TY - JOUR
T1 - Fine scale plant community assessment in coastal meadows using UAV based multispectral data
AU - Villoslada, Miguel
AU - Bergamo, Thaisa Fernandes
AU - Ward, Raymond
AU - Burnside, Niall
AU - Joyce, Chris
AU - Bunce, Robert
AU - Sepp, Kalev
PY - 2020/1/14
Y1 - 2020/1/14
N2 - Coastal meadows worldwide are subjected to habitat degradation due to abandonment, intensification and the impacts of global change. In order to protect and restore these habitats and ensure the supply of valuable ecosystem services, it is necessary to know the extent and location of plant communities in coastal meadows. In this study, five plant communities were mapped at very high resolution in three different study sites in West Estonia. A fixed wing UAV was used to obtain multispectral images and derive a set of vegetation indices. Two different image classification techniques were used to cluster the vegetation indices maps and produce plant community distribution maps. The highest classification accuracy was obtained using a Random Forest classifier and 13 vegetation indices. Additionally, the spectral characteristics of the training samples were correlated with aboveground biomass and species diversity. Both biomass and species diversity were positively correlated with the spectral diversity of training samples and are thus likely to have an effect on the classification accuracy. The results of this study highlight the need to utilize a wide array of vegetation indices and assess the spectral characteristics of training samples in order to obtain high classification accuracies and understand the nature of misclassification errors. The resulting maps provide a solid foundation for global change impact assessment and habitat management and restoration in coastal meadows.
AB - Coastal meadows worldwide are subjected to habitat degradation due to abandonment, intensification and the impacts of global change. In order to protect and restore these habitats and ensure the supply of valuable ecosystem services, it is necessary to know the extent and location of plant communities in coastal meadows. In this study, five plant communities were mapped at very high resolution in three different study sites in West Estonia. A fixed wing UAV was used to obtain multispectral images and derive a set of vegetation indices. Two different image classification techniques were used to cluster the vegetation indices maps and produce plant community distribution maps. The highest classification accuracy was obtained using a Random Forest classifier and 13 vegetation indices. Additionally, the spectral characteristics of the training samples were correlated with aboveground biomass and species diversity. Both biomass and species diversity were positively correlated with the spectral diversity of training samples and are thus likely to have an effect on the classification accuracy. The results of this study highlight the need to utilize a wide array of vegetation indices and assess the spectral characteristics of training samples in order to obtain high classification accuracies and understand the nature of misclassification errors. The resulting maps provide a solid foundation for global change impact assessment and habitat management and restoration in coastal meadows.
KW - Coastal plant communities
KW - UAV
KW - vegetation indices
KW - Random Forests
KW - unsupervised classification
KW - Vegetation indices
KW - Unsupervised classification
KW - Random forests
UR - http://www.scopus.com/inward/record.url?scp=85077769097&partnerID=8YFLogxK
U2 - 10.1016/j.ecolind.2019.105979
DO - 10.1016/j.ecolind.2019.105979
M3 - Article
SN - 1470-160X
VL - 111
SP - 1
EP - 13
JO - Ecological Indicators
JF - Ecological Indicators
M1 - 105979
ER -