GSMNP

<div>Source Data</div><div><p><a href='https://nrcs.app.box.com/v/naip/folder/59364119980' rel='nofollow ugc'><span style='font-size:12.0pt; font-family:&quot;Segoe UI&quot;,sans-serif;'>The
National Agriculture Imagery Program (NAIP) Color Infrared Imagery, captured in
2018</span></a><span style='font-size:12.0pt; font-family:&quot;Segoe UI&quot;,sans-serif;'> </span></p></div><div><br /></div><div>Processing Methods</div><div><ol><li>downloaded NAIP imagery tiles for all Southern Appalachian sky islands with spruce forest type present.  </li><li>Mosaiced individual imagery tiles by sky island.  This step resulted in a single, seamless imagery raster dataset for each sky island.</li><li>Changed the raster band combination of the mosaiced sky island imagery to visually enhance the spruce forest type from the other forest types.  Typically, the band combination was Band 2 for Red, Band 3 for Green, and Band 1 for Blue. </li><li>Utilizing the ArcGIS Pro Image Analyst extension, performed an image segmentation of the mosaiced sky island imagery.  Segmentation is a process in which adjacent pixels with similar multispectral or spatial characteristics are grouped together. These objects represent partial or complete features on the landscape.  In this case, it simplified the imagery to be more uniform by forest type present in the imagery, especially for the spruce forest type.</li><li>Utilizing the segmented mosaiced sky island imagery, training samples were digitized.  Training samples are areas in the imagery that contain representative sites of a classification type that are used to train the imagery classification.  Adequate training samples were digitized for every classification type required for the imagery classification.  The spruce forest type was included for every sky island.  </li><li>Classified the segmented mosaiced sky island imagery utilizing a Support Vector Machine (SVM) classifier.  The SVM provides a powerful, supervised classification method that is less susceptible to noise, correlated bands, and an unbalanced number or size of training sites within each class and is widely used among researchers.  This step took the segmented mosaiced sky island imagery and created a classified raster dataset based on the training sample classification scheme.  </li><li>Reclassified the classified dataset only retaining the spruce forest type and shadows class.</li><li>Converted the spruce and shadows raster dataset to polygon.</li></ol></div><div><br /></div>