The förecast 2.0 project addresses its scalability in terms of computational development, geoprocessing optimization, and business model.
To improve the current functionalities of förecast and to address its scalability objectives, förecast 2.0 will improve the architecture and procedures under a Big Data framework and integrate procedures and protocols to upgrade forest inventory using LiDAR data. förecast has a module devoted to forest monitoring (change detection) using Sentinel-2 satellite imagery. förecast 2.0 pretends to analize the large-scale, geospatial data, greatly reducing the time required for computation.
Thank to EUHubs4Data, we have been able to give a big step forward with forecast. This step has been possible through the use of the improvements in the platform architecture and the supercomputation power, that will boost förecast’s solutions.
Main Results of the 1st stage of the programme: Architecture enhancement (development and optimization of the forest inventory solver and redesigning of the architecture to minimize procedures), establishment of hierarchies of master and slaves to solve inventories and development and application of algorithms in the new architecture, especially monthly estimates with multivariable and paralleled processing.
The main objective is to improve the current functionalities of förecast in terms of computational development, geoprocessing optimisation, and business model.
- An intelligent workflow architecture to combine the different image layers and forestry algorithms, that reflects the real situation of every forest.
- Automatic and on demand processes to increase the simultaneous working capacity of the platform.
- förecast 2.0 offers real-time information of forest characteristics and changes. This functionality is key for the reduction of the risk of forest fires. Lost forest areas and their associated biodiversity may be avoided with proper forest information and subsequent decision making.
- A data-driven Sustainable Forest Management is proven to optimize the carbon uptake of the forests, that account for absorbing around the 10% of the global emissions in Europe.
- Real-time and precise forest information allows better decisions and, subsequently, leads to more resilient but, also, profitable forests.
A fully updated version of forecast to cope with a future high demand enhancing computation and geospatial capabilities, as well as developing business tools for an optimal commercial roadmap.
KPI 1: Scaling services in forest LiDAR inventory. KPI 2: Spatial geoprocessing modelling for forest monitoring service. KPI 3: Big Data geoprocessing application. KPI 4: Business innovation consulting.