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Mapping chlorophyll-a concentrations in a cyanobacteria- and algae-impacted Vaal Dam using Landsat 8 OLI data

机译:使用Landsat 8 Oli数据映射叶绿素和藻类撞击的Vaal Dam中的浓度

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摘要

Mapping chlorophyll-a (chl-a) is crucial for water quality management in turbid and productive case II water bodies, which are largely influenced by suspended sediment and phytoplankton. Recent developments in remote sensing technology offer new avenues for water quality assessment and chl-a detection for inland water bodies. In this study, the red to near-infrared (NIR-red) bands were tested for the Vaal Dam in South Africa to classify chl-a concentrations using Landsat 8 Operational Land Imager (OLI) data for 2014–2016 by means of stepwise logistic regression (SLR). The moderate-resolution imaging spectroradiometer (MODIS) data were also used for validating chl-a concentration classes. The chl-a concentrations were classified into low and high concentrations. The SLR applied on 2014 images yielded an overall accuracy of 80% and kappa coefficient (κ) of 0.74 on April 2014 data, while an overall accuracy of 65% and κ=0.30 were obtained for the May 2015 Landsat data. There was a significant (p<0.05) negative correlation between chl-a classes and red band in all analyses, while the NIR band showed a positive correlation (0.0001; p<0.89) for April 2014 data set. The 2015 image classification yielded an overall accuracy of 83% and κ=0.43. The difference vegetation index showed a significant (p<0.003) positive correlation with chl-a concentrations for May 2015 and July 2016, with chl-a ranges of between 2.5 μg/L and 1219 μg/L. These correlations show that a class increase in chl-a (from low to high) is in response to an increase in greenness within the Vaal Dam. We have demonstrated the applicability of Landsat 8 OLI data for inland water quality assessment.Significance:• The magnitude of the algae problem in the Vaal Dam is highlighted.• Landsat 8 OLI satellite data have potential in mapping chl-a in inland water bodies.• Both the red and the near infrared wavelengths were significant in mapping chl-a concentrations in the Vaal Dam.• Satellite earth observation can be instrumental for water quality monitoring and decision making.
机译:测绘叶绿素-A(CHL-A)对于浑浊和生产案例II水体中的水质管理至关重要,这主要受到悬浮沉积物和浮游植物的影响。遥感技术的最新发展为内陆水体的水质评估和CHL-A检测提供了新的途径。在这项研究中,对南非的Vaal大坝测试了红色到近红外(Nir-red)频带,以通过逐步的物流使用Landsat 8运营土地成像器(Oli)数据来分类CHL-A浓度。回归(SLR)。中等分辨率成像光谱辐射器(MODIS)数据也用于验证CHL-A浓度类。将CHL-A浓度分为低浓度和高浓度。应用于2014年图像的SLR在2014年4月的数据中产生了80%和Kappa系数(κ)的整体准确性,而2015年5月LANDSAT数据则获得了65%和κ= 0.30的整体准确性。在所有分析中,CHL-A类和红色频段之间存在显着(P <0.05)的负相关,而NIR带显示了2014年4月数据集的正相关(0.0001; P <0.89)。 2015年图像分类产生了83%和κ= 0.43的整体精度。差异植被指数与2015年5月至2016年7月的CHL-A浓度显着(P <0.003)阳性相关性,CHL-A的范围为2.5μg/ L和1219μg/ L.这些相关性表明CHL-A(从低到高)增加了CHL-A的增加是响应于VAAL坝内的绿色的增加。我们已经证明了Landsat 8 Oli数据对内陆水质评估的适用性。意义:•突出了Vaal Dam中的藻类问题的幅度。•Landsat 8 Oli卫星数据具有在内陆水体中映射CHL-A的潜力。•红色和近红外波长均在映射CHL-A浓度在Vaal Dam中进行了显着性。•卫星地球观测可以是水质监测和决策的乐器。

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