Long‐term and seasonal variation of open‐surface water bodies in the <scp>Yellow River Basin</scp> during 1990–2020

Jiao, Chentai; Wang, Shuai; Song, Shuang; Fu, Bojie

doi:10.1002/hyp.14846

Cited by 3 publications

(1 citation statement)

References 51 publications

(74 reference statements)

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“…Another important consideration is that the composition and extent of WSs are highly variable in space and time [5], [26], [27]. These variations depend on various factors, such as terrain characteristics -including rock types, soil, vegetation -and human activities -associated with agriculture and civil construction -which accelerate river systems' hydrodynamic and morphodynamic processes [28].…”

Section: Introductionmentioning

confidence: 99%

WaterLog: A New Spectral Index for Mapping Aquatic Surfaces in Urban Contexts

Justiniano,

Santos Junior,

de Melo

et al. 2024

Preprint

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This paper proposes a new spectral index called waterLog, aiming to enhance the delineation and performance of water body mapping in urban areas using satellite imagery. To evaluate the effectiveness of the proposed index, we compare its performance in water surfaces (WSs) in different cities in South America with the results obtained by other well-known water indices in the literature (namely, NDWI, MNDWI, AWEIsh, and AWEInsh). We utilized composite images from the Sentinel satellite (2A and 2B), all acquired in June 2021, and in cities located south of the Tropic of Capricorn to obtain images with the highest incidence of shading. This strategy enhances confusion involving mapping water classes and shadows associated with urban infrastructures, thereby allowing for a better assessment of index effectiveness. In this scenario, the present study revealed that selecting the best single index for mapping all WSs is impossible, as their performances varied across the analyzed locations. Overall, the performance of the indexes - evaluated by partial ROC curve analysis and non-water misclassification points restriction - revealed quite similar results, especially considering waterLog, NDWI, and MNDWI. Despite the similarity, in the six areas analyzed, Waterlog outperformed in three. Additionally, when considering a single threshold for all the cities, waterLog showed the best result in mapping, reducing confusion involving water and shading. Such results, therefore, are interpreted as an essential contribution to water body mapping, considering its practical applications in environmental monitoring and water resource management in urbanized areas.

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