Application of a geographically‐weighted regression analysis to estimate net primary production of Chinese forest ecosystems

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.

[1]  D. Marks,et al.  A global perspective of regional vegetation and hydrologic sensitivities from climatic change , 1994 .

[2]  J. Ni,et al.  Synthesis and analysis of biomass and net primary productivity in Chinese forests , 2001 .

[3]  Changhui Peng,et al.  Modelling the spatial pattern of net primary productivity in Chinese forests , 1999 .

[4]  A. Bondeau,et al.  Comparing global models of terrestrial net primary productivity (NPP): overview and key results , 1999 .

[5]  Jack J. Lennon,et al.  Red-shifts and red herrings in geographical ecology , 2000 .

[6]  E. Rastetter,et al.  Potential Net Primary Productivity in South America: Application of a Global Model. , 1991, Ecological applications : a publication of the Ecological Society of America.

[7]  Jesslyn F. Brown,et al.  Measuring phenological variability from satellite imagery , 1994 .

[8]  Emilio A. Laca,et al.  Calibration of remotely sensed, coarse resolution NDVI to CO2 fluxes in a sagebrush–steppe ecosystem , 2003 .

[9]  Sharon E. Nicholson,et al.  On the use of NDVI for estimating rainfall fields in the Kalahari of Botswana , 1997 .

[10]  Clifford M. Hurvich,et al.  Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion , 1998 .

[11]  James Rowland,et al.  A weighted least-squares approach to temporal NDVI smoothing , 1999 .

[12]  H. Seino,et al.  Agroclimatic Evaluation of Net Primary Productivity of Natural Vegetations , 1985 .

[13]  Thomas M. Smith,et al.  A global land primary productivity and phytogeography model , 1995 .

[14]  B. Holben.  Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[15]  Timothy G. F. Kittel,et al.  Regional Analysis of the Central Great Plains , 1991 .

[16]  Jessica Gurevitch,et al.  Ecography 25: 601 -- 615, 2002 , 2022 .

[17]  G. Foody.  Geographical weighting as a further refinement to regression modelling: An example focused on the NDVI–rainfall relationship , 2003 .

[18]  C. Tucker,et al.  North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer , 1985, Vegetatio.

[19]  Stephen D. Prince,et al.  Terrestrial net primary production estimates for 0.5o grid cells from field observations-a contribution to global biogeochemical modeling , 2003 .

[20]  P. Legendre.  Spatial Autocorrelation: Trouble or New Paradigm? , 1993 .

[21]  J. Diniz‐Filho,et al.  Spatial autocorrelation and red herrings in geographical ecology , 2003 .

[22]  M. Wand.  Local Regression and Likelihood , 2001 .

[23]  C. Tucker,et al.  Satellite remote sensing of total dry matter production in the Senegalese Sahel , 1983 .

[24]  A. McGuire,et al.  Global climate change and terrestrial net primary production , 1993, Nature.

[25]  T. Hobbs,et al.  The use of NOAA-AVHRR NDVI data to assess herbage production in the arid rangelands of Central Australia , 1995 .

[26]  Giles M. Foody,et al.  Spatial nonstationarity and scale-dependency in the relationship between species richness and environmental determinants for the sub-Saharan endemic avifauna , 2004 .

[27]  Jeffrey Q. Chambers,et al.  TROPICAL FORESTS : AN EVALUATION AND SYNTHESIS OF EXISTING FIELD DATA , 2022 .

[28]  L. Holdridge.  Life zone ecology. , 1967 .

[29]  Shu Tao,et al.  Relations between AVHRR NDVI and ecoclimatic parameters in China , 2002 .

[30]  Huiquan Bi,et al.  Modeling spatial variation in tree diameter–height relationships , 2004 .

[31]  J. Faundeen,et al.  The 1 km AVHRR global land data set: first stages in implementation , 1994 .

[32]  A. Huete,et al.  MODIS Vegetation Index Compositing Approach: A Prototype with AVHRR Data , 1999 .

[33]  J. Ni,et al.  Net primary productivity in forests of China: scaling-up of national inventory data and comparison with model predictions , 2003 .

[34]  Inez Y. Fung,et al.  Application of Advanced Very High Resolution Radiometer vegetation index to study atmosphere‐biosphere exchange of CO2 , 1987 .

[35]  Yude Pan,et al.  BIOMASS AND NPP ESTIMATION FOR THE MID-ATLANTIC REGION (USA) USING PLOT-LEVEL FOREST INVENTORY DATA , 2001 .

[36]  H. Lieth.  Modeling the Primary Productivity of the World , 1975 .

[37]  Carol A. Gotway,et al.  Spatial Data Analysis in the Social and Environmental , 1992 .

[38]  F. Woodward,et al.  Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models , 2001 .

[39]  C. Tucker,et al.  Satellite estimates of productivity and light use efficiency in United States agriculture, 1982–98 , 2002 .

[40]  C. Tucker,et al.  Satellite remote sensing of primary production , 1986 .

[41]  Brent N. Holben,et al.  Accuracy of the AVHRR vegetation index as a predictor of biomass, primary productivity and net CO2 flux , 1989, Vegetatio.

[42]  T. Simons,et al.  Spatial autocorrelation and autoregressive models in ecology , 2002 .

[43]  Limin Yang,et al.  An analysis of relationships among climate forcing and time-integrated NDVI of grasslands over the U.S. northern and central Great Plains , 1998 .

[44]  R. Haining.  Spatial Data Analysis in the Social and Environmental Sciences , 1990 .

[45]  Luc Anselin,et al.  Under the hood , 2002 .

[46]  C. Tucker,et al.  Remote Sensing of Total Dry-Matter Accumulation in Winter Wheat , 1981 .

[47]  B. Duchemin,et al.  Normalisation of directional effects in 10-day global syntheses derived from VEGETATION/SPOT: , 2002 .

[48]  S. Kalluri,et al.  The Pathfinder AVHRR land data set: An improved coarse resolution data set for terrestrial monitoring , 1994 .

[49]  J. Paruelo,et al.  ANPP ESTIMATES FROM NDVI FOR THE CENTRAL GRASSLAND REGION OF THE UNITED STATES , 1997 .

[50]  Walter Jetz,et al.  Local and global approaches to spatial data analysis in ecology , 2005 .

[51]  R. K. Dixon,et al.  Carbon Pools and Flux of Global Forest Ecosystems , 1994, Science.

[52]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .