Assessing Ice Break-Up Trends in Slave River Delta through Satellite Observations and Random Forest Modeling
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 pra...
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ftdoajarticles:oai:doaj.org/article:6dfeb8d0bdbd4256b246b43fcc4d73d2 2024-09-15T18:08:13+00:00 Assessing Ice Break-Up Trends in Slave River Delta through Satellite Observations and Random Forest Modeling Ida Moalemi Homa Kheyrollah Pour K. Andrea Scott 2024-06-01T00:00:00Z https://doi.org/10.3390/rs16122244 https://doaj.org/article/6dfeb8d0bdbd4256b246b43fcc4d73d2 EN eng MDPI AG https://www.mdpi.com/2072-4292/16/12/2244 https://doaj.org/toc/2072-4292 doi:10.3390/rs16122244 2072-4292 https://doaj.org/article/6dfeb8d0bdbd4256b246b43fcc4d73d2 Remote Sensing, Vol 16, Iss 12, p 2244 (2024) Slave River Delta Great Slave Lake machine learning random forest modeling classification Science Q article 2024 ftdoajarticles https://doi.org/10.3390/rs16122244 2024-08-05T17:49:05Z 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. Article in Journal/Newspaper Great Slave Lake Slave River Directory of Open Access Journals: DOAJ Articles Remote Sensing 16 12 2244 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Slave River Delta Great Slave Lake machine learning random forest modeling classification Science Q |
spellingShingle |
Slave River Delta Great Slave Lake machine learning random forest modeling classification Science Q Ida Moalemi Homa Kheyrollah Pour K. Andrea Scott Assessing Ice Break-Up Trends in Slave River Delta through Satellite Observations and Random Forest Modeling |
topic_facet |
Slave River Delta Great Slave Lake machine learning random forest modeling classification Science Q |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Ida Moalemi Homa Kheyrollah Pour K. Andrea Scott |
author_facet |
Ida Moalemi Homa Kheyrollah Pour K. Andrea Scott |
author_sort |
Ida Moalemi |
title |
Assessing Ice Break-Up Trends in Slave River Delta through Satellite Observations and Random Forest Modeling |
title_short |
Assessing Ice Break-Up Trends in Slave River Delta through Satellite Observations and Random Forest Modeling |
title_full |
Assessing Ice Break-Up Trends in Slave River Delta through Satellite Observations and Random Forest Modeling |
title_fullStr |
Assessing Ice Break-Up Trends in Slave River Delta through Satellite Observations and Random Forest Modeling |
title_full_unstemmed |
Assessing Ice Break-Up Trends in Slave River Delta through Satellite Observations and Random Forest Modeling |
title_sort |
assessing ice break-up trends in slave river delta through satellite observations and random forest modeling |
publisher |
MDPI AG |
publishDate |
2024 |
url |
https://doi.org/10.3390/rs16122244 https://doaj.org/article/6dfeb8d0bdbd4256b246b43fcc4d73d2 |
genre |
Great Slave Lake Slave River |
genre_facet |
Great Slave Lake Slave River |
op_source |
Remote Sensing, Vol 16, Iss 12, p 2244 (2024) |
op_relation |
https://www.mdpi.com/2072-4292/16/12/2244 https://doaj.org/toc/2072-4292 doi:10.3390/rs16122244 2072-4292 https://doaj.org/article/6dfeb8d0bdbd4256b246b43fcc4d73d2 |
op_doi |
https://doi.org/10.3390/rs16122244 |
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Remote Sensing |
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16 |
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12 |
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2244 |
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