Spatial evolution of the December 2013 Metaponto plain (Basilicata, Italy) flood event using multi-source and high-resolution remotely sensed data

We present a multi-layer, multi-temporal flood map of the event occurred on December 2013 in Basilicata (southern Italy), documenting the spatial evolution of the inundated areas through time, as well as some ground effects of floodwaters inferred from the imagery. The map, developed within a GIS and consisting of four, 1:20,000 scale, different layers, was prepared using image processing, visual image interpretation and field survey controls. We used two COSMO-SkyMed synthetic aperture radar (SAR) images, acquired during the event, and a Plèiades-1B High-Resolution optical image, acquired at the end of the event. We also used the information derived from the satellite imagery to update some local features of the OpenStreetMap (OSM) geospatial database, and then integrated it within the flood map. A classified multi-temporal dynamic map of inundation and flood effects has been produced in the form of a multi-layer pdf file (Main Map)

Spatial evolution of the December 2013 Metaponto plain (Basilicata, Italy) flood event using multi-source and high-resolution remotely sensed data

We present a multi-layer,multi-temporal flood map of the event occurred on December 2013 in Basilicata (southern Italy), documenting the spatial evolution of the inundated areas through time, as well as some ground effects of flood waters inferred from the imagery. The map, developed within a GIS and consisting of four, 1:20,000 scale, different layers, was prepared using image processing, visual image interpretation and field survey controls. We used two COSMO-SkyMed synthetic aperture radar (SAR) images, acquired during the event, and a Plèiades-1B High-Resolution optical image, acquired at the end of the event. We also used the information derived from the satellite imagery to update some local features of the OpenStreetMap (OSM) geospatial database, and then integrated it within the flood map. A classified multi-temporal dynamic map of inundation and flood effects has been produced in the form of a multi-layer pdf fil

REMOTELY SENSED FLOOD MAPS IN THE METAPONTO PLAIN (BASILICATA)

Recent availability of multisource high-resolution remote sensing data and of flood detection algorithms (D’Addabbo A. et al. 2016; Rajapaksha, J., & Dampegama 2016; Mason, D. et al. 2012; Martinis, S. Et al. 2006; Nico, G. et al. 2000), poses the problem of how to integrate them into ready to use emergency flood maps. We present a flood map of the event occurred on December 2013 in Basilicata (southern Italy) that documents both the ground effects (Figure 1) and the spatial evolution of the inundated areas through time (Figure 2). The multilayer map, consisting of four 1:25.000 scale different layers, was prepared using image processing, visual image interpretation and field survey controls. We used two COSMO-SkyMed synthetic aperture radar (SAR), stripmap (3 m resolution) images, acquired during the event, and a Plèiades-1B High Resolution (2 m) satellite optical image, acquired at the end of the event. We also included data on vulnerability obtained through out the OpenStreetMap (OSM) database update, and then integrating it within the flood map. Our map shows how recent advances in flood detection algorithms, and the availability of high resolution Optical and SAR data can be integrated to have a flood event synoptic representation, but also a ready-to-use map in a relatively short time, to be used immediately after the event for hazard, vulnerability and damage assessment. Figure 1 (A) Optical satellite detail image acquired on 5 December 2013, showing different ground effect traces; (B) derived inundation map, showing different ground effects observed by visual image interpretation. Figure 2 Inundation map details which show water evidences decrease from the image acquired during flood event peak (1-2 Dec. 2013) until the last image acquired far from the event; (A & B) show water evidence observed in RGB SAR composition, obtained from 2 December and 3 December SAR image elaboration; (C) water evidences observed from Plèiades optical satellite image acquired on 5 December 2013. References D'Addabbo, A., Refice, A., Pasquariello, G., Lovergine, F. P., Capolongo, D., & Manfreda, S. (2016). A bayesian network for flood detection combining SAR imagery and ancillary data. IEEE Transactions on Geoscience and Remote Sensing, 54(6), 3612-3625. Martinis, S., Twele, A., & Voigt, S. (2009). Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data. Natural Hazards and Earth System Sciences, 9(2), 303-314. Mason, D. C., Davenport, I. J., Neal, J. C., Schumann, G. J. P., & Bates, P. D. (2012). Near real-time flood detection in urban and rural areas using high-resolution synthetic aperture radar images. IEEE transactions on Geoscience and Remote Sensing, 50(8), 3041-3052. Nico, G., Pappalepore, M., Pasquariello, G., Refice, A., & Samarelli, S. (2000). Comparison of SAR amplitude vs. coherence flood detection methods-a GIS application. International Journal of Remote Sensing, 21(8), 1619-1631. Rajapaksha, J., & Dampegama, L. S. S. Emergency flood mapping from synthetic aperture radar; a simple fuzzy logic approach. Conference Paper October 2016; Conference: Asian Conference on Remote Sensing, At Colombo, Volume: 37

Sentinel-1 and Sentinel-2 Data to Detect Irrigation Events: Riaza Irrigation District (Spain) Case Study

This paper investigates the use of high resolution (~100 m) surface soil moisture (SSM) maps to detect irrigation occurrences, in time and space. The SSM maps have been derived from time series of Copernicus Sentinel-1 (S-1) and Sentinel-2 (S-2) observations. The analysis focused on the Riaza irrigation district in the Castilla y León region (Spain), where detailed information on land use, irrigation scheduling, water withdrawal, meteorology and parcel borders is available from 2017 to 2021. The well-documented data basis has supported a solid characterization of the sources of uncertainties affecting the use of SSM to map and monitor irrigation events. The main factors affecting the irrigation detection are meteo-climatic condition, crop type, water supply and spatial and temporal resolution of Earth observation data. Results indicate that approximately three-quarters of the fields irrigated within three days of the S-1 acquisition can be detected. The specific contribution of SSM to irrigation monitoring consists of (i) an early detection, well before vegetation indexes can even detect the presence of a crop, and (ii) the identification of the irrigation event in time, which remains unfeasible for vegetation indexes. Therefore, SSM can integrate vegetation indexes to resolve the irrigation occurrences in time and space

Synergistic use of synthetic aperture radar and optical imagery to monitor surface accumulation of cyanobacteria in the Curonian Lagoon

Phytoplankton blooms in internal water bodies are an unpleasant sight that often emerges on top like a layer of foam containing high concentrations of toxins (scum event). Monitoring the concentration of algae and the occurrence of scum in lakes and lagoons has become a topic of interest for management and science. Optical remote sensing is a validated tool but unfortunately it is highly hindered by clouds. For regions with frequent cloud cover, such as the Baltic region, this means loss of data, which limits the purpose of sensing to spatially and temporally characterize any scum for a comprehensive ecological analysis. The aim of this paper is to investigate whether the use of synthetic aperture radar (SAR) images can compensate for the weaknesses of optical images for cyanobacteria bloom monitoring purposes in the event of cloudy skies. A “ready to use” approach to detect cyanobacteria bloom in the Curonian Lagoon based on the level 2 ocean product of Sentinel-1 images is proposed. This method is empirically validated for the images of summer/autumn 2018 of the Curonian Lagoon

High-Resolution Flood Monitoring Based on Advanced Statistical Modeling of Sentinel-1 Multi-Temporal Stacks

High-resolution flood monitoring can be achieved relying on multi-temporal analysis of remote sensing SAR data, through the implementation of semi-automated systems. Exploiting a Bayesian inference framework, conditioned probabilities can be estimated for the presence of floodwater at each image location and each acquisition date. We developed a procedure for efficient monitoring of floodwaters from SAR data cubes, which adopts a statistical modelling framework for SAR backscatter time series over normally unflooded areas based on Gaussian processes (GPs), in order to highlight flood events as outliers, causing abrupt variations in the trends. We found that non-parametric time series modelling improves the performances of Bayesian probabilistic inference with respect to state-of-the-art methodologies using, e.g., parametric fits based on periodic functions, by both reducing false detections and increasing true positives. Our approach also exploits ancillary data derived from a digital elevation model, including slopes, normalized heights above nearest drainage (HAND), and SAR imaging parameters such as shadow and layover conditions. It is here tested over an area that includes the so-called Metaponto Coastal Plain (MCP), in the Basilicata region (southern Italy), which is recurrently subject to floods. We illustrate the ability of our system to detect known (although not ground-truthed) and smaller, undocumented inundation events over large areas, and propose some consideration about its prospective use for contexts affected by similar events, over various land cover scenarios and climatic settings