An Integrated Approach for Landscape Element Detection and Characterization using Sentinel-2 AND EnMAP Data

Landscape elements, such as hedges, tree rows, and green strips in agroecosystems provide important ecosystem services. However detailed and up-to date information on landscape elements is often limited. This study proposes a two-step approach for the identification and characterization of landscape elements, using multitemporal Sentinel-2 and hyperspectral EnMAP data. The first step aims on landscape element detection using Sentinel-2 data, using a TensorFlow U-Net model. The second component is based on spectral unmixing of hyperspectral EnMAP data, aiming on the previously detected landscape elements.Overall, the proposed method proofs useful. Results show that the U-Net model used is capable of detecting landscape elements with a high degree of certainty (> 93 % OA and > 0.84 IoU on independent test data). Preliminary results of spectral unmixing are promising, indicating that the research presented here represents an approach that can be built upon for future, large-scale applications.