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引用本文:杜雨春子,青松,包玉海,郝艳玲.乌梁素海沉水植物群落光谱特征及其受覆盖度的影响分析.海洋与湖沼,2022,53(1):74-83.
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乌梁素海沉水植物群落光谱特征及其受覆盖度的影响分析
杜雨春子1,2, 青松1,2, 包玉海1,2, 郝艳玲3
1.内蒙古师范大学地理科学学院 呼和浩特 010022;2.内蒙古师范大学内蒙古自治区遥感与地理信息系统重点实验室 呼和浩特 010022;3.内蒙古大学生态与环境学院 呼和浩特 010021
摘要:
沉水植物是湖泊生态系统重要组成,也是水质与健康状况的指标性类群,可以采用遥感技术对其生长和分布情况进行监测。基于实测光谱数据,分析了乌梁素海沉水植物光谱特征,研究了覆盖度对乌梁素海沉水植物反射光谱的影响,建立了乌梁素海沉水植物覆盖度反演模型。结果表明:沉水植物群落的光谱反射率随覆盖度减少而下降,覆盖度为1%~20%时,沉水植物群落光谱反射率与水体反射率非常接近,但在813 nm和1 069 nm处仍存在差异。在710~746 nm波段附近,沉水植物群落光谱反射率与覆盖度呈显著正相关。在建立的单波段/波段比沉水植物覆盖度反演模型中,波段比覆盖度反演模型要优于单波段反演模型,波段比反演模型的决定系数(coefficient of determination)R2>0.716,均方根误差(root mean square error,RMSE)小于14.90%,平均相对于误差(mean relative percentage error,MRPE)小于35.65%,反演精度较高,适用于60%~100%沉水植物覆盖度反演。利用波段响应函数,将实测光谱反射率积分到Sentinel-2 MSI波段上,建立了multi spectral instrument (MSI)覆盖度反演模型。波段比二次多项式反演效果最好(R2为0.94,RMSE为4.62%,MRPE为6.22%),可以用于乌梁素海沉水植物覆盖度的反演。
关键词:  沉水植物  光谱反射率  覆盖度  相关分析  回归模型  乌梁素海
DOI:10.11693/hyhz20210900207
分类号:
基金项目:国家自然科学基金项目,41961057号,61461034号;内蒙古自治区高等学校青年科技英才支持计划项目NJYT-17-B04号;内蒙古自治区自然科学基金项目,2019MS04013号,2019MS03023号。
附件
SPECTRAL FEATURES OF SUBMERGED AQUATIC VEGETATION UNDER COVERAGE IMPACT IN THE ULANSUHAI LAKE
DU Yu-Chunzi1,2, QING Song1,2, BAO Yu-Hai1,2, HAO Yan-Ling3
1.College of Geographical Science, Inner Mongolia Normal University, Inner Mongolia, Hohhot 010022, China;2.Inner Mongolian Key Laboratory of Remote Sensing & Geography Information System, Inner Mongolia Normal University, Hohhot 010022, China;3.School of Ecology and Environment, Inner Mongolia University, Inner Mongolia, Hohhot 010021, China
Abstract:
Submerged plants play an important role in lake ecosystems and act as an indicator of water quality and aquatic ecosystem health. Remote sensing technology offers opportunity to improve the monitoring on growth and distribution of submerged plants. To analyze the spectral characteristics of submerged aquatic vegetation in Ulansuhai Lake, Inner Mongolia, China, the effects of coverage on the reflectance spectra of submerged plants in the lake was explored based on measured spectral data. A submerged plant coverage retrieval model was established. Results show that the spectral reflectance of submerged plants decreased with the coverage decrease. The spectral reflectance of submerged plants was close to that of water at a coverage of 1%~20% although the differences remained at 813 nm and 1 069 nm. The spectral reflectance of submerged plants was significantly and positively correlated with the coverage in 710~746 nm band. Among models established of submerged plant coverage retrieval, the band ratio retrieval model was better than that of the single band retrieval model in performance. The band ratio retrieval model had the highest accuracy and was shown to be suitable for monitoring submerged plants with coverage of 60%~100%. In addition, the multi-spectral instrument (MSI) coverage retrieval model was established by integrating the measured spectral reflectance into the Sentinel-2 MSI bands using the band response function. The band ratio quadratic polynomial coverage retrieval model achieved the best effect. Therefore, the submerged plant coverage retrieval model developed in this study can be used to inverse the coverage of submerged aquatic vegetation in the Ulansuhai Lake.
Key words:  submerged aquatic vegetation  spectral features  coverage  correlation analysis  regression model  Ulansuhai Lake
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