Integration of classification tree analyses and spatial metrics to assess changes in supraglacial lakes in the Karakoram Himalaya

Bulley, H. N.; Bishop, M. P.; Shroder, John F.; Haritashya, U. K.

doi:10.1080/01431161.2012.705915

Cited by 8 publications

(7 citation statements)

References 49 publications

(62 reference statements)

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“…These lakes show up as dark green in satellite images and are surrounded by exposed moraine. In the Central Himalayas, glacial lakes have developed in situ and are generally grouped into four categories: Supraglacial lakes, Proglacial lakes, Periglacial lakes, and extraglacial lakes [19], [26], [27], [28]. A large proportion of moraine-dammed glacial lakes is formed by seasonal melt water from the frontal moraines of dead-ice dams and is developed at the debris-covered glacier terminus [26].…”

Section: A Study Areamentioning

confidence: 99%

“…The use of this method at a large scale is limited by region, owing to inconsistencies in the segment parameters [18], and by the extensive postprocessing required. The limitations of these traditional methods restrict accurate glacial lake extraction at large scales, therefore, new methods are required to accurately extract glacial lakes by either combining the various traditional methods or to improve upon them [19].…”

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confidence: 99%

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A Systematic Extraction Approach for Mapping Glacial Lakes in High Mountain Regions of Asia

Zhao

Chen

Zhang

2018

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Glacial lakes are highly sensitive indicators of global climate change. The accurate extraction of glacial lakes from image data is of great importance to the evaluation of the hydrological environment in high mountain regions. This paper demonstrates a systematic approach-TSCV (threshold and simplified C-V)-that integrates the advantages of the threshold segmentation method and the active contour model to improve the effect of glacial lake extraction, by overcoming the problems involved in the efficient extraction of small, overlooked glacial lakes and in the removal of some mountain shadows. Three typical areas of glacial lake development in High Asia were selected for glacial lake extraction from Landsat-8 imagery and the results were then compared with those obtained using the threshold segmentation method and the fuzzy clustering method (FCM). The values of the kappa coefficient (KC) and average extraction accuracy (AEA) shows that the TSCV (KC = 0.895, AEA = 0.739) performed superior than other two methods, especially for the small glacial lakes, but also that the threshold segmentation method (KC = 0.869, AEA = 0.631) requires the least time and that the FCM (KC = 871, AEA = 0.663) has the lowest commission error. Our findings lead to a new approach to improve the accuracy of glacial lake extraction in situation where a large proportion of the lakes are small and with a potential for automated glacial lake mapping in rugged mountain areas at a large scale.

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Section: A Study Areamentioning

confidence: 99%

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confidence: 99%

A Systematic Extraction Approach for Mapping Glacial Lakes in High Mountain Regions of Asia

Zhao

Chen

Zhang

2018

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Visual interpretation method can be used to accurately delineate glacier lake boundaries, but these methods are time-consuming, laborious, costly, and the corresponding subjective factors are relatively large; thus, these methods are not suitable for mapping glacial lakes or conducting dynamic monitoring research in remote or large-scale regions. Thus, many automatic extraction methods have emerged for delineating water bodies, such as classification methods [22,23], threshold segmentation methods [24][25][26][27][28], machine learning methods [29][30][31][32], and other improved methods [33,34] (e.g., methods in which classification tree-based image segmentation and object-oriented spatial metrics are implemented to improve existing water body indices). However, it is still difficult to distinguish glacial lakes or their boundaries in some images due to the small sizes of lakes, lakes being shadowed by steep terrain, or lakes being ice-covered or snow-covered; these factors can change dramatically over a year or even over several months [32].…”

Section: Introductionmentioning

confidence: 99%

Inventory and Spatiotemporal Patterns of Glacial Lakes in the HKH-TMHA Region from 1990 to 2020

Wang

Gao

et al. 2022

Remote Sensing

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The Himalayan, Karakoram, and Hindu Kush (HKH-TMHA) are the three main mountain ranges in the high-mountain Asia region, covering the China–Pakistan Economic Corridor (CPEC). In this study, we identified glacial lakes in the HKH-TMHA region based on multitemporal Landsat images taken from 1990 to 2020. We analyzed the spatial distribution and evolution of glacial lakes in the HKH-TMHA region from the perspective of their elevation, size, and terrain aspect; then, we described their temporal changes. The results showed that approximately 84.56% of the glacial lakes (84.1% of the total lake area) were located at elevations between 4000 m and 5500 m, and glacial lakes with areas ranging from 0.01–0.5 km2 accounted for approximately 95.21% of the number and 63.01% of the total area of glacial lakes. The number (38.64%) and area (58.83%) of south-facing glacial lakes were largest in HKH-TMHA and expanded significantly over time. There were 5835 (664.84 ± 89.72 km2) glacial lakes in 1990; from 1990 to 2020, the number of glacial lakes in the HKH-TMHA region increased by 5974 (408.93 km2) in total; and the annual average increase in the area of glacial lakes reached 13.63 km2 (11.15%). In 2020, the total number of glacial lake reached to 9673 (899.66 ± 120.63 km2). In addition, most glacial lakes were located in the Eastern Himalayan, China, and the Indus Basin. Based on the precipitation and temperature analyses performed in our study area, we found inconsistent climate characteristics and changes in the three mountain ranges. In general, the daily precipitation (temperature) increased by 1.0766 mm (1.0311 °C), 0.8544 mm (0.8346 °C), and 0.8245 mm (−0.1042 °C) on the yearly, summer, and winter scales, respectively. Glacial melting and climate change are common contributors to glacial lake expansion. The investigation of glacial lakes in this region can provide basic supporting data for research on glacial lake-related disasters, land cover, and climate change in the high-mountain Asia region.

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“…Because of its simplicity, K-means is widely used for unsupervised remote sensing image classification. A typical example is the method based on BOVW [15] [16], in which the visual dictionary (code book) is the K-means algorithm obtained by performing k-mean clustering operation on a group of local features, which is easy to understand, low complexity, and able to process massive data in a short time, with reasonable clustering effect.…”

Section: A Remotely Sensed Imagery Classification Methods Based On Mamentioning

confidence: 99%

“…The normalized difference index method refers to the index generated by the operation between special bands that can represent the obvious characteristics of the target. For example, the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), and the normalized difference snow cover index (NDSI) are respectively used to extract the information of plant, water and snow [16]. The characteristic curve analysis method is based on the probabilistic similarity of samples of the same kind in the spectral space, and the main methods include maximum likelihood method and minimum distance method [17], etc.…”

Section: Introductionmentioning

confidence: 99%

A Novel Perspective Invariant Dissociaton Hash Method for Remotely Sensed Imagery Plusieurs Target Classification

Zhang

Xiao

2021

IEEE Access

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Plusieurs target classification based on remotely sensed imagery is a hot and difficult-ridden topic in recent years. Specifically, the time and space complexity of the classical pattern recognition model in the classification task based on remote sensing image is usually high, and the remotely sensed imagery is usually not normally captured. For the former, we introduce the low delay and low storage hash method into the plusieurs target classification of remotely sensed imagery. Aiming at the latter, in order to improve the effectiveness of the proposed model in monitoring perspective transformation data, a dissociation perspective invariant model is constructed. By fusing these two solutions, a perspective invariant dissociaton hash model for remotely sensed imagery plusieurs target classification is obtained. By adding perspective invariant constraint to the supervised dissociaton hash method, our method forces the same type of target to share the same binary code, increases the similarity of the hash code of the same type of target, and thus improves the performance. In order to verify the validity and universality of our method, two different data sets were used for experiments, in which different hash methods and different classification methods are compared. The experimental results show that, compared with the comparison methods, the proposed method improves the accuracy of plusieurs target classes with a small number of samples, thus achieving a higher overall classification accuracy. In addition, the advantages of hash method in low storage make the speed of proposed method improved compared with the classical pattern recognition method.

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Integration of classification tree analyses and spatial metrics to assess changes in supraglacial lakes in the Karakoram Himalaya

Cited by 8 publications

References 49 publications

A Systematic Extraction Approach for Mapping Glacial Lakes in High Mountain Regions of Asia

A Systematic Extraction Approach for Mapping Glacial Lakes in High Mountain Regions of Asia

Inventory and Spatiotemporal Patterns of Glacial Lakes in the HKH-TMHA Region from 1990 to 2020

A Novel Perspective Invariant Dissociaton Hash Method for Remotely Sensed Imagery Plusieurs Target Classification

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