Assessment of the Water Area in the Lowland Region of the Mekong River Using MODIS EVI Time Series

Van, Chien Pham; Nguyen-Van, Giang

doi:10.1007/978-3-030-38364-0_18

Cited by 2 publications

(1 citation statement)

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“…That means when MNDWI cannot extract the effective water body, it can be extracted through the joint calculation of EVI and MNDWI. Similarly, when This study jointly used three water-body indices (the MNDWI [8], NDVI [30], and EVI [31]) to extract water-body information from Landsat_Collection 1_T1_SR remote sensing imagery. Although some studies have shown that the water-body extraction performance of MNDWI is higher than that of NDWI and EVI, MNDWI is not 100% effective, especially in alpine regions with snow cover.…”

Section: Extracting Water Bodies From Satellite-based Databasesmentioning

confidence: 99%

Monitoring and Predicting Channel Morphology of the Tongtian River, Headwater of the Yangtze River Using Landsat Images and Lightweight Neural Network

et al. 2022

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The Tongtian River is the source of the Yangtze River and is a national key ecological reserve in China. Monitoring and predicting the changes and mechanisms of the Tongtian River channel morphology are beneficial to protecting the “Asian Water Tower”. This study aims to quantitatively monitor and predict the accretion and erosion area of the Tongtian River channel morphology during the past 30 years (1990–2020). Firstly, the water bodies of the Tongtian River were extracted and the accretion and erosion areas were quantified using 1108 Landsat images based on the combined method of three water-body indices and a threshold, and the surface-water dataset provided by the European Commission Joint Research Centre. Secondly, an intelligent lightweight neural-network model was constructed to predict and analyze the accretion and erosion area of the Tongtian River. Results indicate that the Tongtian River experienced apparent accretion and erosion with a total area of 98.3 and 94.9 km2, respectively, during 1990–2020. The braided (meandering) reaches at the upper (lower) Tongtian River exhibit an overall trend of accretion (erosion). The Tongtian River channel morphology was determined by the synergistic effect of sediment-transport velocity and streamflow. The lightweight neural network well-reproduced the complex nonlinear processes in the river-channel morphology with a final prediction error of 0.0048 km2 for the training session and 4.6 km2 for the test session. Results in this study provide more effective, reasonable, and scientific decision-making aids for monitoring, protecting, understanding, and mining the evolution characteristics of rivers, especially the complex change processes of braided river channels in alpine regions and developing countries.

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Section: Extracting Water Bodies From Satellite-based Databasesmentioning

confidence: 99%