期刊
REMOTE SENSING
卷 14, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/rs14051290
关键词
accuracy; classification; four-dimensional (4D) structure-from-motion (SfM); grassland; sagebrush; semi-arid; UAV; remote sensing
UAVs can measure vegetation spectral patterns inexpensively and accurately, improving classification accuracy. Multiple UAV measurements can identify fine-scale phenological variability in complex mixed grass/shrub vegetation.
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.
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