Curriculum Vitae
- 2018–present: Postdoctoral Researcher, Institute of Zoology and LaSTIG (University of Paris Est, France)
- 2014–2017: PhD Researcher, DYNAFOR Lab (INRA & University of Toulouse, France)
- 2013–2014: Study engineer, Center for the Study of the Biosphere from Space (CESBIO, France)
- 2010–2013: Diploma in Agronomy Engineering (equivalent to a Masters degree), National School of Agricultural Science and Engineering (ENSAT, France)
- 2012: Postgraduate student in Geographic Information Science (Exchange semester),University of Georgia (USA)
ResearchGate Mailys Lopes
Research Interests
My research is focused on developing remote sensing tools to support ecological and biodiversity related studies. I use high spatial resolution satellite imagery acquired with the latest generation of satellites (Sentinel-2, Sentinel-1) to monitor biodiversity from space. I am looking at how the information contained in remote sensing data can relate to natural vegetation state and properties, and more specifically to vegetation phenology and seasonal changes using dense satellite image time series. My work involves natural and semi-natural vegetation mapping, monitoring of vegetation phenology and producing biodiversity indicators derived from satellite imagery.
Current Research
New generation satellites (Sentinel-1 and Sentinel-2) have been launched recently and they provide high spatial and temporal resolution multispectral and radar images of the terrestrial surfaces. They offer new opportunities to map vegetation at fine spatio-temporal scale. However, the full capacity of these sensors (very high revisit frequency, fusion of optical and radar images) is still generally not exploited for biodiversity monitoring. Hence, little is known about the potential of dense satellite image time series to improve natural vegetation mapping and even less about the contribution of combining time series analyses and fusion approaches to mapping natural habitats. My postdoc project is about assessing the potential of dense optical and radar satellite image time series to map natural vegetation in a large savannah conservation area: the W-Arly-Pendjari (WAP). The WAP is a vast transboundary protected area located in West Africa. It is a key biodiversity hotspot, with natural habitats that support the only surviving population of cheetah in West Africa and more than half of endangered West African lions. However, agricultural expansion is a major threat to biodiversity in this area. This project will provide a detailed map of natural resources in the WAP that will enable the quantification of the effects of land cover change across the WAP on biodiversity.
Supervisors
Nathalie Pettorelli, Institute of Zoology, Zoological Society of London
Pierre-Louis Frison, LaSTIG (IGN, University of Paris Est),
Funding
2018–2019: AgreenSkills+ international postdoctoral fellowship, programme co-funded by the European Union (Marie-Curie COFUND-FP7 People Programme).
2018–2019: Toulouse-INP International Mobility Support.
2014–2019: INRA-INRIA Young Scientist Contract which finances 3 years of PhD and 2 years of post-doctorate.
Publications
Carrié, R., Lopes, M., Ouin, A. and Andrieu, E. Bee diversity in crop fields is influenced by remotely-sensed nesting resources in surrounding permanent grasslands. Ecological Indicators. 2018. 90: 606-614.
Lopes, M., Fauvel, M., Ouin, A. and Girard, S. Spectro-Temporal Heterogeneity Measures from Dense High Spatial Resolution Satellite Image Time Series: Application to Grassland Species Diversity Estimation. Remote Sensing, 2017. 9(10):993.
Lopes, M., Fauvel, M., Girard, S. and Sheeren, D. Object-Based Classification of Grasslands from High Resolution Satellite Image Time Series Using Gaussian Mean Map Kernels. Remote Sensing. 2017. 9(7):688.
Lopes, M., Fauvel, M., Ouin, A. and Girard, S. Potential of Sentinel-2 and SPOT5 (Take5) time series for the estimation of grasslands biodiversity indices, 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp). 2017.
Lopes, M., Fauvel, M., Girard, S., and Sheeren, D. High dimensional Kullback-Leibler divergence for grassland management practices classification from high resolution satellite image time series, 2016 IEEE International Geoscience And Remote Sensing Symposium (IGARSS). 2016 pp. 3342-3345.
Sheeren, D., Fauvel, M., Josipović, V., Lopes, M., Planque, C., Willm, J. and Dejoux, J.-F. Tree species classification in temperate forests using Formosat-2 satellite image time series. Remote Sensing. 2016. 8(9):734
