CN108647623A - A kind of potential organic C storage remote sensing estimation method of forest based on resource constraint - Google Patents
A kind of potential organic C storage remote sensing estimation method of forest based on resource constraint Download PDFInfo
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- CN108647623A CN108647623A CN201810417220.2A CN201810417220A CN108647623A CN 108647623 A CN108647623 A CN 108647623A CN 201810417220 A CN201810417220 A CN 201810417220A CN 108647623 A CN108647623 A CN 108647623A
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Abstract
The invention discloses a kind of potential organic C storage remote sensing estimation method of forest based on resource constraint, including data acquisition and processing, forest cover region and type acquisition, leaf area index calculating, the high model construction of maximal tree, potential organic C storage model construction and inverting etc..The data acquisition and input data and preprocess method that processing is the method for the present invention；The forest cover region and type are retrieved as determining wood land and type based on support vector machine method using Landsat TM data；The leaf area index is calculated as being based on neural network method inverting leaf area index using Landsat TM；The high model construction of maximal tree is law of conservation of energy based on forest ecology and resource constraint establishes potential tree height inversion model and the potential tree height of inverting；The potential organic C storage model construction of the forest and inverting are that analysis forest tree is high and organic C storage statistical data forms the potential organic C storage appraising model of forest, and is obtained according to model and study the potential organic C storage of area forest.
Description
Technical field
The present invention relates to forest carbon storages to estimate field, and in particular to a kind of potential carbon of forest based on resource constraint
Reserves remote sensing estimation method.
Background technology
The carbon cycle of terrestrial ecosystems is the important component of global carbon, is sent out during global warming
Wave very important effect.As the main body of terrestrial ecosystems, the biomass of forest accounts for land vehicles total biomass
85%-90%.The biomass and organic C storage of forest ecosystem play vital effect in global carbon.
From signing the holding to Copenhagen World Climate Conference of the Kyoto Protocol, many countries of the world just pass through
Forest realizes that reduction of greenhouse gas discharge is in agreement.Correspondingly, domestic and international forest researcher and climatologists are directed to
The dynamic change of forest carbon storage and forest cover carbon storehouse has also carried out a large amount of research in country and regional scale, these grind
Study carefully achievement and provides important evidence for the forest emission reduction ability on assessment country and regional scale.
China is used as maximum developing country, and what reduction of greenhouse gas discharge had become that Chinese Economy Development must solve asks
Topic.With the fast development of remote sensing technology, obtainable different resolution remotely-sensed data is more and more, and novel sensor is also continuous
Occur.Laser radar data, polarization S Λ R datas and multiple-angle thinking data can high-precision acquisition Forest Vertical knots
Structure parameter, many intermediate-resolution remote sensing satellites have carried out continuous observation for many years to the whole world.These are all high-precision, big ruler
Degree, long-term sequence estimating forest carbon storage provide a fast and easily approach.Currently with remote sensing technology to forest
Organic C storage, which is estimated, has carried out many researchs, some results of study also have very high-precision.But these researchs collect mostly
In forest carbon storage is estimated in smaller survey region, and using the method for empirical model, can not be in large scale area
The high-precision estimation of forest carbon storage is realized on domain, while it is even more impossible to realize potential (maximum) forest carbon storage on regional scale
Estimation.
Estimate mechanism model by forest species and forest based on the forest carbon storage of forest ecology theory and remote sensing technology
The influence very little of variation of ecology and environment has stronger versatility, is effectively applied to the Forest Carbon storage of Large-scale areas
In amount estimation research.Meanwhile using resources supplIes constrained procedure, can predict potential (maximum) forest carbon storage, to be system
Determine Carbon budget report, the reliable technical support of international carbon trade barrier offer is provided.
Invention content
1. it is latent that in view of the deficiencies of the prior art, the purpose of the present invention is to provide a kind of forests based on resource constraint
In organic C storage remote sensing estimation method, the remote sensing appraising of the potential organic C storage of forest is realized.
2. to achieve the above object, the technical solution adopted by the present invention is：A kind of forest based on resource constraint is latent
In organic C storage remote sensing estimation method, algorithm flow chart is as shown in Figure 1, include step：
Step 1, the image data of Landsat land imager is obtained, and image data is pre-processed；It obtains
Take temperature, precipitation, wind speed, relative humidity, solar radiation data, and by the resampling of meteorological data interpolation at land imager shadow
As data spatial resolution having the same；Obtain Λ STER dem datas；
Step 2, it is obtained using the sorting technique of support vector machines using Landsat land imager reflectivity image gloomy
Woods overlay area, described includes coniferous forest, broad-leaf forest and theropencedrymion；
Step 3, all types of Forest Leaf Area Indexes in actual measurement sample ground, are adopted using Landsat land imager reflectivity data
Leaf area index information is obtained with neural network method；
Step 4, all types of forest trees are high with surveying sample, the energy followed according to the acquired solar energy of tree and precipitation
It measures law of conservation and builds the potential tree height inversion model of forest；
Step 5, acquisition research area forest tree height and organic C storage historical statistical data, between analysis forest tree height and organic C storage
Scaling relation equation, build the potential organic C storage appraising model of forest；Forest is obtained according to the potential organic C storage appraising model of forest
The potential organic C storage inversion result of overlay area forest.
Further, the step 1 includes to the pretreatment of image data:Geometric correction, radiant correction, atmospheric correction and
Topographical correction；The step 1 uses Kriging regression method to meteorological data interpolation resampling；
Further, the step 2 uses Landsat land imager reflectivity image the classification of support vector machines
Method obtains forest cover region, first carries out the Image Segmentation of object-oriented, then is supported the image classification of vector machine, obtains
Forest cover region, including：Coniferous forest, broad-leaf forest and theropencedrymion；
Further, the step 3 builds the leaf area index nerve net based on land imager Reflectivity for Growing Season data
Network inverse model is with being built upon a large amount of actual measurement samples on all types of Forest Leaf Area Index sample basis；
Further, all types of forest trees are high with surveying sample for the step 4, according to tree acquired solar energy and drop
Law of conservation of energy (Fig. 2 show the law of conservation of energy schematic diagram that tree growth follows) structure potential tree of forest that water is followed
High inverse model, is as follows：
Step 41, all types of forest trees are high with surveying sample；
Step 42, the obtainable current passband (Q of structure treep) equation：By the pick-up rate p of precipitationincAnd vegetation root
Swept area and absorption efficiency γ determine；According to the scale growth relationship of root radius and tree between high, obtains tree and can get
Current passband (Qp) equation is
P in formulaincFor precipitation, γ is root rate of water absorption, β3≈ 0.42 is the mass ratio of tree root and trunk, and h is
Maximal tree is high；
Step 43, the energy budget law of conservation for analyzing Vegetation canopy builds the practical flow water-use ratio (Q of treee) side
Journey：Qe=Qe(hmax,LAI,ψ,A,Rinc,TA,u,RH)；
In formula, variable indicates respectively：Maximal tree height (hmax), leaf area index (LAI), solar zenith angle (ψ), height above sea level
(A), the solar radiation energy (R obtained under per unit areainc), atmospheric temperature (TA), wind speed (u) and relative humidity
(RH)；
Step 44, the relation of equality of actual rate of evaporation and obtainable water flux density rate based on maximal tree Gao Shu, structure
The potential high estimation equation of maximal tree:hmax=H (LAI, ψ, A, Rinc,TA,u,RH,pinc)
In formula, variable indicates respectively：Under leaf area index (LAI), solar zenith angle (ψ), height above sea level (A), per unit area
Obtained solar radiation energy (Rinc), atmospheric temperature (TA), wind speed (u) relative humidity (RH) and precipitation (pinc)。
Description of the drawings
The potential organic C storage remote sensing appraising algorithm flow chart of Fig. 1 forests.
Fig. 2 trees grow the law of conservation of energy schematic diagram followed.
Claims (6)
1. a kind of potential organic C storage remote sensing estimation method of forest based on resource constraint, which is characterized in that including step：
Step 1, the image data of Landsat land imager is obtained, and image data is pre-processed；Obtain gas
Image data (temperature, precipitation, wind speed, relative humidity, solar radiation), and by the resampling of meteorological data interpolation at land imager
Image data spatial resolution having the same；Obtain the ASTER with land imager image data same spatial resolution
DEM image datas；
Step 2, forestland distribution is obtained using the sorting technique of support vector machines according to the reflectivity image obtained after pretreatment
Domain and Forest Types；
Step 3, leaf area index information is obtained using neural network method using the reflectivity image obtained after pretreatment；
Step 4, the potential height inverse model structure of forest；The energy followed according to the acquired solar energy of tree and precipitation
Law of conservation and resource constraint build the potential tree height inversion model of forest；It is latent that forest is obtained according to the potential high model of tree of forest
In height；
Step 5, research area forest tree height and organic C storage historical statistical data, the high pass between organic C storage of analysis forest tree are obtained
It is equation, builds the potential organic C storage appraising model of forest；It is latent that research area forest is obtained according to the potential organic C storage appraising model of forest
In organic C storage inversion result.
2. the potential organic C storage remote sensing estimation method of the forest according to claim 1 based on resource constraint, feature
It is, the step 1 includes to the pretreatment of image data:Geometric correction, radiant correction, atmospheric correction and topographical correction；Institute
It states step 1 and Kriging regression method is used to meteorological data interpolation resampling.
3. the potential organic C storage remote sensing estimation method of the forest according to claim 2 based on resource constraint, feature
It is, the step 2 obtains forest cover region using the sorting technique of support vector machines, first carries out the image point of object-oriented
It cuts, then is supported the image classification of vector machine, form the image data for only including forest cover.
4. the potential organic C storage remote sensing estimation method of the forest according to claim 3 based on resource constraint, feature
It is, the step 3 surveys the leaf area index of wood land first；Secondly, structure is based on land imager Reflectivity for Growing Season
The leaf area index Neural Network Inversion model of data；The final leaf area index inverting for realizing wood land.
5. the potential organic C storage remote sensing estimation method of the forest according to claim 4 based on resource constraint, feature
It is, the potential height inverse model of step 4 forest, its base can be met by being the current passband according to tree for transpiration
The required current passband of this growth is received no more than this core concept of the obtained current passband in its root and leaf again
Solar radiation energy, law of conservation of energy structure needed for transpiration between energy and off-energy three；Model
Input data includes：Temperature, precipitation, wind speed, solar radiation, relative humidity, leaf area index, height above sea level, solar zenith angle；Model
Output data is that the potential tree of corresponding forest is high.
6. the potential organic C storage remote sensing estimation method of the forest according to claim 5 based on resource constraint, feature
It is, the potential organic C storage appraising model of step 5 forest, is that corresponding climatic province type is divided according to temperature, precipitation, is drawing
The high power-exponent eqution between organic C storage of climatic province inner member tree divided, utilizes forest tree height and organic C storage historical statistics number
It is fitted optimization according to the power-exponent eqution coefficient to structure.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111537018A (en) * | 2019-02-06 | 2020-08-14 | 印度电子信息技术部 | Estimating sequestered CO2Automatic carbon sequestration estimation system and method for quantities of |
| CN112819365A (en) * | 2021-02-23 | 2021-05-18 | 中国科学院空天信息创新研究院 | Carbon sink detection method and device, storage medium and electronic equipment |
| CN112836610A (en) * | 2021-01-26 | 2021-05-25 | 平衡机器科技(深圳)有限公司 | Land use change and carbon reserve quantitative estimation method based on remote sensing data |
| CN113449976A (en) * | 2021-06-21 | 2021-09-28 | 广东翁源滃江源国家湿地公园管理处 | Forestry carbon metering method based on ecological process model |
| CN114462579A (en) * | 2022-02-10 | 2022-05-10 | 中南大学 | Soil organic carbon content estimation method based on terrain and remote sensing data |
| CN114462701A (en) * | 2022-01-29 | 2022-05-10 | 武汉大学 | Monthly-scale land vegetation carbon reserve calculation optimization method based on land water reserve |
| CN115082274A (en) * | 2022-06-09 | 2022-09-20 | 贵州师范学院 | Earth surface vegetation carbon sink estimation and transaction method and system based on satellite remote sensing |
| CN115131370A (en) * | 2022-07-04 | 2022-09-30 | 东北林业大学 | Forest carbon sequestration and oxygen release capacity and benefit evaluation method and equipment |
| CN115561773A (en) * | 2022-12-02 | 2023-01-03 | 武汉大学 | Forest carbon reserve inversion method based on ICESat-2 satellite-borne LiDAR data and multispectral data |
| CN116563718A (en) * | 2023-07-11 | 2023-08-08 | 成都垣景科技有限公司 | Remote sensing mapping-based carbon reserve estimation method |
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Cited By (14)
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| CN111537018A (en) * | 2019-02-06 | 2020-08-14 | 印度电子信息技术部 | Estimating sequestered CO2Automatic carbon sequestration estimation system and method for quantities of |
| CN112836610A (en) * | 2021-01-26 | 2021-05-25 | 平衡机器科技(深圳)有限公司 | Land use change and carbon reserve quantitative estimation method based on remote sensing data |
| CN112819365A (en) * | 2021-02-23 | 2021-05-18 | 中国科学院空天信息创新研究院 | Carbon sink detection method and device, storage medium and electronic equipment |
| CN113449976A (en) * | 2021-06-21 | 2021-09-28 | 广东翁源滃江源国家湿地公园管理处 | Forestry carbon metering method based on ecological process model |
| CN114462701A (en) * | 2022-01-29 | 2022-05-10 | 武汉大学 | Monthly-scale land vegetation carbon reserve calculation optimization method based on land water reserve |
| CN114462579B (en) * | 2022-02-10 | 2024-09-17 | 中南大学 | Soil organic carbon content estimation method based on terrain and remote sensing data |
| CN114462579A (en) * | 2022-02-10 | 2022-05-10 | 中南大学 | Soil organic carbon content estimation method based on terrain and remote sensing data |
| CN115082274A (en) * | 2022-06-09 | 2022-09-20 | 贵州师范学院 | Earth surface vegetation carbon sink estimation and transaction method and system based on satellite remote sensing |
| CN115131370B (en) * | 2022-07-04 | 2023-04-18 | 东北林业大学 | Forest carbon sequestration and oxygen release capacity and benefit evaluation method and equipment |
| CN115131370A (en) * | 2022-07-04 | 2022-09-30 | 东北林业大学 | Forest carbon sequestration and oxygen release capacity and benefit evaluation method and equipment |
| CN115561773A (en) * | 2022-12-02 | 2023-01-03 | 武汉大学 | Forest carbon reserve inversion method based on ICESat-2 satellite-borne LiDAR data and multispectral data |
| CN115561773B (en) * | 2022-12-02 | 2023-03-10 | 武汉大学 | Forest carbon reserve inversion method based on ICESat-2 satellite-borne LiDAR data and multispectral data |
| CN116563718A (en) * | 2023-07-11 | 2023-08-08 | 成都垣景科技有限公司 | Remote sensing mapping-based carbon reserve estimation method |
| CN116563718B (en) * | 2023-07-11 | 2023-09-05 | 成都垣景科技有限公司 | Remote sensing mapping-based carbon reserve estimation method |
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