Water Body Extraction Method Research Articles

A Water Body Extraction Methods Comparison Based on FengYun Satellite Data: A Case Study of Poyang Lake Region, China

Water body extraction can help eco-environmental policymakers to intuitively grasp surface water resources. Remote sensing technology can accurately and quickly extract surface water information, which is of great significance for monitoring surface water changes. Fengyun satellite images have the advantages of high time resolution and multispectral bands. This provides important image data suitable for high-frequency surface water monitoring. Based on Fengyun 3 medium resolution spectral imager (FY-3/MERSI) data, 7 methods were applied in this study, which include single-band threshold method, water body index method, knowledge decision tree classification method, supervised classification method, unsupervised classification method, spectral matching based on discrete particle swarm optimization (SMDPSO), and improved spectral matching based on discrete particle swarm optimization with linear feature enhancement (SMDPSO+LFE). These methods were used to extract the land surface water of Poyang Lake, check the samples from the Landsat image with similar times to the FY-3 images, and calculate the classification accuracy via the confusion matrix. The results showed that the overall classification accuracy (OA) of the SMDPSO+LFE is 97.64%, and the Kappa coefficient is 0.95. To analyze the stability of the surface water extracted by SMDPSO+LFE in different regions, this paper selected eight test sites with different surface water types, landscapes, and terrains to extract surface water. Based on an analysis of the land surface water results at the eight test sites, every OA in the eight sites was higher than 94.5%, the Kappa coefficient was greater than 0.88. In conclusion, the SMDPSO+LFE is found to be the most suitable method among the 7 methods and effectively distinguish between different surface water bodies and backgrounds with good stability.

A new accuracy evaluation method for water body extraction

ABSTRACT This paper employs the Normalized Difference Water Index (NDWI), Automated Water Extraction Index (AWEI), Modified Normalized Difference Water Index (MNDWI) and Near-Infrared (NIR) threshold methods to extract the boundaries of Hongjiannao Lake, Zasak reservoir and Changjiagou reservoir. The real boundary of the water bodies was obtained by visual interpretation from high-resolution imagery. The Digital Shoreline Analysis System (DSAS) is further used to calculate the net shoreline movement (NSM) between the real lake boundary and the lake boundary extracted by the NDWI, AWEI, MNDWI and NIR threshold methods. We quantitatively evaluated the accuracy of each water body extraction method by NSM, which was competed with kappa coefficient (κ) and edge detection. The results showed that the average of the |NSM| of the NDWI, MNDWI, AWEI and NIR threshold methods are 12.77 m, 16.76 m, 28.65 m and 31.43 m, respectively. The κ is 0.9869, 0.9855, 0.9747 and 0.9736, respectively and the correct extracted (T) ratios for the edge detection are 84.06%, 82.68%, 56.8% and 53.52%, respectively. The mean value of |NSM| for NDWI is the smallest, while κ and T are the highest. It indicates that the accuracy of NDWI is the highest in Hongjiannao lake. The smaller the |NSM| is, the larger κ and the higher T are. This shows that the |NSM| is consistent with the commonly used accuracy verification method such as κ and edge detection. The results of water extraction from the other two reservoirs also support this conclusion. Therefore, the accuracy verification of NSM can reflect the spatial position information and has reliability. It provides a new approach in verifying the accuracy of water body extraction methods. The optimum water extraction index for different study areas is different. The NIR band threshold method and AWEI can accurately extract water boundaries with a small amount of aquatic vegetation. However, NDWI and MNDWI are more suitable for extracting water bodies located in complex terrains.

A Multi-Scale Water Extraction Convolutional Neural Network (MWEN) Method for GaoFen-1 Remote Sensing Images

Automatic water body extraction method is important for monitoring floods, droughts, and water resources. In this study, a new semantic segmentation convolutional neural network named the multi-scale water extraction convolutional neural network (MWEN) is proposed to automatically extract water bodies from GaoFen-1 (GF-1) remote sensing images. Three convolutional neural networks for semantic segmentation (fully convolutional network (FCN), Unet, and Deeplab V3+) are employed to compare with the water bodies extraction performance of MWEN. Visual comparison and five evaluation metrics are used to evaluate the performance of these convolutional neural networks (CNNs). The results show the following. (1) The results of water body extraction in multiple scenes using the MWEN are better than those of the other comparison methods based on the indicators. (2) The MWEN method has the capability to accurately extract various types of water bodies, such as urban water bodies, open ponds, and plateau lakes. (3) By fusing features extracted at different scales, the MWEN has the capability to extract water bodies with different sizes and suppress noise, such as building shadows and highways. Therefore, MWEN is a robust water extraction algorithm for GaoFen-1 satellite images and has the potential to conduct water body mapping with multisource high-resolution satellite remote sensing data.

Changes in the Lake Area of Tonle Sap: Possible Linkage to Runoff Alterations in the Lancang River?

Tonle Sap Lake is the largest freshwater lake in Southeast Asia. Water development infrastructures are increasingly being constructed in the Lancang–Mekong River Basin, which is a major concern considering its potential impact on Tonle Sap Lake. This study aimed to investigate variations in the area of the lake and discuss their possible linkage to runoff alterations in the Lancang River (Upper Mekong) by comparing runoff at the Yunjinghong hydrological station before and after significant changes in runoff trends that occurred in 2008. First, four commonly used water body extraction methods (MNDWI, NDWI, NDVI, and EVI) were compared and MNDWI was found to provide a better and more stable performance. Based on MOD09A1 data, MNDWI was used to extract the water area of the lake from 2000 to 2014, and characteristics of variations in the area before and after 2008 were analyzed. The water area of Tonle Sap Lake displayed an overall decreasing trend, and specifically decreased by 8.3% during the flood season and by 1.5% on average during the dry season after 2008. Seasonal variations in the water area of Tonle Sap Lake were dominantly influenced by runoff from the Mekong River. Compared with the period 2000–2007, runoff at Yunjinghong station were increased during the dry season (20.74%) and decreased during the flood season (34.25%) between 2008 and 2014. Changes in upstream runoff contributed to runoff at the Stung Treng station in the lower Mekong River by 6.17% (dry season) and −2.41% (flood season). Evidently, the operation of dams in the Lancang River does not primarily account for the area decrease of Tonle Sap Lake during the flood season. In contrast, runoff increase during the dry season mitigates the area decrease of Tonle Sap Lake to a certain extent.

Surface water bodies mapping in Zimbabwe using landsat 8 OLI multispectral imagery: A comparison of multiple water indices

Water is integral for the survival of humans, animals, as well as vegetation and information on its spatial distribution is important for water resources management. Various methods have been applied to detect and map land surface water, especially due to the availability of a large range of satellite sensors. The main methods involved are either general feature classification techniques or thematic water body information extraction. Thematic water body extraction methods make use of band information and in this study; spectral indices are used to delineate land surface water for Zimbabwe using Landsat 8 OLI images. Multiple algorithms i.e. spectral indices were used to extract surface water bodies from Landsat 8 imagery.Then overall accuracies and Kappa coefficients and average reliability were then applied to assess the performance of the seven applied models. The results have shown that the LSWI plus VI can map surface water bodies with an overall accuracy of 86%, kappa statistic of 0.70 and a reliability accuracy of 89%. Comparatively, the index produced good results followed by the mNDWI which had an overall classification accuracy of 84% and an average reliability of 87%. Following on the list in terms of performance was the AWEIsh, mMDWI plus VI, NDWI and NDWI plus VI. However, other indices had slightly lower results comparatively. The study noted that LSWI plus VI applied on Landsat 8 OLI data can reliably delineate land surface water in a subtropical environment.

Summary of water body extraction methods based on ZY-3 satellite

Extracting from remote sensing images is one of the main means of water information extraction. Affected by spectral characteristics, many methods can be not applied to the satellite image of ZY-3. To solve this problem, we summarize the extraction methods for ZY-3 and analyze the extraction results of existing methods. According to the characteristics of extraction results, the method of WI& single band threshold and the method of texture filtering based on probability statistics are explored. In addition, the advantages and disadvantages of all methods are compared, which provides some reference for the research of water extraction from images. The obtained conclusions are as follows. 1) NIR has higher water sensitivity, consequently when the surface reflectance in the study area is less similar to water, using single band threshold method or multi band operation can obtain the ideal effect. 2) Compared with the water index and HIS optimal index method, object extraction method based on rules, which takes into account not only the spectral information of the water, but also space and texture feature constraints, can obtain better extraction effect, yet the image segmentation process is time consuming and the definition of the rules requires a certain knowledge. 3) The combination of the spectral relationship and water index can eliminate the interference of the shadow to a certain extent. When there is less small water or small water is not considered in further study, texture filtering based on probability statistics can effectively reduce the noises in result and avoid mixing shadows or paddy field with water in a certain extent.

An effective modified water extraction method for Landsat-8 OLI imagery of mountainous plateau regions

Water body extraction from remote sensing imagery is an efficient way to investigate and monitor water resources. In the study area of this research, a mountainous plateau near Kashgar, China, sparse vegetation and seasonal rivers affect water body extraction. In order to extract water bodies, a modified water body extraction method is proposed in this paper and tested using Landsat-8 OLI imagery. Following this method, binary images are first generated using a classification, a Tasseled Cap transform, and a normalized difference water index, respectively, and then combined to yield a mask. Next, water bodies are delineated by masking the Landsat-8 OLI imagery and then refined by eliminating false areas using a supervised classification. It is demonstrated from the resulting water body maps that terrain related shadows in imagery were effectively eliminated and river tributaries and artificial ditches were precisely delineated, with accuracy up to 94%. Compared with several current water body extraction methods, the modified method yielded water body maps with better visualization and slightly improved accuracy.

An automatic integrated image segmentation, registration and change detection method for water-body extraction using HSR images and GIS data

Abstract. Automatic water-body extraction from remote sense images is a challenging problem. Using GIS data to update and extract waterbody is an old but active topic. However, automatic registration and change detection of the two data sets often presents difficulties. In this paper, a novel automatic water-body extraction method is proposed. The core idea is to integrate image segmentation, image registration and change detection with GIS data as a whole processing. A new iterative segmentation and registration strategy is proposed. Here, the so-called visual attention model and level set segmentation algorithm is introduced to find interesting areas with suspected water-bodies and to obtain initial objects contours. And an improved shape curve similarity (ISCS) is presented to match image segmentation objects and GIS water-bodies with overall spatial constraints. Furthermore buffer based change detection algorithm is designed to obtain unchanged water-bodies and SVM classifier is used to verify changed ones. Experiment results show that proposed method was effective in rapid extraction and change detection of water-bodies.

Water Body Extraction Methods Study Based on RS and GIS

Water body extraction by using remote sensing has been the most important method in the investigation of water resources, flood hazard prediction assessment and water planning with fast and accurate effectiveness. Multiple methods including unsupervised classification, supervised classification, single-band threshold, inter spectrum relation method and water index method (normalized difference water index, modified normalized difference water index, and new water index) are analyzed. Miyun reservoir and Miyun cit zone are selected as study area to find the most effective water extraction method with different usage in different time.