Evaluating floodplain vegetation after valley‐scale restoration with unsupervised classification of National Agriculture Imagery Program data in semi‐arid environments

Abstract Monitoring vegetation response to valley‐scale floodplain restoration to evaluate effectiveness can be costly and time‐consuming. We used publicly available National Agriculture Imagery Program (NAIP) data and commonly used ArcGIS software to assess land cover change over time at five study sites located in semi‐arid environments of eastern Oregon and north‐central California. Accuracy assessments of our unsupervised classifications were used to evaluate effectiveness. Overall accuracy across sites and years ranged from 64.2% to 89.2% with mean and median accuracy of 79.1% and 80.6%, respectively. Further, we compared our classifications with high‐resolution uncrewed aerial systems (UAS)‐based data collected in the same timeframe. Restored areas classified as dense vegetation were within 4% of the UAS study, water was within 6%, and post‐restoration classifications of sparse vegetation and bare ground classes were within 6% and 4% of the UAS study, respectively. This comparison demonstrates that our unsupervised NAIP data classification of land cover change across entire valley‐scale restoration projects can be used to monitor riparian vegetation change over time as accurately as UAS‐based methods, but at lower cost. Additionally, our methods leverage existing fine‐resolution, pre‐restoration vegetation density data that were not collected as part of project planning.