Browsing by Subject "DSM"
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(2010)The objective of this study was to develop a method for estimation of forest stand variables and updating the forest resource data, based on a well known and widely used method among forest sector, aerial photography. The second objective was to produce information of cost-effectiveness and accuracy of digital surface model (DSM) generated from very high resolution aerial images in comparison of methods based on aerial laser scanning (ALS). The study area covering circa 2000 hectares is located in state owned forest in Hämeenlinna, Southern Finland. The study material consisted of 85 digitised and orthorectified colour-infrared (CIR) aerial photographs, LiDAR measurements of the corresponding area and field measurements of 402 concentric circular plots. Both the remote sensing data and the field measurements were acquired in 2009. In this study, the accuracy of DSM generated from very high resolution CIR - aerial images was examined in the estimation of forest stand variables. Estimation of forest stand variables was made using non-parametric k-nearest neighbour method. Sequential forward selection was used for selecting features from remote sensing data and the examination of accuracy was done with cross validation. The variables examined were mean diameter, basal area, mean height, dominant height and mean volume. Relative RMSE -values of DMS estimation were at the best with mean diameter, basal area, mean height, dominant height and mean volume 33,67 %, 36,23 %, 25,33 %, 23,53 % and 40,39 %. For the reference ALS-data, relative RMSE-values were 25,26 %, 27,89 %, 19,94 %, 16,76 % ja 31,26 %. Photogrammetric DSM was best suited for estimating dominant and mean height and produced estimates slightly more inaccurate than those of reference ALS-data. When estimating mean diameter, photogrammetric DSM was slightly better, but at mean volume estimation, ALS-data proved again to be a little more a accurate than photogrammetric DSM. At basal area estimation, ALS-data gave considerably better results than photogrammetric DSM. This research showed that the photogrammetric DSM suits well for updating the forest resource data, and also satisfies the requirements in a more economic way.
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(2016)Major Depressive Disorder (MDD) is a mental health disorder, which requires a diagnosis or identifying an illness in order to be treated. There are nine criteria symptoms for MDD, and to acquire a diagnosis, an individual must exhibit five to nine of these symptoms (DSM-5). The weight of the criteria symptoms is assumed to be equal and a sum-score is calculated. The sum-score determines whether the MDD in question is mild, moderate or severe. The sum-scores simplify MDD because it is a heterogeneous syndrome. The diagnostic criteria see MDD as a common cause network: criteria symptoms are equally weighted indicators of an illness called MDD. Symptom-based networks are presented as an alternative, more precise way of modeling MDD. In symptom-based networks MDD is seen as consisting of causal connections between symptoms so that a certain existing symptom is likely to result in the manifestation of another symptom. Symptom-based networks are well-suited for explaining the heterogeneity of MDD, the vast spectrum of symptom combinations and the interrelationships of symptoms. In addition, symptom-based networks bypass entirely the problematic assumption that MDD is a latent phenomenon. The heterogeneity hidden in the MDD diagnosis is critically examined through research evaluating the diagnosis and pathogenesis of MDD as well as symptom studies. The results from depression studies and clinical practice strongly indicate that the official view of the scientific community on the etiology of MDD is wrong. A tremendous amount of heterogeneity is hidden behind the MDD diagnosis.
Now showing items 1-2 of 2