Rasmus Astrup

Moose Alces alces habitat use at multiple temporal scales in a human-altered landscape

Abstract Habitat alteration by humans may change the supply of food and cover for wild ungulates, but few studies have examined how these resources are utilised over time by individuals of different sex and reproductive status. We examined circadian and seasonal variation in habitat utilisation within a moose Alces alces population in central Norway. Our study area covers forests and open habitats, both influenced by human alterations (e.g. forestry and agriculture). We expected moose to select habitats with good forage and cover in all seasons, but to select open foraging habitats mainly during night-time. Moose selected good foraging habitats, such as young forest stands and cultivated land during night, whereas the utilisation of older forest stands providing cover increased during daytime. This circadian pattern changed throughout the year, seemingly related to variation in hours of daylight and provision of forage. Young forest stands provided higher density of preferred food plants compared to older stands and were highly selected from spring until autumn. Relative to young forest, the selection for older forest stands increased towards winter, likely due to provision of higher plant quality late in the growing season, and to reduced accumulation of movement-impeding snow during winter. Selection of cultivated land varied among seasons, being highest when crop biomass was high. We also found some indications of state-dependent habitat selection as reproducing females avoided open, food rich areas in the first months after their calves were born, whereas males and females without young selected these areas in spring and summer. Our results clearly show that moose exploit the variations in cover and food caused by forestry and agriculture. This is particularly relevant for moose in Norway as current changes in forestry practice lead to a reduction in young, food-rich forest stands, possibly aggravating the already declining body conditions and recruitment rates of moose.

Competitive interactions across a soil fertility gradient in a multispecies forest

Summary 1. Whether plant competition grows stronger or weaker across a soil fertility gradient is an area of great debate in plant ecology. We examined the effects of competition and soil fertility and their interaction on growth rates of the four dominant tree species in the sub-boreal spruce forest of British Columbia. 2. We tested separate soil nutrient and moisture indices and found much stronger support for models that included the nutrient index as a measure of soil fertility. 3. Competition, soil fertility and their interaction affected radial growth rates for all species. 4. Each species supported a different alternate hypothesis for how competitive interactions changed with soil fertility and whether competition intensity was stronger or weaker overall as soil fertility increased depended on the context, specifically, species, neighbourhood composition and type of competition (shading vs. crowding). 5. The four species varied slightly in their growth response to soil fertility. 6. Individual species had some large variations in the shapes of their negative relationships between shading, crowding and tree growth, with one species experiencing no net negative effects of crowding at low soil fertility. 7. Goodness-of-fit was not substantially increased by models including competition–soil fertility interactions for any species. Tree size, soil fertility, shading and crowding predicted most of the variation in tree growth rates in the sub-boreal spruce forest. 8. Synthesis. The intensity of competition among trees across a fertility gradient was species- and context-specific and more complicated than that predicted by any one of the dominant existing theories in plant ecology.

Deriving forest monitoring variables from X-band InSAR SRTM height.

The suitability of interferometric X-band radar for forest monitoring was investigated. Working in a spruce-dominated forest in southeast Norway, top height, mean height, stand density, stem volume, and biomass were related to space shuttle interferometric height above ground. A ground truth dataset was produced for each radar data pixel in the study area by combining a field inventory and automatic tree detection with airborne laser scanning data. Pixels were aggregated to forest stands. Interferometric height was strongly related to all of the five forest variables, and most strongly to top height with R2 = 0.71 and RMSE = 13% at the pixel level and R2 = 0.82 and RMSE = 5.6% at the stand level. Interferometric height was linearly related to stem volume and biomass up to 400 m3/ha and 200 t/ha, respectively, and RMSE was approximately 19% for both variables. These errors contain error components caused by the 3.5-year time lag between the radar acquisition and the laser scanning. It is concluded that interferometric X-band radar has potential for use in forest monitoring.

Empirical coverage of model-based variance estimators for remote sensing assisted estimation of stand-level timber volume

Due to the availability of good and reasonably priced auxiliary data, the use of model-based regression-synthetic estimators for small area estimation is popular in operational settings. Examples are forest management inventories, where a linking model is used in combination with airborne laser scanning data to estimate stand-level forest parameters where no or too few observations are collected within the stand. This paper focuses on different approaches to estimating the variances of those estimates. We compared a variance estimator which is based on the estimation of superpopulation parameters with variance estimators which are based on predictions of finite population values. One of the latter variance estimators considered the spatial autocorrelation of the residuals whereas the other one did not. The estimators were applied using timber volume on stand level as the variable of interest and photogrammetric image matching data as auxiliary information. Norwegian National Forest Inventory (NFI) data were used for model calibration and independent data clustered within stands were used for validation. The empirical coverage proportion (ECP) of confidence intervals (CIs) of the variance estimators which are based on predictions of finite population values was considerably higher than the ECP of the CI of the variance estimator which is based on the estimation of superpopulation parameters. The ECP further increased when considering the spatial autocorrelation of the residuals. The study also explores the link between confidence intervals that are based on variance estimates as well as the well-known confidence and prediction intervals of regression models.

Estimating single-tree branch biomass of Norway spruce with terrestrial laser scanning using voxel-based and crown dimension features

Abstract Many remote sensing-based methods estimating forest biomass rely on allometric biomass models for field reference data. Terrestrial laser scanning (TLS) has emerged as a tool for detailed data collection in forestry applications, and the methods have been proposed to derive, e.g. tree position, diameter-at-breast-height, and stem volume from TLS data. In this study, TLS-derived features were related to destructively sampled branch biomass of Norway spruce at the single-tree level, and the results were compared to conventional allometric models with field measured diameter and height. TLS features were derived following two approaches: one voxel-based approach with a detailed analysis of the interaction between individual voxels and each laser beam. The features were derived using voxels of size 0.1, 0.2, and 0.4 m, and the effect of the voxel size was assessed. The voxel-derived features were compared to features derived from crown dimension measurements in the unified TLS point cloud data. TLS-derived variables were used in regression models, and prediction accuracies were assessed through a Monte Carlo cross-validation procedure. The model based on 0.4 m voxel data yielded the best prediction accuracy, with a root mean square error (RMSE) of 32%. The accuracy was found to decrease with an increase in voxel size, i.e. the model based on the 0.1 m voxel yielded the lowest accuracy. The model based on crown measurements had an RMSE of 34%. The accuracies of the predictions from the TLS-based models were found to be higher than from conventional allometric models, but the improvement was relatively small.

Empirical harvest models and their use in regional business-as-usual scenarios of timber supply and carbon stock development

Abstract Harvest activity directly impacts timber supply, forest conditions, and carbon stock. Forecasts of the harvest activity have traditionally relied on the assumption that harvest is carried out according to forest management guidelines or to maximize forest value. However, these rules are, in practice, seldom applied systematically, which may result in large discrepancies between predicted and actual harvest in short-term forecasts. We present empirical harvest models that predict final felling and thinning based on forest attributes such as site index, stand age, volume, slope, and distance to road. The logistic regression models were developed and fit to Norwegian national forest inventory data and predict harvest with high discriminating power. The models were consistent with expected landowners behavior, that is, areas with high timber value and low harvest cost were more likely to be harvested. We illustrate how the harvest models can be used, in combination with a growth model, to develop a national business-as-usual scenario for forest carbon. The business-as-usual scenario shows a slight increase in national harvest levels and a decrease in carbon sequestration in living trees over the next decade.

A comment to “Large-scale bioenergy from additional harvest of forest biomass is neither sustainable nor greenhouse gas neutral”: Important insights beyond greenhouse gas accounting

RYAN M. BR IGHT * , FRANCESCO CHERUB IN I * , RASMUS ASTRUP † , NE I L B IRD ‡ , ANNETTE L . COWIE § , MARK J . DUCEY ¶ , GREGG MARLAND k, K IM P INGOUD* * , I LKKA SAVOLA INEN* * and ANDERS H. STRØMMAN* *Industrial Ecology Program, Department of Energy and Process Engineering, Norwegian University of Science and Technology, Trondheim, Norway, †Department of Forest Resources, Norwegian Forest and Landscape Institute, Ås, Norway, ‡Joanneum Research, Resources, Institute for Water, Energy and Sustainability, Graz, Austria, §Department of Primary Industries, Rural Climate Solutions, University of New England/NSW, Armidale, Australia, ¶Department of Natural Resources & the Environment, University of New Hampshire, Durham, N. H, USA, kResearch Institute for Environment, Energy, and Economics, Appalachian State University, Boone, North Carolina, USA, **VTT Technical Research Center of Finland, Espoo, Finland

Temporal Stability of X-Band Single-Pass InSAR Heights in a Spruce Forest: Effects of Acquisition Properties and Season

We investigated the stability of TanDEM-X interferometric synthetic aperture radar (InSAR) heights across eight repeated acquisitions. With InSAR height we mean the height above ground of the scattering phase center. We obtained InSAR heights by subtracting a digital terrain model generated from airborne laser scanning. The acquisitions varied in polarization, normal baseline, and season. The study area was a spruce forest in southeastern Norway. We established 179 field plots within 26 selected forest stands and obtained aboveground biomass (AGB) from field inventory. The InSAR heights were generally stable across the acquisitions as was the relationship between AGB and InSAR height, although systematic and random variations were noted. Two acquisitions had close-to-identical technical properties and weather conditions, and they produced close-to-identical InSAR heights. InSAR heights were fairly stable across a range in temperature and precipitation through spring, summer, and autumn, across a range in baseline values and for both HH and VV polarizations. However, a winter acquisition at temperatures of -7°C had much deeper penetration into the canopy and generated considerably lower InSAR heights and, hence, a very different relationship with biomass. Higher random errors were noted in a cross-pol data set due to lower backscatter and when the normal baseline was very small or very large. A height of ambiguity around 20-50 m appeared to be optimal. Interferometric X-band SAR can be used for monitoring coniferous boreal forests as long as the season and technical properties of the acquisition are kept within certain ranges.

Adjusting for nondetection in forest inventories derived from terrestrial laser scanning

Nondetection of trees is a serious problem for the use of terrestrial laser scanning (TLS) in forest inventory applications. The use of multiple coregistered scans can reduce nondetection but may not eliminate it, and it carries substantial field and post-processing costs. We examined and extended previously developed theoretical approaches to modeling nondetection. The results suggested that tree size as well as multiple stand structural characteristics may be factors, but the theoretical models do not lend themselves to empirical estimation. We then used distance sampling techniques to identify detection probabilities and develop adjusted estimates for trees per hectare and basal area in nine forest stands in southern Norway. The results compared favorably with field estimates based on fixed-area plots. The estimated detection probabilities indicate that correction for nondetection is needed unless the search for trees is limited to very small distances from the scanner. Distance sampling appears promising when TLS is used in the context of temporary-plot forest inventories.

Creating a regional MODIS satellite-driven net primary production dataset for european forests

Net primary production (NPP) is an important ecological metric for studying forest ecosystems and their carbon sequestration, for assessing the potential supply of food or timber and quantifying the impacts of climate change on ecosystems. The global MODIS NPP dataset using the MOD17 algorithm provides valuable information for monitoring NPP at 1-km resolution. Since coarse-resolution global climate data are used, the global dataset may contain uncertainties for Europe. We used a 1-km daily gridded European climate data set with the MOD17 algorithm to create the regional NPP dataset MODIS EURO. For evaluation of this new dataset, we compare MODIS EURO with terrestrial driven NPP from analyzing and harmonizing forest inventory data (NFI) from 196,434 plots in 12 European countries as well as the global MODIS NPP dataset for the years 2000 to 2012. Comparing these three NPP datasets, we found that the global MODIS NPP dataset differs from NFI NPP by 26%, while MODIS EURO only differs by 7%. MODIS EURO also agrees with NFI NPP across scales (from continental, regional to country) and gradients (elevation, location, tree age, dominant species, etc.). The agreement is particularly good for elevation, dominant species or tree height. This suggests that using improved climate data allows the MOD17 algorithm to provide realistic NPP estimates for Europe. Local discrepancies between MODIS EURO and NFI NPP can be related to differences in stand density due to forest management and the national carbon estimation methods. With this study, we provide a consistent, temporally continuous and spatially explicit productivity dataset for the years 2000 to 2012 on a 1-km resolution, which can be used to assess climate change impacts on ecosystems or the potential biomass supply of the European forests for an increasing bio-based economy. MODIS EURO data are made freely available at ftp://palantir.boku.ac.at/Public/MODIS_EURO.