|Title (Primary)||Assessing biodiversity in forests using very high-resolution images and unmanned aerial vehicles|
|Author||Getzin, S.; Wiegand, K.; Schöning, I.|
|Journal||Methods in Ecology and Evolution|
|Keywords||biodiversity; coarse-filter approach; forest understorey; gap shape complexity index; unmanned aerial vehicles|
1. Structural diversity and niche differences within habitats are important for stabilizing species coexistence. However, land-use change leading to environmental homogenization is a major cause for the dramatic decline of biodiversity under global change. The difficulty in assessing large-scale biodiversity losses urgently requires new technological advances to evaluate land-use impact on diversity timely and efficiently across space.
2. While cost-effective aerial images have been suggested for potential biodiversity assessments in forests, correlation of canopy object variables such as gaps with plant or animal diversity has so far not been demonstrated using these images.
3. Here, we show that aerial images of canopy gaps can be used to assess floristic biodiversity of the forest understorey. This approach is made possible because we employed cutting-edge unmanned aerial vehicles and very high-resolution images (7 cm pixel−1) of the canopy properties. We demonstrate that detailed, spatially implicit information on gap shape metrics is sufficient to reveal strong dependency between disturbance patterns and plant diversity (R2 up to 0·74). This is feasible because opposing disturbance patterns such as aggregated and dispersed tree retention directly correspond to different functional and dispersal traits of species and ultimately to different species diversities.
4. Our findings can be used as a coarse-filter approach to conservation in forests wherever light strongly limits regeneration and biodiversity.
|Persistent UFZ Identifier||https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=11938|
|Getzin, S., Wiegand, K., Schöning, I. (2012):
Assessing biodiversity in forests using very high-resolution images and unmanned aerial vehicles
Methods Ecol. Evol. 3 (2), 397 - 404