Abstract
The upper Huaihe River is the water-producing area of the Huaihe River Basin and the major grain and oil-producing area in China. The changing global climate over the recent years has increased the frequency of extreme weather in the upper reaches of the Huaihe River. Research on the responses of surface water bodies to extreme climates has become increasingly important. Based on all utilizable Landsat 4–8 T1–SR data and frequency mapping, the spatio-temporal extraction of surface water and its response to extreme climate were studied. We generated high-precision frequency maps of surface water, and a comparison of cartographic accuracy evaluation indices and spatial consistency was also carried out. The high-precision interpretation of small waterbodies constructs a surface water distribution with better continuity and integrity. Furthermore, we investigated the effect of El Niño/La Niña events on precipitation, temperature, and surface water along the upper Huaihe River, using the Mann–Kendall mutation tests. The results show: in 1987–2018, periods of abrupt changes in precipitation coincide with EI Niño/La Niña events, indicating that the precipitation was sensitive to EI Niño/La Niña events, which also strongly correlated with surface water area during wet and dry years. The effect of extreme events on seasonal water was smaller than permanent water. Surface water area showed an insignificant declining trend after 1999 and a significant drop in 2012. The phenomenon of topographic enhancement of precipitation controlled the spatial distribution of permanent water, with human activities having a substantial effect on the landscape pattern of seasonal water. Finally, discussions and applications related to the Markov Chain probability calculation theory in the paper contributed to enriching the theories on frequency mapping. The relevant results provide a theoretical basis and case support for the formulation of long-term water resources utilization and allocation policies.
Highlights
We conducted a human–computer interactive interpretation by employing six scenes of Landsat 8 Operational Land Imager (OLI) images from 18 October 2015 to 3 December 2015, with 96% overall mapping accuracy. We considered this map as the surface water base map for validation of water classification mapping in 2015
Upon analyzing the years with complete data, we found no significant difference between the two sets of data on permanent water, as indicated by the mean and standard deviations of the two datasets
Compared with the JRC global surface water dataset, we found a clear difference in the seasonal water area
Summary
Time-series data have clear advantages for monitoring changes in environmental characteristics [1]. Remote sensing cloud platform for remote sensing data processing and analysis on the large scale will be a key direction of development in the field of land cover mapping [2]. Multi-seasonal imagery has been shown to improve the accuracy of forest biomass estimation [4]. The benefits of multi-temporal data for the estimation of successional processes have been acknowledged [5]. In time-series monitoring remote sensing mapping, the same sensor spectrum products are superior to multi-source image products in terms of spatial consistency. Landsat satellite images have been favored by scholars because of their long acquisition duration, abundant archived data, and high spatial resolution [6]. Series satellite images have specific data missing and poor data quality [7], posing a challenge to the production of time series
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