A new image mosaic of Greenland using Landsat-8 OLI images

Chen, Zhuoqi; Chi, Zhaohui; Zinglersen, Karl B.; Tian, Ying; Wang, Kaijia; Hui, Fengming; Cheng, Xiao

doi:10.1016/j.scib.2020.01.014

Cited by 16 publications

(9 citation statements)

References 12 publications

(14 reference statements)

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“…Water bodies were masked using a 1 km buffer around a modified version of the MEaSUREs GIMP 15 m ice mask (produced from a 1999 to 2001 image mosaic, https://nsidc.org/data/NSIDC-0714/versions/1 ) 33 , 61 . The ice mask was modified manually using coinciding Sentinel-2 imagery and a Landsat 8 image mosaic 62 to update marked changes in the ice margin and outlet positions. Finally, the inventory was filtered and verified manually for quality purposes, as is standard practise in similar studies such as those looking at the Himalaya 63 – 65 .…”

Section: Methodsmentioning

confidence: 99%

Greenland-wide inventory of ice marginal lakes using a multi-method approach

How

Messerli

Mätzler

et al. 2021

Sci Rep

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Ice marginal lakes are a dynamic component of terrestrial meltwater storage at the margin of the Greenland Ice Sheet. Despite their significance to the sea level budget, local flood hazards and bigeochemical fluxes, there is a lack of Greenland-wide research into ice marginal lakes. Here, a detailed multi-sensor inventory of Greenland’s ice marginal lakes is presented based on three well-established detection methods to form a unified remote sensing approach. The inventory consists of 3347 ($$\pm 8$$ ± 8 %) ice marginal lakes ($$>0.05\,{{\text{ km }}^{2}}$$ > 0.05 km 2 ) detected for the year 2017. The greatest proportion of lakes lie around Greenland’s ice caps and mountain glaciers, and the southwest margin of the ice sheet. Through comparison to previous studies, a $$\sim 75$$ ∼ 75 % increase in lake frequency is evident over the west margin of the ice sheet since 1985. This suggests it is becoming increasingly important to include ice marginal lakes in future sea level projections, where these lakes will form a dynamic storage of meltwater that can influence outlet glacier dynamics. Comparison to existing global glacial lake inventories demonstrate that up to 56% of ice marginal lakes could be unaccounted for in global estimates of ice marginal lake change, likely due to the reliance on a single lake detection method.

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Section: Methodsmentioning

confidence: 99%

Greenland-wide inventory of ice marginal lakes using a multi-method approach

How

Messerli

Mätzler

et al. 2021

Sci Rep

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“…GrIS could cause a global sea level rise of 7 m if it melts completely, with the uncertainty at 35% [1]. GrIS has shown accelerated mass loss and contribution to sea level rise in recent decades due to global warming [2,3]. GrIS has made a total contribution of 13.7 ± 1.1 mm to the global sea level rise since 1972 [4].…”

Section: Introductionmentioning

confidence: 99%

Automatic Extraction of Supraglacial Lakes in Southwest Greenland during the 2014–2018 Melt Seasons Based on Convolutional Neural Network

Yuan

Chi

Cheng

et al. 2020

Water

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The mass loss of the Greenland Ice Sheet (GrIS) has implications for global sea level rise, and surface meltwater is an important factor that affects the mass balance. Supraglacial lakes (SGLs), which are representative and identifiable hydrologic features of surface meltwater on GrIS, are a means of assessing surface ablation temporally and spatially. In this study, we have developed a robust method to automatically extract SGLs by testing the widely distributed SGLs area—in southwest Greenland (68°00′ N–70°00′ N, 48°00′ W–51°30′ W), and documented their dynamics from 2014 to 2018 using Landsat 8 OLI images. This method identifies water using Convolutional Neural Networks (CNN) and then extracts SGLs with morphological and geometrical algorithms. CNN combines spectral and spatial features and shows better water identification results than the widely used adaptive thresholding method (Otsu), and two machine learning methods (Random Forests (RF) and Support Vector Machine (SVM)). Our results show that the total SGLs area varied between 158 and 393 km2 during 2014 to 2018; the area increased from 2014 to 2015, then decreased and reached the lowest point (158.73 km2) in 2018, when the most limited surface melting was observed. SGLs were most active during the melt season in 2015 with a quantity of 700 and a total area of 393.36 km2. The largest individual lake developed in 2016, with an area of 9.30 km2. As for the elevation, SGLs were most active in the area, with the elevation ranging from 1000 to 1500 m above sea level, and SGLs in 2016 were distributed at higher elevations than in other years. Our work proposes a method to extract SGLs accurately and efficiently. More importantly, this study is expected to provide data support to other studies monitoring the surface hydrological system and mass balance of the GrIS.

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“…The study area was the entire Greenland region which is known as the largest island in the world with a highest elevation of ~3700 m and an estimated area of ~2.20 million km 2 [36,37] (Figure 1). The GrIS is the second-largest continental ice sheet in the world after the Antarctic ice sheet, with an area of 183.39 × 10 4 km 2 and an average ice thickness of 2300 m [38,39].…”

Section: Study Areamentioning

confidence: 99%

“…However, because the SGLs on the GrIS are generally small and unstable, tracing their variation is limited by significant amount of manual interpretation and editing [10,35]. The GEE platform, which has open and dense Sentinel-2 archive data, offers the advantage of fast batch data processing for large study areas [10,36].…”

Section: Study Areamentioning

confidence: 99%

Distribution and Evolution of Supraglacial Lakes in Greenland during the 2016–2018 Melt Seasons

Huang

Chi

et al. 2021

Remote Sensing

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In recent decades, the melting of the Greenland Ice Sheet (GrIS) has become one of the major causes of global sea-level rise. Supraglacial lakes (SGLs) are typical hydrological features produced on the surface of the GrIS during the melt seasons. The existence and evolution of SGLs play an important role in the melting process of the ice sheet surface. To understand the distribution and recent changes of SGLs in Greenland, this study developed a random forest (RF) algorithm incorporating the texture and morphological features to automatically identify SGLs based on the Google Earth Engine (GEE) platform. Sentinel-2 imagery was used to map the SGLs inventory in Greenland during the 2016–2018 melt seasons and to explore the spatial and temporal variability characteristics of SGLs. Our results show changes in SGLs from 2016 to 2018, with the total area decreasing by ~1152.22 km2 and the number increasing by 1134; SGLs are mainly distributed in western Greenland (SW, CW, NW) and northeastern Greenland (NE), where the NE region has the largest number of observed SGLs and the largest SGL was with the surface area of 16.60 km2 (2016). SGLs were found to be most active in the area with the elevation of 800–1600 m and the slope of 0–5°, and showed a phenomenon of retreating to lower elevation areas and developing to steeper slope areas. Our work provided a method for rapid inventory of SGLs. This study will help monitor the mass balance of the GrIS and predict future rapid ice loss from Greenland.

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A new image mosaic of Greenland using Landsat-8 OLI images

Cited by 16 publications

References 12 publications

Greenland-wide inventory of ice marginal lakes using a multi-method approach

Greenland-wide inventory of ice marginal lakes using a multi-method approach

Automatic Extraction of Supraglacial Lakes in Southwest Greenland during the 2014–2018 Melt Seasons Based on Convolutional Neural Network

Distribution and Evolution of Supraglacial Lakes in Greenland during the 2016–2018 Melt Seasons

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