Towards a large-scale tool for estimating potential land-cover impacts from Remote Sensing
Daniel E. Pabon-Moreno
Land cover changes affect the climate system at local, regional, and global scales. Previous studies have indicated that the changes in vegetation distribution have an impact on the land surface temperature and the energy balance at local and global scales. Assessing the effect of land cover change on climate variables is a fundamental step in understanding how deforestation and reforestation processes will impact the climate dynamics. At the same time, this knowledge can be of utmost importance for the design of reforestation or afforestation plans, such as those envisaged within the European Union’s Green New Deal, but also more generally, in any part of the world. In this talk, we will present some preliminary results on the effect of land cover change on climate variables for Europe and Africa. To develop the studies, we will develop a first technical implementation of the space for time technique in the programming language Julia. The space for time technique estimates the average change of local climate if the land cover changes from one class to another. For example, savannas are usually hotter than neighboring forest, then contrasting the local climate conditions we can evaluate the potential effect of the transition from forest to savannas. In the current state of Earth observation using satellite imagery, downloading large amounts of information is no longer feasible because the amount of information is many times larger than the infrastructure of research institutions and organizations. In this context, the development of software compatible with cloud computing infrastructure is more important than ever. Julia is a dynamic programming language focused on high-performance computation and easy scalability, following the philosophy of “write like python, run like C”. Despite being a relatively new programming language (11 years old), the use of Julia in science and cloud computing has grown exponentially recently, as more and more institutes and companies adopt it. For these reasons, our implementation of the space-for-time technique on the Julia programming language, will allow scientists and organizations to efficiently perform the analysis from laptops to remote servers and platforms.
