CN115311628A - Forest canopy chlorophyll content inversion and dynamic monitoring method - Google Patents
Forest canopy chlorophyll content inversion and dynamic monitoring method Download PDFInfo
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Abstract
The invention discloses a forest canopy chlorophyll content inversion and dynamic monitoring method, which is characterized in that a copula algorithm is applied to first data to generate a leaf character parameter sample; applying the sample to a coupling model of PROSPECT-5B +4SAIL, PROSPECT-5B + GeoSAIL and LIBERTY + GeoSAIL to simulate the spectral reflectivity of the canopy of broad-leaved forest and coniferous forest; simulating the spectral reflectivity of the canopy of the mixed forest by utilizing a linear spectral mixing method of the pin forest and the broad-leaved forest; establishing a forest canopy chlorophyll content inversion model by adopting a machine algorithm; migrating the model to a Google Earth engine cloud computing platform, and according to the third data and the fourth data, applying the model to invert the content of the chlorophyll in the canopy of the forest so as to realize inversion and dynamic monitoring of the content of the chlorophyll in the canopy of the forest; and according to the second data, estimating the actual accuracy of the inversion model by combining the inversion result. The invention accurately simulates the edge distribution of the leaf traits and the dependency relationship between the edge distribution and the edge distribution according to the coniferous leaves and the broad leaves, accurately simulates the canopy spectral characteristics of different forest types, and realizes the accurate inversion and dynamic monitoring of the chlorophyll content of the forest canopy.
Description
Technical Field
The invention relates to a quantitative remote sensing inversion and dynamic monitoring technology of vegetation physiological parameters, in particular to an inversion and dynamic monitoring method of forest canopy chlorophyll content.
Background
Chlorophyll in plant leaves can convert CO2 into monosaccharides through photosynthesis, convert absorbed solar radiation into stored chemical energy, and promote mass exchange and energy flux between biospheres and the atmosphere, which is central to carbon, water and energy exchange and terrestrial ecosystem function. Forests are an important component of the terrestrial ecosystem, possessing powerful carbon sequestration capacity, playing a critical role in land-gas matter and energy exchange. Therefore, under the background of climate change, the inversion and dynamic monitoring of the forest canopy chlorophyll content are helpful for mastering forest conditions and predicting carbon dynamics, and certain data support is provided for 'carbon peak reaching' and 'carbon neutralization'.
Currently, for inversion of chlorophyll content of vegetation canopy, an empirical parameter setting method and a PROSPECT-5B +4SAIL leaf-canopy coupling model are generally adopted, and the following problems mainly exist: (1) Coniferous and broadleaf models are not adopted to simulate respectively; (2) Aiming at parameter setting in the coupling model, the distribution of blade properties and the dependency relationship between the blade properties cannot be accurately described; (3) The heterogeneous and discontinuous characteristics of the forest canopy cannot be accurately simulated; (4) In addition, a simple and efficient monitoring method is lacked for dynamic change of chlorophyll content of forest canopy.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a brand-new forest canopy chlorophyll inversion and dynamic monitoring method, so that accurate inversion and dynamic change of forest canopy chlorophyll can be conveniently and efficiently monitored.
The invention is realized by the following technical scheme:
the forest canopy chlorophyll content inversion and dynamic monitoring method comprises the following steps:
s1: acquiring leaf character data as first data, wherein the leaf character data comprise chlorophyll content, leaf dry matter content, leaf equivalent water thickness, leaf carotenoid content, leaf lignin content and leaf nitrogen content;
acquiring chlorophyll content data of a vegetation canopy of the sample plot as second data;
acquiring a remote sensing image as third data;
acquiring forest type data as fourth data;
s2: according to the first data, multivariate distribution among related leaf traits is respectively described by adopting a copula method, and random sampling is carried out to generate a large number of samples;
s3: according to the generated leaf character sample, simulating the spectral reflectivity of broadleaf forest by using a PROSPECT-5B +4SAIL (forest vegetation) and PROSPECT-5B + GeoSAIL (forest canopy) coupling model, simulating the spectral reflectivity of conifer forest by using a PROSPECT-5B +4SAIL (forest vegetation) and LIBERTY + GeoSAIL (forest canopy) coupling model, and simulating the spectral reflectivity of mixed forest by using a method of linearly mixing the spectral reflectivities of broadleaf forest and conifer forest;
s4: establishing a regression model of the chlorophyll content of the forest canopy by adopting a machine algorithm according to the leaf area index, the chlorophyll content of the leaves and the simulated spectral reflectivity corresponding to the chlorophyll content of the leaves, which are set in the coupling model;
s5: applying the established regression model to the third data and the fourth data to perform forest canopy chlorophyll content inversion;
s6: evaluating the precision of the forest canopy chlorophyll content inversion model according to the second data and observation point canopy chlorophyll content data obtained by inversion;
s7: and applying the forest canopy chlorophyll content inversion model to a Google earth engine cloud computing platform to dynamically monitor the canopy chlorophyll content.
In the existing forest canopy chlorophyll inversion and dynamic monitoring technology, a plurality of related documents disclose the technology, but the inventor finds that the following defects still exist in the prior art: (1) Coniferous leaves and broadleaf leaves are not simulated by adopting a model with a leaf scale respectively; (2) Aiming at parameter setting in the coupling model, the distribution of blade properties and the dependency relationship between the blade properties cannot be accurately described; (3) The heterogeneous and discontinuous characteristics of the forest canopy cannot be accurately simulated; (4) Aiming at the dynamic change of the chlorophyll content of the forest canopy, a simple, convenient and efficient monitoring method is lacked.
Aiming at the defects in the current forest canopy chlorophyll inversion and dynamic monitoring technology, the applicant innovatively provides a brand-new forest canopy chlorophyll inversion and dynamic monitoring method. In the application, the preset area is generally an area of interest which needs forest canopy chlorophyll inversion and dynamic monitoring, and a representative sample plot is arranged in the field to carry out field investigation to obtain first data and second data. In addition, the first data also needs to be combined with specific operations such as laboratory weighing, scanning, drying, chemical analysis and the like. And the third data is obtained by imaging the preset area by adopting a remote sensing technology. Fourth data is obtained by collecting existing forest type data or performing supervised or unsupervised classification on the third data.
Aiming at different characteristics of coniferous leaves and broadleaf leaves, PROSPECT-5B and LIBERTY models are respectively adopted to accurately simulate the radiation transmission process of the leaf scale. Aiming at the problem that the distribution of the leaf traits and the dependency relationship between the leaf traits are lack of accurate description, the copula functions are creatively established according to different forest vegetation types, so that the accurate expression of the distribution rule of the leaf traits and the dependency relationship between the leaf traits is realized. In addition, the built copula function can also generate pseudo observation samples containing similar distribution rules and dependency relationships in batch for application to the PROSPECT-5B and LIBERTY models.
The inventor creatively proposes that a PROSPECT-5B +4SAIL (vegetation under the forest) and a PROSPECT-5B + GeoSAIL (forest canopy) coupling model are respectively adopted to simulate the spectral reflectivity of broadleaf forests, a PROSPECT-5B +4SAIL (vegetation under the forest) and LIBERTY + GeoSAIL (forest canopy) coupling model is adopted to simulate the spectral reflectivity of coniferous forests, and a linear spectrum mixing method is adopted to simulate the spectral reflectivity of mixed forests, so that the accurate description of the special layered structure, heterogeneity and discontinuity of the forest canopy is realized.
In addition, the inventor also selects the wave band reflectivity, the vegetation index and the vegetation type as characteristic variables, establishes a forest canopy chlorophyll content inversion model by using a machine algorithm, and evaluates the theoretical precision of the inversion model by adopting cross validation. Meanwhile, the inversion model of the chlorophyll content of the forest canopy is transferred to a Google Earth engine cloud computing platform, and a preset area, time and a target image are input, so that inversion and dynamic monitoring of the chlorophyll content of the forest canopy are achieved.
Further, the step S1 includes the following substeps:
s11: establishing a representative sample plot in the field, and carrying out sample plot investigation;
s12: carrying out laboratory treatment and analysis on samples collected in field investigation, and collating data to generate first data;
s13: calculating the first data by combining the area index of the sample plot leaves measured in the field to generate second data;
s14: acquiring a remote sensing image meeting the requirement of a research target in a preset area as third data;
s15: collecting the existing forest type data of the preset area, or classifying the third data by adopting a supervised or unsupervised classification method to obtain fourth data.
Further, the step S2 includes the following substeps:
classifying parameters of chlorophyll content, dry matter content of leaves, leaf equivalent water thickness, carotenoid content of leaves, lignin content of leaves and nitrogen content of leaves into forest types, and describing univariate distribution and dependency among variables by copula;
and randomly sampling based on the constructed copula function of the leaf characters to generate a large number of pseudo-observation samples.
Further, the step S3 includes the following substeps:
for broad-leaved forest, simulating the spectral reflectance of the broad-leaved forest by using a PROSPECT-5B +4SAIL and PROSPECT-5B + GeoSAIL coupling model according to the generated broad-leaved leaf character sample;
for coniferous forest, according to the generated coniferous leaf character sample, simulating the spectral reflectivity of the coniferous forest by using a PROSPECT-5B +4SAIL and LIBERTY + GeoSAIL coupling model;
for the mixed forest, the spectral reflectivity is simulated by adopting a method of linearly mixing the spectral reflectivities of the broad-leaved forest and the coniferous forest.
Further, the step S4 includes the following substeps:
selecting characteristic variables according to the forest type, the wave band reflectivity and the vegetation index obtained by calculating the same, and performing regression modeling on the chlorophyll content of the forest canopy by using a machine algorithm;
and performing regression analysis on the predicted value and the observed value of the final regression model by adopting a ten-fold cross validation method so as to evaluate the theoretical precision of the regression model.
Further, the step S6 includes the following sub-steps:
extracting a corresponding inversion value of the content of the chlorophyll in the canopy according to the coordinates and time of the observation points in the second data;
and carrying out regression analysis on the inversion value and the observed value of the chlorophyll content of the canopy, and evaluating the accuracy of the forest canopy chlorophyll content inversion model.
Further, the step S7 includes the following substeps:
implementing a forest canopy chlorophyll content inversion model in a Google earth engine cloud computing platform;
the dynamic monitoring is realized by defining a preset area, setting time, calling remote sensing images and forest type data, applying an inversion model to generate the space-time distribution of the chlorophyll content of the forest canopy, and further analyzing the dynamic change of the result.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. aiming at different characteristics of coniferous leaves and broadleaf leaves, the invention respectively adopts PROSPECT-5B and LIBERTY models to accurately simulate the radiation transmission process of the leaf scale; aiming at the problem that the distribution of the leaf traits and the dependency relationship between the leaf traits are lack of accurate description, the method creatively provides that copula functions are respectively established according to different forest vegetation types, so that the accurate expression of the distribution rules of the leaf traits and the dependency relationship between the leaf traits is realized, and the established copula functions can also generate pseudo observation samples containing similar distribution rules and dependency relationship in batches so as to be applied to PROSPECT-5B and LIBERTY models;
2. the invention creatively provides that the spectral reflectivity of broad-leaved forests is simulated by respectively adopting PROSPECT-5B +4SAIL (under-forest vegetation) and PROSPECT-5B + GeoSAIL (forest canopy), the spectral reflectivity of coniferous forests is simulated by adopting PROSPECT-5B +4SAIL (under-forest vegetation) and LIBERTY + GeoSAIL (forest canopy), and the spectral reflectivity of mixed forests is simulated by adopting a linear spectrum mixing method, so that the accurate description of the specific layered structure, heterogeneity and discontinuity of forest canopy is realized;
3. the invention selects the wave band reflectivity, the vegetation index and the vegetation type as characteristic variables, establishes a forest canopy chlorophyll content inversion model by using a machine algorithm, evaluates the theoretical precision of the inversion model by adopting ten-fold cross validation, migrates the forest canopy chlorophyll content inversion model to a Google earth engine cloud computing platform, inputs a preset area, time and a target image, and realizes inversion and dynamic monitoring of the forest canopy chlorophyll content.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
FIG. 1 is a schematic diagram of the process steps of the present invention;
FIG. 2 is a graph of the distribution and dependency relationship of herbaceous plant leaf trait samples based on Gaussian copula simulation in example 2 of the present invention; wherein, the (a), (b), (c), (d), (e) and (f) are respectively the description of sample distribution and dependence relationship of the dry matter content and the water content of the leaves of the herbaceous plant, the dry matter content and the chlorophyll content, the dry matter content and the carotenoid content, the water content and the chlorophyll content, the water content and the carotenoid content;
FIG. 3 is a graph of sample distribution and dependency relationship of leaf traits of broad-leaved trees based on Gaussian copula simulation in example 2 of the present invention; wherein, the (a), (b), (c), (d), (e) and (f) are respectively the description of sample distribution and dependence relationship of the dry matter content and the water content of the leaves of the broad leaf tree species, the dry matter content and the chlorophyll content, the dry matter content and the carotenoid content, the water content and the chlorophyll content, the water content and the carotenoid content;
FIG. 4 is a graph of distribution and dependency relationship of leaf trait samples of conifer species based on Gaussian copula simulation in embodiment 2 of the present invention; wherein, (a), (b), (c), (d), (e) and (f) are respectively descriptions of sample distribution and dependence of water content and chlorophyll content, water content and lignin content, water content and nitrogen content, chlorophyll content and lignin content, chlorophyll content and nitrogen content and lignin content and nitrogen content of conifer seed leaves;
FIG. 5 is a theoretical accuracy diagram of an inversion model of forest canopy chlorophyll content in example 2 of the present invention;
FIG. 6 is a graph of inversion accuracy evaluation of forest canopy chlorophyll quantity in example 2 of the present invention;
fig. 7 is a diagram of inversion and dynamic monitoring of water content of forest canopy in embodiment 2 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Example 1
As shown in fig. 1, the present invention comprises the steps of:
s1: acquiring leaf character data as first data, wherein the leaf character data comprise chlorophyll content, leaf dry matter content, leaf equivalent water thickness, leaf carotenoid content, leaf lignin content and leaf nitrogen content;
acquiring chlorophyll content data of a vegetation canopy of the sample plot as second data;
acquiring a remote sensing image as third data;
acquiring forest type data as fourth data;
s2: according to the first data, multivariate distribution among related leaf traits is respectively described by adopting a copula method, and random sampling is carried out to generate a large number of samples;
s3: according to the generated leaf character sample, simulating the spectral reflectivity of broadleaf forest by using a PROSPECT-5B +4SAIL (forest vegetation) and PROSPECT-5B + GeoSAIL (forest canopy) coupling model, simulating the spectral reflectivity of conifer forest by using a PROSPECT-5B +4SAIL (forest vegetation) and LIBERTY + GeoSAIL (forest canopy) coupling model, and simulating the spectral reflectivity of mixed forest by using a method of linearly mixing the spectral reflectivities of broadleaf forest and conifer forest;
s4: establishing a regression model of the chlorophyll content of the forest canopy by adopting a machine algorithm according to the leaf area index, the chlorophyll content of the leaves and the simulated spectral reflectivity corresponding to the chlorophyll content of the leaves, which are set in the coupling model;
s5: applying the established regression model to the third data and the fourth data to perform forest canopy chlorophyll content inversion;
s6: evaluating the precision of the forest canopy chlorophyll content inversion model according to the second data and observation point canopy chlorophyll content data obtained by inversion;
s7: and applying the forest canopy chlorophyll content inversion model to a Google earth engine cloud computing platform to dynamically monitor the canopy chlorophyll content.
When the embodiment is implemented, the applicant innovatively provides a novel forest chlorophyll content inversion and dynamic monitoring method. In this embodiment, the preset area is generally an area of interest that needs forest canopy chlorophyll inversion and dynamic monitoring, and the first data and the second data are obtained by setting a representative sample plot in the field and performing field investigation. In addition, the first data also needs to be combined with specific operations such as laboratory weighing, scanning, drying, chemical analysis and the like. And the third data is obtained by imaging the preset area by adopting a remote sensing technology. Fourth data is obtained by collecting existing forest type data or performing supervised or unsupervised classification on the third data.
Aiming at different characteristics of coniferous leaves and broadleaf leaves, PROSPECT-5B and LIBERTY models are respectively adopted to accurately simulate the radiation transmission process of the leaf scale. Aiming at the problem that the distribution of the leaf traits and the dependency relationship between the leaf traits are lack of accurate description, the copula functions are creatively established according to different forest vegetation types, so that the accurate expression of the distribution rule of the leaf traits and the dependency relationship between the leaf traits is realized. In addition, the built copula function can also generate pseudo observation samples containing similar distribution rules and dependency relationships in batch for application to the PROSPECT-5B and LIBERTY models.
The inventor creatively proposes that a PROSPECT-5B +4SAIL (vegetation under the forest) and a PROSPECT-5B + GeoSAIL (forest canopy) coupling model are respectively adopted to simulate the spectral reflectivity of broadleaf forests, a PROSPECT-5B +4SAIL (vegetation under the forest) and LIBERTY + GeoSAIL (forest canopy) coupling model is adopted to simulate the spectral reflectivity of coniferous forests, and a linear spectrum mixing method is adopted to simulate the spectral reflectivity of mixed forests, so that the accurate description of the special layered structure, heterogeneity and discontinuity of the forest canopy is realized.
In addition, the inventor also selects the wave band reflectivity, the vegetation index and the vegetation type as characteristic variables, establishes a forest canopy chlorophyll content inversion model by using a machine algorithm, and evaluates the theoretical precision of the inversion model by adopting ten-fold cross validation. Meanwhile, the inversion model of the chlorophyll content of the forest canopy is transferred to a Google Earth engine cloud computing platform, and a preset area, time and a target image are input, so that inversion and dynamic monitoring of the chlorophyll content of the forest canopy are achieved.
By setting the steps, the invention fully describes the property distribution rule and the dependency relationship of the coniferous and broadleaf, more accurately simulates the radiation transmission process of coniferous forest, broadleaf forest and mixed forest from the leaf to the canopy scale, and realizes the rapid and efficient dynamic change monitoring of the chlorophyll content of the canopy of the forest.
In this embodiment, the step S1 includes the following sub-steps:
s11: establishing a square sample plot in the field, and carrying out sample plot investigation;
s12: carrying out laboratory treatment and analysis on a tree leaf sample collected in field investigation, and collating data to generate first data;
s13: calculating the first data by combining the area index of the sample plot leaves measured in the field to generate second data;
s14: obtaining an MODIS remote sensing image as third data for a preset area;
s15: and collecting the existing preset region MODIS classification data to obtain fourth data.
Although the present embodiment discloses obtaining the MODIS image as the third data and the MODIS classification data as the fourth data, the Landsat-8, landsat-9, sentinel-2 images and other shared classification products or existing forest data that can achieve the same function should be considered as equivalent to the present embodiment.
In the present embodiment, step S2 includes the following substeps:
s21: dividing parameters of chlorophyll content, leaf dry matter content, leaf equivalent water thickness, leaf carotenoid content, leaf lignin content and leaf nitrogen content into forest types, and describing the edge distribution of each univariate and the dependency relationship among variables by adopting Gauss copula;
s22: and randomly sampling based on the constructed leaf character Gaussian copula function to generate a large number of observation samples.
In the implementation of the present embodiment, although gaussian copula is disclosed to describe the distribution and coupling relationship of the leaf trait variables, the multivariate copula such as Vine copula similar to this should be considered as equivalent to the present embodiment.
As a specific embodiment, step S3 comprises the following sub-steps:
s31: for broad-leaved forest, according to the generated broad-leaved leaf trait sample, simulating the spectral reflectivity of the broad-leaved forest by using a PROSPECT-5B +4SAIL and PROSPECT-5B + GeoSAIL coupling model;
s32: for coniferous forest, according to the generated coniferous forest characteristic sample, simulating the spectral reflectivity of coniferous forest by using PROSPECT-5B +4SAIL and LIBERTY + GeoSAIL coupling model;
s33: for the mixed forest, the spectral reflectivity is simulated by adopting a method of linearly mixing the spectral reflectivities of the broad-leaved forest and the coniferous forest.
In the present embodiment, step S4 includes the following substeps:
s41: selecting characteristic variables according to the forest type, the waveband reflectivity and the vegetation index obtained by calculation, and performing regression modeling on the chlorophyll content of the forest canopy by using a random forest regression algorithm;
s42: and performing regression analysis on the predicted value and the observed value of the final regression model by adopting a ten-fold cross validation method so as to evaluate the theoretical precision of the regression model.
In the implementation of the present embodiment, although the random forest regression algorithm is disclosed to perform the regression modeling of the chlorophyll content in the forest canopy, other machine algorithms capable of achieving similar purposes should be considered as equivalent to the present embodiment.
In the present embodiment, step S6 includes the following sub-steps:
s61: extracting a corresponding inversion value of the content of the chlorophyll in the canopy according to the coordinates and time of the observation points in the second data;
s62: and performing regression analysis on the inversion value and the observed value of the canopy chlorophyll content, and evaluating the accuracy of the forest canopy chlorophyll content inversion model.
In one embodiment, step S7 comprises the following sub-steps:
s71: implementing a forest canopy chlorophyll content inversion model in a Google earth engine cloud computing platform;
s72: the dynamic monitoring is realized by defining a preset area, setting time, calling remote sensing images and forest type data, applying an inversion model to generate the space-time distribution of the chlorophyll content of the forest canopy, and further analyzing the dynamic change of the result.
When the method is implemented, inversion and dynamic monitoring of the chlorophyll content in the canopy of the forest are realized by means of the characteristics of rich remote sensing data of a Google earth engine cloud computing platform and forest classification products, high computing speed, complete analysis function, visual and convenient achievement and the like.
Example 2
On the basis of the above embodiment, the embodiment discloses a more specific implementation manner: the method is characterized by selecting a north American land vegetation coverage area as a preset area, and more effectively testing the feasibility of the method according to plant leaf character observation data and forest canopy chlorophyll amount actual measurement data based on a Google Earth engine cloud computing platform.
Step 1: the method comprises the steps of generating first data through pretreatment by using herbaceous plant and tree leaf character data acquired by an ecological observation network (NEON) of LOPEX1993 and ANGERS2003 leaf optical database acquired through field investigation; multiplying the area index (LAI) of the forest sample plot and the chlorophyll content (EWT) measured by NEON to obtain sample plot canopy chlorophyll content data which is second data; taking an MODIS waveband reflectivity product (MCD 43A 4) as a third data set; taking an MODIS land cover type product (MCD 12Q 1) as a fourth data set;
step 2: for the first data, dividing herbage, conifer species and broad-leaved tree species into three types, respectively realizing Gaussian copula by using an opentruns library in Python, simulating the distribution and coupling relation of four variables of dry matter, water, chlorophyll and carotenoid contents of herbage and conifer leaves, as shown in figures 2 to 4, and respectively generating 5000 random samples;
wherein, in fig. 2, (a), (b), (c), (d), (e) and (f) are respectively descriptions of sample distributions and dependence relationships of the dry matter content and water content, the dry matter content and chlorophyll content, the dry matter content and carotenoid content, the water content and chlorophyll content, the water content and carotenoid content, and the chlorophyll content and carotenoid content of the herb leaves; in fig. 3, (a), (b), (c), (d), (e), (f) are descriptions of sample distributions and dependence relationships of the dry matter content and water content, the dry matter content and chlorophyll content, the dry matter content and carotenoid content, the water content and chlorophyll content, the water content and carotenoid content, and the chlorophyll content and carotenoid content of the leaves of the broad-leaved tree species, respectively; in fig. 4, (a), (b), (c), (d), (e), and (f) are descriptions of sample distributions and dependence relationships of water content and chlorophyll content, water content and lignin content, water content and nitrogen content, chlorophyll content and lignin content, chlorophyll content and nitrogen content, lignin content and nitrogen content, and lignin content and nitrogen content, respectively, of conifer species leaves;
and step 3: in MATLAB, bringing a herbaceous random sample into a PROSPECT-5B +4SAIL coupling model to simulate the spectrum of vegetation under forest;
and 4, step 4: in MATLAB, combining the generated under-forest vegetation spectrum, bringing a broad-leaf random sample into a PROSPECT-5B + GeoSAIL coupling model, and simulating a broad-leaf canopy spectrum;
and 5: combining the generated under-forest vegetation spectrum in MATLAB, bringing the needle leaf random sample into a PROSPECT-5B + GeoSAIL coupling model, and simulating the spectrum of the needle leaf forest canopy;
step 6: in MATLAB, the spectra of canopy of coniferous forest and broadleaf forest are mixed linearly to simulate the spectrum of canopy of mixed forest;
and 7: in MATLAB, preprocessing the simulated spectrum by an AMRTO tool such as resampling and the like to obtain a wave band consistent with MODIS;
and 8: in the statistical analysis and drawing software R, by taking the wave band reflectivity and the vegetation type as characteristic variables, establishing a regression model of the chlorophyll content of the forest canopy by using a random forest algorithm, and evaluating the theoretical precision of the model by adopting a ten-fold cross validation method, as shown in FIG. 5;
and step 9: calling third data and fourth data in a Google Earth engine, and utilizing a random forest regression model to perform inversion to obtain the chlorophyll content of the canopy of the North American forest;
step 10: and extracting the forest canopy chlorophyll content inversion value corresponding to the space-time in a Google Earth engine according to the second data, and then performing linear regression analysis in statistical analysis and drawing software R to obtain the actual precision of the inversion model, as shown in FIG. 6.
Step 11: in the google earth engine, dynamic monitoring is realized through algorithm application and UI interface setting, as shown in fig. 7.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only examples of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (8)
1. The forest canopy chlorophyll content inversion and dynamic monitoring method is characterized by comprising the following steps:
s1: acquiring forest leaf character data as first data;
acquiring chlorophyll content data of a vegetation canopy of the forest sample plot as second data;
acquiring a forest remote sensing image as third data;
acquiring forest type data as fourth data;
s2: according to the first data, a copula function is adopted to respectively describe multivariate distribution among related leaf traits, and random sampling is carried out to generate a large number of leaf trait samples;
s3: according to the generated leaf character sample, simulating the spectral reflectivities of broad-leaved forests and coniferous forests by using a coupling radiation transmission model, and then simulating the spectral reflectivities of the mixed forests by adopting a method of linearly mixing the spectral reflectivities of the broad-leaved forests and the coniferous forests;
s4: establishing a regression model of the chlorophyll content of the forest canopy by adopting a machine algorithm according to the leaf area index, the chlorophyll content of the leaves and the simulated spectral reflectivity corresponding to the chlorophyll content of the leaves, which are set in the coupling model;
s5: applying the established regression model to the third data and the fourth data, and performing forest canopy chlorophyll content inversion to obtain forest canopy chlorophyll content data;
s6: performing regression analysis according to the second data and the forest canopy chlorophyll content data obtained by inversion to evaluate the accuracy of the forest canopy chlorophyll content inversion model;
s7: and applying the forest canopy chlorophyll content inversion model to a Google earth engine cloud computing platform to dynamically monitor the canopy chlorophyll content.
2. The forest canopy chlorophyll content inversion and dynamic monitoring method according to claim 1, wherein the step S1 further comprises:
s11: establishing a sample plot in the field and carrying out sample plot investigation;
s12: carrying out laboratory treatment and analysis on a tree leaf sample collected in field investigation, and sorting data to generate first data;
s13: calculating the first data by combining the field measured sample plot and leaf surface indexes to generate second data;
s14: acquiring a remote sensing image meeting the requirement of a research target from a preset area as third data;
s15: collecting the existing forest type data of the preset area, or classifying the third data by adopting a supervised or unsupervised classification method and taking the third data as fourth data.
3. The forest canopy chlorophyll content inversion and dynamic monitoring method according to claim 1, wherein the forest leaf trait data in step S1 includes chlorophyll content, leaf dry matter content, leaf equivalent water thickness, leaf carotenoid content, leaf lignin content, leaf nitrogen content.
4. The forest canopy chlorophyll content inversion and dynamic monitoring method according to claim 3, wherein the step S2 further comprises the steps of: dividing the parameters of chlorophyll content, leaf dry matter content, leaf equivalent water thickness, leaf carotenoid content, leaf lignin content and leaf nitrogen content into forest types, and describing the distribution of each univariate and the dependency relationship among variables by adopting copula function;
and randomly sampling based on the constructed leaf character copula function to generate a large number of observation samples.
5. The forest canopy chlorophyll content inversion and dynamic monitoring method according to claim 1, wherein the step S3 further comprises the steps of:
aiming at broad leaf forest, according to the generated broad leaf blade character sample, simulating the spectral reflectivity of the broad leaf forest by using a PROSPECT-5B +4SAIL and PROSPECT-5B + GeoSAIL coupling model;
aiming at coniferous forest, according to the generated coniferous leaf characteristic sample, simulating the spectral reflectivity of the coniferous forest by using a PROSPECT-5B +4SAIL and LIBERTY + GeoSAIL coupling model;
aiming at the mixed forest, the spectral reflectivity of the mixed forest is simulated by adopting a method of linearly mixing the spectral reflectivities of broad-leaved forest and coniferous forest.
6. The forest canopy chlorophyll content inversion and dynamic monitoring method according to claim 1, wherein the step S4 further comprises the steps of:
selecting characteristic variables according to the forest type, the wave band reflectivity and the vegetation index obtained by calculating the same, and performing regression modeling on the chlorophyll content of the forest canopy by using a machine algorithm;
and performing regression analysis on the predicted value and the observed value of the final regression model by adopting a ten-fold cross validation method so as to evaluate the theoretical precision of the regression model.
7. The forest canopy chlorophyll content inversion and dynamic monitoring method according to claim 1, wherein the step S6 further comprises the steps of:
extracting a corresponding inversion value of the content of the chlorophyll in the canopy according to the coordinates and time of the observation points in the second data;
and performing regression analysis on the inversion value and the observed value of the canopy chlorophyll content, and evaluating the accuracy of the forest canopy chlorophyll content inversion model.
8. The forest canopy chlorophyll content inversion and dynamic monitoring method according to claim 1, wherein the step S7 further comprises the steps of:
implementing a forest canopy chlorophyll content inversion model in a Google Earth engine cloud computing platform;
the dynamic monitoring is realized by defining a preset area, setting time, calling remote sensing images and forest type data, applying an inversion model to generate the space-time distribution of the chlorophyll content of the forest canopy, and further analyzing the dynamic change of the result.
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