Remote Sensing Temporal Reconstruction of the Flooded Area in "Tablas de Daimiel" Inland Wetland 2000-2021

Sensors (Basel). 2023 Apr 19;23(8):4096. doi: 10.3390/s23084096.


Tablas de Daimiel National Park (TDNP) is a unique inland wetland located in the Mancha plain (Spain). It is recognized at the international level, and it is protected by different figures, such as Biosphere Reserve. However, this ecosystem is endangered due to aquifer overexploitation, and it is at risk of losing its protection figures. The objective of our study is to analyze the evolution of the flooded area between the year 2000 and 2021 by Landsat (5, 7 and 8) and Sentinel-2 images, and to assess the TDNP state through an anomaly analysis of the total water body surface. Several water indices were tested, but the NDWI index for Sentinel-2 (threshold -0.20), the MNDWI for Landsat-5 (threshold -0.15), and the MNDWI for Landsat-8 (threshold -0.25) showed the highest accuracy to calculate the flooded surface inside the protected area's limits. During the period 2015-2021, we compared the performance of Landsat-8 and Sentinel-2 and an R2 value of 0.87 was obtained for this analysis, indicating a high correspondence between both sensors. Our results indicate a high variability of the flooded areas during the analyzed period with significant peaks, the most notorious in the second quarter of 2010. Minimum flooded areas were observed with negative precipitation index anomalies since fourth quarter of 2004 to fourth quarter of 2009. This period corresponds to a severe drought that affected this region and caused important deterioration. No significant correlation was observed between water surface anomalies and precipitation anomalies, and the significant correlation with flow and piezometric anomalies was moderate. This can be explained because of the complexity of water uses in this wetland, which includes illegal wells and the geological heterogeneity.

Keywords: Landsat series; Sentinel-2; Tablas de Daimiel National Park; inland wetland; water remote sensing index.

Grants and funding

This research received no external funding.