Developing raster bands computed from LiDAR to improve forest

This study examines the use of di erent features derived from remotely sensed data in segmentation of forest stands. Surface interpolation methods were applied to LiDAR points in order to represent data in the form of grayscale images. Median and mean shift ltering was applied to the data for noise reduction. The ability of di erent compositions of rasters obtained from LiDAR data and an aerial image to maximize stand homogeneity in the segmentation was evaluated. The quality of forest stand delineations was assessed by the Akaike information criterion. The research was performed in co-operation with Arbonaut Ltd., Joensuu, Finland.

Design of Personalization of Massive Open Online Courses

Massive Open Online Courses have been in the center of attention in the recent years. However, the main problem of all online learning environments is their lack of personalization according to the learners’ knowledge, learning styles and other learning preferences. This research explores the parameters and features used for personalization in the literature and based on them, evaluates to see how well the current MOOC platforms have been personalized. Then, proposes a design framework for personalization of MOOC platforms that fulfills most of the personalization parameters in the literature including the learning style as well as personalization features. The result of an assessment made for the proposed design framework shows that the framework well supports personalization of MOOCs.

Near-infraread observation of supernovae with the Nordic Optical Telescope (NOT)

Supernova (SN) is an explosion of a star at the end of its lifetime. SNe are classified to two types, namely type I and II through the optical spectra. They have been categorised based on their explosion mechanism, to core collapse supernovae (CCSNe) and thermonuclear supernovae. The CCSNe group which includes types IIP, IIn, IIL, IIb, Ib, and Ic are produced when a massive star with initial mass more than 8 M⊙ explodes due to a collapse of its iron core. On the other hand, thermonuclear SNe originate from white dwarfs (WDs) made of carbon and oxygen, in a binary system. Infrared astronomy covers observations of astronomical objects in infrared radiation. The infrared sky is not completely dark and it is variable. Observations of SNe in the infrared give different information than optical observations. Data reduction is required to correct raw data from for example unusable pixels and sky background. In this project, the NOTCam package in the IRAF was used for the data reduction. For measuring magnitudes of SNe, the aperture photometry method with the Gaia program was used. In this Master’s thesis, near-infrared (NIR) observations of three supernovae of type IIn (namely LSQ13zm, SN 2009ip and SN2011jb), one type IIb (SN2012ey), in addition to one type Ic (SN2012ej) and type IIP (SN 2013gd) are studied with emphasis on luminosity and colour evolution. All observations were done with the Nordic Optical Telescope (NOT). Here, we used the classification by Mattila & Meikle (2001) [76], where the SNe are differentiated by the infrared light curves into two groups, namely ’ordinary’ and ’slowly declining’. The light curves and colour evolution of these supernovae were obtained in J, H and Ks bands. In this study, our data, combined with other observations, provide evidence to categorize LSQ13zm, SN 2012ej and SN 2012ey as being part of the ordinary type. We found interesting NIR behaviour of SN 2011jb, which lead it to be classified as a slowly declining type.