Mapping gradients of community composition with nearest-neighbour imputation: Extending plot data for landscape analysis

Journal of Vegetation Science

Volumn 22, Issue 4, 2011, Pages 660-676

  Ohmann, Janet L ^a   Gregory, Matthew J ^b   Henderson, Emilie B ^b   Roberts, Heather M ^b  

^a PACIFIC NORTHWEST RESEARCH STATION   (United States)

^b OREGON STATE UNIVERSITY   (United States)

Author keywords

Canonical correspondence analysis; Constrained ordination; Gradient analysis; Gradient nearest neighbour; kNN; Landscape scenario analysis; Oregon; Species distribution modelling

Indexed keywords

ABUNDANCE; ACCURACY ASSESSMENT; CANONICAL ANALYSIS; COEXISTENCE; COMMUNITY COMPOSITION; CONSERVATION PLANNING; CORRESPONDENCE ANALYSIS; DATA SET; DISTURBANCE; ECOLOGICAL MODELING; ERROR ANALYSIS; FOREST ECOSYSTEM; FUZZY MATHEMATICS; GRADIENT ANALYSIS; LANDSCAPE CHANGE; LANDSCAPE ECOLOGY; NUMERICAL MODEL; ORDINATION; PIXEL; PLANT COMMUNITY; POPULATION DISTRIBUTION; SPATIAL ANALYSIS; TOPOGRAPHIC EFFECT; VEGETATION MAPPING; WOODLAND;

OREGON; UNITED STATES;

EID: 79960100484     PISSN: 11009233     EISSN: 16541103     Source Type: Journal    
DOI: 10.1111/j.1654-1103.2010.01244.x     Document Type: Article

References (51)

1. Agee, J.K. 1993. Fire ecology of Pacific Northwest forests. Island Press, Covelo, CA, US.
2. Austin, M.P. 2002. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecological Modelling 157: 101-118.
3. Breiman, L. 2001. Random forests. Machine Learning 45: 5-32.
4. Cohen, J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20: 37-46.
5. Comer, P., Faber-Langendoen, D., Evans, R., Gawler, S., Josse, C., Kittel, G., Menard, S., Pyne, S., Reid, M., Schulz, K., Snow, K. & Teague, J. 2003. Ecological Systems of the United States: a working classification of U.S. terrestrial systems. NatureServe, Arlington, VA, US.
6. Congalton, R.G. & Green, K. 1999. Assessing the accuracy of remotely sensed data: principles and practices. Lewis Publishers, Boca Raton, FL, US.
7. Crookston, N.L. & Finley, A.O. 2008. yaImpute: an R package for kNN imputation. Journal of Statistical Software 23: 1-16.
8. Daly, C., Halbleib, M., Smith, J.L., Gibson, W.P., Doggett, M.K., Taylor, G.H., Curtis, J. & Pasteris, P.P. 2008. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology 28: 2031-2064.
9. Dirnböck, T., Dullinger, S., Gottfried, M., Ginzler, C. & Grabherr, G. 2003. Mapping alpine vegetation based on image analysis, topographic variables and Canonical Correspondence Analysis. Applied Vegetation Science 6: 85-96.
10. Eskelson, B.N.I., Temesgen, H., Lemay, V., Barrett, T.M., Crookston, N.L. & Hudak, A.T. 2009. The roles of nearest neighbor methods in imputing missing data in forest inventory and monitoring databases. Scandinavian Journal of Forest Research 24: 235-246.
11. Ferrier, S. & Guisan, A. 2006. Spatial modelling of biodiversity at the community level. Journal of Applied Ecology 43: 393-404.
12. Finley, A.O. & Mcroberts, R.E. 2008. Efficient k-nearest neighbor searches for multi-source forest attribute mapping. Remote Sensing of Environment 112: 2203-2211.
13. Franco-Lopez, H., Ek, A.R. & Bauer, M.E. 2001. Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method. Remote Sensing of Environment 77: 251-274.
14. Franklin, J. 2009. Mapping species distributions. Cambridge University Press, New York, NY, US, 320pp.
15. Franklin, J.F. & Dyrness, C.T. 1973. Natural vegetation of Oregon and Washington. US Department of Agriculture, Forest Service, General Technical Report PNW-8.
16. Gopal, S. & Woodcock, C.E. 1994. Theory and methods for accuracy assessment of thematic maps using fuzzy sets. Photogrammetric Engineering and Remote Sensing 60: 181-188.
17. Gottfried, M., Pauli, H. & Grabherr, G. 1998. Prediction of vegetation patterns at the limits of plant life: a new view of the alpine-nival ecotone. Arctic and Alpine Research 30: 207-221.
18. Grossmann, E.B., Ohmann, J.L., Kagan, J., May, H.K. & Gregory, M.J. 2010. Mapping ecological systems with a random forest model: tradeoffs between errors and bias. USGS Gap Analysis Bulletin, No. 17; February 2010; pp 16-22.
19. Guisan, A., Weiss, S.B. & Weiss, A.D. 1999. GLM versus CCA spatial modeling of plant species distribution. Plant Ecology 143: 107-122.
20. Hanley, J.A. & McNeil, B.J. 1982. The meaning and use of the area under a receiver operating characteristics curve. Radiology 143: 29-36.
21. Hudak, A.T., Crookston, N.L., Evans, J.S., Hall, D.E. & Falkowski, M.J. 2008. Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data. Remote Sensing of Environment 112: 2232-2245.
22. Kagan, J.S., Ohmann, J.L., Gregory, M. & Tobalske, C. 2008. Land cover map for map zones 8 and 9 developed from SAGEMAP, GNN, and SWReGAP: a pilot for NWGAP. Gap Analysis Bulletin, No. 15; February 2008; pp 15-19.
23. Kruskal, J.B. & Wish, M. 1978. Multidimensional scaling. Sage Publications, Beverly Hills, CA, US, 93pp.
24. Magnussen, S., Mcroberts, R.E. & Tomppo, E.O. 2009. Model-based mean square error estimators for k-nearest neighbor predictions and applications using remotely sensed data for forest inventories. Remote Sensing of Environment 113: 476-488.
25. McCune, B. & Grace, J.B. 2002. Analysis of ecological communities. MjM Software Design, Glenedon Beach, OR, US, 300pp.
26. Mcroberts, R.E. 2009. Diagnostic tools for nearest neighbors techniques when used with satellite imagery. Remote Sensing of Environment 113: 489-499.
27. Mcroberts, R.E., Nelson, M.D. & Wendt, D.G. 2002. Stratified estimation of forest area using satellite imagery, inventory data, and the k-nearest neighbors technique. Remote Sensing of Environment 82: 457-468.
28. Mcroberts, R.E., Tomppo, E.O., Finley, A.O. & Heikkinen, J. 2007. Estimating areal means and variances of forest attributes using the k-Nearest Neighbors technique and satellite imagery. Remote Sensing of Environment 111: 466-480.
29. Mcroberts, R.E., Tomppo, E.O. & Næsset, E. 2010. Advances and emerging issues in national forest inventories. Scandinavian Journal of Forest Research 25: 368-381.
30. Moeur, M. & Stage, A.R. 1995. Most Similar Neighbor: an improved sampling inference procedure for natural resource planning. Forest Science 41: 337-359.
31. Moeur, M., Ohmann, J., Hemstrom, M., Burcsu, T. & Merzenich, J. 2009. Projecting watershed condition with Interagency Mapping and Assessment Project (IMAP) vegetation data and landscape models. In: Bayer, J.M. & Schei, J.L. (eds.) PNAMP special publication: remote sensing applications for aquatic resource monitoring. Chapter 11. pp. 83-91. Pacific Northwest Aquatic Monitoring Partnership, Cook, WA, US.
32. Ohmann, J.L. & Gregory, M.J. 2002. Predictive mapping of forest composition and structure with direct gradient analysis and nearest neighbor imputation in coastal Oregon, USA. Canadian Journal of Forest Research 32: 725-741.
33. Ohmann, J.L. & Spies, T.A. 1998. Regional gradient analysis and spatial pattern of woody plant communities of Oregon forests. Ecological Monographs 68: 151-182.
34. Ohmann, J.L., Gregory, M.J. & Spies, T.A. 2007. Influence of environment, disturbance, and ownership on forest vegetation of coastal Oregon. Ecological Applications 17: 18-33.
35. Økland, R.H. 1999. On the variation explained by ordination and constrained ordination axes. Journal of Vegetation Science 10: 131-136.
36. Palmer, M. 1993. Putting things in even better order: the advantages of canonical correspondence analysis. Ecology 74: 2215-2230.
37. Pierce, K.B., Lookingbill, T.R. & Urban, D.L. 2005. A simple method for estimating potential relative radiation (PRR) for landscape-scale vegetation analysis. Landscape Ecology 20: 137-147.
38. Pierce, K.B. Jr., Ohmann, J.L., Wimberly, M.C., Gregory, M.J. & Fried, J.S. 2009. Mapping wildland fuels and forest structure for land management: a comparison of nearest neighbor imputation and other methods. Canadian Journal of Forest Research 39: 1901-1916.
39. Riemann, R., Wilson, B.T., Lister, A. & Parks, S. 2010. An effective assessment protocol for continuous geospatial datasets of forest characteristics using USFS Forest Inventory and Analysis (FIA) data. Remote Sensing of Environment 114: 2337-2352.
40. Roberts, D.W. 2009. Comparison of multi-dimensional fuzzy set ordination with CCA and DB-RDA. Ecology 90: 2622-2634.
41. Schmidtlein, S., Zimmermann, P., Schupferling, R. & Weiss, C. 2007. Mapping the floristic continuum: ordination space position estimated from imaging spectroscopy. Journal of Vegetation Science 18: 131-140.
42. Spies, T.A., Johnson, K.N., Burnett, K.M., Ohmann, J.L., McComb, B.C., Reeves, G.H., Bettinger, P., Kline, J.D. & Garber-Yonts, B. 2007. Cumulative ecological and socioeconomic effects of forest policies in coastal Oregon. Ecological Applications 17: 5-17.
43. Stage, A.R. & Crookston, N.L. 2007. Partitioning error components for accuracy assessment of near-neighbor methods of imputation. Forest Science 53: 62-72.
44. Temesgen, H., LeMay, V., Froese, K.L. & Marshall, P.L. 2003. Imputing tree lists from aerial attributes for complex stands of south-eastern British Columbia. Forest Ecology and Management 177: 277-285.
45. ter Braak, C.J.F. 1986. Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology 67: 1167-1179.
46. Thessler, S., Ruokolainen, K., Tuomisto, H. & Tomppo, E. 2005. Mapping gradual landscape-scale floristic changes in Amazonian primary rain forests by combining ordination and remote sensing. Global Ecology & Biogeography 14: 315-325.
47. Thessler, S., Sesnie, S., Ramos, Z.S.B., Ruokolainen, K., Tomppo, E. & Finegan, B. 2008. Using k-NN and discriminant analyses to classify rain forest types in a Landsat TM image over northern Costa Rica. Remote Sensing of Environment 112: 2485-2494.
48. Tomppo, E. 1991. Satellite image-based national forest inventory of Finland. International Archives of Photogrammetry and Remote Sensing 28: 419-424.
49. Tomppo, E., Gagliano, C., De Natale, F., Katila, M. & Mcroberts, R.E. 2009. Predicting categorical forest variables using an improved k-nearest neighbour estimator and Landsat imagery. Remote Sensing of Environment 113: 500-517.
50. USDA NRCS. 2000. The PLANTS database (http://plants.usda.gov). National Plant Data Center, Baton Rouge, LA, US.
51. Václavík, T., Kanaskie, A., Hansen, E., Ohmann, J.L. & Meentemeyer, R. 2010. Modeling potential vs. actual distribution of sudden oak death in Oregon: prioritizing landscape contexts for early detection and eradication of disease outbreaks. Forest Ecology and Management 260: 1026-1035.