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these systems can detect vegetation, they are not able to detect small IAS (Blackshaw
et al. 1998) or distinguish between the crop and IAS. They thus rely solely on a
priori assumptions about timing of emergence of IAS relative to crop species.
In summary, using RS in agroecosystems is only useful to growers within the
timeline of crop cycles. IAS control is most effective when plants are small, but this
is when they are also most difficult to detect and differentiate from the crop. IAS
detection often requires high spatial and temporal resolution due to synchronous
and asynchronous IAS emergence with the crop and sometimes high spectral resolution to deal with similar appearances during early growth stages. Implementing
data-based management decisions is difficult if monitoring is not near constant due
to the necessity of rapid responses. Thus, the use RS for the control of IAS has seen
limited adoption in agriculture, despite a long history of research. However, UAS
have become more common because the technology now meets several of the
requirements for RS of IAS in agricultural settings.
12.2.5 Urban Ecosystems
More than half of all people live in urban areas, and this proportion is expected to
increase substantially during this century. Urban ecosystems differ from agricultural or natural systems in terms of structural properties related to the built/natural
ratio of the landscape; built area includes impervious and permeable built environments and the connecting infrastructure. Urban ecosystems have been colonized by
increasing numbers of IAS (Paap et al. 2017; Hui et al. 2017). These ecosystems are
unique because trees and other ornamental species in private and public city gardens
are often non-native and can be sources of IAS to surrounding areas (Paap et al.
2017; Mayer-Pinto et al. 2017). IAS richness in urban areas is positively correlated
with housing density (Gavier-Pizarro et al. 2010), urban wastelands (Bonthoux
et al. 2014; Maurel et al. 2010), green infrastructure (Hostetler et al. 2011), and
roads (Rupprecht et al. 2015). By harboring IAS, cities may unwittingly act as
sources of IAS to surrounding agroecosystems and natural ecosystems (Paap et al.
2017; McLean et al. 2017).
Use of RS for IAS detection and mapping in urban environments is essential to
gauge the affect of urban plants, which are often non-native, on the surrounding
ecosystems. Detection has been successful with many forms of RS. For example,
Shouse et al. (2012) used a combination of 0.3 m color aerial photographs and multispectral Landsat data to map bush honeysuckle (Lonicera maackii) under the forest canopy in an urban park in Louisville, Kentucky, USA. They conducted an
object-based classification, a supervised classification, and constructed a species
distribution model, with accuracies above 75%, especially for the object-based classification. This high accuracy can be attributed to extended greened-up seasons and
high spatial resolution. Hyperspectral data has been used to detect Himalayan
blackberry (Rubus armeniacus) and English ivy (Hedera helix) in nonforested areas
of Surrey, British Columbia, Canada (Chance et al. 2016a). Classification accuracies
E. A. Bolch et al.
these systems can detect vegetation, they are not able to detect small IAS (Blackshaw
et al. 1998) or distinguish between the crop and IAS. They thus rely solely on a
priori assumptions about timing of emergence of IAS relative to crop species.
In summary, using RS in agroecosystems is only useful to growers within the
timeline of crop cycles. IAS control is most effective when plants are small, but this
is when they are also most difficult to detect and differentiate from the crop. IAS
detection often requires high spatial and temporal resolution due to synchronous
and asynchronous IAS emergence with the crop and sometimes high spectral resolution to deal with similar appearances during early growth stages. Implementing
data-based management decisions is difficult if monitoring is not near constant due
to the necessity of rapid responses. Thus, the use RS for the control of IAS has seen
limited adoption in agriculture, despite a long history of research. However, UAS
have become more common because the technology now meets several of the
requirements for RS of IAS in agricultural settings.
12.2.5 Urban Ecosystems
More than half of all people live in urban areas, and this proportion is expected to
increase substantially during this century. Urban ecosystems differ from agricultural or natural systems in terms of structural properties related to the built/natural
ratio of the landscape; built area includes impervious and permeable built environments and the connecting infrastructure. Urban ecosystems have been colonized by
increasing numbers of IAS (Paap et al. 2017; Hui et al. 2017). These ecosystems are
unique because trees and other ornamental species in private and public city gardens
are often non-native and can be sources of IAS to surrounding areas (Paap et al.
2017; Mayer-Pinto et al. 2017). IAS richness in urban areas is positively correlated
with housing density (Gavier-Pizarro et al. 2010), urban wastelands (Bonthoux
et al. 2014; Maurel et al. 2010), green infrastructure (Hostetler et al. 2011), and
roads (Rupprecht et al. 2015). By harboring IAS, cities may unwittingly act as
sources of IAS to surrounding agroecosystems and natural ecosystems (Paap et al.
2017; McLean et al. 2017).
Use of RS for IAS detection and mapping in urban environments is essential to
gauge the affect of urban plants, which are often non-native, on the surrounding
ecosystems. Detection has been successful with many forms of RS. For example,
Shouse et al. (2012) used a combination of 0.3 m color aerial photographs and multispectral Landsat data to map bush honeysuckle (Lonicera maackii) under the forest canopy in an urban park in Louisville, Kentucky, USA. They conducted an
object-based classification, a supervised classification, and constructed a species
distribution model, with accuracies above 75%, especially for the object-based classification. This high accuracy can be attributed to extended greened-up seasons and
high spatial resolution. Hyperspectral data has been used to detect Himalayan
blackberry (Rubus armeniacus) and English ivy (Hedera helix) in nonforested areas
of Surrey, British Columbia, Canada (Chance et al. 2016a). Classification accuracies
E. A. Bolch et al.