Sawyer Mill Dam Removal Bellamy River Reservoir Response Training Polygons, Test Polygons, and Updated Accuracy Assessment Points

These are the training & testing polygons as well as the updated accuracy assessment points made from the test polygons for the Evans et al. Sawyer Mill dam removal Bellamy River Reservoir (Dover, New Hampshire, USA) response manuscript. These polygons were used to train and test machine learning classifiers for mapping vegetation structure in a drained reservoir environment pre-/post-dam removal. For more information on these shapefiles and how they were created and used in the SfM/ML classification workflow, please see the Evans et al. manuscript. The study name and sample date (written mddyyyy) are noted in the file name, as well as whether the file contains test polygons, training polygons, or accuracy assessment points.
Note: The train/test polygon sets have varying projections depending on the sampling date. In hindsight, this was due to some of the polygons being drawn in different/earlier preliminary work. The polygons here, as is, were fed into the relevant ArcGIS Pro tools along with the GE raster datasets in WGS 1984 in the classification workflow, and it appears that ArcGIS Pro was able to reconcile these differences during processing based on the classification results. When the polygons here are overlaid with the corresponding orthomosaics in WGS 1984 in ArcGIS, upon visual inspection they designate the areas of cover correctly. This breakaway from keeping projections consistent across spatial data files was unnoticed until the data was being prepared for publication, and it was assumed to not have impacted the results in any meaningful way.
These materials were made using resources from an NSF EPSCoR funded project “RII Track-2 FEC: Strengthening the scientific basis for decision-making about dams: Multi-scale, coupled-systems research on ecological, social, and economic trade-offs” (a.k.a. "Future of Dams"). Support for this project is provided by the National Science Foundation’s Research Infrastructure Improvement NSF #IIA-1539071. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.