Cork oaks (Quercus suber L.) characterize many Mediterranean forest landscapes where they play important socio-economic and ecological functions for nature. This study, carried out in Mount Scrisi (Calabria Region, Italy) aims to map cork oak forests by using WorldView-3 (WV-3) high-resolution satellite image. For this aim, a supervised classification on WV-3’s images was implemented to assess the potential performance of this sensor either in detecting the presence of cork oak woodlands and in distinguishing them from other spectrally similar tree species. Particular attention was paid to the distinction of cork oaks from olive (Olea europaea L.) and chestnut trees (Castanea sativa, Mill.). By exploiting the panchromatic image with 31 cm resolution and multispectral image bands through pansharpening, the objective was achieved obtaining a high accuracy in classification (OA = 0.88). Results confirm the usefulness of WV-3 in the applications of remote sensing (RS) on forestry for mapping species distribution and monitoring vegetation and environmental health.
Preliminary Results in the Use of WorldView-3 for the Detection of Cork Oak (Quercus Suber L.): A Case in Calabria (Italy)
Modica GiuseppeUltimo
2022-01-01
Abstract
Cork oaks (Quercus suber L.) characterize many Mediterranean forest landscapes where they play important socio-economic and ecological functions for nature. This study, carried out in Mount Scrisi (Calabria Region, Italy) aims to map cork oak forests by using WorldView-3 (WV-3) high-resolution satellite image. For this aim, a supervised classification on WV-3’s images was implemented to assess the potential performance of this sensor either in detecting the presence of cork oak woodlands and in distinguishing them from other spectrally similar tree species. Particular attention was paid to the distinction of cork oaks from olive (Olea europaea L.) and chestnut trees (Castanea sativa, Mill.). By exploiting the panchromatic image with 31 cm resolution and multispectral image bands through pansharpening, the objective was achieved obtaining a high accuracy in classification (OA = 0.88). Results confirm the usefulness of WV-3 in the applications of remote sensing (RS) on forestry for mapping species distribution and monitoring vegetation and environmental health.Pubblicazioni consigliate
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