Analysis of the melting glaciers in Southeast Tibet by ALOS-PALSAR data

Du, Wenjie; Liu, Xin; Guo, Jinyun; Shen, Yi; Li, Wang; Chang, Xiaotao

doi:10.3319/tao.2018.07.09.03

Cited by 16 publications

(24 citation statements)

References 39 publications

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“…Wang et al (2011) selected five parameters, i.e., mother glacier area, distance between lake and glacier terminus, slope between lake and glacier, mean slope of moraine dam, and mother glacier snout steepness, to identify the first order dangerous glacial lakes in southeastern Tibetan Plateau using Landsat images, ALOS AVNIR-2 and DEM. The recent study have found strong melting and deformation of southeastern Tibetan glaciers using satellite data (Du et al 2019). Wang et al (2012) used five indices (dam type, size of lake, changes in area, dam characteristics and distance between lake and parent glacier) to identify PDGL in the Chinese Himalaya using ASTER images and digitized DEM from topographic maps.…”

Section: A Summary On Remote Sensing Techniques For Identifying Dangementioning

confidence: 99%

The state of six dangerous glacial lakes in the Nepalese Himalaya

Khadka¹,

Zhang²,

Chen³

2019

Terr. Atmos. Ocean. Sci.

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Glaciers in the Himalaya are increasingly retreating and thinning due to climate change. This process is the primary cause of glacial lakes expansion and has increased the possibilities of the glacial lake outburst floods (GLOFs) that have been responsible for heavy loss of life and damage to downstream infrastructures. This study examines the status of the existing potentially dangerous glacial lakes in the Nepalese Himalaya such as Imja Tsho, Tsho Rolpa, Thulagi, Chamlang South, Barun Tsho, and Lumding Tsho; which were more susceptible to GLOF after the devastating earthquake in 2015. We examined the evolution and decadal expansion rate of lakes from 1987 to 2016 using Landsat images. The results show significant expansion of Imja Tsho, Barun Tsho, and Lumding Tsho at the rates of 42.1, 46.8, and 32.9% respectively, during 2006-2016; while other glacial lakes (i.e., Chamlang South, Barun Tsho, and Lumding Tsho) are relatively stable. Although the current status of glacial lakes may be stable in term of burst risk, high expanding lakes must be prioritized for detail studies. Continuous model-based monitoring and risk assessment, mitigation measures and disaster management strategies are necessary for reducing the impact of GLOFs.

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Section: A Summary On Remote Sensing Techniques For Identifying Dangementioning

confidence: 99%

The state of six dangerous glacial lakes in the Nepalese Himalaya

Khadka¹,

Zhang²,

Chen³

2019

Terr. Atmos. Ocean. Sci.

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“…The global foF2 and hmF2 maps provided by IZMIRAN can be regarded as new GIM-TEC products [32]. The GIM-TECs are estimated by the dual-frequency observations obtained from hundreds of International GNSS Service (IGS) stations with spherical harmonics and have been widely applied in scientific research and practical productions [33][34][35][36]. Therefore, the IZMIRAN maps were capable of meeting the requirements of this study.…”

Section: Validation Of Accuracy Over the Greenwich Meridianmentioning

confidence: 96%

Advanced Machine Learning Optimized by The Genetic Algorithm in Ionospheric Models Using Long-Term Multi-Instrument Observations

Zhao

Hé

et al. 2020

Remote Sensing

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The ionospheric delay is of paramount importance to radio communication, satellite navigation and positioning. It is necessary to predict high-accuracy ionospheric peak parameters for single frequency receivers. In this study, the state-of-the-art artificial neural network (ANN) technique optimized by the genetic algorithm is used to develop global ionospheric models for predicting foF2 and hmF2. The models are based on long-term multiple measurements including ionospheric peak frequency model (GIPFM) and global ionospheric peak height model (GIPHM). Predictions of the GIPFM and GIPHM are compared with the International Reference Ionosphere (IRI) model in 2009 and 2013 respectively. This comparison shows that the root-mean-square errors (RMSEs) of GIPFM are 0.82 MHz and 0.71 MHz in 2013 and 2009, respectively. This result is about 20%-35% lower than that of IRI. Additionally, the corresponding hmF2 median errors of GIPHM are 20% to 30% smaller than that of IRI. Furthermore, the ANN models present a good capability to capture the global or regional ionospheric spatial-temporal characteristics, e.g., the equatorial ionization anomaly and Weddell Sea anomaly. The study shows that the ANN-based model has a better agreement to reference value than the IRI model, not only along the Greenwich meridian, but also on a global scale. The approach proposed in this study has the potential to be a new three-dimensional electron density model combined with the inclusion of the upcoming Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC-2) data.Remote Sens. 2020, 12, 866 2 of 17 space-borne and ground-based instruments and data processed techniques, more attention has been paid to the development and improvement of these ionospheric models [3].In the previous studies, the artificial neural network (ANN), nonlinear least squares and AdaBoost techniques have been used to predict ionospheric variations with a satisfactory accuracy [4][5][6][7]. Among them, the ANN technique has proven to be a successful tool in the modeling of ionospheric variations as well as solving the forecast problems in many geophysical applications over a single station, regional area and global scale [3,[8][9][10]. The neural network was first applied to build the foF2 model over the Grahamstown station (33 • S, 26 • E) using the data range set of 1973-1983 [11]. This model can predict the daily noon foF2 value with a root-mean-square error (RMSE) of 0.95 MHz and the monthly averaged RMSE value was 0.48 MHz. Afterward, this approach has been widely applied in the Asia/Pacific sector [5], equatorial region [12], European region [13] and the auroral zone [14]. These studies showed an improvement in the accuracy of the ANN-based models over the International Reference Ionosphere (IRI) model.With the advancement of data acquisition, more ionospheric measurements have become available for the global modelling of foF2. For instance, Oyeyemi et al. [15] used the foF2 data from 59 globally distributed ionospheric stations to establish th...

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“…(4) Glacier and landslide dynamics from SAR Du et al (2019) used ALOS_PALSAR images to show the lowering of glacier elevation in southeastern Himalayas, where GRACE shows the largest loss of glacier mass (Fig. 2).…”

Section: (1) Satellite-based Global Gravity Modelmentioning

confidence: 99%

“…Figure 3 shows the coverage of the SAR images over the Himalayas from Sentinel-1A, which is in a 12-day repeat observing cycle. The SAR-related works in this special issue are Du et al (2019), Liu et al (2019), and Tseng et al (2019). With more publicly available SAR images from satellite missions like ESA's Sentinel-1A/-1B, and JAXA's ALOS and ALOS-2, publicly available SAR data processing software systems such as GMTSAR and others, SAR estimates of glacier melt rate, landslide and surface deformation in Tibet have been demonstrated.…”

mentioning

confidence: 99%