CN111814326A - Method for estimating aboveground biomass of swamp wetland reeds - Google Patents

Method for estimating aboveground biomass of swamp wetland reeds Download PDF

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CN111814326A
CN111814326A CN202010633030.1A CN202010633030A CN111814326A CN 111814326 A CN111814326 A CN 111814326A CN 202010633030 A CN202010633030 A CN 202010633030A CN 111814326 A CN111814326 A CN 111814326A
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biomass
reed
vegetation index
wetland
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神祥金
姜明
吕宪国
安雨
刘波
郝明旭
张振卿
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Northeast Institute of Geography and Agroecology of CAS
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Abstract

A method for estimating biomass on a marsh wetland reed land relates to a method for estimating biomass on a marsh wetland vegetation land. The invention aims to solve the technical problems that the field measurement of biomass on the reed field of the existing marsh wetland has large workload, long period and high cost, and the biomass can not be quickly and accurately estimated and predicted, and the method comprises the following steps: acquiring normalized vegetation index data, surface observation meteorological data and actually measured swamp wetland reed aboveground biomass data, and preprocessing; interpolating the earth surface observation meteorological data to obtain a numerical value spatial distribution data set of each meteorological element; resampling the numerical value spatial distribution data sets of all meteorological elements, and unifying projection coordinates; acquiring a data set of the maximum value of the annual vegetation index by using a maximum synthesis method; extracting the maximum value of the annual vegetation index and each meteorological element value corresponding to the biomass sampling point; establishing a biomass simulation and prediction model. The method can accurately estimate and predict the aboveground biomass of the swamp wetland reeds, and has the advantages of small workload, short period and low cost.

Description

Method for estimating aboveground biomass of swamp wetland reeds
Technical Field
The invention relates to a method for estimating biomass on a marsh wetland, in particular to a method for estimating the biomass on the marsh wetland reed land in a region based on remote sensing data and predicting the biomass on the marsh wetland reed land under future climate change through meteorological data.
Background
The wetland is one of important natural ecosystems, and plays an important role in protecting the ecological environment, maintaining biological diversity, serving social and economic sustainable development and the like. As an important wetland type, the marsh wetland not only can promote the storage and purification of water resources and adjust the regional climate, but also can provide a habitat for the propagation of wild animals. The reed is one of the main vegetation types of the marsh wetland ecosystem, has higher economic value besides the common ecological function of marsh wetland plants, can provide valuable material resources for the production and the life of human beings, for example, the reed has higher cellulose content in the reed stem, can be used for manufacturing paper, reed mats, artificial fibers and the like, and is also called as a second forest because the reed can save a large amount of wood for the country.
Aboveground biomass refers to the total weight of the aboveground parts of a plant in a unit area. The overground biomass of the swamp wetland reeds can directly reflect the growth condition of the reeds, is one of the material basis and the important ecological quality parameters of the reeds, and can be used as a key index for measuring the yield of the swamp wetland reeds. The aboveground biomass of the swamp wetland reeds is determined, and scientific basis can be provided for the growth condition and yield evaluation of the swamp wetland reeds. The method has the advantages that the aboveground biomass of the reeds in the marsh wetland can be accurately estimated and predicted, important data support can be provided for quantitative measurement and calculation of the yield of the reeds in the region, and scientific basis can be provided for carbon cycle research of the marsh wetland under the influence of climate change.
Currently, there are two main methods related to the acquisition of aboveground biomass of swamp wetland reeds, one is a real measurement method, and a representative reed sample is selected for field harvest measurement; the second method is a traditional model estimation method, and calculates the aboveground biomass of the reeds by using a model method by means of remote sensing image data such as vegetation indexes. The first practical measurement method has the characteristics of large workload, long period, high cost, relatively small survey scale and the like, so that the method is difficult to obtain the aboveground biomass data of the reeds in a regional scale in a short time. The second model estimation method only uses the vegetation index remote sensing image data in the historical period to carry out model simulation, lacks actual measurement data verification and cannot predict the overground biomass of the reeds in the future area.
Disclosure of Invention
The invention aims to solve the technical problems that the field measurement of the biomass on the reed ground of the existing marsh wetland has large workload, long period and high cost, and the biomass of regional reeds cannot be quickly and accurately estimated and the future biomass cannot be predicted, and provides a method for estimating the biomass on the reed ground of the marsh wetland.
A method for estimating aboveground biomass of swamp wetland reeds comprises the following steps:
firstly, acquiring a medium resolution imaging spectrometer (MODIS) normalized vegetation index data set, a ground meteorological observation data set and actually measured swamp wetland reed aboveground biomass data covering a research area, and preprocessing the data;
secondly, interpolating the ground meteorological observation data by adopting a common kriging interpolation method to obtain a numerical value spatial distribution data set of each meteorological element of the long time sequence in the research time period;
thirdly, resampling the long-time sequence meteorological element value spatial distribution data sets with the same resolution as vegetation index (NDVI) data respectively, and unifying the data sets to be under the same projection and coordinate system as the vegetation index data sets;
fourthly, acquiring a maximum value data set of annual vegetation index in a research time period by utilizing a maximum synthesis method according to the vegetation index data set obtained in the first step;
fifthly, extracting the maximum value of the annual vegetation index and the numerical value of each meteorological element corresponding to all sampling points in the research area by utilizing the spatial distribution data set of each meteorological element numerical value and the maximum value data set of the annual vegetation index of the long-time sequence obtained in the third step and the fourth step according to the latitude and longitude of the biomass sampling points on the swamp wetland reed land;
sixthly, constructing a wetland reed aboveground biomass estimation model based on the vegetation annual vegetation index maximum value by utilizing unitary linear regression analysis according to the actually measured wetland reed aboveground biomass data and the corresponding annual vegetation index maximum value, wherein the formula is as follows:
Biomass(reed)＝c+b×NDVImaxor Biomass(reed)＝c×Exp(b×NDVImax) Formula (1)
Wherein Biomass(reed)Is a field biomass value of Phragmites communis, NDVImaxThe value is the maximum value of the annual vegetation index of vegetation;
seventhly, according to the extracted annual vegetation index maximum values and all meteorological element values corresponding to all sampling points, a model for predicting the annual vegetation index maximum values under the influence of climate change is constructed through multivariate stepwise regression analysis, and the formula is as follows:
NDVImax＝a+a1X1+a2X2+a3X3+…+akXkformula (2)
Wherein NDVImaxIs the maximum value of the annual vegetation index, X1、X2、X3、XkRespectively the relevant meteorological element values of the research time period;
eighthly, combining the models obtained in the sixth step and the seventh step, and establishing a wetland reed aboveground biomass prediction model under future climate change, wherein the formula is as follows:
Biomass(reed)＝c+b×(a+a1X1+a2X2+a3X3+…+akXk)
or Biomass(reed)＝c×Exp[b×(a+a1X1+a2X2+a3X3+…+akXk)]And (3) finishing the estimation of the biomass on the sward land of the swamp wetland.
The reed is a perennial herb of the genus Phragmites of the family Gramineae, and the aboveground biomass of the natural marsh wetland reeds is mainly influenced by climate change. Under the climate change background, the accurate estimation and prediction of the aboveground biomass of the swamp wetland reeds have important significance for the evaluation of the yield and the ecological function of the swamp wetland reeds. The aboveground biomass of Chinese herbaceous plants and the annual NDVI maximum value are in a linear relationship, and the aboveground biomass of the plants can be well estimated by using NDVI data; the annual NDVI maximum value is obviously influenced by climate change, so that the overground biomass of the herbaceous swamp plants in the future can be predicted by analyzing the correlation between relevant meteorological elements and the NDVI maximum value and utilizing future climate change data.
The invention provides a method for estimating the biomass of swamp wetland reeds on the ground based on remote sensing data by combining long-time sequence vegetation NDVI data, actually measuring the biomass of swamp wetland reeds on the ground and surface observation meteorological data sets, and predicting the biomass of swamp wetland reeds on the ground under the influence of climate change by performing multiple regression fitting on the vegetation NDVI and various meteorological elements.
The invention provides a method for estimating the aboveground biomass of regional marsh wetland reeds based on remote sensing data and predicting the aboveground biomass of the marsh wetland reeds under future climate change through meteorological data on the basis of a traditional aboveground biomass model estimation method of the marsh wetland reeds. The method overcomes the defects that the traditional model estimation method cannot accurately simulate the aboveground biomass of regional reeds and cannot predict the aboveground biomass of the swamp wetland reeds under the future climate change. The invention provides a new method for estimating and predicting the aboveground biomass of the swamp wetland reeds on the regional scale by utilizing remote sensing vegetation data and ground observation meteorological data. The method can accurately estimate and predict the aboveground biomass of the swamp wetland reeds, and has the advantages of small workload, short period and low cost.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a spatial distribution diagram of aboveground biomass sampling points of swamp wetland reeds in the first experiment;
FIG. 3 is a graph showing the correlation between aboveground biomass of reed and annual NDVI maximum in the first experiment;
FIG. 4 is a graph comparing the measured aboveground biomass of reeds with the predicted aboveground biomass of reeds in the first experiment.
Detailed Description
The technical solution of the present invention is not limited to the following specific embodiments, but includes any combination of the specific embodiments.
The first embodiment is as follows: the method for estimating the aboveground biomass of the swamp wetland reeds in the embodiment comprises the following steps:
acquiring a normalized vegetation index data set, a ground meteorological observation data set and actually measured swamp wetland reed aboveground biomass data of a medium-resolution imaging spectrometer covering a research area, and preprocessing the data;
secondly, interpolating the ground meteorological observation data by adopting a common kriging interpolation method to obtain a numerical value spatial distribution data set of each meteorological element of the long time sequence in the research time period;
thirdly, resampling the long-time sequence meteorological element value spatial distribution data sets with the same resolution as the vegetation index data respectively, and unifying the sampled data sets to be under the same projection and coordinate system as the vegetation index data sets;
fourthly, acquiring a maximum value data set of annual vegetation index in a research time period by utilizing a maximum synthesis method according to the vegetation index data set obtained in the first step;
fifthly, extracting the maximum value of the annual vegetation index and the numerical value of each meteorological element corresponding to all sampling points in the research area by utilizing the spatial distribution data set of each meteorological element numerical value and the maximum value data set of the annual vegetation index of the long-time sequence obtained in the third step and the fourth step according to the latitude and longitude of the biomass sampling points on the swamp wetland reed land;
sixthly, constructing a wetland reed aboveground biomass estimation model based on the vegetation annual vegetation index maximum value by utilizing unitary linear regression analysis according to the actually measured wetland reed aboveground biomass data and the corresponding annual vegetation index maximum value, wherein the formula is as follows:
Biomass(reed)＝c+b×NDVImaxor Biomass(reed)＝c×Exp(b×NDVImax) Formula (1)
Wherein Biomass(reed)Is a field biomass value of Phragmites communis, NDVImaxThe value is the maximum value of the annual vegetation index of vegetation;
seventhly, according to the extracted annual vegetation index maximum values and all meteorological element values corresponding to all sampling points, a model for predicting the annual vegetation index maximum values under the influence of climate change is constructed through multivariate stepwise regression analysis, and the formula is as follows:
NDVImax＝a+a1X1+a2X2+a3X3+…+akXkformula (2)
Wherein NDVImaxIs the maximum value of the annual vegetation index, X1、X2、X3、XkRespectively the relevant meteorological element values of the research time period;
eighthly, combining the models obtained in the sixth step and the seventh step, and establishing a wetland reed aboveground biomass prediction model under future climate change, wherein the formula is as follows:
Biomass(reed)＝c+b×(a+a1X1+a2X2+a3X3+…+akXk)
or Biomass(reed)＝c×Exp[b×(a+a1X1+a2X2+a3X3+…+akXk)]Formula (3)
And then the estimation of the biomass on the reed land of the marsh wetland is finished.
The second embodiment is as follows: the difference between this embodiment and the first embodiment is that the method for preprocessing data in the first step is as follows:
firstly, atmospheric correction, radiation correction and geometric correction are carried out on vegetation index data sets;
and secondly, averaging the repeated sample value of each sampling point of the biomass on the reed ground, and representing the biomass value on the reed ground of the sampling point by using the average value of the biomass on the reed ground of all the samples corresponding to each sampling point. The rest is the same as the first embodiment.
The following experiments are adopted to verify the effect of the invention:
experiment one:
according to actual measurement data of biomass on the wetland reed land in 2015 of the national marsh wetland resource and main ecological environmental benefit comprehensive survey of basic work special item of the Ministry of science and technology, selecting a marsh wetland reed distribution area in the Qinghai-Tibet plateau area as an implementation area, as shown in figures 1-4, and figure 1 illustrates a specific step of estimating and predicting the biomass on the marsh wetland reed land in the area;
a method for estimating aboveground biomass of swamp wetland reeds comprises the following steps:
firstly, acquiring initial remote sensing image data and earth surface observation meteorological data, and preprocessing the data.
Respectively obtaining an 2015 year-by-ten-day MODIS NDVI data set, an 2014-plus 2015 year earth surface month-by-month meteorological observation data set and 2015 year measured biomass data on the wetland reed land covering the Qinghai-Tibet plateau; carrying out atmospheric correction, radiation correction and geometric correction on the NDVI data set; and averaging the repeated sample value of each sampling point of the biomass on the reed ground, and representing the biomass value on the reed ground of the sampling point by using the average value of the biomass on the reed ground of all the samples corresponding to each sampling point.
Secondly, interpolating the ground meteorological observation data by adopting a common kriging interpolation method to obtain a numerical value spatial distribution data set of each meteorological element of the long time sequence in the research time period;
thirdly, resampling the long-time sequence meteorological element value spatial distribution data sets with the same resolution as the vegetation index data respectively, and unifying the sampled data sets to be under the same projection and coordinate system as the vegetation index data sets;
fourthly, acquiring a maximum value data set of annual vegetation index in a research time period by utilizing a maximum synthesis method according to the vegetation index data set obtained in the first step;
fifthly, extracting the maximum value of the annual vegetation index and the numerical value of each meteorological element corresponding to all sampling points in the research area by utilizing the spatial distribution data set of each meteorological element numerical value and the maximum value data set of the annual vegetation index of the long-time sequence obtained in the third step and the fourth step according to the latitude and longitude of the biomass sampling points on the swamp wetland reed land;
sixthly, constructing a wetland reed aboveground biomass estimation model based on the vegetation annual vegetation index maximum value by utilizing unitary linear regression analysis according to the actually measured wetland reed aboveground biomass data and the corresponding annual vegetation index maximum value, wherein the formula is as follows:
Biomass(reed)＝c+b×NDVImaxor Biomass(reed)＝c×Exp(b×NDVImax) Formula (1)
Wherein Biomass(reed)Is a field biomass value of Phragmites communis, NDVImaxThe value is the maximum value of the annual vegetation index of vegetation;
the final formula is as follows:
Biomass(reed)＝1611×NDVImax 0.875(R20.542) formula (1)
Seventhly, according to the extracted annual vegetation index maximum values and all meteorological element values corresponding to all sampling points, a model for predicting the annual vegetation index maximum values under the influence of climate change is constructed through multivariate stepwise regression analysis, and the formula is as follows:
NDVImax＝a+a1X1+a2X2+a3X3+…+akXkin the formula (2),
wherein NDVImaxIs the maximum value of the annual vegetation index, X1、X2、X3、XkRespectively the relevant meteorological element values of the research time period;
the annual NDVI maximum value of vegetation in the Qinghai-Tibet plateau is mainly influenced by average temperature in spring, winter and summer and precipitation in summer.
The formula is as follows:
NDVImax ═ 1.565+0.005Tmean1+0.54Tmean2-0.055Tmean3+0.001P (R2 ═ 0.51, P < 0.01) formula (2)
Wherein NDVImaxIs the value of the maximum value of the annual vegetation NDVI, Tmean1、Tmean2、Tmean3The values of the spring average temperature (3-5 months), the winter average temperature (12-2 months) and the summer average temperature (6-8 months) in the research time period are respectively, and P is the value of summer rainfall.
Eighthly, combining the models obtained in the sixth step and the seventh step, and establishing a wetland reed aboveground biomass prediction model under future climate change, wherein the formula is as follows:
Biomass(reed)＝1611×(1.565+0.005Tmean1+0.054Tmean2-0.055Tmean3+0.001P)0.875formula (3)
And then the estimation of the biomass on the reed land of the marsh wetland is finished.
And ninthly, verifying the result of the prediction model.
In order to verify the simulation effect of the prediction model, the predicted value of the biomass on the wetland reed field in the swamp wetland in the Qinghai-Tibet plateau region in 2015 is calculated according to the prediction model by utilizing the 2014-charge 2015 monthly meteorological data in the Qinghai-Tibet plateau region. The actual aboveground biomass value of reed in 2015 is compared with the model prediction result by utilizing the actual aboveground biomass value of 50 sampling points of swamp wetland reed in Qinghai-Tibet plateau by basic work special items of the Ministry of science and technology. The results show that the estimated value of the above-ground biomass model of the reed and the measured value of the ground have high correlation (the correlation coefficient R is 0.995), and reach an extremely significant level (the significance P is 0.000). The verification result shows that the regression model can be applied to estimation and prediction of aboveground biomass of swamp wetland reeds in the region.

Claims (2)

1. A method for estimating aboveground biomass of marsh wetland reeds is characterized by comprising the following steps:
acquiring a normalized vegetation index data set, a ground meteorological observation data set and actually measured swamp wetland reed aboveground biomass data of a medium-resolution imaging spectrometer covering a research area, and preprocessing the data;
secondly, interpolating the ground meteorological observation data by adopting a common kriging interpolation method to obtain a numerical value spatial distribution data set of each meteorological element of the long time sequence in the research time period;
thirdly, resampling the long-time sequence meteorological element value spatial distribution data sets with the same resolution as the vegetation index data respectively, and unifying the sampled data sets to be under the same projection and coordinate system as the vegetation index data sets;
fourthly, acquiring a maximum value data set of annual vegetation index in a research time period by utilizing a maximum synthesis method according to the vegetation index data set obtained in the first step;
fifthly, extracting the maximum value of the annual vegetation index and the numerical value of each meteorological element corresponding to all sampling points in the research area by utilizing the spatial distribution data set of each meteorological element numerical value and the maximum value data set of the annual vegetation index of the long-time sequence obtained in the third step and the fourth step according to the latitude and longitude of the biomass sampling points on the swamp wetland reed land;
sixthly, constructing a wetland reed aboveground biomass estimation model based on the vegetation annual vegetation index maximum value by utilizing unitary linear regression analysis according to the actually measured wetland reed aboveground biomass data and the corresponding annual vegetation index maximum value, wherein the formula is as follows:
Biomass(reed)＝c+b×NDVImaxor Biomass(reed)＝c×Exp(b×NDVImax) Formula (1)
Wherein Biomass(reed)Is a field biomass value of Phragmites communis, NDVImaxThe value is the maximum value of the annual vegetation index of vegetation;
seventhly, according to the extracted annual vegetation index maximum values and all meteorological element values corresponding to all sampling points, a model for predicting the annual vegetation index maximum values under the influence of climate change is constructed through multivariate stepwise regression analysis, and the formula is as follows:
NDVImax＝a+a1X1+a2X2+a3X3+…+akXkformula (2)
Wherein NDVImaxIs the maximum value of annual vegetation index，X1、X2、X3、XkRespectively the relevant meteorological element values of the research time period;
eighthly, combining the models obtained in the sixth step and the seventh step, and establishing a wetland reed aboveground biomass prediction model under future climate change, wherein the formula is as follows:
Biomass(reed)＝c+b×(a+a1X1+a2X2+a3X3+…+akXk)
or Biomass(reed)＝c×Exp[b×(a+a1X1+a2X2+a3X3+…+akXk)]Formula (3)
And then the estimation of the biomass on the reed land of the marsh wetland is finished.
2. The method for estimating the aboveground biomass of the swamp wetland reeds according to claim 1, wherein the method for preprocessing the data in the first step is as follows:
firstly, atmospheric correction, radiation correction and geometric correction are carried out on vegetation index data sets;
and secondly, averaging the repeated sample value of each sampling point of the biomass on the reed ground, and representing the biomass value on the reed ground of the sampling point by using the average value of the biomass on the reed ground of all the samples corresponding to each sampling point.
CN202010633030.1A 2020-07-02 2020-07-02 Method for estimating aboveground biomass of swamp wetland reeds Pending CN111814326A (en)

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CN113449984A (en) * 2021-06-25 2021-09-28 中国水利水电科学研究院 Reed resource quantity evaluation method
CN113361952A (en) * 2021-06-25 2021-09-07 河北工程大学 Reed growth condition evaluation method
CN113449984B (en) * 2021-06-25 2023-12-29 中国水利水电科学研究院 Reed resource quantity assessment method
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CN114241331B (en) * 2021-12-16 2024-04-05 中国科学院南京地理与湖泊研究所 Remote sensing modeling method for ground biomass of reed in wetland by taking UAV as ground and Septinel-2 medium
CN116739133A (en) * 2023-03-20 2023-09-12 北京师范大学 Regional reed NDVI pattern simulation prediction method based on natural-social dual-drive analysis
CN116739133B (en) * 2023-03-20 2024-06-04 北京师范大学 Regional reed NDVI pattern simulation prediction method based on natural-social dual-drive analysis
CN116663783A (en) * 2023-07-28 2023-08-29 中国科学院东北地理与农业生态研究所 System for statistical analysis of carbon reserves of marsh wetland ecosystem
CN116663783B (en) * 2023-07-28 2023-10-13 中国科学院东北地理与农业生态研究所 System for statistical analysis of carbon reserves of marsh wetland ecosystem

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Application publication date: 20201023