Seedling stands are areas in forest landscapes where young trees, typically from newly planted or naturally regenerated seedlings, grow. These stands are in the early stages of forest development and are crucial for the renewal and future growth of the forest. They represent a vital phase in the forest's lifecycle, for which careful management is often employed to ensure the successful establishment and growth of young crop trees.
To address the data-gathering requirements of forest management, seedling stands are typically assessed through field visits, a process that is considered time-consuming, expensive, and labor-intensive. As trees in the seedling stands are small and often densely stocked, they are difficult to assess in operational remote sensing-based forest inventories. However, recent developments in remote sensing, especially in laser scanning and the use of drones, could open new pathways to developing methods for the spatially explicit and timely inventorying of seedling stands; such methods could complement or even replace field visits.
The aim here was to develop and assess remote sensing methods of estimating the tree density, mean tree height, and species of seedling stands, which are the key characteristics supporting forest management. For this purpose, new remote sensing techniques–namely drone photogrammetric point clouds, hyper- and multi-spectral imagery (studies I and IV), and multi-spectral and single-photon airborne laser scanning (ALS; studies II and III) data–were investigated over seedling stands located in three study sites in the boreal forests of Finland. Performance of leaf-off and leaf-on hyper-spectral drone imagery and multi-spectral ALS data was explored in seedling stands in studies I and II. A canopy-thresholding method (Cth) was also optimized to minimize the interference of understory vegetation (study II), and the performance of single-photon ALS was examined in study III. In that study, an area-based approach (ABA) that included single-tree features and corrected the effect of edge trees (ABAEdgeITD) was developed and compared to conventional ABA. In study IV, a new approach for feeding multispectral drone images to convolutional neural networks was proposed and validated for the classification of seedling tree species.
The findings of this thesis demonstrated that drone imagery yielded more accurate tree density estimates, while dense multispectral ALS data outperformed other tested methods of tree height estimation (both when using leaf-on data). The use of ABAEdgeITD improved the tree density and height estimates compared to conventional ABA, although it was less accurate than the individual tree-based methods used in studies I and II. Characterization of advanced seedling stands was more accurate than that of early-growth stage stands (mean height < 1.3 m), which remained challenging. Finally, the image pre-processing approach, together with the convolutional neural network, used in study IV improved the species classification accuracy of seedlings. This thesis shows that the remote sensing methods used can be applied in operational forest inventories to complement or replace field visits. These new technologies are valuable approaches to increasing the efficiency and sustainability of forest management.