[2016-01-11 Mon] Corinne Corcher is starting her master internship within the MUESLI project. She is going to work on the field survey (dendrometry).
[2016-01-04 Mon] Vincent Thierion officially starts his post-doc position in the MUESLI project. He will be the coordinators of the fields works for the first semester of 2016 and will further investigate the data in September. He will be in the DYNAFOR team for 1 year and half.
[2016-03-24 Thu] The tender is for the acquisition of the hyperspectral and LiDAR data is open. More information can be found : https://www.marches-publics.gouv.fr/?page=entreprise.EntrepriseAdvancedSearch&AllCons&refConsultation=286507&orgAcronyme=f2h
[2016-05-13 Fri] The first field campaign of MUESLI project finished on [2016-03-04 Fri]. It corresponded to the measurements of dendrometric structure of living and dead trees (basal area and timber quality) as well as the identification of biodiversity indicators (microhabitats such as woodpecker feeding and living holes, dendrotelmas, cavities, large dead branches, epiphytes, cracks, etc.) to calculate a Potential Biodiversity Index.
All these measurements are dedicated to assess different ecosystem services of little forests (less than 5 ha) on the “Vallées et Coteaux de Gascogne” Long Term Ecological Research (LTER) site in South West of France.
- [2016-06-07 Tue] The data acquisition has been done ! Both the hyperspectral and the lidar data have been acquired and should be in our database in September. Can’t wait !
MUESLI is the acronym of /MUltiscale mapping of Ecosystem Services by very high spatial resolution hyperspectraL and LiDAR remote sensing Imagery/. The project is funded by the University of Toulouse.
The objective of the MUESLI project is to monitor ecosystem services using high spatial resolution hyperspectral and LiDAR images. Regulating services, such as pollination and biological control, and provisioning services will be considered on a Long Term Ecological Research site. Unlike conventional approaches that use a crisp description of the landscape, i.e., by defining spatial objects from pre-defined classes (forest, agricultural field, river, meadow, …), it is proposed to adopt a finer representation that considers the continuous nature of landscape. Indeed, an object for a given spatial resolution is usually not made of only one material. A more realistic representation is to consider that an object can be a mixture of several materials. Using such a representation of the landscape, it is expected to identify new spatial patterns that provide significant regulating services. Furthermore, the object representation will allow to perform a multiscale analysis that better preserves the original landscape configuration.
Remote Sensing Images
Hyperspectral images provides a rich spectral information, which is necessary to assess correctly the heterogeneity of the landscape while LiDAR data provides topographic information, which is complementary to the spectral information. However, this multi-source data is challenging to process accurately and specific developments will be done during the project. In particular, the definition a mixture model on objects (rather than on pixels) will be addressed.
Three major steps are planned during the project for a total duration of 36 months. The first step concerns the data acquisition (remote sensing images and field data). The second step concerns the performances assessment of the state-of-art method and the data base construction. The last step concerns the definition and the implementation of the proposed representation of the landscape. The new developments will be compared to conventional approaches.