| Title | Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos |
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| Author | Fernandes, R ;
Prevost, C; Canisius, F; Leblanc, S G; Maloley, M; Oakes, S;
Holman, K; Knudby, A |
| Source | The Cryosphere vol. 12, issue 11, 2018 p. 3535-3550, https://doi.org/10.5194/tc-12-3535-2018  Open Access |
| Image |  |
| Year | 2018 |
| Alt Series | Natural Resources Canada, Contribution Series 20190614 |
| Publisher | EGU |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Subjects | geophysics; environmental geology; Nature and Environment; Science and Technology; remote sensing; photogrammetric surveys; snow; models; vegetation; topography; Methodology; snowpacks; drones;
elevations |
| Illustrations | satellite images; tables; graphs; time series; models; profiles; plots |
| Program | Canada Centre for Remote Sensing Divsion |
| Released | 2018 11 13 |
| Abstract | Accurate estimates of evapotranspiration (ET) in arid ecosystems are important for sustainable water resource management due to competing water demands between human and ecological environments. Several
empirical remotely sensed ET models have been constructed and their potential for regional scale ET estimation in arid ecosystems has been demonstrated. Generally, these models were built using combined measured ET and corresponding remotely sensed
and meteorological data from diverse sites. However, there are usually different vegetation types or mixed vegetation types in these sites, and little information is available on the estimation uncertainty of these models induced by combining
different vegetation types from diverse sites. In this study, we employed the most popular one of these models and recalibrated it using datasets from two typical vegetation types (shrub Tamarix ramosissima and arbor Populus euphratica) in arid
ecosystems of northwestern China. The recalibration was performed in the following two ways: using combined datasets from the two vegetation types, and using a single dataset from specific vegetation type. By comparing the performance of the two
methods in ET estimation for Tamarix ramosissima and Populus euphratica, we investigated and compared the accuracy of ET estimation at the site scale and the difference in annual ET estimation at the regional scale. The results showed that the
estimation accuracy of daily, monthly, and yearly ET was improved by distinguishing the vegetation types. The method based on the combined vegetation types had a great influence on the estimation accuracy of annual ET, which overestimated annual ET
about 9.19% for Tamarix ramosissima and underestimated annual ET about 11.50% for Populus euphratica. Furthermore, substantial difference in annual ET estimation at regional scale was found between the two methods. The higher the vegetation coverage,
the greater the difference in annual ET. Our results provide valuable information on evaluating the estimation accuracy of regional scale ET using empirical remotely sensed ET models for arid ecosystems. © 2019 by the authors. |
| GEOSCAN ID | 321943 |
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