TY - THES ID - 147265662 TI - Remote sensing-derived structural variation to identify old-growth forests in Romania AU - Geuskens, Marijke AU - Lhermitte, Stefaan AU - Muys, Bart AU - KU Leuven. Faculteit Bio-ingenieurswetenschappen. Opleiding Master of Bioscience Engineering. Agro- and Ecosystems Engineering (Leuven) PY - 2024 PB - Leuven KU Leuven. Faculteit Bio-ingenieurswetenschappen DB - UniCat UR - https://www.unicat.be/uniCat?func=search&query=sysid:147265662 AB - One of the last strongholds of old-growth forests in Europe can be found in the Romanian Carpathians. These forests provide a myriad of ecosystems services and therefore require strict protection, yet Romania’s old-growth forests are currently threatened due to inadequate policy. Mapping old-growth forests is a first step towards their protection. Since current mapping efforts are based on incomplete inventories with different focuses, omission errors are likely high. Therefore, an approach based on remote sensing is proposed. The high structural variation of old-growth forests is a common element across the many existing definitions. This variation is exploited in the proposed method, which aims to answer (i) how well old-growth forests can be distinguished from other forests based on their horizontal variation in forest structure, (ii) what the added value is of vertical variation derived from space-borne LiDAR (GEDI) in the identification of old-growth forests, and (iii) what the necessary minimal area is to identify old-growth forest patches as such. Sentinel-2 data was employed to assess the horizontal variation using the coefficient of variation of the NDVI, NDWI and SI, and using the mean, contrast and entropy of the grey-level co-occurrence matrix for the green, NIR and SWIR bands. Vertical variation was assessed using metrics derived from the Global Ecosystem Dynamics Investigation (GEDI) spaceborne LiDAR. As the magnitude of variation depends on the measurement scale, horizontal variation was assessed for windows of increasing size and vertical variation was assessed in transects of increasing length. Support vector machine models based on both types of variation separately gave unsatisfactory results because of the limited gradient in non-old-growth forests and class imbalance in the available training data. The optimal support vector machine model based on horizontal variation used a minimum area of 1 ha, which is in accordance with the minimum area for set-aside old-growth patches to be ecologically functional when combined with larger old-growth forests in a network. Although field inventories remain necessary to identify specific indicators, such as the presence of habitat trees, remote sensing can provide a first delineation of old-growth forests based on their structural variation, if the proposed improvements to the method are taken into account. ER -