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Combining machine learning and remotely sensed bandratios to investigate chlorophyll content and photosynthetic processes.

机译：结合机器学习和遥感带菌研究叶绿素含量和光合作用过程。

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摘要

Photosynthesis in aquatic and terrestrial ecosystems is the key component of the food chain and the most important driver of the global carbon cycle. Therefore, estimation of photosynthesis at large spatial scales is of great scientific importance and can only practically be achieved by remote sensing data and techniques. In this dissertation, remotely sensed information and techniques, as well as field measurements, are used to improve current approaches of assessing photosynthetic processes. More specifically, three topics are the focus here: (1) investigating the application of spectral vegetation indices as proxies for terrestrial chlorophyll in a mangrove ecosystem, (2) evaluating and improving one of the most common empirical ocean-color algorithms (OC4), and (3) developing an improved approach based on sunlit-to-shaded scaled photochemical reflectance index (sPRI) ratios for detecting drought signals in a deciduous forest at eastern United States. The results indicated that although the green normalized difference vegetation index (GNDVI) is an efficient proxy for terrestrial chlorophyll content, there are opportunities to improve the performance of vegetation indices by optimizing the band weights. In regards to the second topic, we concluded that the parameters of the OC4 algorithm and similar empirical models should be tuned regionally and the addition of sea-surface temperature makes the global ocean-color approaches more valid. Results obtained from the third topic showed that considering shaded and sunlit portions of the canopy (i.e., two-leaf models instead of single big leaf models) and taking into account the divergent stomatal behavior of the species (i.e. isohydric and anisohydric) can improve the capability of sPRI in detecting drought.;In addition to investigating the photosynthetic processes, the other common theme of the three research topics is the evaluation of "off- the-shelf" solutions to remote-sensing problems. Although widely used approaches such as normalized difference vegetation index (NDVI) are easy to apply and are often efficient choices in remote sensing applications, the use of these approaches should be justified and their shortcomings need to be considered in the context of the research application. When developing new remote sensing approaches, special attention should be paid to (1) initial data analysis such as statistical data transformations (e.g. Tukey ladder-of-powers transformation) and (2) rigorous validation design by creating separate training and validation data sets preferably using both field measurements and satellite-based data. Developing a sound approach and applying a rigorous validation methodology go hand in hand. In sum, all approaches have advantages and disadvantages or as George Box puts it, "all models are wrong but some are useful".
机译：水生和陆地生态系统中的光合作用是食物链的关键组成部分，也是全球碳循环的最重要驱动力。因此，在大的空间尺度上光合作用的估计具有重大的科学意义，并且只能通过遥感数据和技术来实际实现。本文利用遥感信息和技术，以及实地测量，来改进目前评估光合作用过程的方法。更具体地说，这里是三个主题：（1）研究光谱植被指数在红树林生态系统中作为陆地叶绿素代理的应用；（2）评估和改进一种最常见的经验海洋颜色算法（OC4）， （3）根据日光与阴影比例的光化学反射指数（sPRI）比，开发一种改进的方法来检测美国东部落叶林中的干旱信号。结果表明，尽管绿色归一化差异植被指数（GNDVI）可以有效替代陆地叶绿素含量，但仍有机会通过优化谱带权重来改善植被指数的性能。关于第二个主题，我们得出的结论是，应该对OC4算法和类似的经验模型的参数进行局部调整，加上海面温度的增加，使全球海洋颜色方法更加有效。从第三个主题获得的结果表明，考虑冠层的阴影和阳光照射部分（即，两叶模型而不是单个大叶模型），并考虑该物种的不同气孔行为（即等渗和等渗）可以改善除了研究光合作用过程外，这三个研究主题的另一个共同主题是评估遥感问题的“现成”解决方案。尽管诸如归一化植被指数（NDVI）之类的广泛使用的方法易于应用，并且在遥感应用中通常是有效的选择，但应证明这些方法的使用是合理的，并且在研究应用中应考虑其缺点。在开发新的遥感方法时，应特别注意（1）初始数据分析，例如统计数据转换（例如Tukey幂阶变换），以及（2）最好通过创建单独的训练和验证数据集进行严格的验证设计同时使用野外测量和基于卫星的数据。开发合理的方法并应用严格的验证方法是齐头并进的。总之，所有方法都有其优点和缺点，或者正如乔治·博克斯（George Box）所说，“所有模型都是错误的，但有些模型是有用的”。

著录项

  • 作者

    Gholizadeh, Hamed.;

  • 作者单位

    Indiana University.;

  • 授予单位 Indiana University.;
  • 学科 Remote sensing.
  • 学位 Ph.D.
  • 年度 2016
  • 页码 138 p.
  • 总页数 138
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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