Assessing current biodiversity status (CBS) from remote sensing data in boreal forests
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Insinööritieteiden korkeakoulu |
Master's thesis
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Authors
Date
2023-10-09
Department
Major/Subject
Geoinformatics
Mcode
Degree programme
Master's Programme in Geoinformatics (GIS)
Language
en
Pages
63+3
Series
Abstract
Boreal forests cover a third of Earth's forested land. The variety of species in different ecosystems is massive. The ongoing biodiversity loss due to human acts has accelerated the need for biodiversity monitoring. Nature reserves are needed to preserve the biodiversity of boreal forests. The aim of this master’s thesis was to examine a biodiversity assessment-method, and its suitability for assessing the current status of biodiversity in boreal forests. The method and chosen indicators used in this study are based on Aleksandra Holmlund’s and Martin Pilstjärna’s (2022) biodiversity assessment-method: Current Biodiversity Status (CBS). The exploited data has been obtained from 150 sample plots in Kopparfors, Sweden, using Airborne LiDAR, along with Drone-LiDAR, multispectral Drone imagery, and Sentinel-2 image mosaics. The CBS-method was implemented by using an overlay analysis with vector data and three different spatial units (grid sizes). The different grid sizes gave various results in the same locations. Terrain also had an influence on the analysis’ ability to extract values with certain grid size. Overall, 35mx35m grid resulted the largest amount of the highest CBS-values. The 35mx35m grid was also examined from the inside using smaller spatial units. The 16mx16m grid was the most capable to extract higher CBS-values within the surrounding 35mx35m grid. The study showed that the exploited method and the data were suitable to be used with a spatial overlay analysis and easily modifiable regarding the required parameters. However, I could not validate my findings because the study was lacking ground truth data. Therefore, the improved version of the method could include support from ground truth data, as well more accurate classification considering the indicators. The level of automatization in the analysis could be increased as well to make the analysis faster and more reliable.Description
Supervisor
Rautiainen, MiinaThesis advisor
Kauranne, TuomoShah, Dipal
Keywords
biodiversity, GIS, remote sensing, boreal forest