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，该技术将亚像素遥感数据和景观指标相结合，与传统的聚合技术相比，可以保留更多的空间信息。地理学有将空间数据分组为面积单位（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造成障碍。地理学家熟悉这些偏差，称之为可修改面积单位问题（MAUP），生态学家则认为这些偏差是生态谬论的根源。本研究开发的数据汇总技术将捕获亚像元遥感分类中增加的土地覆盖信息，并在将较小的空间单元合并为较大的空间单元时保留这些信息，从而减少统计偏差。然后，该研究将通过在非常精细的空间分辨率下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汇总技术消除了多少统计偏差。最后，研究将展示这些发现的实际生态应用，利用它们进行超分辨率制图。超分辨率制图是遥感的一个子领域，涉及以比原始传感器集合更精细的分辨率绘制数据。本研究的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，同时也为使用传统遥感分类时MAUP和生态谬误的原因提供了理论理解。这项研究将展示先进的遥感技术如何有助于减轻这些偏见。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基于地面的生态空间数据结合起来。最终，这些进展可用于利用遥感和地面调查数据研究个体行为及其对土地的影响。其长期影响超出了地理和空间科学的理论范畴，并将影响应用科学研究，包括基于主体的人类影响分析建模、空间流行病学和人口动态。社区外展将包括为一门特殊教育地球科学课程制定课程计划，向学生传授地理空间技术的应用，并强调未来职业道路的机会。作为博士论文研究进步奖，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.