Time Series Remote Sensing Data-Based Identification of the Dominant Factor for Inland Lake Surface Area Change: Anthropogenic Activities or Natural Events?

Abstract

Inland lake variations are considered sensitive indicators of global climate change. However, human activity is playing as a more and more important role in inland lake area variations. Therefore, it is critical to identify whether anthropogenic activity or natural events is the dominant factor in inland lake surface area change. In this study, we proposed a method that combines the Douglas-Peucker simplification algorithm and the bend simplification algorithm to locate major lake surface area disturbances. These disturbances were used to extract the features that been used to classify disturbances into anthropogenic or natural. We took the nine lakes in Yunnan Province as test sites, a 31-year long (from 1987 to 2017) time series Landsat TM/OLI images and HJ-1A/1B used as data sources, the official records were used as references to aid the feature extraction and disturbance identification accuracy assessment. Results of our method for disturbance location and disturbance identification could be concluded as follows: (1) The method can accurately locate the main lake changing events based on the time series lake surface area curve. The accuracy of this model for segmenting the time series of lake surface area in our study area was 94.73%. (2) Our proposed method achieved an overall accuracy of 87.75%, with an F-score of 85.71 for anthropogenic disturbances and an F-score of 88.89 for natural disturbances. (3) According to our results, lakes in Yunnan Province of China have undergone intensive disturbances. Human-induced disturbances occurred almost twice as much as natural disturbances, indicating intensified disturbances caused by human activities. This inland lake area disturbance identification method is expected to uncover whether a disturbance to inland lake area is human activity-induced or a natural event, and to monitor whether disturbances of lake surface area are intensified for a region.

Anthropogenic and Climatic Factors Differentially Affect Waterbody Area and Connectivity in an Urbanizing Landscape: A Case Study in Zhengzhou, China

“…We found that the length of water pipe was correlated with an increase in surface area of lakes and canals, and precipitation correlated with an increase in surface area of seasonal rivers and streams. Liu et al (2020) [94] also found that anthropogenic variables had a stronger effect on lakes than natural variables, but they mainly found a negative effect on lake area change. This disparity may be explained by the fact that Liu et al ( 2020) focused on natural lakes, whereas our "lake" category included constructed lakes as well.…”

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

“…Characterizing how and where lakes are changing on broad scales is, therefore, critical to understanding the drivers of change. However, studies that have investigated patterns of waterbody surface area change, rather than quantifying variability or linear change, are typically performed on short-term time series (∼2 years), long-term but low-resolution time series (i.e., only five measurements in 22 years), or on few (<30) waterbodies. − Recently, the Reservoir and Lake Surface Area Time series (ReaLSAT) data set, a long-term, spatially extensive water body data set, has become available which makes possible, using new analytic tools, a comprehensive analysis of long-term patterns of change across broad spatial scales.…”

Mapping and Monitoring the Multi-Decadal Dynamics of Australia’s Open Waterbodies Using Landsat

“…The delineation of waterbodies provides an object-based analysis, which characterises the dynamics of the whole waterbody, not its individual pixels. This allows analysis of waterbody characteristics such as the change in surface area over time [24,33], facilitating study of the events observed to be impacting individual waterbodies [33]. This in turn allows environmental water managers to evaluate the efficacy of environmental flow events in providing aquatic ecosystem provision objectives [34].…”