Geographically weighted regression based on estimation of regional forest carbon storage

GUO Hanru; ZHANG Maozhen; XU Lihua; YUAN Zhenhua; CHEN Tiange

doi:10.11833/j.issn.2095-0756.2015.04.002

Volume 32 Issue 4

Jul. 2015

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GUO Hanru, ZHANG Maozhen, XU Lihua, YUAN Zhenhua, CHEN Tiange. Geographically weighted regression based on estimation of regional forest carbon storage[J]. Journal of Zhejiang A&F University, 2015, 32(4): 497-508. doi: 10.11833/j.issn.2095-0756.2015.04.002

Citation:

GUO Hanru, ZHANG Maozhen, XU Lihua, YUAN Zhenhua, CHEN Tiange. Geographically weighted regression based on estimation of regional forest carbon storage[J]. Journal of Zhejiang A&F University, 2015, 32(4): 497-508. doi: 10.11833/j.issn.2095-0756.2015.04.002

Geographically weighted regression based on estimation of regional forest carbon storage

doi: 10.11833/j.issn.2095-0756.2015.04.002

1.
Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration, Zhejiang A & F University, Lin’an 311300, Zhejiang, China;
2.
School of Environmental & Resource Sciences, Zhejiang A & F University, Lin’an 311300, Zhejiang, China;
3.
Hualong District Science & Technology Bureau of Puyang City, Puyang 457001, Henan, China

Funds:

国家自然科学基金资助项目（30972360，41201563）;浙江省林业碳汇与计量创新团队资助项目（2012R10030-01）

Received Date: 2014-09-19
Rev Recd Date: 2014-11-01
Publish Date: 2015-08-20

Abstract

Global climate issues have confirmed the irreplaceable role forest carbon stocks play in the global carbon cycle. To research whether the geographically weighted regression （GWR） model method which considers the role of survey factors spatial heterogeneity and establish the local regression model, can improve the estimation accuracy of forest carbon stocks, instead of the more commonly used methods of global regression model such as ordinary least squares analysis（OLS）, we used forest management inventory data in Xianju County, Zhejiang Province, combined with Landsat TM image data developing local models using GWR to estimate forest carbon stock and its density. Available of geographically and altitudinal weighted regression （GAWR） model was then tested in smooth terrain. Analysis is included comparison to traditional regression and co-kriging interpolation. Results showed that the total forest aboveground carbon stocks estimated by the GWR（T） model for Xianju County were 3.132 106 Mg, and carbon density ranged from 0 to 89.964 Mghm－2 with a mean value of 15.555 Mghm－2. Meanwhile, the total forest aboveground carbon stocks calculated from diameter measurements were 3.192 106 Mg with a mean value of 15.854 Mghm－2. The overall result from GWR（T） model was lower than diameter measured by 1.880%, R2 = 0.654 （P＜0.01）, and carbon density distribution was consistent with the actual situation. The estimated results also had a higher accuracy with the RRMSE = 9.802 （P＜0.01） than traditional regression method with the RRMSE = 15.033 （P＜0.01） and co-kriging interpolation method with the RRMSE = 16.427 （P＜0.01）. GWR method can effectively estimate the regional forest aboveground carbon stocks reasonably and accurately, however, the GAWR model is not applicable for the areas with smooth terrain. Adding altitude as an explanatory variable in the modeling could improve estimation accuracy but would in turn create a multi-collinearity problem.［Ch, 6 fig. 9 tab. 26 ref.］

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通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

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Geographically weighted regression based on estimation of regional forest carbon storage

1. Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration, Zhejiang A & F University, Lin’an 311300, Zhejiang, China;

2. School of Environmental & Resource Sciences, Zhejiang A & F University, Lin’an 311300, Zhejiang, China;

3. Hualong District Science & Technology Bureau of Puyang City, Puyang 457001, Henan, China

Funds:

国家自然科学基金资助项目（30972360，41201563）;浙江省林业碳汇与计量创新团队资助项目（2012R10030-01）

Received Date: 2014-09-19

Rev Recd Date: 2014-11-01

Publish Date: 2015-08-20

Key words:

forest ecology /
forest carbon storage /
spatial heterogeneity /
geographically weighted regression （GWR） /
geographically and altitudinal weighted regression （GAWR）

Abstract: Global climate issues have confirmed the irreplaceable role forest carbon stocks play in the global carbon cycle. To research whether the geographically weighted regression （GWR） model method which considers the role of survey factors spatial heterogeneity and establish the local regression model, can improve the estimation accuracy of forest carbon stocks, instead of the more commonly used methods of global regression model such as ordinary least squares analysis（OLS）, we used forest management inventory data in Xianju County, Zhejiang Province, combined with Landsat TM image data developing local models using GWR to estimate forest carbon stock and its density. Available of geographically and altitudinal weighted regression （GAWR） model was then tested in smooth terrain. Analysis is included comparison to traditional regression and co-kriging interpolation. Results showed that the total forest aboveground carbon stocks estimated by the GWR（T） model for Xianju County were 3.132 106 Mg, and carbon density ranged from 0 to 89.964 Mghm－2 with a mean value of 15.555 Mghm－2. Meanwhile, the total forest aboveground carbon stocks calculated from diameter measurements were 3.192 106 Mg with a mean value of 15.854 Mghm－2. The overall result from GWR（T） model was lower than diameter measured by 1.880%, R2 = 0.654 （P＜0.01）, and carbon density distribution was consistent with the actual situation. The estimated results also had a higher accuracy with the RRMSE = 9.802 （P＜0.01） than traditional regression method with the RRMSE = 15.033 （P＜0.01） and co-kriging interpolation method with the RRMSE = 16.427 （P＜0.01）. GWR method can effectively estimate the regional forest aboveground carbon stocks reasonably and accurately, however, the GAWR model is not applicable for the areas with smooth terrain. Adding altitude as an explanatory variable in the modeling could improve estimation accuracy but would in turn create a multi-collinearity problem.［Ch, 6 fig. 9 tab. 26 ref.］

Geographically weighted regression based on estimation of regional forest carbon storage

doi: 10.11833/j.issn.2095-0756.2015.04.002

Abstract

References

Proportional views

通讯作者: 陈斌, bchen63@163.com

Related

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