Automatic Inundation Mapping Using Sentinel-2 Data Applicable to Both Camargue and Doñana Biosphere Reserves

Abstract

Flooding periodicity is crucial for biomass production and ecosystem functions in wetland areas. Local monitoring networks may be enriched by spaceborne derived products with a temporal resolution of a few days. Unsupervised computer vision techniques are preferred, since human interference and the use of training data may be kept to a minimum. Recently, a novel automatic local thresholding unsupervised methodology for separating inundated areas from non-inundated ones led to successful results for the Doñana Biosphere Reserve. This study examines the applicability of this approach to Camarque Biosphere Reserve, and proposes alternatives to the original approach to enhance accuracy and applicability for both Camargue and Doñana wetlands in a scientific quest for methods that may serve accurately biomes at both protected areas. In particular, it examines alternative inputs for automatically estimating thresholds while applying various algorithms for estimating the splitting thresholds. Reference maps for Camargue are provided by local authorities, and generated using Sentinel-2 Band 8A (NIR) and Band 12 (SWIR-2). The alternative approaches examined led to high inundation mapping accuracy. In particular, for the Camargue study area and 39 different dates, the alternative approach with the highest overall Kappa coefficient is 0.84, while, for the Doñana Biosphere Reserve and Doñana marshland (a subset of Doñana Reserve) and 7 different dates, is 0.85 and 0.94, respectively. Moreover, there are alternative approaches with high overall Kappa for all areas, i.e., at 0.79 for Camargue, over 0.91 for Doñana marshland, and over 0.82 for Doñana Reserve. Additionally, this study identifies the alternative approaches that perform better when the study area is extensively covered by temporary flooded and emergent vegetation areas (i.e., Camargue Reserve and Doñana marshland) or when it contains a large percentage of dry areas (i.e., Doñana Reserve). The development of credible automatic thresholding techniques that can be applied to different wetlands could lead to a higher degree of automation for map production, while enhancing service utilization by non-trained personnel.

Introducing WIW for Detecting the Presence of Water in Wetlands with Landsat and Sentinel Satellites

“…Switching the threshold values among satellites or using different atmospheric correction methods provided similar water maps, suggest that our approach is robust and replicable. Furthermore, when applied to a Sentinel 2 scene of the Doñana marshes in southern Spain [79], the WIW logical rules provides a Kappa coefficient of similar value (0.84) to the one obtained for the Camargue wetlands. Overall, Sentinel 2 scenes systematically provide better classifications, presumably because of their higher spatial resolution compared to Landsat sensors.…”

An AI-Enabled Framework for Real-Time Generation of News Articles Based on Big EO Data for Disaster Reporting

“…For this reason, architectures for image segmentation [9][10][11] have been reviewed. Other methods of processing of EO images used for the implementation of the platform concern the detection of the affected areas from floods and water in general [12,13] and fire [14,15].…”

Editorial for Special Issue “Remote Sensing in Applications of Geoinformation”

“…The study by Kordelas et al [5] examines the applicability of a novel automatic local thresholding unsupervised methodology for separating inundated areas from noninundated ones and proposes alternatives to the original approach to enhance accuracy and applicability for both Camargue (France) and Doñana (Spain) wetlands. Each examined alternative approach relies on a specific band or band combination, acknowledged as effective by the underlying physics, and a specific approach for estimating splitting thresholds.…”