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Related Experiment Video

Updated: Jul 14, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
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Mapping invasive aquatic vegetation in the Sacramento-San Joaquin Delta using hyperspectral imagery.

E C Underwood1, M J Mulitsch, J A Greenberg

  • 1Center for Spatial Technologies and Remote Sensing, University of California, Davis, California, USA. eunderwoodrussell@ucdavis.edu

Environmental Monitoring and Assessment
|June 3, 2006
PubMed
Summary
This summary is machine-generated.

Hyperspectral imagery shows promise for mapping invasive aquatic plants like Brazilian waterweed and water hyacinth in California

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Area of Science:

  • Ecology
  • Remote Sensing
  • Environmental Management

Background:

  • Invasive species pose significant ecological and economic threats.
  • Effective management requires accurate mapping of invasion extent and severity.
  • Aquatic invasive plants are a particular concern in the Sacramento-San Joaquin Delta.

Purpose of the Study:

  • To evaluate hyperspectral imagery for mapping invasive aquatic plants.
  • To assess mapping accuracy at fine and broad spatial scales.
  • To identify challenges for remote sensing of invasive species in dynamic waterways.

Main Methods:

  • Acquisition of HyMap hyperspectral imagery over 2,139 km(2) of the Sacramento-San Joaquin Delta.
  • Field data collection of GPS locations for target invasive species.
  • Spectral mixture analysis to classify Brazilian waterweed (Egeria densa) and water hyacinth (Eichhornia crassipes).

Main Results:

  • High classification accuracies (93% for Brazilian waterweed, 73% for water hyacinth) at fine scales (average 51 ha).
  • Lower accuracies at the Delta-wide scale (29% for Brazilian waterweed, 65% for water hyacinth).
  • Accuracy variations attributed to water turbidity and tidal fluctuations.

Conclusions:

  • Hyperspectral imagery is a valuable tool for mapping invasive aquatic plants in the Sacramento-San Joaquin Delta.
  • Further development of classification algorithms is needed to address environmental variability.
  • Improved mapping supports better management decisions for invasive species control and monitoring.