This thesis explores the potential of Neural Radiance Fields (NeRF) for generating orthophotos in forestry applications. Traditional orthophoto production methods, such as those implemented in Pix4D, require high image overlap and significant data collection. NeRF, a novel 3D scene reconstruction technique, shows potential for reducing these requirements by effectively reconstructing scenes with lower image overlaps. This study compares the orthophotos produced by NeRF and Pix4D using various degrees of image overlap, evaluating the results based on geometric accuracy, image quality, and robustness to data variations. The findings indicate that NeRF can produce orthophotos from low-overlap images with geometric accuracy comparable to orthophotos produced by Pix4D from high-overlap images, though with some trade-offs in image sharpness. These results suggest potential cost savings and operational efficiencies in forestry applications, providing a viable alternative to traditional photogrammetric techniques.

Since 2009, Swedish municipalities have the possibility of limiting the coastal protection in rural areas with the LIS-tool in order to promote countryside development there. In 2013, Ronneby Kommun pointed out 28 such areas. The goal of this thesis is to describe the development in the coastal areas in Ronneby Kommun between 1960 and 2018, with a special focus on the recent development in the designated LIS areas since 2013. A proposal for the future application of the LIS-tool in the municipality is an additional goal. For the analysis of the LIS-tool as such, the author conducted two expert interviews and compared the results with the quantitative GIS-based analysis, using orthophotos, of the building activity along the shores in Ronneby Kommun. The data shows very low building activity within the LIS areas and a generally low building activity in the municipality recently. For the further application of the LIS-tool in its present form in Ronneby Kommun, the author proposes to either combine it with other measures to increase the attractivity of the existing LIS areas such as infrastructure improvements, or to wait some more time until the pressure on the housing market increases and thereby the general building activity. The municipality can then evaluate the individual areas independently of each other and adjust them to meet the demand on the housing market. In addition, the author and the experts suggest changing the LIS-tool in order to make it more forceful, independent and regional specific. In general, the results of this thesis are regional specific on Ronneby Kommun and, therefore, this thesis can be a comparison base for other research but does not generalize.

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Digital urban tree registers serve many porposes and facilitate the administration, care and management of urban trees within a city or municipality. Currently, mapping of urban tree stands is carried out manually with methods which are both laborious and time consuming. The aim of this study is to establish a way of operation based on the use of existing LiDAR data and othophotos to automatically detect individual trees. By using the extensions LIDAR Analyst and Feature Analyst for ArcMap a tree extraction was performed. This was carried out over the extent of the city district committee area of Östermalm in the city of Stockholm, Sweden. The results were compared to the city’s urban tree register and validated by calculating its Precision and Recall. This showed that FeatureAnalyst generated the result with the highest accuracy. The derived trees were represented by polygons which despite their high accuracy make the result unsuitable for detecting individual tree positions. Even though the use of LIDAR Analyst resulted in a less precise tree mapping result, individual tree positions were detected satisfactory. This especially in areas with more sparse, regular tree stands. The study concludes that the use of both tools complement each other and compensate the shortcomings of the other. FeatureAnalyst maps an acceptable tree coverage while LIDAR Analyst more accurately identifies individual tree positions. Thus, a combination of the two results could be used for individual tree mapping. / Digitala urbana trädregister tjänar många syften och underlättar för städer och kommuner att administrera, sköta och hantera sina park- och gatuträd. Dagens kartering av urbana trädbestånd sker ofta manuellt med metoder vilka är både arbetsintensiva och tidskrävande. Denna studie syftar till att utveckla ett arbetssätt för att med hjälp av befintliga LiDAR-data och ortofoton automatiskt kartera individuella träd. Med hjälp av tilläggen LIDAR Analyst och FeatureAnalyst för ArcMap utfördes en trädkartering över Östermalms stadsdelsnämndsområde i Stockholms stad. Efter kontroll mot stadens träddatabas och validering av resultatet genom beräknandet av Precision och Recall konstaterades att användningen av FeatureAnalyst resulterade i det bästa trädkarteringsresultatet. Dessa träd representeras av polygoner vilket medför att resultatet trots sin goda täckning inte lämpar sig för identifierandet av enskilda trädpositioner. Även om användningen av LIDAR Analyst resulterade i ett mindre precist karteringsresultat erhölls goda positionsbestämmelser för enskilda träd, främst i områden med jämna, glesa trädbestånd. Slutsatsen av detta är att användandet av de båda verktygen kompenserar varandras tillkortakommanden där FeatureAnalyst ger en godtagbar trädtäckning medan LIDAR Analyst bättre identifierar enskilda trädpositioner. En kombination av de båda resultaten skulle alltså kunna användas i trädkarteringssyfte.

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