ACFNet: A Feature Fusion Network for Glacial Lake Extraction Based on Optical and Synthetic Aperture Radar Images

Wang, Jinxiao; Chen, Fang; Zhang, Meimei; Yu, Bo

doi:10.3390/rs13245091

Cited by 13 publications

(3 citation statements)

References 70 publications

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“…RF performs well with noisy datasets and is adaptable to different types of data thanks to the information entropy, making it suitable for complicated datasets (Hillebrand et al, 2021; F. Wang, Li, et al, 2020). Previous research results show that it performs well in image semantic segmentation using point data (Brenning, 2009; Y. J. Lu et al, 2020; J. X. Wang, Chen, et al, 2021) and could be well applied in this study.…”

Section: Methodsmentioning

confidence: 73%

The response of glaciers and glacial lakes to climate change in the Southeastern Tibetan Plateau over the past three decades

Dou,

Fan,

Wang

et al. 2023

Land Degrad Dev

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With global warming, changes in glaciers and glacial lakes on the Tibetan Plateau call for a serious inspection. The Southeastern Tibetan Plateau (SETP) is a typical monsoonal marine glacier area that is extremely vulnerable to climate change. It is affected by both humid and warm Indian Ocean currents. Outlines of glaciers and glacial lakes in SETP have been extracted from Landsat satellite images with an automatic extraction method based on image compositing with Google Earth Engine and Random Forest algorithm. Alongside the outlines, meteorological data have been collected from 1990 to 2021. The results show that since 1990 the glacier area of SETP has retreated by about 2165.33 km2 (~25.28%), the glacial lake area has increased by about 36.41 km2 (~21.45%), and their number has increased by 477 (~27.32%). The reliability and scientific validity of this study is proven by comparison with existing glacier inventories. By analyzing the meteorological data, the inverse correlation between the glacier area and temperature has been found. Despite decreases in cumulative precipitation, the glacial lake level rise and consequent area expansion have been observed. A fitting equation for the response pattern of glacier changes to temperature and precipitation change is presented and will provide a basis for future studies.

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Section: Methodsmentioning

confidence: 73%

The response of glaciers and glacial lakes to climate change in the Southeastern Tibetan Plateau over the past three decades

Dou,

Fan,

Wang

et al. 2023

Land Degrad Dev

View full text Add to dashboard Cite

show abstract

“…However, UNet is an early deep learning network with limited performance. Advanced deep learning networks that have been developed based on more effective architectures have demonstrated improved identification accuracy compared with UNet [30,31]. Despite the ability of deep learning networks, limited sample data lead to inadequate model training and result in false positive errors.…”

Section: Introductionmentioning

confidence: 99%

Identifying Alpine Lakes in the Eastern Himalayas Using Deep Learning

Feng

Sui

et al. 2023

Water

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Alpine lakes, which include glacial and nonglacial lakes, are widely distributed in high mountain areas and are sensitive to climate and environmental changes. Remote sensing is an effective tool for identifying alpine lakes over large regions, but in the case of small lakes, the complex terrain and extreme weather make their accurate identification extremely challenging. This paper presents an automated method for alpine lake identification developed by leveraging deep learning algorithms and multi-source high-resolution satellite data. The method is able to detect the outlines and types of alpine lakes from high-resolution optical and Synthetic Aperture Radar (SAR) satellite data. In this study, a total of 4584 alpine lakes (including 2795 glacial lakes) were identified in the Eastern Himalayas from Sentinel-1 and Sentinel-2 data acquired during 2016–2020. The average area of the lakes was 0.038 km2, and the average elevation was 4974 m. High accuracy was reported for the dataset for both segmentation (mean Intersection Over Union (MIoU) > 72%) and classification (Overall Accuracy, User’s and Producer’s Accuracies, and F1-Score are all higher than 85%). A higher accuracy was found for the combination of optical and SAR data than relying on single-sourced data, for which the MIoU increased by at least 12%, suggesting that the combination of optical and SAR data is critical for improving the identification of alpine lakes. The deep learning-based method demonstrated a significant improvement over traditional spectral extraction methods.

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“…However, it had high requirements for the data sources and lacked general applicability. In addition to optical images, SAR images (mostly Sentinel-1 SAR images) were also utilized to demarcate glacial lake outlines [24][25][26][27], especially for some regions that were heavily affected by clouds. Typically, the backscatter coefficients of SAR imagery were preprocessed first, including intensity ratios and median filtering.…”

Section: Introductionmentioning

confidence: 99%

Inventory of Glacial Lake in the Southeastern Qinghai-Tibet Plateau Derived from Sentinel-1 SAR Image and Sentinel-2 MSI Image

Zhang,

Zhao,

Yao

et al. 2023

Remote Sensing

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The glacial lakes in the Southeastern Qinghai–Tibet Plateau (SEQTP) have undergone dramatic expansion in the context of global warming, leading to several glacial lake outburst floods (GLOFs) disasters. However, there is a gap and incompleteness in glacial lake inventories across this area due to the heavy cloud cover. In this study, an updated and comprehensive glacial lake inventory was produced by object-based image analysis (OBIA) and manual vectorization based on the Sentinel-1 SAR and Sentinel-2 MSI images acquired in 2022. Detailed steps regarding the OBIA were provided, and the feature set of Sentinel-1 SAR images suitable for extracting glacial lakes was also determined in this paper. We found that the mean combination of ascending-orbit and descending-orbit images is appropriate for mapping glacial lakes. VV-polarized backscattering coefficients from ascending-orbit achieved a better performance for delineating glacial lakes within the study area. Moreover, the distribution of glacial lakes was characterized in terms of four aspects: size, type, elevation, and space. There were 3731 glacial lakes with a total area of 1664.22 ± 0.06 km2 in the study area; most of them were less than 0.07 km2. Ice-contacted lakes were primarily located in the Palongzangbo basin (13.24 ± 0.08 km2). Nyang Qu basin had the most abundant glacial lake resources (2456 and 93.32 ± 0.18 km2). A comparison with previously published glacial lake datasets demonstrated that our dataset is more complete. This inventory is useful for evaluating water resources, studying glacier–glacial lake interactions, and assessing GLOFs’ susceptibility in the SEQTP.

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ACFNet: A Feature Fusion Network for Glacial Lake Extraction Based on Optical and Synthetic Aperture Radar Images

Cited by 13 publications

References 70 publications

The response of glaciers and glacial lakes to climate change in the Southeastern Tibetan Plateau over the past three decades

The response of glaciers and glacial lakes to climate change in the Southeastern Tibetan Plateau over the past three decades

Identifying Alpine Lakes in the Eastern Himalayas Using Deep Learning

Inventory of Glacial Lake in the Southeastern Qinghai-Tibet Plateau Derived from Sentinel-1 SAR Image and Sentinel-2 MSI Image

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