Application of a geographically‐weighted regression analysis to estimate net primary production of Chinese forest ecosystems
By Quan Wang, J. Ni, John Tenhunen. 2005
239 citations recorded by the source corpus.
Aim The objective of this paper is to obtain a net primary production (NPP) regression model based on the geographically weighted regression (GWR) method, which includes spatial non-stationarity in the parameters estimated for forest ecosystems in China. Location We used data across China. Methods We e xamine the relationships between NPP of Chinese forest ecosystems and environmental variables, specifically altitude, temperature, precipitation and time-integrated normalized difference vegetation index (TINDVI) based on the ordinary least squares (OLS) regression, the spatial lag model and GWR methods. Results The GWR method made significantly better predictions of NPP in simulations than did OLS, as indicated both by corrected Akaike Information Criterion (AIC c ) and R 2 . GWR provided a value of 4891 for AIC c and 0.66 for R 2 , compared with 5036 and 0.58, respectively, by OLS. GWR has the potential to reveal local patterns in the spatial distribution of a parameter, which would be ignored by the OLS approach. Furthermore, OLS may provide a false general relationship between spatially nonstationary variables. Spatial autocorrelation violates a basic assumption of the OLS method. The spatial lag model with the consideration of spatial autocorrelation had improved performance in the NPP simulation as compared with OLS (5001 for AIC c and 0.60 for R 2 ), but it was still not as good as that via the GWR method. Moreover, statistically significant positive spatial autocorrelation remained in the NPP residuals with the spatial lag model at small spatial scales, while no positive spatial autocorrelation across spatial scales can be found in the GWR residuals. Conclusions We conclude that the regression analysis for Chinese forest NPP with respect to environmental factors and based alternatively on OLS, the spatial lag model, and GWR methods indicated that there was a significant improvement in model performance of GWR over OLS and the spatial lag model.
Subfields: Economics and Econometrics, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/5b9213a68bf74b2c165f92175e41d7a3eaaea00d
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In Economics and Econometrics
- Y. Yamagata
- Daisuke Murakami
- Takahiro Yoshida
- Hajime Seya
- Ning Wang
- Daniel A. Griffith
- Changlin Mei
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In Global and Planetary Change
- Ning Wang
- Takahiro Yoshida
- Daniel A. Griffith
- Changlin Mei
- Hajime Seya
- Y. Yamagata
- Xiaodong Yan
- Daisuke Murakami
Citations in the corpus
- Local Linear Estimation of Spatially Varying Coefficient Models: An Improvement on the Geographically Weighted Regression Technique (2008, methodology): “Recently, Wang et al (2005) used GWR to estimate the net primary production of Chinese forest ecosystems.”
- A Moran coefficient-based mixed effects approach to investigate spatially varying relationships (2016, methodology): “s (SVCs). Thus far, GWR models have attained remarkable success with various applications, including social economic studies (e.g., Bitter et al., 2007; Huang et al., 2010), ecological studies (e.g., Wang et al., 2005; Austin, 2007), and health studies (e.g., Nakaya et al., 2005; Hu et al., 2012). D”