Using airborne laser scanning data for detecting canopy gaps and their understory type in mature boreal forest

• Introduction   

Canopy gap dynamics in old-growth boreal forests is a result of tree mortality caused by insects, diseases, or meteorological phenomena. Canopy gaps improve the possibilities of natural regeneration, and concentrations of decomposed deadwood are often found in these natural openings, which provide specific habitats for many deadwood-dependent species and organisms.     

Biodegradability Studies of Certain Wood Preservatives in Groundwater as Determined by the Respirometric Bod Oxitop Method

This study focuses on the biodegradability measurements of tall oil-based wood preservatives and their raw materials in groundwater as determined by the respirometric BOD OxiTop method. Certain substances were also analyzed in standard conditions described by OECD 301 F. Results show that creosote oil, a traditional wood preservative, does not biodegrade at all in groundwater over a period of 28 days, while tall oil and linseed oil biodegrade to some degree (13.4% and 14.8%, respectively). On the other hand, degrees of biodegradation of linseed oil, tall oil and creosote oil in the extra mineral and microbe including conditions described by OECD 301 F were 72.9%, 54.3% and 24.9% after 28 days, respectively. Studied raw materials of tall oil-based wood preservative production biodegrade to some degree in groundwater (variability from 11.9% to 18%) and quite effectively in OECD 301 F test (variability from 39% to 71.7%) over a period of 28 days. Abiotic degradation may also be significant with the studied substances.     

Predicting species-specific basal areas in urban forests using airborne laser scanning and existing stand register data

The aim of this work was to examine how well species-specific stand attributes can be predicted using a combination of airborne laser scanning (ALS) and existing stand register data in urban forests. In this context, the ability of three data combinations: ALS data and stand register data, ALS data and digital aerial images and all of these combined, was tested in the prediction of species-specific basal areas. We divided tree species into seven and three different tree species strata and applied two prediction methods: (1) regression method, in which the predicted total basal area was divided into tree species based on tree species proportions from stand register data, and (2) the nearest neighbour (NN) method, in which tree species proportions were used as predictor variables for species-specific basal areas. Prediction models were built based on training data of 205 field plots, and the accuracy of the models was tested based on validation data of 52 forests stands. Our results showed that species-specific predictions of seven tree species were more accurate when tree species proportions from stand register data were used in the prediction. Both the regression and the NN method provided reasonable accuracy. This study showed that tree species information from existing stand register data could be used as an alternative for aerial images in ALS-based forests inventories. The use of ALS data together with stand register data and small field data could also be economically beneficial in an inventory of urban forests.     

Carbon budget for Scots pine trees: effects of size, competition and site fertility on growth allocation and production

Time series of carbon fluxes in individual Scots pine (Pinus sylvestris L.) trees were constructed based on biomass measurements and information about component-specific turnover and respiration rates. Foliage, branch, stem sapwood, heartwood and bark components of aboveground biomass were measured in 117 trees sampled from 17 stands varying in age, density and site fertility. A subsample of 32 trees was measured for belowground biomass excluding fine roots. Biomass of fine roots was estimated from the results of an earlier study. Statistical models were constructed to predict dry mass (DW) of components from tree height and basal area, and time derivatives of these models were used to estimate biomass increments from height growth and basal area growth. Biomass growth (G) was estimated by adding estimated biomass turnover rates to increments, and gross photosynthetic production (P) was estimated by adding estimated component respiration rates to growth. The method, which predicts the time course of G, P and biomass increment in individual trees as functions of height growth and basal area growth, was applied to eight example trees representing different dominance positions and site fertilities. Estimated G and P of the example trees varied with competition, site fertility and tree height, reaching maximum values of 22 and 43 kg(DW) year(-1), respectively. The site types did not show marked differences in productivity of trees of the same height, although height growth was greater on the fertile site. The G:P ratio decreased with tree height from 65 to 45%. Growth allocation to needles and branches increased with increasing dominance, whereas growth allocation to the stem decreased. Growth allocation to branches decreased and growth allocation to coarse roots increased with increasing tree size. Trees at the poor site allocated 49% more to fine roots than trees at the fertile site. The belowground parts accounted for 25 to 55% of annual G, increasing with tree size and decreasing with site fertility. Annual G and P per unit needle mass varied over the ranges 1.9-2.4 and 3.5-4.0 kg(DW) kg(-1), respectively. The relationship between P and needle mass in the example trees was linear and relatively independent of competition, site fertility and age.     

Mapping of urban roadside trees – A case study in the tree register update process in Helsinki City

This case study describes a method for utilizing leaf-off airborne laser scanning (ALS) data for mapping characteristics of urban trees. ALS data were utilized to detect and update all street trees in the tree inventory of the City of Helsinki, Finland. The inventory consists of roughly 20,000 street trees with mean diameter at breast height (DBH) of 24 cm and mean height of 10.6 m. The large number of trees makes the manual updating process very laborious. The automatic mapping procedure presented in this paper detected 88.8% of all trees in the inventory. Tree height was predicted with root mean square error (RMSE) of 1.27 meters and tree DBH with RMSE of 6.9 cm. The presented method provides a practical and cost–effective tool for the mapping of urban tree characteristics. The cost–efficiency was further enhanced because the used ALS data were originally collected for other urban planning purposes.     

Identification of boreal forest stands with high herbaceous plant diversity using airborne laser scanning

Boreal forest stands with high herbaceous plant species diversity have been found to be one of the main habitats for many endangered species, but the locations and sizes of these herb-rich forest stands are not well known in many areas. Better identification of the stands could improve both their conservation and management. A new approach is proposed here for locating the mature herb-rich forest stands using airborne laser scanner (ALS) data and logistic regression, or the k-NN classifier. We show that ALS technology is capable of distinguishing the ecologically important herb-rich forests from those growing on less fertile site types, mainly on the basis of unique but quantifiable crown structure and vertical profile that characterise forests on high fertility sites. The study site, Koli National Park, is located on the border of the southern and middle boreal vegetation zones in Finland, and includes 63 herb-rich forest stands of varying sizes. The model and test data comprised 274 forest stands belonging to five forest site types varying from very fertile to poor. The best overall classification accuracy achieved with the k-NN method was 88.9%, the herb-rich forests being classified correctly in 65.0% of cases and the other forest site types in 95.7%. The best overall classification accuracy achieved with logistic regression was 85.6%, being 55.0% for the herb-rich forests and 94.3% for the other forest site types. Both methods demonstrated promising potential for separating herb-rich forests from other forest site types, although slightly better results were obtained with the non-parametric k-NN method, which was capable of utilising a higher number of explanatory variables. It is concluded that ALS-based data analysis techniques are applicable to the detection of mature boreal herb-rich forests in large-scale forest inventories.     

Applied 3D texture features in ALS-based forest inventory

Airborne laser scanning (ALS) data are not usually considered to be very informative with respect to tree species, and this information is often obtained by combining such data with spectral image material. The aim was to test the ability of height, density, intensity and applied 2D and 3D texture variables derived solely from a very high-density ALS point cloud to describe the crown shape and structure characteristics required for tree species discrimination. Linear discriminant analysis was used to find optimal combinations of variables within the predictor groups, and classifications based on variables from different groups were compared. The third power of the tree diameter was used as a stem volume approximate, and rather than examining species alone, the classification was evaluated with respect to the volume approximates assigned to the predicted species. The sensitivity of pulse density to the methodology presented here was determined by simulating thinned data sets by reducing the initial pulse density. The reliability of the estimates was analysed both with functions generated using the original data and with new functions for each thinning level. Alpha shape metrics developed for describing tree crowns constructed from the 3D point clouds proved capable of discriminating between all three species groups evaluated, and several height distribution and textural variables were found to discriminate between the coniferous tree species. The results demonstrate the importance of species interpretation in forest inventories based on allometric modelling, but then indicate that species-specific estimation could be carried out using ALS-derived variables alone.     

Aerosol particles in the developing world; a comparison between New Delhi in India and Beijing in China

In developing countries, aerosol particles damage the health of hundreds of millions of people. Migration from the country side to megacities increases emissions and exposure to particles. Some countries have started to limit emissions based on particulate mass, but this may increase particle number concentrations. In this study we discuss some earlier measurements carried out in the developing world and compare results from one-week measurement campaigns concerning the particle number size distribution and PM₁₀ mass concentrations in New Delhi, India and Beijing, China. Our results show that submicron particle concentrations are high in both places. The average PM₁₀ concentration was 360 μg/m³ in New Delhi and 120 μg/m³ in Beijing. The corresponding total particle number concentrations in the size range 3–800 nm were 63 000 cm^?3 and 35 000 cm^?3. Number and mass concentrations and their characteristics showed significantly different behaviour between these two locations, which stresses the importance of long-term simultaneous measurements of both quantities in different types of megacities.