This work has focused on the application of remote sensing to continuous cover forestry (CCF), primarily within Britain, with the intent to identify new methods of inventory, monitoring and biomass quantification. CCF is a silvicultural approach with a focus on sustainability through which forest stands, often of varied species composition, are manipulated to create irregular stand structures through practices of partial harvesting in a manner that retains constant forest cover of a site and allows for natural regeneration. Owing to the great differences between CCF and traditional approaches of forestry, in which even-aged monocultures are maintained, the traditional methods of assessment, such as productivity (yield class) calculations, are less applicable. There is a need to identify new methods of inventory, biomass estimation and stand monitoring for use in operational forestry and research environments and remote sensing has been identified as a potential tool to meet this need. The hypotheses of this work relate to the ways in which remote sensing can overcome the challenges posed by the complexity introduced by the adoption of CCF and the aims of this work relate to demonstrating methods for working with remote sensing and CCF. This work addresses multiple different approaches to remote sensing; aerial laser scanning (ALS), ground-based laser scanning (TLS and MLS), and photogrammetry.
The first part of this work reviews the extent of existing research that addresses the application of remote sensing in CCF and considers the transferability of remote sensing methodologies from other complex forest ecosystems to CCF.
Following from this, is a summation of contributions made towards a greater effort within the European Cooperation in Science and Technology to collate information on forestry-specific ground-based point cloud processing solutions and their functions, presented as a brief review of tools. The intent of the work and the greater effort it contributes to is to improve accessibility to and promote democratisation of such tools for forestry researchers and professionals.
This work then moves onto remote sensing in complex forest systems demonstrating how ALS timeseries data can be used for detecting disturbance directly and the importance of remote sensing for modelling the structural traits of a forest ecosystem. This chapter finds that maps of change in LiDAR metric descriptors of forest structure can be used to detect selective logging activities and visualise stand growth over time. An attempt to develop a more accurate model for AGB using three forest structural metrics was made, however the results indicated no improvement over an existing, widely adopted, single variable model.
Following on from the exploration of ground-based point cloud processing tools, an exploration of how well three of these tools can be employed to replicate and expand upon existing traditional inventory methodologies in complex CCF stands and ancient forest. We compare plot level distributions of stem diameters extracted from point clouds against those from field data. This work demonstrates that it is currently possible to use TLS as an alternative means of inventory data collection to traditional, manual measurements, though this is subject to the correct processing methods and data quality.
Finally, this work closes with a discussion of how this work is justified in light of the ongoing climate crisis, how this work addresses the needs for remote sensing research in CCF, shortcomings, and future directions for work.