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    <titleInfo>
        <title>Assessing Ice Break-Up Trends in Slave River Delta through Satellite Observations and Random Forest Modeling</title>
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    <name type="personal">
        <namePart type="given">Ida</namePart>
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    <name type="personal">
        <namePart type="given">Homa</namePart>
        <namePart type="given">Kheyrollah</namePart>
        <namePart type="family">Pour</namePart>
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    <name type="personal">
        <namePart type="given">K</namePart>
        <namePart type="given">Andrea</namePart>
        <namePart type="family">Scott</namePart>
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            <title>Remote Sensing, Volume 16, Issue 12</title>
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            <publisher>MDPI AG</publisher>
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        <genre authority="marcgt">periodical</genre>
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    <abstract>The seasonal temperature trends and ice phenology in the Great Slave Lake (GSL) are significantly influenced by inflow from the Slave River. The river undergoes a sequence of mechanical break-ups all the way to the GSL, initiating the GSL break-up process. Additionally, upstream water management practices impact the discharge of the Slave River and, consequently, the ice break-up of the GSL. Therefore, monitoring the break-up process at the Slave River Delta (SRD), where the river meets the lake, is crucial for understanding the cascading effects of upstream activities on GSL ice break-up. This research aimed to use Random Forest (RF) models to monitor the ice break-up processes at the SRD using a combination of satellite images with relatively high spatial resolution, including Landsat-5, Landsat-8, Sentinel-2a, and Sentinel-2b. The RF models were trained using selected training pixels to classify ice, open water, and cloud. The onset of break-up was determined by data-driven thresholds on the ice fraction in images with less than 20% cloud coverage. Analysis of break-up timing from 1984 to 2023 revealed a significant earlier trend using the Mann–Kendall test with a p-value of 0.05. Furthermore, break-up data in recent years show a high degree of variability in the break-up rate using images in recent years with better temporal resolution.</abstract>
    <identifier type="citekey">Moalemi-2024-Assessing</identifier>
    <identifier type="doi">10.3390/rs16122244</identifier>
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        <url>https://gwf-uwaterloo.github.io/gwf-publications/G24-15001</url>
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    <part>
        <date>2024</date>
        <detail type="volume"><number>16</number></detail>
        <detail type="issue"><number>12</number></detail>
        <detail type="page"><number>2244</number></detail>
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%0 Journal Article
%T Assessing Ice Break-Up Trends in Slave River Delta through Satellite Observations and Random Forest Modeling
%A Moalemi, Ida
%A Pour, Homa Kheyrollah
%A Scott, K. Andrea
%J Remote Sensing, Volume 16, Issue 12
%D 2024
%V 16
%N 12
%I MDPI AG
%F Moalemi-2024-Assessing
%X The seasonal temperature trends and ice phenology in the Great Slave Lake (GSL) are significantly influenced by inflow from the Slave River. The river undergoes a sequence of mechanical break-ups all the way to the GSL, initiating the GSL break-up process. Additionally, upstream water management practices impact the discharge of the Slave River and, consequently, the ice break-up of the GSL. Therefore, monitoring the break-up process at the Slave River Delta (SRD), where the river meets the lake, is crucial for understanding the cascading effects of upstream activities on GSL ice break-up. This research aimed to use Random Forest (RF) models to monitor the ice break-up processes at the SRD using a combination of satellite images with relatively high spatial resolution, including Landsat-5, Landsat-8, Sentinel-2a, and Sentinel-2b. The RF models were trained using selected training pixels to classify ice, open water, and cloud. The onset of break-up was determined by data-driven thresholds on the ice fraction in images with less than 20% cloud coverage. Analysis of break-up timing from 1984 to 2023 revealed a significant earlier trend using the Mann–Kendall test with a p-value of 0.05. Furthermore, break-up data in recent years show a high degree of variability in the break-up rate using images in recent years with better temporal resolution.
%R 10.3390/rs16122244
%U https://gwf-uwaterloo.github.io/gwf-publications/G24-15001
%U https://doi.org/10.3390/rs16122244
%P 2244

Markdown (Informal)

[Assessing Ice Break-Up Trends in Slave River Delta through Satellite Observations and Random Forest Modeling](https://gwf-uwaterloo.github.io/gwf-publications/G24-15001) (Moalemi et al., GWF 2024)

• Assessing Ice Break-Up Trends in Slave River Delta through Satellite Observations and Random Forest Modeling (Moalemi et al., GWF 2024)

ACL

• Ida Moalemi, Homa Kheyrollah Pour, and K. Andrea Scott. 2024. Assessing Ice Break-Up Trends in Slave River Delta through Satellite Observations and Random Forest Modeling. Remote Sensing, Volume 16, Issue 12, 16(12):2244.