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

使用遥感技术 (DART) 检测考古残留物

• 批准号: AH/H032673/1
• 负责人: Anthony Cohn
• 金额: $ 88.58万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=AH%2FH032673%2F1
• 关键词: Detection; Archaeological; residues; using; Remote

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.

该项目将增加有关考古遗迹遥感的知识，并建立这方面可转让的专门知识。目前的考古遥感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。该项目将重点分析影响AR对比度动态的物理和环境因素，总体目标是改进场地和特征检测。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对比度的能力。AR检测受到许多因素的影响-降水，温度，作物应力/类型，土壤类型和结构以及土地管理技术的变化。这些因素随季节和日变化，这意味着使用特定传感器检测AR的能力随时间而变化。如果不了解影响AR可见性和检测（直接和通过代理）的过程，勘探技术将仍然是临时和机会主义的。加强对人工反应的了解对于长期保护日益减少的遗产非常重要，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中非常重要该项目及时考虑到最近开发的高空间和光谱分辨率地面、空气和卫星传感器，该项目包括4个阶段：1确定适当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地点的实地样品3物理建模，反馈，知识表达4评估地点的选择将基于对比的人工植被、土壤和土地管理条件等。与策展机构（有挖掘数据）的密切联系是必要的，以确保识别出具有代表性的人工植被类型。重要的是要包括具有不同环境条件和AR类型的场地（掩埋土壤，“负面”特征，如沟渠，掩埋砖石和表面材料）。为了确定对比因素，将在一天和一年的不同时间在AR上和周围进行战略性采样和测量，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调查、热剖面、土壤湿度和光谱反射率。对样品的实验室分析将包括地球化学和颗粒大小，并将建立模型，将这些物理值转化为光谱、磁性、电学和声学测量，以确定对比参数。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。总之，该项目将影响和发展：1关于AR对比度过程和保存动态的基线理解和知识：a.导致更好的管理和策展B.提供数据以模拟环境对人工呼吸的影响c. 2.确定适当的传感器及其使用条件（和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），以提高AR识别4遥感，土壤科学，计算和遗产专业人员5研究人员更有效地访问数据档案的技术我们相信，研究结果将产生全国性的影响，并有可能进行转让在全世界都有。

项目成果

Long-term monitoring to inform the geophysical detection of archaeological ditch anomalies in different climatic conditions

长期监测为不同气候条件下考古沟渠异常的地球物理检测提供信息

• DOI: 10.1002/arp.1902
• 发表时间: 2023
• 期刊: Archaeological Prospection
• 影响因子: 1.8
• 作者: Boddice D

Capability assessment and challenges for quantum technology gravity sensors for near surface terrestrial geophysical surveying

• DOI: 10.1016/j.jappgeo.2017.09.018
• 发表时间: 2017-11-01
• 期刊: JOURNAL OF APPLIED GEOPHYSICS
• 影响因子: 2
• 作者: Boddice, Daniel;Metje, Nicole;Tuckwell, George
• 通讯作者: Tuckwell, George

Archaeological applications of multi/hyper-spectral data - challenges and potential

多光谱/高光谱数据的考古应用 - 挑战和潜力

• 发表时间: 2010
• 作者: Beck A R

Unique insight into the seasonal variability of geophysical properties of field soils: practical implications for near-surface investigations

对田间土壤地球物理特性季节性变化的独特见解：对近地表调查的实际影响

• DOI: 10.3997/1873-0604.2017020
• 发表时间: 2017
• 期刊: Near Surface Geophysics
• 影响因子: 1.6
• 作者: Boddice D

Interpreting Archaeological Topography: 3D Data, Visualisation and Observation

解读考古地形：3D 数据、可视化和观察

• 发表时间: 2013
• 作者: Beck, A R