Monitoring the spatiotemporal dynamics of surface water body of the Xiaolangdi Reservoir using Landsat-5/7/8 imagery and Google Earth Engine

Abstract

Xiaolangdi Reservoir is a key control project to control the water and sediment in the lower Yellow River, and a timely and accurate grasp of the reservoir’s water storage status is essential for the function of the reservoir. This study used all available Landsat images (789 scenes) and adopted the modified normalized difference water index, enhanced vegetation index, and normalized difference vegetation index to map the surface water from 1999 to 2019 in Google Earth Engine (GEE) cloud platform. The spatiotemporal characteristics of the surface water body area changes in the Xiaolangdi Reservoir in the past 21 years are analyzed from the water body type division, area change, type conversion, and the driving force of the Xiaolangdi water body area changes was analyzed. The results showed that (1) the overall accuracy of the water body extraction method was 98.86%, and the kappa coefficient was 0.96; (2) the maximum water body area of the Xiaolangdi Reservoir varies greatly between inter-annual and intra-annual, and seasonal water body and permanent water body have uneven spatiotemporal distribution; (3) in the conversion of water body types, the increased seasonal water body area of the Xiaolangdi Reservoir from 1999 to 2019 was mainly formed by the conversion of permanent water body, and the reduced permanent water body area was mainly caused by non-water conversion; and (4) the change of the water body area of the Xiaolangdi Reservoir has a weak negative correlation with natural factors such as precipitation and temperature, and population. It is positively correlated with seven indicators such as runoff and regional gross domestic product (GDP). The findings of the research will provide necessary data support for the management and planning of soil and water resources in the Xiaolangdi Reservoir.

Inner Niger Delta Inundation Extent (2010–2022) Based on Landsat Imagery and the Google Earth Engine

“…However, the universal application of the index-and threshold-based approaches faces some challenges since the ideal thresholds vary with time and location, and shadow noise in some regions cannot be effectively removed [26]. These indices can also be used as additional bands in an image classification process to improve the classification [1,7,8,10,[40][41][42]. Therefore, to ensure a better mapping of the spatiotemporal dynamics of open water surfaces and flooded areas of the IND, a combination of multiple indices and spectral classification was used.…”

“…The most widely used multitemporal and multispectral satellite observations for this purpose are derived from Landsat (LS) [7][8][9][10], Sentinel [11][12][13], Moderate Resolution Imaging Spectroradiometer (MODIS) [6,[14][15][16], and SPOT (Satellite Pour l'Observation de la Terre) [17,18]. Much of the available literature on water-body extraction focusses on one type of water body (e.g., lakes, rivers, etc.)…”

“…Water-body extraction methods include, among others, the digitisation of images through visual interpretation, index-and threshold-based algorithms, spectral mixture analysis, and pixel-based classification [8,13]. Threshold-based methods are commonly used, with multiple algorithms developed for this purpose.…”

Remote Sensing of Surface Water Dynamics in the Context of Global Change—A Review

“…[116] uses Sentinel-2 along with Landsat 8 data for improved temporal resolution. Studies that focus on lakes or reservoirs include [12,69,81,228,231,242,248]. A smaller number of studies focuses on rivers and deltas throughout China [13,43,115,172,192] or monitor changes in aquaculture area [103,106,244].…”

Investigation of Meteorological Effects on Çivril Lake, Turkey, with Sentinel-2 Data on Google Earth Engine Platform

“…The GEE platform offers support for both JavaScript and Python languages. It offers several advantages, including computing, analytical operations, data analysis, and the capacity to make maps and export these maps [43].…”