Deep learning methods for naturalness evaluation of forests based on canopy height model - Bauner, Andreas (My University, 2024)
Forest evaluation has historically been done through field surveys by experts from national forest agencies or from forestry companies. This is costly manual labor that consumes a lot of time. A solution could be to use remote sensing ecological data and automate the naturalness evaluation of forests with the use of computers. The aim of this thesis is to develop a machine learning model that could help automate naturalness evaluation of forests. The remote sensing data is in the form of a Canopy Height Model (CHM), that is height of trees obtained from airborne laser-scanning. Ground truth data for forest naturalness is given in the form of annotated, georeferenced polygons. The study area is limited to a 50×50 km2 area north-east of the city of Jönköping in Sweden. After applying different processing steps on the data, it is then used for training a convolutional neural network, based on U-Nets, on this semantic segmentation task. The evaluation of the model shows good results, achieving an accuracy of 94.1% on the test set. This performance is competitive with currently used models for related tasks and shows the feasibility of using machine learning in the relatively new field of automated naturalness evaluation of forests.
Keywords: machine learning; artificial intelligence; U-Net; forests; canopy height model; remote sensing; semantic segmentation