Remote sensing of peatland responses to hydrological change

低地沼泽地通过光合作用吸收大气中二氧化碳的能力意味着它们在调节全球气候方面发挥着重要作用。在全球范围内，泥炭沼泽含有陆地生物圈中储存的约三分之一的碳。气候变化可能会影响沼泽的水平衡，这反过来又会对碳储存产生影响。这是因为退化的泥炭地释放出二氧化碳等温室气体，而不是吸收它们。人为干扰，如泥炭切割和排水也可能影响碳储存。因此，有必要加强对低地凸起沼泽的空间分布水文特性的了解。凸起的沼泽具有分布在其表面的特征，包括丘、脊、草坪、凹地和水池。表面结构和沼泽水文学之间存在公认的联系。表面结构也可以决定泥炭地对水文变化的反应，使其成为监测气候变化的重要变量。生活在沼泽表面的植物群落也可以指示一个凸起的沼泽的水文状况。大量的泥炭藓是潮湿和活跃泥炭形成的标志。相比之下，沼泽的干燥地区的特点是物种与粗糙的树冠，如石楠。这些植物具有不同的空间特征，这是由树冠的粗糙度造成的。这为沼泽水文学提供了另一种结构指标。本研究旨在利用新的遥感技术监测沼泽结构，从而，水文状况。遥感是一种从远处测量地球表面的方法，通常使用基于卫星或飞机的成像仪器。遥感方法提供的天气视图比基于实地的监测技术具有明显的优势，因为凸起的沼泽表面通常无法接近，难以在地面上进行调查。遥感新技术提供了探测和测量地表结构的手段。如前所述，结构是沼泽水文学的一个关键指标，因此，如果这可以量化和建模使用遥感，这提供了一个可重复的监测和管理这些栖息地的手段。英国坎布里亚郡的一个重要的凸起沼泽地将被用作一套新的监测和建模方法的试验台。选择Wedholme流是因为它包含了一系列的条件类别，从裸露的泥炭到完整的沼泽。现场的实地研究将侧重于描述生态和水文特征，并根据这些数据将沼泽表面划分为条件等级（将使用“低地隆起沼泽清单”（LRBI）分类）。这项研究将采用一种名为激光雷达（光探测和测距）的机载地形测绘系统，该系统提供关于植被冠层结构的高空间分辨率数据。将利用空间统计学分析这些数据，以提取关键的远距离碰撞影响指数类别的典型特征。将使用变差函数模型对这些特征进行建模。地面结构的实地测量，类似于那些由机载系统收集（但在更精细的空间分辨率）将比较那些从空中数据，以便可以得出的结论，最佳的空间采样需要区分关键的水文类。这些分析将导致空间模型的发展，将结构与低地沼泽条件类别联系起来。这里开发和测试的技术将提供湿地水文，发育地形，生物多样性和遥感结构变量之间的联系的深入了解。这项研究的更广泛应用涉及到这些研究结果将提供一种严格和有效监测全球湿地资源的方法。

The ability of lowland raised bogs to absorb atmospheric carbon dioxide through photosynthesis means that they play a major role in moderating global climate. Globally, peat bogs contain around one third of the carbon stored in the terrestrial biosphere. Climate change may affect the bog's water balance, which will in turn, have an effect on carbon storage. This is because degraded peatlands release greenhouse gases such as carbon dioxide, instead of absorbing them. Human disturbances such as peat cutting and draining may also affect carbon storage. As a result, there is a research imperative for an enhanced understanding of the spatially distributed hydrological properties of lowland raised bogs. Raised bogs have characteristic features distributed across their surface, including hummocks, ridges, lawns, hollows and pools. There is a recognised link between the structure of the surface, and bog hydrology. Surface structure can also determine peatland responses to hydrological variability, making this an important variable to monitor in relation to climate change. Plant communities living on the bog surface can also indicate the hydrological status of a raised bog. Extensive mats of Sphagnum mosses are indicative of wetness and active peat formation. In contrast, drier areas of the bog are characterised by species with coarser canopies such as heather. These plants have a different spatial signature, caused by the roughness of the canopy. This provides an alternative structural indicator of bog hydrology. This research aims to use novel remote sensing techniques for monitoring raised bog structure, and thus, hydrological status. Remote sensing is a method of measuring the Earth's surface from a distance, and usually employs satellite- or aircraft-based imaging instruments. The synoptic view offered by a remote sensing approach has obvious advantages over field-based monitoring techniques because raised bog surfaces are often inaccessible and difficult to survey on the ground. New techniques in remote sensing offer a means of detecting and measuring surface structure. As previously mentioned, structure is a key indicator of raised bog hydrology, and therefore if this can be quantified and modelled using remote sensing, this offers a repeatable means of monitoring and managing these habitats. An important raised bog site in Cumbria, UK will be used as the test-bed for a suite of new monitoring and modelling approaches. Wedholme Flow has been chosen because it contains a range of condition categories ranging from cutover bare peat to intact raised bog. Field research at the site will focus on characterising the ecology and hydrology and classifying the bog surface into condition classes based on these data (the 'Lowland Raised Bog Inventory' (LRBI) classification will be used). The research will employ an airborne topographic mapping system called LiDAR (Light Detection and Ranging), which provides fine spatial resolution data on the structure of the vegetation canopy. Using spatial statistics, these data will be analysed to extract typical signatures for key LRBI categories. These signatures will be modelled using variogram models. Field measurements of surface structure, similar to those collected by the airborne system (but at much finer spatial resolution) will be compared to those from the airborne data so that conclusions can be reached on the optimal spatial sampling required to discriminate key hydrological classes. These analyses will result in development of spatial models linking structure to lowland raised bog condition categories. Techniques developed and tested here will provide an in-depth understanding of the link between wetland hydrology, developmental topography, biodiversity, and remotely sensed structural variables. The wider application of this research relates to the way in which these findings will provide a means of rigorously and efficiently monitoring global wetland resources.