Wasser- und Uferkäfer auf Råg-Öarna und bei Baltischport an der estländischen NW-Küste

http://www.ester.ee/record=b4182924*es

Estimation of 3D vegetation structure from waveform and discrete return airborne laser scanning data

This study presents and compares new methods to describe the 3D canopy structure with Airborne Laser Scanning (ALS) waveform data as well as ALS point data. The ALS waveform data were analyzed in three different ways; by summing the intensity of the waveforms in height intervals (a); by first normalizing the waveforms with an algorithm based on Beer-Lambert law to compensate for the shielding effect of higher vegetation layers on reflection from lower layers and then summing the intensity (b); and by deriving points from the waveforms (c). As a comparison, conventional, discrete return ALS point data from the laser scanning system were also analyzed (d). The study area was located in hemi-boreal, spruce dominated forest in the southwest of Sweden (Lat. 58° N, Long. 13° E). The vegetation volume profile was defined as the volume of all tree crowns and shrubs in 1 dm height intervals in a field plot and the total vegetation volume as the sum of the vegetation volume profile in the field plot. The total vegetation volume was estimated for 68 field plots with 12 m radius from the proportion between the amount of ALS reflections from the vegetation and the total amount of ALS reflections based on Beer-Lambert law. ALS profiles were derived from the distribution of the ALS data above the ground in 1 dm height intervals. The ALS profiles were rescaled using the estimated total vegetation volume to derive the amount of vegetation at different heights above the ground. The root mean square error (RMSE) for cross validated regression estimates of the total vegetation volume was 31.9% for ALS waveform data (a), 27.6% for normalized waveform data (b), 29.1% for point data derived from the ALS waveforms (c), and 36.5% for ALS point data from the laser scanning system (d). The correspondence between the estimated vegetation volume profiles was also best for the normalized waveform data and the point data derived from the ALS waveforms and worst for ALS point data from the laser scanning system as demonstrated by the Reynolds error index. The results suggest that ALS waveform data describe the volumetric aspects of vertical vegetation structure somewhat more accurately than ALS point data from the laser scanning system and that compensation for the shielding effect of higher vegetation layers is useful. The new methods for estimation of vegetation volume profiles from ALS data could be used in the future to derive 3D models of the vegetation structure in large areas

Classification of tree species classes in a hemi-boreal forest from multispectral airborne laser scanning data using a mini raster cell method

Classification of tree species or species classes is still a challenge for remote sensing-based forest inventory. Operational use of Airborne Laser Scanning (ALS) data for prediction of forest variables has this far been dominated by area-based methods where laser scanning data have been used for estimation of forest variables within raster cells. Classification of tree species has however not been achieved with sufficient accuracy with area-based methods using only ALS data. Furthermore, analysis of tree species at the level of raster cells with typical size of 15 m ? 15 m is not ideal in the case of mixed species stands. Most ALS systems for terrestrial mapping use only one wavelength of light. New multispectral ALS systems for terrestrial mapping have recently become operational, such as the Optech Titan system with wavelengths 1550 nm, 1064 nm, and 532 nm. This study presents an alternative type of area-based method for classification of tree species classes where multispectral ALS data are used in combination with small raster cells. In this ?mini raster cell method? features for classification are derived from the intensity of the different wavelengths in small raster cells using a moving window average approach to allow for a heterogeneous tree species composition. The most common tree species in the Nordic countries are Pinus sylvestris and Picea abies, constituting about 80% of the growing stock volume. The remaining 20% consists of several deciduous species, mainly Betula pendula and Betula pubescens, and often grow in mixed forest stands. Classification was done for pine (Pinus sylvestris), spruce (Picea abies), deciduous species and mixed species in middle-aged and mature stands in a study area located in hemi-boreal forest in the southwest of Sweden (N 58?27?, E 13?39?). The results were validated at plot level with the tree species composition defined as proportion of basal area of the tree species classes. The mini raster cell classification method was slightly more accurate (75% overall accuracy) than classification with a plot level area-based method (68% overall accuracy). The explanation is most likely that the mini raster cell method is successful at classifying homogenous patches of tree species classes within a field plot, while classification based on plot level analysis requires one or several heterogeneous classes of mixed species forest. The mini raster cell method also results in a high-resolution tree species map. The small raster cells can be aggregated to estimate tree species composition for arbitrary areas, for example forest stands or area units corresponding to field plots

Forest Variable Estimation Using a High Altitude Single Photon Lidar System

As part of the digitalization of the forest planning process, 3D remote sensing data is an important data source. However, the demand for more detailed information with high temporal resolution and yet still being cost efficient is a challenging combination for the systems used today. A new lidar technology based on single photon counting has the possibility to meet these needs. The aim of this paper is to evaluate the new single photon lidar sensor Leica SPL100 for area-based forest variable estimations. In this study, it was found that data from the new system, operated from 3800 m above ground level, could be used for raster cell estimates with similar or slightly better accuracy than a linear system, with similar point density, operated from 400 m above ground level. The new single photon counting lidar sensor shows great potential to meet the need for efficient collection of detailed information, due to high altitude, flight speed and pulse repetition rate. Further research is needed to improve the method for extraction of information and to investigate the limitations and drawbacks with the technology. The authors emphasize solar noise filtering in forest environments and the effect of different atmospheric conditions, as interesting subjects for further research

Effect of an educational intervention for telephone triage nurses on out-of-hours attendance: a pragmatic randomized controlled study

Background: Telephone triage has been established in many countries as a response to the challenge of non-urgent use of out-of-hours primary care services. However, limited evidence is available regarding the effect of training interventions on clinicians’ telephone consultation skills and patient outcomes. Methods: This was a pragmatic randomized controlled educational intervention for telephone triage nurses in 59 Norwegian out-of-hours general practitioners’ (GPs) cooperatives, serving 59% of the Norwegian population. Computer-generated randomization was performed at the level of out-of-hours GP cooperatives, stratified by the population size. Thirty-two out-of-hours GP cooperatives were randomized to intervention. One cooperative did not accept the invitation to participate in the educational programme, leaving 31 cooperatives in the intervention group. The intervention comprised a 90-minute e-learning course and 90-minute group discussion about respiratory tract infections (RTIs), telephone communication skills and local practices. We aimed to assess the effect of the intervention on out-of-hours attendance and describe the distribution of RTIs between out-of-hours GP cooperatives and list-holding GPs. The outcome was the difference in the number of doctor’s consultations per 1000 inhabitants between the intervention and control groups during the winter months before and after the intervention. A negative binomial regression model was used for the statistical analyses. The model was adjusted for the number of nurses who had participated in the e-learning course, the population size and patients’ age groups, with the out-of-hours GP cooperatives defined as clusters. Results: The regression showed that the intervention did not change the number of consultations for RTIs between the two groups of out-of-hours GP cooperatives (incidence rate ratio 0.99, 95% confidence interval 0.91–1.07). The winter season’s out-of-hours patient population was younger and had a higher proportion of RTIs than the patient population in the list-holding GP offices. Laryngitis, sore throat, and pneumonia were the most common diagnoses during the out-of-hours primary care service. Conclusions: The intervention did not influence the out-of-hours attendance. This finding may be due to the intervention’s limited scope and the intention-to-treat design. Changing a population’s out-of-hours attendance is complicated and needs to be targeted at several organizational levels.publishedVersio