Peatlands have been drained for land use for a long time and on a large scale, turning them from carbon and nutrient sinks into respective sources, diminishing water regulation capacity, causing surface height loss and destroying biodiversity. Over the last decades, drained peatlands have been rewetted for biodiversity restoration and, as it strongly decreases greenhouse gas emissions, also for climate protection. We quantify restoration success by comparing 320 rewetted fen peatland sites to 243 near-natural peatland sites of similar origin across temperate Europe, all set into perspective by 10k additional European fen vegetation plots. Results imply that rewetting of drained fen peatlands induces the establishment of tall, graminoid wetland plants (helophytisation) and long-lasting differences to pre-drainage biodiversity (vegetation), ecosystem functioning (geochemistry, hydrology), and land cover characteristics (spectral temporal metrics). The Paris Agreement entails the rewetting of 500,000 km2 of drained peatlands worldwide until 2050-2070. A better understanding of the resulting locally novel ecosystems is required to improve planning and implementation of peatland rewetting and subsequent management.
Future spaceborne imaging spectroscopy data will offer new possibilities for mapping ecosystems globally, including urban environments. The high spectral information content of such data is expected to improve accuracies and thematic detail of maps on urban composition and urban environmental condition. This way, urgently needed information for environmental models will be provided, for example, for microclimate or hydrological models. The diverse vertical structures, highly frequent spatial change and a great variety of materials cause challenges for urban environmental mapping with Earth observation data, especially at the 30 m spatial resolution of data from future spaceborne imaging spectrometers. This paper gives an overview of the state-of-the-art in urban imaging spectroscopy considering decreasing spatial resolution, the related user requirements and existing knowledge gaps, as well as expected future directions for the work with new data sets.
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