Browsing by Author "Prieto-Blanco, Ana"

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Temperature, heat flux, and reflectance of common subarctic mosses and lichens under field conditions: might changes to community composition impact climate-relevant surface fluxes?
(2012-06) Stoy, Paul C.; Street, Lorna E.; Johnson, Aiden V.; Prieto-Blanco, Ana; Ewing, Stephanie A.
Bryophytes and lichens are ubiquitous in subarctic ecosystems, but their roles in controlling energy fluxes are rarely studied at the species level despite large, recent observed shifts in subarctic vegetation. We quantified the surface and subsurface temperatures and spectral reflectance of common moss and lichen species at field sites in Alaska and Sweden. We also used MODIS observations to determine if the removal of Cladonia spp. by reindeer overgrazing impacts land surface albedo and temperature. Radiometric surface temperature of a feather moss (Pleurozium schreberi) exceeded 50 °C on occasion when dry, up to 20 °C higher than co-located Sphagnum fuscum or C. rangiferina. Spectral reflectance of S. fuscum was on average higher than Polytrichum piliferum across the 350–1400 nm range, with substantial within-species variability. MODIS albedo was significantly higher on the Norwegian (relatively undisturbed) side versus the Finnish (disturbed) side of a border reindeer fence by an average of 1% during periods without snow cover. MODIS nighttime land surface temperatures were often significantly higher on the Norwegian side of the fence by an average of 0.7 °C despite higher albedo, likely due to poor conductance of heat to the subsurface as observed in C. rangiferina in the field. Changes to bryophyte and lichen community composition alter the surface energy balance, and future work must determine how to best incorporate these effects into Earth system models.
Upscaling as ecological information transfer: A simple framework with application to arctic ecosystem carbon exchange
(2009-06) Stoy, Paul C.; Williams, Mathew; Prieto-Blanco, Ana; Huntley, Brian; Baxter, Robert; Lewis, Philip
Transferring ecological information across scale often involves spatial aggregation, which alters information content and may bias estimates if the scaling process is nonlinear. Here, a potential solution, the preservation of the information content of fine-scale measurements, is highlighted using modeled net ecosystem exchange (NEE) of an Arctic tundra landscape as an example. The variance of aggregated normalized difference vegetation index (NDVI), measured from an airborne platform, decreased linearly with log(scale), resulting in a linear relationship between log(scale) and the scale-wise modeled NEE estimate. Preserving three units of information, the mean, variance and skewness of fine-scale NDVI observations, resulted in upscaled NEE estimates that deviated less than 4% from the fine-scale estimate. Preserving only the mean and variance resulted in nearly 23% NEE bias, and preserving only the mean resulted in larger error and a change in sign from CO2 sink to source. Compressing NDVI maps by 70–75% using wavelet thresholding with the Haar and Coiflet basis functions resulted in 13% NEE bias across the study domain. Applying unique scale-dependent transfer functions between NDVI and leaf area index (LAI) decreased, but did not remove, bias in modeled flux in a smaller expanse using handheld NDVI observations. Quantifying the parameters of statistical distributions to preserve ecological information reduces bias when upscaling and makes possible spatial data assimilation to further reduce errors in estimates of ecological processes across scale.
Upscaling tundra CO2 exchange from chamber to eddy covariance tower
(2013-05) Stoy, Paul C.; Williams, Mathew; Evans, Jonathan G.; Prieto-Blanco, Ana; Disney, Mathias; Hill, Timothy C.; Ward, Helen C.; Wade, Thomas J.; Street, Lorna E.
Extrapolating biosphere-atmosphere CO2 flux observations to larger scales in space, part of the so-called “upscaling” problem, is a central challenge for surface-atmosphere exchange research. Upscaling CO2 flux in tundra is complicated by the pronounced spatial variability of vegetation cover. We demonstrate that a simple model based on chamber observations with a pan-Arctic parameterization accurately describes up to 75% of the observed temporal variability of eddy covariance—measured net ecosystem exchange (NEE) during the growing season in an Abisko, Sweden, subarctic tundra ecosystem, and differed from NEE observations by less than 4% for the month of June. These results contrast with previous studies that found a 60% discrepancy between upscaled chamber and eddy covariance NEE sums. Sampling an aircraft-measured normalized difference vegetation index (NDVI) map for leaf area index (L) estimates using a dynamic flux footprint model explained less of the variability of NEE across the late June to mid-September period, but resulted in a lower root mean squared error and better replicated large flux events. Findings suggest that ecosystem structure via L is a critical input for modeling CO2 flux in tundra during the growing season. Future research should focus on quantifying microclimate, namely photosynthetically active radiation and air temperature, as well as ecosystem structure via L, to accurately model growing season tundra CO2 flux at chamber and plot scales.
Using information theory to determine optimum pixel size and shape for ecological studies: Aggregating land surface characteristics in arctic ecosystems
(2009-03) Stoy, Paul C.; Williams, Mathew; Bell, Robert A.; Spadavecchia, Luke; Prieto-Blanco, Ana; Evans, J. G.; van Wijk, Mark T.
Quantifying vegetation structure and function is critical for modeling ecological processes, and an emerging challenge is to apply models at multiple spatial scales. Land surface heterogeneity is commonly characterized using rectangular pixels, whose length scale reflects that of remote sensing measurements or ecological models rather than the spatial scales at which vegetation structure and function varies. We investigated the ‘optimum’ pixel size and shape for averaging leaf area index (LAI) measurements in relatively large (85 m2 estimates on a 600 × 600-m2 grid) and small (0.04 m2 measurements on a 40 × 40-m2 grid) patches of sub-Arctic tundra near Abisko, Sweden. We define the optimum spatial averaging operator as that which preserves the information content (IC) of measured LAI, as quantified by the normalized Shannon entropy (E S,n) and Kullback–Leibler divergence (D KL), with the minimum number of pixels. Based on our criterion, networks of Voronoi polygons created from triangulated irregular networks conditioned on hydrologic and topographic indices are often superior to rectangular shapes for averaging LAI at some, frequently larger, spatial scales. In order to demonstrate the importance of information preservation when upscaling, we apply a simple, validated ecosystem carbon flux model at the landscape level before and after spatial averaging of land surface characteristics. Aggregation errors are minimal due to the approximately linear relationship between flux and LAI, but large errors of approximately 45% accrue if the normalized difference vegetation index (NDVI) is averaged without preserving IC before conversion to LAI due to the nonlinear NDVI-LAI transfer function.