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Nonlinear partial least square optimizing model-based forest carbon sink remote sensing evaluation method

A partial least squares, optimization model technology, applied in measurement devices, material analysis through optical means, instruments, etc., can solve problems such as errors, inaccurate estimates, and regression analysis that cannot meet basic assumptions

Active Publication Date: 2014-03-26
ZHEJIANG FORESTRY UNIVERSITY
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Problems solved by technology

[0004] Multiple linear regression based on ordinary least squares assumes that there is a linear relationship between remote sensing data and biophysical attributes, and assumes that the independent variables (spectral bands of remote sensing data) are independent, but most of the spectral responses of biophysical attributes are curvilinear, and there is a high relationship between the bands. Therefore, the regression analysis is often difficult to meet the basic assumptions, resulting in inaccurate estimates; in addition, if there are measurement errors in the spectral reflectance and biophysical attribute variables, the ordinary least squares regression may get a wrong model, but the spectral Albedo and biophysical property variables are not possible without error

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  • Nonlinear partial least square optimizing model-based forest carbon sink remote sensing evaluation method

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Embodiment Construction

[0024] The present invention is described in detail below in conjunction with embodiment and with reference to accompanying drawing:

[0025] The specific implementation of each step of the present invention is now described by taking the remote sensing estimation of the carbon storage of the aboveground part of the moso bamboo forest as an example.

[0026] Step 1: Set up survey plots in the research area, observe the biomass in the plots and convert them into carbon stocks, and obtain remote sensing images corresponding to the observation time. The multi-band spectral reflectance of remote sensing images is used as an independent variable, and the carbon storage is used as a dependent variable.

[0027] As for the example of this moso bamboo forest, 55 survey plots of 30m×30m were set up in the research area. The size of the sample plots was equal to the spatial resolution of the Landsat5TM remote sensing image used, and the carbon storage of the moso bamboo forest in the sa...

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Abstract

The invention relates to a nonlinear partial least square optimizing model-based forest carbon sink remote sensing evaluation method which comprises the following main steps of: (1) mapping the original variable to a high-dimension space to obtain a new variable by adopting a kernel function and carrying out standardization treatment; (2) carrying out regression analysis on the extracted component by adopting a least square method and reducing a regression coefficient; (3) evaluating a model by adopting LOO cross effectiveness; (4) repeating the step (2) to step (3) and adding 1 to the component number every repetition till the extracted component number reaches the maximal value; (5) repeating the step (1) to the step (4) and adding 1 to the subsection number M in the step (1) every repetition till M is equal to the preset number; and (6) searching the model with maximal related coefficient of an estimating value and a practical value from all the models and modeling with the M and extracted component number at the moment for being used as a final estimation model. The invention uses the optimized nonlinear partial least square regression for establishing a forest carbon storage predicting model and improves the forest carbon storage predicting precision.

Description

【Technical field】 [0001] The invention relates to a forest carbon sink remote sensing estimation method based on a statistical optimization model. 【Background technique】 [0002] Forests are an important part of the global carbon balance and play a key role in the terrestrial carbon cycle. Therefore, accurate estimation of forest carbon storage is of great significance for the study of global change. Bamboo forest is a special forest type in subtropical regions of China (such as Zhejiang, Anhui, Jiangxi, Fujian and other provinces). Recent studies have shown that bamboo forest resources have huge carbon storage, and its contribution to global carbon balance has begun to attract attention. [0003] Remote sensing is an important means of estimating aboveground carbon storage in forests. However, remote sensing technology cannot directly measure biomass, carbon storage and their changes. It requires a series of processing and conversion of remote sensing observation data, and ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01N21/84
Inventor 杜华强周国模范渭亮
Owner ZHEJIANG FORESTRY UNIVERSITY
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