Doctoral Dissertation Research: Overcoming Scale Disparities Through Sub-Pixel Remote Sensing Classifications and Spatial Pattern Metrics

这个DDRI项目研究了一种新的数据聚合技术，该技术使用亚像素遥感数据和景观指标的组合，以保留更大量的空间信息相比，传统的聚合技术。地理学有着将空间数据分组为区域单位的悠久传统（例如，人口普查区域、像素等）这是观察和测量的基础。数据汇总对于绘制真实世界的现象和进行空间分析是必要的，但这一过程会引入统计偏差，对结果产生不利影响，并为整合来自不同来源和跨学科的数据集制造障碍。这些偏见是地理学家所熟悉的可修改的面积单位问题（MAUP），生态学家认为这是生态谬误的根源。在这项研究中开发的数据聚合技术将捕获增加的土地覆盖信息存在于亚像素遥感分类，并保留这些信息作为较小的空间单位合并成较大的单位，从而减少统计偏差。然后，这项研究将通过以非常精细的空间分辨率预测土地覆被模式来检验数据汇总技术的结果。这些预测的准确性将取决于通过数据汇总技术消除了多少统计偏差。最后，本研究将通过使用这些发现来执行超分辨率映射来展示这些发现的实际生态应用。超分辨率制图是遥感领域的一个分支，它关注的是比原始传感器集合更精细分辨率的制图数据，这项研究的更广泛影响包括通过重新定义数据聚合来推进基础空间科学，同时还提供了对使用传统遥感分类时MAUP和生态谬误的原因的理论理解。这项研究将展示先进的遥感技术如何帮助减轻这些偏见。该项目将促进跨学科合作，最终使研究能够更准确地将来自遥感的全球空间数据源与地面生态空间数据结合起来。最终，这些进展可以用于研究个人的行为和影响的土地使用遥感和地面调查数据。长期影响超出了地理和空间科学的理论层面，并将影响应用科学研究，包括基于代理人的人类影响分析，空间流行病学和人口动态建模。社区外联活动将包括为地球科学特别教育班制定一个课程计划，向学生讲授地理空间技术的应用，并突出未来职业道路的机会。作为博士论文研究改进奖，该项目将提供支持，使有前途的学生建立一个独立的研究生涯。

This DDRI project investigates a new data aggregation technique that uses a combination of sub-pixel remote sensing data and landscape metrics to retain a greater amount of spatial information compared to traditional aggregation techniques. Geography has a long tradition of grouping spatial data into areal units (e.g., census tracts, pixels, etc.) that form the basis for observation and measurement. Data aggregation is necessary to map real-world phenomena and perform spatial analysis, but the process introduces statistical biases that adversely affect results and create obstacles for integrating datasets from different sources and across disciplines. These biases are familiar to geographers as the modifiable areal unit problem (MAUP) and are known to ecologists as the root of ecological fallacies. The data aggregation technique developed in this research will capture the increased land cover information present in sub-pixel remote sensing classifications and retain this information as smaller spatial units are combined into larger units, thereby reducing statistical biases. The study will then test the results of the data aggregation technique by predicting land cover patterns at very fine spatial resolutions. The accuracy of these predictions will depend on how much statistical bias was removed through the data aggregation technique. Lastly, the research will demonstrate a practical ecological application for these findings by using them to perform super-resolution mapping. Super-resolution mapping is a subfield of remote sensing concerned with mapping data at a finer resolution than the original sensor collection.The broader impacts of this research include advancing fundamental spatial science by redefining data aggregation while also providing a theoretical understanding of the causation of MAUP and ecological fallacies when using traditional remote sensing classifications. The research will demonstrate how advanced remote sensing techniques can help mitigate those biases. The project will promote interdisciplinary collaborations by ultimately allowing studies to integrate global spatial data sources from remote sensing with ground-based ecological spatial data more accurately. Eventually these advances can be used to study individual behaviors and impacts to the land using remote sensing and ground-based survey data. The long-term impacts reach beyond the theoretical aspects of geographical and spatial sciences and will impact applied scientific research including agent-based modeling for human impact analysis, spatial epidemiology, and population dynamics. Community outreach will include developing a lesson plan for a special education Earth Science class to teach students applications of geospatial technologies and highlight opportunities for future career paths. As a Doctoral Dissertation Research Improvement award, this project will provide support to enable a promising student to establish an independent research career.