Modeling spatial variation in tree diameter–height relationships
By Lianjun Zhang, Huiquan Bi, P. Cheng, C. J. Davis. 2004
101 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Nature and Landscape Conservation
View Paper: https://www.semanticscholar.org/paper/9808af7c429546cda36a1345c7bebf940c0a2c62
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In Economics and Econometrics
In Nature and Landscape Conservation
Citations in the corpus
- Application of a geographically‐weighted regression analysis to estimate net primary production of Chinese forest ecosystems (2005, methodology): “The comparison on the range of the GWR local parameter estimates with a confidence interval (CI) around the OLS global estimate of the equivalent parameter is suggested by the former studies (e.g. Fotheringham et al., 2002; Zhang et al., 2004).”
- Local Linear Estimation of Spatially Varying Coefficient Models: An Improvement on the Geographically Weighted Regression Technique (2008, methodology): “GWR was also employed by Zhang et al (2004) and Zhang and Shi (2004) to analyze the spatial characteristics of tree growth.”
- Spatial Assessment of Model Errors from Four Regression Techniques (2005, methodology): “…51(4) 2005 geographically weighted regression (GWR) (e.g., Austin and Meyers 1996, Preisler et al. 1997, Lehmann 1998, Moisen and Edwards 1999, Moisen et al. 1999, Frescino et al. 2001, Guisan et al. 2002, Moisen and Frescino 2002, Lehmann et al. 2003, Zhang and Shi 2004, Zhang et al. 2004).”
- High Precision Individual Tree Diameter and Perimeter Estimation from Close-Range Photogrammetry (2018, background): “The diameter is related strongly to other descriptors of tree size such as total height [3] and crown dimensions [4].”