Assessing the impact of different landscape features on post-fire forest recovery with multitemporal remote sensing data: the case of Mount Taygetos (southern Greece)
Anastasia Christopoulou A , Giorgos Mallinis B , Emmanuel Vassilakis C , Georgios-Pavlos Farangitakis D , Nikolaos M. Fyllas E , Giorgos D. Kokkoris F and Margarita Arianoutsou A G
+ Author Affiliations
- Author Affiliations
A Department of Ecology and Systematics, Faculty of Biology, National and Kapodistrian University of Athens, Panepistimiopolis, 15784 Athens, Greece.
B Department of Forestry and Management of Environment and Natural Resources, Democritus University of Thrace, 68200 Orestiada, Greece.
C Department of Geography and Climatology, Faculty of Geology, National and Kapodistrian University of Athens, Panepistimiopolis, 15784 Athens, Greece.
D Department of Earth Sciences, Durham University, Science Site, Durham DH1 3 LE, United Kingdom.
E Biodiversity Conservation Laboratory, Department of Environment, University of the Aegean, University Hill, Mytilene, Lesvos Island GR81100, Greece.
F Department of Marine Sciences, Faculty of the Environment, University of the Aegean, University Hill, Mytilene, Lesvos Island GR81100, Greece.
G Corresponding author. Email: marianou@biol.uoa.gr
International Journal of Wildland Fire 28(7) 521-532 https://doi.org/10.1071/WF18153
Submitted: 13 September 2018 Accepted: 22 May 2019 Published: 11 July 2019
Journal compilation © IAWF 2019 Open Access CC BY-NC-ND
Abstract
Fires affecting large areas usually create a mosaic of recovering plant communities reflecting their pre-fire composition and local conditions of burning. However, post-fire recovery patterns may also reveal the effects of landscape heterogeneity on the natural regeneration process of plant communities. This study combines field data and remote sensing image interpretation techniques to assess the role of various landscape characteristics in the post-fire recovery process in a mountainous region of Greece burned by a severe wildfire. Remote sensing techniques were used to accurately map secluded, large burned areas. By introducing a temporal component, we explored the correlation between post-fire regeneration and underlying topography, soils and basement rock. Pre-fire forest cover was reduced by more than half 8 years after fire. Regarding the dominant pre-fire forest trees, Abies cephalonica did not regenerate well after fire and most pre-fire stands were converted to grasslands and shrublands. In contrast, Pinus nigra regenerated sufficiently to return to its pre-fire cover, especially in areas underlain by softer basement rock. The use of different time series of high-resolution images improved the quality of the results obtained, justifying their use despite their high cost.
Additional keywords: black pine, conifer forest, GEOBIA, geomorphology, multitemporal, random forest.
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