Abstract
Flood-damages on crops are related to several factors concerning both flood event and crops characteristics. In particular, flooded area size and water level are critical parameters while assessing yield loss and damages related to agricultural infrastructure and irrigation systems. With reference to the Sesia river (North-Western Italy) flood, occurred on 3rd October 2020, a methodology to detect flooded areas and estimate water level above ground was proposed based on Sentinel-1 (S1) data and Digital Terrain Model (DTM). S1 imagery was collected and processed by GEE. In particular, S1 VV image difference (pre- post-event) was analyzed by Otsu’s method to define a threshold able to map flooded pixels. A watershed segmentation was performed on DTM to locate terrain depressions patches that, once coupled with flooded areas map, made possible to get an estimate of water level and map it. Agricultural crops were recognized based on the CORINE Land Cover 2018 level 3 map. A Normalized Difference Vegetation Index map from Sentinel-2 data was also obtained to describe crop activity before the event and associated to crops. This made possible to investigate which “active” crops were flooded and get an estimate of the water level that affected each area. This issue, together with water time persistence, strictly influences the degree of damage that a crop can suffer from. With these premised, the proposed methodology could be intended as a useful tool to support agricultural-related damages models generating a preliminary and rapid map of flooded crops taking care of the local topography.
F. Ghilardi—Private author.
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We would like to thank Dr. Sara Martinengo for providing reference data information useful to reach the results presented in this work.
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Ghilardi, F., De Petris, S., Sarvia, F., Borgogno-Mondino, E. (2022). A Proposal for Crop Damage Assessment by Floods Based on an Integrated Approach Relying on Copernicus Sentinel Data and DTMs. In: Borgogno-Mondino, E., Zamperlin, P. (eds) Geomatics and Geospatial Technologies. ASITA 2021. Communications in Computer and Information Science, vol 1507. Springer, Cham. https://doi.org/10.1007/978-3-030-94426-1_4
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