The increasing use of unmanned aerial vehicles (UAV) and high spatial resolution imagery from associated sensors necessitates the continued advancement of efficient means of image processing to ensure these tools are utilized effectively. This is exemplified in the field of forest management, where the extraction of individual tree crown information stands to benefit operational budgets. We explored training a region-based convolutional neural network (Mask R-CNN) to automatically delineate individual tree crown (ITC) polygons in regenerating forests (14 years after harvest) using true colour red-green-blue (RGB) imagery with an average ground sampling distance (GSD) of 3 cm. We predicted ITC polygons to extract height information using canopy height models generated from digital aerial photogrammetric (DAP) point clouds. Our approach yielded an average precision of 0.98, an average recall of 0.85, and an average F1 score of 0.91 for the delineation of ITC. Remote height measurements were strongly correlated with field height measurements (r² = 0.93, RMSE = 0.34 m). The mean difference between DAP-derived and field-collected height measurements was −0.37 m and −0.24 m for white spruce (Picea glauca) and lodgepole pine (Pinus contorta), respectively. Our results show that accurate ITC delineation in young, regenerating stands is possible with fine-spatial resolution RGB imagery and that predicted ITC can be used in combination with DAP to estimate tree height.

Unmanned aerial vehicles (UAVs) and deep learning are important tools at the forefront of automated forest monitoring research, where classification of individual tree species is a critical forest management goal. Near-infrared (NIR) information provided by specialized UAV sensors may improve classification accuracy at the cost of added operational complexity; however, this potential for improvement is context-dependent and, therefore, may not be necessary. We assessed the performance of conventional red-green-blue (RGB) versus NIR imagery when classifying regenerating lodgepole pine and white spruce crowns automatically delineated by a trained deep learning algorithm. Models trained on NIR imagery slightly outperformed those trained on RGB imagery. Models trained on spectral bands outperformed those trained on spectral indices. The minor difference in performance between the two sets of imagery showed that accurate classification of lodgepole pine and white spruce can be carried-out using conventional RGB imagery.

Chapter 1 outlines an ontology of hybridity; a way of approaching the study of the social world that moves us away from “either/or” thinking and toward “not only but also” thinking. It shows how this ontology of hybridity develops out of diverse and multidisciplinary strands in the social sciences and how the themes and dispositions of this body of research may, with appropriate theoretical and empirical development, generate new ways of exploring some of the classic concerns of political communication. Hybrid thinking also serves as a platform for understanding the three other central conceptual themes of this book: power, the idea of a system, and media logics, all of which are discussed in this chapter.