| 摘要: |
| 【目的】构建并选取适于监测黄河流域典型内陆湖泊（乌梁素海）复杂水环境的挺水植被、沉水植被和漂浮藻类的光谱指标，构建决策树分类模型以实现乌梁素海长时间序列的水生植被类群与藻华监测。【方法】基于实测光谱数据，通过光谱特征分析，提取出改进的归一化土壤指数（MNDSI）进行水生植被类群、黄苔藻华和水体外类型的剔除，利用光谱水体指数（NDWI）提取开阔水体，基于改进增强型植被指数（AEVI）提取挺水植被，利用改进的漂浮藻类光谱指数（MFAI）区分黄苔藻华与沉水植被。在此基础上，构建决策树分类模型对1986—2018年乌梁素海的水生植被类群和藻华进行遥感监测，分析其时空分布特征和变化趋势。【结果】通过实测数据和GF-2 PMS人工视觉解译进行精度验证，分类总体精度均大于90%，表明本研究所提出的方法可有效区分水生植被与黄苔藻华，可用于乌梁素海植被类群和藻华变化特征分析及藻华暴发事件监测。【结论】1986—2018年，挺水植被类群呈缓慢增长趋势。沉水植被类群1986—2013年呈减少趋势，2013年后呈快速增长趋势。黄苔藻华无明显变化趋势，但具有一定的暴发性。 |
| 关键词: 水生植被类群；黄苔藻华；光谱指数；决策树；乌梁素海 |
| DOI：10.13522/j.cnki.ggps.2022336 |
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| Using Remote Sensing to Monitor Aquatic Vegetation and Algal Blooms in Lakes in Yellow River Basin |
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CHEN Yonggui, ZHU Yuxiang
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Henan Collage of Surveying and Mapping, Zhengzhou 451464, China
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| Abstract: |
| 【Background and objective】Global warming and anthropogenic activities will change ecological functions of lakes, resulting in eutrophication and algal blooms, for example. Monitoring the change in aquatic vegetation and algae is important to improve lake management but difficult, especially at large scale. Remot sensing can plug this gap and in this paper, we study the feasibility of using it to acquire vegetation dynamics in lakes.【Method】The studied site is Ulansuhai lake in the upper reach of the Yellow River basin. Emergent and submerged vegetation as well as floating algae were calculated based on spectral feature analysis of the spectral data acquired from the Landsat8 imageries. A decision tree classification model was constructed to analyze the aquatic vegetation and algal blooms. A MNDSI spectral index was proposed to identify the aquatic vegetation and yellow moss algal blooms; a NDWI spectral water index was used to extract water bodies that were free of vegetation and algae. An improved enhanced vegetation index (AEVI) was used to extract the emergent vegetation; a modified macroalgal index (MFAI) was used to distinguish the submerged vegetation from the yellow moss algal blooms. The accuracy of the method was tested against ground-truth data. 【Result】The spectral index combined with the decision tree classification model is effective to distinguish the aquatic vegetation from the yellow moss algal blooms. Using the Landsat8 imageries acquired from 1986 to 2018 revealed that the emergent vegetation taxa grew slowly, and the coverage area of the submerged vegetation showed periodic changes, decreasing 1986 to 2013 and increasing after 2013. The yellow moss algal bloom did not show identifiable trends, with their outbreaks occurring irregularly from 1986 to 2018. |
| Key words: groups of aquatic vegetation; yellow algal blooms; spectral index; decision tree; Ulansuhai Lake |
