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

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 bounda...

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Main Authors: Jingxiao Zhang, Li Jia, Massimo Menenti, Guangcheng Hu
Format: Article
Language:English
Published: MDPI AG 2019-02-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/11/4/452
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spelling doaj-6cb8f79433dc4c569f61446ddb8bde412020-11-25T00:02:24ZengMDPI AGRemote Sensing2072-42922019-02-0111445210.3390/rs11040452rs11040452Glacier Facies Mapping Using a Machine-Learning Algorithm: The Parlung Zangbo Basin Case StudyJingxiao Zhang0Li Jia1Massimo Menenti2Guangcheng Hu3State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, ChinaGlaciers 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 (&lt;1 km<sup>2</sup>) were 88.3% distributed at lower elevations compared to larger size glaciers (&#8805;1 km<sup>2</sup>). In addition, the majority of glaciers (both in terms of glacier number and area) were characterized by a mean slope ranging between 20&#176; and 30&#176;, and 42.1% of glaciers had a northeast and north orientation in the Parlung Zangbo basin.https://www.mdpi.com/2072-4292/11/4/452automatic glacier facies mappingRandom ForestLandsatParlung Zangbo basin
collection DOAJ
language English
format Article
sources DOAJ
author Jingxiao Zhang
Li Jia
Massimo Menenti
Guangcheng Hu
spellingShingle Jingxiao Zhang
Li Jia
Massimo Menenti
Guangcheng Hu
Glacier Facies Mapping Using a Machine-Learning Algorithm: The Parlung Zangbo Basin Case Study
Remote Sensing
automatic glacier facies mapping
Random Forest
Landsat
Parlung Zangbo basin
author_facet Jingxiao Zhang
Li Jia
Massimo Menenti
Guangcheng Hu
author_sort Jingxiao Zhang
title Glacier Facies Mapping Using a Machine-Learning Algorithm: The Parlung Zangbo Basin Case Study
title_short Glacier Facies Mapping Using a Machine-Learning Algorithm: The Parlung Zangbo Basin Case Study
title_full Glacier Facies Mapping Using a Machine-Learning Algorithm: The Parlung Zangbo Basin Case Study
title_fullStr Glacier Facies Mapping Using a Machine-Learning Algorithm: The Parlung Zangbo Basin Case Study
title_full_unstemmed Glacier Facies Mapping Using a Machine-Learning Algorithm: The Parlung Zangbo Basin Case Study
title_sort glacier facies mapping using a machine-learning algorithm: the parlung zangbo basin case study
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2019-02-01
description 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 (&lt;1 km<sup>2</sup>) were 88.3% distributed at lower elevations compared to larger size glaciers (&#8805;1 km<sup>2</sup>). In addition, the majority of glaciers (both in terms of glacier number and area) were characterized by a mean slope ranging between 20&#176; and 30&#176;, and 42.1% of glaciers had a northeast and north orientation in the Parlung Zangbo basin.
topic automatic glacier facies mapping
Random Forest
Landsat
Parlung Zangbo basin
url https://www.mdpi.com/2072-4292/11/4/452
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