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Vegetation Survey of the San Carlos Apache Reservation, Arizona and Surrounding Area (September to November 2017).

February 26, 2019

This zip file contains spatial, descriptive and digital camera image data for a vegetation field dataset collected on the San Carlos Apache Reservation and surrounding area in Arizona, and used for analysis in the associated publication. Data consists of vector point data, vegetation community type, field observations, and digital camera images that correspond with the images in the associated directory. This field data was used, in addition to historical field data from the SWReGAP project, to characterize the accuracy of the vegetation maps generated by the techniques described in the associated publication. The maps generated by these techniques did not improve on the accuracy of existing maps and are, therefore, not published. The abstract for the associated publication is as follows: 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.