Multiple UAV flights across the growing season can characterize fine scale phenological heterogeneity within and among vegetation functional groups
Grasslands and shrublands exhibit pronounced spatial and temporal variability in structure and function with differences in phenology that can be difficult to observe. Unpiloted aerial vehicles (UAVs) can measure vegetation spectral patterns relatively cheaply and repeatably at fine spatial resolution. We tested the ability of UAVs to measure phenological variability within vegetation functional groups and to improve classification accuracy at two sites in Montana, U.S.A. We tested four flight frequencies during the growing season. Classification accuracy based on reference data increased by 5–10% between a single flight and scenarios including all conducted flights. Accuracy increased from 50.6% to 61.4% at the drier site, while at the more mesic/densely vegetated site, we found an increase of 59.0% to 64.4% between a single and multiple flights over the growing season. Peak green-up varied by 2–4 weeks within the scenes, and sparse vegetation classes had only a short detectable window of active phtosynthesis; therefore, a single flight could not capture all vegetation that was active across the growing season. The multi-temporal analyses identified differences in the seasonal timing of green-up and senescence within herbaceous and sagebrush classes. Multiple UAV measurements can identify the fine-scale phenological variability in complex mixed grass/shrub vegetation.
Citation Information
| Publication Year | 2022 |
|---|---|
| Title | Multiple UAV flights across the growing season can characterize fine scale phenological heterogeneity within and among vegetation functional groups |
| DOI | 10.3390/rs14051290 |
| Authors | David J. A. Wood, Todd M. Preston, Scott Powell, Paul C. Stoy |
| Publication Type | Article |
| Publication Subtype | Journal Article |
| Series Title | Remote Sensing |
| Index ID | 70232158 |
| Record Source | USGS Publications Warehouse |
| USGS Organization | Northern Rocky Mountain Science Center |
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