WANG Hui,WANG Yongqian,LI Jianfeng,et al.Estimation of Chlorophyll a Concentration in Three Gorges Reservoir Branch based on UAV Multispectral Data——A Case Study of Xiaojiang River[J].Journal of Chengdu University of Information Technology,2024,39(2):233-239.[doi:10.16836/j.cnki.jcuit.2024.02.015]
基于无人机多光谱数据的三峡库区支流叶绿素a浓度估算——以小江为例
- Title:
- Estimation of Chlorophyll a Concentration in Three Gorges Reservoir Branch based on UAV Multispectral Data——A Case Study of Xiaojiang River
- 文章编号:
- 2096-1618(2024)02-0233-07
- Keywords:
- UAV; multispectral; important tributaries of the Three Gorges; chlorophyll a; regression model
- 分类号:
- X821
- 文献标志码:
- A
- 摘要:
- 以三峡库区重要支流——小江为研究区,基于大疆P4M无人机多光谱数据与原位测量叶绿素浓度数据,建立叶绿素a浓度估算模型。结果表明,采样点叶绿素浓度值在5～260 μg/L,水体光谱曲线表现为各自的光谱特征,高叶绿素浓度水体的蓝(450 nm)、红(650 nm)波段附近出现叶绿素吸收峰,且随叶绿素浓度增加而加深; 高悬浮物浓度水体光谱反射峰值高于其他水体,反射峰位置存在向长波方向移动的“红移”现象; 使用MERIS陆地叶绿素指数(MTCI)建立的叶绿素a浓度估算模型精度最高,模型决定系数R2为0.89,平均绝对误差MAE为7.72 μg/L,均方根误差RMSE为9.34 μg/L,平均绝对误差百分比MAPE为35.72%; 无人机水体成像应在环境光照稳定、太阳高度角适宜的时间段进行。
- Abstract:
- This paper takes Xiaojiang River, an important tributary of the Three Gorges Reservoir, as the study area, establishes a chlorophyll a concentration estimation model based on the multi-spectral data of P4M UAV in Xinjiang and the chlorophyll concentration data measured in situ. The results showed that the chlorophyll concentration at the sampling point was between 5-260 μg/L,and the spectral curves of water showed their own spectral characteristics. Chlorophyll absorption peaks appeared near the blue(450 nm)and red(650 nm)bands of water with high chlorophyll concentration, and grow with the increase of chlorophyll concentration. The spectral reflection peak of water body with high suspended matter concentration is higher than that of other water bodies, and the position of reflection peak has a “red shift” phenomenon moving to the long wave direction. The MERIS land chlorophyll index(MTCI)was used to establish the chlorophyll a concentration estimation model with the highest accuracy. The model determination coefficient R2 was 0.89, the average absolute error MAE was 7.72 μg/L, the root mean square error RMSE was 9.34 μg/L, and the mean absolute percentage error MAPE was35.72%. UAV water body imaging should be carried out in the period when light is stable and the solar altitude angle is appropriate.
参考文献/References:
[1] 赵英时.遥感应用分析原理与方法[M].北京:科学出版社,2003.
[2] Isenstein E M,Park M H.Assessment of nutrientdistributions in Lake Champlain using satellite remote sensing[J].Journal of Environmental Sciences,2014,26(9):1831-1836.
[3] Keith D,Rover J,Green J,et al.Monitoring algal blooms in drinking water reservoirs using the Landsat-8 Operational Land Imager[J].International journal of remote sensing,2018,39(9-10):2818-2846.
[4] Lisboa F,Brotas V,Santos F D,et al.Spatial Variability and Detection Levels for Chlorophyll-a Estimates in High Latitude Lakes Using Landsat Imagery[J].Remote Sensing,2020,12(18):2898.
[5] 李冰,刘镕源,刘素红,等.基于低空无人机遥感的冬小麦覆盖度变化监测[J].农业工程学报,2012,28(13):160-165.
[6] 高林,杨贵军,王宝山,等.基于无人机遥感影像的大豆叶面积指数反演研究[J].中国生态农业学报,2015,23(7):868-876.
[7] 洪运富,杨海军,李营,等.水源地污染源无人机遥感监测[J].中国环境监测,2015,31(5):163-166.
[8] 刘彦君,夏凯,冯海林,等.基于无人机多光谱影像的小微水域水质要素反演[J].环境科学学报,2019,39(4):1241-1249.
[9] 杨振,卢小平,武永斌,等.无人机高光谱遥感的水质参数反演与模型构建[J].测绘科学,2020,45(9):60-64.
[10] 董月群,冒建华,梁丹,等.城市河道无人机高光谱水质监测与应用[J].环境科学与技术,2021,44(S1):289-296.
[11] 李优.三峡库区回水区叶绿素a浓度遥感反演方法研究[D].北京:中国地质大学,2017.
[12] 中国环境监测总站.2015年长江三峡工程生态与环境监测公报[EB/OL].http://www.cnemc.cn/zzjj/jgsz/sts/gzdt_sts/201601/W020181 008664072105866.pdf,2016-01-04.
[13] 胡鹏飞.三峡库区澎溪河与磨刀溪水华程度差异的机理研究[D].重庆:西南大学,2020.
[14] 深圳市大疆创新科技有限公司.精灵4多光谱版图像处理指南v1.0[EB/OL].https://dl.djicdn.com/downloads/p4-multispectral/202007 17/P4_Multispectral_Image_Processing_Guide_CHS.pdf,2020-07-17.
[15] Moses W J,Gitelson A A,Berdnikov S,et al.Satellite estimation of chlorophyll-a concentration using the red and NIR bands of MERIS—The Azov Sea case study[J].IEEE Geoscience and Remote Sensing Letters,2009,6(4):845-849.
[16] Gitelson A A,Gurlin D,Moses W J,et al.A bio-optical algorithm for the remote estimation of the chlorophyll-a concentration in case 2 waters[J].Environmental Research Letters,2009,4(4):5.
[17] Mishra S,Mishra D R.Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters[J].RemoteSensing of Environment,2012,117:394-406.
[18] Dash J,Curran P J.Evaluation of the MERIS terrestrial chlorophyll index(MTCI)[J].Advances in Space Research,2007,39(1):100-104.
[19] 疏小舟,尹球,匡定波.内陆水体藻类叶绿素浓度与反射光谱特征的关系[J].遥感学报,2000,4(1):41-45.
[20] Gitelson A A.The peak near 700 nm on radiance spectra of algae and water:relationships of its magnitude and position with chlorophyll concentration[J].International Journal of Remote Sensing,1992,13(17):3367-3373.
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备注/Memo
收稿日期:2022-10-09
基金项目:中国气象局大气探测重点开放实验室联合基金开放课题资助项目(U2021Z07)
通信作者:李剑锋.E-mail:lijianfeng_ljf@126.com