CN115796344A - Method for estimating carbon reserves of forest vegetation on regional scale - Google Patents
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Abstract
The invention relates to the technical field of forest ecology, statistics and 3S, in particular to an estimation method of forest vegetation carbon reserves on a regional scale. The estimation method comprises the steps of collecting standard sample plot data, downloading remote sensing data and preprocessing, estimating partial carbon reserves on arbor forests and shrub forests, estimating through remote sensing inversion, estimating carbon reserves on under-forest shrub layers, herbaceous layers, litter, root systems and soil, and estimating through a spatial interpolation technology utilizing statistics. The method for estimating the carbon reserves of the forest vegetation on the regional scale is simple to operate, can quickly and dynamically estimate the carbon reserves of the forest vegetation ecosystem of the region, and has high accuracy of the predicted value. The method can be suitable for estimating the carbon reserves of the forest vegetation ecosystems under different scales, and the method can be popularized and has negative values.
Description
Technical Field
The invention relates to the technical field of forest ecology, statistics and 3S, in particular to a method for estimating carbon reserves of forest vegetation on a regional scale.
Background
At present, accurate estimation of carbon reserves and carbon sinks of forest vegetation is a difficulty in the academic world all the time, and transaction of forest carbon sinks is limited. At present, methods for estimating carbon reserves and sinks of forest vegetation mainly comprise a biomass conversion factor method, a carbon flux method, a process model method, an IPPC method, a remote sensing model method and the like. The biomass conversion factor continuous function method is a method which is widely applied in China, but the method ignores carbon sink resources such as shrubbery, sparse forest, non-grown forest and the like, and causes a low regional forest carbon sink estimation result. The observation range of the carbon flux method based on the vorticity correlation technology is only hundreds of meters, and due to the factors of limitation of the number of stations, great influence of environmental factors and the like, the data measured by the method has great uncertainty and is still in an exploration stage. The model method has the defects that most models are developed abroad, required parameters are difficult to obtain, the results of the models are difficult to verify, and the like, and the remote sensing model has the advantages of high obtaining speed, simple operation, dynamic and repeatable evaluation, and the defect that part of carbon sink amount under forest cannot be estimated. The method utilizes a remote sensing technology and a spatial interpolation technology to combine with ground sample plot survey data to estimate the carbon reserves and the carbon sinks of the parts of the forest vegetation ground, the earth surface and the underground of the area. The method has the advantages that the carbon reserves and the carbon sinks of the forest vegetation on the regional scale can be quickly, dynamically and accurately estimated, and the method is suitable for different spatial scales. The disadvantage of this method is the lack of biophysiological processes of vegetation.
The forest carbon density is an important index for evaluating the forest carbon sink function, and has become a main method for estimating the regional forest carbon reserve and the carbon sink due to certain correlation between remote sensing wave band information and the forest carbon density. Jiang Jiuhua and the likeThe carbon reserves of the forest ecological system in the Beijing mountain area are evaluated and analyzed by utilizing remote sensing image data and standard sample plot survey data and combining an InVEST model carbon reserve module, and the result is that the average carbon density of the forest ecological system in the Beijing mountain area is 99.95Mg/hm 2[1] . Zhang Qinyu and the like estimate the carbon reserves of forests in Hunan province based on a reference plot method and domestic high-grade data, and the total carbon reserve in Hunan province is 22.28Mt [2] . Zhang Guilian adopts sample plot survey data and Landsat OLI remote sensing image to construct an estimation model based on combination of a multiple stepwise regression model and common kriging residual error correction to estimate the total carbon reserve of forest in Shanghai city to be 2.87Mt [3] . Liu et al estimated the average carbon density of the overground part of the Zhejiang bamboo forest by using an improved BIOMEBGC model and combining sample plot survey and remote sensing data, and the estimation result shows that the carbon density of the Zhejiang bamboo forest is increased from 6.75Mg/ha in 2000 to 19.07Mg/ha in 2014, and the average annual increase is 0.88Mg/ha [4] . Although the scholars research regional forest carbon reserves and carbon density, most researches only estimate the forest carbon reserves of the overground part, but do not estimate the forest carbon reserves of the overground part, the overground part and the underground part respectively, and the estimation result is often low. Some models have complex structures, and parameters are difficult to obtain, so that the estimation result has large deviation. Therefore, in order to scientifically evaluate the carbon reserves of forest ecosystems, a new, reproducible and generalizable estimation method needs to be researched.
Disclosure of Invention
The invention aims to solve the defects and provides a method for estimating the carbon reserves of forest vegetation on a regional scale.
In order to overcome the defects in the background art, the technical scheme adopted by the invention for solving the technical problems is as follows: the method for estimating the carbon reserve of the forest vegetation on the regional scale comprises the following steps,
the method comprises the following steps of firstly, collecting standard sample data:
a. setting standard sample plots of a study area of a arbor forest and a bush forest, and setting the number of the standard sample plots according to the size of the study area, wherein the number of the sample plots is required to be uniformly distributed in a forest vegetation coverage area of the study area as much as possible;
b. calculating the standard sample carbon density of the arbor forest, comprising:
b1, arbor forest biomass calculation: checking the size of each tree in the arbor forest standard land, recording the breast diameter and tree height data, substituting the recorded breast diameter or breast diameter and tree height into a unitary biomass model or a binary biomass model, and calculating the biomass of the arbor forest standard land; for a standard sample plot lacking a biomass model, adopting an average standard wood method, felling standard wood, weighing fresh weights of stems, branches, leaves and roots on the spot, respectively collecting 300-500g of samples of the stems, branches, leaves and roots, sending the samples back to a laboratory, placing the samples in an oven at 100 ℃ for drying until the weights are constant, calculating biomass of the standard wood, and calculating the biomass of the standard sample plot;
b2, calculating biomass of shrubs, herbs and litter under the forest: harvesting all shrub branches, shrub leaves, shrub trunks, shrub roots, herbs and withered matters in the sample prescription by adopting a total harvesting method, weighing the fresh weights, respectively recording data values, taking 100-200g of fresh weights of the shrub branches, the shrub leaves, the shrub trunks, the shrub roots, the herbs and the withered matters in each sample prescription, sending the fresh weights to a laboratory, placing the laboratory in a drying oven at 100 ℃, drying the fresh weights to constant weights, and calculating the biomass of the shrubs, the herbs and the withered matters in the sample prescription;
b3, digging out all root systems in the sample prescription, removing soil, weighing the fresh weight of the root systems, collecting 100-200g of samples, sending the samples back to a laboratory, drying the samples in a drying oven at 100 ℃ to constant weight, and calculating the biomass of the root systems in the sample prescription;
b4, respectively calculating standard inner shrubs, herbs, litter and root biomass according to the sample prescription and the standard area;
b5, measuring the average carbon content of the trees, shrubs, herbs, litter and roots in a laboratory, and respectively calculating the carbon density of the trees, shrubs, herbs, litter and roots in the standard sample plot according to the standard biomass and the average carbon content of the trees;
c. calculating the carbon density of a standard sample plot of the shrubby forest, calculating biomass by adopting a harvesting method, measuring the carbon content of each part, and calculating the carbon density of the sample plot;
d. calculating the carbon density of soil in a standard sample plot, digging a soil section in the sample plot of the standard sample plot, and respectively sampling the soil by 5 layers of soil, wherein the soil is 0-10 cm, 10-20 cm, 20-30 cm, 30-40 cm and 40-60 cm;
e. counting the data of each standard sample plot, importing the data into an ARCGIS, and establishing a standard sample plot database Geodatabase;
secondly, downloading remote sensing data and preprocessing:
a. downloading remote sensing data of a research area;
b. predicting the downloaded remote sensing data by utilizing remote sensing data processing software of ENVI 5.6;
thirdly, estimating partial carbon reserves on arbor forest and shrub forest lands:
a. extracting ecological remote sensing factors, namely extracting remote sensing ecological factors such as sample plot single-waveband, vegetation indexes, image transformation, textural features, terrain, elevation, slope direction and the like in the ARCGIS by using standard sample plot coordinates;
b. performing correlation analysis, namely performing correlation analysis on the carbon density of the arbor forest and the shrubbery in the sample plot and the extracted remote sensing ecological factors by using SPSS software, and selecting the remote sensing ecological factors which have correlation and statistical significance to participate in constructing a carbon density remote sensing inversion model;
c. constructing a carbon density remote sensing inversion model, carrying out multivariate regression analysis by using SPSS software and using the carbon density of arbor forests and shrubbery forests at sample points as dependent variables and the remote sensing ecological factors at the sample points as independent variables, establishing a multivariate regression model, and carrying out precision verification evaluation on the regression model until the precision meets the requirement;
d. according to the remote sensing inversion model and the forest vegetation area which meet the precision, the overground part carbon reserves of the trees and the shrubs in the research area are inverted and counted, and relevant analysis and drawing are carried out, or dynamic change analysis of the carbon reserves of the trees and the shrubs is carried out;
the fourth step, under-forest brush layer, herbaceous layer, litter, root system and soil carbon reserves estimate through the spatial interpolation technique estimation that utilizes statistics, include:
a. spatial interpolation, including three important theories, is a regionalized variable, a semi-variogram, and a covariance function:
a1, regionalizing a variable to be a random variable with spatial information characteristics or phenomena;
a2, a half-variogram, wherein the Kriging interpolation firstly determines the half-variogram of the regionalized variable, the half-variogram is an important component of the Kriging interpolation, the half-variogram reflects the similarity degree of the regionalized variables Z (x) and Z (x + h), the function determines the value of an unsampled point, and the formula of the half-variogram is as follows:
z (x) in the formula i )、z(x i + h) is a regionalized variable, the two are separated by a distance of h;
a3, a covariance function, wherein the covariance function reflects a difference between the regionalized variables Z (x) and Z (x + h), and the function is also a value for determining an unsampled point, and both the covariance function and the variance function reflect spatial information characteristics or phenomena correlation coefficients, and the variance function is adopted:
z (x) in the formula i )、z(x i + h) is a regionalized variable, separated by a distance of h,
are respectively z (x) i )、z(x i Average number of samples of + h);
b. preprocessing the interpolation data, wherein the Kriging interpolation requires that the interpolation data obey normal distribution, if the interpolation data does not obey the normal distribution, the data must be preprocessed before the Kriging interpolation is adopted, the main method of data preprocessing is logarithmic transformation, the data without normal distribution can obey the normal distribution or basically obey the normal distribution after the logarithmic transformation, and the interpolation requirement is met;
c. selecting an interpolation model, wherein the types of Kriging interpolation mainly comprise ordinary, simple, cooperative, bayesian, probabilistic and other interpolation, once the types of Kriging interpolation are selected, a corresponding variation function model is selected, and main parameter values of the variation function model comprise a gold value, a variation range, a base station value, a deviation value and a standard average value;
d. and performing cross validation and precision validation on interpolation results, namely performing curve fitting validation on all measured values and predicted values by using SPSS software according to automatic cross validation results in ArcGIS, analyzing whether the correlation between the measured values and the predicted values is obvious, extracting predicted data by using a check sample plot, performing precision validation on the measured data and the predicted data of the check sample plot, and analyzing whether the correlation between the measured values and the predicted data is obvious and the precision of the interpolation results.
According to another embodiment of the present invention, the method further comprises the steps of setting standard sample areas of arbor forest and shrub forest research areas in step a of the first step, setting 3 under-forest shrub sample areas of 1m × 1m,3 herbaceous sample areas of 1m × 1m,3 litter sample areas of 1m × 1m,3 root sample areas of 1m × 1m × 1m,3 soil profile and root sample directions on diagonal lines, and recording longitude and latitude coordinates of center points of each standard sample area.
According to another embodiment of the invention, the soil sampling in the step d in the first step is respectively sampled by using a cutting ring, an aluminum box and a self-sealing bag, the samples are numbered and recorded, the numbers are sent back to a laboratory, the volume weight, the water content and the soil organic matter of the soil are measured, and the carbon density of the soil is calculated.
According to another embodiment of the invention, the method further comprises the step a of downloading Landsat series, sentinel2 and GF1 or downloading a series of remote sensing data according to the research scale size from the research area remote sensing data in the step a in the second step to perform dynamic analysis and research area DEM.
According to another embodiment of the present invention, the prediction process further comprising step b in the second step comprises the procedures of geometric fine correction, radiometric calibration, atmospheric correction, mosaicing, research area cropping and supervised classification, and the image information and enhancement process of the single-band, vegetation index, image transformation and texture features is performed.
The invention has the beneficial effects that: the method for estimating the carbon reserves of the forest vegetation on the regional scale is simple to operate, can quickly and dynamically estimate the carbon reserves of the forest vegetation ecosystem of the region, and has higher predicted value precision. The method can be suitable for estimating the carbon reserves of the forest vegetation ecosystems under different scales, and the method can be popularized and has negative values.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a schematic structural diagram of the same root system sample of 3 soil sections of the standard sample of the present invention;
FIG. 2 is a schematic block diagram of a flow chart for carrying out the present invention;
FIG. 3 is a carbon density distribution diagram of forest vegetation in Qinhuang island;
FIG. 4 is a spatial distribution diagram of carbon density of under-forest shrubs, herbs, dead branches, fallen leaves and soil layers.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 2, an implementation flowchart of the method for estimating the carbon reserves of forest vegetation on the regional scale specifically includes:
the method comprises the following steps of firstly, standard sample data acquisition:
a. setting standard sample plots of a study area of a arbor forest and a bush forest, and setting the number of the standard sample plots according to the size of the study area, wherein the number of the sample plots is required to be uniformly distributed in a forest vegetation coverage area of the study area as much as possible;
b. calculating the standard sample carbon density of the arbor forest, comprising:
b1, arbor forest biomass calculation: checking the size of each tree in the arbor forest standard land, recording the breast diameter and tree height data, substituting the recorded breast diameter or breast diameter and tree height into a unitary biomass model or a binary biomass model, and calculating the biomass of the arbor forest standard land; for a standard sample plot lacking a biomass model, adopting an average standard wood method, felling standard wood, weighing fresh weights of stems, branches, leaves and roots on the spot, respectively collecting 300-500g of samples of the stems, branches, leaves and roots, sending the samples back to a laboratory, placing the samples in an oven at 100 ℃ for drying until the weights are constant, calculating biomass of the standard wood, and calculating the biomass of the standard sample plot;
b2, calculating biomass of shrubs, herbs and litter under the forest: harvesting all shrub branches, shrub leaves, shrub trunks, shrub roots, herbs and withered matters in the sample prescription by adopting a total harvesting method, weighing the fresh weights, respectively recording data values, taking 100-200g of fresh weights of the shrub branches, the shrub leaves, the shrub trunks, the shrub roots, the herbs and the withered matters in each sample prescription, sending the fresh weights to a laboratory, placing the laboratory in a drying oven at 100 ℃, drying the fresh weights to constant weights, and calculating the biomass of the shrubs, the herbs and the withered matters in the sample prescription;
b3, digging out all root systems in the sample prescription, removing soil, weighing the fresh weight of the root systems, collecting 100-200g of samples, sending the samples back to a laboratory, drying the samples in a drying oven at 100 ℃ to constant weight, and calculating the biomass of the root systems in the sample prescription;
b4, respectively calculating standard inner shrubs, herbs, litter and root biomass according to the sample prescription and the standard area;
b5, measuring the average carbon content of the arbor, shrub, herb, litter and root in a laboratory, and respectively calculating the carbon density of standard sample arbor forest, under-forest shrub, under-forest herb, litter and root system according to the biomass of standard arbor forest land and the average carbon content;
c. calculating the carbon density of a standard sample plot of the shrubby forest, calculating biomass by adopting a harvesting method, measuring the carbon content of each part, and calculating the carbon density of the sample plot;
d. calculating the carbon density of soil in a standard sample plot, digging a soil section in the sample plot of the standard sample plot, and respectively sampling the soil by 5 layers of soil, wherein the soil is 0-10 cm, 10-20 cm, 20-30 cm, 30-40 cm and 40-60 cm;
e. counting the data of each standard sample plot, importing the data into an ARCGIS, and establishing a standard sample plot database Geodatabase;
secondly, downloading remote sensing data and preprocessing:
a. downloading remote sensing data of a research area;
b. predicting the downloaded remote sensing data by utilizing remote sensing data processing software of ENVI 5.6;
thirdly, estimating partial carbon reserves on arbor forest and shrub forest lands:
a. extracting ecological remote sensing factors, namely extracting remote sensing ecological factors such as sample plot single-waveband, vegetation indexes, image transformation, texture features, terrain and the like by utilizing standard sample plot coordinates in the ARCGIS;
b. performing correlation analysis, namely performing correlation analysis on the carbon density of the arbor forest and the bush forest in the sample plot and the extracted remote sensing ecological factors by using SPSS software, and selecting the remote sensing ecological factors with correlation and statistical significance to participate in constructing a carbon density remote sensing inversion model;
c. constructing a carbon density remote sensing inversion model, carrying out multivariate regression analysis by using SPSS software and using the carbon density of arbor forests and shrubbery forests at sample points as dependent variables and the remote sensing ecological factors at the sample points as independent variables, establishing a multivariate regression model, and carrying out precision verification evaluation on the regression model until the precision meets the requirement;
d. according to the remote sensing inversion model and the forest vegetation area which meet the precision, the overground part carbon reserves of the trees and the shrubs in the research area are inverted and counted, and relevant analysis and drawing are carried out, or dynamic change analysis of the carbon reserves of the trees and the shrubs is carried out;
and fourthly, estimating the carbon reserves of the shrub layer, the herbaceous layer, the litter, the root system and the soil under the forest, wherein the carbon reserves are estimated by utilizing a statistical spatial interpolation technology, and the carbon reserves have important significance for maintaining soil fertility, forest ecosystem energy flow and material circulation, soil C reservoir balance and the like. Because of the optical image wavelength reasons such as landsat8, its penetration capacity is limited, can't estimate under the forest brush layer, herbaceous layer, withered branch and fallen leaves layer, carbon reserves such as soil, this partial carbon reserves estimation mainly utilizes statistical spatial interpolation technique, includes:
a. spatial interpolation, including three important theories, is a regionalized variable, a semi-variant function, and a covariance function:
a 1. Regionalized variable is a variable with spatial informationA random variable of a feature or phenomenon, and a regionalized variable is a random variable having a spatial information feature or phenomenon. Assuming that the study area is a, Z (x) is a variable therein, x represents the spatial position, and Z (x) can be represented by a spatial point function: z (X) = Z (X) x ，x y ，x z ),x x ，x y ，x z The spatial information may be three-dimensional spatial information or two-dimensional spatial information. Z (x) at a known sampling point x i The information characteristic value at (i =1, 2 … …, n) is Z (x) i ). The regionalized variable also has a certain degree of spatial correlation, namely the value Z (x) of the variable at the point x and the point x + h which is at a distance h has spatial correlation with Z (x + h), and the normal distribution and the second-order stationarity are also met;
a2, a half-variogram, wherein the Kriging interpolation firstly determines the half-variogram of the regionalized variable, the half-variogram is an important component of the Kriging interpolation, the half-variogram reflects the similarity degree of the regionalized variables Z (x) and Z (x + h), the function determines the value of an unsampled point, and the formula of the half-variogram is as follows:
z (x) in the formula i )、z(x i + h) is a regionalization variable, the two being separated by a distance of h;
a3, a covariance function, wherein the covariance function reflects a difference between the regionalized variables Z (x) and Z (x + h), and the function is also a value for determining an unsampled point, and both the covariance function and the variance function reflect spatial information characteristics or phenomena correlation coefficients, and the variance function is adopted:
z (x) in the formula i )、z(x i + h) is a regionalized variable, separated by a distance of h,
are respectively z (x) i )、z(x i Average number of samples of + h);
b. preprocessing the interpolation data, wherein the Kriging interpolation requires that the interpolation data obey normal distribution, if the interpolation data does not obey the normal distribution, the data must be preprocessed before the Kriging interpolation is adopted, the main method of data preprocessing is logarithmic transformation, the data without normal distribution can obey the normal distribution or basically obey the normal distribution after the logarithmic transformation, and the interpolation requirement is met;
c. and selecting an interpolation model, wherein the types of Kriging interpolation mainly comprise ordinary, simple, cooperative, bayesian, probabilistic and other interpolation, once the types of Kriging interpolation are selected, a corresponding variogram model is selected, main parameter values of the variogram model comprise a gold value, a variation range, a base station value, a bias station value and a standard average value, and the selection of the interpolation type and the model is important for a prediction result. The commonly used variation function models in ArcGIS at present mainly include an exponential model, a spherical model, a Gaussian model, a circular model and the like. The selection of the optimal model is mainly determined by identifying the size of the parameter values of the model. The main parameter values of the variation function model are a lump value, a variation range, a base station value, a deviation station value, a standard average value and the like. According to the evaluation standard of the optimal model: the gold blocking value is minimum, the structure ratio is maximum, the standard average value is closest to 0, the average standard error is closest to 1, and the root mean square is minimum;
d. and performing curve fitting verification on all measured values and predicted values by using SPSS software according to the automatic cross verification result in ArcGIS, analyzing whether the correlation between the measured values and the predicted values is obvious or not, extracting predicted data by using a test sample plot, performing precision verification on the measured data and the predicted data of the test sample plot, and analyzing whether the correlation between the measured values and the predicted data is obvious or not and the precision of the interpolation result.
Preferably, in the step a of the first step, standard sample plots of arbor forest and shrub forest research areas are set on diagonal lines, 3 under-forest shrub sample plots are set to be 1m × 1m,3 herb sample plots are set to be 1m × 1m,3 litter sample plots are set to be 1m × 1m,3 root sample plots are set to be 1m × 1m,3 soil profile same root sample directions are set, and longitude and latitude coordinates of center points of each standard sample plot are recorded as shown in fig. 1.
In a preferred embodiment, the soil sampling in the step d in the first step is respectively sampled by using a cutting ring, an aluminum box and a self-sealing bag, the serial numbers are recorded and sent back to a laboratory, the volume weight, the water content and the organic matter of the soil are measured, and the carbon density of the soil is calculated.
Preferably, the remote sensing data of the research area in the step a in the second step downloads Landsat series, sentinel2 and GF1 or downloads a series of remote sensing data according to the research scale size to perform dynamic analysis and research area DEM.
Preferably, the prediction processing in step b in the second step includes procedures of geometric fine correction, radiometric calibration, atmospheric correction, mosaicing, research area cropping and supervised classification, and image information and enhancement processing of single-band, vegetation index, image transformation and texture features are performed.
Example (b):
by taking forest vegetation in Qinhuang island city as a research object, 181 standard lands are established in total, the whole research area is covered, and the main forest vegetation types in the research area can be represented. According to the technical scheme of the invention:
firstly, carrying out standard sample plot survey, calculating the carbon density of the standard sample plot, and establishing a standard sample plot Geodabase database;
and secondly, collecting Landsat8 remote sensing data and DEM topographic data of the research area, carrying out preprocessing such as data geometric fine correction, radiometric calibration, atmospheric correction, mosaic, research area cutting and the like, supervising and classifying the predicted research data, and extracting forest vegetation information of the research area. Carrying out image information and enhancement processing such as single-waveband, vegetation index, image transformation, texture feature and the like on the predicted research data, wherein the total number of 78 independent variable factors is single-waveband factors b1, b2, b3, b4, b5, b6 and b7; vegetation index factors NDVI, RVI, DIV, EVI, SAVI, MSAVI; K-T image transform factor B, G, W; K-L image transformation factors P1, P2, P3;8 texture feature values (56 feature values) which are respectively mean values M1, M2, M3, M4, M5, M6, M7, variances V1, V2, V3, V4, V5, V6, V7, homogeneity H1, H2, H3, H4, H5, H6, H7, contrasts C1, C2, C3, C4, C5, C6, C7, dissimilarities D1, D2, D3, D4, D5, D6, D7, entropy E1, E2, E3, E4, E5, E6, E7, angular second moments A1, A2, A3, A4, A5, A6, A7, correlations C1, C2, C3, C4, C5, C6, C7; terrain factor E, S, P;
and thirdly, constructing an underground carbon density remote sensing inversion model and verifying the model precision. And (3) randomly selecting about 80% of standard sample plots, namely selecting 146 sample plot carbon density values and independent variable factors for correlation analysis, selecting correlation coefficients reaching a significant level or extremely significant level factors to participate in the construction of the remote sensing inversion model, and verifying the model accuracy. And (4) selecting independent variable factors with high correlation to participate in stepwise regression modeling by using SPSS software. The regression model constructed was:
Y＝-35.319+0.023×Elevation-0.068×PAC 3 +20.119×E 6 +20.921×A 3 +10.277×C 2 +28.223×A 2 the standard sample of about 20% remained for verification. The prediction precision of the model is 80.53 percent, and the model precision meets the requirement.
And constructing a remote sensing inversion model of the carbon density of the overground part of the forest vegetation. Utilizing the constructed remote sensing inversion model to obtain the total forest vegetation carbon reserve of 11.88 multiplied by 10 in the research area through inversion 6 t, average carbon density of 30.49t/hm 2 As shown in fig. 3. The carbon reserve of arbor is 11.46X 10 6 t, average carbon density of 33.49t/hm 2 (ii) a The shrub forest is 0.42 × 10 6 t, average carbon density 8.87t/hm 2 。
And fourthly, estimating carbon reserves of the shrub layer, the herbaceous layer, the litter, the root system and the soil layer under the forest, and respectively estimating the carbon reserves and the carbon density of the shrub layer, the herbaceous layer, the withered branch and fallen leaf layer and the soil layer under the forest vegetation according to the interpolation principle, method and steps. Firstly, estimating the carbon density of the shrub layer under the forest vegetation layer in the research area by using a Co-Kriging interpolation method, then extracting a distribution map of the carbon density of the shrub under the forest by using the boundary of the forest vegetation, and finally counting the carbon reserves of the shrub layer under the forest as follows: 0.83X 10 6 t, average carbon density of 2.13t/hm 2 And is shown as a in fig. 4. Similarly, the method is used for estimating the carbon storage of the herbaceous layer, the withered branch and fallen leaf layer and the soil layerThe amount and carbon density are estimated to be 0.23 x 10 carbon storage of the understory herbaceous layer 6 t, average carbon density of 0.58t/hm 2 As shown in B in FIG. 4, the carbon reserve of the under-forest dry branch and deciduous leaf layer was 1.03X 10 6 t, average carbon density of 2.63t/hm 2 C in FIG. 4, the reserve of organic carbon in the understory soil layer is 23.08X 10 6 t, average carbon density of 58.72t/hm 2 And D in fig. 4.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered as the technical solutions and the inventive concepts of the present invention within the technical scope of the present invention.
Claims (5)
1. A method for estimating the carbon reserve of forest vegetation on an area scale is characterized by comprising the following steps of,
the method comprises the following steps of firstly, standard sample data acquisition:
a. setting standard sample plots of a study area of a arbor forest and a bush forest, and setting the number of the standard sample plots according to the size of the study area, wherein the number of the sample plots is required to be uniformly distributed in a forest vegetation coverage area of the study area as much as possible;
b. calculating the standard sample carbon density of the arbor forest, comprising:
b1, arbor forest biomass calculation: checking the size of each tree in the arbor forest standard land, recording the breast diameter and tree height data, substituting the recorded breast diameter or breast diameter and tree height into a unitary biomass model or a binary biomass model, and calculating the biomass of the arbor forest standard land; for a standard sample plot lacking a biomass model, adopting an average standard wood method, felling standard wood, weighing fresh weights of stems, branches, leaves and roots on the spot, respectively collecting 300-500g of samples of the stems, branches, leaves and roots, sending the samples back to a laboratory, placing the samples in an oven at 100 ℃ for drying until the weights are constant, calculating biomass of the standard wood, and calculating the biomass of the standard sample plot;
b2, calculating biomass of shrubs, herbs and withered objects under the forest: harvesting all shrub branches, shrub leaves, shrub trunks, shrub roots, herbs and withered matters in the sample prescription by adopting a total harvesting method, weighing the fresh weights, respectively recording data values, taking 100-200g of fresh weights of the shrub branches, the shrub leaves, the shrub trunks, the shrub roots, the herbs and the withered matters in each sample prescription, sending the fresh weights to a laboratory, placing the laboratory in a drying oven at 100 ℃, drying the fresh weights to constant weights, and calculating the biomass of the shrubs, the herbs and the withered matters in the sample prescription;
b3, digging out all root systems in the sample prescription, removing soil, weighing the fresh weight of the root systems, collecting 100-200g of samples, sending the samples back to a laboratory, drying the samples in a drying oven at 100 ℃ to constant weight, and calculating the biomass of the root systems in the sample prescription;
b4, respectively calculating the biomass of standard inner shrubs, herbs, litter and root systems according to the sample prescription and the standard land area;
b5, measuring the average carbon content of the trees, shrubs, herbs, litter and roots in a laboratory, and respectively calculating the carbon density of the trees, shrubs, herbs, litter and roots in the standard sample plot according to the standard biomass and the average carbon content of the trees;
c. calculating the carbon density of a standard sample plot of the shrubbery, calculating the biomass by adopting a harvesting method, measuring the carbon content of each part, and calculating the carbon density of the sample plot;
d. calculating the carbon density of soil in a standard sample plot, digging a soil section in the sample plot of the standard sample plot, and respectively sampling the soil by 5 layers of soil, wherein the soil is 0-10 cm, 10-20 cm, 20-30 cm, 30-40 cm and 40-60 cm;
e. counting the data of each standard sample plot, importing the data into an ARCGIS, and establishing a standard sample plot database Geodatabase;
secondly, downloading remote sensing data and preprocessing:
a. downloading remote sensing data of a research area;
b. predicting the downloaded remote sensing data by utilizing remote sensing data processing software of ENVI 5.6;
thirdly, estimating partial carbon reserves on arbor forest and shrub forest lands:
a. extracting ecological remote sensing factors, namely extracting remote sensing ecological factors of single wave bands, vegetation indexes, image transformation, textural features, landforms, elevations and slopes of sample plot positions by utilizing standard sample plot coordinates in the ARCGIS;
b. performing correlation analysis, namely performing correlation analysis on the carbon density of the arbor forest and the bush forest in the sample plot and the extracted remote sensing ecological factors by using SPSS software, and selecting the remote sensing ecological factors with correlation and statistical significance to participate in constructing a carbon density remote sensing inversion model;
c. constructing a carbon density remote sensing inversion model, carrying out multivariate regression analysis by using SPSS software and using the carbon density of arbor forests and shrubbery forests at sample points as dependent variables and the remote sensing ecological factors at the sample points as independent variables, establishing a multivariate regression model, and carrying out precision verification evaluation on the regression model until the precision meets the requirement;
d. according to the remote sensing inversion model and the forest vegetation area which meet the precision, the overground part carbon reserves of the trees and the shrubs in the research area are inverted and counted, and relevant analysis and drawing are carried out, or dynamic change analysis of the carbon reserves of the trees and the shrubs is carried out;
the fourth step, under forest brush layer, herbaceous layer, litter, root system and soil carbon reserves estimate, through utilizing statistical spatial interpolation technique to estimate, include:
a. spatial interpolation, including three important theories, is a regionalized variable, a semi-variant function, and a covariance function:
a1, regionalization variable is a random variable with spatial information characteristics or phenomena;
a2, half variogram, the half variogram of the regional variable is determined by Kriging interpolation, the half variogram is an important component of Kriging interpolation, which reflects the similarity between the regional variables Z (x) and Z (x + h), the function determines the value of the non-sampling point, and the formula of the half variogram is as follows:
z (x) in the formula i )、z(x i + h) is a regionalized variable, the two are separated by a distance of h;
a3, a covariance function, wherein the covariance function reflects a difference between the regionalized variables Z (x) and Z (x + h), and the function is also a value for determining an unsampled point, and both the covariance function and the variance function reflect spatial information characteristics or phenomena correlation coefficients, and the variance function is adopted:
z (x) in the formula i )、z(x i + h) is a regionalized variable, separated by a distance of h,
are respectively z (x) i )、z(x i Average number of samples of + h);
b. preprocessing the interpolation data, wherein the Kriging interpolation requires that the interpolation data obey normal distribution, if the interpolation data does not obey the normal distribution, the data must be preprocessed before the Kriging interpolation is adopted, the main method of data preprocessing is logarithmic transformation, the data without normal distribution can obey the normal distribution or basically obey the normal distribution after the logarithmic transformation, and the interpolation requirement is met;
c. selecting an interpolation model, wherein the types of Kriging interpolation mainly comprise ordinary, simple, cooperative, bayesian, probabilistic and other interpolation, once the types of Kriging interpolation are selected, a corresponding variation function model is selected, and main parameter values of the variation function model comprise a gold value, a variation range, a base station value, a deviation value and a standard average value;
d. and performing cross validation and precision validation on interpolation results, namely performing curve fitting validation on all measured values and predicted values by using SPSS software according to automatic cross validation results in ArcGIS, analyzing whether the correlation between the measured values and the predicted values is obvious, extracting predicted data by using a check sample plot, performing precision validation on the measured data and the predicted data of the check sample plot, and analyzing whether the correlation between the measured values and the predicted data is obvious and the precision of the interpolation results.
2. The method as claimed in claim 1, wherein the standard sample of the forest and shrub research area is set in step a of the first step, wherein 3 under-forest shrubs are 1m × 1m,3 herbs are 1m × 1m,3 litter are 1m × 1m,3 root systems are 1m × 1m,3 soil sections have the same root system sample direction, and the longitude and latitude coordinates of the center point of each standard sample are recorded.
3. The method for estimating the carbon reserves of the forest vegetation on the regional scale according to claim 1, wherein the soil sampling of the step d in the first step is respectively sampled by a cutting ring, an aluminum box and a self-sealing bag, the samples are numbered and recorded, the numbers are sent back to a laboratory, the volume weight, the water content and the soil organic matters of the soil are measured, and the carbon density of the soil is calculated.
4. The method for estimating the carbon reserves of the forest vegetation on the regional scale as claimed in claim 1, wherein the remote sensing data of the research region in the step a in the second step downloads Landsat series, sentinel2 and GF1 or a series of remote sensing data according to the research scale size to perform dynamic analysis and research on the regional DEM.
5. The method according to claim 1, wherein the prediction process of step b in the second step comprises geometric fine correction, radiometric calibration, atmospheric correction, mosaicing, region cropping, supervised classification, and single band, vegetation index, image transformation, texture feature image information, and enhancement.
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