Multiscale analysis of the relationship between topography and aboveground biomass in the tropical rainforests of Sulawesi, Indonesia

This article aims to explore spatial and altitudinal non-stationarity in the relationship between aboveground biomass (AGB) of tropical rainforest in Sulawesi (Indonesia) and topography. An autoregressive model through a geographically weighted regression (GWR) framework was used to study the relationship between ground-measured values of AGB and altitude above sea level at 85 sampling plots. The relationships between AGB and altitude were found to be significantly spatially variable and scale-dependent. The results also suggested high altitudinal variability in the examined relationship. Both the strength of the AGB–altitude relationship (ρ) and the altitudinal gradient (α) showed a high changeability in the horizontal and vertical dimensions. The complex spatio-altitudinal patterns in the GWR-based local estimates of the ρ and α parameters gave rise to both spatial and altitudinal variations in the scale effects. The approach presented in this study enables finding the most appropriate scale for data analysis within different altitudinal bands. The study found that the changes of the gradient α along altitudinal transects relate to prevalent environmental conditions observed at different altitudes, whereas the optimal bandwidth was related to the terrain surface heterogeneity.

[1]  A. Stewart Fotheringham,et al.  Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity , 2010 .

[2]  Q. Ketterings,et al.  Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests , 2001 .

[3]  D. F. Grigal,et al.  NITROGEN MINERALIZATION AND PRODUCTIVITY IN 50 HARDWOOD AND CONIFER STANDS ON DIVERSE SOILS , 1997 .

[4]  D. Lieberman,et al.  TROPICAL FOREST STRUCTURE AND COMPOSITION ON A LARGE-SCALE ALTITUDINAL GRADIENT IN COSTA RICA , 1996 .

[5]  Chris Brunsdon,et al.  Geographically Weighted Regression: The Analysis of Spatially Varying Relationships , 2002 .

[6]  Martin Charlton,et al.  The Geography of Parameter Space: An Investigation of Spatial Non-Stationarity , 1996, Int. J. Geogr. Inf. Sci..

[7]  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 .

[8]  Koichi Takahashi,et al.  Changes with altitude of the stand structure of temperate forests on Mount Norikura, central Japan , 2007, Journal of Forest Research.

[9]  M. Charlton,et al.  Some Notes on Parametric Significance Tests for Geographically Weighted Regression , 1999 .

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

[11]  Martin Charlton,et al.  Spatial Nonstationarity and Autoregressive Models , 1998 .

[12]  K. Yasue,et al.  Effects of climate on the radial growth of tree species in the upper and lower distribution limits of an altitudinal ecotone on Mount Norikura, central Japan , 2003, Ecological Research.

[13]  Nicholas C. Coops,et al.  Procedures for predicting habitat and structural attributes in eucalypt forests using high spatial resolution remotely sensed imagery , 1998 .

[14]  Peter E. Thornton,et al.  Parameterization and Sensitivity Analysis of the BIOME–BGC Terrestrial Ecosystem Model: Net Primary Production Controls , 2000 .

[15]  K. Kitayama,et al.  Structure, composition and species diversity in an altitude-substrate matrix of rain forest tree communities on Mount Kinabalu, Borneo , 1999, Plant Ecology.

[16]  E. Tanner,et al.  STUDIES ON THE BIOMASS AND PRODUCTIVITY IN A SERIES OF MONTANE RAIN FORESTS IN JAMAICA , 1980 .

[17]  Pavel Propastin,et al.  Spatial non-stationarity and scale-dependency of prediction accuracy in the remote estimation of LAI over a tropical rainforest in Sulawesi, Indonesia. , 2009 .

[18]  A. O. Nicholls,et al.  Determining species response functions to an environmental gradient by means of a β‐function , 1994 .

[19]  A. Nelson,et al.  Multi‐scale correlations between topography and vegetation in a hillside catchment of Honduras , 2007, Int. J. Geogr. Inf. Sci..

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

[21]  P. Propastin RELATIONS BETWEEN LANDSAT ETM+ IMAGERY AND FOREST STRUCTURE PARAMETERS IN TROPICAL RAINFORESTS: A CASE STUDY FROM LORE-LINDU NATIONAL PARK IN SULAWESI, INDONESIA , 2009 .

[22]  Colin L. Mallows,et al.  Some Comments on Cp , 2000, Technometrics.

[23]  Muslimin Mustafa,et al.  The Ecology of Sulawesi , 1987 .

[24]  J. Mennis Mapping the Results of Geographically Weighted Regression , 2006 .

[25]  Martin Kappas,et al.  Application of Geographically Weighted Regression to Investigate the Impact of Scale on Prediction Uncertainty by Modelling Relationship between Vegetation and Climate , 2007, Int. J. Spatial Data Infrastructures Res..

[26]  Chris Brunsdon,et al.  Spatial variations in the average rainfall–altitude relationship in Great Britain: an approach using geographically weighted regression , 2001 .

[27]  Peter M. Vitousek,et al.  PRIMARY PRODUCTIVITY AND ECOSYSTEM DEVELOPMENT ALONG AN ELEVATIONAL GRADIENT ON MAUNA LOA, HAWAI‘I , 1997 .

[28]  Robert M. May,et al.  The tropical rainforest , 1975, Nature.

[29]  C. Mallows Some Comments on Cp , 2000, Technometrics.

[30]  T. Kamijo,et al.  Altitudinal zonation and structure of warm-temperate forests on Mikura-jima Island, Izu Islands, Japan , 2001 .

[31]  John Tenhunen,et al.  Application of a geographically‐weighted regression analysis to estimate net primary production of Chinese forest ecosystems , 2005 .

[32]  Martin Kappas,et al.  Reducing Uncertainty in Modeling the NDVI-Precipitation Relationship: A Comparative Study Using Global and Local Regression Techniques , 2008 .

[33]  A. Twele,et al.  Monitoring inter-annual land cover dynamics at the rainforest margin in Central Sulawesi, Indonesia , 2007 .

[34]  J. Chambers,et al.  Tree allometry and improved estimation of carbon stocks and balance in tropical forests , 2005, Oecologia.

[35]  Mark S. Pearce,et al.  Geographically weighted regression: A method for exploring spatial nonstationarity , 1999 .

[36]  Zbigniew Bochenek,et al.  New Developments and Challenges in Remote Sensing , 2007 .

[37]  D. Hertel,et al.  Altitudinal Change in LAI and Stand Leaf Biomass in Tropical Montane Forests: a Transect Study in Ecuador and a Pan-Tropical Meta-Analysis , 2007, Ecosystems.

[38]  Cindy Q. Tang,et al.  Zonal transition of evergreen, deciduous, and coniferous forests along the altitudinal gradient on a humid subtropical mountain, Mt. Emei, Sichuan, China , 1997, Plant Ecology.