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

ASJC Scopus subject areas

  • Computers in Earth Sciences

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