Nicolas Dobigeon and I have an open post-doc position for the MUESLI project (Multiscale Mapping of Ecosystem Services by Very High Spatial Resolution Hyperspectral and Lidar Remote Sensing Imagery).
The objectives of the post-doctoral position concern the application and the definition of unmixing models to extract relevant description of landscapes that takes into account its continuous nature. In particular, two points will be addressed in priority during this post-doctoral work:
- First, the high spatial resolution of the images will impose to apply contextual unmixing. If the proportion of each material is important, the spatial configuration of these materials is also an important feature to describe landscapes.
- Second, the physical natures of the hyperspectral and LiDAR data are complementary and this information must be fully exploited during the processing.
The extracted variables will be used in predictive models to link ecosystem services and the landscape descriptors. A field mission is actually conducted from February, 2016 to July, 2016. Several services in forests and agricultural fields are monitored and the data will be available in September 2016.
The implementation will be done using open software such as the Orfeo Toolbox (C++ library from the CNES) and Python with a graphical interface in QIGS. Obtained results will be disseminate in the scientific community by publications in journals or by communications in conference and national workshops.
The candidate should send (in English) an extended CV (including formation, experiences, list of publication and scientific responsibilities), a motivation letter and reference’s contacts to mathieu.fauvel@ensat.fr and nicolas.dobigeon@enseeiht.fr. Review of applications begins on March, 2016, and will be closed when the position is filled.
Details are given proposalMUESLI.pdf.
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