Large-scale mapping of the riverbanks, mud flats and salt marshes of the Scheldt basin, using airborne imaging spectroscopy and LiDAR
Bertels, L.; Houthuys, R.; Sterckx, S.; Knaeps, E.; Deronde, B. (2011). Large-scale mapping of the riverbanks, mud flats and salt marshes of the Scheldt basin, using airborne imaging spectroscopy and LiDAR. Int. J. Remote Sens. 32(10): 2905-2918. https://dx.doi.org/10.1080/01431161003745632
In: International Journal of Remote Sensing. Taylor & Francis: London. ISSN 0143-1161; e-ISSN 1366-5901, more
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| Abstract |
For maintaining the tidal waterways in the Scheldt basin, including the rivers Rupel and Durme and a large part of the Nete catchment, and for ecological monitoring of the mud flats, salt marshes and riverbank vegetation, the Flemish government needs detailed maps of these rivers and their bank structures. These maps indicate not only vegetation types, plant associations and sediment types but also hard structures, such as quays, locks, sluices and roads. Different remote sensing techniques were used to collect the data necessary to produce the required detailed maps. During the months of July and August 2007 an airborne flight campaign took place to collect hyperspectral and LiDAR data of the Scheldt basin and the Nete catchments. These rivers have a total length of about 240 km. The Airborne Imaging Spectrometer for Applications (AISA) Eagle sensor acquired hyperspectral data in 32 spectral bands covering the visible/near-infrared (VIS/NIR) part of the electromagnetic spectrum with a ground resolution of 1 m. A multiple binary classification algorithm based on Fisher's linear discriminant analysis (LDA) was used to map the salt marshes and riverbank vegetation. Ground truth information, that is vegetation and sediment types, together with their geographical locations collected around the time of the flight campaign, was used to train the classifier in the later classification step. Laser scanning was performed using the Riegl LMS-Q560. The LiDAR dataset obtained had a resolution of at least 1 point per m2 and was used to produce a digital elevation model (DEM) that contains all elements of the terrain. From this DEM a digital terrain model (DTM) was derived by applying appropriate filtering techniques. The elevation models were used primarily to derive information on the height, slope and aspect of the banks and dikes, but they also served as expert knowledge in the classification of the mud flats and bank vegetation.Overall, this work illustrates how airborne hyperspectral and LiDAR data can be used to derive highly detailed maps of the sediments, vegetation and hard structures along tidal rivers in large river basins. It also shows how large datasets can be handled in an expert system, in combination with different classification techniques, to produce the required result and accuracy. |
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