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Mapping Presence and Predicting Phenological Status of Invasive Buffelgrass in Southern Arizona Using MODIS, Climate and Citizen Science Observation Data

C. Wallace,Jessica J. Walker,S. Skirvin,C. Patrick-Birdwell,J. Weltzin,H. Raichle

Published 2016 in Remote Sensing

ABSTRACT

The increasing spread and abundance of an invasive perennial grass, buffelgrass (Pennisetum ciliare), represents a critical threat to the native vegetation communities of the Sonoran desert in southern Arizona, USA, where buffelgrass eradication is a high priority for resource managers. Herbicidal treatment of buffelgrass is most effective when the vegetation is actively growing, but the remoteness of infestations and the erratic timing and length of the species’ growth periods confound effective treatment. The goal of our research is to promote buffelgrass management by using remote sensing data to detect where the invasive plants are located and when they are photosynthetically active. We integrated citizen scientist observations of buffelgrass phenology in the Tucson, Arizona area with PRISM precipitation data, eight-day composites of 250-m Moderate-resolution Imaging Spectroradiometer (MODIS) satellite imagery, and aerially-mapped polygons of buffelgrass presence to understand dynamics and relationships between precipitation and the timing and amount of buffelgrass greenness from 2011 to 2013. Our results show that buffelgrass responds quickly to antecedent rainfall: in pixels containing buffelgrass, higher correlations (R2 > 0.5) typically occur after two cumulative eight-day periods of rain, whereas in pixels dominated by native vegetation, four prior 8-day periods are required to reach that threshold. Using the new suite of phenometrics introduced here—Climate Landscape Response metrics—we accurately predicted the location of 49% to 55% of buffelgrass patches in Saguaro National Park. These metrics and the suggested guidelines for their use can be employed by resource managers to treat buffelgrass during optimal time periods.

PUBLICATION RECORD

  • Publication year

    2016

  • Venue

    Remote Sensing

  • Publication date

    2016-06-24

  • Fields of study

    Computer Science, Environmental Science

  • Identifiers
    DOI 10.3390/RS8070524
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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