Implications of differing input data sources and approaches upon forest carbon stock estimation

Site index is an important forest inventory attribute that relates productivity and growth expectation of forests over time. In forest inventory programs, site index is used in conjunction with other forest inventory attributes (i.e., height, age) for the estimation of stand volume. In turn, stand volumes are used to estimate biomass (and biomass components) and enable conversion to carbon. In this research, we explore the implications and consequences of different estimates of site index on carbon stock characterization for a 2,500-ha Douglas-fir-dominated landscape located on Eastern Vancouver Island, British Columbia, Canada. We compared site index estimates from an existing forest inventory to estimates generated from a combination of forest inventory and light detection and ranging (LIDAR)-derived attributes and then examined the resultant differences in biomass estimates generated from a carbon budget model (Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3)). Significant differences were found between the original and LIDAR-derived site indices for all species types and for the resulting 5-m site classes (p < 0.001). The LIDAR-derived site class was greater than the original site class for 42{\%} of stands; however, 77{\%} of stands were within +/-1 site class of the original class. Differences in biomass estimates between the model scenarios were significant for both total stand biomass and biomass per hectare (p < 0.001); differences for Douglas-fir-dominated stands (representing 85{\%} of all stands) were not significant (p = 0.288). Overall, the relationship between the two biomass estimates was strong (R(2) = 0.92, p < 0.001), suggesting that in certain circumstances, LIDAR may have a role to play in site index estimation and biomass mapping

Leaf litter decomposition -- Estimates of global variability based on Yasso07 model

Litter decomposition is an important process in the global carbon cycle. It accounts for most of the heterotrophic soil respiration and results in formation of more stable soil organic carbon (SOC) which is the largest terrestrial carbon stock. Litter decomposition may induce remarkable feedbacks to climate change because it is a climate-dependent process. To investigate the global patterns of litter decomposition, we developed a description of this process and tested the validity of this description using a large set of foliar litter mass loss measurements (nearly 10 000 data points derived from approximately 70 000 litter bags). We applied the Markov chain Monte Carlo method to estimate uncertainty in the parameter values and results of our model called Yasso07. The model appeared globally applicable. It estimated the effects of litter type (plant species) and climate on mass loss with little systematic error over the first 10 decomposition years, using only initial litter chemistry, air temperature and precipitation as input variables. Illustrative of the global variability in litter mass loss rates, our example calculations showed that a typical conifer litter had 68% of its initial mass still remaining after two decomposition years in tundra while a deciduous litter had only 15% remaining in the tropics. Uncertainty in these estimates, a direct result of the uncertainty of the parameter values of the model, varied according to the distribution of the litter bag data among climate conditions and ranged from 2% in tundra to 4% in the tropics. This reliability was adequate to use the model and distinguish the effects of even small differences in litter quality or climate conditions on litter decomposition as statistically significant.Comment: 19 Pages, to appear in Ecological Modellin

Soil biota in boreal urban greenspace : Responses to plant type and age

Plant functional type influences the abundance and distribution of soil biota. With time, as root systems develop, such effects become more apparent. The relationship of plant type and time with the structure and abundance of soil microbial and invertebrate communities has been widely investigated in a variety of systems. However, much less is known about long-term soil community dynamics within the context of urban environments. In this study, we investigated how soil microbes, nematodes and earthworms respond to different plant functional types (lawns only and lawns with deciduous or evergreen trees) and park age in 41 urban parks in southern Finland. As non-urban controls we included deciduous and evergreen trees in 5 forest sites. We expected that microbial biomass and the relative abundance of fungi over bacteria would increase with time. We also expected major differences in soil microbial and nematode communities depending on vegetation: we hypothesized that i) the presence of trees, and evergreens in particular, would support a greater abundance of fungi and fungal-feeding nematodes over bacteria and bacterial-feeding nematodes and ii) the fungi to bacteria ratio would be lowest in lawns, with deciduous trees showing intermediate values. In contrast to our predictions, we showed that old deciduous trees, rather than evergreens, supported the highest fungal abundances and fungal-feeding nematodes in the soil. Consistent with our predictions, microbial biomass in urban park soils tended to increase with time, whereas - in contrast to our hypotheses - fungal-feeding nematode abundance declined. Even in the oldest parks included in the current study, microbial biomass estimates never approximated those in the minimally managed natural forests, where biomass estimates were three times higher. Anecic earthworm abundance also increased with time in urban parks, whereas abundances of fungal-feeding, plant-feeding and omnivorous nematodes, as well as those of epigeic and endogeic earthworms remained constant with time and without any distinct differences between urban parks and the control forests. Our findings highlight that although urban park soils harbor diverse soil communities and considerable microbial biomass, they are distinct from adjacent natural sites in community composition and biomass.Peer reviewe

Assessing tower flux footprint climatology and scaling between remotely sensed and eddy covariance measurements

We describe pragmatic and reliable methods to examine the influence of patch-scale heterogeneities on the uncertainty in long-term eddy-covariance (EC) carbon flux data and to scale between the carbon flux estimates derived from land surface optical remote sensing and directly derived from EC flux measurements on the basis of the assessment of footprint climatology. Three different aged Douglas-fir stands with EC flux towers located on Vancouver Island and part of the Fluxnet Canada Research Network were selected. Monthly, annual and interannual footprint climatologies, unweighted or weighted by carbon fluxes, were produced by a simple model based on an analytical solution of the Eulerian advection-diffusion equation. The dimensions and orientation of the flux footprint depended on the height of the measurement, surface roughness length, wind speed and direction, and atmospheric stability. The weighted footprint climatology varied with the different carbon flux components and was asymmetrically distributed around the tower, and its size and spatial structure significantly varied monthly, seasonally and inter-annually. Gross primary productivity (GPP) maps at 10-m resolution were produced using a tower-mounted multi-angular spectroradiometer, combined with the canopy structural information derived from airborne laser scanning (Lidar) data. The horizontal arrays of footprint climatology were superimposed on the 10-m-resolution GPP maps. Monthly and annual uncertainties in EC flux caused by variations in footprint climatology of the 59-year-old Douglas-fir stand were estimated to be approximately 15-20{\%} based on a comparison of GPP estimates derived from EC and remote sensing measurements, and on sensor location bias analysis. The footprint-variation-induced uncertainty in long-term EC flux measurements was mainly dependent on the site spatial heterogeneity. The bias in carbon flux estimates using spatially-explicit ecological models or tower-based remote sensing at finer scales can be estimated by comparing the footprint-weighted and EC-derived flux estimates. This bias is useful for model parameter optimizing. The optimization of parameters in remote-sensing algorithms or ecosystem models using satellite data will, in turn, increase the accuracy in the upscaled regional carbon flux estimation