Temporal and Spatial Variation Analysis of Lake Area Based on the ESTARFM Model: A Case Study of Qilu Lake in Yunnan Province, China

Abstract

Qilu Lake is one of the nine plateau lakes in Yunnan Province, China. In recent years, under the influence of extreme climate and human activities, the area of Qilu Lake has shrunk significantly, the water level has dropped, and the problem of water shortage has become increasingly serious. Based on the Landsat and MODIS image data from 2000 to 2020, this study applied the ESTARFM spatiotemporal fusion model to unify the data images used in the study to February, used three kinds of water body indexes, selected the water body index most suitable for the study area to extract relevant information, and analyzed the spatiotemporal change characteristics of Qilu Lake area in the last 20 years. The results showed that: (1) Based on the ESTARFM model, the Landsat and MODIS data on 18 January 2020, the Landsat and MODIS data on 9 May 2020, and the MODIS data on the date to be predicted (February 13) were fused to obtain the Landsat image data of the predicted date, which met the accuracy requirements; (2) Taking 2005 as an example, the NDWI, MNDWI, and AWEIsh indexes were used to extract the water body with the precisions of 99.0%, 99.6% and 98.6%, respectively, and then the MNDWI water body index was selected to extract the lake area; (3) In the past 20 years, the overall area of Qilu Lake has shown a downward trend, with the area reduced by 0.7132 km2. From 2000 to 2010, the lake area was relatively stable, fluctuating up and down around 36 km2. From 2010 to 2015, the lake area decreased sharply, with a change rate of −40%. After 2015, the lake area gradually increased; (4) The spatial change of Qilu Lake area mainly occurred in the southwest and west, which decreased by 0.44 km2 and 0.49 km2, respectively, and there were small fluctuations in other directions. In the past two decades, the shape index of Qilu Lake has shown a downward trend as a whole; the contour of the lake tends to be simplified, the contour is complex and stable from 2000 to 2010, and the shape index decreases from 2.17 to 1.74 from 2010 to 2020; (5) The change in the Qilu Lake area is positively correlated with the change in the water level. Polynomial models with different times were selected as the model for retrieving water level elevation from the Qilu Lake water surface area, with a highest correlation coefficient of 0.9259. The temporal and spatial changes of the Qilu Lake area in the last 20 years are the result of the joint action of natural factors and socio-economic factors. According to the analysis, the annual average temperature, annual precipitation, annual average sunshine hours, and population density are the main driving forces leading to the change. In the future, the government and relevant researchers should strengthen real-time monitoring and regular research, formulate and optimize emergency plans to deal with changes in the ecological environment of lakes, and promote the sustainable development of the ecological environment and social economy of the basin.

Using Enhanced Gap-Filling and Whittaker Smoothing to Reconstruct High Spatiotemporal Resolution NDVI Time Series Based on Landsat 8, Sentinel-2, and MODIS Imagery

“…For instance, Gao et al [37] proposed the spatial and temporal adaptive reflectance fusion model (STARFM), which fuses Landsat and MODIS images by leveraging information from similar neighboring pixels for refined increment estimation [38]. Subsequently, Zhu et al [39] introduced an improved version, ESTARFM, which incorporates additional data pairs on the benchmark date and adopts a linear hybrid model to enhance the fusion performance in a heterogeneous region [40]. Rao et al [41] presented a demixing model known as the linear mixed growth model (LMGM), which decomposes coarse increments into fine increments.…”