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Remote sensing analysis of vegetation at the San Carlos Apache Reservation, Arizona and surrounding area

June 1, 2018

Mapping of vegetation types is of great importance to the San Carlos Apache Tribe and their management of forestry and fire fuels. Various remote sensing techniques were applied to classify multitemporal Landsat 8 satellite data, vegetation index, and digital elevation model data. A multitiered unsupervised classification generated over 900 classes that were then recoded to one of the 16 generalized vegetation/land cover classes using the Southwest Regional Gap Analysis Project (SWReGAP) map as a guide. A supervised classification was also run using field data collected in the SWReGAP project and our field campaign. Field data were gathered and accuracy assessments were generated to compare outputs. Our hypothesis was that a resulting map would update and potentially improve upon the vegetation/land cover class distributions of the older SWReGAP map over the 24,000  km2 study area. The estimated overall accuracies ranged between 43% and 75%, depending on which method and field dataset were used. The findings demonstrate the complexity of vegetation mapping, the importance of recent, high-quality-field data, and the potential for misleading results when insufficient field data are collected.

Citation Information

Publication Year 2018
Title Remote sensing analysis of vegetation at the San Carlos Apache Reservation, Arizona and surrounding area
DOI 10.1117/1.JRS.12.026017
Authors Laura M. Norman, Barry R. Middleton, Natalie R. Wilson
Publication Type Article
Publication Subtype Journal Article
Series Title Journal of Applied Remote Sensing
Series Number
Index ID 70197465
Record Source USGS Publications Warehouse
USGS Organization Western Geographic Science Center

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