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DESCRIPTION:Geography and the Environment Colloquium Series.  Guest speaker
  Susan Meerdinck\, University of Iowa.&nbsp\;&nbsp\;Invasive plant species
  remain one of the primary threats to ecosystems because they alter critic
 al ecosystem functions and suppress native species' ability to respond to 
 changes in environmental conditions. Accumulation of invaders is projected
  to increase over the next two decades. Detection and long-term monitoring
  of invasions provide valuable ecological information and ultimately guide
  management decisions. One of the challenges of managing invasives across 
 landscapes is determining where they are located\, particularly at the ear
 ly stages of an invasion\, where they occur at low densities mixed with na
 tive vegetation. Traditional surveys through ground efforts are often infe
 asible due to cost and logistics\, especially when considering repeat surv
 eys are needed to determine spread. Remote sensing could improve response 
 to invasions by providing accurate and affordable repeat imagery as a more
  cost-effective way to monitor spread. This research explores our ability 
 to detect invasives using multi-spectral remote sensing and a machine-lear
 ning approach adapted to detect invasives at low densities with imprecisio
 n in the dataset. Here we show data from the Everglades National Park\, wh
 ich presents an ideal case study for using remote sensing to capture invas
 ive species spread because the majority of the park is accessible only by 
 watercraft or helicopter\, making monitoring and managing invasive plant p
 opulations exceptionally difficult and costly.&nbsp\;
DTEND:20240405T203000Z
DTSTAMP:20260512T084926Z
DTSTART:20240405T190000Z
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SUMMARY:Monitoring Dynamics of Invasive Plants Using Remote Sensing and Mac
 hine Learning Tools
UID:RFCALITEM639141581668933699
X-ALT-DESC;FMTTYPE=text/html:<p>Geography and the Environment Colloquium Se
 ries.  </p><p>Guest speaker Susan Meerdinck\, University of Iowa.&nbsp\;&n
 bsp\;</p><p>Invasive plant species remain one of the primary threats to ec
 osystems because they alter critical ecosystem functions and suppress nati
 ve species' ability to respond to changes in environmental conditions. Acc
 umulation of invaders is projected to increase over the next two decades. 
 Detection and long-term monitoring of invasions provide valuable ecologica
 l information and ultimately guide management decisions. </p><p>One of the
  challenges of managing invasives across landscapes is determining where t
 hey are located\, particularly at the early stages of an invasion\, where 
 they occur at low densities mixed with native vegetation. Traditional surv
 eys through ground efforts are often infeasible due to cost and logistics\
 , especially when considering repeat surveys are needed to determine sprea
 d. Remote sensing could improve response to invasions by providing accurat
 e and affordable repeat imagery as a more cost-effective way to monitor sp
 read. </p><p>This research explores our ability to detect invasives using 
 multi-spectral remote sensing and a machine-learning approach adapted to d
 etect invasives at low densities with imprecision in the dataset. Here we 
 show data from the Everglades National Park\, which presents an ideal case
  study for using remote sensing to capture invasive species spread because
  the majority of the park is accessible only by watercraft or helicopter\,
  making monitoring and managing invasive plant populations exceptionally d
 ifficult and costly.&nbsp\;</p>
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