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Book
ISBN: 0128238941 0128242116 9780128242117 9780128238943 Year: 2022 Publisher: London : Academic Press,
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LiDAR Principles, Processing and Applications in Forest Ecology introduces the principles of LiDAR technology and explains how to collect and process LiDAR data from different platforms based on real-world experience. The book provides state-of the-art algorithms on how to extract forest parameters from LiDAR and explains how to use them in forest ecology. It gives an interdisciplinary view, from the perspective of remote sensing and forest ecology. Because LiDAR is still rapidly developing, researchers must use programming languages to understand and process LiDAR data instead of established software. In response, this book provides Python code examples and sample data. Sections give a brief history and introduce the principles of LiDAR, as well as three commonly seen LiDAR platforms. The book lays out step-by-step coverage of LiDAR data processing and forest structure parameter extraction, complete with Python examples. Given the increasing usefulness of LiDAR in forest ecology, this volume represents an important resource for researchers, students and forest managers to better understand LiDAR technology and its use in forest ecology across the world. The title contains over 15 years of research, as well as contributions from scientists across the world. Presents LiDAR applications for forest ecology based in real-world experience Lays out the principles of LiDAR technology in forest ecology in a systematic and clear way Provides readers with state-of the-art algorithms on how to extract forest parameters from LiDAR Offers Python code examples and sample data to assist researchers in understanding and processing LiDAR data Contains over 15 years of research on LiDAR in forest ecology and contributions from scientists working in this field across the world.
Forest ecology. --- Forest mapping. --- Optical radar. --- Laser radar --- Lidar --- Laser communication systems --- Optical communications --- Optoelectronic devices --- Radar --- Forests and forestry --- Forest surveys --- Vegetation mapping --- Forest ecosystems --- Ecology --- Mapping
Dissertation
Year: 2022 Publisher: Liège Université de Liège (ULiège)
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In Wallonia, the regional forest resources are estimated from field methods that may present biases for estimating fast-growing forest species area like poplars, thus requiring support from remote sensing-based solutions. The objectives of this master thesis concern the mapping and characterization of the poplar resource in the province of Hainaut. Specifically, it investigates (i) the potential of S2 super-resolution images and (ii) the use of orthoimages through a deep learning-based approach to map the poplar resource, followed by (iii) the ability of an aerial photogrammetry CHM to characterize the latter. The used methods are divided into two approaches: classification of super-resolved s2 images using Random Forest algorithm (Breiman, 2001), semantic segmentation of ortho-images through a Deep Layer Aggregation (Yu et al., 2018) Neural Network. Both approaches involve 5 steps: data preparation, supervised learning, map production, height classification and accuracy assessment. The results for the first approach map, with a F1-score of 0.923, is limited in detecting young poplar plantations and overestimates the poplar resource. Then, the second approach produced a map presenting great potential to detect poplar trees with an average accuracy of 1m between the position of correctly predicted and observed poplars, but still contains many False Negatives, resulting in a F1-score of 0.653. Finally, poplar resource characterization shows for the first and second approach a respective ratio of properly identified height classes of 50% and 69%, these results are contrasted by poor ground truth data and a convincing visual assessment. To conclude, the super-resolution of sentinel-2 image seems to bring a higher accuracy compared to the poplar resource map made on S2 images by (Bolyn, Latte, Colson, et al., 2020a). Furthermore, a potential to map the poplar resource from orthoimages using a deep learning-based approach has been highlighted in this project, despite a low accuracy to be the subject of a management tool at this time. Lastly, although contrasting results, it would seem that aerial photogrammetry CHM could be appropriate to characterize the poplar resource in this project, but would require field validation.
Forest mapping --- Forest characterization --- Poplar resource --- Random Forests --- Deep Learning --- Deep Layer Aggregation --- Sentinel-2 super-resolution images --- Orthoimages --- Aerial photogrammetric canopy height model --- Ingénierie, informatique & technologie > Multidisciplinaire, généralités & autres
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