At the Remote sensing and natural resources modelling (REMOTE) group, we are capitalizing on a blend of remote sensing data obtained from space- and air-borne platforms, as well as in-situ measured data, for producing information on the status of natural resources for public and private stakeholders.Eventually, we rely on our competences in remote sensing, and environmental sciences such as hydrology, climatology, plant physiology, etc. to improve our capacity to monitor variations of Earth’s biotic and abiotic resources at unprecedented temporal and spatial resolution.
Moreover, we aim to integrate remote sensing data with in situ measured data, land surface models and satellite and terrestrial communication services in order to provide evidence-based decision support in near real time in a variety of thematic domains (i.e. disaster risk reduction, precision agriculture, viticulture and forestry, preservation and management of natural resources, maritime surveillance). This body of work largely connects with other lines of research carried out by our colleagues in the AGRO and CAT groups (e.g., climate modelling, remote sensing, hydrologic and hydraulic modelling).
Remote sensing and numerical modelling of key environmental variables, design and development of robust and resilient communication infrastructure in the following thematic areas:
Our research activities are wired around fundamental and applied questions related to:
This includes research on:
We rely on our long-standing expertise in remote sensing, satellite and terrestrial communication services and environmental modelling to carry out research in the thematic areas of:
We leverage EO and RS-based information for gaining a better understanding of fundamental functions of agroecosystems and forests. The effects of global change call for new decision and management support tools (e.g., precision agriculture and viticulture).
We rely on scientific and technical EO and RS-based knowledge for gaining a better understanding of Land Surface Processes. For investigating eco-hydrological extremes in a non-stationary context, we focus on biosphere-atmosphere interactions at multiple spatio-temporal scales.
With global change increasingly triggering hydro-climatological extremes, we aim at improving satellite EO-based tools for monitoring, modelling and predicting natural disasters such as floods and droughts (including early-warning systems) at large scale.
We develop scientific and technical EO and RS-based knowledge to better understand, protect and manage coastal environments, as well as vessel and ocean monitoring techniques for ensuring maritime safety and security.
High-performance processing chains enabling an automated production of key environmental variables from multi source remote sensing data:
Complementarily to the available spaceborne sensors and with the objective to monitor terrestrial subsurface and surface water bodies, the hydro-ecological processes and their related impacts, the research group operates:
Sentinel-1 InSAR Coherence to Detect Floodwater in Urban Areas: Houston and Hurricane Harvey as A Test Case. Chini, M., Pelich, R., Pulvirenti, L., Pierdicca, N., Hostache, R., & Matgen, P. (2019). Remote Sensing, 11, 107
Challenges and Future Perspectives of Multi-/Hyperspectral Thermal Infrared Remote Sensing for Crop Water-Stress Detection: A Review. Gerhards, M., Schlerf, M., Mallick, K., & Udelhoven, T. (2019). Remote Sensing, 11(10).
Large-scale automatic vessel monitoring based on dual-polarization Sentinel-1 and AIS data. Pelich, R., Chini, M., Hostache, R., Matgen, P., Lopez-Martinez, C., Nuevo, M., Ries, P., & Eiden, G. (2019). Remote Sensing, 11(9), 1078
Towards a 20 m Global Building Map from Sentinel-1 SAR Data. Chini, M., Pelich, R., Hostache, R., Matgen, P., & Lopez-Martinez, C. (2018). Remote Sens., 10(11), 1833
“Analysis of Airborne Optical and Thermal Imagery for Detection of Water Stress Symptoms”, Gerhards, M., Schlerf, M., Rascher, U., Udelhoven, T., Juszczak, R., Alberti, G., Miglietta, F., Inoue, Y. (2018). Remote Sens., 10, 1139.
Near‐Real‐Time Assimilation of SAR‐Derived Flood Maps for Improving Flood Forecasts. Hostache, R., Chini, M., Giustarini, L., Neal, J., Kavetski, D., Wood, M., Corato, G., Pelich, R., & Matgen, P. (2018). Water Resources Research, 54(8), 5516-5535