Last week, iecolab attended a workshop on modelling and data assimilation organised by ECOPOTENTIAL Project. This project aims at generating tools for improving the management of Protected Areas by using remote sensing and Earth Observation.
During two days, remote sensers, modellers and other scientists participated in discussions regarding the use of remote sensing in the design and validation of different types of models, from hydrology to species distribution.
Discussions highlighted the value of remote sensing in the modelling process: it can be used as input data for a model and it allows the validation and calibration of models which could not be done by other means due to the high costs of ground-based measurements or the impossibility to monitor large areas. Moreover, remote sensing can also be used for data assimilation, this way improving the performance of developed models and improving the accuracy of their predictions.
The role of uncertainty in the modelling process was also part of the discussions. It was acknowledged that a deep understanding of the sources of uncertainty within a model is crucial to design and develop reliable models. A working group dealing with uncertainty has been set up within the Project and will start working after summer holidays.
Some time was also spend in understanding the concept of data assimilation, for those unfamiliar with the topic, as well as getting to know OpenDA. OpenDA is an open interface developed by Deltares, which provides a set of ready-to-use set of tools for data assimilation.
Finally, further steps within the Project were discussed. In the coming moths, work will focus on preparing the models to be implemented within a virtual laboratory, this way allowing the execution of such models by a wider user community and improving its visibility.
- alumnos internos
- Análisis de datos ecológicos en R. V edición
- cambio global
- datos ecológicos
- Fundación Biodiversidad
- global change
- jóvenes investigadores
- modelos mixtos
- pine forests
- protected areas
- Remote Sensing
- sierra nevada
- sistemas de información
- Socio Ecological Systems