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ABSTRACT
Long-term and large-scale lake statistics are meaningful for the study of environment change, but many of the existing methods are labour-intensive and time-consuming. To overcome this problem, a novel method for long-term and large-scale lake extraction by shape-factors- and machine-learning-based water body classification is proposed. An experiment was conducted to extract the lakes in the Yangtze River basin (YRB) from 2008 to 2018 with the Joint Research Centre's Global Surface Water Dataset (JRC GSW) data and OSM data. The results show: 1) The proposed method is automatically and successfully executed. 2) The number of river–lake complexes is between 3008 and 4697, representing 3.56%–5.70% of the total water bodies. 3) The areas of the lakes and rivers in the YRB were obtained, and the accuracy of water classification in each year was stable between 90.2% and 93.6%. Comparing the back propagation neural network, random forest, and support vector machine models, we found that the three machine learning models have similar classification accuracy for the scenario. 4) Fragmented and incomplete small rivers in the JRC GSW data, unchecked training samples, and overlapped shape factors are the three error sources. Future work will focus on addressing these three error sources.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data Availability Statement
The JRC GSW dataset is referenced by the paper (Pekel et al. Citation2016). The openstreetmap data is referenced by the website (https://www.openstreetmap.org). The results of this paper can be asked by corresponding email.