Exploration of Glacier Surface FaciesMapping Techniques Using Very High Resolution Worldview-2 Satellite Data

Jawak, Shridhar D.; Wankhede, Sagar F.; Luis, Alvarinho J.

doi:10.3390/ecrs-2-05152

Cited by 7 publications

(5 citation statements)

References 9 publications

(6 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Comparative assessments between OBIA and PBIA have indicated that the high accuracies achieved by OBIA hold steadfast when highand very high-resolution imageries are under consideration [51,52]. With the rise in cryosphere monitoring using very high resolution (VHR) imagery, the efficacy of OBIA against PBIA for glaciological applications has been tested with impressive results [53][54][55][56]. A large number of glacier-related information extraction from images is carried out through techniques that involve spectral band manipulations, such as band ratioing and algorithms [57].…”

Section: Glacier Facies Via Remote Sensingmentioning

confidence: 99%

“…Engaging the protocol outlined and tested elsewhere [53][54][55]60], the individual ortho-rectified corrected WV-2 raw tiles were subjected to a two-step radiometric corrective procedure. The steps were as follows: (1) Transformation of the raw digital number (DN) values to at sensor spectral radiance; (2) transformation of the at-sensor spectral radiance to apparent surface reflectance (atmospheric correction).…”

Section: Preprocessingmentioning

confidence: 99%

See 1 more Smart Citation

Explorative Study on Mapping Surface Facies of Selected Glaciers from Chandra Basin, Himalaya Using WorldView-2 Data

2019

Self Cite

View full text Add to dashboard Cite

Mapping of surface glacier facies has been a part of several glaciological applications. The study of glacier facies in the Himalayas has gained momentum in the last decade owing to the implications imposed by these facies on the melt characteristics of the glaciers. Some of the most commonly reported surface facies in the Himalayas are snow, ice, ice mixed debris, and debris. The precision of the techniques used to extract glacier facies is of high importance, as the result of many cryospheric studies and economic reforms rely on it. An assessment of a customized semi-automated protocol against conventional and advanced mapping algorithms for mapping glacier surface facies is presented in this study. Customized spectral index ratios (SIRs) are developed for effective extraction of surface facies using thresholding in an object-based environment. This method was then tested on conventional and advanced classification algorithms for an evaluation of the mapping accuracy for five glaciers located in the Himalayas, using very high-resolution WorldView-2 imagery. The results indicate that the object-based image analysis (OBIA) based semi-automated SIR approach achieved a higher average overall accuracy of 87.33% (κ = 0.85) than the pixel-based image analysis (PBIA) approach. Among the conventional methods, the Maximum Likelihood performed the best, with an overall accuracy of 78.71% (κ = 0.75). The Constrained Energy Minimization, with an overall accuracy of 68.76% (κ = 0.63), was the best performer of the advanced algorithms. The advanced methods greatly underperformed in this study. The proposed SIRs show a promise in the mapping of minor features such as crevasses and in the discrimination between ice-mixed debris and debris. We have efficiently mapped surface glacier facies independently of short-wave infrared bands (SWIR). There is a scope for the transferability of the proposed SIRs and their performance in varying scenarios.

show abstract

Section: Glacier Facies Via Remote Sensingmentioning

confidence: 99%

Section: Preprocessingmentioning

confidence: 99%

Explorative Study on Mapping Surface Facies of Selected Glaciers from Chandra Basin, Himalaya Using WorldView-2 Data

2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Additionally, unlike bare ice extraction, the identification of supraglacial debris often requires the integration of multiple features, including thermal infrared radiation, topographic factors, and SAR, among others [18,28,[41][42][43]. High-resolution imagery provides spectral features, such as shape and texture, which are essential for distinguishing supraglacial debris [44][45][46], and DEMs are useful in glacier extraction due to the significant elevation difference between glacier and non-glacier areas [18,47]; there are notable temperature differences between glacier and non-glacier regions: the glacier temperatures are typically lower in comparison to those of supraglacial debris [19,41]. These temperature variations can be attributed to differences in reflectivity.…”

Section: Introductionmentioning

confidence: 99%

Mapping Debris-Covered Glaciers Using High-Resolution Imagery (GF-2) and Deep Learning Algorithms

Yang,

Xie,

Liu

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Glacier inventories are fundamental in understanding glacier dynamics and glacier-related environmental processes. High-resolution mapping of glacier outlines is lacking, although high-resolution satellite images have become available in recent decades. Challenges in development of glacier inventories have always included accurate delineation of boundaries of debris-covered glaciers, which is particularly true for high-resolution satellite images due to their limited spectral bands. To address this issue, we introduced an automated, high-precision method in this study for mapping debris-covered glaciers based on 1 m resolution Gaofen-2 (GF-2) imagery. By integrating GF-2 reflectance, topographic features, and land surface temperature (LST), we used an attention mechanism to improve the performance of several deep learning network models (the U-Net network, a fully convolutional neural network (FCNN), and DeepLabV3+). The trained models were then applied to map the outlines of debris-covered glaciers, at 1 m resolution, in the central Karakoram regions. The results indicated that the U-Net model enhanced with the Convolutional Block Attention Module (CBAM) outperforms other deep learning models (e.g., FCNN, DeepLabV3+, and U-Net model without CBAM) in terms of precision for supraglacial debris identification. On the testing dataset, the CBAM-enhanced U-Net model achieved notable performance metrics, with its accuracy, F1 score, mean intersection over union (MIoU), and kappa coefficient reaching 0.93, 0.74, 0.79, and 0.88. When applied at the regional scale, the model even exhibits heightened precision (accuracies = 0.94, F1 = 0.94, MIoU = 0.86, kappa = 0.91) in mapping debris-covered glaciers. The experimental glacier outlines were accurately extracted, enabling the distinction of supraglacial debris, clean ice, and other features on glaciers in central Karakoram using this trained model. The results for our method revealed differences of 0.14% for bare ice and 10.36% against the manually interpreted glacier boundary for supraglacial debris. Comparison with previous glacier inventories revealed raised precisions of 8.74% and 4.78% in extracting clean ice and with supraglacial debris, respectively. Additionally, our model demonstrates exceptionally high exclusion for bare rock outside glaciers and could reduce the influence of non-glacial snow on glacier delineation, showing substantial promise in mapping debris-covered glaciers.

show abstract

“…Ali et al, (2017), assessed the effects of changing glacier facies on the dimensions of the glacier through recession, deglaciation and meteorological parameters. Thus, it can be inferred that these superficial expressions (facies) are visual cues of the current health of the glacier (Jawak et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Pixel and Object-Based Classification Techniques for Glacier Facies Extraction

Jawak

Wankhede

Luis

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

Self Cite

View full text Add to dashboard Cite

Abstract. Glacier facies are zones of snow on a glacier that have certain specific spectral characteristics that enable their characterization. The accuracy of their extraction will determine the end accuracy of the distributed mass balance model calibrated by this information. Therefore, coarse to medium resolution satellites are not preferable for this particular function as the data derived from such sensors will potentially blur out the minute spatial variations on the surface of a glacier. Very high resolution (VHR) sensors (such as, WorldView (WV)-1, 2, 3) are thus much more suited for this particular task. Hence, this study aims to extract the available glacier facies on the Sutri Dhaka glacier, Himalayas, using very high-resolution WorldView-2 (WV-2) imagery. Extensive pre-processing of the imagery was performed to prepare the data for this purpose. The steps incorporated for this purpose consist of 1) Data Calibration, 2) Mosaicking, 3) Pan Sharpening, 4) Generation of 3D surface, and 5) Digitization. Using image classification as the primary method of information extraction, this study tests the ever-popular pixel-based classification technique against the uprising object-based classification technique. In doing so, this study aims to determine the most accurate technique of information extraction for the WV-2 imagery in the given scenario. The presence of unique bands (Coastal (0.40&ndash;0.45&mu;m), Red Edge (0.705&ndash;0.745&mu;m), NIR-1 (0.770&ndash;0.895&mu;m) and NIR-2 (0.86&ndash;1.04&mu;m) in the multispectral range of WV-2, allows this study to perform facies classification through the development of customized spectral index ratios (SIRs) in the object-based domain. Establishment of thresholds was hence necessitated for information extraction through the developed SIRs. Three supervised classifiers, namely, a) Mahalanobis distance, b) Maximum likelihood, and c) Minimum distance to mean, were then used to perform classification, thereby allowing a comparative analysis between the classification schemes. Accuracy assessment for each classification scheme was performed using error matrices. The object-based approach achieved an overall accuracy of 90% (&kappa;=0.88) and the highest overall accuracy among the pixel-based classification methods is 78.57% (&kappa;=0.75). The results clearly portray that the object-based method delivered much higher accuracy than the pixel-based methods. The carry home message is that future studies must examine the transferability and accuracy of the customized SIRs in varying scenarios, as different scenarios will require varying threshold adjustments. Forthcoming studies can also develop sensor specific and unique indices for other sensors that are suitable for such applications.

show abstract

Exploration of Glacier Surface FaciesMapping Techniques Using Very High Resolution Worldview-2 Satellite Data

Cited by 7 publications

References 9 publications

Explorative Study on Mapping Surface Facies of Selected Glaciers from Chandra Basin, Himalaya Using WorldView-2 Data

Explorative Study on Mapping Surface Facies of Selected Glaciers from Chandra Basin, Himalaya Using WorldView-2 Data

Mapping Debris-Covered Glaciers Using High-Resolution Imagery (GF-2) and Deep Learning Algorithms

Comparison of Pixel and Object-Based Classification Techniques for Glacier Facies Extraction

Contact Info

Product

Resources

About