High Precision Extraction of Surface Water from Complex Terrain in Bosten Lake Basin Based on Water Index and Slope Mask Data

Abstract

The surface water extraction algorithm based on satellite remote sensing images is advantageous as it is able to obtain surface water information in a relatively short time. However, when it is used to extract information on surface water in large-scale, long-time series and complex terrain areas, there will be a large number of misclassified pixels, and a large amount of image preprocessing work is required. The accuracy verification is time-consuming and laborious, and the results may not be accurate. The complex climatic and topographic conditions in Bosten Lake Basin make it more difficult to monitor and control surface water bodies. Therefore, based on the GEE (Google Earth Engine) cloud platform, and the studies of the effect of nine kinds of water indexes on the surface water extraction in Bosten Lake Basin, this paper adds a slope mask to remove misclassified pixels and finds the best extraction method of surface water extraction in the basin by means of accuracy verification and visual discrimination through continuous iteration of index threshold and slope mask threshold. The results show that when the threshold value is −0.25 and the slope mask is 8 degrees, the index WI2019 has the best effect on the surface water information extraction of Bosten Lake Basin, effectively eliminating the interference of shadow and snow. The effect of water extraction in the long-time series is discussed and it was found that the precision of water extraction in the long-time series is also better than other indexes. The effects of various indexes on surface water extraction under complex terrain are compared. It can quickly and accurately realize the long-time series of surface water extraction under large-area complex terrain and provides useful guiding significance for water resources management and allocation as well as a water resources ecological assessment of Bosten Lake Basin.

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

“…Water is generally reflected in the visible light range and shows almost no reflection in the infrared range, making it very distinct from other surfaces. 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].…”

“…The literature describes a wide range of machine learning algorithms for classification, including, but not limited to, Naive Bayes (NB), recursive partitioning and regression trees (RPART), neural networks (NNET), support vector machines (SVM), random forest (RF), gradient boosted machines (GBM) [24,25], and deep learning [26].…”

Research on Remote-Sensing Identification Method of Typical Disaster-Bearing Body Based on Deep Learning and Spatial Constraint Strategy

“…Additionally, c, f, and i depict the corresponding topographic landforms in the dam area. Upon analyzing water bodies and topographic features, it becomes evident that the accuracy of dam extraction is heavily influenced by the effectiveness of water body extraction [40,41] and the precision of the terrain model generated by DEM [42][43][44]. Inaccurate water body extraction can lead to the omission of dams, thereby compromising extraction accuracy.…”

A study of Great Lakes dam regulation based on water level and flow prediction models: application of particle swarm algorithm and LSTM neural network

“…For the amount of water in each lake, consider the factors that affect the inflow and outflow of the river, as well as the amount of rainfall and evaporation [7][8]. Based on the river flow obtained from the above equation, the volume change of each lake at any given moment can be calculated:…”