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- Ebenhard, Torbjörn, et al.
(författare)
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Environmental effects of brushwood harvesting for bioenergy
- 2017
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Ingår i: Forest Ecology and Management. - : Elsevier BV. - 0378-1127 .- 1872-7042. ; 383, s. 85-98
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Tidskriftsartikel (refereegranskat)abstract
- Sweden aims to increase the proportion of renewable energy sources, ultimately to be able to phase out fossil fuels. To achieve this, new energy sources need to be explored. In this multi-disciplinary article, we examine the technical, economical, ecological and legal possibilities to commercially and sustainably harvest brushwood for bioenergy, while simultaneously gaining positive environmental effects, both for biological diversity, the cultural heritage, and the climate. The Swedish open landscape is becoming covered with secondary brushwood regrowth through natural succession, except where it is kept open. Brushwood is spreading along roads, railway lines, edge zones, in power line corridors, abandoned semi-natural grasslands, nature reserves, and in marginal land in urban areas. Such brushwood consists of saplings, bushes and young trees of a range of deciduous plant species, e.g. birch, aspen, alder and goat willow, sometimes mixed with conifers, often forming dense thickets. Such secondary brushwood regrowth could be systematically utilised as a new source of renewable bioenergy. Commercial brushwood harvesting in Sweden may contribute 26 PJ of energy annually, which may be a small but significant contribution, considering the favourable energy ratio (E-r = 28), indicating that large emission reductions can be achieved, if fossil fuels are replaced. Growing brushwood does not require fertilizers or pesticides, soil tillage or crop management, and it does not compete with any other potential land use. Many brushwood habitats are already being managed to clear brushwood, for other purposes, minimizing the added harvesting cost. Apart from providing bioenergy, it has also been suggested that brushwood harvesting would benefit biological diversity. A large number of nationally redlisted species are dependent on the active management of open habitats, including semi-natural grasslands, and man-made habitats such as road verges and power line corridors. Our literature review shows that brushwood harvesting could benefit both biological diversity and the cultural heritage, and contribute to the management of the open cultural landscape. There are however certain limitations. Brushwood harvesting would favour a certain set of species, including many redlisted, but it may also threaten another set of species, especially species associated with early successional stages of forest regeneration, as well as forest edge species, depending on how and where it is applied. Harvesting may be affected by legislation imposing limitations regarding habitats of particular importance for biodiversity. The environmental and legal constraints would probably reduce the profitability of brushwood harvesting in certain areas, as well as the annual production of bioenergy.
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- Ehlers, Sarah, et al.
(författare)
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Assessing Error Correlations in Remote Sensing-Based Estimates of Forest Attributes for Improved Composite Estimation
- 2018
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Today, non-expensive remote sensing (RS) data from different sensors and platforms can be obtained at short intervals and be used for assessing several kinds of forest characteristics at the level of plots, stands and landscapes. Methods such as composite estimation and data assimilation can be used for combining the different sources of information to obtain up-to-date and precise estimates of the characteristics of interest. In composite estimation a standard procedure is to assign weights to the different individual estimates inversely proportional to their variance. However, in case the estimates are correlated, the correlations must be considered in assigning weights or otherwise a composite estimator may be inefficient and its variance be underestimated. In this study we assessed the correlation of plot level estimates of forest characteristics from different RS datasets, between assessments using the same type of sensor as well as across different sensors. The RS data evaluated were SPOT-5 multispectral data, 3D airborne laser scanning data, and TanDEM-X interferometric radar data. Studies were made for plot level mean diameter, mean height, and growing stock volume. All data were acquired from a test site dominated by coniferous forest in southern Sweden. We found that the correlation between plot level estimates based on the same type of RS data were positive and strong, whereas the correlations between estimates using different sources of RS data were not as strong, and weaker for mean height than for mean diameter and volume. The implications of such correlations in composite estimation are demonstrated and it is discussed how correlations may affect results from data assimilation procedures.
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- Ekström, Magnus, et al.
(författare)
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Logistic regression for clustered data from environmental monitoring programs
- 2018
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Ingår i: Ecological Informatics. - : Elsevier. - 1574-9541 .- 1878-0512. ; 43, s. 165-173
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Tidskriftsartikel (refereegranskat)abstract
- Large-scale surveys, such as national forest inventories and vegetation monitoring programs, usually have complex sampling designs that include geographical stratification and units organized in clusters. When models are developed using data from such programs, a key question is whether or not to utilize design information when analyzing the relationship between a response variable and a set of covariates. Standard statistical regression methods often fail to account for complex sampling designs, which may lead to severely biased estimators of model coefficients. Furthermore, ignoring that data are spatially correlated within clusters may underestimate the standard errors of regression coefficient estimates, with a risk for drawing wrong conclusions. We first review general approaches that account for complex sampling designs, e.g. methods using probability weighting, and stress the need to explore the effects of the sampling design when applying logistic regression models. We then use Monte Carlo simulation to compare the performance of the standard logistic regression model with two approaches to model correlated binary responses, i.e. cluster-specific and population-averaged logistic regression models. As an example, we analyze the occurrence of epiphytic hair lichens in the genus Bryoria; an indicator of forest ecosystem integrity. Based on data from the National Forest Inventory (NFI) for the period 1993-2014 we generated a data set on hair lichen occurrence on > 100,000 Picea abies trees distributed throughout Sweden. The NFI data included ten covariates representing forest structure and climate variables potentially affecting lichen occurrence. Our analyses show the importance of taking complex sampling designs and correlated binary responses into account in logistic regression modeling to avoid the risk of obtaining notably biased parameter estimators and standard errors, and erroneous interpretations about factors affecting e.g. hair lichen occurrence. We recommend comparisons of unweighted and weighted logistic regression analyses as an essential step in development of models based on data from large-scale surveys.
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- Esseen, Per-Anders, et al.
(författare)
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Broad-scale distribution of epiphytic hair lichens correlates more with climate and nitrogen deposition than with forest structure
- 2016
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Ingår i: Canadian Journal of Forest Research. - : Canadian Science Publishing. - 0045-5067 .- 1208-6037. ; 46:11, s. 1348-1358
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Tidskriftsartikel (refereegranskat)abstract
- Hair lichens are strongly influenced by forest structure at local scales, but their broad-scale distributions are less understood. We compared the occurrence and length of Alectoria sarmentosa (Ach.) Ach., Bryoria spp., and Usnea spp. in the lower canopy of > 5000 Picea abies (L.) Karst. trees within the National Forest Inventory across all productive forest in Sweden. We used logistic regression to analyse how climate, nitrogen deposition, and forest variables influence lichen occurrence. Distributions overlapped, but the distribution of Bryoria was more northern and that of Usnea was more southern, with Alectoria's distribution being intermediate. Lichen length increased towards northern regions, indicating better conditions for biomass accumulation. Logistic regression models had the highest pseudo R2 value for Bryoria, followed by Alectoria. Temperature and nitrogen deposition had higher explanatory power than precipitation and forest variables. Multiple logistic regressions suggest that lichen genera respond differently to increases in several variables. Warmingdecreased the odds for Bryoria occurrence at all temperatures. Corresponding odds for Alectoria and Usnea decreased in warmer climates, but in colder climates, they increased. Nitrogen addition decreased the odds for Alectoria and Usnea occurrence under high deposition, but under low deposition, the odds increased. Our analyses suggest major shifts in the broad-scale distribution of hair lichens with changes in climate, nitrogen deposition, and forest management.
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- Fridman, Jonas, et al.
(författare)
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Burial of downed deadwood is strongly affected by log attributes, forest ground vegetation, edaphic conditions, and climate zones
- 2016
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Ingår i: Canadian Journal of Forest Research. - : Canadian Science Publishing. - 0045-5067 .- 1208-6037. ; 46, s. 1451-1457
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Tidskriftsartikel (refereegranskat)abstract
- Deadwood can represent a substantial portion of forest ecosystem carbon stocks and is often reported following good practice guidance associated with national greenhouse gas inventories. In high-latitude forest ecosystems, a substantial proportion of downed deadwood is overgrown by ground vegetation and buried in the humus layer. Such burial obfuscates the important process of deadwood carbon transfer to other pools (e.g., litter and soil) and emission to the atmosphere (i.e., rates of decay). Using data from the Swedish National Forest Inventory, we found that the proportion of downed logs that is buried increased from temperate to boreal forests. Several factors affect the probability of burial, including log attributes (e.g., decay class), ground vegetation (e.g., moss dominance, type of moss cover), and edaphic conditions (e.g., soil type, depth of organic layer). Combined assessments suggest that about 24% of the carbon in the aboveground downed deadwood pool was found to be buried in boreal forests. Deadwood burial has important implications for forest carbon dynamics and associated monitoring (e.g., United Nations Framework Convention on Climate Change reporting) as such a pool typically decomposes much slower compared with aboveground deadwood.
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