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

Abstract

Many wetlands are characterized by a vegetation cover of variable height and density over time. Tracking spatio-temporal changes in inundation patterns of these wetlands remains a challenge for remote sensing. Water In Wetlands (WIW) was predicted using a dichotomous partitioning of reflectance values encoded based on ground-truth (n = 4038) and optical-space derived (n = 7016) data covering all land cover types (n = 17) found in the Rhône delta, southern France. The models were developed with spectral data from Sentinel 2, Landsat 7, and Landsat 8 sensors, hence providing a monitoring tool that covers a 35-year period (same sensor for Landsat 5 and 7). A single model combining the near infrared (NIR ≤ 0.1558 to 0.1804, depending on sensors) and short-wave infrared (SWIR2 ≤ 0.0871 to 0.1131) wavelengths was identified by three independent analyses, each one using a different satellite. Overall accuracy of water maps ranged from 89% to 94% for the training samples and from 90% to 94% for the validation samples, encompassing standard water indices that systematically underestimate flooding duration under vegetation cover. Sentinel 2 provided the highest performance with a kappa coefficient of 0.82 for both samples. Such tool will be most useful for monitoring the water dynamics of seasonal wetlands, which are particularly sensitive to climate change while providing multiple services to humankind. Considering the high temporal resolution of Sentinel 2 (every 5 days), cumulative water maps built with the WIW logical rule could further be used for mapping a wide range of wetlands which are either periodically or permanently flooded.

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

“…The tile products are mosaicked and clipped to the extent of the shapefile. The reference maps for Camargue were obtained by dichotomous partitioning of reflectance values encoded as 1 for water presence and 0 for water absence based on ground-truth (n = 1229) and optical-space derived (n = 2603) reference points covering the whole Biosphere Reserve area and all the main habitat types [22]. Ground-truth data refer to water level measures in different wetland types, focusing on those with a dense vegetation cover.…”

“…Commonly used indices include the Normalized Difference Water Index (NDWI) [15,16,19], Modified NDWI [14,[18][19][20], and Automated Water Extraction Index [17][18][19]21]. Several approaches use information from Shortwave infrared (SWIR) spectral ranges to identify shallow inundated wetland areas, since it is less sensitive to sediment-filled waters and, hence, more efficient for registering the boundaries between water and dry areas in shallow wetlands [13,[22][23][24]. Automatic thresholding approaches can be applied to different areas and are computationally inexpensive, but they may wrongly classify dark objects (i.e., shadows and buildings) as water when their spectral characteristics are similar [25].…”

“…Machine-learning algorithms, including supervised and unsupervised ones, have also been used for detecting water bodies from multispectral imagery. Supervised approaches applied for water detection include random forests [30,31], neural networks [32], support vector machines [33,34], deep neural networks [35,36], and decision trees [13,22,37], while frequently used unsupervised approaches for water mapping include K-means [38] and Iterative Self-Organizing Data Analysis Technique (ISODATA) [39]. Even though machine-learning approaches usually exhibit higher accuracy than thresholding ones [30], they have several limitations.…”

“…Most of the previously mentioned approaches aim to detect water in open-water bodies such as rivers, lakes, reservoirs, and watersheds [17,19,20,30,33,37,43], while a part of them focuses on wetland areas [13,[22][23][24].…”

“…The different methods for estimating splitting thresholds include: (i) minimum entropy thresholding, and (ii) Otsu's algorithm. The results of the alternative approaches are compared against reference maps, provided for Doñana and Camargue by local research institutes, based on locally developed water detection models [22,24].…”