223
Ustin SL, Gamon JA (2010) Remote sensing of plant functional types. New Phytol 186(4):795–
816. https://doi.org/10.1111/j.1469-8137.2010.03284.x
Vallejos R, Osorio F (2014) Effective sample size of spatial process models. Spat Stat 9:66–92.
https://doi.org/10.1016/j.spasta.2014.03.003
Wallace AR (1860) On the zoological geography of the Malay archipelago. Zool J Linnean Soc
4(16):172–184. https://doi.org/10.1111/j.1096-3642.1860.tb00090.x
Waltari E, Schroeder R, McDonald K et al (2014) Bioclimatic variables derived from remote
sensing: assessment and application for species distribution modelling. Methods Ecol Evol
5(10):1033–1042. https://doi.org/10.1111/2041-210x.12264
Wang Z, Townsend PA, Schweiger AK et al (2019) Mapping foliar functional traits and their
uncertainties across three years in a grassland experiment. Remote Sens Environ 221:405–416.
https://doi.org/10.1016/j.rse.2018.11.016
Warren DL (2012) In defense of “niche modeling”. Trends Ecol Evol 27(9):497–500. https://doi.
org/10.1016/j.tree.2012.03.010
Warren DL, Beaumont LJ, Dinnage R et al (2018) New methods for measuring ENM breadth and
overlap in environmental space. Ecography 42:444. https://doi.org/10.1111/ecog.03900
West AM, Evangelista PH, Jarnevich CS et al (2016) Integrating remote sensing with species distribution models; mapping tamarisk invasions using the software for assisted habitat modeling
(SAHM). J Vis Exp (116). https://doi.org/10.3791/54578
Wood SN (2006) Generalized additive models. Chapman and Hall/CRC, Boca Raton
Xiao Z, Liang S, Wang J et al (2014) Use of general regression neural networks for generating
the GLASS leaf area index product from time-series MODIS surface reflectance. IEEE Trans
Geosci Remote Sens 52(1):209–223. https://doi.org/10.1109/tgrs.2013.2237780
Zimmermann NE, Edwards TC, Moisen GG et al (2007) Remote sensing-based predictors improve
distribution models of rare, early successional and broadleaf tree species in Utah. J Appl Ecol
44(5):1057–1067. https://doi.org/10.1111/j.1365-2664.2007.01348.x
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give appropriate
credit to the original author(s) and the source, provide a link to the Creative Commons license and
indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative
Commons license, unless indicated otherwise in a credit line to the material. If material is not
included in the chapter's Creative Commons license and your intended use is not permitted by
statutory regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder.
9 Using Remote Sensing for Modeling and Monitoring Species Distributions
Ustin SL, Gamon JA (2010) Remote sensing of plant functional types. New Phytol 186(4):795–
816. https://doi.org/10.1111/j.1469-8137.2010.03284.x
Vallejos R, Osorio F (2014) Effective sample size of spatial process models. Spat Stat 9:66–92.
https://doi.org/10.1016/j.spasta.2014.03.003
Wallace AR (1860) On the zoological geography of the Malay archipelago. Zool J Linnean Soc
4(16):172–184. https://doi.org/10.1111/j.1096-3642.1860.tb00090.x
Waltari E, Schroeder R, McDonald K et al (2014) Bioclimatic variables derived from remote
sensing: assessment and application for species distribution modelling. Methods Ecol Evol
5(10):1033–1042. https://doi.org/10.1111/2041-210x.12264
Wang Z, Townsend PA, Schweiger AK et al (2019) Mapping foliar functional traits and their
uncertainties across three years in a grassland experiment. Remote Sens Environ 221:405–416.
https://doi.org/10.1016/j.rse.2018.11.016
Warren DL (2012) In defense of “niche modeling”. Trends Ecol Evol 27(9):497–500. https://doi.
org/10.1016/j.tree.2012.03.010
Warren DL, Beaumont LJ, Dinnage R et al (2018) New methods for measuring ENM breadth and
overlap in environmental space. Ecography 42:444. https://doi.org/10.1111/ecog.03900
West AM, Evangelista PH, Jarnevich CS et al (2016) Integrating remote sensing with species distribution models; mapping tamarisk invasions using the software for assisted habitat modeling
(SAHM). J Vis Exp (116). https://doi.org/10.3791/54578
Wood SN (2006) Generalized additive models. Chapman and Hall/CRC, Boca Raton
Xiao Z, Liang S, Wang J et al (2014) Use of general regression neural networks for generating
the GLASS leaf area index product from time-series MODIS surface reflectance. IEEE Trans
Geosci Remote Sens 52(1):209–223. https://doi.org/10.1109/tgrs.2013.2237780
Zimmermann NE, Edwards TC, Moisen GG et al (2007) Remote sensing-based predictors improve
distribution models of rare, early successional and broadleaf tree species in Utah. J Appl Ecol
44(5):1057–1067. https://doi.org/10.1111/j.1365-2664.2007.01348.x
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give appropriate
credit to the original author(s) and the source, provide a link to the Creative Commons license and
indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative
Commons license, unless indicated otherwise in a credit line to the material. If material is not
included in the chapter's Creative Commons license and your intended use is not permitted by
statutory regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder.
9 Using Remote Sensing for Modeling and Monitoring Species Distributions