Effects of Sampling Interval on Spatial Patterns and Statistics of Watershed Nitrogen Concentration

This study investigates how spatial patterns and statistics of a 30 m resolution, model-simulated, watershed nitrogen concentration surface change with sampling intervals from 30 m to 600 m for every 30 m increase for the Little River Watershed (Georgia, USA). The results indicate that the mean, standard deviation, and variogram sills do not have consistent trends with increasing sampling intervals, whereas the variogram ranges remain constant. A sampling interval smaller than or equal to 90 m is necessary to build a representative variogram. The interpolation accuracy, clustering level, and total hot spot areas show decreasing trends approximating a logarithmic function. The trends correspond to the nitrogen variogram and start to level at a sampling interval of 360 m, which is therefore regarded as a critical spatial scale of the Little River Watershed.

[1]  Wsd Wong,et al.  Statistical Analysis of Geographic Information with ArcView GIS And ArcGIS , 2005 .

[2]  T. Seastedt,et al.  Spatial analysis of selected soil attributes across an alpine topographic/snow gradient , 2004, Landscape Ecology.

[3]  Qing Zhu,et al.  Effects of Various Factors on the Accuracy of DEMs: An Intensive Experimental Investigation , 2000 .

[4]  S. Morris.  Spatial distribution of fungal and bacterial biomass in southern Ohio hardwood forest soils: fine scale variability and microscale patterns , 1999 .

[5]  Zhilin Li.  VARIATION OF THE ACCURACY OF DIGITAL TERRAIN MODELS WITH SAMPLING INTERVAL , 2006 .

[6]  R. Young,et al.  AGNPS: A nonpoint-source pollution model for evaluating agricultural watersheds , 1989 .

[7]  David K. Oline,et al.  Scaling patterns of biomass and soil properties: an empirical analysis , 2004, Landscape Ecology.

[8]  P. Burrough,et al.  Principles of geographical information systems , 1998 .

[9]  Norbert Silvera,et al.  Accuracy of interpolation techniques for the derivation of digital elevation models in relation to landform types and data density , 2006 .

[10]  Jean-Daniel Boissonnat,et al.  Natural neighbor coordinates of points on a surface , 2001, Comput. Geom..

[11]  Peter M. Atkinson,et al.  Geostatistics and remote sensing , 1998 .

[12]  Günter Blöschl,et al.  On the spatial scaling of soil moisture , 1999 .

[13]  Achim Dobermann,et al.  Sampling optimization based on secondary information and its utilization in soil carbon mapping , 2006 .

[14]  Manuel A. Aguilar,et al.  Effects of Terrain Morphology, Sampling Density, and Interpolation Methods on Grid DEM Accuracy , 2005 .

[15]  F. D. Whisler,et al.  Spatial Variability Analysis of Soil Physical Properties of Alluvial Soils , 2005 .

[16]  H. Elsenbeer,et al.  Scale dependency in spatial patterns of saturated hydraulic conductivity , 2004 .

[17]  R. K. Hubbard,et al.  Little River Experimental Watershed database , 2007 .

[18]  Alfred Stein,et al.  Optimization of environmental sampling using interactive GIS. , 1997 .

[19]  Ian D. Moore,et al.  Terrain analysis : integration into the agricultural nonpoint source (AGNPS) pollution model , 1991 .

[20]  E. Lynn Usery,et al.  Geospatial data resampling and resolution effects on watershed modeling: A case study using the agricultural non-point source pollution model , 2004, J. Geogr. Syst..

[21]  Michael J. Oimoen,et al.  The National Elevation Dataset , 2002 .

[22]  J. Ord,et al.  Local Spatial Autocorrelation Statistics: Distributional Issues and an Application , 2010 .

[23]  A. Getis.  The Analysis of Spatial Association by Use of Distance Statistics , 2010 .

[24]  C. Woodcock,et al.  Multiscale analysis and validation of the MODIS LAI product: I. Uncertainty assessment , 2002 .