UHI Research Database pdf download summary Modelling the effect of spacing and site exposure on spiral grain angle on Sitka spruce (Picea sitchensis (Bong.) Carr.) in Northern Britain Link to author version on UHI Research Database Citation for published

. Modelling the effect of spacing and site exposure on spiral grain angle on Sitka spruce (Picea sitchensis (Bong.) Carr.) in Northern Britain. Forestry, 86(3), 331-342. DOI: 10.1093/forestry/cpt002 General rights Copyright and moral rights for the publications made accessible in the UHI Research Database are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights: 1) Users may download and print one copy of any publication from the UHI Research Database for the purpose of private study or research. 2) You may not further distribute the material or use it for any profit-making activity or commercial gain 3) You may freely distribute the URL identifying the publication in the UHI Research Database Take down policy If you believe that this document breaches copyright please contact us at [email protected] providing details; we will remove access to the work immediately and investigate your claim. Large grain angles in timber can have a negative effect on wood quality by reducing dimensional stability, strength properties and performance, and causing twisting and warping in sawn timber and poles. In this study, we assessed the impact of tree spacing and site exposure on grain angle, based on the measurements from 360 discs cut from Sitka spruce (Picea sitchensis (Bong.) Carr.) trees from Northern Britain (Scotland and Northern England). The results show that planting on sites exposed to strong prevailing winds, planting at wider spacing or undertaking heavy thinning can significantly increase spiral grain angle. Using the mixed-effect model, we found that between-tree variation ranged from 3.6 to 33.3 per cent, variation due to disc position within the tree ranged from 5.7 to 47.8 per cent; and between-ring variations within discs ranged from 0 to 38.5 per cent. Residual variation ranged from 25.7 to 64.8 per cent. Grain angles are greater at the juvenile stage than at the mature stage of wood development. A non-linear model, developed to predict grain angle, explained only 16 per cent of total variation in grain angle, despite the inclusion of several stand covariates into the model. Although the mixed-effect model improved the root mean square error (RMSE) by 26 per cent, and the coefficient of determination (R 2 ) rose to 53 per cent, its usage requires that a sample of grain angle measurements be made on the particular site for model calibration. The silvicultural response to reduced grain angle would be to plant at closer spacing and delay thinning or thin lightly, or to avoid planting Sitka spruce on very exposed sites. Given that grain angle is highly heritable, a further option is to reduce grain angle through selective breeding

Translation of Remote Sensing data into Sustainable Development Indicators

In 2015, member countries of the United Nations adopted the 17 Sustainable Development Goals (SDGs) at the Sustainable Development Summit in New York. These global goals have 169 targets and 232 indicators which are based on the three pillars of sustainable development: economic, social and environmental. Substantial challenges remain in obtaining data of the required quality, especially in developing countries, given the limited resources involved. One promising and innovative way of addressing this issue of data availability is to use Earth Observation (EO). This paper presents the results of research to analyse and optimise the potential of EO approaches to populate the SDG indicators and targets. We present a matrix of EO technologies with respect to the full set of current SDG indicators which shows the potential for direct or proxy calibrations across the span of the social, economic and environmental SDG indicators. We have focussed particularly on those SDG indicators covering the social and economic dimensions of sustainable development as these are relatively unexplored from an EO context. Results suggest that EO can make an important contribution towards populating the SDG indicators, but there is a spectrum from at one end the sole use of EO to the other end where the EO derived data have to be used in concert with data collected via non-EO means (surveys etc.). Complicating factors also include the lack of driving force and pressure indicators in the SDG framework and the use of ‘proxy’ indicators not part of the SDG framework but more amenable to EO-derived assessment. The next phase of the research will involve the presenting of these ideas to experts in the EO and indicator arenas for their assessment

Translation of Remote Sensing data into Sustainable Development Indicators

In 2015, member countries of the United Nations adopted the 17 Sustainable Development Goals (SDGs) at the Sustainable Development Summit in New York. These global goals have 169 targets and 232 indicators which are based on the three pillars of sustainable development: economic, social and environmental. Substantial challenges remain in obtaining data of the required quality, especially in developing countries, given the limited resources involved. One promising and innovative way of addressing this issue of data availability is to use Earth Observation (EO). This paper presents the results of research to analyse and optimise the potential of EO approaches to populate the SDG indicators and targets. We present a matrix of EO technologies with respect to the full set of current SDG indicators which shows the potential for direct or proxy calibrations across the span of the social, economic and environmental SDG indicators. We have focussed particularly on those SDG indicators covering the social and economic dimensions of sustainable development as these are relatively unexplored from an EO context. Results suggest that EO can make an important contribution towards populating the SDG indicators, but there is a spectrum from at one end the sole use of EO to the other end where the EO derived data have to be used in concert with data collected via non-EO means (surveys etc.). Complicating factors also include the lack of driving force and pressure indicators in the SDG framework and the use of ‘proxy’ indicators not part of the SDG framework but more amenable to EO-derived assessment. The next phase of the research will involve the presenting of these ideas to experts in the EO and indicator arenas for their assessment

Development of ecoregion-based merchantable volume systems for Pinus brutia Ten. and Pinus nigra Arnold. in southern Turkey

Estimating individual tree volume is one of the essential building blocks in forest growth and yield models. Ecologically based taper equations provide accurate volume predictions and allow classification by merchantable sizes, assisting in sustainable forest management. In the present study, ecoregion-based compatible volume systems for brutian pine and black pine in the three ecoregions of southern Turkey were developed. Several well-known taper functions were evaluated. A second-order continuous-time autoregressive error structure was used to correct the inherent autocorrelation in the hierarchical data, allowing the model to be applied to irregularly spaced and unbalanced data. The compatible segmented model of Fang et al. (For Sci 461–12, 2000) best described the experimental data. It is therefore recommended for estimating diameter at a specific height, height to a specific diameter, merchantable volume, and total volume for the three ecoregions and two species analyzed. The nonlinear extra sum of squares method indicated differences in ecoregion and tree-specific taper functions. A different taper function should therefore be used for each pine species and ecoregion in southern Turkey. Using ecoregion-specific taper equations allows making more robust estimations and, therefore, will enhance the accuracy of diameter at different heights and volume predictions. © 2015, Northeast Forestry University and Springer-Verlag Berlin Heidelberg