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

Mullen, Andrew; Watts, Jennifer D.; Rogers, Brendan M.; Carroll, M.; Elder, C.; Noomah, Jonas; Williams, Z.; Caraballo-Vega, J.A.; Bredder, Allison; Rickenbaugh, Eliza; Levenson, Eric; Cooley, S. W.; Hung, Jacqueline K.Y.; Fiske, Greg; Potter, Stefano; Yang, Yili; Miller, Charles E.; Natali, Susan M.; Douglas, Thomas A.; Kyzivat, E. D.

doi:10.1029/2022gl102327

Cited by 10 publications

(8 citation statements)

References 85 publications

(105 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data fusion with three high‐resolution (HR) remotely‐sensed data sets (Kyzivat et al., 2019a; Mullen et al., 2022; Muster et al., 2017a) was used to supplement HydroLAKES coverage (Text S1 in Supporting Information ). While suitable for small lake extrapolation, these data sets under‐represent large lakes, due to their small, sometimes airborne‐derived, extents.…”

Section: Methodsmentioning

confidence: 99%

“…Data fusion with three high-resolution (HR) remotely-sensed data sets (Kyzivat et al, 2019a;Mullen et al, 2022;Muster et al, 2017a) was used to supplement HydroLAKES coverage (Text S1 in Supporting Information S1).…”

Section: Estimation Of Small Water Bodiesmentioning

confidence: 99%

“…The following publicly‐available data sets were used in this analysis: HydroLAKES (Messager et al., 2016) from the World Wildlife Fund; GSW (Pekel et al., 2016) from the European Commission's Joint Research Centre; BAWLD (Olefeldt et al., 2021a) and BAWLD‐CH4 (Kuhn et al., 2021a) from the National Science Foundation Arctic Data Center; WBD (Feng & Sui, 2020) from the Chinese National Tibetan Plateau/Third Pole Environment Data Center, available in English; LEV (Kyzivat et al., 2021) from the Oak Ridge National Lab (ORNL) Distributed Active Archive Center (DAAC); HR lakes (Kyzivat et al., 2019a; Mullen et al., 2022; Muster et al., 2017a) from ORNL DAAC, ORNL DAAC, and PANGEA, respectively; and ERA5 (Muñoz Sabater, 2019) from Copernicus Climate Change Service Climate Data Store. New data and a python toolbox generated for this manuscript are archived in the Arctic Data Center (Kyzivat & Smith, 2023).…”

Section: Data Availability Statementmentioning

confidence: 99%

“…In contrast to wetlands (Melton et al., 2013), lakes lack methane emission models (Tan & Zhuang, 2015; Zhuang et al., 2023) and are most commonly upscaled using individual lake area (Bastviken et al., 2004; DelSontro et al., 2018; Holgerson & Raymond, 2016; Rosentreter et al., 2021), although some studies use lake type (Johnson et al., 2022; Wik et al., 2016) or chlorophyll (DelSontro et al., 2018). Finer‐scale studies have identified other drivers including shoreline fluctuations (Hondula et al., 2021; Mullen et al., 2023), nutrient concentrations (Bastviken et al., 2004), and littoral zones (Larmola et al., 2004). While having modest correlation coefficients over broad scales (e.g., r 2 < 0.3, Savignano et al., 2023), lake area and temperature are nonetheless among the most predictive of available variables (Aben et al., 2022; Bastviken et al., 2004; Sanches et al., 2019; Yvon‐Durocher et al., 2014) and a minimum requirement for upscaling lake methane emissions over large, data‐poor areas such as the pan‐Arctic.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Closer Look at the Effects of Lake Area, Aquatic Vegetation, and Double‐Counted Wetlands on Pan‐Arctic Lake Methane Emissions Estimates

Kyzivat,

Smith

2023

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

Lake methane emissions are commonly upscaled from lake area, with recognition that smaller, non‐inventoried lakes emit more per unit area. There is also growing awareness of the importance of lake aquatic vegetation and potential “double‐counting” with wetlands, but lack of consensus on which is most impactful. Here, we combine high‐resolution data with the comprehensive lake inventory HydroLAKES to rank these three variables based on emissions sensitivity. Including non‐inventoried small lakes <0.1 km2 (+30 [range: 9.0 to 82]% change) is greatest, followed by double‐counting (−20 [−11 to −34]%) and lake aquatic vegetation (+14 [2.7 to 43]%). Significantly, emissions from non‐inventoried lakes contribute far less than the ∼40% previously determined globally through statistical area extrapolation. We produce a first pan‐Arctic estimate of lake aquatic vegetation in 1.37 million km2 of lakes, but after correcting for persistent double‐counting, its net effect is to decrease emissions estimates by 9%. Thus, previous global emissions estimates are likely too high.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Estimation Of Small Water Bodiesmentioning

confidence: 99%

Section: Data Availability Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Closer Look at the Effects of Lake Area, Aquatic Vegetation, and Double‐Counted Wetlands on Pan‐Arctic Lake Methane Emissions Estimates

Kyzivat,

Smith

2023

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, icedammed lakes can be short-lived and may experience spontaneous drainage (Hewitt and Liu, 2010;Carrivick et al, 2017;Jacquet et al, 2017), therefore they require continuous monitoring to allow advance warning. PlanetScope satellite imagery provides daily coverage at 3 m resolution (Qayyum et al, 2020;Mullen et al, 2023), but the data is not publicly available. Recent studies have utilized UAS data to supplement satellite data to combine the benefits of both platforms and obtain information quickly and inexpensively with greater temporal and spatial resolution (Wigmore and Mark, 2017;Gaffey and Bhardwaj, 2020).…”

Section: Glacial Lake Outburst Floodingmentioning

confidence: 99%

UAS remote sensing applications to abrupt cold region hazards

Verfaillie,

Cho,

Dwyre

et al. 2023

Front. Remote Sens.

View full text Add to dashboard Cite

Unoccupied aerial systems (UAS) are an established technique for collecting data on cold region phenomenon at high spatial and temporal resolutions. While many studies have focused on remote sensing applications for monitoring long term changes in cold regions, the role of UAS for detection, monitoring, and response to rapid changes and direct exposures resulting from abrupt hazards in cold regions is in its early days. This review discusses recent applications of UAS remote sensing platforms and sensors, with a focus on observation techniques rather than post-processing approaches, for abrupt, cold region hazards including permafrost collapse and event-based thaw, flooding, snow avalanches, winter storms, erosion, and ice jams. The pilot efforts highlighted in this review demonstrate the potential capacity for UAS remote sensing to complement existing data acquisition techniques for cold region hazards. In many cases, UASs were used alongside other remote sensing techniques (e.g., satellite, airborne, terrestrial) and in situ sampling to supplement existing data or to collect additional types of data not included in existing datasets (e.g., thermal, meteorological). While the majority of UAS applications involved creation of digital elevation models or digital surface models using Structure-from-Motion (SfM) photogrammetry, this review describes other applications of UAS observations that help to assess risks, identify impacts, and enhance decision making. As the frequency and intensity of abrupt cold region hazards changes, it will become increasingly important to document and understand these changes to support scientific advances and hazard management. The decreasing cost and increasing accessibility of UAS technologies will create more opportunities to leverage these techniques to address current research gaps. Overcoming challenges related to implementation of new technologies, modifying operational restrictions, bridging gaps between data types and resolutions, and creating data tailored to risk communication and damage assessments will increase the potential for UAS applications to improve the understanding of risks and to reduce those risks associated with abrupt cold region hazards. In the future, cold region applications can benefit from the advances made by these early adopters who have identified exciting new avenues for advancing hazard research via innovative use of both emerging and existing sensors.

show abstract

Challenges and Prospects in Using Very High-Resolution Optical Satellite Imagery for Urban Water Body Extraction—A Study Utilizing GeoEye Remote Sensing Data for Surface Water Management Perspective

Ferdos,

Rahman

2024

GIScience and Geo-Environmental Modelling

View full text Add to dashboard Cite

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

Cited by 10 publications

References 85 publications

A Closer Look at the Effects of Lake Area, Aquatic Vegetation, and Double‐Counted Wetlands on Pan‐Arctic Lake Methane Emissions Estimates

A Closer Look at the Effects of Lake Area, Aquatic Vegetation, and Double‐Counted Wetlands on Pan‐Arctic Lake Methane Emissions Estimates

UAS remote sensing applications to abrupt cold region hazards

Challenges and Prospects in Using Very High-Resolution Optical Satellite Imagery for Urban Water Body Extraction—A Study Utilizing GeoEye Remote Sensing Data for Surface Water Management Perspective

Contact Info

Product

Resources

About