Sentinel-2 Application to the Surface Characterization of Small Water Bodies in Wetlands

Abstract

Developing indicators to monitor environmental change in wetlands with the aid of Earth Observation Systems can help to obtain spatial data that is not feasible with in situ measures (e.g., flooding patterns). In this study, we aim to test Sentinel-2A/B images suitability for detecting small water bodies in wetlands characterized by high diversity of temporal and spatial flooding patterns using previously published indices. For this purpose, we used medium spatial resolution Sentinel-2A/B images of four representative coastal wetlands in the Valencia Region (East Spain, Mediterranean Sea), and on three different dates. To validate the results, 60 points (30 in water areas and 30 in land areas) were distributed randomly within a 20 m buffer around the border of each digitized water polygon for each date and wetland (600 in total). These polygons were mapped using as a base map orthophotos of high spatial resolution. In our study, the best performing index was the NDWI. Overall accuracy and Kappa index results were optimal for −0.30 threshold in all the studied wetlands and dates. The consistency in the results is key to provide a methodology to characterize water bodies in wetlands as generalizable as possible. Most studies developed in wetlands have focused on calculating global gain or loss of wetland area. However, inside of wetlands which hold protection figures, the main threat is not necessarily land use change, but rather water management strategies. Applying Sentinel-2A/B images to calculate the NDWI index and monitor flooded area changes will be key to analyse the consequence of these management actions.

Estimation of Water Coverage in Permanent and Temporary Shallow Lakes and Wetlands by Combining Remote Sensing Techniques and Genetic Programming: Application to the Mediterranean Basin of the Iberian Peninsula

“…However, AWEIsh showed better accuracy results in the estimation of water pixels (k = 0.8 and OA = 92%) than those of AWEInsh in Palmones marshes site. This result agrees with those obtained by Fisher et al [46], where AWEIsh achieved greater accuracy in the estimation of water pixels for images with urban areas, as is the case of Palmones marshes site. NDWI, WRI, and WTI indices assessed surface water coverage with lower accuracy than GP and AWEI methods [12,20,35,46].…”

“…This result agrees with those obtained by Fisher et al [46], where AWEIsh achieved greater accuracy in the estimation of water pixels for images with urban areas, as is the case of Palmones marshes site. NDWI, WRI, and WTI indices assessed surface water coverage with lower accuracy than GP and AWEI methods [12,20,35,46]. The water cover estimation errors by the former were basically linked to the spectral characteristics of the water bodies themselves.…”

“…GP was originally developed and tested across several water bodies including temporary shallow lakes [20], whereas AWEI was tested by its authors under a wide range of environmental conditions and water body types, such as small freshwater reservoirs, large lakes, harbors, and the sea [35]. In our study, AWEI presented better results for permanent lake/wetland types than for temporary lake/wetland types, which could possibly be explained by the fact that it was developed and tested with permanent water bodies [45][46][47][48]. In general, AWEInsh behave better than AWEIsh, mainly due to the absence of shadows in the scenes [35].…”

“…For all the applied methods, results showed some discrepancies (k < 0.4) in the estimation of water cover in lakes/wetlands with presence of high coverage of helophytic vegetation and an irregular and highly fluctuating distribution of flooded areas along the water bodies. Recently, Pena-Regueiro et al [46] found that the best performing index for four representative coastal wetlands with this characteristics was the NDWI with a threshold value of −0.3, which showed overall accuracy and kappa coefficient results above 75% and 0.5 for all the wetlands studied, respectively.…”

“…The spatial resolution of Landsat-7 ETM+, 30 m, facilitates the mixture of land-cover types, this effect being more significant in edge pixels, resulting in difficulties for mapping flooded areas in heterogeneous wetlands [12,20,46]. Furthermore, as was mentioned in the results section, the SLC failure of Landsat-7 imageries caused errors in the estimation of the total water area of some lakes and lakes/wetlands presenting irregular shapes could be more vulnerable to this problem.…”