CN106384171A - Fir forest carbon reserve estimation model suitable for local research area - Google Patents
Fir forest carbon reserve estimation model suitable for local research area Download PDFInfo
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Abstract
The invention discloses a fir forest carbon reserve estimation model suitable for a local research area. Actually measured fir small class data, Landsat8OLT images and research area DEM data are collected; the collected original data are preprocessed in a remote sensing processing system, including geometrical precise correction, landform correction, research area cutting, radiometric calibration and atmospheric correction so that the data are enabled to meet the requirements of further analysis, and vegetation growth factors, remote sensing factors and earth-science factors are respectively extracted from the preprocessed data; all the correlated factors extracted in advance are imported for statistical analysis to perform construction of a biomass multivariable regression model, and precision verification is performed on the output module through the actually measured data so that an actually applicable fir ground carbon reserve multivariable regression estimation model is obtained. Compared with the models in the prior art, the model has higher targeted performance so that the reliability of fir forest carbon reserve estimation of the research area can be greatly enhanced and the estimation precision and the estimation efficiency can be substantially enhanced.
Description
Technical field
The present invention relates to belonging to the crossing domain between forestry, mathematical statisticss and remote sensing treatment technology three, especially relate to
And a kind of it is applied to the local Abies fabri forest organic C storage estimation models in research area.
Background technology
With the continuous development of remote sensing technology, by its unique advantage in terms of acquisition of information, can quickly and efficiently estimate
Calculate the organic C storage of forest in research area.
At present, forest carbon storage evaluation method is varied.The more commonly used method has：Remote sensing information parameter is intended with Biomass
Close evaluation method, remotely-sensed data and process model matching evaluation method, benchmark sample plot method and artificial nerve network model method etc..This
Invention is exactly using remote sensing information parameter and Biomass matching evaluation method, sets up fir ground biomass multiple regression estimation mould
Type, and then obtain fir belowground biomass multiple regression appraising model.By appraising model in forest remote sensing information, Forest biont
Set up dependency relation between amount and forest carbon storage, forest carbon storage estimation problem can be efficiently solved.The method prominent
Advantage be can macroscopic view, continuous, detect Vegetation carbon storage exactly, and simple and practical it is adaptable to when one a ground one thing situation,
Ratio traditional method low cost, destroys little.But it recognizes not enough, shortage biophysicss meaning to Physical Mechanism.
In recent years, existing researcher Abies fabri forest carbon is stored up using remote sensing information parameter and Biomass matching evaluation method
Amount and its association area are furtherd investigate, such as Xu Xinliang, Cao Mingkui etc. [forest biomass remote sensing appraising and applied analysis [J].
Earth Information Science, 2006 (4):122-128.] comparison and summary to forest biomass evaluation method, illustrate remote sensing information ginseng
The pluses and minuses of number and Biomass matching evaluation method and its applicable elements.Huang Congde, Zhang Jian et al. [Sichuan Province and Chongqing region
Forest cover organic C storage is dynamic [J]. Acta Ecologica Sinica, 2008 (3):966-975.] the fir biomass estimation model that proposes and its
Efficiency of carbon con version.Fang Jingyun, Liu Guohua, Xu Songling et al. [the Biomass net production [J] of China forest cover. Acta Ecologica Sinica,
1996,16(5):497--508.] the systematic study biological productivity of China forest cover.Result shows, China's Forest biont
The Rule of Geographical Distribution of the productivity is consistent with world's general trend, but amount is upper variant, is in particular in:China's forest biomass
Meansigma methodss are less than world average level, and net production seems higher.Han Aihui [forest biomass and organic C storage remote sensing monitoring
Technique study [J]. Beijing Forestry University, 2009,63.] using multiple regression method, manage meteorological factor in combination, grind and build
Forest biomass estimation models with CBERS-WFI data and MODIS data as chief source of information.
Content of the invention
The purpose of the present invention is that and provides to solve the above problems that a kind of to be applied to the local fir in research area gloomy
Woods organic C storage estimation models.
The present invention is achieved through the following technical solutions above-mentioned purpose：
The present invention comprises the following steps：
Step one, collects actual measurement fir bottom class data, Landsat8OLI image and research area dem data；
Step 2, in remote sensing processing system, to collect come initial data carry out pretreatment, including geometric accurate correction,
Topographical correction, research area's cutting, radiation calibration and atmospheric correction so as to meet the requirement analyzed further, and from above-mentioned pre- from
In data after reason, extract the vegetation growth factor, the remote sensing factor and ground respectively and learn the factor；
Step 3, imports statistical analysiss each correlation factor extracting in advance, carries out Biomass multivariate regression models structure
Build, and precision test is carried out to output model by measured data, as below standard in model accuracy, then rebuild model, otherwise
Then obtain fir ground biomass model；
Step 4, according to the relation between fir Biomass and organic C storage, obtain can practical application the carbon storage of fir ground
Amount multiple regression appraising model.
The beneficial effects of the present invention is：
The present invention is a kind of Abies fabri forest organic C storage estimation models being applied to research area locality, compared with prior art,
It is more targeted for the present invention, is greatly improved the reliability of research area Abies fabri forest organic C storage estimation, significantly improves estimation essence
Degree and estimation efficiency.
Brief description
Fig. 1 is the implementing procedure figure of the present invention.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings：
As shown in Figure 1：Comprise the following steps：
Step one, collects actual measurement fir bottom class data, Landsat8OLI image and research area dem data；
Step 2, in remote sensing processing system, to collect come initial data carry out pretreatment, including geometric accurate correction,
Topographical correction, research area's cutting, radiation calibration and atmospheric correction so as to meet the requirement analyzed further, and from above-mentioned pre- from
In data after reason, extract the vegetation growth factor, the remote sensing factor and ground respectively and learn the factor；
Step 3, imports statistical analysiss each correlation factor extracting in advance, carries out Biomass multivariate regression models structure
Build, and precision test is carried out to output model by measured data, as below standard in model accuracy, then rebuild model, otherwise
Then obtain fir ground biomass model；
Step 4, according to the relation between fir Biomass and organic C storage, obtain can practical application the carbon storage of fir ground
Amount multiple regression appraising model.
Embodiment：The present invention, with Barkam County Suo Mo township of Sichuan Province fir forest as object of study, sets up 90 sample points, greatly
The little sample prescription for 30m × 30m.First, collect 2013 Nian Suomo township fir forest forest governmance bottom class's data and the same year
Landsat8OLI remote sensing image data, and remote sensing image data is done with pretreatment, such as：Wave band synthesis, geometric correction, image melt
Conjunction, atmospheric correction and cutting etc. are processed, and reach requirement with the precision ensureing follow-up work with reliability.Then, with sample point it is
Benchmark, 20 correlation factors in extraction bottom class's data and remote sensing images are such as：Canopy density, coverage, the age of stand, B1, B2, B3, B4,
B5, B6, B7, RVI, DVI, NDVI, GVI, BVI, WVI, VI3, the gradient, slope aspect and height above sea level.Again according to mathematical statistics, adopt
Gradually linear regression method, extract before 20 correlation factors are carried out correlation analysiss.With organic C storage as dependent variable, set up each
The dependency relation of organic C storage correlation factor, rejects the little factor of dependency relation, retains the big factor of dependency relation as independent variable,
Build the multiple linear regression model of research area ground fir Biomass.And precision test is done to the Biomass Models drawing, test
Card result shows, model accuracy reaches estimation and requires.Finally, the transformational relation according to Biomass and organic C storage, draws Suo Mo township
Fir belowground biomass model is：
C=35.507L+1.245F+0.723G-152.638B1-66.020
In formula：C is organic C storage (t/hm2)；L is the age of stand；F is vegetation coverage；G is the gradient；B1 is first band.
Ultimate principle and principal character and the advantages of the present invention of the present invention have been shown and described above.The technology of the industry
, it should be appreciated that the present invention is not restricted to the described embodiments, the simply explanation described in above-described embodiment and description is originally for personnel
The principle of invention, without departing from the spirit and scope of the present invention, the present invention also has various changes and modifications, these changes
Change and improvement both falls within scope of the claimed invention.Claimed scope by appending claims and its
Equivalent thereof.
Claims (1)
1. a kind of local Abies fabri forest organic C storage estimation models in research area that are applied to are it is characterised in that comprise the following steps：
Step one, collects actual measurement fir bottom class data, Landsat8OLI image and research area dem data；
Step 2, in remote sensing processing system, carries out pretreatment to collecting the initial data coming, including geometric accurate correction, landform
Correct, study area's cutting, radiation calibration and atmospheric correction so as to meet the requirement analyzed further, and after above-mentioned pretreatment
Data in, respectively extract the vegetation growth factor, the remote sensing factor with ground learn the factor；
Step 3, imports statistical analysiss each correlation factor extracting in advance, carries out Biomass multivariate regression models structure, and
Precision test is carried out to output model by measured data, as below standard in model accuracy, then rebuild model, otherwise then obtain
Fir ground biomass model；
Step 4, according to the relation between fir Biomass and organic C storage, obtain can practical application fir belowground biomass many
First regression estimation model.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107247809A (en) * | 2017-07-19 | 2017-10-13 | 南京林业大学 | A kind of new method of artificial forest different age forest space mapping |
| CN109190178A (en) * | 2018-08-07 | 2019-01-11 | 广西壮族自治区林业科学研究院 | A kind of China fir carbon density calculation method based on DEM terrain factor |
| CN112434617A (en) * | 2020-11-26 | 2021-03-02 | 南京观微空间科技有限公司 | Forest biomass change monitoring method and system based on multi-source remote sensing data |
| 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 |
| CN113177744A (en) * | 2021-06-09 | 2021-07-27 | 西安建筑科技大学 | Urban green land system carbon sink amount estimation method and system |
| US11481904B1 (en) | 2022-01-04 | 2022-10-25 | Natural Capital Exchange, Inc. | Automated determination of tree inventories in ecological regions using probabilistic analysis of overhead images |
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107247809A (en) * | 2017-07-19 | 2017-10-13 | 南京林业大学 | A kind of new method of artificial forest different age forest space mapping |
| CN107247809B (en) * | 2017-07-19 | 2020-05-26 | 南京林业大学 | New method for forest age space mapping of artificial forest |
| CN109190178A (en) * | 2018-08-07 | 2019-01-11 | 广西壮族自治区林业科学研究院 | A kind of China fir carbon density calculation method based on DEM terrain factor |
| CN109190178B (en) * | 2018-08-07 | 2022-11-01 | 广西壮族自治区林业科学研究院 | Method for calculating carbon density of fir based on DEM (digital elevation model) terrain factor |
| CN112434617A (en) * | 2020-11-26 | 2021-03-02 | 南京观微空间科技有限公司 | Forest biomass change monitoring method and system based on multi-source remote sensing data |
| 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 |
| CN112836610B (en) * | 2021-01-26 | 2022-05-27 | 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 |
| CN113177744A (en) * | 2021-06-09 | 2021-07-27 | 西安建筑科技大学 | Urban green land system carbon sink amount estimation method and system |
| CN113177744B (en) * | 2021-06-09 | 2024-03-01 | 西安建筑科技大学 | Urban green land system carbon sink estimation method and system |
| US11481904B1 (en) | 2022-01-04 | 2022-10-25 | Natural Capital Exchange, Inc. | Automated determination of tree inventories in ecological regions using probabilistic analysis of overhead images |
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Application publication date: 20170208 |