Potential of Airborne LiDAR Derived Vegetation Structure for the Prediction of Animal Species Richness at Mount Kilimanjaro
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Potential of Airborne LiDAR Derived Vegetation Structure for the Prediction of Animal Species Richness at Mount Kilimanjaro. / Ziegler, Alice; Meyer, Hanna; Otte, Insa; Peters, Marcell K.; Appelhans, Tim; Behler, Christina; Böhning-Gaese, Katrin; Classen, Alice; Detsch, Florian; Deckert, Jürgen; Eardley, Connal D.; Ferger, Stefan W.; Fischer, Markus; Gebert, Friederike; Haas, Michael; Helbig-Bonitz, Maria; Hemp, Andreas; Hemp, Claudia; Kakengi, Victor; Mayr, Antonia V.; Ngereza, Christine; Reudenbach, Christoph; Röder, Juliane; Rutten, Gemma; Costa, David Schellenberger; Schleuning, Matthias; Ssymank, Axel; Steffan-Dewenter, Ingolf; Tardanico, Joseph; Tschapka, Marco; Vollstädt, Maximilian G. R.; Wöllauer, Stephan; Zhang, Jie; Brandl, Roland; Nauss, Thomas.
In: Remote Sensing, Vol. 14, No. 3, 786, 2022.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Potential of Airborne LiDAR Derived Vegetation Structure for the Prediction of Animal Species Richness at Mount Kilimanjaro
AU - Ziegler, Alice
AU - Meyer, Hanna
AU - Otte, Insa
AU - Peters, Marcell K.
AU - Appelhans, Tim
AU - Behler, Christina
AU - Böhning-Gaese, Katrin
AU - Classen, Alice
AU - Detsch, Florian
AU - Deckert, Jürgen
AU - Eardley, Connal D.
AU - Ferger, Stefan W.
AU - Fischer, Markus
AU - Gebert, Friederike
AU - Haas, Michael
AU - Helbig-Bonitz, Maria
AU - Hemp, Andreas
AU - Hemp, Claudia
AU - Kakengi, Victor
AU - Mayr, Antonia V.
AU - Ngereza, Christine
AU - Reudenbach, Christoph
AU - Röder, Juliane
AU - Rutten, Gemma
AU - Costa, David Schellenberger
AU - Schleuning, Matthias
AU - Ssymank, Axel
AU - Steffan-Dewenter, Ingolf
AU - Tardanico, Joseph
AU - Tschapka, Marco
AU - Vollstädt, Maximilian G. R.
AU - Wöllauer, Stephan
AU - Zhang, Jie
AU - Brandl, Roland
AU - Nauss, Thomas
N1 - Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022
Y1 - 2022
N2 - 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 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.
AB - 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 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.
KW - Arthropods
KW - Bats
KW - Biodiversity
KW - Birds
KW - Elevation
KW - LiDAR
KW - Partial least square regression
KW - Predictive modeling
KW - Species richness
U2 - 10.3390/rs14030786
DO - 10.3390/rs14030786
M3 - Journal article
AN - SCOPUS:85124570815
VL - 14
JO - Remote Sensing
JF - Remote Sensing
SN - 2072-4292
IS - 3
M1 - 786
ER -
ID: 299204059