Browsing by discipline "Geoinformatics (GIMP)"
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(2020)Understanding the factors that affect the climate and the resulting chemical and physical processes will help to develop better climate models. This requires long-term measurements of carbon exchange between the earth and the atmosphere, as well as information on the spatial distribution of different bog nutrient classes and their microtopographic forms, in order to better understand the effects of climate change on different temporal and spatial scales. My master's thesis focuses on the separation of hummocks, interfaces and wet surfaces in the Simoskanaapa bog, which belongs to the Ostrobothnia-Kainuu bog zone, by means of remote sensing, image processing and guided classification. The material used was a high-resolution Optical satellite image of WorldView-2 and an altitude model interpolated to a pixel size of 2 meters, created from the laser scanning data of the National Land Survey of Finland. Object-based classification and support vector machines were used in the guided classification. Object-based classification is suitable for data containing noise, such as remote sensing data taken from bogs. The classification was successful in the ombrotrophic raised bog area of the Simoskanaava bog: the classification accuracy of the six microtopographic forms was calculated to be 84.1% (kappa 0.672). At the aapa-mire, the accuracy of the overall classification was slightly lower for the five classes (76.3%, kappa 0.650), due to the mixing of the interface wet surface levels and intermediate-wet surface levels. Object-based classification is well suited for the classification of certain bog microsites. In my study, it was possible to distinguish well the ridges and wet surfaces of the aapa mire, ridges and wet surfaces of the ombrotrophic raised bog area, and the intermediate sphagnum sp. surfaces. Further research can use more accurate laser scanning data as well as high-resolution satellite imagery to classify the bog into bog types for which emission factors are calculated using the bog's carbon balances
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(2019)Tiivistelmä/Referat – Abstract African Savanna elephant (Loxodonta africana) is the largest terrestrial mammal. Due to its size, elephants consume large amount of food and water each day and thus modify the environment around them greatly. At the same time, they create living areas for other species. On the other hand, too large number of elephants in confined areas will eventually lead into the destruction of the environment. Humans and elephants have also a twofold relationship: elephants attract tourist but at the same time they can destroy crops, property or even kill humans. The African elephant is thought to be a vulnerable specie that is in risk on becoming endangered in the near future due to the changing environment and the pressure from growing human populations. The population size has been long decreasing and for conservational work to be effective, knowledge about the suitable environments and the needs of the specie is needed. Species distribution modelling (SDM) uses computer algorithms to combine the environmental variables and species occurrence data. SDM can be used for example to predict suitable habitats for species and the method is regularly used in conservational work and is an important part of it. The aim of this study is to increase the knowledge of elephant distribution patterns in Taita Taveta County in Kenya. In contrary to the overall trend of the elephant numbers, the population in Taita Taveta County has been growing. The changes in the population have been monitored from the 60’s and data from three different years, 2005, 2008 and 2011, have been used in this study. The study was divided in to three questions: (1) How elephants are distributed in the area in different years, (2) What environmental variables correlate with elephants in different years, and (3) Can the distribution of the elephants in the area be predicted? Different spatial analyzes and visual comparison was used to study the distribution of elephants in the county. Spearman Rho Rank correlation analyses was used to study the correlation of environmental variables and elephants and predicting of the elephant distribution was done using species distribution modelling method MaxEnt. The results show that the elephant distribution changes each year, but certain key areas can be found in each year that elephants favor. The meaningful environmental variables change each year and between the protected areas in the county and the areas that are not protected. In protected areas the meaning of water sources is highlighted and in the other areas the meaning of human activities grows in importance. The variables used for this study did not create well performing predictions, and thus it would not be advisable to use them for predictions. Presumably, the environmental variables used are not enough to explain the distribution of elephants. Elephants can live in many different habitats and they move around a lot, which also further decrease the performance of the predictions
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