Abstract
Abstract. Remote sensing is a potentially very useful source of information for spatial monitoring of natural or cultivated vegetation. The latest advances, in particular the arrival of new image acquisition programs, are changing the temporal approach to monitoring vegetation. The latest European satellites launched, delivering an image every 5 days for each point on the globe, allow the end of a growing season to be monitored. The main objective of this work is to identify and map the vegetation in the Pays de Brest area by using a multi sensors stacking of Sentinel-1 and Sentinel-2 satellites data via Random Forest, Rotation forests (RoF) and Canonical Correlation Forests (CCFs). RoF and CCF create diverse base learners using data transformation and subset features. Twenty four radar images and optical dataa representing different dates in 2017 were processed in time series stacks. The results of RoF and CCF were compared with the ones of RF.
Highlights
Environmental vegetation monitoring is constantly increasing with a desire to preserve natural habitats and ecosystems
The use of intra-annual time series of satellite images acquired by sensors like Sentinel-2 and Sentinel-1 with a high revisit capacity and a high spatial resolution makes it possible to acquire a larger number of images on a same area and improve the identification and characterization of different classes of vegetation and cultures
Different feature extraction methods were tested for the operation of Rotation Forests
Summary
Environmental vegetation monitoring is constantly increasing with a desire to preserve natural habitats and ecosystems. It is possible to follow the intra- and inter-annual evolution of ecosystems at a fine spatial resolution at the scale of the territory. This massive flow of earth observation data provides a rich and detailed description of ecosystems and cultures, allowing control over their state and evolution. The use of intra-annual time series of satellite images acquired by sensors like Sentinel-2 and Sentinel-1 with a high revisit capacity and a high spatial resolution makes it possible to acquire a larger number of images on a same area and improve the identification and characterization of different classes of vegetation and cultures
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