Professional summary

France has over 25 years of experience in remote sensing of vegetation and land in a wide range of ecosystems.  She graduated at the University of Ghent Belgium as an Agricultural Engineer in Forestry and did an MSc in Environmental GIS and Remote Sensing at Aberdeen University. In her PhD she used 3-dimensional light-canopy interaction modelling to test if tree canopy shadowing could be used to differentiate tropical forest types on multi-view satellite imagery. Her work focuses on the development of remotely sensed-derived data to constrain and validate models, support process understanding and monitor vegetation. Using field radiometry, camera, drone, aircraft and satellite remote sensing, she and her team have worked on characterising wetland and flood dynamics, tropical leaf/plant phenology, woody cover and habitat condition, and on mapping land cover, forest types, vegetation communities, burnt area, plant species and flowers. 

Throughout her career France has also managed a variety of interdisciplinary research projects. The NERC AHRC funded Newton project PARAGUAS (Colombia-Bio programme) is one of the most recent ones. PARAGUAS studied how the diversity of plants and activities of people contribute to the water regulation and provision of the Colombian páramos. The remote sensing part involved the mapping of páramo vegetation communities across 12 micro-catchments of Guántiva-La Rusia using drones.  She is currently also managing the remote sensing work package of the Horizon2020 MAMBO project. The work package is responsible for developing pipelines extracting habitat condition metrics from 3 dimensional point clouds from drone LiDAR or Structure from motion data. In the MRC-GCRF grants KFD and IndiaZooRisk, her team is characterising seasonal surface water and paddy dynamics and forest fragmentation across regions of India. 

Selected publications

Thornley Rachael H. et al. , (2023), Prediction of grassland biodiversity using measures of spectral variance: a meta-analytical review. Remote Sensing, 15, http://dx.doi.org/10.3390/rs15030668

Broughton Richard K. et al. , (2022), Slow development of woodland vegetation and bird communities during 33 years of passive rewilding in open farmland. PLoS ONE, 17, http://dx.doi.org/10.1371/journal.pone.0277545

Thornley Rachael Helen et al. , (2022), The feasibility of leaf reflectance-based taxonomic inventories and diversity assessments of species-rich grasslands: a cross-seasonal evaluation using waveband selection. Remote Sensing, 14, http://dx.doi.org/10.3390/rs14102310

Thornley Rachael et al. , (2022), Intra-annual taxonomic and phenological drivers of spectral variance in grasslands. Remote Sensing of Environment, 271, http://dx.doi.org/10.1016/j.rse.2022.112908

Adzhar Rahayu et al. , (2022), MODIS Vegetation Continuous Fields tree cover needs calibrating in tropical savannas. Biogeosciences, 19, 1377-1394, http://dx.doi.org/10.5194/bg-19-1377-2022

Taylor Christopher M. et al. , (2022), “Late-stage” deforestation enhances storm trends in coastal West Africa. Proceedings of the National Academy of Sciences, 119, https://doi.org/10.1073/pnas.2109285119

Roy D.B. et al. , (2022), Developing technologies for Agri-environment monitoring. https://randd.defra.gov.uk/ProjectDetails?ProjectId=20551

Broughton Richard K. et al. , (2021), A regional-scale study of associations between farmland birds and linear woody networks of hedgerows and trees. Agriculture, Ecosystems and Environment, 310, https://doi.org/10.1016/j.agee.2021.107300

Zhang Ce et al. , (2020), Identifying and mapping individual plants in a highly diverse high-elevation ecosystem using UAV imagery and deep learning. ISPRS Journal of Photogrammetry and Remote Sensing, 169, 280-291, http://dx.doi.org/10.1016/j.isprsjprs.2020.09.025

Gerard France F. et al. , (2020), Leaf phenology amplitude derived from MODIS NDVI and EVI: maps of leaf phenology synchrony for Meso‐ and South America. Geoscience Data Journal, 7, 13-26, http://dx.doi.org/10.1002/gdj3.87