield of bamboo shoots. The normally growing phyllostachys praecox forest usually has the characteristics of simple maintenance of new bamboos, density of about 1000 plants per mu of standing bamboos, reasonable forest stand structure, thick breast diameter, large quantity of bamboo rhizome and bud, high branch height, balanced soil nutrients and the like, and researchers often diagnose and evaluate the phyllostachys praecox forest land blocks in specific areas by utilizing the characteristics.
In the prior art, the most main evaluation method of the degeneration degree of the phyllostachys praecox forest is to directly judge whether the phyllostachys praecox forest is degenerated according to the bamboo shoot yield of the phyllostachys praecox land, and the judgment of the degeneration degree cannot be completed before entering the bamboo shoot producing period as a post judgment means.
In the prior art, forest stand parameters, physiological parameters and soil parameters are used for evaluating the degeneration degree of the phyllostachys praecox forest in scientific research institutions. Although the method is accurate, the cost is high, and the efficiency is low.
In the prior art, degradation evaluation is usually performed on a certain bamboo forest land block as a basic unit, evaluation targets are often distributed on a map in a point form, and macro analysis cannot be rapidly formed on a large area range.
Disclosure of Invention
The invention aims to at least solve the problem that macro analysis cannot be rapidly formed on a large area range in the prior art.
In view of the above, an object of the present invention is to provide a method for predicting the degradation degree of a phyllostachys praecox forest based on physiological parameters of phyllostachys praecox and remote sensing data products.
Another object of the present invention is to provide an electronic device.
The invention provides a method for predicting the degeneration degree of a phyllostachys praecox forest based on phyllostachys praecox physiological parameters and remote sensing data products, which comprises the following steps: determining the degradation degree of a sample area according to parameter information of the sample area, wherein the parameter information comprises the following sub-parameters: the planting age limit of the sample area, the phyllostachys praecox coverage age limit of the sample area, the bamboo shoot yield of the sample area, the number of new bamboos, the diameter of the breast of a new bamboo, the height of branches of a new bamboo, the height of joints of a new bamboo, the diameter of the breast of a old bamboo, the height of branches of a old bamboo and the height of joints of a old bamboo;
acquiring remote sensing information of the sample area according to the longitude and latitude information of the sample area, wherein the remote sensing information comprises multispectral data information and remote sensing data information;
training an initial phyllostachys pracecox forest degradation degree prediction model according to the degradation degrees of the plurality of sample areas and the remote sensing information to obtain a phyllostachys pracecox forest degradation degree prediction model;
inputting the remote sensing information of the target area into the bamboo forest degradation degree prediction model to obtain the predicted degradation degree of the target area output by the bamboo forest degradation degree prediction model.
Optionally, the determining the degradation degree of the sample area according to the parameter information of the sample area includes:
determining 5 sub-parameters with the highest accumulated contribution rate in the parameter information;
determining a degradation index of the sample area according to the 5 sub-parameters with the highest contribution rates;
and determining the degradation degree of the sample area according to the degradation index of the sample area.
Optionally, the remote sensing data information includes:
MOD15 based leaf area index, MOD16 based transpiration, MOD17 based net primary productivity.
Optionally, the wavelength range of the multispectral data information is 458-875 nm.
Optionally, the remote sensing information is determined by:
and synthesizing the remote sensing data information and the multispectral data information into a tif raster image by using a wave band synthesis function.
Optionally, the initial thunderforest degradation degree prediction model is a support vector machine model, and before the initial thunderforest degradation degree prediction model is trained according to the degradation degrees of the plurality of square areas and the remote sensing information to obtain the thunderforest degradation degree prediction model, the method further includes:
setting parameters of the initial Phyllostachys praecox forest degradation degree prediction model: setting a Kernel Type Parameter as a Radial Basis Function, setting a Gamma in Kernel Function Parameter as 0.1, setting a Penalty Parameter as 100, setting a Pyramid Levels Parameter as 0, and setting a Classification Prohability Threshold Parameter as 0.
Optionally, the degree of degradation comprises: no degeneration, mild degeneration, moderate degeneration and severe degeneration.
Optionally, the method further comprises:
and outputting the thematic image of the area to be evaluated according to the predicted degradation degree of the target area.
Optionally, the size of the sample area and the target area is 5m × 5 m.
A second aspect of the present invention provides an electronic device, comprising: a memory having a computer program stored thereon; a processor for executing the computer program in the memory to implement the steps of the method provided by the first aspect of the invention.
Through the technical scheme, the initial phyllostachys pracecox degradation degree prediction model can be trained according to the degradation degrees and the remote sensing information of a plurality of sample areas, so that the phyllostachys pracecox degradation degree prediction model is obtained. Subsequently, the remote sensing information of the target area can be input into the Redball degradation degree prediction model to obtain the predicted degradation degree of the target area output by the Redball degradation degree prediction model. Therefore, through the trained phyllostachys praecox forest degradation degree prediction model, the prediction degradation degrees of a plurality of target areas can be obtained, and then the macro analysis can be quickly formed on a large area range.
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 chart of a method for predicting the degradation degree of a Phyllostachys praecox forest based on Phyllostachys praecox physiological parameters and remote sensing data products according to an embodiment of the present invention;
FIG. 2 is a table of correspondence of degradation levels to degradation indices according to one embodiment of the invention;
FIG. 3 is a table of multispectral data according to an embodiment of the invention;
FIG. 4 is a thematic image of an area to be evaluated according to one embodiment of the present invention;
FIG. 5 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 and 2.
Referring to fig. 1, a first aspect of the present invention provides a method for predicting a degradation degree of a phyllostachys praecox forest based on physiological parameters of phyllostachys praecox and remote sensing data products, comprising:
step S11, determining the degradation degree of the sample area according to the parameter information of the sample area, wherein the parameter information includes the following sub-parameters: planting age limit of the sample area, phyllostachys praecox covering age limit of the sample area, bamboo shoot yield of the sample area, number of new bamboos, diameter of breast height of the new bamboos, height of joints of the new bamboos, diameter of breast height of old bamboos, height of branches of old bamboos and height of joints of old bamboos;
step S12, obtaining remote sensing information of the sample area according to the longitude and latitude information of the sample area, wherein the remote sensing information comprises multispectral data information and remote sensing data information;
step S13, training an initial Rebamboos degradation degree prediction model according to the degradation degrees of a plurality of sample areas and remote sensing information to obtain a Rebamboos degradation degree prediction model;
and step S14, inputting the remote sensing information of the target area into the Redball degradation degree prediction model to obtain the predicted degradation degree of the target area output by the Redball degradation degree prediction model.
Therefore, the initial thunderforest degradation degree prediction model can be trained according to the degradation degrees of a plurality of sample areas and the remote sensing information, and the thunderforest degradation degree prediction model is obtained. Subsequently, the remote sensing information of the target area can be input into the Redball degradation degree prediction model to obtain the predicted degradation degree of the target area output by the Redball degradation degree prediction model. Therefore, through the trained phyllostachys praecox forest degradation degree prediction model, the prediction degradation degrees of a plurality of target areas can be obtained, and then the macro analysis can be quickly formed on a large area range.
According to the technical scheme, the remote sensing satellite image before the bamboo shoot producing period can be used for analysis, and the judgment of the bamboo forest degradation degree is completed before the bamboo shoot producing period so as to carry out follow-up decision.
The technical scheme can realize the analysis of the degradation degree of the planar area and is different from the traditional method of performing the evaluation of the degradation of the phyllostachys praecox in the form of isolated points due to the characteristics of high spatial resolution, time resolution and large-range coverage of the remote sensing image.
Illustratively, the degree of degradation includes: no degeneration, mild degeneration, moderate degeneration and severe degeneration.
For example, 10 pieces of bamboo in the sample area are selected to respectively count the following information: a. the age of bamboo; b. breast diameter; c. the branch is high; d. the bamboo joint is high. After statistics is completed, classifying the new bamboo and the old bamboo, and calculating the mean value of the following parameters in a sampling party: the diameter of the new bamboo breast, the height of the new bamboo branch, the height of the new bamboo joint, the diameter of the old bamboo breast, the height of the old bamboo branch and the height of the old bamboo joint.
Illustratively, determining the degradation degree of the sample area according to the parameter information of the sample area includes: determining 5 sub-parameters with the highest accumulated contribution rate in the parameter information; determining a degradation index of a sample area according to the 5 sub-parameters with the highest contribution rate; and determining the degradation degree of the sample area according to the degradation index of the sample area.
In this embodiment, since the sub-parameters are normalized and the principal component analysis is performed, the cumulative contribution rate of 5 principal components (i.e., the main principal parameters) exceeds 80%, the first 5 principal components are taken to replace the original plurality of sub-parameters and are denoted as F1 to F5. The degradation index is marked as A, and the relationship between A and F1-F5 is established. The degree of degradation is classified into 4 levels according to the degradation index definition label: non-degeneration, mild degeneration, moderate degeneration, and severe degeneration (see fig. 2).
Illustratively, the remotely sensed data information includes: MOD15 based leaf area index, MOD16 based transpiration, MOD17 based net primary productivity.
For example, the remote sensing data product is the data product of MODIS, where MOD15 land level 3 product (leaf area index), MOD16 land level 4 product (transpiration), MOD17 land level 4 product (net primary productivity) will be selected. The same multispectral data source is obtained from the geographic monitoring cloud platform Google Earth Engine.
Illustratively, the wavelength range of the multispectral data information is 458-875 nm.
For example, multispectral data is from Sentinel-2 Sentinel images, is a high-resolution multispectral imaging satellite, has a height of 786 kilometers, carries a multispectral imager for land monitoring, and can provide images of vegetation, soil and water coverage, inland waterways, 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 comes from a geographic monitoring cloud platform Google Earth Engine, 2A data which is subjected to radiation correction is selected, and B2, B3, B4, B5, B6, B7 and B8 wave bands are selected as part of sample remote sensing information.
Illustratively, the remote sensing information is determined by: and synthesizing the remote sensing data information and the multispectral data information into a tif raster image by using a wave band synthesis function.
Exemplarily, the initial thunderbolt forest degradation degree prediction model is a support vector machine model, and before the initial thunderbolt forest degradation degree prediction model is trained according to the degradation degrees of a plurality of sample areas and remote sensing information to obtain the thunderbolt forest degradation degree prediction model, the method further includes: setting parameters of an initial Rebamboos forest degradation degree prediction model: setting a Kernel Type Parameter as a Radial Basis Function, setting a Gamma in Kernel Function Parameter as 0.1, setting a Penalty Parameter as 100, setting a Pyramid Levels Parameter as 0, and setting a Classification Prohability Threshold Parameter as 0.
The support vector machine classification is a machine learning method based on a statistical learning theory. Compared with the traditional statistics, the statistical learning theory is a theory for specially researching the learning rule of the small samples. This step will be performed here in the supervised classification tool of ENVI.
Firstly, importing a finished label into an ROI, selecting a Classification- > Supervised- > Support Vector Machine tool from a main menu, and selecting an image to be used for training. Parameters of an initial phyllostachys pracecox forest degradation degree prediction model can be set in the ENVI; and after parameter setting is finished, selecting an output path and a file name, and executing.
It should be noted that in ENVI, generating the trainer and using the trainer for prediction are two links of the same step.
Illustratively, the specification of the sample area and the target area is 5m × 5 m. Therefore, the pixel position corresponding to the remote sensing image can be found on the remote sensing image; and analyzing the relation between the reflectivity, the vegetation index equivalence and the degradation degree of the pixel point position, and manufacturing a classifier (namely training an initial phyllostachys pracecox forest degradation degree prediction model to obtain the phyllostachys pracecox forest degradation degree prediction model).
In this way, the scheme firstly selects parameters for judging the bamboo forest degradation, and a statistical approach is used for judging the bamboo forest degradation degree of a specific area; marking the longitude and latitude of the bamboo forest plots with different degradation degrees, and finding out the pixel position corresponding to the bamboo forest plots on the remote sensing image; analyzing the relation between the reflectivity, the vegetation index equivalence and the degradation degree of the pixel point position, and manufacturing a classifier; and finally, judging the degeneration degree of the phyllostachys praecox in the whole area by using the classifier.
Illustratively, the method further comprises: and outputting the thematic image of the area to be evaluated according to the predicted degradation degree of the target area. (see, for example, figure 4).
Fig. 5 is a block diagram illustrating an electronic device 700 according to an example embodiment. As shown in fig. 5, the electronic device 700 may include: a processor 701 and a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an input/output (I/O) interface 704, and a communication component 705.
The processor 701 is configured to control the overall operation of the electronic device 700, so as to complete all or part of the steps of the method for predicting the degradation degree of a phyllostachys praecox forest based on the physiological parameters of the phyllostachys praecox and the remote sensing data product. The memory 702 is used to store various types of data to support operation at the electronic device 700, such as instructions for any application or method operating on the electronic device 700 and application-related data, such as contact data, transmitted and received messages, pictures, audio, video, and so forth. The Memory 702 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia components 703 may include screen and audio components. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 702 or transmitted through the communication component 705. The audio assembly further comprises at least one speaker for outputting audio signals. The I/O interface 704 provides an interface between the processor 701 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless Communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, which is not limited herein. The corresponding communication component 705 may thus include: Wi-Fi module, Bluetooth module, NFC module, etc.
In an exemplary embodiment, the electronic Device 700 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components, for performing the above-described method for predicting the degree of degradation of the bamboo based on physiological parameters of the bamboo and remote sensing data products.
In another exemplary embodiment, a computer readable storage medium comprising program instructions for implementing the steps of the method for predicting the degradation degree of a phyllostachys praecox forest based on physiological parameters of phyllostachys praecox and a remote sensing data product is provided. For example, the computer readable storage medium may be the memory 702 comprising program instructions executable by the processor 701 of the electronic device 700 to perform the method for predicting the degradation degree of a phyllostachys praecox forest based on physiological parameters of phyllostachys praecox and remote sensing data products.
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 may 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 (10)
1. A method for predicting the degradation degree of a phyllostachys praecox forest based on physiological parameters of phyllostachys praecox and remote sensing data products is characterized by comprising the following steps:
determining the degradation degree of a sample area according to parameter information of the sample area, wherein the parameter information comprises the following sub-parameters: the planting age limit of the sample area, the phyllostachys praecox coverage age limit of the sample area, the bamboo shoot yield of the sample area, the number of new bamboos, the diameter of the breast of a new bamboo, the height of branches of a new bamboo, the height of joints of a new bamboo, the diameter of the breast of a old bamboo, the height of branches of a old bamboo and the height of joints of a old bamboo;
acquiring remote sensing information of the sample area according to the longitude and latitude information of the sample area, wherein the remote sensing information comprises multispectral data information and remote sensing data information;
training an initial phyllostachys pracecox forest degradation degree prediction model according to the degradation degrees of the plurality of sample areas and the remote sensing information to obtain a phyllostachys pracecox forest degradation degree prediction model;
inputting the remote sensing information of the target area into the bamboo forest degradation degree prediction model to obtain the predicted degradation degree of the target area output by the bamboo forest degradation degree prediction model.
2. The method according to claim 1, wherein the determining the degradation degree of the sample area according to the parameter information of the sample area comprises:
determining 5 sub-parameters with the highest accumulated contribution rate in the parameter information;
determining a degradation index of the sample area according to the 5 sub-parameters with the highest contribution rates;
and determining the degradation degree of the sample area according to the degradation index of the sample area.
3. The method of claim 1, wherein the remotely sensed data information comprises:
MOD15 based leaf area index, MOD16 based transpiration, MOD17 based net primary productivity.
4. The method as claimed in claim 3, wherein the wavelength range of the multispectral data information is 458-875 nm.
5. The method of claim 4, wherein the remote sensing information is determined by:
and synthesizing the remote sensing data information and the multispectral data information into a tif raster image by using a wave band synthesis function.
6. The method according to any one of claims 1 to 5, wherein the initial Reo bamboo forest degradation degree prediction model is a support vector machine model, and before the training of the initial Reo bamboo forest degradation degree prediction model according to the degradation degrees of the plurality of the sample areas and the remote sensing information to obtain the Reo bamboo forest degradation degree prediction model, the method further comprises:
setting parameters of the initial Phyllostachys praecox forest degradation degree prediction model: setting a Kernel Type Parameter as a Radial Basis Function, setting a Gamma in Kernel Function Parameter as 0.1, setting a Penalty Parameter as 100, setting a Pyramid Levels Parameter as 0, and setting a Classification Prohability Threshold Parameter as 0.
7. The method of any one of claims 1 to 5, wherein the degree of degradation comprises: no degeneration, mild degeneration, moderate degeneration and severe degeneration.
8. The method according to any one of claims 1 to 5, further comprising:
and outputting the thematic image of the area to be evaluated according to the predicted degradation degree of the target area.
9. The method according to any one of claims 1 to 5,
the specification of the sample area and the target area is 5m × 5 m.
10. 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 8.
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| CN115294460B (en) * | 2022-10-08 | 2023-01-17 | 杭州领见数字农业科技有限公司 | Method for determining degradation degree of phyllostachys praecox forest, medium and electronic device |
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