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Peatlands contribute to a wide range of ecosystem services. They play an important role as carbon sinks in their natural state, but when they are drained, they cause carbon emissions. Rewetting drained peatlands is required to reduce carbon emissions and create new carbon sinks. However, drained peatlands are commonly used as grassland or croplands; therefore, alternative agriculture schemes are required following rewetting. Paludiculture, i.e., agriculture on wet and rewetted peatlands, is an option in these areas after rewetting to produce biomass sustainably. Monitoring of peatland management is challenging, yet needed to ensure a successful rewetting and plantation of, e.g., Phragmites australis and Typha spp., two plants which are commonly used in paludiculture. Remote sensing is an excellent tool for monitoring the vegetation composition of vast rewetted peatland regions. However, because many peatland species have similar spectral characteristics, such monitoring is ideally based on high-spatial, high-temporal hyperspectral images. Data that complies with all these requirements does not exist on a regular basis. Therefore, we assessed the potential for mapping peatland vegetation communities in the Peene and Trebel river basins of the federal state of Mecklenburg-Western Pomerania, Germany, using multi-date hyperspectral (PRISMA) data. We used regression-based unmixing to map fractions of different peatland vegetation classes. Results were analyzed with regard to the contribution of multi-date observations and, in comparison, to multispectral datasets (Landsat-8/Sentinel-2). Our results showed that different classes are best mapped at different observation dates. The multi-date hyperspectral datasets produced less Mean Absolute Error (MAE = 16.4%) than the single-date hyperspectral images (ΔMAE + 1%), with high accuracies for all classes of interest. Compared to the results obtained with multispectral data from similar acquisition dates and annual spectral-temporal metrics (STM), the results from hyperspectral data were always clearly superior (ΔMAE + 4%). Besides the superior performance during comparisons, our results also indicate that information that can be derived from the hyperspectral data with the regression-based unmixing goes clearly beyond that of discrete classification. With more hyperspectral sensors coming up and an expected higher availability of multi-data hyperspectral imagery, these data can be expected to play a bigger role in the future monitoring of peatlands.
Peatlands in the European Union are largely drained for agriculture and emit 25% of the total agricultural greenhouse gas emissions. Drainage-based peatland use has also negative impacts on water quality, drinking water provision and biodiversity. Consequently, key EU environmental policy objectives include the rewetting of all drained peatlands as an essential nature-based solution. Rewetting of peatlands can be combined with site-adapted land use, so-called paludiculture. Paludiculture produces biomass from wet and rewetted peatlands under conditions that maintain the peat body, facilitate peat accumulation and can provide many of the ecosystem services associated with natural, undrained peatlands. The biomass can be used for a wide range of traditional and innovative food, feed, fibre and fuel products. Based on examples in Germany, we have analysed emerging paludiculture options for temperate Europe with respect to greenhouse gas fluxes, biodiversity and indicative business economics. Best estimates of site emission factors vary between 0 and 8 t CO2eq ha−1 y−1. Suitability maps for four peatland-rich federal states (76% of total German peatland area) indicate that most of the drained, agriculturally used peatland area could be used for paludiculture, about one-third of the fen area for any paludiculture type. Fen-specific biodiversity benefits from rewetting and paludiculture, if compared to the drained state. Under favourable conditions, paludiculture can be economically viable, but costs and revenues vary considerably. Key recommendations for large-scale implementation are providing planning security by paludiculture spatial planning, establishing best practice sites and strengthening research into crops, water tables and management options.