The Detection of Archaeological residues using Remote Sensing Techniques (DART)

该项目将增加有关考古遗迹遥感（RS）的知识，并建立可转移的专门知识。目前的考古RS技术随着对所涉及的物理、化学、生物和环境过程的不同理解而发展。因此，目前的检测策略不允许系统的AR评估，导致次优的遗产管理和发展控制。该项目将侧重于分析影响AR对比度动态的物理和环境因素，以提高站点和特征检测的总体目标。考古RS技术依赖于传感器检测AR与其周围环境或矩阵之间的对比度的能力。AR检测受到许多因素的影响——降水、温度、作物胁迫/类型、土壤类型和结构以及土地管理技术的变化。这些因素随季节和每天的变化而变化，这意味着使用特定传感器检测AR的能力会随着时间而变化。如果不了解影响ar可见性和检测的过程（直接或通过代理），勘探技术将在某种程度上仍然是临时的和机会主义的。加强对ARs的了解对于日益减少的遗产的长期管理非常重要，并将为运营工程节省成本（通过更有效的缓解）。这在传统光学航空摄影反应迟钝的环境（如牧场和粘土土壤）中非常重要。该项目及时考虑了高空间和光谱分辨率地面、空中和卫星传感器的最新发展。项目分为四个阶段：1 .确定合适的候选地点和取样方法；2 .实地测量，收集和分析不同条件下的实地样本；3 .物理建模、反馈、知识表达；4 .评估将根据对比ARs、土壤和土地管理条件等选择地点。与策展机构密切联系（有挖掘数据）是必要的，以确保确定具有代表性的AR类型范围。将具有不同环境条件和AR类型（埋藏的土壤、沟渠等“负面”特征、埋藏的砖石和表面材料）的场地纳入其中是很重要的。为了确定对比因素，将在一天和一年的不同时间在AR上和周围进行战略样本和测量，以确保涵盖具有代表性的条件范围。野外测量将包括地球物理和高光谱测量、热剖面、土壤湿度和光谱反射率。样品的实验室分析将包括地球化学和颗粒大小。将开发模型，将这些物理值转化为光谱、磁、电和声学测量，以确定对比度参数。数据融合和知识推理技术将用于开发管理工具，以改进调查的规划。这些工具将用于部署传感器，包括用于评估目的的空中高光谱设备。总之，该项目将影响和发展：1 .对AR对比过程和保存动力学的基本理解和知识；a.促进更好的管理和管理b.为模拟环境对AR的影响提供数据c.增强对资源基础的理解2 .确定合适的传感器及其使用条件（并反馈以改进传感器设计）3 .数据融合技术（物理模型、多传感器数据和领域知识）以改进AR识别4 .遥感、土壤科学、研究人员更有效地访问数据档案的技术。我们相信，研究结果将对国家产生影响，并有可能在全世界范围内转移。

This project will increase the knowledge about, and build transferable expertise in, the remote sensing (RS) of archaeological residues (AR). Current archaeological RS techniques have evolved with variable understanding of the physical, chemical, biological and environmental processes involved. Thus current detection strategies do not allow systematic AR assessment leading to sub-optimal heritage management and development control. This project will focus on analysing the physical and environmental factors that influence AR contrast dynamics with the overall aim of improving site and feature detection.Archaeological RS techniques rely on the ability of a sensor to detect the contrast between an AR and its immediate surroundings or matrix. AR detection is influenced by many factors - changes in precipitation, temperature, crop stress/type, soil type and structure and land management techniques. These factors vary seasonally and diurnally, meaning that the ability to detect an AR with a specific sensor changes over time.Without understanding the processes that affect the visibility and detection of ARs (directly and by proxy), prospection techniques will remain somewhat ad-hoc and opportunistic. Enhanced knowledge of ARs is important in the long-term curation of a diminishing heritage and will provide cost savings to operational works (through more effective mitigation). This is important in environments where traditional optical aerial photography has been unresponsive (e.g pasture and clay soils).The project is timely considering the recent development of high spatial and spectral resolution ground, air and satellite sensors.The project involves 4 stages:1 Identifying appropriate candidate sites and sampling methodology2 Field measurements and collecting and analysing field samples from sites under different conditions3 Physical modelling, feedback, knowledge articulation4 EvaluationSites will be chosen on the basis of contrasting ARs, soil and land management conditions etc. Close liaison with curatorial agencies (with excavation data) is necessary to ensure a representative range of AR types is identified. It will be important to include sites with varying environmental conditions and AR types (buried soils, 'negative' features such as ditches, buried masonry and surface materials).To determine contrast factors strategic samples and measurements will be taken on and around the AR at different times of the day and year to ensure that a representative range of conditions is covered. Field measurements will include geophysical and hyperspectral surveys, thermal profiling, soil moisture and spectral reflectance. Laboratory analysis of samples will include geochemistry and particle size.Models will be developed that translate these physical values into spectral, magnetic, electrical and acoustic measures in order to determine contrast parameters. Data fusion and knowledge reasoning techniques will be used to develop management tools to improve the programming of surveys. These tools will be used to deploy sensors, including aerial hyperspectral devices, for evaluation purposes.In summary, this project will impact on and develop:1 Baseline understanding and knowledge about AR contrast processes and preservation dynamics: a. leading to better management and curation b. providing data to model environmental impact on ARs c. enhancing the understanding of the resource base2 The identification of suitable sensors and conditions for their use (and feedback to improve sensor design)3 Data fusion techniques (physical models, multi-sensor data and domain knowledge) to improve AR identification4 An Interdisciplinary network between remote sensing, soil science, computing and heritage professionals5 Techniques for researchers to access data archives more effectivelyWe believe that the results will have national impact and have the potential for transfer throughout the world.