The search result changed since you submitted your search request. Documents might be displayed in a different sort order.
  • search hit 4 of 4
Back to Result List

Potential of airborne LiDAR derived vegetation structure for the prediction of animal species richness at Mount Kilimanjaro

Please always quote using this URN: urn:nbn:de:bvb:20-opus-262251
  • The monitoring of species and functional diversity is of increasing relevance for the development of strategies for the conservation and management of biodiversity. Therefore, reliable estimates of the performance of monitoring techniques across taxa become important. Using a unique dataset, this study investigates the potential of airborne LiDAR-derived variables characterizing vegetation structure as predictors for animal species richness at the southern slopes of Mount Kilimanjaro. To disentangle the structural LiDAR information fromThe monitoring of species and functional diversity is of increasing relevance for the development of strategies for the conservation and management of biodiversity. Therefore, reliable estimates of the performance of monitoring techniques across taxa become important. Using a unique dataset, this study investigates the potential of airborne LiDAR-derived variables characterizing vegetation structure as predictors for animal species richness at the southern slopes of Mount Kilimanjaro. To disentangle the structural LiDAR information from co-factors related to elevational vegetation zones, LiDAR-based models were compared to the predictive power of elevation models. 17 taxa and 4 feeding guilds were modeled and the standardized study design allowed for a comparison across the assemblages. Results show that most taxa (14) and feeding guilds (3) can be predicted best by elevation with normalized RMSE values but only for three of those taxa and two of those feeding guilds the difference to other models is significant. Generally, modeling performances between different models vary only slightly for each assemblage. For the remaining, structural information at most showed little additional contribution to the performance. In summary, LiDAR observations can be used for animal species prediction. However, the effort and cost of aerial surveys are not always in proportion with the prediction quality, especially when the species distribution follows zonal patterns, and elevation information yields similar results.show moreshow less

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics
Metadaten
Author: Alice Ziegler, Hanna Meyer, Insa Otte, Marcell K. Peters, Tim Appelhans, Christina Behler, Katrin Böhning-Gaese, Alice Classen, Florian Detsch, Jürgen Deckert, Connal D. Eardley, Stefan W. Ferger, Markus Fischer, Friederike Gebert, Michael Haas, Maria Helbig-Bonitz, Andreas Hemp, Claudia Hemp, Victor Kakengi, Antonia V. Mayr, Christine Ngereza, Christoph Reudenbach, Juliane Röder, Gemma Rutten, David Schellenberger Costa, Matthias Schleuning, Axel Ssymank, Ingolf Steffan-Dewenter, Joseph Tardanico, Marco Tschapka, Maximilian G. R. Vollstädt, Stephan Wöllauer, Jie Zhang, Roland Brandl, Thomas Nauss
URN:urn:nbn:de:bvb:20-opus-262251
Document Type:Journal article
Faculties:Fakultät für Biologie / Theodor-Boveri-Institut für Biowissenschaften
Philosophische Fakultät (Histor., philolog., Kultur- und geograph. Wissensch.) / Institut für Geographie und Geologie
Language:English
Parent Title (English):Remote Sensing
ISSN:2072-4292
Year of Completion:2022
Volume:14
Issue:3
Article Number:786
Source:Remote Sensing (2022) 14:3, 786. https://doi.org/10.3390/rs14030786
DOI:https://doi.org/10.3390/rs14030786
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 52 Astronomie / 526 Mathematische Geografie
5 Naturwissenschaften und Mathematik / 59 Tiere (Zoologie) / 590 Tiere (Zoologie)
Tag:LiDAR; arthropods; bats; biodiversity; birds; elevation; partial least square regression; predictive modeling; species richness
Release Date:2023/02/16
Date of first Publication:2022/02/08
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International