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Remote Sensing Estimation of Forest Aboveground Biomass Based on Lasso-SVR

Ping Wang / Sanqing Tan / Gui Zhang / Shuang Wang / Xin Wu

With the Lutou Forest Farm as the research area, the Lasso algorithm was used for characteristic selection, and the optimal combination of variables was input into the support vector regression (SVR) model. The most suitable SVR model was selected to estimate the aboveground biomass of the forest through the comparison of the kernel function and optimal parameters, and the spatial distribution map of the aboveground biomass in the study area was drawn. The significance analysis of special variables showed good correlations between forest aboveground biomass and each vegetation index. There was a more significant correlation with some remote sensing bands, a less significant correlation with some texture features, and a strong correlation with DEM in the terrain features. When the parameters C is 2 and g is 0.01, the SVR model has the highest precision, which can illustrate 73% of the forest aboveground biomass, with the validation set $R^{2}$ being 0.62. The statistical analysis of the results shows that the total aboveground biomass of the Lutou Forest Farm is $4.82 \times 10^{5}$ t. The combination of Lasso with the SVR model can improve the estimation accuracy of forest aboveground biomass, and the model has a strong generalization ability.

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Remote Sensing Estimation of Forest Aboveground Biomass Based on Lasso-SVR

Ping Wang / Sanqing Tan / Gui Zhang / Shuang Wang / Xin Wu

With the Lutou Forest Farm as the research area, the Lasso algorithm was used for characteristic selection, and the optimal combination of variables was input into the support vector regression (SVR) model. The most suitable SVR model was selected to estimate the aboveground biomass of the forest through the comparison of the kernel function and optimal parameters, and the spatial distribution map of the aboveground biomass in the study area was drawn. The significance analysis of special variables showed good correlations between forest aboveground biomass and each vegetation index. There was a more significant correlation with some remote sensing bands, a less significant correlation with some texture features, and a strong correlation with DEM in the terrain features. When the parameters C is 2 and g is 0.01, the SVR model has the highest precision, which can illustrate 73% of the forest aboveground biomass, with the validation set $R^{2}$ being 0.62. The statistical analysis of the results shows that the total aboveground biomass of the Lutou Forest Farm is $4.82 \times 10^{5}$ t. The combination of Lasso with the SVR model can improve the estimation accuracy of forest aboveground biomass, and the model has a strong generalization ability.

Remote Sensing Estimation of Forest Aboveground Biomass Based on Lasso-SVR

Ping Wang / Sanqing Tan / Gui Zhang / Shuang Wang / Xin Wu

With the Lutou Forest Farm as the research area, the Lasso algorithm was used for characteristic selection, and the optimal combination of variables was input into the support vector regression (SVR) model. The most suitable SVR model was selected to estimate the aboveground biomass of the forest through the comparison of the kernel function and optimal parameters, and the spatial distribution map of the aboveground biomass in the study area was drawn. The significance analysis of special variables showed good correlations between forest aboveground biomass and each vegetation index. There was a more significant correlation with some remote sensing bands, a less significant correlation with some texture features, and a strong correlation with DEM in the terrain features. When the parameters C is 2 and g is 0.01, the SVR model has the highest precision, which can illustrate 73% of the forest aboveground biomass, with the validation set $R^{2}$ being 0.62. The statistical analysis of the results shows that the total aboveground biomass of the Lutou Forest Farm is $4.82 \times 10^{5}$ t. The combination of Lasso with the SVR model can improve the estimation accuracy of forest aboveground biomass, and the model has a strong generalization ability.

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Title:

Remote Sensing Estimation of Forest Aboveground Biomass Based on Lasso-SVR

Contributors:

Ping Wang (author) / Sanqing Tan (author) / Gui Zhang (author) / Shuang Wang (author) / Xin Wu (author)

Published in:

Forests, Vol 13, Iss 10, p 1597 (2022)

Publication date:

2022

ISSN:

DOI:

https://doi.org/10.3390/f13101597

Type of media:

Article (Journal)

Type of material:

Electronic Resource

Language:

Unknown

Keywords:

aboveground biomass , remote sensing estimation , Lasso algorithm , support vector regression model , Plant ecology , QK900-989

Metadata by DOAJ is licensed under CC BY-SA 1.0

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