Missions

the candidate will have to process global remote sensing burned area data delivered by ESA fireCCI at fine resolution (Sentinel sensor at 20m) to generate fire patches and compute its descriptive morphological features
The candidate will have to develop efficient pixel aggregation methods for big data processing, and follow the previous standard fire patch description performed MODIS 250m resolution.
The candidate will have to provide a comprehense source code, data set decription as a deliverable report and a data paper.

Activités

The candidate will use the daily burned area 20m resolution delivered by the FireCCI project, and will develop a source code to assemble the pixels into patches according to nearest spatio-temporal rules.
Patches will be describes by their morphological features using landscape metrics and overlapped with 3hourly VIIRS fire detections for fire spread information.
A final global shapefile formated dataset will be provided, with the source and the data description for a data paper and free access to the scientific community.

Compétences

The candidate will have skills in efficient computing and processing of large global remote sensing data and geostatistics.
the candidate will have to deliver clean and reusable codes freely accessible on github, with desciption of metadata and code description.
Skills in scientific writing in english are required

Contexte de travail

The candidate will work within the european space agency ESA FireCCI, at the UMR CEFE lab in montpellier.
the candidate will have to follow the deadlines on deliverables, monthly reports and 6monthly meetings to report on his work.
the candidate will have to answer requests on data deiivery, formating, and eventual data synthesis maps

Contraintes et risques

strict deadlines for deliverables have to be respected with 6monthly evaluations by the project officer.