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Data from: Demography with drones: semi-automated segmentation and crown-to-crown matching for growth and survival of shrubs with structure from motion photogrammetry

Large-scale disturbances, such as megafires are motivating restoration at equally large, management-relevant 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) presents a challenge to effectively measure 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 the detection of demographic rates of individual plants from the air. We generated a time series of UAS flights at two sagebrush (Artemisia tridentata) common gardens to measure survival and growth of individual plants. Accuracy of Bayesian-optimized segmentation of shrubs was high (73-95%, depending on the year and site), and remotely sensed survival estimates were within 10% of field-based survival estimates. Stand age-structure affected remotely sensed estimates of growth; growth was overestimated relative to field-based estimates by 57% at 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, enabled demographic rate measurements at management-relevant scales.

Data Use
Creative Commons Attribution 4.0 License (CC-BY 4.0)
Recommended Citation:
Olsoy PJ, Zaiats A, Delparte DM, Germino MJ, Richardson BA, Roser AV, Forbey JS, Cattau ME, Caughlin TT. 2023. Data from: Demography with drones: semi-automated segmentation and crown-to-crown matching for growth and survival of shrubs with structure from motion photogrammetry [Dataset]. University of Idaho.

US National Science Foundation and Idaho EPSCoR: OIA-1757324
US National Science Foundation and Idaho EPSCoR: OIA-1826801
US National Science Foundation: BIO-2207158

Release Date
Spatial / Geographical Coverage Area
POLYGON ((-116.99 43.303, -116.99 43.322, -115.998 43.322, -115.998 43.303))
Spatial / Geographical Coverage Location
Idaho USA
Temporal Coverage
Thursday, January 1, 2015 - 00:00 to Friday, December 31, 2021 - 00:00
English (United States)
Peter J Olsoy, Andrii Zaiats, Donna Delparte, Matthew J Germino, Bryce A Richardson, Anna V Roser, Jennifer Sorenson Forbey, Megan E Cattau, T Trevor Caughlin
Contact Name
Peter Olsoy
Contact Email
Public Access Level