Spectral diversity area relationships for assessing biodiversity in a wildland-agriculture matrix

Ecological Applications

Volumn 26, Issue 8, 2016, Pages 2756-2766

  Dahlin, Kyla Marie ^a  

^a MICHIGAN STATE UNIVERSITY   (United States)

Author keywords

Airborne Visible Infrared Imaging Spectrometer; biodiversity; community assembly; hyperspectral remote sensing; imaging spectroscopy; Michigan; spectral diversity area relationship

Indexed keywords

AGRICULTURAL LAND; ASSEMBLY RULE; AVIRIS; BIODIVERSITY; DATA ACQUISITION; LAND TYPE; MIXED FOREST; REMOTE SENSING; SPECIES DIVERSITY; SPECIES-AREA RELATIONSHIP; SPECTRAL ANALYSIS;

MICHIGAN; UNITED STATES;

AGRICULTURE; BIODIVERSITY; FOREST; MICHIGAN; PLANT;

AGRICULTURE; BIODIVERSITY; FORESTS; MICHIGAN; PLANTS;

EID: 84990053253     PISSN: None     EISSN: 19395582     Source Type: Journal    
DOI: 10.1002/eap.1390     Document Type: Article

References (59)

1. Asner, G. P., and, A. Alencar,. 2010. Drought impacts on the Amazon forest: the remote sensing perspective. New Phytologist 187: 569-578.
2. Asner, G. P., D. E. Knapp, J. Boardman, R. O. Green, T. Kennedy-Bowdoin, M. Eastwood, R. E. Martin, C. Anderson, and, C. B. Field,. 2012. Carnegie airborne observatory-2: increasing science data dimensionality via high-fidelity multi-sensor fusion. Remote Sensing of Environment 124: 454-465.
3. Asner, G. P., and, R. E. Martin,. 2009. Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests. Frontiers in Ecology and the Environment 7: 269-276.
4. Baldeck, C., and, G. Asner,. 2013. Estimating vegetation beta diversity from airborne imaging spectroscopy and unsupervised clustering. Remote Sensing 5: 2057-2071.
5. Baldeck, C. A., G. P. Asner, R. E. Martin, C. B. Anderson, D. E. Knapp, J. R. Kellner, and, S. J. Wright,. 2015. Operational tree species mapping in a diverse tropical forest with airborne imaging spectroscopy. PLoS ONE 10: e0118403.
6. Castello, J. D., D. J. Leopold, and, P. J. Smallidge,. 1995. Pathogens, patterns, and processes in forest ecosystems: pathogens influence and are influenced by forest development and landscape characteristics. BioScience 45: 16-24.
7. Chen, D., J. Huang, and, T. J. Jackson,. 2005. Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands. Remote Sensing of Environment 98: 225-236.
8. Cornwell, W. K., L. D. W. Schwilk, and, D. D. Ackerly,. 2006. A trait-based test for habitat filtering: convex hull volume. Ecology 87: 1465-1471.
9. Craig, M. D., 1990. Unsupervised unmixing of remotely sensed data. Pages 324-330 in 5th Australasian Remote Sensing Conference. Perth, Western Australia, Australia.
10. Dahlin, K. M., G. P. Asner, and, C. B. Field,. 2013. Environmental and community controls on plant canopy chemistry in a Mediterranean-type ecosystem. Proceedings of the National Academy of Sciences of the United States of America 110: 6895-6900.
11. Damschen, E. I., S. Harrison, D. D. Ackerly, B. M. Fernandez-Going, and, B. L. Anacker,. 2012. Endemic plant communities on special soils: Early victims or hardy survivors of climate change? Journal of Ecology 100: 1122-1130.
12. Díaz, S., et al., 2016. The global spectrum of plant form and function. Nature 529: 167-171.
13. Dirzo, R., H. S. Young, M. Galetti, G. Ceballos, N. J. B. Isaac, and, B. Collen,. 2014. Defaunation in the anthropocene. Science 345: 401-406.
14. Feret, J.-B., and, G. P. Asner,. 2014. Mapping tropical forest canopy diversity using high-fidelity imaging spectroscopy. Ecological Applications 24: 1289-1296.
15. Field, C. B., and, H. A. Mooney,. 1986. The photosynthesis-nitrogen relationship in wild plants. Pages 25-56 in, T. J. Givnish, editor. On the economy of plant form and function. Cambridge University Press, Cambridge, UK.
16. Fisher, R. A., et al., 2015. Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes. Geoscientific Model Development Discussions 8: 3293-3357.
17. Green, R. O., et al., 1998. Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS). Remote Sensing of Environment 65: 227-248.
18. Habel, K., R. Grasman, R. B. Gramacy, A. Stahel, and, D. C. Sterratt,. 2015. geometry: Mesh Generation and Surface Tesselation. R package version 0.3-6. https://CRAN.R-project.org/package=geometry
19. Hamlin, L., R. O. Green, P. Mouroulis, M. L. Eastwood, D. Wilson, M. Dudik, and, C. Paine,. 2011. Imaging spectrometer science measurements for terrestrial ecology: AVIRIS and new developments. Pages 1-7 in 2011 Aerospace Conference.
20. Hubbell, S. P,. 2001. The unified neutral theory of biodiversity and biogeography (MPB-32) (Google eBook). Princeton University Press, Princeton, New Jersey, USA.
21. Jetz, W., et al., 2016. Monitoring plant functional diversity from space. Nature Plants 2: 16024.
22. Kampe, T. U., B. R. Johnson, M. Kuester, and, M. Keller,. 2010. NEON: the first continental-scale ecological observatory with airborne remote sensing of vegetation canopy biochemistry and structure. Journal of Applied Remote Sensing 4: 043510.
23. Keddy, P. A., 1992. Assembly and response rules: two goals for predictive community ecology. Journal of Vegetation Science 3: 157-164.
24. Kleidon, A., J. Adams, R. Pavlick, and, B. Reu,. 2009. Simulated geographic variations of plant species richness, evenness and abundance using climatic constraints on plant functional diversity. Environmental Research Letters 4: 014007.
25. Leibold, M. A., and, M. A. McPeek,. 2006. Coexistence of the niche and neutral perspectives in community ecology. Ecology 87: 1399-1410.
26. Matthews, T. J., and, R. J. Whittaker,. 2014. Neutral theory and the species abundance distribution: recent developments and prospects for unifying niche and neutral perspectives. Ecology and Evolution 4: 2263-2277.
27. McGill, B. J., B. J. Enquist, E. Weiher, and, M. Westoby,. 2006. Rebuilding community ecology from functional traits. Trends in Ecology & Evolution 21: 178-185.
28. Meentemeyer, R. K., N. J. Cunniffe, A. R. Cook, J. A. N. Filipe, R. D. Hunter, D. M. Rizzo, and, C. A. Gilligan,. 2011. Epidemiological modeling of invasion in heterogeneous landscapes: spread of sudden oak death in California (1990-2030). Ecosphere 2: art17.
29. Meyer, D., E. Dimitriadou, A. Hornik, and, F. Leisch,. 2015. e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.6-7. https://CRAN.R-project.org/package=e1071
30. Oldeland, J., D. Wesuls, D. Rocchini, M. Schmidt, and, N. Jürgens,. 2010. Does using species abundance data improve estimates of species diversity from remotely sensed spectral heterogeneity? Ecological Indicators 10: 390-396.
31. Ollinger, S., et al., 2008. Canopy nitrogen, carbon assimilation, and albedo in temperate and boreal forests: functional relations and potential climate feedbacks. Proceedings of the National Academy of Sciences of the United States 105: 19336-19341.
32. Pearlman, J., S. Carman, C. Segal, P. Jarecke, P. Clancy, and, W. Browne,. 2001. Overview of the hyperion imaging spectrometer for the NASA EO-1 mission. IGARSS 2001. Scanning the present and resolving the future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217) 7: 1-3.
33. Pettorelli, N., et al., 2016. Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions. Remote Sensing in Ecology and Conservation doi: 10.1002/rse2.15.
34. Powell, K. I., J. M. Chase, and, T. M. Knight,. 2013. Invasive plants have scale-dependent effects on diversity by altering species-area relationships. Science 339: 316-318.
35. Preston, F. W., 1960. Time and space and the variation of species. Ecology 41: 612-627.
36. R Core Team. 2015. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.r-project.org
37. Rocchini, D., et al., 2010. Remotely sensed spectral heterogeneity as a proxy of species diversity: recent advances and open challenges. Ecological Informatics 5: 318-329.
38. Rocchini, D., T. Wohlgemuth, S. Ghisleni, and, A. Chiarucci,. 2008. Spectral rarefaction: linking ecological variability and plant species diversity. Community Ecology 9: 169-176.
39. Roth, K. L., A. Casas, M. Huesca, S. L. Ustin, M. M. Alsina, S. A. Mathews, and, M. L. Whiting,. 2016. Leaf spectral clusters as potential optical leaf functional types within California ecosystems. Remote Sensing of Environment 184: 229-246.
40. Scheiner, S. M., 2003. Six types of species-area curves. Global Ecology and Biogeography 12: 441-447.
41. Serbin, S. P., A. Singh, B. E. McNeil, C. C. Kingdon, and, P. A. Townsend,. 2014. Spectroscopic determination of leaf morphological and biochemical traits for northern temperate and boreal tree species. Ecological Applications 24: 1651-1669.
42. Skidmore, A. K., et al., 2015. Environmental Science: agree on biodiversity metrics to track from space. Nature 523: 403-405.
43. Smith, A. B., 2010. Caution with curves: caveats for using the species-area relationship in conservation. Biological Conservation 143: 555-564.
44. Smith, A. B., B. Sandel, N. J. B. Kraft, and, S. Carey,. 2013. Characterizing scale-dependent community assembly using the functional-diversity-area relationship. Ecology 94: 2392-2402.
45. Stuffler, T., et al., 2007. The EnMAP hyperspectral imager: an advanced optical payload for future applications in earth observation programmes. Acta Astronautica 61: 115-120.
46. Thomas, R. Q., G. B. Bonan, and, C. L. Goodale,. 2013. Insights into mechanisms governing forest carbon response to nitrogen deposition: a model data comparison using observed responses to nitrogen addition. Biogeosciences 10: 3869-3887.
47. Thomas, R. W., and, S. L. Ustin,. 1987. Discriminating semiarid vegetation using airborne imaging spectrometer data-a preliminary assessment. Remote Sensing of Environment 23: 273-290.
48. Tucker, C. J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8: 127-150.
49. United States Department of Agriculture. 2009. 2007 Census of Agriculture 1: 739.
50. Ustin, S. L., D. A. R. A. Roberts, J. A. Gamon, and, R. O. Green,. 2004. Using imaging spectroscopy to study ecosystem processes and properties. BioScience 54: 523-534.
51. Vellend, M., 2010. Conceptual synthesis in community ecology. Quarterly Review of Biology 85: 183-206.
52. Venables, W. N., and, B. D. Ripley,. 2002. Modern applied statistics with S. Stanford University, Stanford, California, USA.
53. Violle, C., B. J. Enquist, B. J. McGill, L. Jiang, C. H. Albert, C. Hulshof, V. Jung, and, J. Messier,. 2012. The return of the variance: intraspecific variability in community ecology. Trends in Ecology and Evolution 27: 244-252.
54. Violle, C., P. B. Reich, S. W. Pacala, B. J. Enquist, and, J. Kattge,. 2014. The emergence and promise of functional biogeography. Proceedings of the National Academy of Sciences 111: 13690-13696.
55. Weiher, E., and, P. A. Keddy,. 1995. Assembly rules, null models, and trait dispersion: new questions from old patterns. Oikos 74: 159-164.
56. Wickham, H., 2007. Reshaping Data with the reshape Package. Journal of Statistical Software, 21 (12): 1-20. http://www.jstatsoft.org/v21/i12/
57. Wickham, H., 2009. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag, New York.
58. Wright, I. J., et al., 2004. The worldwide leaf economics spectrum. Nature 428: 821-827.
59. Zhang, Q., E. M. Middleton, B. C. Gao, and, Y. Ben Cheng,. 2012. Using EO-1 hyperion to simulate HyspIRI products for a coniferous forest: the fraction of PAR absorbed by chlorophyll (fAPAR chl) and leaf water content (LWC). IEEE Transactions on Geoscience and Remote Sensing 50: 1844-1852.