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.
Highlights
Inland lakes are important aspects of land surface cover that participate in the natural water cycle and are considered highly sensitive to the impacts of climate change and human activities [1,2]
To identify lake disturbances that are dominated by different factors using time series remote sensing data, this paper proposes a lake disturbance identification algorithm that consists of two parts: the first is the Douglas-Peucker algorithm [26] and the bend simplification algorithm [27] to segment the time series of Landsat data derived lake surface areas
Annual Landsat remote sensing images taken during the dry seasons from 1986 to 2017 were used to extract lake surface area information for nine typical lakes in Yunnan Province, China
Summary
Inland lakes are important aspects of land surface cover that participate in the natural water cycle and are considered highly sensitive to the impacts of climate change and human activities [1,2]. Shrinkage or extension of inland lakes can reflect global climate and environment changes [3]. Inland lake variations are considered sensitive indicators of global climate change [4,5]. Lake variations are caused by either natural events or anthropogenic activities. These variations are mostly documented by the local authorities or institutions and are rarely obtained from remote sensing technology. This study focuses on remote sensing methods to identify the dominant factors affecting changes to inland lake surface area. With advantages of wide coverage, high frequency data collection, labor and economic cost-effectiveness, remote sensing technology has been used in previous lake change studies [6,7,8], especially for lakes located in remote and less developed areas where lake surface changes have been only rarely documented [2]
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