The influence of sampling density on geographically weighted regression: A case study using forest canopy height and optical data

International Journal of Remote Sensing

Volumn 33, Issue 9, 2012, Pages 2909-2924

  Chen, Gang ^a   Zhao, Kaiguang ^b   McDermid, Gregory J ^a   Hay, Geoffrey J ^a  

^a UNIVERSITY OF CALGARY   (Canada)

^b DUKE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FORESTRY; INTERPOLATION; REGRESSION ANALYSIS;

GEOGRAPHICALLY WEIGHTED REGRESSION; HIGH SPATIAL RESOLUTION; INVERSE DISTANCE WEIGHTING; OPERATIONAL APPLICATIONS; REMOTE SENSING APPLICATIONS; SPATIAL NON-STATIONARITY; SPATIALLY VARYING COEFFICIENTS; VARIABLE RELATIONSHIPS;

REMOTE SENSING;

FOREST CANOPY; HEIGHT; KRIGING; LEAST SQUARES METHOD; OPTICAL METHOD; QUICKBIRD; REGRESSION ANALYSIS; REMOTE SENSING; SPATIAL ANALYSIS; SPATIAL VARIATION;

FORESTRY; REGRESSION ANALYSIS; REMOTE SENSING;

EID: 84863281647     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2011.624130     Document Type: Article

References (37)

1. Akaike, H. 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19: 716-723.
2. Cassetti, E. 1972. Generating models by the expansion method: applications to geographical research. Geographical Analysis, 4: 81-91.
3. Chen, G., Hay, G.J., Castilla, G., St-Onge, B. and Powers, R. 2011. A multiscale geographic object-based image analysis (GEOBIA) to estimate lidar-measured forest canopy height using Quickbird imagery. International Journal of Geographical Information Science, 25: 877-893.
4. Cohen, W.B., Maiersperger, T.K., Spies, T.A. and Otter, D.R. 2001. Modelling forest cover attributes as continuous variables in a regional context with Thematic Mapper data. International Journal of Remote Sensing, 22: 2279-2310.
5. Donoghue, D.N.M. and Watt, P.J. 2006. Using LiDAR to compare forest height estimates from IKONOS and Landsat ETM+ data in Sitka spruce plantation forests. International Journal of Remote Sensing, 27: 2161-2175.
6. Fitch, D., Stow, D., Hope, A. and Rey, S. 2010. MODIS vegetation metrics as indicators of hydrological response in watersheds of California Mediterranean-type climate zones. Remote Sensing of Environment, 114: 2513-2523.
7. Foody, G.M. 2003. Geographical weighting as a further refinement to regression modelling: an example focused on the NDVI-rainfall relationship. Remote Sensing of Environment, 88: 283-293.
8. Foody, G.M. 2004. Spatial nonstationarity and scale-dependency in the relationship between species richness and environmental determinants for the sub-Saharan endemic avifauna. Global Ecology and Biogeography, 13: 315-320.
9. Foody, G.M. 2005. Mapping the richness and composition of British breeding birds from coarse spatial resolution satellite sensor imagery. International Journal of Remote Sensing, 26: 3943-3956.
10. Fotheringham, A.S., Brunsdon, C. and Charlton, M. 2002. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, 269New York: Wiley.
11. Fotheringham, A.S., Charlton, M.E. and Brunsdon, C. 1998. Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environment and Planning A, 30: 1905-1927.
12. Franklin, J. 1995. Predictive vegetation mapping: geographic modeling of biospatial patterns in relation to environmental gradients. Progress in Physical Geography, 19: 474-499.
13. Franklin, S.E. and McDermid, G.J. 1993. Empirical relations between digital SPOT HRV and CASI spectral response and lodgepole pine (Pinus contorta) forest stand parameters. International Journal of Remote Sensing, 14: 2331-2348.
14. Goodchild, M.F. 1986. Spatial Autocorrelation (Catmog 47), 56Norwich: Geo Books.
15. Hilker, T., Wulder, M.A. and Coops, N.C. 2008. Update of forest inventory data with lidar and high spatial resolution satellite imagery. Canadian Journal of Remote Sensing, 34: 5-12.
16. Hyde, P., Dubayah, P., Walker, W., Blair, J.B., Hofton, M. and Hunsaker, C. 2006. Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/InSAR, ETM+, Quickbird) synergy. Remote Sensing of Environment, 102: 63-73.
17. Jetz, W., Rahbek, C. and Lichstein, J.W. 2005. Local and global approaches to spatial data analysis in ecology. Global Ecology and Biogeography, 14: 97-98.
18. Kimsey, M.J., Moore, J. and McDaniel, P. 2008. A geographically weighted regression analysis of Douglas-fir site index in north central Idaho. Forest Science, 54: 356-366.
19. Lefsky, M.A., Cohen, W.B., Acker, S.A., Parker, G.G., Spies, T.A. and Harding, D. 1999. Lidar remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests. Remote Sensing of Environment, 70: 339-361.
20. Li, H. and Reynolds, J.F. 1995. On definition and quantification of heterogeneity. Oikos, 73: 280-284.
21. Lim, K., Treitz, P., Baldwin, K., Morrison, I. and Green, J. 2003. Lidar remote sensing of biophysical properties of tolerant northern hardwood forests. Canadian Journal of Remote Sensing, 29: 658-678.
22. Man Shrestha, P. 2006. "Comparison of ordinary least square regression, spatial autoregression, and geographically weighted regression for modeling forest structural attributes using a geographical information system (GIS)/remote sensing (RS) approach". In Master thesis, University of Calgary Canada, 204
23. McBratney, A.B. and Webster, R. 1983. Optimal interpolation and isarithmic mapping of soil properties: V. Coregionalization and multiple sampling strategy. Journal of Soil Science, 34: 137-162.
24. McCoy, R.M. 2005. Field Methods in Remote Sensing, 159New York: The Guilford Press.
25. Means, J.E., Acker, S.A., Harding, D.J., Blair, J.B., Lefsky, M.A., Cohen, W.B., Harmon, M.E. and McKee, W.A. 1999. Use of large-footprint scanning airborne lidar to estimate forest stand characteristics in the Western Cascades of Oregon. Remote Sensing of Environment, 67: 298-308.
26. Morgenstern, K., Black, T.A., Humphreys, E.R., Griffis, T.J., Drewitt, G.B., Cai, T.B., Nesic, Z., Spittlehouse, D.L. and Livingstone, N.J. 2004. Sensitivity and uncertainty of the carbon balance of a Pacific Northwest Douglas-fir forest during an El Nino La Nina cycle. Agricultural and Forest Meteorology, 123: 201-219.
27. Oliver, M.A. and Webster, R. 1990. Kriging: a method of interpolation for geographical information systems. International Journal of Geographic Information Systems, 4: 313-332.
28. Peng, H., Long, F. and Ding, C. 2005. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27: 1226-1238.
29. Propastin, P.A. 2009. Spatial non-stationarity and scale-dependency of prediction accuracy in the remote estimation of LAI over a tropical rainforest in Sulawesi, Indonesia. Remote Sensing of Environment, 113: 2234-2242.
30. Salas, C., Ene, L., Gregoire, T.G., Næsset, E. and Gobakken, T. 2010. Modelling tree diameter from airborne laser scanning derived variables: a comparison of spatial statistical models. Remote Sensing of Environment, 114: 1277-1285.
31. Wang, Q., Ni, J. and Tenhunen, J. 2005. Application of a geographically-weighted regression analysis to estimate net primary production of Chinese forest ecosystems. Global Ecology and Biogeography, 14: 379-393.
32. Watson, D.F. and Philip, G.M. 1985. A refinement of inverse distance weighted interpolation. Geoprocessing, 2: 315-327.
33. Welch, R. and Ahlers, W. 1987. Merging multiresolution SPOT HRV and Landsat TM data. Photogrammetric Engineering and Remote Sensing, 53: 301-303.
34. Wulder, M., Niemann, K.O. and Goodenough, D.G. 2000. Local maximum filtering for the extraction of tree locations and basal area from high spatial resolution imagery. Remote Sensing of Environment, 73: 103-114.
35. Zhang, L., Ma, Z. and Guo, L. 2009. An evaluation of spatial autocorrelation and heterogeneity in the residuals of six regression models. Forest Science, 55: 533-548.
36. Zhao, K. and Popescu, S. 2009. Lidar-based mapping of leaf area index and its use for validating GLOBCARBON satellite LAI product in a temperate forest of the southern USA. Remote Sensing of Environment, 113: 1628-1645.
37. Zhao, K., Popescu, S. and Nelson, R. 2009. Lidar remote sensing of forest biomass: a scale-invariant estimation approach using airborne lasers. Remote Sensing of Environment, 113: 182-196.