Relating Biomass and Leaf Area Index to Non-destructive Measurements in Order to Monitor Changes in Arctic Vegetation

Abstract

this paper reports an alternative method for seasonal and long-term monitoring of biomass and the leaf area index (LAI) at Arctic tundra sites. Information related to the historical and projected change in abundance and distribution of biomass and LAI is required to address numerous environmental and resource management issues. Observations of earth from satellites could potentially be used to derive seasonal and long-term changes in biomass and the LAI. to realize this potential, seasonal and long-term ground monitoring data for validation are essential; however, the conventional destructive sampling method for measuring biomass and the LAI does not allow repetitive measurements at the same plots and thus is not suitable for monitoring change over time. Alternative methods, such as sampling nearby similar plots, can be laborious and easily subject to large sampling errors, especially in Arctic tundra sites with low vegetation cover. In this study, we developed a practical method for relating non-destructive measurements (percent cover and mean height) to biomass and the LAI for 13 major Arctic plant groups, or seven plant functional types, on the basis of measurements at 196 plots across Canada's Arctic tundra ecosystems. using the method at the plant group level to estimate plot total vascular aboveground biomass, foliage biomass, and LAI, we had r 2 = 0.91-0.95 and relative mean absolute error of 25-29%. By this method, one could monitor seasonal and long-term changes in biomass and the LAI through repeated, non-destructive observations of percent cover and mean height at the same permanent plots.

Vegetation Community, Foliar Nitrogen, and Temperature Effects on Tundra CO2Exchange across a Soil Moisture Gradient

“…174 / ARCTIC, ANTARCTIC, AND ALPINE RESEARCH Chen et al (2009) developed field metric methods for assessing AGB for both graminoids and bryophytes; whereas the equations presented here have lower RMAE and MedAPE values, indicating the potential of high spatial resolution remote sensing data for modeling these variables. The strongest result was observed for nonvegetated cover.…”

“…As the primary variable that requires the most post field processing, it was used to determine sample size for all associated variables. Small sample size has been an issue for many studies in remote locations, and in many instances has limited their statistical reliability Laidler et al, 2008;Chen et al, 2009). This study took a statistically rigorous approach to determining sample sizes for each vegetation plot.…”

“…Linear bivariate regression of Arctic biophysical variables and vegetation spectral indices, specifically NDVI, from various satellite sensors is the most commonly applied and easily understood method of analysis (Stow et al, 1993a;Shippert et al, 1995;Laidler et al, 2008;Chen et al, 2009). Biophysical samples and NDVI values were aggregated to the plot quadrant level for analysis with each site (i.e., CB and SL) being analyzed independently.…”

“…Since percentage errors are not scale-dependent, they can be used to compare performance across data sets (Swanson et al, 2011). As in Chen et al (2009), two relative statistics, the Relative Mean Absolute Error (RMAE) and the Median Absolute Percentage Error (MedAPE), were selected as the non-scale-dependent estimates of error. RMAE is computed as the Mean Absolute Error (MAE) divided by the corresponding average value of the dependent variable and is more sensitive to errors if the dependent variable has a large value.…”

“…RMAE is computed as the Mean Absolute Error (MAE) divided by the corresponding average value of the dependent variable and is more sensitive to errors if the dependent variable has a large value. In contrast, the value of MedAPE generally gives more weight to percentage errors corresponding to low values for the dependent variable (Chen et al, 2009;Swanson et al, 2011). The coefficient of determination (r 2 ) is the most common statistic given describing the fit of an empirical model and can be interpreted as the proportion of the total variation in the dependent variable explained by the independent variable.…”