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Keith A.M.,UK Center for Ecology and Hydrology | Rowe R.L.,UK Center for Ecology and Hydrology | Parmar K.,UK Center for Ecology and Hydrology | Perks M.P.,Center for Sustainable Forestry and Climate Change | And 3 more authors.
GCB Bioenergy | Year: 2015

Land-use change can have significant impacts on soil and aboveground carbon (C) stocks and there is a clear need to identify sustainable land uses which maximize C mitigation potential. Land-use transitions from agricultural to bioenergy crops are increasingly common in Europe with one option being Short Rotation Forestry (SRF). Research on the impact on C stocks of the establishment of SRF is limited, but given the potential for this bioenergy crop in temperate climates, there is an evident knowledge gap. Here, we examine changes in soil C stock following the establishment of SRF using combined short (30 cm depth) and deep (1 m depth) soil cores at 11 sites representing 29 transitions from agriculture to SRF. We compare the effects of tree species including 9 coniferous, 16 broadleaved and 4 Eucalyptus transitions. SRF aboveground and root biomass were also estimated in 15 of the transitions using tree mensuration data allowing assessments of changes in total ecosystem C stock. Planting coniferous SRF, compared to broadleaved and Eucalyptus SRF, resulted in greater accumulation of litter and overall increased soil C stock relative to agricultural controls. Though broadleaved SRF had no overall effect on soil C stock, it showed the most variable response suggesting species-specific effects and interactions with soil types. While Eucalyptus transitions induced a reduction in soil C stocks, this was not significant unless considered on a soil mass basis. Given the relatively young age and limited number of Eucalyptus plantations, it is not possible to say whether this reduction will persist in older stands. Combining estimates of C stocks from different ecosystem components (e.g., soil, aboveground biomass) reinforced the accumulation of C under coniferous SRF, and indicates generally positive effects of SRF on whole-ecosystem C. These results fill an important knowledge gap and provide data for modelling of future scenarios of LUC. © 2014 NERC Centre for Ecology and Hydrology.

Hetroy-Wheeler F.,French Institute for Research in Computer Science and Automation | Casella E.,Center for Sustainable Forestry and Climate Change | Boltcheva D.,University of Lorraine
International Journal of Remote Sensing | Year: 2016

This article describes a new semi-automatic method to cluster terrestrial laser scanning (TLS) data into meaningful sets of points to extract plant components. The approach is designed for small plants with distinguishable branches and leaves, such as tree seedlings. It first creates a graph by connecting each point to its most relevant neighbours, then embeds the graph into a spectral space, and finally segments the embedding into clusters of points. The process can then be iterated on each cluster separately. The main idea underlying the approach is that the spectral embedding of the graph aligns the points along the shape’s principal directions. A quantitative evaluation of the segmentation accuracy, as well as of leaf area (LA) estimates, is provided on a poplar seedling mock-up. It shows that the segmentation is robust with false-positive and false-negative rates of around 1%. Qualitative results on four contrasting plant species with three different scan resolution levels each are also shown. © 2016 Informa UK Limited, trading as Taylor & Francis Group.

Alexander P.,Scotland's Rural College | Moran D.,Scotland's Rural College | Smith P.,University of Aberdeen | Hastings A.,University of Aberdeen | And 9 more authors.
GCB Bioenergy | Year: 2014

To achieve the UK Government's aim of expansion in the growth of perennial energy crops requires farmers to select these crops in preference to conventional rotations. Existing studies estimating the total potential resource have either only simplistically considered the farmer decision-making and opportunity costs, for example using an estimate of annual land rental charge; or have not considered spatial variability, for example using representative farm types. This paper attempts to apply a farm-scale modelling approach with spatially specific data to improve understanding of potential perennial energy crop supply. The model main inputs are yield maps for the perennial energy crops, Miscanthus and willow grown as short-rotation coppice (SRC), and regional yields for conventional crops. These are used to configure location specific farm-scale models, which optimize for profit maximization with risk aversion. Areas that are unsuitable or unavailable for energy crops, due to environmental or social factors, are constrained from selection. The results are maps of economic supply, assuming a homogenous farm-gate price, allowing supply cost curves for the UK market to be derived. The results show a high degree of regional variation in supply, with different patterns for each energy crop. Using estimates of yields under climate change scenarios suggests that Miscanthus supply may increase under future climates while the opposite effect is suggested for SRC willow. The results suggest that SRC willow is only likely to able to supply a small proportion of the anticipated perennial energy crop target, without increases in market prices. Miscanthus appears to have greater scope for supply, and its dominance may be amplified over time by the effects of climate change. Finally, the relationship to the demand side of the market is discussed, and work is proposed to investigate the factors impacting how the market as a whole may develop. © 2013 John Wiley & Sons Ltd.

Akerblom M.,Tampere University of Technology | Raumonen P.,Tampere University of Technology | Kaasalainen M.,Tampere University of Technology | Casella E.,Center for Sustainable Forestry and Climate Change
Remote Sensing | Year: 2015

One way to model a tree is to use a collection of geometric primitives to represent the surface and topology of the stem and branches of a tree. The circular cylinder is often used as the geometric primitive, but it is not the only possible choice. We investigate various geometric primitives and modelling schemes, discuss their properties and give practical estimates for expected modelling errors associated with the primitives. We find that the circular cylinder is the most robust primitive in the sense of a well-bounded volumetric modelling error, even with noise and gaps in the data. Its use does not cause errors significantly larger than those with more complex primitives, while the latter are much more sensitive to data quality. However, in some cases, a hybrid approach with more complex primitives for the stem is useful. © 2015 by the authors licensee MDPI, Basel, Switzerland.

Xia J.,University of Oklahoma | Niu S.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Ciais P.,French Climate and Environment Sciences Laboratory | Janssens I.A.,University of Antwerp | And 43 more authors.
Proceedings of the National Academy of Sciences of the United States of America | Year: 2015

Terrestrial gross primary productivity (GPP) varies greatly over time and space. A better understanding of this variability is necessary for more accurate predictions of the future climate-carbon cycle feedback. Recent studies have suggested that variability in GPP is driven by a broad range of biotic and abiotic factors operating mainly through changes in vegetation phenology and physiological processes. However, it is still unclear howplant phenology and physiology can be integrated to explain the spatiotemporal variability of terrestrial GPP. Based on analyses of eddy-covariance and satellite-derived data, we decomposed annual terrestrial GPP into the length of the CO2 uptake period (CUP) and the seasonalmaximal capacity of CO2 uptake (GPPmax). The product of CUP and GPPmax explained >90% of the temporal GPP variability in most areas of North America during 2000-2010 and the spatial GPP variation among globally distributed eddy flux tower sites. It also explained GPP response to the European heatwave in 2003 (r2 = 0.90) and GPP recovery after a fire disturbance in South Dakota (r2 = 0.88). Additional analysis of the eddy-covariance flux data shows that the interbiome variation in annual GPP is better explained by that in GPPmax than CUP. These findings indicate that terrestrial GPP is jointly controlled by ecosystem-level plant phenology and photosynthetic capacity, and greater understanding of GPPmax and CUP responses to environmental and biological variations will, thus, improve predictions of GPP over time and space.

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