Sand dynamics along the Belgian coast based on airborne hyperspectral data and lidar data

The goal of this project was to explore the possibilities of airborne hyperspectral data and airborne lidar data to study sand dynamics on the Belgian backshore and foreshore. The Belgian coast is formed by a sandy strip at the southern edge of the North Sea Basin which is commonly known as the Southern Bight. Since the beach is prone to structural and occasional erosion, it is very important to obtain a better understanding of the processes controlling it. The combination of multi-temporal hyperspectral data and lidar data provides a suitable tool for follow-up of the Belgian coastline, and sandy coastlines in general. Hyperspectral imagery generates a reflectance spectrum for each pixel in the image. The shape of this spectrum is influenced by the composition of the topsoil of the beach, being mainly the mineralogical composition and the grain size. A Spectral Angle Mapper (SAM) algorithm was used to perform a supervised classification of the hyperspectral images in order to distinguish between different sand types. Digital terrain models (DTM’s) with a mean vertical accuracy of 5 cm were generated from lidar data. By differencing a DTM from September 2000 and one from September 2001 a map with sedimentation and erosion zones was generated. By combining the erosion/sedimentation map with the classified hyperspectral images, dating from August 2000 and August 2001, an appropriate and cost-effective method was found for studying the processes of sand transport along the Belgian coastline

Remote sensing of coastal vegetation in the Netherlands and Belgium

Vegetation maps are frequently used in conservation planning and evaluation. Monitoring commitments, a.o. in relation to the European Habitat Directive, increase the need for efficient mapping tools. This paper explores methods of vegetation mapping with particular attention to automated classification of remotely sensed images. Characteristics of two main types of imagery are discussed, very high spatial resolution false colour images on the one hand and hyperspectral images on the other. The first type has proved its qualities for mapping of - mainly - vegetation structure in dunes and salt marshes. Hyperspectral imagery enables thematic detail but encounters more technical problems

Foreword to the Special Issue on Analysis of Multitemporal Remote Sensing Images

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Classifying hyperspectral airborne imagery for vegetation survey along coastlines

This paper studies the potential of airborne hyperspectral imagery for classifying vegetation along the Belgian coastlines. Here, the aim is to build vegetation maps using automatic classification. Besides a general linear multiclass classifier (Linear Discriminant Analysis), several strategies for combining binary classifiers are proposed: one based on a hierarchical decision tree, one based on the Hamming distance between the codewords obtained by binary classifiers and one based on the coupling of posterior probabilities. In addition, a new procedure is proposed for spatial classification smoothing. This procedure takes into account spatial information by letting the decision for classification of a pixel depend on the classification probabilities of neighboring pixels. This is shown to render smoother classification images

TideSed: intertidal sediment characterization using HyMap imagery

The Scheldt Estuary is internationally known for its nature and as an important commercial shipping route. Obtaining accurate data on the basic biological, chemical and physical processes in intertidal sediments is expensive and difficult: the accessibility to the site is limited, and estuaries are characterized by a wide spatial heterogeneity. Remote sensing methods can produce detailed information on intertidal sediments in a cost-effective manner. Hyperspectral HyMap imagery is combined with intensive ground truthing to quantify the most important biological and physical parameters. To achieve these goals a consortium consisting of five research institutes with complementary skills in remote sensing, marine (and coastal zone) ecology and sediment mechanics, joined forces in the TIDESED project