Information extraction of aquaculture ponds in the Jianghan Plain based on Sentinel-2 time-series data
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摘要: 近年来, 水产养殖业的迅速发展引发了一系列的生态环境问题。江汉平原作为我国最重要的淡水养殖基地之一, 研究其养殖池变化对我国的生态保护至关重要。因此, 该文面向江汉平原区域, 基于谷歌地球引擎(Google Earth Engine, GEE)与Sentinel-2密集时间序列影像, 提出了一种结合K均值聚类(K-means)与层次决策树分类算法的养殖池提取与变化监测方法, 实现了2016—2022年逐年的江汉平原养殖池精确提取及时空格局分析。结果表明:结合K-means与融入时间变化特征的层次决策树算法能够实现精准的养殖池分类, 每年总体分类精度均达到91.90%以上, Kappa系数达到0.84以上; 2022年江汉平原的水产养殖池面积为2 126.43 km2, 其中, 43.24%的养殖池集中分布于荆州市, 宜昌市养殖池面积最小仅占0.76%; 江汉平原养殖池在2016—2022年期间的动态变化呈现出明显的空间异质性, 整体呈现增加的趋势, 总面积从1 947.43 km2增加到2 126.43 km2, 增加了9.19%。所提方法为养殖池的精准监测提供了重要参考, 所得数据集对支持江汉平原地区生态保护和对可持续发展目标的评估具有重要的借鉴价值和现实意义。
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关键词:
- 江汉平原 /
- 内陆养殖池 /
- K-means /
- 时间序列数据 /
- Google Earth Engine
Abstract: In recent years, the rapid expansion of the aquaculture pond industry has given rise to a series of ecological and environmental issues. The Jianghan Plain is recognized as one of the most important freshwater aquaculture bases in China, and investigating changes in its aquaculture ponds is crucial for China’s ecological conservation. Focusing on the Jianghan Plain, this study proposed a method for extracting and monitoring changes in aquaculture ponds using Google Earth Engine (GEE) and Sentinel-2 dense time-series images. Using this method, which combined K-means clustering and a hierarchical decision tree classification algorithm, this study achieved accurate information extraction and spatiotemporal pattern analyses of aquaculture ponds in the plain in each year from 2016 to 2022. The results indicate that the combination of K-means and the hierarchical decision tree algorithm that integrated time-varying features allowed for accurate classification of aquaculture ponds, with an overall classification accuracy of 91.90% and a Kappa coefficient exceeding 0.84. In 2022, the aquaculture pond area of Jianghan Plain is 2 126.43 km2. Among these area of aquaculture ponds, 43.24% were concentrated in Jingzhou City, while Yichang City had the fewest area of aquaculture ponds, accounting for only 0.76%. From 2016 to 2022, aquaculture ponds in the Jianghan Plain exhibited an upward trend overall and dynamics with pronounced spatial heterogeneity. Specifically, the total area increased to 2 126.43 km2 from 1 947.43 km2, increasing by 9.19%. The proposed methodology provides an important reference for the precise monitoring of aquaculture ponds, and the resulting dataset serves as a valuable reference and holds great practical significance for the ecological conservation and the assessment of sustainable development goals in the Jianghan Plain.-
Key words:
- Jianghan Plain /
- inland aquaculture pond /
- K-means /
- time series data /
- Google Earth Engine
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