Glacial geomorphology of the Parlung Zangbo Valley, southeastern Tibetan Plateau

Chen, Renrong; Zhou, Shangzhe; Li, Yingkui; Deng, Yangfan

doi:10.1080/17445647.2015.1069765

Cited by 16 publications

(13 citation statements)

References 42 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A total of 1334 articles describing the use of GE were published in 547 journals relating to fields such as remote sensing, computer science, the environment, and GIS (Figure 2a). The top ten journals were Remote Sensing (89) (16). The publications referencing GE spanned a broad spectrum of journals.…”

Section: Statistical Characteristicsmentioning

confidence: 99%

“…A total of ten GEE clusters were identified, and these included papers related to tidal flats, Landsat time series, land surface temperature, wetlands, the recursive hierarchical segmentation method (RHSEG), central Asia, land-use change, nighttime lights, the NDVI, and yield estimation. The clustering results suggested that GE was widely applied as a virtual Earth in studies on ship detection [77][78][79][80][81][82][83], land-conversion mapping [84][85][86], glacial geomorphology [6,[87][88][89][90][91], and the Earth's lithosphere [92].…”

Section: Knowledge Base Analysismentioning

confidence: 99%

See 1 more Smart Citation

Progress and Trends in the Application of Google Earth and Google Earth Engine

Zhao

et al. 2021

Remote Sensing

142

View full text Add to dashboard Cite

Earth system science has changed rapidly due to global environmental changes and the advent of Earth observation technology. Therefore, new tools are required to monitor, measure, analyze, evaluate, and model Earth observation data. Google Earth (GE) was officially launched by Google in 2005 as a ”geobrowser”, and Google Earth Engine (GEE) was released in 2010 as a cloud computing platform with substantial computational capabilities. The use of these two tools or platforms in various applications, particularly as used by the remote sensing community, has developed rapidly. In this paper, we reviewed the applications and trends in the use of GE and GEE by analyzing peer-reviewed articles, dating up to January 2021, in the Web of Science (WoS) core collection using scientometric analysis (i.e., by using CiteSpace) and meta-analysis. We found the following: (1) the number of articles describing the use of GE or GEE increased substantially from two in 2006 to 530 in 2020. The number of GEE articles increased much faster than those concerned with the use of GE. (2) Both GE and GEE were extensively used by the remote sensing community as multidisciplinary tools. GE articles covered a broader range of research areas (e.g., biology, education, disease and health, economic, and information science) and appeared in a broader range of journals than those concerned with the use of GEE. (3) GE and GEE shared similar keywords (e.g., “land cover”, “water”, “model”, “vegetation”, and “forest”), which indicates that their application is of great importance in certain research areas. The main difference was that articles describing the use of GE emphasized its use as a visual display platform, while those concerned with GEE placed more emphasis on big data and time-series analysis. (4) Most applications of GE and GEE were undertaken in countries, such as the United States, China, and the United Kingdom. (5) GEE is an important tool for analysis, whereas GE is used as an auxiliary tool for visualization. Finally, in this paper, the merits and limitations of GE and GEE, and recommendations for further improvements, are summarized from an Earth system science perspective.

show abstract

Section: Statistical Characteristicsmentioning

confidence: 99%

Section: Knowledge Base Analysismentioning

confidence: 99%

Progress and Trends in the Application of Google Earth and Google Earth Engine

Zhao

et al. 2021

Remote Sensing

142

View full text Add to dashboard Cite

show abstract

“…The climatic pattern of the Parlung Zangbo basin is highly influenced by the Indian summer monsoon [36]. Therefore, monsoonal maritime glaciers are widely distributed in this region and the annual precipitation is approximately 2500-3000 mm near the ELA [37,39]. The sub glacial erosion is strong and the ablation and deposition rates are high, so that these maritime glaciers created abundant glacial landforms with a mixture of debris-free and debris-covered ice in this region [36,39].…”

mentioning

confidence: 99%

Glacier Facies Mapping Using a Machine-Learning Algorithm: The Parlung Zangbo Basin Case Study

Zhang

Menenti

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Glaciers in the Tibetan Plateau are an important indicator of climate change. Automatic glacier facies mapping utilizing remote sensing data is challenging due to the spectral similarity of supraglacial debris and the adjacent bedrock. Most of the available glacier datasets do not provide the boundary of clean ice and debris-covered glacier facies, while debris-covered glacier facies play a key role in mass balance research. The aim of this study was to develop an automatic algorithm to distinguish ice cover types based on multi-temporal satellite data, and the algorithm was implemented in a subregion of the Parlung Zangbo basin in the southeastern Tibetan Plateau. The classification method was built upon an automated machine learning approach: Random Forest in combination with the analysis of topographic and textural features based on Landsat-8 imagery and multiple digital elevation model (DEM) data. Very high spatial resolution Gao Fen-1 (GF-1) Panchromatic and Multi-Spectral (PMS) imagery was used to select training samples and validate the classification results. In this study, all of the land cover types were classified with overall good performance using the proposed method. The results indicated that fully debris-covered glaciers accounted for approximately 20.7% of the total glacier area in this region and were mainly distributed at elevations between 4600 m and 4800 m above sea level (a.s.l.). Additionally, an analysis of the results clearly revealed that the proportion of small size glaciers (<1 km 2 ) were 88.3% distributed at lower elevations compared to larger size glaciers (≥1 km 2 ). In addition, the majority of glaciers (both in terms of glacier number and area) were characterized by a mean slope ranging between 20 • and 30 • , and 42.1% of glaciers had a northeast and north orientation in the Parlung Zangbo basin.In the past few decades, much work has been accomplished to map the extent of clean glacial ice and to quantify changes over time using satellite image data [6]. Methods applied range from visual interpretation [7] to segmentation of band ratio or spectral indices (e.g., the Normalized Difference Snow Index) images [8] and different unsupervised (e.g., the Iterative Self Organizing Data Analysis Techniques Algorithm, ISODATA) [9] and supervised (e.g., the Maximum Likelihood algorithm) classification [10] and decision tree methods [5,11]. For extracting debris-covered glaciers using multispectral imagery, fully manual onscreen digitizing is widely considered to be a common classification approach [12]. However, the accuracy of results using manual approaches depends greatly on the researcher's experience. Due to the laborious work of manual delineation, many researchers have further proposed semi-automated methods to extract the debris-covered glacial surface [13,14]. The use of Unmanned Aerial Vehicles (UAVs) and terrestrial remote sensing techniques offers new ways to monitor the debris-covered glaciers on a detailed spatial scale [15,16].Nevertheless, the spatial heterogeneity of the g...

show abstract

“…It started uplifting during the early Cenozoic as the Central Asian and Indian plates collided, with profound consequences for landscape evolution and global and regional climate change (Clark et al, 2005;Raymo & Ruddiman, 1992;Wang et al, 2008Wang et al, , 2012Yao et al, 2012b). Because of its importance for climate dynamics, considerable efforts have been made to reconstruct climate change from the history of glaciation on the Tibetan Plateau and surrounding mountain ranges through glacial geomorphological mapping and geochronological studies (Blomdin et al, 2016a(Blomdin et al, , 2016b(Blomdin et al, , 2018Chen et al, 2016;Chevalier & Replumaz, 2019;Gribenski et al, 2018;Heyman et al, 2008Heyman et al, , 2009Heyman et al, , 2011aHeyman et al, , 2011bLehmkuhl & Owen, 2005). Most of these recent studies conclude that alpine-style glaciation has dominated on the Tibetan Plateau, with strong regional differences in the timing of maximum glaciation across the region (Blomdin et al, 2016b;.…”

Section: Study Area and Previous Researchmentioning

confidence: 99%

“…The Tibetan Plateau and location of the study area (yellow polygon; Figure 2) and previously published glacial geomorphological maps (black polygons). (a) Shaluli Shan (Fu et al, 2012); (b) Bayan Har Shan (Heyman et al, 2008); (c) Dalijia Shan (Kassab et al, 2013); (d) Parlung Zangbo Valley,(Chen et al, 2016); (e) Maidika region(Lindholm & Heyman, 2016); (f) Tangula Shan(Morén et al, 2011). Overview map of the Tibetan Plateau, indicating the mapping area of this and of previous mapping studies.…”

mentioning

confidence: 97%

Paleoglacial footprint and fluvial terraces of the Shaluli Shan, SE Tibetan Plateau

Schneider

Blomdin

et al. 2021

Journal of Maps

View full text Add to dashboard Cite

This study provides mapping of glacial and fluvial geomorphology in the Shaluli Shan region on the eastern margin of the south-eastern Tibetan Plateau. Based on TanDEM-X 12 m elevation data and GoogleEarth imagery, glacial valleys, ice-marginal moraines, glacial lineations, scoured terrain and fluvial terraces were mapped. Covering around 11,000 km 2 , this map is the first for this region to display geomorphology at a spatial resolution of 0.4 arcsec (= c. 11 m) and to include fluvial terraces. Its glacial landform distribution is largely consistent with previous mapping. The substantially higher level of detail in this study is reflected in an approximately tenfold number and smaller median sizes of individual landforms such as moraines and glacial lineations. These results underscore the importance of high-resolution DEM data such as TanDEM-X for the identification of glacial and fluvial geomorphology. The map presented here will be used for detailed paleoglacial reconstructions and landscape evolution studies combining both glacial and fluvial landforms.

show abstract

Glacial geomorphology of the Parlung Zangbo Valley, southeastern Tibetan Plateau

Cited by 16 publications

References 42 publications

Progress and Trends in the Application of Google Earth and Google Earth Engine

Progress and Trends in the Application of Google Earth and Google Earth Engine

Glacier Facies Mapping Using a Machine-Learning Algorithm: The Parlung Zangbo Basin Case Study

Paleoglacial footprint and fluvial terraces of the Shaluli Shan, SE Tibetan Plateau

Contact Info

Product

Resources

About