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Open AccessArticle

A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation

School of Engineering, University of Basilicata, 10 Viale dell’Ateneo Lucano, 85100 Potenza, Italy
Italian National Research Council, IMAA C.da Santa Loja, Tito Scalo, Potenza 85050, Italy
Department of Methods and Models for Economics, Territory and Finance, University of Rome “La Sapienza” Via Del Castro Laurenziano 9, Roma 00161, Italy
Author to whom correspondence should be addressed.
Academic Editors: Martin Behnisch, Gotthard Meinel and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2016, 5(7), 109;
Received: 12 April 2016 / Revised: 20 June 2016 / Accepted: 27 June 2016 / Published: 6 July 2016
PDF [5996 KB, uploaded 6 July 2016]


The negative impacts of land take on natural components and economic resources affect planning choices and territorial policies. The importance of land take monitoring, in Italy, has been only recently considered, but despite this awareness, in the great part of the country, effective monitoring and containment measures have not been started, yet. This research proposes a methodology to map and monitor land use changes. To this end, a time series from 1985–2010, based on the multi-temporal Landsat data Thematic Mapper (TM), has been analyzed in the Vulture Alto-Bradano area, a mountain zone of the Basilicata region (Southern Italy). Results confirm a double potentiality of using these data: on the one hand, the use of multi-temporal Landsat data allows going very back in time, producing accurate datasets that provide a phenomenon trend over time; on the other hand, these data can be considered a first experience of open data in the field of spatial information. The proposed methodology provides agencies, local authorities and practitioners with a valuable tool to implement monitoring actions. This represents the first step to pursue territorial governance methods based on sustainability, limiting the land take. View Full-Text
Keywords: remote sensing; urban growth; urban sprawl; supervised classification remote sensing; urban growth; urban sprawl; supervised classification

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Di Palma, F.; Amato, F.; Nolè, G.; Martellozzo, F.; Murgante, B. A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation. ISPRS Int. J. Geo-Inf. 2016, 5, 109.

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