Vegetation and Substrate Mapping with Hyperspectral Airborne Remote Sensing

Christina.Mehrtens [ at ]


Vegetation monitoring is an important tool in coastal zone management and environmental research. Common ground-based methods such as vegetation mapping or quantitative sampling are time-consuming in the field and often spatially restricted, but provide highly dissolved data (on species level). Hyperspectral airborne remote sensing, in contrast, also provides highly dissolved data, but on a different hierarchical scale (community level). The advantage is the fast and synoptic data collection and evaluation of big or partially inaccessible areas. Our approach to airborne remote sensing uses the hyperspectral imaging sensor AISA Eagle (400-970 nm, spectral resolution 3 nm, ground resolution approx. 1 m). Within two projects, Innohyp and CoastEye, the sensor AISA Eagle was integrated into a motor glider in co-operation with OHB System AG, Bremen, and in cooperation with OHB and FIELAX GmbH, Bremerhaven, sensor calibration and data pre- and post-processing is developed. Specifically, we measure the reflectance spectra of vegetation units and different substrates to create detailed thematic maps of the ground. The current research area is the rocky intertidal of Helgoland, characterized by a rich macroalgal vegetation. Our particular research addresses the classification of macroalgal species and analysis of their spatio-temporal change through different seasons in order to quantify change and to develop a synoptic and objective monitoring tool for the future. Additionaly, we collect reflectance spectra with a field spectrometer to validate the airborne sensor data and to build up a spectral library.The project is supported by BIS Bremerhaven (project code: 68202/2-Z) and BIG Bremen (project code: INNO 104 1B)

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Conference (Poster)
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Presented at the AWI PhD Days 2008 in Bremerhaven..
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Mehrtens, C. , Bartsch, I. , Kaschell, T. and Herber, A. (2008): Vegetation and Substrate Mapping with Hyperspectral Airborne Remote Sensing , Presented at the AWI PhD Days 2008 in Bremerhaven. .

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