10.7923/XJ7R-1D86 Olsoy, Peter Peter Olsoy https://orcid.org/0000-0002-8785-0459 Agricultural Research Service Boise State University Zaiats, Andrii Andrii Zaiats https://orcid.org/0000-0001-8978-4152 Boise State University Delparte, Donna Donna Delparte https://orcid.org/0000-0002-9107-5117 Idaho State University Germino, Matthew Matthew Germino https://orcid.org/0000-0001-6326-7579 United States Geological Survey Richardson, Bryce Bryce Richardson https://orcid.org/0000-0001-9521-4367 Rocky Mountain Research Station US Forest Service Roser, Anna Anna Roser https://orcid.org/0000-0002-5184-2916 Boise State University Sorensen Forbey, Jennifer Jennifer Sorensen Forbey https://orcid.org/0000-0001-6069-4049 Boise State University Cattau, Megan Megan Cattau https://orcid.org/0000-0003-2164-3809 Boise State University Caughlin, T. Trevor T. Trevor Caughlin https://orcid.org/0000-0001-6752-2055 Boise State University University of Idaho 2023 Dataset FOS: Earth and related environmental sciences FOS: Biological sciences FOS: Environmental engineering Child, Andrew Wright Andrew Wright Child https://orcid.org/0000-0001-6666-0739 University of Idaho en 10.1111/rec.14106 2024 image/tiff shp text/xml text/txt Creative Commons Attribution 4.0 International Large-scale disturbances, such as megafires, motivate restoration at equally large extents. Measuring the survival and growth of individual plants plays a key role in current efforts to monitor restoration success. However, the scale of modern restoration (e.g., >10,000 ha) challenges measurements of demographic rates with field data. In this study, we demonstrate how unoccupied aerial system (UAS) flights can provide an efficient solution to the tradeoff of precision and spatial extent in detecting demographic rates from the air. We flew two, sequential UAS flights at two sagebrush (Artemisia tridentata) common gardens to measure the survival and growth of individual plants. The accuracy of Bayesian-optimized segmentation of individual shrub canopies was high (73–95%, depending on the year and site), and remotely sensed survival estimates were within 10% of ground-truthed survival censuses. Stand age structure affected remotely sensed estimates of growth; growth was overestimated relative to field-based estimates by 57% in the first garden with older stands, but agreement was high in the second garden with younger stands. Further, younger stands (similar to those just after disturbance) with shorter, smaller plants were sometimes confused with other shrub species and bunchgrasses, demonstrating a need for integrating spectral classification approaches that are increasingly available on affordable UAS platforms. The older stand had several merged canopies, which led to an underestimation of abundance but did not bias remotely sensed survival estimates. Advances in segmentation and UAS structure from motion photogrammetry will enable demographic rate measurements at management-relevant extents. Idaho USA -116.99 -115.998 43.303 43.322 National Science Foundation https://doi.org/10.13039/100000001 OIA-1757324 RII Track-1: Linking Genome to Phenome to Predict Adaptive Responses of Organisms to Changing Landscapes National Science Foundation https://doi.org/10.13039/100000001 OIA-1826801 RII Track-2 FEC: Genomics Underlying Toxin Tolerance (GUTT): Identifying Molecular Innovations that Predict Phenotypes of Toxin Tolerance in Wild Vertebrate Herbivores National Science Foundation https://doi.org/10.13039/100000001 BIO-2207158 EAGER: Scaling Up Plant Demographic Rates with Imagery from Unoccupied Aerial Systems