Regional assessment of boreal forest productivity using an ecological process model and remote sensing parameter maps

Abstract

An ecological process model (BIOME-BGC) was used to assess boreal forest regional net primary production (NPP) and response to short-term, year-to-year weather fluctuations based on spatially explicit, land cover and biomass maps derived by radar remote sensing, as well as soil, terrain and daily weather information. Simulations were conducted at a 30-m spatial resolution, over a 1205 km(2) portion of the BOREAS Southern Study Area of central Saskatchewan, Canada, over a 3-year period (1994-1996). Simulations of NPP for the study region were spatially and temporally complex, averaging 2.2 (+/- 0.6), 1.8 (+/- 0.5) and 1.7 (+/- 0.5) Mg C ha(-1) year(-1) for 1994, 1995 and 1996, respectively. Spatial variability of NPP was strongly controlled by the amount of aboveground biomass, particularly photosynthetic leaf area, whereas biophysical differences between broadleaf deciduous and evergreen coniferous vegetation were of secondary importance. Simulations of NPP were strongly sensitive to year-to-year variations in seasonal weather patterns, which influenced the timing of spring thaw and deciduous bud-burst. Reductions in annual NPP of approximately 17 and 22% for 1995 and 1996, respectively, were attributed to 3- and 5-week delays in spring thaw relative to 1994. Boreal forest stands with greater proportions of deciduous vegetation were more sensitive to the timing of spring thaw than evergreen coniferous stands. Similar relationships were found by comparing simulated snow depth records with 10-year records of aboveground NPP measurements obtained from biomass harvest plots within the BOREAS region. These results highlight the importance of sub-grid scale land cover complexity in controlling boreal forest regional productivity, the dynamic response of the biome to short-term interannual climate variations, and the potential implications of climate change and other large-scale disturbances.

Influence of changed vegetations fields on regional climate simulations in the Barents Sea Region

“…For example, the duration of the non-snow season and the timing of the spring melt has a great impact on the surface energy balance and on plant productivity in the Arctic. For a similarly regionthe boreal forest in Canada - Kimball et al (2000) found a relationship between the date of first thaw and biomass production. They have shown a decrease of net primary production by 9-17% for 10-day delay in spring thaw.…”

Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation

“…The process model employed for scaling GPP was the Biome-BGC model (Kimball, Keyser, Running, & Saatchi, 2000;Kimball, Running, & Saatchi, 1999;Kimball, Thornton, White, & Running, 1997;Running, 1994;Running & Hunt, 1993). A version similar to that used in this study has been applied and tested in temperate (Coops, Waring, Brown, & Running, 2001;Running, 1994) and boreal (Kimball et al, 1997(Kimball et al, , 1999 forests.…”

“…Less than 20% of the days at either site required filling in missing data with measurements from elsewhere. The effectiveness of the model itself has been documented to some degree with regard to NPP in boreal (Kimball et al, 2000(Kimball et al, , 1999(Kimball et al, , 1997 and temperate (Running, 1994) forests. The used of binned GPP data at the tower makes it difficult to closely evaluate the effectiveness of modeled GPP responses to day-to-day variation in meteorology but the model output is clearly tracking most of the oscillations during the growing season.…”

Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation

“…Various methods such as process models and remote sensing-based approaches have been developed and used [4][5][6][7]. The process models-based methods do not generate spatially explicit predictions and often lead to a large amount of uncertainty for specific sites, partly because too many variables and input parameters are required to run the models and partly because different source data such as climate and soil data have very coarse spatial resolutions [8][9][10]. In contrast, remote sensing-based approaches have become popular due to their unique characteristics in data collection and presentation; that is, multitemporal remote sensing images not only reveal spatial variability, spatial distributions, and patterns of forests but also provide the potential to estimate their changes over time [4][5][6][7].…”