Introduction: Efforts to collect ecological data have intensified over the last decade. This is especially true for freshwater habitats, which are among the most impacted by human activity and yet lagging behind in terms of data availability. Now, to support conservation programmes and management decisions, these data need to be analyzed and interpreted; a process that can be complex and time consuming. The South African Biodiversity Data Pipeline for Wetlands and Waterbirds (BIRDIE) aims to help fast and efficient information uptake, bridging the gap between raw ecological datasets and the information final users need. <br /><br /> Methods: BIRDIE is a full data pipeline that takes up raw data, and estimates indicators related to waterbird populations, while keeping track of their associated uncertainty. At present, we focus on the assessment of species abundance and distribution in South Africa using two citizen-science bird monitoring datasets, namely: the African Bird Atlas Project and the Coordinated Waterbird Counts. These data are analyzed with occupancy and state-space models, respectively. In addition, a suite of environmental layers help contextualize waterbird population indicators, and link these to the ecological condition of the supporting wetlands. Both data and estimated indicators are accessible to end users through an online portal and web services. <br /><br /> Results and discussion: We have designed a modular system that includes tasks, such as: data cleaning, statistical analysis, diagnostics, and computation of indicators. Envisioned users of BIRDIE include government officials, conservation managers, researchers and the general public, all of whom have been engaged throughout the project. Acknowledging that conservation programmes run at multiple spatial and temporal scales, we have developed a granular framework in which indicators are estimated at small scales, and then these are aggregated to compute similar indicators at broader scales. Thus, the online portal is designed to provide spatial and temporal visualization of the indicators using maps, time series and pre-compiled reports for species, sites and conservation programmes. In the future, we aim to expand the geographical coverage of the pipeline to other African countries, and develop more indicators specific to the ecological structure and function of wetlands.
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RBINS Staff Publications 2023
Canopy laser scanning to study the complex architecture of large old trees Barbara D'hont1 , Professor Kim Calders1 , Professor Alexandre Antonelli6 , Dr. Thomas Berg7 , Dr. Karun Dayal1 , Dr. Leonard Hambrecht5 , Dr. Maurice Leponce2,3, Prof. Arko Lucieer5 , Olivier Pascal4 , Professor Pasi Raumonen8, Professor Hans Verbeeck1 1Q-ForestLab, Department of Environment, Ghent University, Ghent, Belgium, 2Royal Belgian Institute of Natural Sciences, Brussels, Belgium, 3Université Libre de Bruxelles, Brussels, Belgium, 4Fonds de Dotation Biotope Pour La Nature, France, 5School of Geography, Planning, and Spatial Sciences, University of Tasmania, , Australia, 6Royal Botanic Gardens, Kew, Richmond, Surrey, United Kingdom, 7ARAÇÁ Project, Nova Friburgo, Rio de Janeiro, Brazil, 8Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland Large trees are keystone structures providing multiple ecosystem functions in forests all around the world: they disproportionately contribute to forest biomass and biodiversity. Large trees can have an extremely complex structure, housing many epiphytes on their stem and branches. High point-density 3D point clouds, in which leaves and epiphytes in the tree can be distinguished, are useful to make the link between the distribution of organisms on the tree, the tree architecture and its microclimate. In addition, a comprehensive branching model can improve above ground biomass (AGB) estimates. Highly detailed, complete point clouds of large trees are, however, exceptionally difficult to derive. With terrestrial laser scanning, the state-of-the-art method to capture 3D tree structure, the plant material blocks the view of (or, occludes) the top part of the dense crown. Drone or airborne laser scanning data on the other hand, lacks detail in the subcanopy. Combining these two methods minimises occlusion; however, increased distance from the tree to the scanner still leads to a relatively low resolution of the canopy point clouds. To improve the level of precision of the tree point clouds, we introduce a new concept, called canopy laser scanning (CLS). With CLS, a laser scanner is operated statically inside the tree canopy, reducing the distance between the area of interest and the instrument. We lifted a high-end laser scanner (RIEGL vz-400(i)) inside the canopy of six large emergent trees. Four of these trees are located in different types of tropical rainforests in Colombia, Brazil and Peru. They are part of biodiversity programs in which organisms and their spatial distributions are studied (Life On Trees, Araçá). The two other trees are famous giants located in the wet temperate eucalypt forests of southern Tasmania. We will present the practical aspects of CLS, evaluate the extra value of using canopy scans, looking at occlusion and point cloud precision, estimate epiphyte cover and AGB. We demonstrate that canopy laser scanning opens up new opportunities in sciences in which multi-disciplinary teams perform in depth research on large individual trees.
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