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Using High-Resolution Satellite Imagery and Deep Learning to Track Dynamic Seasonality in Small Water Bodies

Publication ,  Journal Article
Mullen, AL; Watts, JD; Rogers, BM; Carroll, ML; Elder, CD; Noomah, J; Williams, Z; Caraballo-Vega, JA; Bredder, A; Rickenbaugh, E; Levenson, E ...
Published in: Geophysical Research Letters
April 16, 2023

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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Published In

Geophysical Research Letters

DOI

EISSN

1944-8007

ISSN

0094-8276

Publication Date

April 16, 2023

Volume

50

Issue

7

Related Subject Headings

  • Meteorology & Atmospheric Sciences
 

Citation

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Mullen, A. L., Watts, J. D., Rogers, B. M., Carroll, M. L., Elder, C. D., Noomah, J., … Kyzivat, E. D. (2023). Using High-Resolution Satellite Imagery and Deep Learning to Track Dynamic Seasonality in Small Water Bodies. Geophysical Research Letters, 50(7). https://doi.org/10.1029/2022GL102327
Mullen, A. L., J. D. Watts, B. M. Rogers, M. L. Carroll, C. D. Elder, J. Noomah, Z. Williams, et al. “Using High-Resolution Satellite Imagery and Deep Learning to Track Dynamic Seasonality in Small Water Bodies.” Geophysical Research Letters 50, no. 7 (April 16, 2023). https://doi.org/10.1029/2022GL102327.
Mullen AL, Watts JD, Rogers BM, Carroll ML, Elder CD, Noomah J, et al. Using High-Resolution Satellite Imagery and Deep Learning to Track Dynamic Seasonality in Small Water Bodies. Geophysical Research Letters. 2023 Apr 16;50(7).
Mullen, A. L., et al. “Using High-Resolution Satellite Imagery and Deep Learning to Track Dynamic Seasonality in Small Water Bodies.” Geophysical Research Letters, vol. 50, no. 7, Apr. 2023. Scopus, doi:10.1029/2022GL102327.
Mullen AL, Watts JD, Rogers BM, Carroll ML, Elder CD, Noomah J, Williams Z, Caraballo-Vega JA, Bredder A, Rickenbaugh E, Levenson E, Cooley SW, Hung JKY, Fiske G, Potter S, Yang Y, Miller CE, Natali SM, Douglas TA, Kyzivat ED. Using High-Resolution Satellite Imagery and Deep Learning to Track Dynamic Seasonality in Small Water Bodies. Geophysical Research Letters. 2023 Apr 16;50(7).
Journal cover image

Published In

Geophysical Research Letters

DOI

EISSN

1944-8007

ISSN

0094-8276

Publication Date

April 16, 2023

Volume

50

Issue

7

Related Subject Headings

  • Meteorology & Atmospheric Sciences