Influence of nitrogen and soil physical characteristics on belowground carbon flux dynamics of woody plants

At ecosystem level soil respiration (Rs) represents the largest carbon (C) flux after gross primary productivity, being mainly generated by root respiration (autotrophic respiration, Ra) and soil microbial respiration (heterotrophic respiration, Rh). In the case of terrestrial ecosystems, soils contain the largest C-pool, storing twice the amount of C contained in plant biomass. Soil organic matter (SOM), representing the main C storage in soil, is decomposed by soil microbial community. This process produces CO2 which is mainly released as Rh. It is thus relevant to understand how microbial activity is influenced by environmental factors like soil temperature, soil moisture and nutrient availability, since part of the CO2 produced by Rh, directly increases atmospheric CO2 concentration and therefore affects the phenomenon of climate change. Among terrestrial ecosystems, agricultural fields have traditionally been considered as sources of atmospheric CO2. In agricultural ecosystems, in particular apple orchards, I identified the role of root density, soil temperature, soil moisture and nitrogen (N) availability on Rs and on its two components, Ra and Rh. To do so I applied different techniques to separate Rs in its two components, the ”regression technique” and the “trenching technique”. I also studied the response of Ra to different levels of N availability, distributed either in a uniform or localized way, in the case of Populus tremuloides trees. The results showed that Rs is mainly driven by soil temperature, to which it is positively correlated, that high levels of soil moisture have inhibiting effects, and that N has a negligible influence on total Rs, as well as on Ra. Further I found a negative response of Rh to high N availability, suggesting that microbial decomposition processes in the soil are inhibited by the presence of N. The contribution of Ra to Rs was of 37% on average.

New techniques for the remote sensing of foliar nitrogen concentration in forest ecosystems.

During my Doctoral study I researched about the remote detection of canopy N concentration in forest stands, its potentials and problems, under many overlapping perspectives. The study consisted of three parts. In S. Rossore 2000 dataset analysis, I tested regressions between N concentration and NIR reflectances derived from different sources (field samples, airborne and satellite sensors). The analysis was further expanded using a larger dataset acquired in year 2009 as part of a new campaign funded by the ESA. In both cases, a good correlation was observed between Landsat NIR, using both TM (2009) and ETM+ (2000) imagery, and N concentration measured by a CHN elemental analyzer. Concerning airborne sensors I did not obtain the same good results, mainly because of the large FOV of the two instruments, and to the anisotropy of vegetation reflectance. We also tested the relation between ground based ASD measures and nitrogen concentration, obtaining really good results. Thus, I decided to expand my study to the regional level, focusing only on field and satellite measures. I analyzed a large dataset for the whole of Catalonia, Spain; MODIS imagery was used, in consideration of its spectral characteristics and despite its rather poor spatial resolution. Also in this case a regression between nitrogen concentration and reflectances was found, but not so good as in previous experiences. Moreover, vegetation type was found to play an important role in the observed relationship. We concluded that MODIS is not the most suitable satellite sensor in realities like Italy and Catalonia, which present a patchy and inhomogeneous vegetation cover; so it could be utilized for the parameterization of eco-physiological and biogeochemical models, but not for really local nitrogen estimate. Thus multispectral sensors similar to Landsat Thematic Mapper, with better spatial resolution, could be the most appropriate sensors to estimate N concentration.