Long-Term Change of the Secchi Disk Depth in Lake Maninjau, Indonesia Shown by Landsat TM and ETM+ Data
Long-Term Change of the Secchi Disk Depth in Lake Maninjau, Indonesia Shown by Landsat TM and ETM+ Data
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DOI：
10.3390/rs11232875
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发表时间：
2019-12
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影响因子：
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通讯作者：
F. Setiawan;B. Matsushita;R. Hamzah;Dalin Jiang;T. Fukushima
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文献类型：
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作者：
F. Setiawan;B. Matsushita;R. Hamzah;Dalin Jiang;T. Fukushima
印度尼西亚的大多数湖泊都面临着富营养化、沉积和溶解氧耗尽等环境问题。由于资金限制，印度尼西亚用于支持湖泊管理的水质数据非常有限。为了解决这一问题，通常使用卫星数据来检索水质数据。在这里，我们开发了一个经验模型，利用从9个印度尼西亚湖泊/水库(SD值0.5-18.6m)收集的数据，从Landsat TM/ETM+数据估计Secchi盘深度(SD)。为了提高模型的稳健性，我们做了两个方面的工作。首先，在使用陆地卫星数据之前，我们进行了一系列的图像预处理步骤(即去除受污染的水像元、对图像进行滤波、减轻大气影响)。其次，我们选择了两个波段比率(蓝/绿和红/绿)作为标度预测因子；这与以前研究的推荐不同。验证结果表明，所建立的模型可以反演印度尼西亚马尼焦湖的SD值，其R2为0.60，均方根误差为1.01m(SD值范围为0.5~5.8m，n=74)。然后，我们将开发的模型应用于230个经过预处理的Landsat TM/ETM+图像场景，以生成1987-2018年间马尼焦湖的长期SD数据库。对现场测量的SD值和卫星估计的SD值以及几个事件(例如藻华、水闸打开和养鱼)的目测比较表明，基于陆地卫星的SD值很好地捕捉到了马尼焦湖水透明度的变化趋势，因此这些估计将为湖泊管理者和政策制定者提供有用的数据。
Most of the lakes in Indonesia are facing environmental problems such as eutrophication, sedimentation, and depletion of dissolved oxygen. The water quality data for supporting lake management in Indonesia are very limited due to financial constraints. To address this issue, satellite data are often used to retrieve water quality data. Here, we developed an empirical model for estimating the Secchi disk depth (SD) from Landsat TM/ETM+ data by using data collected from nine Indonesian lakes/reservoirs (SD values 0.5–18.6 m). We made two efforts to improve the robustness of the developed model. First, we carried out an image preprocessing series of steps (i.e., removing contaminated water pixels, filtering images, and mitigating atmospheric effects) before the Landsat data were used. Second, we selected two band ratios (blue/green and red/green) as SD predictors; these differ from previous studies’ recommendation. The validation results demonstrated that the developed model can retrieve SD values with an R2 of 0.60 and the root mean square error of 1.01 m in Lake Maninjau, Indonesia (SD values ranged from 0.5 to 5.8 m, n = 74). We then applied the developed model to 230 scenes of preprocessed Landsat TM/ETM+ images to generate a long-term SD database for Lake Maninjau during 1987–2018. The visual comparison of the in situ-measured and satellite estimated SD values, as well as several events (e.g., algal bloom, water gate open, and fish culture), showed that the Landsat-based SD estimations well captured the change tendency of water transparency in Lake Maninjau, and these estimations will thus provide useful data for lake managers and policy-makers.