Measurement, modeling, and remote sensing of snow cover in areas of heterogeneous vegetation Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/rj4307072

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  • Numerous studies have demonstrated that vegetation canopies affect snow accumulation and ablation processes. In addition, estimates of remotely sensed snow covered area can be biased by the presence of an overlying vegetation canopy. Consequently, any attempts to measure, model, or map the distribution of snow in a region with heterogeneous vegetation cover would benefit from a more complete understanding of both the relationship between vegetation density and snow cover on the ground as well as the relationship between remotely sensed snow covered area and actual snow covered area under various vegetation densities. The research presented here explores both of these relationships. Chapter 2 describes, qualitatively and quantitatively, the relationship between canopy gap fraction (the inverse of canopy density) and snow accumulation at fine spatial scales in Glacier National Park, Montana. Gap fraction and snow cover data from two winters were compared along eight vegetation-snow transects representing a range of landscape types, including dense forest, variable density forests with openings, forest-grassland mosaics, and burned-unburned forest mosaics. The data suggest that the relationship between gap fraction and snow accumulation depends on the range of gap fraction values considered. For gap fraction values less than 40%, a significant positive linear relationship exists between gap fraction and snow accumulation. For gap fraction values between 40% and 90%, the relationship is poorly defined, most likely due to the influence of the spatial patterning of vegetation on wind scouring/deposition of snow which cannot be captured by a simple metric such as gap fraction. When gap fraction exceeds - 90%, snow cover is almost always shallow or nonexistent due to wind scouring and high solar radiation loads. The poorly defined relationship between gap fraction and snow accumulation in the range of 40-90% gap fraction is not highly problematic because this gap fraction range represents only 24% of the landscape, and the 60-90% range of gap fraction where the gap fraction-snow accumulation relationship is least pronounced represents only 5% of the landscape. The results from these vegetation-snow surveys indicate that at fine spatial scales where topographic variability is minimal, canopy density can explain a substantial portion of the variability in snow accumulation that would otherwise remain unexplained. The high variance in snow accumulation in the 60-90% gap fraction range and the relatively small sample size presented here make it unrealistic, however, to infer an optimum gap fraction for snow accumulation in Glacier National Park or anywhere else. Chapter 3 provides an assessment of methods for modeling and mapping spatiotemporal variability in snow cover in Glacier National Park. SnowModel, a relatively new physically-based snow evolution model that accounts for the influence of vegetation on snow processes, was used to simulate the spatial distribution of snow water equivalent at hourly time steps for an 850 km2 model domain in eastern Glacier National Park. The standard implementation of SnowModel uses an image of land cover type to adjust snow accumulation and ablation for the effects of vegetation. In this non-standard implementation, the model was parameterized using a weighting scheme that allowed the model to utilize a Landsat-derived image of gap fraction to adjust snow accumulation and ablation in a more precise manner than would have been possible if only land cover type information was available. In situ measurements suggest the model did a reasonable job simulating snow evolution patterns and the differences in snow evolution associated with different vegetation densities. Weaknesses in this implementation of SnowModel appear to be its tendency to overestimate snow in the easternmost portion of the model domain (where a significant rain shadow effect exists) and overestimate snow in exposed areas. Due to a lack of in situ measurements at the scale of the model output, it was not possible to conclusively determine if the incorporation of fine scale (28.5 m pixel) information on forest canopy density improved model accuracy. MODIS-derived images of binary and fractional snow covered area were also evaluated. The binary product consistently mapped a higher percentage of the study area as snow covered than the fractional product. Spatial patterns of snow covered area were similar for the MODIS-derived products and the results from the implementation of SnowModel. Unfortunately, the remotely sensed snow covered area products could not be used to evaluate the model's treatment of snow evolution under different vegetation conditions because gap fraction influences the mapping of snow covered area for the remotely sensed products. Understanding how remotely sensed estimates of snow covered area are influenced by gap fraction density will hopefully allow for these products to be used as a validation tool for spatially distributed model results in areas of heterogeneous vegetation in the future.
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  • Master files scanned at 600 ppi (256 Grayscale) using Capture Perfect 3.0 on a Canon DR-9080C in TIF format. PDF derivative scanned at 300 ppi (256 B&W), using Capture Perfect 3.0, on a Canon DR-9080C. CVista PdfCompressor 4.0 was used for pdf compression and textual OCR.
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