Blar i forfatter "Björk, Sara Maria"

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    • Deep convolutional regression modelling for forest parameter retrieval 

      Björk, Sara Maria (Doctoral thesis; Doktorgradsavhandling, 2023-10-06)
      <p>Accurate forest monitoring is crucial as forests are major global carbon sinks. Additionally, accurate prediction of forest parameters, such as forest biomass and stem volume (SV), has economic importance. Therefore, the development of regression models for forest parameter retrieval is essential. <p>Existing forest parameter estimation methods use regression models that establish pixel-wise ...

    • An initial assessment of the possibilities of fish catch prediction using Gaussian processes 

      Björk, Sara Maria (Master thesis; Mastergradsoppgave, 2016-12-15)
      The fishing and aquaculture industry is one of the largest industries of Norway. Enhanced knowledge of the distribution of fish in the ocean is important for an economical and sustainable fishing industry. This study investigates the possibilities of using Gaussian processes for regression within fish catch prediction. A dataset that combines catch reports from the Norwegian shipping company Havfisk ...

    • On the Potential of Sequential and Nonsequential Regression Models for Sentinel-1-Based Biomass Prediction in Tanzanian Miombo Forests 

      Björk, Sara Maria; Anfinsen, Stian Normann; Næsset, Erik; Gobakken, Terje; Zahabu, Eliakimu (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-06-03)
      This study derives regression models for aboveground biomass (AGB) estimation in miombo woodlands of Tanzania that utilize the high availability and low cost of Sentinel-1 data. The limited forest canopy penetration of C-band SAR sensors along with the sparseness of available ground truth restricts their usefulness in traditional AGB regression models. Therefore, we propose to use AGB predictions ...