Upscaling as ecological information transfer: a simple framework with application to Arctic ecosystem carbon exchange

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.     

Modelling biomass and leaf area index in a sub-arctic Scandinavian mountain area

Estimates of biomass and leaf area index (LAI) are important variables in ecological and climate models. However, very little is known about the biomass and LAI of the vegetation in the Scandinavian mountain area. In this study, extensive field data consisting of diameter at breast height for 13?000 trees and height for 550 trees were collected. Furthermore, biomass and leaf area (LA) measurements for 46 mountain birch trees [Betula pubescens ssp. czerepanovii (Orlowa) Hämet-Ahti] and biomass and LA measurements for shrubs (e.g. Salix spp., Betula nana) at 36 sample plots were carried out. Multiplicative linear models for trees were fitted to tree biomass and LA measurements using basal area at breast height, height, crown diameter and diameter at stump height as explanatory variables. Additive linear models were fitted to shrub biomass and LAI measurements using coverage of shrubs, topographic variables and soil type as explanatory variables. The functions were then used to predict the biomass and LAI for trees and shrubs for the entire test area, which covers an area of 84 km² and is located at latitude 68° N. The mean total biomass estimates were 27?493 kg ha^?1 for the forest and 7650 kg ha^?1 for snow-protected heath and meadow vegetation. The LAIs were 2.06 and 0.52, respectively. For monitoring biomass and LAI in the Scandinavian mountain area, the functions could also be applied to data from traditional field-based inventories and the estimates might further be improved by combining the estimates from the test area with auxiliary information such as remote sensing images.