Using High‐Resolution Satellite Imagery and Deep Learning to Track Dynamic Seasonality in Small Water Bodies
Abstract Small water bodies (i.e., ponds; <0.01 km2) play an important role in Earth System processes, including carbon cycling and emissions of methane. Detection and monitoring of ponds using satellite imagery has been extremely difficult and many water maps are biased toward lakes (>0.01 km...
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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-04-01
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| Series: | Geophysical Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2022GL102327 |
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| Summary: | Abstract Small water bodies (i.e., ponds; <0.01 km2) play an important role in Earth System processes, including carbon cycling and emissions of methane. Detection and monitoring of ponds using satellite imagery has been extremely difficult and many water maps are biased toward lakes (>0.01 km2). We leverage high‐resolution (3 m) optical satellite imagery from Planet Labs and deep learning methods to map seasonal changes in pond and lake areal extent across four regions in Alaska. Our water maps indicate that changes in open water extent over the snow‐free season are especially pronounced in ponds. To investigate potential impacts of seasonal changes in pond area on carbon emissions, we provide a case study of open water methane emission budgets using the new water maps. Our approach has widespread applications for water resources, habitat and land cover change assessments, wildlife management, risk assessments, and other biogeochemical modeling efforts. |
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| ISSN: | 0094-8276 1944-8007 |