Classified Cover Mapping to Support Preliminary Assessment of Aquatic Invasive Vegetation (Ludwigia spp.) in the Willamette River, Oregon: Albany and Salem Reaches

Since 2008, large-scale restoration programs have been implemented along the Willamette River, Oregon, to address historical losses of floodplain habitats for native fish. For much of the Willamette River floodplain, direct enhancement of floodplain habitats through restoration activities is needed because the underlying hydrologic, geomorphic, and vegetation processes that historically created and sustained complex floodplain habitats have been fundamentally altered by dam construction, bank protection, large wood removal, land conversion, and other influences (for example, Hulse and others, 2002; Wallick and others, 2013). An emerging management issue in the Willamette River floodplain and focus of river restoration efforts from 2015-2021 was the treatment of invasive, non-native, mat-forming emergent aquatic macrophyte, water primrose (Ludwigia hexapetala and L. peploides - henceforth Ludwigia). Ludwigia has been widely observed within off-channel features of the Willamette River in northwestern Oregon, raising questions about the effects of this plant on habitat conditions for native fish and wildlife. This document describes repeat digital maps of Ludwigia and other cover classes that were developed using random forest classification in R statistical analysis software of WorldView 2 and 3 satellite images acquired in 2012, 2015, and 2018 in two reaches of the Willamette River near Albany and Salem, Oregon. The Albany reach extends from floodplain kilometer (FPKM) 160 to 138, and the Salem reach extends from FPKM 116 to 91. This mapping effort is intended as preliminary reconnaissance to aid in determining the spatial and temporal patterns of Ludwigia presence and inform more comprehensive, river-scale mapping efforts in the future. The digital maps are rasters representing coverage of Ludwigia and seven other land cover classes produced from random forest classification mapping techniques. Mapping datasets are accompanied by training polygons and accuracy assessments of the classification models.