FUSE: Floodplain Underground SEnsors- A high-density, wireless, underground Sensor Network to quantify floodplain hydro-ecological interactions

FUSE：洪泛区地下传感器 - 高密度、无线、地下传感器网络，用于量化洪泛区水文生态相互作用

• 批准号: NE/I006877/1
• 负责人: B Marchant
• 金额: $ 3.86万
• 依托单位: Rothamsted Research
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FI006877%2F1
• 关键词: FUSE; Floodplain; Underground; SEnsors; density

Improved understanding of the functioning of hydrological systems and dependent ecology is essential for optimal environmental management. Floodplains in particular are important due to the ecosystem services they provide. The species composition of floodplain vegetation and their ecosystem functions (e.g. leaf CO2 uptake and transpiration) are very sensitive to the soil hydrological regime, which is highly variable both spatially and temporally. The hydrological regime also affects the temperature and nutrient regime of the root environment, leading to indirect impacts on vegetation. However, the mechanisms controlling these interdependencies are not well established. The proposed project, FUSE, aims to advance this knowledge at a variety of scales. A better understanding of these vulnerable ecosystems will allow improved environmental management, under current and future conditions. A field study is proposed in the Oxford Floodplain (OFP). This study will build upon an existing hydrological monitoring network currently in place in the Oxford Meadows Special Area of Conservation (SAC). The aims will be achieved by a sophisticated combination of environmental data and computer models. This involves state-of-the-art tools: a Wireless Underground Sensor Network (WUSN) and related monitoring of environmental variables, as well as high-resolution Earth Observation (EO, i.e. satellite) data. WSNs are a relatively recent application of technology; uptake of this technology by environmental scientists enables continuous monitoring that is both scalable and less intrusive on its surroundings. It is desirable for land-based sensor networks to have few or no above-ground components, for aesthetic and security reasons, as well as to avoid interference with land management practices. Recently, this has led to the introduction of WUSNs where all or at least the majority of the sensing and transmitting components are underground. WUSNs are rare, especially in the UK, and have not been tested long-term in a challenging environment such as the OFP. Reliability and the potential distance of data transmission depend on a number of factors, including the soil type, sensor installation depth, soil moisture content and technological factors. These will be researched extensively in the FUSE project, initially using existing data on the OFP hydrological regime, soils and vegetation height/density. The precise design of the WUSN will be determined with the aid of a geostatistical procedure. FUSE will allow researchers to reliably measure underground spatial variability at hitherto unachievable resolutions of less than a metre. The project will use a mesh of simple wireless sensor nodes previously developed at Imperial College ('Beasties'). These nodes will gather environmental data, and route these to a base-station that transmits to a remote database via GPRS. The low-cost, low-power Beasties have been used extensively in similar, but less challenging environments. The enhanced sensor technology will be entirely transferable. Theoretical tools in FUSE comprise of a simulation model (SCOPE_SUB), that can be used to describe and predict the interaction between the soil (soil moisture content, soil temperature and nutrient status), the vegetation (root water/nutrient uptake, CO2 uptake and transpiration), and the hydrometeorological regime. Furthermore we will use geospatial models to spatially interpolate between measured, modelled and EO data, thereby increasing data-density. EO data will serve to guide the continuous (in time) simulation model predictions. In that way high resolution maps of key soil and vegetation variables can be constructed. Computer Science tools, e.g. a so-called Integrated Development Environment to help environmental scientist to set up and test the WUSN, and a Web portal for quality control, sensor calibration, time series- and geospatial-analysis, parameter estimation and real-time model output, will be developed.

提高对水文系统和依赖生态功能的了解对于最佳环境管理至关重要。洪泛平原尤其重要，因为它们提供了生态系统服务。河漫滩植被的种类组成及其生态系统功能（如叶片CO2吸收和蒸腾）对土壤水文状况非常敏感，土壤水文状况在空间和时间上都是高度可变的。水文状况也影响根环境的温度和营养状况，导致对植被的间接影响。然而，控制这些相互依赖关系的机制还没有很好地建立起来。拟议的项目FUSE旨在在各种尺度上推进这一知识。更好地了解这些脆弱的生态系统将有助于改善当前和未来条件下的环境管理。建议在牛津洪泛区（OFP）进行实地研究。这项研究将建立在牛津草地特别保护区（SAC）现有的水文监测网络的基础上。这些目标将通过环境数据和计算机模型的复杂结合来实现。这涉及到最先进的工具：无线地下传感器网络（WUSN）和相关的环境变量监测，以及高分辨率地球观测（EO，即卫星）数据。无线传感器网络是一项相对较新的技术应用；环境科学家采用这项技术，可以实现可扩展且对周围环境干扰较小的连续监测。出于美观和安全的考虑，以及为了避免干扰土地管理实践，陆上传感器网络最好只有很少或没有地面上的组件。最近，这导致了wusn的引入，其中所有或至少大部分传感和传输组件都在地下。wusn很少见，特别是在英国，而且还没有在OFP等具有挑战性的环境中进行过长期测试。数据传输的可靠性和潜在距离取决于许多因素，包括土壤类型、传感器安装深度、土壤含水量和技术因素。这些将在FUSE项目中进行广泛的研究，最初使用OFP水文状况、土壤和植被高度/密度的现有数据。在地质统计程序的帮助下，将确定wsn的精确设计。FUSE将使研究人员能够可靠地测量地下空间的变化，其分辨率不到一米，迄今为止还无法实现。该项目将使用之前由帝国理工学院（“Beasties”）开发的简单无线传感器节点网。这些节点将收集环境数据，并将这些数据发送到通过GPRS传输到远程数据库的基站。低成本、低功耗的Beasties已经广泛应用于类似但挑战性较小的环境中。增强的传感器技术将完全可转让。FUSE的理论工具包括一个模拟模型（SCOPE_SUB），该模型可用于描述和预测土壤（土壤含水量、土壤温度和养分状况）、植被（根系水分/养分吸收、二氧化碳吸收和蒸腾）和水文气象制度之间的相互作用。此外，我们将使用地理空间模型在测量、建模和EO数据之间进行空间插值，从而增加数据密度。EO数据将用于指导连续（及时）模拟模型预测。通过这种方式，可以构建关键土壤和植被变量的高分辨率地图。将开发计算机科学工具，例如所谓的集成开发环境，以帮助环境科学家建立和测试自然环境网络，以及用于质量控制、传感器校准、时间序列和地理空间分析、参数估计和实时模型输出的门户网站。

项目成果

How should a spatial-coverage sample design for a geostatistical soil survey be supplemented to support estimation of spatial covariance parameters?

• DOI: 10.1016/j.geoderma.2017.12.022
• 发表时间: 2018-06
• 期刊: Geoderma
• 影响因子: 6.1
• 作者: R. Lark;B. Marchant

Characterization of a Highly Biodiverse Floodplain Meadow Using Hyperspectral Remote Sensing within a Plant Functional Trait Framework

• DOI: 10.3390/rs8020112
• 发表时间: 2016-02
• 期刊: Remote. Sens.
• 作者: S. Punalekar;A. Verhoef;I. Tatarenko;C. Tol;D. Macdonald;B. Marchant;F. Gerard;K. White;D. Gowing