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首页> 外文期刊>Journal of Environmental Management >Integrating multisensor satellite data merging and image reconstruction in support of machine learning for better water quality management
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Integrating multisensor satellite data merging and image reconstruction in support of machine learning for better water quality management

机译：集成多传感器卫星数据合并和图像重建以支持机器学习，以实现更好的水质管理

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摘要

Monitoring water quality changes in lakes, reservoirs, estuaries, and coastal waters is critical in response to the needs for sustainable development. This study develops a remote sensing-based multiscale modeling system by integrating multi-sensor satellite data merging and image reconstruction algorithms in support of feature extraction with machine learning leading to automate continuous water quality monitoring in environmentally sensitive regions. This new Earth observation platform, termed “cross-mission data merging and image reconstruction with machine learning” (CDMIM), is capable of merging multiple satellite imageries to provide daily water quality monitoring through a series of image processing, enhancement, reconstruction, and data mining/machine learning techniques. Two existing key algorithms, including Spectral Information Adaptation and Synthesis Scheme (SIASS) and SMart Information Reconstruction (SMIR), are highlighted to support feature extraction and content-based mapping. Whereas SIASS can support various data merging efforts to merge images collected from cross-mission satellite sensors, SMIR can overcome data gaps by reconstructing the information of value-missing pixels due to impacts such as cloud obstruction. Practical implementation of CDMIM was assessed by predicting the water quality over seasons in terms of the concentrations of nutrients and chlorophyll-a, as well as water clarity in Lake Nicaragua, providing synergistic efforts to better monitor the aquatic environment and offer insightful lake watershed management strategies.
机译：响应可持续发展的需求，监测湖泊，水库，河口和沿海水域的水质变化至关重要。这项研究通过集成多传感器卫星数据合并和图像重建算法，以支持特征提取和机器学习，从而开发了基于遥感的多尺度建模系统，从而实现了对环境敏感地区的连续水质监测的自动化。这个新的地球观测平台被称为“跨任务数据合并和机器学习图像重建”（CDMIM），能够合并多个卫星图像，以通过一系列图像处理，增强，重建和数据提供日常水质监测采矿/机器学习技术。突出显示了两个现有的关键算法，包括光谱信息自适应和综合方案（SIASS）和SMart信息重构（SMIR），以支持特征提取和基于内容的映射。 SIASS可以支持各种数据合并工作，以合并从跨任务卫星传感器收集的图像，而SMIR可以通过重建由于云阻塞等影响而丢失的值丢失像素的信息来克服数据缺口。通过预测营养素和叶绿素-a的季节变化以及尼加拉瓜湖水的清澈度来评估CDMIM的实际实施情况，从而为更好地监测水生环境和提供有见地的湖泊流域管理策略做出协同努力。

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  • 来源
    《Journal of Environmental Management》 |2017年第1期|227-240|共14页
  • 作者

    Chang Nibin; Bai Kaixu; Chen Chifarn;

  • 作者单位

    Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL, United States;

    Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, China,School of Geographic Sciences, East China Normal University, Shanghai, China;

    Center for Space and Remote Sensing Research, National Central University, Taoyuan County, Taiwan;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Enabling technology; Machine learning; Remote sensing; Water quality; Watershed management;

    机译：使能技术;机器学习;遥感;水质;流域管理;

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