Applications using estimates of forest parameters derived from satellite and forest inventory data

From the combination of optical satellite data, digital map data, and forest inventory plot data, continuous estimates have been made for several forest parameters (wood volume, age and biomass). Five different project areas within Sweden are presented which have utilized these estimates for a range of applications. The method for estimating the forest parameters was a ”k-Nearest Neighbor” algorithm, which used a weighted mean value of k spectrally similar reference plots. Reference data were obtained from the Swedish National Forest Inventory. The output was continuous estimates at the pixel level for each of the variables estimated. Validation results show that accuracy of the estimates for all parameters was low at the pixel level (e.g., for total wood volume RMSE ranged from 58-80%), with a tendency toward the mean, and an underestimation of higher values while overestimating lower values. However, when the accuracy of the estimates is assessed over larger areas, the errors are lower, with best results being 10% RMSE over a 100 ha aggregation, and 17% RMSE over a 19 ha aggregation. Applications presented in this paper include moose and bird habitat studies, county level planning activities, use as input information to prognostic programs, and computation of statistics on timber volume within drainage basins and smaller land holdings. This paper provides a background on the kNN method and gives examples of how end users are currently applying satellite-produced estimation data such as these

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

Species-specific forest variable estimation using non-parametric modeling of multi-spectral photogrammetric point cloud data

The recent development in software for automatic photogrammetric processing of multispectral aerial imagery, and the growing nation-wide availability of Digital Elevation Model (DEM) data, are about to revolutionize data capture for forest management planning in Scandinavia. Using only already available aerial imagery and ALS-assessed DEM data, raster estimates of the forest variables mean tree height, basal area, total stem volume, and species-specific stem volumes were produced and evaluated. The study was conducted at a coniferous hemi-boreal test site in southern Sweden (lat. 58° N, long. 13° E). Digital aerial images from the Zeiss/Intergraph Digital Mapping Camera system were used to produce 3D point-cloud data with spectral information. Metrics were calculated for 696 field plots (10 m radius) from point-cloud data and used in k-MSN to estimate forest variables. For these stands, the tree height ranged from 1.4 to 33.0 m (18.1 m mean), stem volume from 0 to 829 m3 ha-1 (249 m3 ha-1 mean) and basal area from 0 to 62.2 m2 ha-1 (26.1 m2 ha-1 mean), with mean stand size of 2.8 ha. Estimates made using digital aerial images corresponding to the standard acquisition of the Swedish National Land Survey (Lantmäteriet) showed RMSEs (in percent of the surveyed stand mean) of 7.5% for tree height, 11.4% for basal area, 13.2% for total stem volume, 90.6% for pine stem volume, 26.4 for spruce stem volume, and 72.6% for deciduous stem volume. The results imply that photogrammetric matching of digital aerial images has significant potential for operational use in forestry

Breast cancer patients with lobular cancer more commonly have a father than a mother diagnosed with cancer

Background: The association between lobular breast cancer and family history is not clear. The aim of the study was to possibly identifying new hereditary patterns predisposing for cancer in the different histopathologic subtypes of breast cancer, with focus on patients with lobular breast cancer and cancer in their first degree relatives. Methods: In 1676 consecutive breast cancer patients detailed family history of cancer was related to histopathologic subtype of breast cancer. Results: Patients with lobular breast cancer were found to be significantly positively associated with having a father diagnosed with cancer, OR 2.17 (95% confidence interval (CI) 1.37-3.46). The finding persisted after excluding breast cancer in the family. Ductal breast cancer was associated with having a mother diagnosed with cancer. There was a significant association between lobular breast cancer and having a father with prostate cancer, OR 2.4 (CI 1.1-5.3). The occurrence of having a father with prostate cancer for lobular breast cancer patients was higher in the younger patient group, OR 2.9 (CI 1.1-7.8), and was still high but lost statistical significance in the older patient group, OR 1.9 (CI 0.5-7.4). The association between lobular breast cancer and a father remained significant after excluding fathers with prostate cancer, OR 1.94 (CI 1.20-3.14). Other commonly occurring tumor types in the father included sarcoma and leukemia. Conclusion: We propose that lobular breast cancer is associated with having a father diagnosed with cancer, most commonly prostate carcinoma. Since the association remained after excluding family history of breast cancer, the association seems independent of classical breast cancer heredity. The association with a father diagnosed with cancer also remained after removing prostate cancer, indicating an independence from prostate cancer as well. The reason for this association is genetically unclear, but could involve sex-specific imprinting

Biochemical characterization of the Arctic char (<it>Salvelinus alpinus</it>) ovarian progestin membrane receptor

<p>Abstract</p> <p>Membrane progestin receptors are involved in oocyte maturation in teleosts. However, the maturation-inducing steroid (MIS) does not appear to be conserved among species and several progestins may fulfill this function. So far, complete biochemical characterization has only been performed on a few species. In the present study we have characterized the membrane progestin receptor in Arctic char (Salvelinus alpinu<it>s</it>) and show that the 17,20beta-dihydroxy-4-pregnen-3-one (17,20beta-P) receptor also binds several xenobiotics, thus rendering oocyte maturation sensitive to environmental pollutants. We identified a single class of high affinity (K_d, 13.8 ± 1.1 nM), low capacity (B_max, 1.6 ± 0.6 pmol/g ovary) binding sites by saturation and Scatchard analyses. Receptor binding displayed rapid association and dissociation kinetics typical of steroid membrane receptors, with t_1/2s of less than 1 minute. The 17,20beta-P binding also displayed tissue specificity with high, saturable, and specific 17,20beta-P binding detected in ovaries, heart and gills while no specific binding was observed in muscle, brain or liver. Changes in 17,20beta-P binding during oocyte maturation were consistent with its identity as the oocyte MIS membrane receptor. Incubation of fully-grown ovarian follicles with gonadotropin induced oocyte maturation, which was accompanied by a five-fold increase in 17,20beta-P receptor binding. In addition, competition studies with a variety of steroids revealed that receptor binding is highly specific for 17,20beta-P, the likely maturation-inducing steroid (MIS) in Arctic char. The relative-binding affinities of all the other progestogens and steroids tested were less than 5% of that of 17,20beta-P for the receptor. Several ortho, para derivatives of DDT also showed weak binding affinity for the 17,20beta-P receptor supporting the hypothesis that xenobiotics may bind steroid receptors on the oocyte's surface and might thereby interfere with oocyte growth and maturation.</p

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

Relation between the rate of tumour cell proliferation and latency time in radiation associated breast cancer.

Background: Patients with possible radiation induced cancer could be used to study if the rate of tumour cell proliferation is related to latency time. Such a finding could help researcher to find time periods when other initiating risk factors operate. Methods: Seventeen women with breast cancer, with a prior history of radiation treatment towards the parts or the whole breast, exclusive of the primary treatment of a breast cancer were identified. Most women had received treatment for benign disorders as hemangiomas, shoulder pain or skin infections. Three patients had been treated with mantle radiation for Hodgkin's disease prior to developing breast cancer. DNA analysis were performed, on remaining tumour tissue after hormone receptor analysis had been done, measuring the fraction of tumour cells in S-phase. Latency time (time between diagnosis and previous radiation treatment) was calculated and related to the S-phase fraction. Results: A significant inverse relationship between latency time and S-phase was found (p < 0.0025), indicating that tumours with a high S-phase had a short latency time and vice versa. Among the possible radiation induced tumours, median S-phase was 14%, comparable with a median latency time of 22 years. Very high S-phase values were associated with short latency times (eg a S-phase of 35% would be compatible with a latency time of 7 years). Conclusion: Our preliminary results indicate that S-phase is related to latency time and that the median latency time maybe as long as 22 years. Our data may also explain why breast cancer is rare before 30 years of age and if patients are diagnosed at early ages, tumours often show high S-phase values and bad prognostic signs. We postulate that these results from radiation induced breast cancer may be used to extrapolate possible latency times in patients with non radiation induced breast tumours in order to isolate possible time periods for research after other initiating events

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