Advancing peatland vegetation mapping by spaceborne imaging spectroscopy

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 (& UDelta;MAE + 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 (& UDelta;MAE + 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 multidata hyperspectral imagery, these data can be expected to play a bigger role in the future monitoring of peatlands.