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GIUSEPPE CIRAOLO

Monitoring the invasion of an exotic tree (Ailanthus altissima) (Mill.) Swingle with Landsat satellite time series imagery in urban forest.

  • Autori: Sala, G.; LA MANTIA, T.; Buscemi, I.; Ciraolo, G.
  • Anno di pubblicazione: 2015
  • Tipologia: Proceedings (TIPOLOGIA NON ATTIVA)
  • Parole Chiave: Remote sensing, Time-series, Invasive Alien Species, Mediterranean area
  • OA Link: http://hdl.handle.net/10447/147706

Abstract

In the Mediterranean area, one the most threat tree to various ecosystems is Ailanthus altissima (Mill.) Swingle. This is an aggressive invasive species common in natural and semi-natural habitat. Monitoring and mapping of invasive species is an important information for the conservation and management of ecosystems. The study of distribution and diffusion of invasive species are useful to assess their environmental impacts, formulate effective control strategies, and forecast potential spread. The main target of this work is to examine the feasibility of mapping the expansion of A. altissima using remote sensing techniques in a highly complex urban forest setting. Remote sensing has been a useful tool to map the invasive plant. We mapped the pattern of ailanthus expansion from 1990 to 2015 in a suburban area of Palermo, the Favorita park, using time series of Landsat image. This images are nowadays available at no cost. We used that images to analyze larger areas but the 30 m resolution does not permit mapping of individual trees; the combinations of dates and medium spatial resolution with the phenology information allowed the detection of the species. Indeed, the ailanthus is a deciduous tree that we compared with the other evergreen vegetation. Time series of Normalized difference vegetation index (NDVI) and supervised classification were used as a dataset in the classification process. These results provide a basis for more detailed investigations on invasive species and the possibility to increase the spatial resolution with the new platforms might lead to further improvements plant species identification and their distribution patterns recognition.