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Franklin SE, Ahmed OS (2018) Deciduous tree species classification using object-based analysis and machine learning with unmanned aerial vehicle multispectral data. Int J Remote Sens
39:5236–5245. https://doi.org/10.1080/01431161.2017.1363442
Gaitán JJ et al (2013) Evaluating the performance of multiple remote sensing indices to predict
the spatial variability of ecosystem structure and functioning in Patagonian steppes. Ecol Indic
34:181–191. https://doi.org/10.1016/j.ecolind.2013.05.007
Gamon JA (2015) Reviews and syntheses: optical sampling of the flux tower footprint.
Biogeosciences 12:4509–4523. https://doi.org/10.5194/bg-12-4509-2015
Gamon JA et al (2004) Remote sensing in BOREAS: lessons leamed. Remote Sens Environ
89:139–162. https://doi.org/10.1016/j.rse.2003.08.017
Gholizadeh H, Gamon JA, Zygielbaum AI, Wang R, Schweiger AK, Cavender-Bares J (2018)
Remote sensing of biodiversity: soil correction and data dimension reduction methods improve
assessment of alpha-diversity (species richness) in prairie ecosystems. Remote Sens Environ
206:240–253. https://doi.org/10.1016/j.rse.2017.12.014
Gholizadeh H et al (2019) Detecting prairie biodiversity with airborne remote sensing. Remote
Sens Environ 221:38–49. https://doi.org/10.1016/j.rse.2018.10.037
Goodman JA, Ustin SL (2007) Classification of benthic composition in a coral reef environment
using spectral unmixing. J Appl Remote Sens 1:011501. https://doi.org/10.1117/1.2815907
Gould W (2000) Remote sensing of vegetation, plant species richness, and regional biodiversity
hotspots. Ecol Appl 10:1861–1870. https://doi.org/10.2307/2641244
Heffernan JB et al (2014) Macrosystems ecology: understanding ecological patterns and processes
at continental scales. Front Ecol Environ 12:5–14. https://doi.org/10.1890/130017
Hooper DU et al (2012) A global synthesis reveals biodiversity loss as a major driver of ecosystem
change. Nature 486:105–108. https://doi.org/10.1038/nature11118
Hopkin M (2006) Spying on nature. Nature 444:420–421
Jetz W et al (2016) Monitoring plant functional diversity from space. Nature Plants 2. https://doi.
org/10.1038/nplants.2016.24
Justice CO, Townshend JRG (1981) Integrating ground data with remote sensing. In: Townshend
JRG (ed) Terrain analysis and remote sensing. Allen and Unwin, London, pp 38–101
Kampe TU, Johnson BR, Kuester M, Keller M (2010) NEON: the first continental-scale ecological observatory with airborne remote sensing of vegetation canopy biochemistry and structure.
J Appl Remote Sens 4:043510. https://doi.org/10.1117/1.3361375
Kerr JT, Southwood TRE, Cihlar J (2001) Remotely sensed habitat diversity predicts butterfly species richness and community similarity in Canada. Proc Natl Acad Sci U S A 98:11365–11370.
https://doi.org/10.1073/pnas.201398398
Kissling WD et al (2018) Towards global data products of Essential Biodiversity Variables on species traits. Nat Ecol Evol 2:1531–1540. https://doi.org/10.1038/s41559-018-0667-3
Knyazikhin Y et al (2013) Hyperspectral remote sensing of foliar nitrogen content. Proc Natl Acad
Sci U S A 110:E185–E192. https://doi.org/10.1073/pnas.1210196109
Kruse FA, Lefkoff AB, Boardman JW, Heidebrecht KB, Shapiro AT, Barloon PJ, Goetz
AFH (1993) The spectral image processing system (SIPS) - interactive visualization and
analysis of imaging spectrometer data. Remote Sens Environ 44:145–163. https://doi.
org/10.1016/0034-4257(93)90013-n
Levin SA (1992) The problem of pattern and scale in ecology. Ecology 73:1943–1967. https://doi.
org/10.2307/1941447
Lucas KL, Carter GA (2008) The use of hyperspectral remote sensing to assess vascular plant species richness on Horn Island, Mississippi. Remote Sens Environ 112:3908–3915. https://doi.
org/10.1016/j.rse.2008.06.009
Madritch MD, Kingdon CC, Singh A, Mock KE, Lindroth RL, Townsend PA (2014) Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales. Philos Trans
R Soc B Biol Sci 369:20130194. https://doi.org/10.1098/rstb.2013.0194
Magurran AE (2004) Measuring biological diversity. Blackwell Publishing, Malden
J. Gamon et al.
Franklin SE, Ahmed OS (2018) Deciduous tree species classification using object-based analysis and machine learning with unmanned aerial vehicle multispectral data. Int J Remote Sens
39:5236–5245. https://doi.org/10.1080/01431161.2017.1363442
Gaitán JJ et al (2013) Evaluating the performance of multiple remote sensing indices to predict
the spatial variability of ecosystem structure and functioning in Patagonian steppes. Ecol Indic
34:181–191. https://doi.org/10.1016/j.ecolind.2013.05.007
Gamon JA (2015) Reviews and syntheses: optical sampling of the flux tower footprint.
Biogeosciences 12:4509–4523. https://doi.org/10.5194/bg-12-4509-2015
Gamon JA et al (2004) Remote sensing in BOREAS: lessons leamed. Remote Sens Environ
89:139–162. https://doi.org/10.1016/j.rse.2003.08.017
Gholizadeh H, Gamon JA, Zygielbaum AI, Wang R, Schweiger AK, Cavender-Bares J (2018)
Remote sensing of biodiversity: soil correction and data dimension reduction methods improve
assessment of alpha-diversity (species richness) in prairie ecosystems. Remote Sens Environ
206:240–253. https://doi.org/10.1016/j.rse.2017.12.014
Gholizadeh H et al (2019) Detecting prairie biodiversity with airborne remote sensing. Remote
Sens Environ 221:38–49. https://doi.org/10.1016/j.rse.2018.10.037
Goodman JA, Ustin SL (2007) Classification of benthic composition in a coral reef environment
using spectral unmixing. J Appl Remote Sens 1:011501. https://doi.org/10.1117/1.2815907
Gould W (2000) Remote sensing of vegetation, plant species richness, and regional biodiversity
hotspots. Ecol Appl 10:1861–1870. https://doi.org/10.2307/2641244
Heffernan JB et al (2014) Macrosystems ecology: understanding ecological patterns and processes
at continental scales. Front Ecol Environ 12:5–14. https://doi.org/10.1890/130017
Hooper DU et al (2012) A global synthesis reveals biodiversity loss as a major driver of ecosystem
change. Nature 486:105–108. https://doi.org/10.1038/nature11118
Hopkin M (2006) Spying on nature. Nature 444:420–421
Jetz W et al (2016) Monitoring plant functional diversity from space. Nature Plants 2. https://doi.
org/10.1038/nplants.2016.24
Justice CO, Townshend JRG (1981) Integrating ground data with remote sensing. In: Townshend
JRG (ed) Terrain analysis and remote sensing. Allen and Unwin, London, pp 38–101
Kampe TU, Johnson BR, Kuester M, Keller M (2010) NEON: the first continental-scale ecological observatory with airborne remote sensing of vegetation canopy biochemistry and structure.
J Appl Remote Sens 4:043510. https://doi.org/10.1117/1.3361375
Kerr JT, Southwood TRE, Cihlar J (2001) Remotely sensed habitat diversity predicts butterfly species richness and community similarity in Canada. Proc Natl Acad Sci U S A 98:11365–11370.
https://doi.org/10.1073/pnas.201398398
Kissling WD et al (2018) Towards global data products of Essential Biodiversity Variables on species traits. Nat Ecol Evol 2:1531–1540. https://doi.org/10.1038/s41559-018-0667-3
Knyazikhin Y et al (2013) Hyperspectral remote sensing of foliar nitrogen content. Proc Natl Acad
Sci U S A 110:E185–E192. https://doi.org/10.1073/pnas.1210196109
Kruse FA, Lefkoff AB, Boardman JW, Heidebrecht KB, Shapiro AT, Barloon PJ, Goetz
AFH (1993) The spectral image processing system (SIPS) - interactive visualization and
analysis of imaging spectrometer data. Remote Sens Environ 44:145–163. https://doi.
org/10.1016/0034-4257(93)90013-n
Levin SA (1992) The problem of pattern and scale in ecology. Ecology 73:1943–1967. https://doi.
org/10.2307/1941447
Lucas KL, Carter GA (2008) The use of hyperspectral remote sensing to assess vascular plant species richness on Horn Island, Mississippi. Remote Sens Environ 112:3908–3915. https://doi.
org/10.1016/j.rse.2008.06.009
Madritch MD, Kingdon CC, Singh A, Mock KE, Lindroth RL, Townsend PA (2014) Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales. Philos Trans
R Soc B Biol Sci 369:20130194. https://doi.org/10.1098/rstb.2013.0194
Magurran AE (2004) Measuring biological diversity. Blackwell Publishing, Malden
J. Gamon et al.