Water body mapping method with HJ-1A/B satellite imagery

Lu, Shanlong; Wu, Bingfang; Niu, Y.; Wang, Hao

doi:10.1016/j.jag.2010.09.006

Cited by 180 publications

(109 citation statements)

References 26 publications

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“…The NDVI enables rapid and simple extraction of water bodies, and is thus considered to be one of the most successful water index methods [19,20]. The NDWI and MNDWI have been widely used to extract water body information in a highly targeted manner [21][22][23][24]. In some studies, NDWI has been recommended for extracting water body information; however, NDWI may be unreliable for shallow-water areas with a varied boundary [4,25].…”

Section: Introductionmentioning

confidence: 99%

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

Luo

et al. 2018

Remote Sensing

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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.

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Section: Introductionmentioning

confidence: 99%

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

Luo

et al. 2018

Remote Sensing

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“…To address the variation in the optimal thresholds and noise contamination issues, some studies have combined WIs with other methods, including color-space transformation [15], principal components analysis [16], image segmentation [16], topographic masking [17], linear discriminant analysis [18] and a combination of digital image processing and geographical information system techniques [5].…”

Section: Introductionmentioning

confidence: 99%

An Automated Method for Extracting Rivers and Lakes from Landsat Imagery

et al. 2014

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Abstract:The water index (WI) is designed to highlight inland water bodies in remotely sensed imagery. The application of WI for water body mapping is mainly based on the thresholding method. However, there are three primary difficulties with this method: (1) inefficient identification of mixed water pixels; (2) confusion of water bodies with background noise; and (3) variation in the threshold values according to the location and time of image acquisitions. Considering that mixed water pixels usually appear in narrow rivers or shallow water at the edge of lakes or wide rivers, an automated method is proposed for extracting rivers and lakes by combining the WI with digital image processing techniques to address the above issues. The data sources are the Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) images for three representative areas in China. The results were compared with those from existing thresholding methods. The robustness of the new method in combination with different WIs is also assessed. Several metrics, which include the Kappa coefficient, omission and OPEN ACCESSRemote Sens. 2014, 6 5068 commission errors, edge position accuracy and completeness, were calculated to assess the method's performance. The new method generally outperformed the thresholding methods, although the degree of improvement varied among WIs. The advantages and limitations of the proposed method are also discussed.

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“…The constellation of the two satellites theoretically has a higher revisiting frequency of 2 days. With both the advantages of spatial and temporal resolution, HJ-1A/B satellites are regarded as an effective tool for monitoring and post-flood assessment [24].…”

Section: Datasetsmentioning

confidence: 99%

Flood Inundation Mapping from Optical Satellite Images Using Spatiotemporal Context Learning and Modest AdaBoost

et al. 2017

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Due to its capacity for temporal and spatial coverage, remote sensing has emerged as a powerful tool for mapping inundation. Many methods have been applied effectively in remote sensing flood analysis. Generally, supervised methods can achieve better precision than unsupervised. However, human intervention makes its results subjective and difficult to obtain automatically, which is important for disaster response. In this work, we propose a novel procedure combining spatiotemporal context learning method and Modest AdaBoost classifier, which aims to extract inundation in an automatic and accurate way. First, the context model was built with images to calculate the confidence value of each pixel, which represents the probability of the pixel remaining unchanged. Then, the pixels with the highest probabilities, which we define as 'permanent pixels', were used as samples to train the Modest AdaBoost classifier. By applying the strong classifier to the target scene, an inundation map can be obtained. The proposed procedure is validated using two flood cases with different sensors, HJ-1A CCD and GF-4 PMS. Qualitative and quantitative evaluation results showed that the proposed procedure can achieve accurate and robust mapping results.

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Water body mapping method with HJ-1A/B satellite imagery

Cited by 180 publications

References 26 publications

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

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

An Automated Method for Extracting Rivers and Lakes from Landsat Imagery

Flood Inundation Mapping from Optical Satellite Images Using Spatiotemporal Context Learning and Modest AdaBoost

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