GAF AG (DE)
A range of EO based prototype capabilities have been developed and tested in relation to the implementation of REDD+.
With the launch of Sentinel-1A/B and Sentinel-2A/B, a new era of frequent coverage of the earth surface by high resolution (HR) satellite imagery was initiated. Together with the Landsat and other satellite missions, it is now possible to build up dense time series with sufficient spectral and geometrical resolution which allow new analysis methods for improved forest and land cover mapping.
The application of dense time series of Sentinel and the HR data provides the possibility to overcome mapping inaccuracies caused by seasonal changes of forest cover (leaf fall in dry season), to compensate data gaps caused by cloud coverage, to improve the analysis of human induced changes and to make an early detection of deforestation and forest degradation events possible.
However, the data volumes of dense time series data stacks from Sentinel and other satellite systems are, compared with traditional processing methods (mono- and bi-temporal analysis), tremendously increasing and therefore require a sophisticated IT infrastructure to compute wall-to-wall land cover maps. It has been proven more efficient for the European Service Providers to make use of cloud processing options instead of purchasing, maintaining and constantly upgrading existing IT infrastructure. On the other side, the handling of huge data volumes and the application of complex processing algorithms pose an enormous infrastructure and capacity challenge for developing countries. Thus, technology transfer and capacity building are major pillars of development cooperation programmes but however, the status of having up-to-date hardware and software is almost always lacking behind the requirements of a fast developing technology.
Therefore, working on cloud-based processing chains will be an opportunity for improved technology transfer and capacity building to developing countries.
The overall goal of the current project is to enable Stakeholders and Users from developing countries to create sophisticated applications for forest monitoring and assessment within an innovative cloud-based Front Office which unifies the Big Data functionalities of the C-DIAS back storage with already verified processing algorithms for tropical dry forest mapping.
In particular the project outcomes are expected to be provision to Users from developing countries of improved access and processing methods for cloud based forest monitoring, based on Sentinel-2 data, a web based Graphical User Interface (GUI) to select, pre-process and classify Sentinel-2 data and capacity building activities related to testing, validation and training on the developed system.