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

该项目将增加关于考古遗迹遥感(AR)的知识，并建立可转让的专门知识。目前的考古遥感技术已经演变成对所涉及的物理、化学、生物和环境过程的不同理解。因此，目前的检测战略不允许进行系统性的AR评估，从而导致次优的遗产管理和开发控制。该项目将重点分析影响AR对比动态的物理和环境因素，总体目标是改进遗址和特征检测。考古遥感技术依赖于传感器检测AR与其邻近环境或基质之间的对比的能力。AR探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。这些因素随着季节和昼夜的变化而变化，这意味着使用特定传感器检测AR的能力会随着时间的推移而变化。如果不了解影响AR可见性和检测的过程(直接和通过代理)，预测技术将在某种程度上保持临时和机会主义。加强对人工影响区的认识对于长期管理日益减少的遗产很重要，并将为业务工作节省费用(通过更有效的缓解措施)。这对于传统的光学航空摄影没有反应的环境(如牧场和粘性土壤)很重要。考虑到高空间和光谱分辨率地面、空气和卫星传感器的最新发展，该项目是及时的。该项目包括4个阶段：1确定合适的候选地点和采样方法2现场测量和从不同条件下的地点收集和分析现场样本3物理模型、反馈、知识表达4评估将根据对比AR、土壤和土地管理条件等来选择地点。必须与策展机构密切联系(利用挖掘数据)，以确保确定具有代表性的AR类型。重要的是要包括不同环境条件和AR类型(埋藏的土壤、沟渠、埋藏的砖石和地面材料等‘负面’特征)的场地。为了确定对比系数，将在一天和一年的不同时间对AR及其周围进行战略性抽样和测量，以确保覆盖具有代表性的条件范围。实地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，将这些物理值转换为光谱、磁学、电学和声学测量，以确定对比参数。将利用数据融合和知识推理技术开发管理工具，以改进调查方案编制工作。这些工具将用于部署传感器，包括航空高光谱设备，用于评估目的。总而言之，该项目将影响和发展：1关于AR对比过程和保存动态的基线理解和知识：A.导致更好的管理和管理；B.提供数据以模拟ARS的环境影响；2加强对资源库的理解2识别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、计算和遗产专业技术5使研究人员能够更有效地获取数据档案我们相信，这一成果将对国家产生影响，并有可能在全世界推广。

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.