| Title | Effects of distinguishing vegetation types on the estimates of remotely sensed evapotranspiration in arid regions |
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| Author | Du, T; Wang, L; Yuan, G; Sun, X; Wang, S |
| Source | Remote Sensing vol. 11, 23, 2856, 2019., https://doi.org/10.3390/rs11232856  Open Access |
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| Year | 2019 |
| Alt Series | Natural Resources Canada, Contribution Series 20190628 |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Subjects | geophysics; remote sensing; vegetation |
| Program | Canada Centre for Remote Sensing Divsion |
| Released | 2019 12 01 |
| Abstract | Differencing of digital surface models derived from structure from motion (SfM) processing of airborne imagery has been used to produce snow depth (SD) maps with between ? 2 and ? 15 cm horizontal
resolution and accuracies of ±10 cm over relatively flat surfaces with little or no vegetation and over alpine regions. This study builds on these findings by testing two hypotheses across a broader range of conditions: (i) that the vertical accuracy
of SfM processing of imagery acquired by commercial low-cost unmanned aerial vehicle (UAV) systems can be adequately modelled using conventional photogrammetric theory and (ii) that SD change can be more accurately estimated by differencing
snow-covered elevation surfaces rather than differencing a snow-covered and snow-free surface. A total of 71 UAV missions were flown over five sites, ranging from short grass to a regenerating forest, with ephemeral snowpacks. Point cloud geolocation
performance agreed with photogrammetric theory that predicts uncertainty is proportional to UAV altitude and linearly related to horizontal uncertainty. The root-mean-square difference (RMSD) over the observation period, in comparison to the average
of in situ measurements along ? 50 m transects, ranged from 1.58 to 10.56 cm for weekly SD and from 2.54 to 8.68 cm for weekly SD change. RMSD was not related to microtopography as quantified by the snow-free surface roughness. SD change uncertainty
was unrelated to vegetation cover but was dominated by outliers corresponding to rapid in situ melt or onset; the median absolute difference of SD change ranged from 0.65 to 2.71 cm. These results indicate that the accuracy of UAV-based estimates of
weekly snow depth change was, excepting conditions with deep fresh snow, substantially better than for snow depth and was comparable to in situ methods. © 2018 Author(s). |
| GEOSCAN ID | 321957 |
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