290
approaches such as phenology signatures. Even without high spectral resolution
data, RS of aquatic macrophytes is progressing. For example, through radiative
transfer modeling, it has been shown to be robust for mapping aquatic macrophyte
morphological traits in temperate systems (e.g., leaf area index, fractional cover,
and biomass) across floating, emergent, and submerged macrophytes, which can be
used to better quantify nutrient uptake, community dynamics, and invasion hotspots
(Villa et al. 2014, 2015, 2017). The rapidly developing science of drone and UAS
imagery also raises the potential to map IAS using differences in texture or using
segmentation tools to do object-based mapping, especially when the area being
mapped is small.
12.2.4 Agroecosystems
Agroecosystems are unique ecosystems due to the extraordinarily high anthropogenic interventions and pressures placed on them. Unlike other ecological systems,
agricultural systems have more controlled environmental conditions with limited
plant biodiversity. Crops are often grown as a monoculture, in uniform rows with
highly regulated demography. Though crop species are often robust and herbicide
resistant, many IAS are also developing resistance to herbicide, making them more
invasive with increasing impacts on crops. With a rising global population, there is
increased pressure on agricultural systems to increase productivity. IAS consume
resources meant for crops and reduce yield, productivity, and income for farmers.
In corn and soybean, two of the major crops grown in the United States losses due
to IAS have been estimated at $17 billion in soybean and $27 billion in corn annually, approximately 50% of the yield of each of these crops (Soltani et al. 2016,
2017). IAS can become established in agroecosystems as in any other system,
through both natural (wind, water, animals, forceful dehiscence) and artificial
(machinery, crop seed, livestock feed, spreading of crop, and livestock waste)
means. The application of water and nutrients also complicates the system by
enhancing IAS’ ability to compete with crops and reproduce. Often the effects of
IAS depend on the crops present. Certain IAS may be problematic in some crops but
not others due to crop management practices (time of planting, tillage, irrigation,
mulch, registered herbicides, rotation).
To effectively detect IAS in agricultural systems, RS must meet the challenge of
detecting IAS before they become competitive with crops. Field spectroscopy has
been shown to be effective for discrimination of IAS from crops (Basinger 2018;
Koger et al. 2004a, b; Gray et al. 2009), but it is not the most efficient due to the short
duration of such field campaigns, since detection must then occur within a small
window during one growing cycle. Research has long been published on the use of
satellites or other airborne sensors for IAS detection in agriculture (Hunt et al.
2007; Menges et al. 1985), but these methods often lack the spatial and/or temporal
resolution needed to detect IAS intermixed with a crop species.
E. A. Bolch et al.
approaches such as phenology signatures. Even without high spectral resolution
data, RS of aquatic macrophytes is progressing. For example, through radiative
transfer modeling, it has been shown to be robust for mapping aquatic macrophyte
morphological traits in temperate systems (e.g., leaf area index, fractional cover,
and biomass) across floating, emergent, and submerged macrophytes, which can be
used to better quantify nutrient uptake, community dynamics, and invasion hotspots
(Villa et al. 2014, 2015, 2017). The rapidly developing science of drone and UAS
imagery also raises the potential to map IAS using differences in texture or using
segmentation tools to do object-based mapping, especially when the area being
mapped is small.
12.2.4 Agroecosystems
Agroecosystems are unique ecosystems due to the extraordinarily high anthropogenic interventions and pressures placed on them. Unlike other ecological systems,
agricultural systems have more controlled environmental conditions with limited
plant biodiversity. Crops are often grown as a monoculture, in uniform rows with
highly regulated demography. Though crop species are often robust and herbicide
resistant, many IAS are also developing resistance to herbicide, making them more
invasive with increasing impacts on crops. With a rising global population, there is
increased pressure on agricultural systems to increase productivity. IAS consume
resources meant for crops and reduce yield, productivity, and income for farmers.
In corn and soybean, two of the major crops grown in the United States losses due
to IAS have been estimated at $17 billion in soybean and $27 billion in corn annually, approximately 50% of the yield of each of these crops (Soltani et al. 2016,
2017). IAS can become established in agroecosystems as in any other system,
through both natural (wind, water, animals, forceful dehiscence) and artificial
(machinery, crop seed, livestock feed, spreading of crop, and livestock waste)
means. The application of water and nutrients also complicates the system by
enhancing IAS’ ability to compete with crops and reproduce. Often the effects of
IAS depend on the crops present. Certain IAS may be problematic in some crops but
not others due to crop management practices (time of planting, tillage, irrigation,
mulch, registered herbicides, rotation).
To effectively detect IAS in agricultural systems, RS must meet the challenge of
detecting IAS before they become competitive with crops. Field spectroscopy has
been shown to be effective for discrimination of IAS from crops (Basinger 2018;
Koger et al. 2004a, b; Gray et al. 2009), but it is not the most efficient due to the short
duration of such field campaigns, since detection must then occur within a small
window during one growing cycle. Research has long been published on the use of
satellites or other airborne sensors for IAS detection in agriculture (Hunt et al.
2007; Menges et al. 1985), but these methods often lack the spatial and/or temporal
resolution needed to detect IAS intermixed with a crop species.
E. A. Bolch et al.