Detecting changes on coastal primary sand dunes using multi-temporal Landsat Imagery Gil Gonçalves *ab , Nuno Duro c , Ercília Sousa bd , Luís Pinto bd , Isabel Figueiredo bd a INESC-Coimbra, Rua Antero de Quental 199, 3000-033 Coimbra; b Dept. of Mathematics, University of Coimbra; c www.bluecover.pt; d CMUC, Apartado 3008, EC Santa Cruz, 3001 - 501 Coimbra ABSTRACT Due to both natural and anthropogenic causes the coastal primary sand dunes, keeps changing dynamically and continuously their shape, position and extend over time. In this paper we use a case study to show how we monitor the Portuguese coast, between the period 2000 to 2014, using free available multi-temporal Landsat imagery (ETM+ and OLI sensors). First, all the multispectral images are panshaperned to meet the 15 meters spatial resolution of the panchromatic images. Second, using the Modification of Normalized Difference Water Index (MNDWI) and kmeans clustering method we extract the raster shoreline for each image acquisition time. Third, each raster shoreline is smoothed and vectorized using a penalized least square method. Fourth, using an image composed by five synthetic bands and an unsupervised classification method we extract the primary sand dunes. Finally, the visual comparison of the thematic primary sand dunes maps shows that an effective monitoring system can be implemented easily using free available remote sensing imagery data and open source software (QGIS and Orfeo toolbox). Keywords: Coastal change detection, Landsat imagery, unsupervised classification, Open source software. 1. INTRODUCTION Primary sand dunes (or fore-dunes for short) are often the nearest seaward feature of coastal aeolian systems and represent the nearest landward part of the sand-sharing system associate with beach dune interaction [1]. Due to both natural and anthropogenic causes the coastal primary sand dunes, these mounds of unconsolidated sandy soil which are continuous to the shoreline, keeps changing dynamically and continuously their shape, position and extend over time. Detection, extraction and monitoring these areas using remote sensing techniques and technologies are important issues for countries like Portugal where 85% of its GDP (Gross Domestic Product) is generated in the coastal zone and in which lives more than 75% of its population. Moreover, delineate automated procedures for detecting changes in the primary sand dunes using free available data sources and software is also an important issue nowadays. To monitor the coastal zones it is necessary to use the knowledge from a large number of disciplines [2]. In this paper we use a Remote Sensing perspective to show how we monitor the Portuguese coast, between the period 2000 to 2014, using free available multi-temporal Landsat imagery (ETM+ and OLI sensors). First, in order to monitor the shoreline for the period under analysis the ETM+ multispectral images and the OLI images are panshaperned to meet the spatial resolution of the panchromatic bands (15m). This is an essential step as we will increase the spatial resolution while we will maintain the spectral quality of the Landsat imagery. Second, for monitoring the primary sand dunes an unsupervised classification (kmeans) is used together with a visual analysis to track the changes in the land cover classes obtained for each classified image. 2. STUDY AREA AND IMAGE DATA The study area is a sandy beach stretch of 75km located in the Portuguese north-west coast and is one of the most dynamic areas of the Portuguese west littoral [3]. This coastline stretch is characterised morphologically by a sandy shore, exposed directly to highly energetic waves, and by primary sand dunes (frontal dunes) with its baseline forming a * gil@mat.uc.pt; phone: +351239791150 Image and Signal Processing for Remote Sensing XX, edited by Lorenzo Bruzzone, Proc. of SPIE Vol. 9244, 924420 · © 2014 SPIE · CCC code: 0277-786X/14/$18 doi: 10.1117/12.2067189 Proc. of SPIE Vol. 9244 924420-1 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 11/04/2014 Terms of Use: http://spiedl.org/terms