Study on the distribution and key influencing factors of Huangtai algae of Ulansuhai Lake
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摘要:
黄苔暴发是乌梁素海水环境和水生态健康的重要环境问题，识别黄苔分布并探究其关键影响因子对预防黄苔暴发以及进行科学管控具有重要的现实意义。以2013—2020年卫星遥感影像以及无人机航拍数据为基础，利用随机森林方法识别乌梁素海黄苔分布并进行面积统计，利用灰色关联度模型筛选影响黄苔面积变化的关键因子。结果表明：通过十折交叉验证随机森林模型在训练集上的精确度达到99%，在测试集上的平均精确度达到92.8%。从年际变化趋势来看，黄苔面积在2014—2015年、2016—2017年波动较大，前者从18.92 km2降至10.59 km2，后者从13.31 km2升至22.48 km2，而其他年份黄苔面积相对稳定；从空间变化看，黄苔在乌梁素海入口区和湖心区分布更为密集。灰色关联分析结果显示，不同生长期的黄苔面积与水质指标的关联性呈现动态变化，5月黄苔面积受水温和有机物浓度影响显著，6—8月受综合环境因素（水温、有机物及氮磷营养盐浓度）影响较大，9月受氮磷营养盐浓度及生物竞争关系影响较强。研究揭示了黄苔生长与水质指标和生物竞争等影响因素的复杂交互关系。
Abstract:Huangtai algae outbreaks are an important environmental problem for the water environment and ecological health of Ulansuhai Lake, and it is of great practical significance to identify the distribution of Huangtai algae and explore the key factors affecting it for the prevention and scientific control of huangtai algae outbreaks. Based on the satellite remote sensing images and unmanned aerial photography data from 2013 to 2020, the distribution of Huangtai algae in Ulansuhai Lake was identified and the area statistics were conducted by using the random forest (RF) method, and then the key factors affecting the change of Huangtai algae area were screened by using the grey correlation analysis (GRA) model. The results showed that the ten-fold cross-validated random forest model achieves 99% accuracy on the training set and 92.8% average accuracy on the test set. In terms of the interannual trend, the Huangtai algae area fluctuated greatly in 2014-2015 and 2016-2017, decreasing from 18.92 to 10.59 km2 and increasing from 13.31 to 22.48 km2, respectively, while at the same time, the Huangtai algae area of other years was relatively stable. In terms of spatial variation, Huangtai algae were more densely distributed in the inlet area and the center of Ulansuhai Lake. In addition, the results of the GRA showed that the correlation between the area of Huangtai algae and water quality indexes changed dynamically in different growth periods. Specifically, the change of Huangtai algae area was significantly affected by water temperature and organic matter concentration in May, more affected by comprehensive environmental factors (water temperature, organic matter concentration and nitrogen and phosphorus nutrients) from June to August, and more affected by nitrogen, phosphorus and other nutrients as well as competitive relationship in September. The study revealed the complex interaction between the growth of Huangtai algae and water quality factors and biological competition, which could provide a scientific basis for the control of Huangtai algae and the health maintenance of water ecosystem.
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Key words:
- Ulansuhai Lake /
- Huangtai algae /
- random forest /
- grey relation analysis
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图 2 2022年6月17日黄苔样本分布
Figure 2. Distribution of Huangtai algae samples on June 17, 2022
图 3 黄苔识别及其关键因子确定的技术路线
Figure 3. Technical route to Huangtai algae identification and key factors determiantion
图 4 2013—2020年乌梁素海黄苔面积年内和年际变化
Figure 4. Intra- and inter-annual variation of Huangtai algae area in Ulansuhai Lake in 2013-2020
图 5 2013—2020年乌梁素海湖面黄苔分布情况
Figure 5. Distribution of Huangtai algae on the lake surface of Ulansuhai Lake in 2013-2020
图 6 2013—2020年乌梁素海5—9月各水质指标变化
Figure 6. Changes in water quality indicators from May to September in Ulansuhai Lake in 2013-2020
图 7 不同生长期黄苔面积与水质指标之间的灰色关联系数箱线图和灰色关联度图
Figure 7. Grey relation coefficient box plots and grey relation plots between Huangtai algae area and water quality indicators in different growth periods
表 1 十折交叉验证模型精确度
Table 1. Ten-fold cross-validation model accuracy
数据集 训练集精确度/% 测试集精确度/% 第1组 99.98 92.74 第2组 100 92.62 第3组 100 92.95 第4组 99.98 92.51 第5组 99.98 92.83 第6组 100 93.02 第7组 100 93.02 第8组 100 93.02 第9组 100 92.89 第10组 99.98 92.55 平均值 99.99 92.82 
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