The global crises – climate change and biodiversity loss – have created a need for precise and wide-scale information of forests. Airborne laser scanning (ALS) provides a means for collecting such information, as it enables mapping large areas efficiently with a resolution sufficient for object-level information extraction. Deadwood is an important component of the forest environment, as it stores carbon and provides a habitat for a wide variety of species. Mapping deadwood provides information about the valuable areas regarding biodiversity, which can be used in, e.g., conservation and restoration planning. The aim of this thesis was to develop automated methodology for detecting individual fallen and standing dead trees from ALS data.
Studies I and II presented a line detection based method for detecting fallen trees and evaluated its performance on a moderate-density ALS dataset (point density approx. 15 points/m2) and a high point density unmanned aerial vehicle borne laser scanning (ULS) dataset (point density approx. 285 points/m2). In addition, the studies inspected the dataset, methodology, and forest structure related factors affecting the performance of the method. The studies found that the length and diameter of fallen trees significantly impact their detection probability, and that the majority of large fallen trees can be identified from ALS data automatically. Furthermore, study I found that the amount and type of undergrowth and ground vegetation, as well as the size of surrounding living trees determine how accurately fallen trees can be mapped from ALS data. Moreover, study II found that increasing the point density of the laser scanning dataset does not automatically improve the performance of fallen tree detection, unless the methodology is adjusted to consider the increase in noise and detail in the point cloud.
Study III inspected the feasibility of high-density discrete return ULS data for mapping individual standing dead trees. The individual tree detection method developed in the study was based on a three-step process consisting of individual tree segmentation, feature extraction, and machine learning based classification. The study found that, while some of the large standing dead trees could be identified from the ULS dataset, basing detection on discrete return data and the geometrical properties of trees did not suffice for acquiring applicable deadwood information. Thus, spectral information acquired with multispectral laser scanners or aerial imaging, or full-waveform laser scanning is necessary for detecting individual standing dead trees with a sufficient accuracy.
The findings of this thesis contribute to the existing deadwood detection methodology and improve the understanding of factors to take into account when utilizing ALS for detecting dead trees at a single-tree-level. Although remotely sensed deadwood mapping is still far from a resolved topic, these contributions are a step towards operationalizing remotely sensed biodiversity monitoring.
Forests are dynamic ecosystems that are constantly changing. The most common natural reasons for change in forests are the growth and death of trees, as well as the damage occurring to them. Tree growth appears as an increment of its structural dimensions, such as stem diameter, height, and crown volume, which all affect the structure of a tree. Repeated measurements of tree characteristics enable observations of the respective increments indicating tree growth. According to current knowledge, the tree growth process follows the priority theory, where trees aim to achieve sufficient lightning conditions for the tree crown through primary growth, whereas increment in diameter results from the secondary growth. Tree growth is known to have an effect on the carbon sequestration potential of trees as well as on the quality of timber. To improve the understanding of the underlying cause–effect relations driving tree growth, methods to quantify structural changes in trees and forests are needed.
The use of terrestrial laser scanning (TLS) has emerged during the recent decade as an effective tool to determine attributes of individual trees. However, the capacity of TLS point cloud-based methods to measure tree growth remains unexplored. This thesis aimed at developing new methods to measure tree growth in boreal forest conditions by utilizing two-date TLS point clouds. The point clouds were also used to investigate how trees allocate their growth and how the stem form of trees develops, to deepen the understanding of tree growth processes under different conditions and over the life cycle of a tree. The capability of the developed methods was examined during a five- to nine-year monitoring period with two separate datasets consisting of 1315 trees in total.
Study I demonstrated the feasibility of TLS point clouds for measuring tree growth in boreal forests. In studies II and III, an automated point cloud-based method was further developed and tested for measuring tree growth. The used method could detect trees from two-date point clouds, with the detected trees representing 84.5% of total basal area. In general, statistically significant changes in the examined attributes, such as diameter at breast height, tree height, stem volume, and logwood volume, were detected during the monitoring periods. Tree growth and stem volume allocation seemed to be more similar for trees growing in similar structural conditions.
The findings obtained in this thesis demonstrate the capabilities of repeatedly acquired TLS point clouds to be used for measuring the growth of trees and for characterizing the structural changes in forests. This thesis showed that TLS point cloud-based methods can be used for enhancing the knowledge of how trees allocate their growth, and thus help discover the underlying reasons for processes driving changes in forests, which could generate benefits for ecological or silvicultural applications where information on tree growth and forest structural changes is needed.
To better understand the underlying processes of many natural phenomena, accurate observations and measurements must be carried out in space and time. Considering forest ecosystems, monitoring the development and dynamics of tree characteristics is essential in this regard. An era of three-dimensional (3D) sensing techniques and point clouds has revolutionized individual tree observations, enabling measurements at an unprecedented level of detail. The feasibility of using point clouds to characterize trees and tree communities in space and their development in time was investigated in this thesis. The objective was to develop point cloud–based methods for distinguishing and characterizing trees and downed dead wood and to test the feasibility of the developed methods in boreal forest conditions.
Point cloud–based methods for detecting and characterizing forest structure were developed in studies I–III. Downed dead wood trunks could be distinguished from the undergrowth vegetation and near-ground objects by means of their regular, cylindrical geometry. Smooth, cylindrical surfaces and vertical continuity, on the other hand, were the key characteristics of point cloud structures to separate woody structures of standing trees from foliage and a tree stem from branches. The methods were tested in diverse boreal forest structures to validate these methodological principles.
The feasibility of the developed methods for characterizing trees and tree communities in space and time was tested in studies II–V. The structural complexity of a tree community was noted as the most important factor affecting tree-detection accuracy. High performance of the point cloud–based method was achieved on managed forest stands with a low degree of variation in tree size distribution. In controlled thinning experiments, thinning intensity was found to be a more significant factor affecting the performance than thinning type (i.e. thinning from below, thinning from above, and systematic thinning). The hemispherical measurement geometry of terrestrial point clouds was successfully complemented with aerial point clouds acquired from above the canopy to improve the vertical characterization of trees and tree communities. Finally, the capacity of bitemporal terrestrial point clouds to characterize changes in the structure of trees and tree communities was demonstrated. If there was an increase or decrease in the attributes of trees within a tree community detected with conventional forest mensuration techniques, a similar outcome was achieved with the point clouds.
The findings of this thesis improve the current knowledge of the feasibility of using point cloud–based methods in observing tree characteristics. Detailed 3D reconstruction of forests expands the spectrum of tree observations, as the dynamics of trees and tree communities can be monitored in more detail. This increases the understanding of processes shaping ecosystems and provides new approaches to improve ecological knowledge.