Small Water Body Detection and Water Quality Variations with Changing Human Activity Intensity in Wuhan

Small water bodies ranging in size from 1 to 50,000 m2, are numerous, widely distributed, and have various functions in water storage, agriculture, and fisheries. Small water bodies used for agriculture and fisheries are economically significant in China, hence it is important to properly identify and analyze them. In remote sensing technology, water body identification based on band analysis, image classification, and water indices are often designed for large, homogenous water bodies. Traditional water indices are often less accurate for small water bodies, which often contain submerged or floating plants or easily confused with hill shade. Water quality inversion commonly depends on establishing the relationship between the concentration of water constituents and the observed spectral reflectance. However, individual variation in water quality in small water bodies is enormous and often far beyond the range of existing water quality inversion models. In this study, we propose a method for small water body identification and water quality estimation and test its applicability in Wuhan. The kappa coefficient of small water body identification is over 0.95, and the coefficient of determination of the water quality inversion model is over 0.9. Our results show that the method proposed in this study can be employed to accurately monitor the dynamics of small water bodies. Due to the outbreak of the COVID-19 pandemic, the intensity of human activities decreased. As a response, significant changes in the water quality of small water bodies were observed. The results also suggest that the water quality of small water bodies under different production modes (intensive/casual) respond differently in spatial and temporal dimensions to the decrease in human activities. These results illustrate that effective remote sensing monitoring of small water bodies can provide valuable information on water quality.

Combining Spectral Water Indices and Mathematical Morphology to Evaluate Surface Water Extraction in Taiwan

Rivers in Taiwan are characterised by steep slopes and high sediment concentrations. Moreover, with global climate change, the dynamics of channel meandering have become complicated and frequent. The primary task of river governance and disaster prevention is to analyse river changes. Spectral water indices are mostly used for surface water estimation, which separates the water from the background based on a threshold value, but it can be challenging in the case of environmental noise. Edge detection uses a canny edge detector and mathematical morphology for extracting geometrical features from the image and effective edge detection. This study combined spectral water indices and mathematical morphology to capture water bodies based on downloaded remote sensing images. From the findings, this study summarised the applicability of various spectral water body indices to the surface water extraction of different river channel patterns in Taiwan. The normalised difference water index and the modified normalised difference water index are suitable for braided rivers, whereas the automated water extraction index is ideal for meandering rivers.

Use of water indices in the determination of thermokarst lakes according to remote sensing data

The results of application of six spectral indices (AWEI, MNDWI, NDVI, NDWI, TCW, WRI) for the isolation of thermokarst lakes in tundra landscapes of northern Yakutia are presented. To assess the accuracy of decryption of lakes, an average quadratic error (MSE) was calculated. The minimum MSE value is 0.11 km2 and corresponds to the NDWI index. An almost identical result (0.12 km2) is found in the WRI index, slightly worse (0.15 km2) one — in the NDVI index. An MNDWI index has the highest mean square error (7.02 km2). Visual analysis also showed better decryption of water bodies using the NDWI, WRI and NDVI indices, which allows the use of these indices for automatical isolatation water bodies.

Exploring Sentinel-1 and Sentinel-2 diversity for Flood inundation mapping using deep learning

<p>Identification of flood water extent from satellite images has historically relied on either synthetic aperture radar (SAR) or multi-spectral (MS) imagery. But MS sensors may not penetrate cloud cover, whereas SAR is plagued by operational errors such as noise-like speckle challenging their viability to global flood mapping applications. An attractive alternative is to effectively combine MS data and SAR, i.e., two aspects that can be considered complementary with respect to flood mapping tasks. Therefore, in this study, we explore the diverse bands of Sentinel 2 (S2) derived water indices and Sentinel 1 (S1) derived SAR imagery along with their combinations to access their capability in generating accurate flood inundation maps. For this purpose, a fully connected deep convolutional neural network known as U-Net is applied to combinations of S1 and S2 bands to 446 (training: 313, validating: 44, testing: 89) hand labeled flood inundation extents derived from Sen1Floods11 dataset spanning across 11 flood events. The trained U-net was able to achieve a median F1 score of 0.74 when using DEM and S1 bands as input in comparison to 0.63 when using only S1 bands highlighting the active positive role of DEM in mapping floods. Among the, S2 bands, HSV (Hue, Saturation, Value) transformation of Sentinel 2 data has achieved a median F1 score of 0.94 outperforming the commonly used water spectral indices owing to HSV’s transformation’s superior contrast distinguishing abilities. Also, when combined with Sentinel 1 SAR imagery too, HSV achieves a median F1 score 0.95 outperforming all the well-established water indices in detecting floods in majority of test images.</p>

Application of Satellite Imagery and Water Indices to the Hydrography of the Cetina RiverBasin (Middle Adriatic)

The paper gives a brief description of the remote sensing method used for the identification and extraction of water surfaces. Landsat 8 and Sentinel 2 satellite imagery was used to separate land from bodies of water in the complex karst area surrounding the Croatian Cetina River, flowing into the Adriatic Sea. Water indexing methods are presented in detail. The most frequently used water indices were selected: NDWI, MNDWI, AWEI_nsh, AWEI_sh, WRI and LSWI, and their results compared. The combination of satellite imagery and calculated water indices is concluded to be very useful for the identification and mapping of the area and banks of lakes, riverine zones, river mouths and the coastline in the coastal zone. Landsat 8 satellite imagery is slightly inferior to Sentinel 2 due to lower image resolution. The best results were obtained with the NDWI water index and the worst with LSWI.