Close-Range Remote Sensing of Forest Structure for Biodiversity Assessments: A Systematic Literature Review

Abstract

Purpose of Review: Forest biodiversity is heavily influenced by structural conditions. In the past, forest stand structure was primarily quantified by traditional one- or two-dimensional metrics and indices. Close-range remote sensing enables researchers for the first time to reproduce and digitalize the three-dimensional structure of forests in high-resolution. The technological progress creates new possibilities in the field of biodiversity assessments. Since a structured overview of this development is still missing, the current review examines the potential of close-range technologies and elucidates the current state of the art and future perspectives. Recent Findings: A systematic literature review was conducted within the Web of Science and yielded 2204 papers which were further assessed according to our scope. Only 31 of these articles used close-range remote sensing to monitor forest structure in biodiversity assessments. Terrestrial laser scanners were the most popular platform, followed by drone-based and handheld solutions. Most authors calculated density or openness measures to describe forest structure based on point clouds. Mammals, insects, and plants were the most represented organism groups studied by the researchers. Classical biodiversity parameters such as abundance and species richness or diversity, although in various forms, were most frequently used for quantification. Summary: The low number of available studies on the topic points to a significant knowledge gap. The analysis suggests a positive trend for close-range remote sensing in forest biodiversity research, as a great portion of the reviewed studies was released in the last two years. The great diversity of approaches and sampled metrics reveals potential for standardization, especially as the number of studies emerging in this field is expected to increase. While the reviewed studies highlight the added value of close-range remote sensing, the potential of other modern approaches, such as machine learning or different sensors, remain hitherto unexplored.