{'fraction_non_alphanumeric': 0.019081272084805655, 'fraction_numerical': 0.005653710247349823, 'mean_word_length': 5.156521739130435, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Improving Peatland Mapping and Monitoring Capability Across Broad Regions Using SAR in Cloud Computing Platforms

Peatlands occur in ecozones from the tropics to the arctic, and are estimated to globally cover just under 450 million ha, roughly 3-5% of the Earth's land surface [8]. Although they cover a small amount of land globally, peatlands are estimated to store more than 30% of the Earth's soil carbon (C) and are at risk from land use and climate change. An approach of multi-source SAR and optical imagery from multiple dates in machine learning classifiers has demonstrated to be of high value in accurately mapping peatlands from boreal, temperate and tropical regions. Cloud-computing has recently been integrated into a multi-date SAR-optical wetland classification workflow that has high accuracy for peatland mapping. Leveraging the large datasets in Google Earth Engine and Alaska Satellite Facility's OpenSARLabs is improving our capability to access large optical and SAR datasets to integrate into our processing/analysis workflow that was previously cumbersome and time consuming. Such analysis is allowing for larger regional areas to be mapped more efficiently. In this paper we review one study conducted completely outside of cloud computing and two examples of how cloud computing is improving our wetland mapping capability in terms of efficiency, but also in more robust input data sets.