Remote sensing of lake and river ice

Duguay, Claude R.; Bernier, Monique; Gauthier, Yves; Kouraev, Alexei V.

doi:10.1002/9781118368909.ch12

Cited by 65 publications

(64 citation statements)

References 77 publications

(85 reference statements)

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“…Surface‐water gains in boreal regions of Alaska are largely driven by ice‐jam flooding events, as is captured in our trend analysis (Figure ), which have recharged lake basins within riverine and lowland flood plains along the Yukon River (Duguay, Bernier, Gauthier, & Kouraev, ; Jepsen, Walvoord, Voss, & Rover, ). Increases in groundwater discharge, as a result of enhanced permafrost thaw, are likely a contributing factor to surface‐water gains in riverine corridors (Walvoord, Voss, & Wellman, ).…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal remote sensing of ecosystem change and causation across Alaska

Pastick

Jorgenson²,

Goetz

et al. 2018

Global Change Biology

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Contemporary climate change in Alaska has resulted in amplified rates of press and pulse disturbances that drive ecosystem change with significant consequences for socio-environmental systems. Despite the vulnerability of Arctic and boreal landscapes to change, little has been done to characterize landscape change and associated drivers across northern high-latitude ecosystems. Here we characterize the historical sensitivity of Alaska's ecosystems to environmental change and anthropogenic disturbances using expert knowledge, remote sensing data, and spatiotemporal analyses and modeling. Time-series analysis of moderate-and high-resolution imagery was used to characterize land- and water-surface dynamics across Alaska. Some 430,000 interpretations of ecological and geomorphological change were made using historical air photos and satellite imagery, and corroborate land-surface greening, browning, and wetness/moisture trend parameters derived from peak-growing season Landsat imagery acquired from 1984 to 2015. The time series of change metrics, together with climatic data and maps of landscape characteristics, were incorporated into a modeling framework for mapping and understanding of drivers of change throughout Alaska. According to our analysis, approximately 13% (~174,000 ± 8700 km ) of Alaska has experienced directional change in the last 32 years (±95% confidence intervals). At the ecoregions level, substantial increases in remotely sensed vegetation productivity were most pronounced in western and northern foothills of Alaska, which is explained by vegetation growth associated with increasing air temperatures. Significant browning trends were largely the result of recent wildfires in interior Alaska, but browning trends are also driven by increases in evaporative demand and surface-water gains that have predominately occurred over warming permafrost landscapes. Increased rates of photosynthetic activity are associated with stabilization and recovery processes following wildfire, timber harvesting, insect damage, thermokarst, glacial retreat, and lake infilling and drainage events. Our results fill a critical gap in the understanding of historical and potential future trajectories of change in northern high-latitude regions.

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Section: Discussionmentioning

confidence: 99%

Spatiotemporal remote sensing of ecosystem change and causation across Alaska

Pastick

Jorgenson²,

Goetz

et al. 2018

Global Change Biology

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“…These factors result in a lack of continuous and complete historical records on regional climate changes (Kropáček et al, ; Liu & Chen, ). Remote sensing data have been widely used in lake ice monitoring (Duguay et al, ; Wei & Ye, ). Passive microwave sensors such as the Scanning Multichannel Microwave Radiometer, the Special Sensor Microwave/Image (SSM/I), and the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E) have high temporal resolutions (at times twice daily or better) and are suitable for monitoring lake ice changes in large lakes, such as Qinghai Lake (Cai et al, ; Che et al, ) and Nam Co (Ke et al, ) on the TP, and Great Bear Lake and Great Slave Lake (Howell et al, ) in Canada.…”

Section: Introductionmentioning

confidence: 99%

Variations of Lake Ice Phenology on the Tibetan Plateau From 2001 to 2017 Based on MODIS Data

Cai

Cao

et al. 2019

JGR Atmospheres

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Lake ice is a robust indicator of climate change. The availability of information contained in Moderate Resolution Imaging Spectroradiometer daily snow products from 2000 to 2017 could be greatly improved after cloud removal by gap filling. Thresholds based on open water pixel numbers are used to extract the freezeup start and breakup end dates for 58 lakes on the Tibetan Plateau (TP); 18 lakes are also selected to extract the freezeup end and breakup start dates. The lake ice durations are further calculated based on freezeup and breakup dates. Lakes on the TP begin to freezeup in late October and all the lakes start the ice cover period in mid‐January of the following year. In late March, some lakes begin to break up, and all the lakes end the ice cover period in early July. Generally, the lakes in the northern Inner‐TP have earlier freezeup dates and later breakup dates (i.e., longer ice cover durations) than those in the southern Inner‐TP. Over 17 years, the mean ice cover duration of 58 lakes is 157.78 days, 18 (31%) lakes have a mean extending rate of 1.11 day/year, and 40 (69%) lakes have a mean shortening rate of 0.80 day/year. Geographical location and climate conditions determine the spatial heterogeneity of the lake ice phenology, especially the ones of breakup dates, while the physico‐chemical characteristics mainly affect the freezeup dates of the lake ice in this study. Ice cover duration is affected by both climatic and lake specific physico‐chemical factors, which can reflect the climatic and environmental change for lakes on the TP.

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“…Microwave radiometric data have a spatial resolution of about 25 km. Satellite altimetry data themselves, and in conjunction with the data of a microwave radiometer located on board the satellite along with the altimeter, can also be used to identify ice cover (Lebedev, 2013;Duguay et al, 2015;Lebedev, Klyuev, 2018;, its climatic variability (Kouraev et al, 2003;Kouraev et al, 2009;Duguay et al, 2015;Karetnikov et al, 2016;Lebedev, Klyuev, 2018) or the position of the ice edge ( Lebedev et al, 2011).…”

Section: Resultsmentioning

confidence: 99%

Satellite Altimetry of Sea Level and Ice Cover in the Barents Sea

2019

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Satellite altimetry data are used for investigation of the sea level variability and sea ice cover retreat in the Barents Sea in 1992-2018. The data from ERS − 1/2, ENVISAT, SARAL/AltiKa, and Sentinel-3A/3B satellites were used in this study. An increasing trend of the sea level of about 2.31 mm/yr was observed in this time period, which caused a total increase in the Barents Sea level by about 6 cm. Linear trends of the sea level change varied from 1.84 mm/yr in July to 4.29 mm/yr in September. The average velocity of the ice edge retreat along the tracks in the northeastern direction is of 10.9 km/yr for the same period. It was found that the ice edge displacement rate tends to increase by 0.30 km/yr per a degree in longitude in the eastward direction. Thus, the ice edge retreat along the “eastern” tracks goes faster than along the “western” ones, which is likely explained by a change in the water dynamics in the Barents Sea.

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Remote sensing of lake and river ice

Cited by 65 publications

References 77 publications

Spatiotemporal remote sensing of ecosystem change and causation across Alaska

Spatiotemporal remote sensing of ecosystem change and causation across Alaska

Variations of Lake Ice Phenology on the Tibetan Plateau From 2001 to 2017 Based on MODIS Data

Satellite Altimetry of Sea Level and Ice Cover in the Barents Sea

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