Space-Temporal analysis of suspended sediment in low concentration reservoir by remote sensing

Pub Date : 2019-01-01 DOI:10.1590/2318-0331.241920180061
Giancarlo Brugnara Chelotti, Jean-Michel Martinez, H. Roig, Diogo Olivietti
{"title":"Space-Temporal analysis of suspended sediment in low concentration reservoir by remote sensing","authors":"Giancarlo Brugnara Chelotti, Jean-Michel Martinez, H. Roig, Diogo Olivietti","doi":"10.1590/2318-0331.241920180061","DOIUrl":null,"url":null,"abstract":"ABSTRACT The study of small reservoirs with low suspended sediment concentration (CSS) is still a challenge for remote sensing. In this work we estimate CSS from the optical properties of water and orbital imagery. Campaigns were carried out at selected dates according to the calendar of sensor passages, rainfall seasonality and hydrograph of the reservoir for the collection of surface water samples and field spectroradiometry. The calibration between CSS and spectral behavior generated CSS estimation models from MODIS and Landsat 8 data, allowing investigation of their temporal and spatial behavior. The MODIS model generated a time series of CSS from 2000 to 2017, presenting R2 = 0.8105 and RMSE% = 39.91%. The Landsat 8 model allowed the spatial analysis of CSS, with R2 = 0.8352 and RMSE% = 15.12%. The combination of the proposed models allowed the temporal and spatial analysis of the CSS and its relationships with the rainfall regime and the quota variation of the Descoberto reservoir (DF). The results showed that the use of orbital data complements the CSS information obtained by the traditional methods of collecting and analyzing water quality in low CSS reservoirs.","PeriodicalId":0,"journal":{"name":"","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1590/2318-0331.241920180061","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

Abstract

ABSTRACT The study of small reservoirs with low suspended sediment concentration (CSS) is still a challenge for remote sensing. In this work we estimate CSS from the optical properties of water and orbital imagery. Campaigns were carried out at selected dates according to the calendar of sensor passages, rainfall seasonality and hydrograph of the reservoir for the collection of surface water samples and field spectroradiometry. The calibration between CSS and spectral behavior generated CSS estimation models from MODIS and Landsat 8 data, allowing investigation of their temporal and spatial behavior. The MODIS model generated a time series of CSS from 2000 to 2017, presenting R2 = 0.8105 and RMSE% = 39.91%. The Landsat 8 model allowed the spatial analysis of CSS, with R2 = 0.8352 and RMSE% = 15.12%. The combination of the proposed models allowed the temporal and spatial analysis of the CSS and its relationships with the rainfall regime and the quota variation of the Descoberto reservoir (DF). The results showed that the use of orbital data complements the CSS information obtained by the traditional methods of collecting and analyzing water quality in low CSS reservoirs.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
低浓度水库悬沙时空遥感分析
低悬沙浓度(CSS)小型水库的遥感研究仍然是一个挑战。在这项工作中,我们估计CSS从光学性质的水和轨道图像。根据传感器通道的日历、降雨季节和水库的水文曲线,在选定的日期进行运动,以收集地表水样品和实地光谱辐射测量。基于MODIS和Landsat 8数据的CSS和光谱行为之间的校准生成了CSS估计模型,从而可以研究它们的时空行为。MODIS模型生成2000 - 2017年的CSS时间序列,R2 = 0.8105, RMSE% = 39.91%。Landsat 8模型允许对CSS进行空间分析,R2 = 0.8352, RMSE% = 15.12%。结合所提出的模型,可以对CSS及其与降雨状况和Descoberto水库(DF)配额变化的关系进行时空分析。结果表明,利用轨道数据对低碳水饱和度水库水质采集分析的传统方法所获得的碳水饱和度信息进行了补充。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1