[{"publication":"Remote Sensing of Environment","article_processing_charge":"No","scopus_import":"1","article_type":"original","publisher":"Elsevier","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Mapping ice cliffs on debris-covered glaciers using multispectral satellite images","article_number":"112201","author":[{"last_name":"Kneib","first_name":"M.","full_name":"Kneib, M."},{"last_name":"Miles","first_name":"E.S.","full_name":"Miles, E.S."},{"last_name":"Jola","first_name":"S.","full_name":"Jola, S."},{"first_name":"P.","last_name":"Buri","full_name":"Buri, P."},{"full_name":"Herreid, S.","first_name":"S.","last_name":"Herreid"},{"full_name":"Bhattacharya, A.","first_name":"A.","last_name":"Bhattacharya"},{"full_name":"Watson, C.S.","first_name":"C.S.","last_name":"Watson"},{"first_name":"T.","last_name":"Bolch","full_name":"Bolch, T."},{"full_name":"Quincey, D.","first_name":"D.","last_name":"Quincey"},{"id":"b28f055a-81ea-11ed-b70c-a9fe7f7b0e70","full_name":"Pellicciotti, Francesca","last_name":"Pellicciotti","first_name":"Francesca"}],"day":"01","keyword":["Computers in Earth Sciences","Geology","Soil Science"],"language":[{"iso":"eng"}],"issue":"2","doi":"10.1016/j.rse.2020.112201","quality_controlled":"1","publication_identifier":{"issn":["0034-4257"]},"_id":"12590","year":"2021","volume":253,"date_created":"2023-02-20T08:12:00Z","abstract":[{"text":"Ice cliffs play a key role in the mass balance of debris-covered glaciers, but assessing their importance is limited by a lack of datasets on their distribution and evolution at scales larger than an individual glacier. These datasets are often derived using operator-biased and time-consuming manual delineation approaches, despite the recent emergence of semi-automatic mapping methods. These methods have used elevation or multispectral data, but the varying slope and mixed spectral signal of these dynamic features makes the transferability of these approaches particularly challenging. We develop three semi-automated and objective new approaches, based on the Spectral Curvature and Linear Spectral Unmixing of multispectral images, to map these features at a glacier to regional scale. The transferability of each method is assessed by applying it to three sites in the Himalaya, where debris-covered glaciers are widespread, with varying lithologic, glaciological and climatic settings, and encompassing different periods of the melt season. We develop the new methods keeping in mind the wide range of remote sensing platforms currently in use, and focus in particular on two products: we apply the three approaches at each site to near-contemporaneous atmospherically-corrected Pléiades (2 m resolution) and Sentinel-2 (10 m resolution) images and assess the effects of spatial and spectral resolution on the results. We find that the Spectral Curvature method works best for the high spatial resolution, four band Pléaides images, while a modification of the Linear Spectral Unmixing using the scaling factor of the unmixing is best for the coarser spatial resolution, but additional spectral information of Sentinel-2 products. In both cases ice cliffs are mapped with a Dice coefficient higher than 0.48. Comparison of the Pléiades results with other existing methods shows that the Spectral Curvature approach performs better and is more robust than any other existing automated or semi-automated approaches. Both methods outline a high number of small, sometimes shallow-sloping and thinly debris-covered ice patches that differ from our traditional understanding of cliffs but may have non-negligible impact on the mass balance of debris-covered glaciers. Overall these results pave the way for large scale efforts of ice cliff mapping that can enable inclusion of these features in debris-covered glacier melt models, as well as allow the generation of multiple datasets to study processes of cliff formation, evolution and decline.","lang":"eng"}],"date_updated":"2023-02-28T12:53:46Z","month":"02","oa_version":"Published Version","type":"journal_article","intvolume":"       253","extern":"1","citation":{"chicago":"Kneib, M., E.S. Miles, S. Jola, P. Buri, S. Herreid, A. Bhattacharya, C.S. Watson, T. Bolch, D. Quincey, and Francesca Pellicciotti. “Mapping Ice Cliffs on Debris-Covered Glaciers Using Multispectral Satellite Images.” <i>Remote Sensing of Environment</i>. Elsevier, 2021. <a href=\"https://doi.org/10.1016/j.rse.2020.112201\">https://doi.org/10.1016/j.rse.2020.112201</a>.","ieee":"M. Kneib <i>et al.</i>, “Mapping ice cliffs on debris-covered glaciers using multispectral satellite images,” <i>Remote Sensing of Environment</i>, vol. 253, no. 2. Elsevier, 2021.","short":"M. Kneib, E.S. Miles, S. Jola, P. Buri, S. Herreid, A. Bhattacharya, C.S. Watson, T. Bolch, D. Quincey, F. Pellicciotti, Remote Sensing of Environment 253 (2021).","ama":"Kneib M, Miles ES, Jola S, et al. Mapping ice cliffs on debris-covered glaciers using multispectral satellite images. <i>Remote Sensing of Environment</i>. 2021;253(2). doi:<a href=\"https://doi.org/10.1016/j.rse.2020.112201\">10.1016/j.rse.2020.112201</a>","apa":"Kneib, M., Miles, E. S., Jola, S., Buri, P., Herreid, S., Bhattacharya, A., … Pellicciotti, F. (2021). Mapping ice cliffs on debris-covered glaciers using multispectral satellite images. <i>Remote Sensing of Environment</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.rse.2020.112201\">https://doi.org/10.1016/j.rse.2020.112201</a>","ista":"Kneib M, Miles ES, Jola S, Buri P, Herreid S, Bhattacharya A, Watson CS, Bolch T, Quincey D, Pellicciotti F. 2021. Mapping ice cliffs on debris-covered glaciers using multispectral satellite images. Remote Sensing of Environment. 253(2), 112201.","mla":"Kneib, M., et al. “Mapping Ice Cliffs on Debris-Covered Glaciers Using Multispectral Satellite Images.” <i>Remote Sensing of Environment</i>, vol. 253, no. 2, 112201, Elsevier, 2021, doi:<a href=\"https://doi.org/10.1016/j.rse.2020.112201\">10.1016/j.rse.2020.112201</a>."},"status":"public","date_published":"2021-02-01T00:00:00Z","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1016/j.rse.2020.112201"}],"oa":1,"publication_status":"published"},{"author":[{"last_name":"Kraaijenbrink","first_name":"P.D.A.","full_name":"Kraaijenbrink, P.D.A."},{"first_name":"J.M.","last_name":"Shea","full_name":"Shea, J.M."},{"id":"b28f055a-81ea-11ed-b70c-a9fe7f7b0e70","full_name":"Pellicciotti, Francesca","last_name":"Pellicciotti","first_name":"Francesca"},{"last_name":"Jong","first_name":"S.M. de","full_name":"Jong, S.M. de"},{"last_name":"Immerzeel","first_name":"W.W.","full_name":"Immerzeel, W.W."}],"page":"581-595","type":"journal_article","month":"12","oa_version":"None","day":"01","date_updated":"2023-02-24T11:31:58Z","abstract":[{"text":"Debris-covered glaciers in the Himalaya may have spatially-averaged rates of surface height change that are similar to those observed on bare-ice glaciers, despite the insulating effects of thick debris. Spatially heterogeneous melt patterns caused by the development and evolution of ice cliffs and supraglacial pond systems result in substantial mass losses over time. However, mechanisms controlling the formation and survival of cliffs and ponds remain largely unknown. To study the distribution and characteristics of these surface features we deploy an unmanned aerial vehicle (UAV) over a stretch of the debris-covered Langtang Glacier, Nepal. Acquired images are processed into high-resolution orthomosaics and elevation models with the Structure from Motion (SfM) photogrammetry algorithm. Ice cliffs and ponds are classified using object-based image analysis (OBIA) and their morphology and spatial distribution are analysed and evaluated using object, pixel and point cloud approaches. Results show that ice cliffs are predominantly north-facing, and larger ice cliffs are generally coupled with supraglacial ponds, which may affect their evolution considerably. The spatial distribution of ice cliffs indicates that they are more likely to form in areas where high strain rates are expected. The spatial configuration of ponds over the entire tongue reveals high pond density near confluences, possibly due to closure of conduits via transverse compression. We conclude that the combination of OBIA and UAV imagery is a valuable tool in the semi-automatic and objective analysis of surface features on debris-covered glaciers. The technique may also have potential for upscaling to the use of spaceborne imagery, and the use of UAV-derived point clouds to analyse ice cliff undercuts is promising.","lang":"eng"}],"title":"Object-based analysis of unmanned aerial vehicle imagery to map and characterise surface features on a debris-covered glacier","volume":186,"date_created":"2023-02-20T08:14:35Z","publisher":"Elsevier","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2016","_id":"12614","publication":"Remote Sensing of Environment","article_type":"original","scopus_import":"1","article_processing_charge":"No","publication_identifier":{"issn":["0034-4257"]},"publication_status":"published","doi":"10.1016/j.rse.2016.09.013","date_published":"2016-12-01T00:00:00Z","quality_controlled":"1","status":"public","keyword":["Computers in Earth Sciences","Geology","Soil Science"],"extern":"1","intvolume":"       186","language":[{"iso":"eng"}],"citation":{"ieee":"P. D. A. Kraaijenbrink, J. M. Shea, F. Pellicciotti, S. M. de Jong, and W. W. Immerzeel, “Object-based analysis of unmanned aerial vehicle imagery to map and characterise surface features on a debris-covered glacier,” <i>Remote Sensing of Environment</i>, vol. 186. Elsevier, pp. 581–595, 2016.","chicago":"Kraaijenbrink, P.D.A., J.M. Shea, Francesca Pellicciotti, S.M. de Jong, and W.W. Immerzeel. “Object-Based Analysis of Unmanned Aerial Vehicle Imagery to Map and Characterise Surface Features on a Debris-Covered Glacier.” <i>Remote Sensing of Environment</i>. Elsevier, 2016. <a href=\"https://doi.org/10.1016/j.rse.2016.09.013\">https://doi.org/10.1016/j.rse.2016.09.013</a>.","short":"P.D.A. Kraaijenbrink, J.M. Shea, F. Pellicciotti, S.M. de Jong, W.W. Immerzeel, Remote Sensing of Environment 186 (2016) 581–595.","ama":"Kraaijenbrink PDA, Shea JM, Pellicciotti F, Jong SM de, Immerzeel WW. Object-based analysis of unmanned aerial vehicle imagery to map and characterise surface features on a debris-covered glacier. <i>Remote Sensing of Environment</i>. 2016;186:581-595. doi:<a href=\"https://doi.org/10.1016/j.rse.2016.09.013\">10.1016/j.rse.2016.09.013</a>","mla":"Kraaijenbrink, P. D. A., et al. “Object-Based Analysis of Unmanned Aerial Vehicle Imagery to Map and Characterise Surface Features on a Debris-Covered Glacier.” <i>Remote Sensing of Environment</i>, vol. 186, Elsevier, 2016, pp. 581–95, doi:<a href=\"https://doi.org/10.1016/j.rse.2016.09.013\">10.1016/j.rse.2016.09.013</a>.","ista":"Kraaijenbrink PDA, Shea JM, Pellicciotti F, Jong SM de, Immerzeel WW. 2016. Object-based analysis of unmanned aerial vehicle imagery to map and characterise surface features on a debris-covered glacier. Remote Sensing of Environment. 186, 581–595.","apa":"Kraaijenbrink, P. D. A., Shea, J. M., Pellicciotti, F., Jong, S. M. de, &#38; Immerzeel, W. W. (2016). Object-based analysis of unmanned aerial vehicle imagery to map and characterise surface features on a debris-covered glacier. <i>Remote Sensing of Environment</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.rse.2016.09.013\">https://doi.org/10.1016/j.rse.2016.09.013</a>"}},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"Elsevier","publication":"Remote Sensing of Environment","article_processing_charge":"No","scopus_import":"1","article_type":"original","author":[{"full_name":"Immerzeel, W.W.","last_name":"Immerzeel","first_name":"W.W."},{"full_name":"Kraaijenbrink, P.D.A.","first_name":"P.D.A.","last_name":"Kraaijenbrink"},{"first_name":"J.M.","last_name":"Shea","full_name":"Shea, J.M."},{"last_name":"Shrestha","first_name":"A.B.","full_name":"Shrestha, A.B."},{"id":"b28f055a-81ea-11ed-b70c-a9fe7f7b0e70","full_name":"Pellicciotti, Francesca","first_name":"Francesca","last_name":"Pellicciotti"},{"first_name":"M.F.P.","last_name":"Bierkens","full_name":"Bierkens, M.F.P."},{"full_name":"de Jong, S.M.","last_name":"de Jong","first_name":"S.M."}],"day":"01","title":"High-resolution monitoring of Himalayan glacier dynamics using unmanned aerial vehicles","keyword":["Computers in Earth Sciences","Geology","Soil Science"],"language":[{"iso":"eng"}],"issue":"7","publication_identifier":{"issn":["0034-4257"]},"doi":"10.1016/j.rse.2014.04.025","quality_controlled":"1","year":"2014","_id":"12636","page":"93-103","date_updated":"2023-02-24T08:32:39Z","abstract":[{"text":"Himalayan glacier tongues are commonly debris covered and they are an important source of melt water. However, they remain relatively unstudied because of the inaccessibility of the terrain and the difficulties in field work caused by the thick debris mantles. Observations of debris-covered glaciers are therefore scarce and airborne remote sensing may bridge the gap between scarce field observations and coarse resolution space-borne remote sensing. In this study we deploy an Unmanned Aerial Vehicle (UAV) before and after the melt and monsoon season (May and October 2013) over the debris-covered tongue of the Lirung Glacier in Nepal. Based on stereo-imaging and the structure for motion algorithm we derive highly detailed ortho-mosaics and digital elevation models (DEMs), which we geometrically correct using differential GPS observations collected in the field. Based on DEM differencing and manual feature tracking we derive the mass loss and the surface velocity of the glacier at a high spatial accuracy. On average, mass loss is limited and the surface velocity is very small. However, the spatial variability of melt rates is very high, and ice cliffs and supra-glacial ponds show mass losses that can be an order of magnitude higher than the average. We suggest that future research should focus on the interaction between supra-glacial ponds, ice cliffs and englacial hydrology to further understand the dynamics of debris-covered glaciers. Finally, we conclude that UAV deployment has large potential in glaciology and it may revolutionize methods currently applied in studying glacier surface features.","lang":"eng"}],"month":"07","type":"journal_article","oa_version":"None","volume":150,"date_created":"2023-02-20T08:16:56Z","status":"public","intvolume":"       150","extern":"1","citation":{"ista":"Immerzeel WW, Kraaijenbrink PDA, Shea JM, Shrestha AB, Pellicciotti F, Bierkens MFP, de Jong SM. 2014. High-resolution monitoring of Himalayan glacier dynamics using unmanned aerial vehicles. Remote Sensing of Environment. 150(7), 93–103.","mla":"Immerzeel, W. W., et al. “High-Resolution Monitoring of Himalayan Glacier Dynamics Using Unmanned Aerial Vehicles.” <i>Remote Sensing of Environment</i>, vol. 150, no. 7, Elsevier, 2014, pp. 93–103, doi:<a href=\"https://doi.org/10.1016/j.rse.2014.04.025\">10.1016/j.rse.2014.04.025</a>.","apa":"Immerzeel, W. W., Kraaijenbrink, P. D. A., Shea, J. M., Shrestha, A. B., Pellicciotti, F., Bierkens, M. F. P., &#38; de Jong, S. M. (2014). High-resolution monitoring of Himalayan glacier dynamics using unmanned aerial vehicles. <i>Remote Sensing of Environment</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.rse.2014.04.025\">https://doi.org/10.1016/j.rse.2014.04.025</a>","ama":"Immerzeel WW, Kraaijenbrink PDA, Shea JM, et al. High-resolution monitoring of Himalayan glacier dynamics using unmanned aerial vehicles. <i>Remote Sensing of Environment</i>. 2014;150(7):93-103. doi:<a href=\"https://doi.org/10.1016/j.rse.2014.04.025\">10.1016/j.rse.2014.04.025</a>","short":"W.W. Immerzeel, P.D.A. Kraaijenbrink, J.M. Shea, A.B. Shrestha, F. Pellicciotti, M.F.P. Bierkens, S.M. de Jong, Remote Sensing of Environment 150 (2014) 93–103.","ieee":"W. W. Immerzeel <i>et al.</i>, “High-resolution monitoring of Himalayan glacier dynamics using unmanned aerial vehicles,” <i>Remote Sensing of Environment</i>, vol. 150, no. 7. Elsevier, pp. 93–103, 2014.","chicago":"Immerzeel, W.W., P.D.A. Kraaijenbrink, J.M. Shea, A.B. Shrestha, Francesca Pellicciotti, M.F.P. Bierkens, and S.M. de Jong. “High-Resolution Monitoring of Himalayan Glacier Dynamics Using Unmanned Aerial Vehicles.” <i>Remote Sensing of Environment</i>. Elsevier, 2014. <a href=\"https://doi.org/10.1016/j.rse.2014.04.025\">https://doi.org/10.1016/j.rse.2014.04.025</a>."},"publication_status":"published","date_published":"2014-07-01T00:00:00Z"}]