Remote sensing of peatland responses to hydrological change

低地抬高的沼泽能够通过光合作用吸收大气中的二氧化碳，这意味着它们在缓和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碳储量的三分之一左右。气候变化可能会影响沼泽的水平衡，这反过来又会影响碳储存。这是因为退化的泥炭地释放了二氧化碳等温室气体，而不是吸收它们。人为干扰，如泥炭砍伐和排水，也可能影响碳储存。因此，加强对低地沼泽空间分布水文特性的理解势在必行。凸起的沼泽表面分布着一些特征，包括小丘、山脊、草坪、洼地和池塘。在地表结构和沼泽水文之间有一个公认的联系。地表结构还可以决定泥炭地对水文变率的响应，使其成为监测与气候变化有关的一个重要变量。生活在沼泽表面的植物群落也可以指示凸起沼泽的水文状况。广阔的泥炭藓席表明了潮湿和活跃的泥炭形成。相比之下，沼泽的干燥地区的特点是有粗糙的冠层，如石南。这些植物有不同的空间特征，这是由于树冠的粗糙度造成的。这为沼泽水文提供了另一种结构指标。本研究旨在利用新的遥感技术来监测凸起的沼泽结构，从而监测水文状况。遥感是一种从远处测量地球表面的方法，通常使用卫星或飞机成像仪器。遥感方法提供的概貌视图明显优于基于实地的监测技术，因为凸起的沼泽表面往往难以接近，难以在地面进行调查。遥感新技术提供了一种探测和测量地表结构的手段。如前所述，结构是高地沼泽水文的一个关键指标，因此，如果可以利用遥感对其进行量化和建模，这就提供了一种可重复的监测和管理这些生境的手段。英国坎布里亚郡的一个重要的高地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 Flow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凸起沼泽。现场研究将侧重于描述生态和水文特征，并根据这些数据对沼泽表面进行分类（将使用“Lowland raise bog Inventory”（LRBI）分类）。该研究将采用一种名为LiDAR（光探测和测距）的机载地形测绘系统，该系统可提供有关植被冠层结构的精细空间分辨率数据。使用空间统计，将分析这些数据以提取关键LRBI类别的典型签名。这些特征将使用变异函数模型进行建模。地面结构的实地测量，类似于机载系统收集的数据（但空间分辨率要精细得多），将与机载数据进行比较，以便得出区分关键水文类别所需的最佳空间采样的结论。这些分析将导致将结构与低地抬高沼泽条件类别联系起来的空间模型的发展。在这里开发和测试的技术将提供对湿地水文、发育地形、生物多样性和遥感结构变量之间联系的深入了解。这项研究的广泛应用涉及到这些发现将为严格和有效地监测全球湿地资源提供一种方法。

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.