The main objective of the French National Inventory (NFI) created in 2013 is to provide continuous evaluation of forest resources and their evolution. The sampling design is set up to produce estimates at the national and regional scales, and to contribute to forest policies and their evaluation. With the development of bio-economy, there is a need to provide information at a finer scale, i.e. the forest territories.
Multi-source inventory methods were developed to provide more precise estimations of forest attributes at those operational scales, but the establishment of such method in France faces multiple difficulties. French forests are among the more diverse of Europe, due to the topographical and climatic gradients found over the country, and to the diversity of forest management practices. Such diversity requires adapting the methods to the landscape properties, with expected impacts on the genericity of the approach and the precision gains within the various territories.
The main objective of this doctoral research is to contribute to the development of the first multi-source inventory approach adapted to the French forest. To do so, the research will benefits from auxiliary data available over the whole territory and regularly updated, like aerial photograph covers, among others.
Prerequisite: the candidate must have an interest for forest ecosystems. The topic requires competences in statistics, spatial analysis, scientific computing. Knowledge in Forest inventory and remote sensing are also advantageous.
Working environment : the candidate will work with various software in statistics (R), data base management (PostgreSQL), GIS (QGIS, ArcGIS).
Profile: master degree in statistics, applied mathematics, image and signal processing, or forestry with an experience in survey sampling.
The candidate must be fluent in English with demonstrated writing skills.
How to apply: provide a motivation letter, a curriculum vitae and two recommendation letters.
Deadline for applying: 31-05-2019 (the position open until filled)