Abstract
Satellite-based Earth Observation (EO) offers great potential to monitor the status and changes on the earth’s surface regularly and on a large scale. Over the past decades the increasing number of EO sensors and more frequent revisit rates have led to the generation and availability of large datasets. The increasing amount of data, however, requires tools for fast data access, harmonization and processing. In the openEO Platform (https://openeo.cloud/) a federated processing and data access structure is currently being developed and offers fast preprocessing and analysis of extensive EO data on a large scale. It relies on the openEO API and enables the EO processing on several cloud back-ends by providing additional clients and intuitive interfaces. Based on Analysis Ready Data (ARD) and the processing capabilities in openEO Platform, diverse essential analytical building blocks are explored iteratively to foster the best implementation and concatenation of new and existing functions based on large-scale application of EO-data at scale. Two such analytical building blocks in the openEO platform project focus on forest areas since they cover up to one third of the world’s surface and are responsible for key environmental services such as natural risk and disaster prevention, carbon sequestration, water storage and biodiversity. Due to climate change, deforestation and forest degradation, forested areas are under pressure and processes and tools are urgently needed to map and monitor changes consistently, continuously, extensively and through performant functions. The two forest-related analytical building blocks currently being implemented are: near real-time forest change detection and retrieval of forest fractional canopy cover (FCC).
In the forest change detection algorithm pixel-based time-series models are fit to Sentinel-1 and Sentinel-2 ARD for a historical reference period, from September 2016 until end October 2018. The time-series model consists of a harmonic model accounting for seasonality with a complexity depending on data availability, forest types and inherent noise in the data. Predicting each new Sentinel-1 and Sentinel-2 acquisition based on the fitted models allows to detect changes based on deviations from the prediction. We apply the workflow for the European Alps to map the impact of the Vaia storm in 2018. Afterwards, the capabilities of openEO platform are extended by a Random Forest regression modelling. We use very high-resolution (VHR) imagery from PlanetScope to calculate the fractional canopy cover within the spatial resolution of medium-resolution sensor information from Sentinel-1 and Sentinel-2. The resulting regression models are used to calculate the forest’s fractional canopy cover over central Europe directly on the platform back-ends with dedicated openEO processes.
As a result of the forest cover change analysis two new openEO processes were incorporated in the federated cloud environment. First, the user can fit an arbitrary function to a pixel time series. In a second step the function can be used to predict the corresponding values for any day of the year based on the pre-computed model. The resulting forest change maps are currently being validated with reference data of local authorities. Two other processes are currently being implemented for the retrieval of the fractional canopy analysis. The openEO processes will be extended by random forest regression models in order to predict the FCC on a larger scale with the ARD available in the back-ends. The results are two functions: one to train and construct the model, one to apply a random Forest model to a set predictor raster stored in multidimensional data-cubes. Both analytical building blocks have been implemented as open source and are fully reproducible. This way they can serve as template to implement other applications on different study areas or further tailored to the requirements of each individual application.