A bottom-up approach to vegetation mapping of the Lake Tahoe Basin using hyperspatial image analysis

Jonathan A. Greenberg, Solomon Z. Dobrowski, Carlos M. Ramirez, Jahatel L. Tull, Susan L. Ustin

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Increasing demands on the accuracy and thematic resolution of vegetation community maps from remote sensing imagery has created a need for novel image analysis techniques. We present a case study for vegetation mapping of the Lake Tahoe Basin which fulfills many of the requirements of the Federal Geographic Data Committee base-level mapping (FGDC, 1997) by using hyperspatial Ikonos imagery analyzed with a fusion of pixel-based species classification, automated image segmentation techniques to define vegetation patch boundaries, and vegetation community classification using querying of the species classification raster based on existing and novel rulesets. This technique led to accurate FGDC physiognomic classes. Floristic classes such as dominance type remain somewhat problematic due to inaccurate species classification results. Vegetation, tree and shrub cover estimates (FGDC required attributes) were determined accurately. We discuss strategies and challenges to vegetation community mapping in the context of standards currently being advanced for thematic attributes and accuracy requirements.

    Original languageEnglish
    Pages (from-to)581-589
    Number of pages9
    JournalPhotogrammetric Engineering and Remote Sensing
    Volume72
    Issue number5
    DOIs
    StatePublished - May 2006

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