Main objective
The EDYSAN lab seeks a postdoctoral candidate for a 13 months period to assess the potential of terrestrial LiDAR to model forest microclimates. The successful candidate is expected to link microclimate data readily available by the lab to stand structure variables derived from terrestrial laser scanning (TLS). Focusing on a subset of sites monitored by the lab, the successful candidate will collect TLS data with a TRIMBLE X7 recently funded by the CPER ECRIN. Several variables will be derived from the 3D LiDAR point cloud, using a voxelization technique, and subsequently used as predictors to model the temporal dynamic of forest microclimates. Several scans throughout the year will be considered to capture the seasonality in the leaf-on and leaf-off periods.
Some background
The position is part of a regional state plan contract under the framework of a large project entitled “Environment, Climate, Research and Innovation” (ECRIN) which aims at identifying and understanding climate change impacts on the atmosphere, human health and ecosystem services to support mitigation and adaptation strategies. The project also aims at supporting the development of innovative measurement methodologies, novel instrumentation to monitor the environment and new advances in analytical approaches. This project involves 29 institutional partners and 25 research laboratories within the Hauts-de-France region.
The EDYSAN lab, who will host the successful candidate, is involved in work package 2 (WP2) entitled “Impacts of climate change on natural, agricultural and suburban systems”. Within the framework of WP2, EDYSAN aims at studying how climate change is impacting the spatial distribution of forest biodiversity and its associated ecosystem services.
Biodiversity redistribution in response to climate change has direct impacts on ecosystem functioning and human well-being throughout the ecosystem services that biodiversity provides. The forest understory is one of the most biodiversity rich compartment in temperate ecosystems worldwide and it harbours a wealth of ecosystem services, such as natural pest control for neighbouring agricultural fields. However, the current tools that are used to predict species distribution and redistribution as climate changes are too coarse in spatial resolution to capture microclimatic processes in the understory and thus unsuitable for forest-dwelling species living under the shade of trees. Not only those models are too coarse in spatial resolution but they are all relying on macroclimatic conditions as measured by standardised weather stations and thus unlikely to capture temperature fluctuations as experienced by forest-dwelling species within their buffered habitats.
In this context, the EDYSAN lab aims at better characterizing and modelling forest microclimates to improve predictions from species distribution models. A recent synthesis published by the lab members in 2022 in Journal of Ecology features how LiDAR technologies, either airborne or terrestrial, can help researchers unveil biodiversity changes through time as well as forest microclimates. An ongoing project funded by the « Agence nationale de la recherche » (ANR, IMPRINT), starting on October 1st 2019 and ending on March 31st 2024, is using this technology to model forest microclimates. This project has already gathered microclimatic data across several sites in several state forests in France (Aigoual, Blois, Compiègne et Mormal), where airborne LiDAR data are readily available, as well as across several permanent forest plots belonging to the « Réseau national de suivi à long terme des écosystèmes forestiers » (RENECOFOR) managed by the « Office national des forêts » (ONF). An equivalent network of permanent forest plots within the Hauts-de-France region is the « Observatoire régional des écosystèmes forestiers » (OREF), managed by the « Centre national de la propriété forestière » (CNPF). Since 2020, 19 permanent forest plots belonging to OREF have been equipped with temperature data loggers (HOBO). Data on forest stand structure and understory plant species composition are also available for all plots.
Main tasks
The main tasks that are expected from the successful candidate are:
- Acquiring, handling and processing terrestrial LiDAR data from the field;
- Extracting LiDAR-derived variables (e.g., PAD) using voxel-based approaches;
- Using LiDAR-derived variables as predictors to model understory temperatures;
- Writing a scientific article on the benefit of TLS to model forest microclimates.
Expected qualifications
A Ph.D. in remote sensing science, forest sciences or ecoinformatics would be welcome but in general, the candidate we are seeking is expected to have the following skills and qualifications:
- Remote sensing and experience in using and processing terrestrial LiDAR data;
- Programming skills, especially in using R and packages such as forestr and lidR;
- Cutting-edge expertise in modeling and advanced statistical analyses;
- Basic knowledge in forest ecology and microclimate ecology;
- Proven abilities to publish at a high international level;
- Good oral and written communication skills in English;
- Rigor, curiosity, autonomy, abilities to work in group.
Where
At the UMR CNRS 7058 « Ecologie et Dynamique des Systèmes Anthropisés » (EDYSAN), UFR de Pharmacie, 1 rue des Louvels, 80039 AMIENS Cedex 1, France.
Duration and starting period
The postdoctoral position is for 13 months, with a possibility for a one year extension depending on funding opportunities. The position should start in the beginning of spring 2023 and at the latest on September 2023.
Gross salary
To be discussed depending on the experience of the successful candidate.
Others
A driving licence is required to travel to the study sites located within the Hauts-de-France region.
Application instructions
Please send your CV, including a list of recent publications, together with a cover letter and the contact information of 2 references to Jonathan Lenoir (jonathan.lenoir@u-picardie.fr) and the human resources at UPJV (recrutement@u-picardie.fr). The submission deadline is April 30th 2023.
Jonathan Lenoir 