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

该 DDRI 项目研究了一种新的数据聚合技术，与传统聚合技术相比，该技术结合使用亚像素遥感数据和景观指标来保留更多的空间信息。地理学有着将空间数据分组为区域单位（例如人口普查区、像素等）的悠久传统，这些单位构成了观察和测量的基础。数据聚合对于绘制现实世界现象和执行空间分析是必要的，但该过程引入了统计偏差，这些偏差会对结果产生不利影响，并为整合不同来源和跨学科的数据集制造障碍。这些偏差被地理学家称为可修改面积单位问题（MAUP），并被生态学家称为生态谬误的根源。本研究开发的数据聚合技术将捕获亚像素遥感分类中增加的土地覆盖信息，并在较小的空间单元组合成较大的单元时保留这些信息，从而减少统计偏差。然后，该研究将通过以非常精细的空间分辨率预测土地覆盖模式来测试数据聚合技术的结果。这些预测的准确性将取决于通过数据聚合技术消除了多少统计偏差。最后，该研究将通过使用这些发现进行超分辨率绘图来展示这些发现的实际生态应用。超分辨率制图是遥感的一个子领域，涉及以比原始传感器收集更精细的分辨率绘制数据的地图。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，同时还提供对使用传统遥感分类时 MAUP 和生态谬误的因果关系的理论理解。该研究将展示先进的遥感技术如何帮助减轻这些偏差。该项目将最终使研究能够更准确地将遥感全球空间数据源与地面生态空间数据整合起来，从而促进跨学科合作。最终，这些进步可用于利用遥感和地面调查数据来研究个体行为和对土地的影响。长期影响超出了地理和空间科学的理论范围，并将影响应用科学研究，包括用于人类影响分析、空间流行病学和人口动态的基于主体的建模。社区外展活动将包括为特殊教育地球科学课程制定课程计划，以教授学生地理空间技术的应用并强调未来职业道路的机会。作为博士论文研究改进奖，该项目将为有前途的学生建立独立的研究生涯提供支持。

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.