Complexity of Spatial and Categorical Scale in Landcover Characterization: A Statistical and Computational Framework

土地覆盖特征中空间和分类尺度的复杂性：统计和计算框架

• 批准号: 0318209
• 负责人: Eric Kolaczyk
• 金额: $ 53.59万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-01 至 2007-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0318209&HistoricalAwards=false
• 关键词: Complexity; Spatial; Categorical; Scale; Landcover

Land cover characterization is essential for modeling, monitoring, and prediction of the terrestrial ecosystem and climate. Such characterizations are produced based largely on information extracted from satellite remote sensing measurements. However, much of the global and regional land cover consists of heterogeneous mixtures of different land cover types at varying spatial scales, while standard methods for remote sensing land cover characterization operate at a single scale and use a fixed set of categories. This research will develop an integrated statistical and computational framework that will produce land cover characterizations from remote sensing data that allow generalization in spatial resolution and categorical scale simultaneously. The result of this land cover characterization process will no longer be a land cover map, as a map implies a single spatial scale, but rather a land cover database, which can be queried in both traditional manners and ways currently unavailable. In essence, users will be free to select a range of spatial and categorical scales most appropriate for their needs. The key elements involved in the development of this framework include new contributions to the fields of geography and remote sensing, statistics, and computer science. At the foundation is a new class of multi-scale statistical models , called mixlets, from which there will result a new paradigm for representation and visualization of land cover categorization. In turn, these advances will be integrated with new developments in spatial database representations and spatial query systems.Land cover characterizations, critical for many studies involving terrestrial ecosystems and climate, and the human impacts on the natural environment, are needed at many different spatial scales and by a variety of user communities. This project will develop a comprehensive framework and prototype system for producing such characterizations in a manner that adapts automatically to multiple scales of information using remote sensing and GIS data. Our research has direct impact in the fields of ecology, biology, geography, forestry and environmental sciences dealing with multiscale patterns and processes. More broadly, the development and availability of these tools will contribute significantly to the improved understanding within the scientific community, and ultimately in the community at large, of the complexity of land cover.This award is jointly supported by the Division of Mathematical Sciences and the Directorate for Social, Behavioral, and Economic Sciences as part of the Mathematical Sciences Priority Area.

土地覆盖特征对陆地生态系统和气候的建模、监测和预测至关重要。这种描述主要是根据从卫星遥感测量中提取的信息得出的。然而，许多全球和区域土地覆盖是不同空间尺度上不同土地覆盖类型的异质混合，而遥感土地覆盖特征的标准方法在单一尺度上操作并使用一套固定的类别。这项研究将发展一个综合的统计和计算框架，根据遥感数据产生土地覆盖特征，从而同时在空间分辨率和分类尺度上进行推广。这一土地覆盖表征过程的结果将不再是一张土地覆盖图，因为地图意味着一个单一的空间尺度，而是一个土地覆盖数据库，可以通过传统方式和目前无法获得的方式进行查询。从本质上讲，用户可以自由选择一系列最适合他们需要的空间和分类尺度。这一框架的发展涉及的关键因素包括对地理和遥感、统计和计算机科学领域的新贡献。其基础是一类新的多尺度统计模型，称为混合模型，它将产生土地覆盖分类的表示和可视化的新范式。反过来，这些进步将与空间数据库表示和空间查询系统的新发展相结合。土地覆盖特征对于许多涉及陆地生态系统和气候以及人类对自然环境影响的研究至关重要，需要在许多不同的空间尺度和各种用户群体中进行。这个项目将发展一个全面的框架和原型系统，以自动适应使用遥感和地理信息系统数据的多种信息尺度的方式制作这种特征。我们的研究在生态学、生物学、地理学、林业和环境科学领域具有直接影响，涉及多尺度模式和过程。更广泛地说，这些工具的发展和可用性将大大有助于提高科学界，并最终在整个科学界对土地覆盖复杂性的理解。该奖项由数学科学司和社会、行为和经济科学理事会共同支持，作为数学科学优先领域的一部分。