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org/10.1109/IGARSS.2002.1026212
van Kleunen M, Dawson W, Essl F et al (2015) Global exchange and accumulation of non-native
plants. Nature 525:100–103. https://doi.org/10.1038/nature14910
Venugopal G (2002) Monitoring the effects of biological control of water hyacinths using remotely
sensed data: a case study of Bangalore, India. Singap J Trop Geogr 19:91–105. https://doi.
org/10.1111/1467-9493.00027
Verma R, Singh SP, Ganesha Raj K (2003) Assessment of changes in water-hyacinth coverage of
water bodies in northern part of Bangalore city using temporal remote sensing data. Curr Sci
84:795–804
Villa P, Mousivand A, Bresciani M (2014) Aquatic vegetation indices assessment through radiative transfer modeling and linear mixture simulation. Int J Appl Earth Obs Geoinf 30:113–127.
https://doi.org/10.1016/j.jag.2014.01.017
Villa P, Bresciani M, Bolpagni R et al (2015) A rule-based approach for mapping macrophyte communities using multi-temporal aquatic vegetation indices. Remote Sens Environ 171:218–233.
https://doi.org/10.1016/j.rse.2015.10.020
Villa P, Pinardi M, Tóth VR et al (2017) Remote sensing of macrophyte morphological traits: implications for the management of shallow lakes. J Limnol 76:109–126. https://doi.org/10.4081/
jlimnol.2017.1629
Vis C, Hudon C, Carignan R (2003) An evaluation of approaches used to determine the distribution
and biomass of emergent and submerged aquatic macrophytes over large spatial scales. Aquat
Bot 77:187–201. https://doi.org/10.1016/S0304-3770(03)00105-0
Vörösmarty C, McIntyre P, Gessner M et al (2010) Global threats to human water security and river
biodiversity. Nat Commun 467:555–561. https://doi.org/10.1038/nature09440
Walther GR, Roques A, Hulme PE et al (2009) Alien species in a warmer world: risks and opportunities. Trends Ecol Evol 24:686–693. https://doi.org/10.1016/j.tree.2009.06.008
Ward JV, Tockner K, Arscott DB, Claret C (2002) Riverine landscape diversity. Freshw Biol
47:517–539. https://doi.org/10.1046/j.1365-2427.2002.00893.x
Waycott M, Duarte CM, Carruthers TJB et al (2009) Accelerating loss of seagrasses across
the globe threatens coastal ecosystems. Proc Natl Acad Sci 106:12377–12381. https://doi.
org/10.1073/pnas.0905620106
Weisberg PJ, Dilts TE, Baughman OW et al (2017) Development of remote sensing indicators
for mapping episodic die-off of an invasive annual grass (Bromus tectorum) from the Landsat
archive. Ecol Indic 79:173–181. https://doi.org/10.1016/j.ecolind.2017.04.024
West AM, Evangelista PH, Jarnevich CS et al (2017) Using multi-date satellite imagery to monitor
invasive grass species distribution in post-wildfire landscapes: an iterative, adaptable approach
that employs open-source data and software. Int J Appl Earth Obs Geoinf 59:135–146. https://
doi.org/10.1016/j.jag.2017.03.009
Wilfong BN, Gorchov DL, Henry MC (2009) Detecting an invasive shrub in deciduous forest understories using remote sensing. Weed Sci 57:512–520. https://doi.org/10.1614/
WS-09-012.1
Williams SL, Grosholz ED (2008) The invasive species challenge in estuarine and coastal environments: marrying management and science. Estuar Coasts 31:3–20. https://doi.org/10.1007/
s12237-007-9031-6
Williams AP, Hunt ER Jr (2002) Estimation of leafy spurge cover from hyperspectral imagery using
mixture tuned matched filtering. Remote Sens Environ 82:446–456. https://doi.org/10.1016/
S0034-4257(02)00061-5
Winfield IJ, Onoufriou C, O’Connell MJ et al (2007) Assessment in two shallow lakes of a
hydroacoustic system for surveying aquatic macrophytes. In: Gulati RD, Lammens E, De Pauw
N, Van Donk E (eds) Hydrobiologia. Springer Netherlands, Dordrecht, pp 111–119
Wolf AA, Zavaleta ES, Selmants PC (2017) Flowering phenology shifts in response to biodiversity
loss. Proc Natl Acad Sci 114:3463–3468. https://doi.org/10.1073/pnas.1608357114
E. A. Bolch et al.
Ustin SL, DiPietro D, Olmstead K et al (2002) Hyperspectral remote sensing for invasive species detection and mapping. IEEE Int Geosci Remote Sens Symp 3:1658–1660. https://doi.
org/10.1109/IGARSS.2002.1026212
van Kleunen M, Dawson W, Essl F et al (2015) Global exchange and accumulation of non-native
plants. Nature 525:100–103. https://doi.org/10.1038/nature14910
Venugopal G (2002) Monitoring the effects of biological control of water hyacinths using remotely
sensed data: a case study of Bangalore, India. Singap J Trop Geogr 19:91–105. https://doi.
org/10.1111/1467-9493.00027
Verma R, Singh SP, Ganesha Raj K (2003) Assessment of changes in water-hyacinth coverage of
water bodies in northern part of Bangalore city using temporal remote sensing data. Curr Sci
84:795–804
Villa P, Mousivand A, Bresciani M (2014) Aquatic vegetation indices assessment through radiative transfer modeling and linear mixture simulation. Int J Appl Earth Obs Geoinf 30:113–127.
https://doi.org/10.1016/j.jag.2014.01.017
Villa P, Bresciani M, Bolpagni R et al (2015) A rule-based approach for mapping macrophyte communities using multi-temporal aquatic vegetation indices. Remote Sens Environ 171:218–233.
https://doi.org/10.1016/j.rse.2015.10.020
Villa P, Pinardi M, Tóth VR et al (2017) Remote sensing of macrophyte morphological traits: implications for the management of shallow lakes. J Limnol 76:109–126. https://doi.org/10.4081/
jlimnol.2017.1629
Vis C, Hudon C, Carignan R (2003) An evaluation of approaches used to determine the distribution
and biomass of emergent and submerged aquatic macrophytes over large spatial scales. Aquat
Bot 77:187–201. https://doi.org/10.1016/S0304-3770(03)00105-0
Vörösmarty C, McIntyre P, Gessner M et al (2010) Global threats to human water security and river
biodiversity. Nat Commun 467:555–561. https://doi.org/10.1038/nature09440
Walther GR, Roques A, Hulme PE et al (2009) Alien species in a warmer world: risks and opportunities. Trends Ecol Evol 24:686–693. https://doi.org/10.1016/j.tree.2009.06.008
Ward JV, Tockner K, Arscott DB, Claret C (2002) Riverine landscape diversity. Freshw Biol
47:517–539. https://doi.org/10.1046/j.1365-2427.2002.00893.x
Waycott M, Duarte CM, Carruthers TJB et al (2009) Accelerating loss of seagrasses across
the globe threatens coastal ecosystems. Proc Natl Acad Sci 106:12377–12381. https://doi.
org/10.1073/pnas.0905620106
Weisberg PJ, Dilts TE, Baughman OW et al (2017) Development of remote sensing indicators
for mapping episodic die-off of an invasive annual grass (Bromus tectorum) from the Landsat
archive. Ecol Indic 79:173–181. https://doi.org/10.1016/j.ecolind.2017.04.024
West AM, Evangelista PH, Jarnevich CS et al (2017) Using multi-date satellite imagery to monitor
invasive grass species distribution in post-wildfire landscapes: an iterative, adaptable approach
that employs open-source data and software. Int J Appl Earth Obs Geoinf 59:135–146. https://
doi.org/10.1016/j.jag.2017.03.009
Wilfong BN, Gorchov DL, Henry MC (2009) Detecting an invasive shrub in deciduous forest understories using remote sensing. Weed Sci 57:512–520. https://doi.org/10.1614/
WS-09-012.1
Williams SL, Grosholz ED (2008) The invasive species challenge in estuarine and coastal environments: marrying management and science. Estuar Coasts 31:3–20. https://doi.org/10.1007/
s12237-007-9031-6
Williams AP, Hunt ER Jr (2002) Estimation of leafy spurge cover from hyperspectral imagery using
mixture tuned matched filtering. Remote Sens Environ 82:446–456. https://doi.org/10.1016/
S0034-4257(02)00061-5
Winfield IJ, Onoufriou C, O’Connell MJ et al (2007) Assessment in two shallow lakes of a
hydroacoustic system for surveying aquatic macrophytes. In: Gulati RD, Lammens E, De Pauw
N, Van Donk E (eds) Hydrobiologia. Springer Netherlands, Dordrecht, pp 111–119
Wolf AA, Zavaleta ES, Selmants PC (2017) Flowering phenology shifts in response to biodiversity
loss. Proc Natl Acad Sci 114:3463–3468. https://doi.org/10.1073/pnas.1608357114
E. A. Bolch et al.