CN108152212A - Meadow ground biomass inversion method based on high time resolution and spatial resolution Multi-spectral Remote Sensing Data - Google Patents
Meadow ground biomass inversion method based on high time resolution and spatial resolution Multi-spectral Remote Sensing Data Download PDFInfo
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Abstract
The meadow ground biomass inversion method based on high time resolution and spatial resolution Multi-spectral Remote Sensing Data of the present invention, the specific steps are：1) Multi-spectral Remote Sensing Data of covering meadow Growing season is chosen, calculates NDVI, and synthesizes the monthly maximum NDVI in covering research area；2) according to meteorological site the moon samming, moon gross precipitation, moon Globalradiation data, interpolation generation covering research area the moon samming, moon gross precipitation, moon Globalradiation data；3) based on the efficiency of light energy utilization (Light Use Efficiency, LUE it is) theoretical, the Net primary productivity (Net Primary Productivity, NPP) on meadow is calculated using CASA (Carnegie Ames Stanford Approach) model；4) according to NPP and the ratio between ground biomass and underground biomass, meadow ground biomass is calculated.
Description
Technical field
The present invention relates to ecological index calculating field, espespecially a kind of meadow ground biomass based on remote-sensing inversion technology refers to
Mark computational methods.
Background technology
The biomass of phytobiocoenose is the comprehensive quantative attribute for embodying Ecosystem structure and function, community biomass
Variation, the carbon cycle and the climate change to the whole world for influencing whether other processes such as land of the ecosystem.Chinese natural grass
Ground area accounts for more than 40% territory land area, is the important component of terrestrial ecosystems.Therefore, meadow is fully understanded
Biomass, to the carbon cycle long term monitoring of Grassland ecosystems, grassland degeneration evaluation and native pasture it is reasonable using and pipe
Reason has important science and application value.
The computational methods of meadow ground biomass can substantially be divided into two classes：When field measurement method, second is that modeling
Method.Field measurement method is obtained when generally when meadows, aerial standing biomass reaches maximum with the mode that sample prescription harvests.This method is more
It is time-consuming, laborious, and influenced by traffic accessibility.Modeling method can be subdivided into again experience-semiempirical statistical regression method,
Mechanism model method etc..Experience-semiempirical statistics law of return usually utilizes the sampling point data that field measurement method obtains and some vegetation
Index, as NDVI (Normalized Difference Vegetation Index, normalized differential vegetation index) establish it is linear, non-
Linear regression fit formula, and then obtain the analog result of whole region.This method lacks theory support, quality as a result with
Used data source quality, regional location are highly relevant, it is difficult to be generalized to other regions.Mechanism model method is from mechanism to planting
The physiological and ecological process and its impact factor of object, feedback mechanism etc. are simulated, and are by soilplant atmosphere continuum mostly
Consider as an entirety, including multiple submodules such as photosynthesis, respiration, evapotranspiration, stomatal conductances.Mechanism mould
Type has complete theoretical system, but is related to numerous input quantities, such as the soil moisture, humidity, therefore can only be equipped with correlation
The observation station of equipment nearby calculates, and is difficult to be generalized to whole region in actual application.
Invention content
In view of the problems of the existing technology, the present invention, which provides, a kind of utilizes the distant of high time resolution and spatial resolution
Feel data (such as the multispectral data of No. 1 satellite of high score, spatial resolution are 16 meters, and temporal resolution is 2-4 days), with reference to
Meteorological measuring based on meadow efficiency of light energy utilization mechanism model, realizes the Inversion Calculation of a wide range of meadow ground biomass.
To achieve the above object, the grass of the invention based on high time resolution and spatial resolution Multi-spectral Remote Sensing Data
Ground ground biomass inversion method, the specific steps are：1) the multispectral remote sensing number of covering meadow Growing season (such as 5-9 months) is chosen
According to, calculating NDVI, and synthesize the monthly maximum NDVI in covering research area；2) according to meteorological site the moon samming, moon gross precipitation,
Month Globalradiation data, interpolation generation covering research area the moon samming, moon gross precipitation, moon Globalradiation data；3) base
In the efficiency of light energy utilization (Light Use Efficiency, LUE) theory, CASA (Carnegie-Ames-Stanford are utilized
Approach) model calculates the Net primary productivity (Net Primary Productivity, NPP) on meadow；4) according to NPP and
The ratio between ground biomass and underground biomass calculate meadow ground biomass.
Further, Multi-spectral Remote Sensing Data will carry out atmospheric radiation correction in step 1), with 6S the or FLASSH moulds of ENVI
Block is corrected.
Further, after the NDVI that each scape remotely-sensed data has been calculated, maximum value synthesis is carried out to the NDVI of same January,
It is calculated with the Mosaic modules of ArcGIS.
Further, interpolation method uses thin disk spline method in step 2), is realized with ANUSPLIN tools.
Further, interpolation result is grid format, and spatial resolution is consistent with multispectral data.
Further, in step 3) meadow NPP calculate the specific steps are：According to NDVI and grassland types, meadow is calculated to light
Close the assimilation ratio (FPAR) of Net long wave radiation；Meadow life is calculated when monthly mean temperature when reaching highest according to NDVI values in 1 year
Long optimal temperature index；When reaching highest according to NDVI values in 1 year when monthly mean temperature and the temperature on average of each month
Calculate coefficient to efficiency of light energy utilization reduction of the meadow under the conditions of optimal temperature is deviateed；According to the precipitation of each month, meter
The water stress influence factor grown to equation in calculation；The optimal temperature that the solar radiation data of every month, FPAR, meadow are grown
The water stress shadow grown to the coefficient of efficiency of light energy utilization reduction, meadow of index, meadow under the conditions of optimal temperature is deviateed
The factor of sound and meadow Net long wave radiation conversion rate coefficient, obtain the monthly NPP results on meadow；Finally by the Growing season NPP of each month
Results added obtains meadow year NPP results.
Further, the grassland types data for participating in calculating in step 3) are grid format, can be from arrows such as grassland types figures
Data are measured by swearing that grid are converted to, spatial resolution is consistent with multispectral data.
Further, meadow Net long wave radiation conversion rate coefficient is corresponding with grassland types in step 3), each grassland types
There is corresponding meadow Net long wave radiation conversion ratio coefficient value, can be obtained by Literature Consult or actual measurement.
Further, in step 4) meadow ground biomass calculate the specific steps are：Meadow year NPP results and meadow class
Result is calculated by formula in the root/shoot ratio of type, root system turnover rate, under ground portion phosphorus content, aerial part phosphorus content.
Further, in step 4) different grassland types root/shoot ratio, root system turnover rate, under ground portion phosphorus content, overground part
Divide the coefficients such as phosphorus content, Literature Consult can be passed through or actual measurement obtains.
The present invention is directed to meadow ground biomass, it is proposed that a kind of inverse model algorithm based on multi-spectrum remote sensing image.
The characteristics of algorithm synthesis remote sensing image of the present invention obtains a wide range of monitoring data and the theory advantage of mechanism model, can be compared with
For accurate simulated sward ground biomass, ecological ragime monitoring, grazing management available for natural meadow etc..
Description of the drawings
Fig. 1 is the meadow ground biomass the present invention is based on high time resolution and spatial resolution Multi-spectral Remote Sensing Data
The flow chart of inversion method
Specific embodiment
The present invention is based on the meadow ground biomass invertings of high time resolution and spatial resolution Multi-spectral Remote Sensing Data
Method calculation process is as shown in Figure 1.This method algorithm principle is specifically described as follows：
1. NPP principles are calculated based on the efficiency of light energy utilization
NPP represents green plants, and institute can fixed total amount of organic in unit interval, unit area.The efficiency of light energy utilization
Photosynthetically active radiation that model is absorbed by meadow (absorbed photosyn-thetically active radiation,
APAR) and Net long wave radiation conversion ratio (ε) calculates the NPP on meadow：
NPP=APAR (x, t) × ε (x, t)
In formula：X is spatial position, and t is the time
APAR (x, t) represents pixel in the t months systemic photosynthetically active radiation, unit MJ.m-2.month-1.ε (x,
T) represent pixel in the actually active radiation conversion ratio of the t months, unit gC.MJ-1.The photosynthetically active radiation (APAR) that meadow absorbs
By the photosynthetically active radiation (photosynthetically active radiation, PAR) in total solar radiation and meadow pair
The assimilation ratio (FPAR) of photosynthetically active radiation determines that FPAR is represented using normalized differential vegetation index (NDVI) and grassland types, ε
It is the efficiency that meadow is converted to the photosynthetically active radiation (FPAR) of absorption Organic carbon, is mainly influenced by temperature and moisture.
APAR (x, t)=SOL (x, t) × FPAR (x, t) × 0.5
SOL (x, t) represents the solar radiation total amount at pixel (position) x, unit MJ.m in the t months-2；FPAR is meadow layer
To the assimilation ratio (no unit) of incident photosynthetically active radiation；Constant 0.5 represents the sun Net long wave radiation (wave that meadow can utilize
A length of 0.38-0.71 μm) account for the ratio of total solar radiation.
Meadow has maximum light utilization efficiency under ideal conditions, and maximum light utilization efficiency under real world conditions is mainly by temperature
The influence of degree and moisture：
ε (x, t)=Tε1(x, t) × Tε2(x, t) × Wε(x, t) × εmax
The first two factor representation low temperature and high temperature are to the coercion of the efficiency of light energy utilization.Third factor representation water stress
Coefficient is influenced, reflects the influence of moisture condition.Section 4 is the maximum efficiency of light energy utilization under ideal conditions, i.e., Net long wave radiation is converted
Rate (gC.MJ-1)。
Comprehensive above formula calculates NPP and becomes：
NPP=SOL (x, t) × FPAR (x, t) × 0.5 × Tε1(x, t) × Tε2(x, t) × Wε(x, t) × εmax
Wherein：(1) SOL (x, t) is solar radiation total amount, and observing data interpolating by meteorological site obtains：
(2) FPAR (x, t) computational methods
There are linear relationships with NDVI by FPAR (x, t), can be according to the maximum value and minimum value of a certain grassland types NDVI
And corresponding FPAR maximum values and minimum value determine：
The value of FPARmax, FPARmix are unrelated with grassland types in formula, and respectively 0.95,0.001.NDVIi, max,
NDVIi, min are respectively the NDVI maximum values and minimum value of corresponding i-th kind of grassland types.
Further study showed that FPAR (x, t) is with ratio meadow index SR, there is also preferable linear relationships：
SRi, max, SRi, min are respectively the NDVIi, max, NDVIi, min of corresponding i-th kind of grassland types：
The FPAR that NDVI is estimated is higher than measured value, and the FPAR estimated by SR is then less than measured value, but its error is small
In directly by NDVI estimated as a result, the mean value both therefore taken as FPAR estimated values：
FPAR (x, t)=α × FPARNDVI+(1-α)×FPARsR
α is regulation coefficient.
(3)Tε1(x, t) computational methods
Tε1When (x, t) is reflected in low temperature and high temperature in plant biochemical action net first life is reduced to photosynthetic limitation
Force of labor can be represented with formula below：
Tε1(x, t)=0.8+0.02Topt(x)-0.0005[Topt(x)]2
Topt(x) work as monthly mean temperature, the size of NDVI and its change when reaching highest for NDVI values in a certain region 1 year
The upgrowth situation of plant can be reflected by changing, and when NDVI reaches highest, plant growth is most fast, and temperature at this time can be to a certain degree
On represent the optimum temperature of plant growth.
(4)Tε2(x, t) computational methods
Tε2(x, t) represents environment temperature from optimum temperature (Topt) gradual to plant light utilization efficiency when high temperature and low temperature variation
The trend to become smaller, this is because when low temperature and high temperature, high respiration consumption will reduce light utilization efficiency, be grown in and deviate most thermophilic
Under conditions of degree, light utilization efficiency is also bound to reduce：
T (x, t) represents the mean temperature at t month pixels x.When the mean temperature of certain month is than optimum temperature (Topt) 10 ° high
Or when 13 ° low, Tε2(x, t) is optimum temperature (T equal to monthly mean temperature (T (x, t))opt) when Tε2The half of (x, t).
(5)Wε(x, t) computational methods
Water stress influences coefficient Wε(x, t) reflects available moisture condition that plant can utilize to light utilization efficiency
It influences.With the increase of available moisture in environment, Wε(x, t) gradually increases, its value range is for 0.5 (in extreme drought item
Under part) to 1 (very under wet condition), formula is：
EET (x, t) is region actual evapotranspiration (the mm/ months), and PET (x, t) is Regional potential evapotranspiration amount (the mm/ months).EET
(x, t) is asked for according to region actual evapotranspiration model：
In formula, r be precipitation (mm), RnFor net radiation amount (mm).Since general weather station is without earth's surface
Net radiation is observed, RnComputational methods are as follows：
Rn=(Ep×r)1/2·[0.369+0.598·(Ep/r)1/2]
EpFor local Penman-Monteith formula, i.e., fritter is sufficiently humidified so as to the evapotranspiration on ground under the conditions of local climate.EpWith moon samming
The relationship spent between T (DEG C) is as follows, and unit is the mm/ months：
Ep=16 × (10T/I)α
Wherein：
α=(0.675 × I3-77.1×I2+17920×I+492390)×10-6
I is the heat factors of 12 months summations, and a is then the constant to vary in different localities, is the function of I.This relationship is only in gas
Between 0 ° to 26.5 ° of temperature effectively.Potential evapotranspiration is set to 0 when temperature is less than 0 °, and when higher than 26.5 °, potential evapotranspiration is only with temperature
Increase and increase, it is unrelated with I.
Region actual evapotranspiration, local potential evapotranspiration, Regional potential evapotranspiration complementary relationship：
EET (x, t)+Ep=2 × PET (x, t)
So far, Wε(x, t) can be acquired according to moon rainfall with temperature.
2. ground biomass principle is calculated based on the ratio between NPP and above and below ground biomass
According to NPP results and underground ground biomass ratio, meadow ground biomass, i.e. grass yield, unit can be calculated
For g/m2,
NPP=ANPP+BNPP
ANPP is meadow Aboveground Biomass of Young, and BNPP is meadow Underground biomass.Therefore it can pass through all kinds of grass
Ground aerial part and under ground portion productivity ratio estimation meadow grass yield：
GY is meadow grass yield.Wherein BNPP calculations are as follows：
BNPP=BGB × (live BGB/BGB) × turover
Turover=0.0009 × ANPP+0.25
Wherein BGB is meadow under ground portion (root system) biomass, and live BGB/BGB are that root biomass living accounts for total root system
The ratio of biomass, BGB × (liveBGB/BGB) are equal to the ratio between underground living organism amount and ground biomass (R/S),
Turnover is Steppe Plants root system turnover value.
It is to be noted that any deformation that specific embodiment according to the present invention is made, all without departing from the present invention's
The range that spirit and claim are recorded.
Claims (8)
1. a kind of meadow ground biomass inversion method based on high time resolution and spatial resolution Multi-spectral Remote Sensing Data,
The specific steps are：1) Multi-spectral Remote Sensing Data of covering meadow Growing season is chosen, calculates normalization meadow index NDVI, and synthesize
The monthly maximum NDVI in covering research area；2) according to meteorological site the moon samming, moon gross precipitation, moon Globalradiation data,
Interpolation generation covering research area the moon samming, moon gross precipitation, moon Globalradiation data, spatial resolution with it is multispectral distant
The resolution ratio of sense data is consistent；3) it based on the efficiency of light energy utilization (Light Use Efficiency, LUE) theory, utilizes
CASA (Carnegie-Ames-Stanford Approach) model calculates Net primary productivity (the Net Primary on meadow
Productivity, NPP), calculating process is：First according to the normalized differential vegetation index of each grassland types (NDVI) and
Ratio vegetation index (SR) obtains the assimilation ratio (FPAR) of the photosynthetically active radiation on meadow, is secondly grown according to meadow most vigorous
The temperature computation in month obtain meadow growth optimal temperature index Tε1(no unit), then according to the optimal temperature on meadow
Index and monthly the moon samming coefficient T to efficiency of light energy utilization reduction of the meadow under the conditions of optimal temperature is deviateed is calculatedε2
(no unit), calculating the water stress on meadow later influences coefficient Wε(no unit), most at last These parameters with the moon total sun spoke
Penetrate SOL and meadow Net long wave radiation conversion rate coefficient εmaxMultiplication obtains the NPP of Growing season monthly, and each month NPP of Growing season adds up
To meadow year NPP, as shown in formula (1)：
NPP=SOL (x, t) × FPAR (x, t) × 0.5 × Tε1(x, t) × Tε2(x, t) × Wε(x, t) × εmax(1), SOL (x, t)
Represent the solar radiation total amount at pixel (position) x, unit MJ.m in the t months-2, FPAR is meadow layer to incident photosynthetic effective spoke
The assimilation ratio (no unit) penetrated, ε (x, t) represent pixel in the actually active radiation conversion ratio of the t months, unit gC.MJ-1。4)
According to NPP and the ratio between ground biomass and underground biomass, meadow ground biomass is calculated.
It is 2. raw on the ground based on the meadow of high time resolution and spatial resolution Multi-spectral Remote Sensing Data as described in claim 1
Object amount inversion method, which is characterized in that when calculating FPAR, as shown in formula (2)：
The value of FPARmax, FPARmix are unrelated with grassland types in formula, and respectively 0.95,0.001.NDVIi, max, NDVIi,
Min be respectively corresponding i-th kind of grassland types NDVI maximum values and minimum value, SRi, max, SRi, min it is respectively corresponding
The NDVIi of i-th kind of grassland types, max, NDVIi, min, SR (x, t)=(1+NDVI (x, t))/(1-NDVI (x, t)).
It is 3. raw on the ground based on the meadow of high time resolution and spatial resolution Multi-spectral Remote Sensing Data as described in claim 1
Object amount inversion method, which is characterized in that meadow growth optimal temperature index is calculated, as shown in formula (3)：
Tε1(x, t)=0.8+0.02Topt(x)-0.0005[Topt(x)]2 (3)
Topt(x) work as monthly mean temperature when reaching highest for NDVI values in a certain region 1 year.
It is 4. raw on the ground based on the meadow of high time resolution and spatial resolution Multi-spectral Remote Sensing Data as described in claim 1
Object amount inversion method, which is characterized in that calculate the temperature T during growth optimal temperature index of meadowopt(x), it is according to careless for many years
When ground NDVI reaches peak the moon samming mean value calculation obtain.
It is 5. raw on the ground based on the meadow of high time resolution and spatial resolution Multi-spectral Remote Sensing Data as claimed in claim 2
Object amount inversion method, which is characterized in that calculate to obtain meadow being reduced to the efficiency of light energy utilization under the conditions of optimal temperature is deviateed
Coefficient, as shown in formula (4)：
T (x, t) represents the mean temperature at t month pixels x.
It is 6. raw on the ground based on the meadow of high time resolution and spatial resolution Multi-spectral Remote Sensing Data as claimed in claim 2
Object amount inversion method, which is characterized in that calculating the water stress on meadow influences coefficient, as shown in formula (5)：
EET (x, t) is region actual evapotranspiration (the mm/ months), and PET (x, t) is Regional potential evapotranspiration amount (the mm/ months).
It is 7. raw on the ground based on the meadow of high time resolution and spatial resolution Multi-spectral Remote Sensing Data as claimed in claim 2
Object amount inversion method, which is characterized in that according to NPP and the ratio between ground biomass and underground biomass in step 4), calculate grass
Ground ground biomass, calculating process are：First according to the phosphorus content of meadow under ground portion and aerial part, root system turnover rate with
And root/shoot ratio parameter, the ratio between meadow underground NPP and ground NPP (BNPP/ANPP) are obtained, then passes through meadow year NPP and grass
Ground BNPP/ANPP must be calculated to meadow ground biomass, as shown in formula (6)：
GY is meadow grass yield, and ANPP is meadow Aboveground Biomass of Young, and BNPP is meadow Underground biomass.
It is 8. raw on the ground based on the meadow of high time resolution and spatial resolution Multi-spectral Remote Sensing Data as described in claim 1
Object amount inversion method, which is characterized in that BNPP is calculated as shown in formula (7)：
BNPP=BGB × (live BGB/BGB) × turover (7)
Wherein BGB is meadow under ground portion (root system) biomass, and live BGB/BGB are that root biomass living accounts for total root system biology
The ratio of amount, BGB × (live BGB/BGB) are equal to the ratio between underground living organism amount and ground biomass (R/S), turnover
For Steppe Plants root system turnover value.
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| CN114184280B (en) * | 2021-12-07 | 2024-03-19 | 自然资源部国土卫星遥感应用中心 | Surface temperature time normalization method based on heat balance |
| CN114897630A (en) * | 2022-06-21 | 2022-08-12 | 生态环境部卫星环境应用中心 | Method and device for estimating optimum temperature of vegetation growth |
| CN114897630B (en) * | 2022-06-21 | 2022-11-18 | 生态环境部卫星环境应用中心 | Vegetation growth optimum temperature estimation method and device |
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