Browsing by Author "Spadavecchia, Luke"
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Item Topographic controls on the leaf area index and plant functional type of a Fennoscandian tundra ecosystem(2008-11) Spadavecchia, Luke; Williams, Mathew; Bell, Robert; Stoy, Paul C.; Huntley, Brian; van Wijk, Mark T.Leaf area index (LAI) is an emergent property of vascular plants closely linked to primary production and surface energy balance. LAI can vary by an order of magnitude among Arctic tundra communities and is closely associated with plant functional type. 2. We examined topographic controls on vegetation type and LAI distribution at two different scales in an Arctic tundra ecosystem in northern Sweden. ‘Micro-scale’ measurements were made at 0.2-m resolution over a 40 m × 40 m domain, while ‘macro-scale’ data were collected at approximately 10-m resolution over a 500 m × 500 m domain. Tundra LAI varied from 0.1–3.6 at the micro-scale resolution, and from 0.1–1.6 at the macro-scale resolution. 3. The correlation between dominant vascular species and LAI at the micro-scale ( r 2 = 0.40) was greater than the correlation between dominant vegetation and LAI at the macro-scale ( r 2 = 0.14).At the macro-scale, LAI was better explained by topographic parameters and spatial auto-correlation (pseudo r 2 = 0.32) than it was at the micro-scale ( r 2 = 0.16). Exposure and elevation were significantly but weakly correlated with LAI at the micro-scale, while on the macro-scale the most significant explanatory topographic variable was elevation ( r 2 = 0.12). 4. The distribution of plant communities at both scales was significantly associated with topography. Shrub communities, dominated by Betula nana , were associated with low elevation sites at both scales, while more exposed and/or high elevation sites were dominated by cryptogams. 5. Synthesis. Dominant vegetation, topography and LAI were linked at both scales of investigation but, for explaining LAI, topography became more important and dominant vegetation less important at the coarser scale. The explanatory power of dominant species/functional type for LAI variation was weaker at coarser scales, because communities often contained more than one functional type at 10 m resolution. The data suggest that remotely sensed topography can be combined with remotely sensed optical measurements to generate a useful tool for LAI mapping in Arctic environments.Item 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.