Landsat Satellites Observed Dynamics of Snowline Altitude at the End of the Melting Season, Himalayas, 1991–2022

Wang, Jingwen; Tang, Zilong; Deng, Gang; Hu, Guojie; You, Yuanhong; Yun-yu, Zhao

doi:10.3390/rs15102534

Cited by 11 publications

(5 citation statements)

References 79 publications

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“…Concerning the poor performance of the RF, the literature generally agrees upon its general applicability in geospatial contexts as a "go-to" model [35,56,63,66,92]. Since RF uses "bagging", as spatial data is correlated, this resampling violates the assumption of independence [93] provided evidence that these limitations could lead to inferior prediction performance of the RF under spatial dependence, and this could be the reason for the observed performance of the RF.…”

Section: Resultsmentioning

confidence: 90%

“…where ρ Green is the surface reflectance in the green band and ρ SWIR is the surface reflectance in the SWIR band. The use of the NDSI as the only means of glacier detection is a common approach in glacier mapping generally with acceptable results [50,66,67]. In this work, we utilized the NDSI both as a covariate for machine learning models and as an independent method for delineating glacier outlines for comparison.…”

Section: Normalized Difference Snow Index (Ndsi)mentioning

confidence: 99%

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A Comparative Analysis of Machine Learning Techniques for National Glacier Mapping: Evaluating Performance through Spatial Cross-Validation in Perú

Bueno,

Macera,

Montoya

2023

Water

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Accurate glacier mapping is crucial for assessing future water security in Andean ecosystems. Traditional accuracy assessment may be biased due to overlooking spatial autocorrelation during map validation. In recent years, spatial cross-validation (CV) strategies have been proposed in environmental and ecological modeling to reduce bias in predictive accuracy. In this study, we demonstrate the influence of spatial autocorrelation on the accuracy assessment of glacier surface predictive models. This is achieved by comparing the performance of several widely used machine learning algorithms including the gradient-boosting machines (GBM), k-nearest neighbors (KNN), random forest (RF), and logistic regression (LR) for mapping nine main Peruvian glacier regions. Spatial and non-spatial cross-validation methods were used to evaluate the model’s classification errors in terms of the Matthews correlation coefficient. Performance differences of up to 18% were found between bias-reduced (spatial) and overoptimistic (non-spatial) cross-validation results. Regarding only spatial CV, the k-nearest neighbors were the overall best model across Huallanca (0.90), Huayhuasha (0.78), Huaytapallana (0.96), Raura (0.93), Urubamba (0.96), Vilcabamba (0.93), and Vilcanota (0.92) regions, consistently demonstrating the highest performance followed by logistic regression at Blanca (0.95) and Central (0.97) regions. Our validation approach, accounting for spatial characteristics, provides valuable insights for glacier mapping studies and future efforts on glacier retreat monitoring. Incorporating this approach improves the reliability of glacier mapping, guiding future national-level initiatives.

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Section: Resultsmentioning

confidence: 90%

Section: Normalized Difference Snow Index (Ndsi)mentioning

confidence: 99%

A Comparative Analysis of Machine Learning Techniques for National Glacier Mapping: Evaluating Performance through Spatial Cross-Validation in Perú

Bueno,

Macera,

Montoya

2023

Water

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“…Overall, glacier mass growth can be observed more effectively only when temperatures consistently remain in the negative range. The response of different glaciers to temperature and precipitation varies depending on regional terrain, glacier area, elevation, and other factors [60][61][62]. To analyze the main The response of different glaciers to temperature and precipitation varies depending on regional terrain, glacier area, elevation, and other factors [60][61][62].…”

Section: Analysis Of Factors Influencing Mass Balance Variations In T...mentioning

confidence: 99%

“…The response of different glaciers to temperature and precipitation varies depending on regional terrain, glacier area, elevation, and other factors [60][61][62]. To analyze the main The response of different glaciers to temperature and precipitation varies depending on regional terrain, glacier area, elevation, and other factors [60][61][62]. To analyze the main reasons for the spatial variation in GMB in the Tomur Peak Region in detail, this study provided a specific analysis of the autumn temperature and precipitation trend maps in the Tomur Peak Region from 2003-2020 (Figure 13).…”

Section: Analysis Of Factors Influencing Mass Balance Variations In T...mentioning

confidence: 99%

Spatiotemporal Variations of Glacier Mass Balance in the Tomur Peak Region Based on Multi-Source Altimetry Remote Sensing Data

Cheng,

Du,

et al. 2023

Remote Sensing

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Alpine glaciers are sensitive indicators of regional climate change, which can affect regional ecological stability and social development. Variations in glacier mass balance (GMB) are an important parameter in studying glacier change. In this study, data from the Ice, Cloud, and Land Elevation Satellite-1 (ICESat-1), the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), and CryoSat-2 (Ku-band) were combined, and high-resolution ALOS DEM was employed to denoise. After that, the polynomial fitting method was used to analyze the characteristics of glacier surface elevation (GSE) variations from 2003–2020 in the Tomur Peak Region of the Central Asian Tianshan Mountains and the regional GMB was calculated. Research results showed that: (1) From 2003–2020, the GSE of the Tomur Peak Region had an overall −8.95 ± 4.48 m variation, the average rate of which was −0.53 ± 0.26 m/yr (/yr is /year). Overall, elevations of most glaciers in the Tomur Peak Region had downward trends, with a rate of change of −0.5 to 0 m/yr. The fastest rate of elevation decline in the Koxkar Glacier Tongue was −1.5 m/yr. The elevation of some altimetric points in the Eastern Tomur Peak Region showed a rising state, with a maximum rate of variation of 1.0 m/yr. (2) From 2003–2020, the average GMB in the Tomur Peak Region was −1.51 ± 0.04 Gt/yr. In the region of elevation below 4000 m, small glaciers dominated, with a GMB of −0.61 ± 0.04 Gt/yr. With increasing elevation, the melting rate of glaciers gradually slowed down, but overall, the mass balance remained in a state of decline. (3) Climate was the main driving factor of GMB change in the study area. From 2003–2020, in the Tomur Peak Region, the average annual temperature continued to increase at a rate of 0.04 ± 0.02 °C/yr, and this was the main influencing factor for the negative GMB in the Tomur Peak Region. In the same period, the annual precipitation showed a rising trend with a linear variation rate of 0.12 ± 0.06 mm/yr, and the rising precipitation was the influencing factor for the gradually slowing change in the GMB in the study area.

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“…Some have relied on meteorological station data to analyze snow depth and duration changes across different regions, including the eastern, central, and entire plateau, as well as at a national scale [15][16][17][18]. There are also studies that have investigated variations in snow cover and the snowline altitude in the Tibetan Plateau [19][20][21][22]. Others have employed remote sensing data, particularly MODIS snow products, to investigate snow cover extent and snow water equivalents in the plateau region [23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variations in Snow Cover on the Tibetan Plateau from 2003 to 2020

Pu,

Zhou,

Sun

et al. 2024

Water

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The variations in snow cover on the Tibetan Plateau play a pivotal role in comprehending climate change patterns and governing hydrological processes within the region. This study leverages daily snow cover data and the NASA Digital Elevation Model (DEM) from 2003 to 2020 to analyze spatiotemporal snow cover days and assess their responsiveness to climatic shifts by integrating meteorological data. The results reveal significant spatial heterogeneity in snow cover across the Plateau, with a slight decreasing trend in annual average snow cover duration. Snow cover is predominantly observed during the spring and winter seasons, constituting approximately 32% of the total snow cover days annually. The onset and cessation of snow cover occur within a range of 120–220 days. Additionally, an increasing trend in snow cover duration below 5000 m altitude was observed, in addition to a decreasing trend above 5000 m altitude. Sub-basin analysis delineates the Tarim River Basin as exhibiting the lengthiest average annual snow cover duration of 83 days, while the Yellow River Basin records the shortest duration of 31 days. The decreasing trend in snow cover duration closely aligns with climate warming trends, characterized by a warming rate of 0.17 ± 0.54 °C per decade, coupled with a concurrent increase in precipitation at a rate of 3.09 ± 3.81 mm per year. Temperature exerts a more pronounced influence on annual snow cover duration variation compared to precipitation, as evidenced by a strong negative correlation (CC = −0.67). This study significantly augments the comprehension of hydrological cycle dynamics on the Tibetan Plateau, furnishing essential insights for informed decision-making in water resource management and ecological conservation efforts.

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Landsat Satellites Observed Dynamics of Snowline Altitude at the End of the Melting Season, Himalayas, 1991–2022

Cited by 11 publications

References 79 publications

A Comparative Analysis of Machine Learning Techniques for National Glacier Mapping: Evaluating Performance through Spatial Cross-Validation in Perú

A Comparative Analysis of Machine Learning Techniques for National Glacier Mapping: Evaluating Performance through Spatial Cross-Validation in Perú

Spatiotemporal Variations of Glacier Mass Balance in the Tomur Peak Region Based on Multi-Source Altimetry Remote Sensing Data

Spatiotemporal Variations in Snow Cover on the Tibetan Plateau from 2003 to 2020

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