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Improving high-resolution (meter-scale) mapping of snow-covered areas in complex and forested terrains is critical to understanding the responses of species and water systems to climate change. Commercial high-resolution imagery from Planet Labs, Inc. (Planet, San Francisco, CA, USA) can be used in environmental science, as it has both high spatial (0.7–3.0 m) and temporal (1–2 day) resolution. Deriving snow-covered areas from Planet imagery using traditional radiometric techniques have limitations due to the lack of a shortwave infrared band that is needed to fully exploit the difference in reflectance to discriminate between snow and clouds. However, recent work demonstrated that snow cover area (SCA) can be successfully mapped using only the PlanetScope 4-band (Red, Green, Blue and NIR) reflectance products and a machine learning (ML) approach based on convolutional neural networks (CNN). To evaluate how additional features improve the existing model performance, we: (1) build on previous work to augment a CNN model with additional input data including vegetation metrics (Normalized Difference Vegetation Index) and DEM-derived metrics (elevation, slope and aspect) to improve SCA mapping in forested and open terrain, (2) evaluate the model performance at two geographically diverse sites (Gunnison, Colorado, USA and Engadin, Switzerland), and (3) evaluate the model performance over different land-cover types. The best augmented model used the Normalized Difference Vegetation Index (NDVI) along with visible (red, green, and blue) and NIR bands, with an F-score of 0.89 (Gunnison) and 0.93 (Engadin) and was found to be 4% and 2% better than when using canopy height- and terrain-derived measures at Gunnison, respectively. The NDVI-based model improves not only upon the original band-only model’s ability to detect snow in forests, but also across other various land-cover types (gaps and canopy edges). We examined the model’s performance in forested areas using three forest canopy quantification metrics and found that augmented models can better identify snow in canopy edges and open areas but still underpredict snow cover under forest canopies. While the new features improve model performance over band-only options, the models still have challenges identifying the snow under trees in dense forests, with performance varying as a function of the geographic area. The improved high-resolution snow maps in forested environments can support studies involving climate change effects on mountain ecosystems and evaluations of hydrological impacts in snow-dominated river basins.
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The Turbulent Pressure Spectrum Within the Roughness Sublayer of a Subarctic Forest Canopy
Abstract The turbulent static pressure spectrum as a function of longitudinal wavenumber in the roughness sublayer of forested canopies is of interest to a plethora of problems such as pressure transport in the turbulent kinetic energy budget, pressure pumping from snow or forest floor, and coupling between flow within and above canopies. Long term static pressure measurements above a sub‐arctic forested canopy for near‐neutral conditions during the winter and spring were collected and analyzed for three snow cover conditions: trees and ground covered with snow, trees are snow free but the ground is covered with snow, and snow free cover. In all three cases, it is shown that obeys the attached eddy hypothesis at low wavenumbers —with and Kolmogorov scaling in the inertial subrange at higher wavenumbers—with , where is the friction velocity at the canopy top, is the mean turbulent kinetic energy dissipation rate, is the distance from the snow top, and is the boundary layer depth. The implications of these two scaling laws to the normalized root‐mean squared pressure and its newly proposed logarithmic scaling with normalized wall‐normal distance are discussed for snow covered and snow free vegetation conditions. The work here also shows that the in the appears more extensive and robust than its longitudinal velocity counterpart.
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- Award ID(s):
- 2028633
- PAR ID:
- 10650192
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Atmospheres
- Volume:
- 130
- Issue:
- 4
- ISSN:
- 2169-897X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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