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JournalISSN: 1672-6316

Journal of Mountain Science 

Springer Science+Business Media
About: Journal of Mountain Science is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Landslide & Debris flow. It has an ISSN identifier of 1672-6316. Over the lifetime, 2813 publications have been published receiving 28987 citations. The journal is also known as: J Mt Sci.
Topics: Landslide, Debris flow, Debris, Vegetation, Glacier


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Journal ArticleDOI
TL;DR: In this paper, the authors highlight the rich species diversity of higher plants in the Bhabha valley of western Himalaya in India and show that the effect of altitude on species diversity displays a hump-shaped curve which may be attributed to increase in habitat diversity at the median ranges and relatively less habitat diversity in higher altitudes.
Abstract: The present study highlights the rich species diversity of higher plants in the Bhabha Valley of western Himalaya in India. The analysis of species diversity revealed that a total of 313 species of higher plants inhabit the valley with a characteristic of moist alpine shrub vegetation. The herbaceous life forms dominate and increase with increasing altitude. The major representations are from the families Asteraceae, Rosaceae, Lamiaceae and Poaceae, suggesting thereby the alpine meadow nature of the study area. The effect of altitude on species diversity displays a hump-shaped curve which may be attributed to increase in habitat diversity at the median ranges and relatively less habitat diversity at higher altitudes. The anthropogenic pressure at lower altitudes results in low plant diversity towards the bottom of the valley with most of the species being exotic in nature. Though the plant diversity is less at higher altitudinal ranges, the uniqueness is relatively high with high species replacement rates. More than 90 % of variability in the species diversity could be explained using appropriate quantitative and statistical analysis along the altitudinal gradient. The valley harbours 18 threatened and 41 endemic species, most of which occur at higher altitudinal gradients due to habitat specificity.

119 citations

Journal ArticleDOI
TL;DR: In this paper, an integrated model of information value method and logistic regression is proposed by using their merits at maximum and overcoming their weaknesses, which may enhance precision and accuracy of landslide susceptibility assessment.
Abstract: Bailongjiang watershed in southern Gansu province, China, is one of the most landslide-prone regions in China, characterized by very high frequency of landslide occurrence. In order to predict the landslide occurrence, a comprehensive map of landslide susceptibility is required which may be significantly helpful in reducing loss of property and human life. In this study, an integrated model of information value method and logistic regression is proposed by using their merits at maximum and overcoming their weaknesses, which may enhance precision and accuracy of landslide susceptibility assessment. A detailed and reliable landslide inventory with 1587 landslides was prepared and randomly divided into two groups, (i) training dataset and (ii) testing dataset. Eight distinct landslide conditioning factors including lithology, slope gradient, aspect, elevation, distance to drainages, distance to faults, distance to roads and vegetation coverage were selected for landslide susceptibility mapping. The produced landslide susceptibility maps were validated by the success rate and prediction rate curves. The validation results show that the success rate and the prediction rate of the integrated model are 81.7 % and 84.6 %, respectively, which indicate that the proposed integrated method is reliable to produce an accurate landslide susceptibility map and the results may be used for landslides management and mitigation.

116 citations

Journal ArticleDOI
TL;DR: In this article, a morphometric mapping approach employing an ASTER-derived digital elevation model has been used to map glaciers in the Khumbu Himalaya and the Tien Shan, aiming to use the morphometric approach to map large debris-covered glaciers; and to use Landsat and ASTER data and GPS and field measurements to document glacier change over the past four decades.
Abstract: Glaciers in the Himalaya are often heavily covered with supraglacial debris, making them difficult to study with remotely-sensed imagery alone. Various methods such as band ratios can be used effectively to map clean-ice glaciers; however, a thicker layer of debris often makes it impossible to distinguish between supraglacial debris and the surrounding terrain. Previously, a morphometric mapping approach employing an ASTER-derived digital elevation model has been used to map glaciers in the Khumbu Himal and the Tien Shan. This study on glaciers in the Greater Himalaya Range in Zanskar, southern Ladakh, aims (i) to use the morphometric approach to map large debris-covered glaciers; and (ii) to use Landsat and ASTER data and GPS and field measurements to document glacier change over the past four decades. Field work was carried out in the summers of 2008. For clean ice, band ratios from the ASTER dataset were used to distinguish glacial features. For debris-covered glaciers, topographic features such as slope were combined with thermal imagery and supervised classifiers to map glacial margins. The method is promising for large glaciers, although problems occurred in the distal and lateral parts and in the fore field of the glaciers. A multitemporal analysis of glaciers in Zanskar showed that in general they have receded since at least the mid- to late-1970s. However, some few glaciers that advanced or oscillated — probably because of specific local environmental conditions — do exist.

115 citations

Journal ArticleDOI
TL;DR: In this article, a novel approach of the landslide numerical risk factor (LNRF) bivariate model was used in ensemble with linear multivariate regression (LMR) and boosted regression tree (BRT) models, coupled with radar remote sensing data and geographic information system (GIS), for landslide susceptibility mapping (LSM) in the Gorganroud watershed, Iran.
Abstract: In this study, a novel approach of the landslide numerical risk factor (LNRF) bivariate model was used in ensemble with linear multivariate regression (LMR) and boosted regression tree (BRT) models, coupled with radar remote sensing data and geographic information system (GIS), for landslide susceptibility mapping (LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory (70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party (ILWP), Forestry, Rangeland and Watershed Organisation (FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve (AUC), frequency ratio (FR) and seed cell area index (SCAI). Normalised difference vegetation index, land use/ land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models (AUC = 0.912 (91.2%) and 0.907 (90.7%), respectively) had high predictive accuracy than the LNRF model alone (AUC = 0.855 (85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.

104 citations

Journal ArticleDOI
TL;DR: A small-scale field study at the project level of the implementation of these two programs in Baiwu Township, Yanyuan County, Sichuan, casts doubt upon the accuracy and reliability of these claims of success; ground observations revealed utter failure in some sites and only marginal success in others.
Abstract: Ever since the disastrous floods of 1998, the Chinese government has used the Natural Forest Protection and Sloping Land Conversion Programs to promote afforestation and reforestation as means to reduce runoff, control erosion, and stabilize local livelihoods. These two ambitious programs have been reported as large-scale successes, contributing to an overall increase in China's forest cover and to the stated goals of environmental stabilization. A small-scale field study at the project level of the implementation of these two programs in Baiwu Township, Yanyuan County, Sichuan, casts doubt upon the accuracy and reliability of these claims of success; ground observations revealed utter failure in some sites and only marginal success in others. Reasons for this discrepancy are posited as involving ecological, economic, and bureaucratic factors. Further research is suggested to determine whether these discrepancies are merely local aberrations or represent larger-scale failures in reforestation programs.

102 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
2023121
2022225
2021226
2020213
2019208
2018206