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Permafrost estimation model in Upper Indus Basin

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Abstract

Remotely sensed topo-climatic factors, potential incoming solar radiation (PISR), land surface temperature (LST), topographic wetness index (TWI), Surface emissivity, and elevation, and machine learning techniques are used for mapping the spatial distribution of permafrost in the Tso Kar, a sub-basin of Upper Indus Basin (UIB) in Leh, Ladakh (UT). This schematic model is employed to identify remotely sensed parameters which are crucial in assessing permafrost extent over the study region. It is followed by the application and tuning of several machine learning models to deliver an expected accuracy in terms of permafrost classes demarcated over the study region based on literature. Results show that the PISR, LST and TWI are the most significant remotely sensed parameters affecting the permafrost and associated processes. Above 5000 m a.s.l., the proportion of permafrost in the study catchment is higher. Synergistic use of remote sensing image processing and machine learning techniques together provide mapping of permafrost over the region, which is elusive so far.

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References

  • Ali S N, Quamar M F, Phartiyal B and Sharma A 2018 Need for permafrost researches in Indian Himalaya; J. Clim. Change 4(1) 33–36, https://doi.org/10.3233/JCC-180004.

    Article  Google Scholar 

  • Allen S K, Fiddes J, Linsbauer A, Randhawa S S, Saklani B and Salzmann N 2016 Permafrost studies in Kullu district, Himachal Pradesh; Curr. Sci. 111 550–553, https://doi.org/10.18520/cs/v111/i3/550-553.

  • Anderson M C, Norman J M, Kustas W P, Houborg R, Starks P J and Agam N 2008 A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales; Remote Sens. Environ. 112 4227–4241, https://doi.org/10.1016/j.rse.2008.07.009.

    Article  Google Scholar 

  • ArcMap. Area Solar Radiation. The User Document of ArcGIS Tools 2021; https://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/area-solar-radiation.htm.

  • Bandyopadhyay J 2013 Securing the Himalayas as the water tower of Asia; Asia Policy, pp. 45–50.

  • Baral P and Haq M A 2020 Spatial prediction of permafrost occurrence in Sikkim Himalayas using logistic regression, random forests, support vector machines and neural networks; Geomorphology 371 107331, https://doi.org/10.1016/j.geomorph.2020.107331.

  • Baral P, Haq M A and Yaragal S 2020 Assessment of rock glaciers and permafrost distribution in Uttarakhand, India; Permafrost Periglac. 31 31–56, https://doi.org/10.1002/ppp.2008.

    Article  Google Scholar 

  • Becker F and Li Z 1995 Surface temperature and emissivity at various scales: Definition, measurement and related problems; Remote Sens. Rev. 12 225–253, https://doi.org/10.1080/02757259509532286.

    Article  Google Scholar 

  • Bhattacharyya A 1989 Vegetation and climate during the last 30,000 years in Ladakh; Palaeogeogr. Palaeoclimatol. Palaeoecol. 73 25–38, https://doi.org/10.1016/0031-0182(89)90042-4.

    Article  Google Scholar 

  • Blum M, Lensky I M and Nestel D 2013 Estimation of olive grove canopy temperature from MODIS thermal imagery is more accurate than interpolation from meteorological stations; Agr. Forest Meteorol. 176 90–93, https://doi.org/10.1016/j.agrformet.2013.03.007.

    Article  Google Scholar 

  • Bodin X, Schoeneich P, Deline P, Ravanel L, Magnin F, Krysiecki J M and Echelard T 2015 Mountain permafrost and associated geomorphological processes: Recent changes in the French Alps; Rev. Géogr. Alp., https://doi.org/10.4000/rga.2885.

    Article  Google Scholar 

  • Bojinski S, Verstraete M, Peterson T C, Richter C, Simmons A and Zemp M 2014 The concept of essential climate variables in support of climate research, applications, and policy; Bull. Am. Meteorol. Soc. 95 1431–1443, https://doi.org/10.1175/BAMS-D-13-00047.1.

    Article  Google Scholar 

  • Bolton S and Bon C 2009 Analysis of variance; In: Pharmaceutical statistics, 5th edn, CRC Press, pp. 198–237.

    Chapter  Google Scholar 

  • Brenning A and Azócar G F 2010 Statistical analysis of topographic and climatic controls and multispectral signatures of rock glaciers in the dry Andes, Chile (27°–33°S); Permafrost Periglac. 21 54–66, https://doi.org/10.1002/ppp.670.

  • Brown J, Ferrians O J Jr, Heginbottom J A and Melnikov E S 1997 Circum-Arctic map of permafrost and ground ice conditions; USGS Numbered Series 2 1.

    Google Scholar 

  • Carlson T N and Ripley D A 1997 On the relation between NDVI, fractional vegetation cover, and leaf area index; Remote Sens. Environ. 62 241–252.

    Article  Google Scholar 

  • Chadburn S E, Burke E J, Cox P M, Friedlingstein P, Hugelius G and Westermann S 2017 An observation-based constraint on permafrost loss as a function of global warming; Nat. Clim. Change 7 340–344, https://doi.org/10.1038/nclimate3262.

    Article  Google Scholar 

  • Chander G, Markham B L and Helder D L 2009 Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors; Remote Sens. Environ. 113 893–903, https://doi.org/10.1016/j.rse.2009.01.007.

    Article  Google Scholar 

  • Chen S 2004 Permafrost classification on the Tibet plateau based on surface emissivity retrieval from terra-MODIS data; Int. Geosci. Remote Sens. Symp. (IGARSS) 4 2699–2702, https://doi.org/10.1109/igarss.2004.1369857.

    Article  Google Scholar 

  • Cheng G and Jin H 2013 Permafrost and groundwater on the Qinghai–Tibet Plateau and in northeast China; Hydrogeol. J. 21 5–23, https://doi.org/10.1007/s10040-012-0927-2.

    Article  Google Scholar 

  • Cheng G and Wu T 2007 Responses of permafrost to climate change and their environmental significance, Qinghai–Tibet Plateau; J. Geophys. Res.-Earth 112 1–10, https://doi.org/10.1029/2006JF000631.

    Article  Google Scholar 

  • Christiansen H H, Etzelmüller B, Isaksen K, Juliussen H, Farbrot H, Humlum O, Johansson M, Ingeman-Nielsen T, Kristensen L, Hjort J, Holmlund P, Sannel A B K, Sigsgaard C, Åkerman H J, Foged N, Blikra L H, Pernosky M A and Ødegård R S 2010 The thermal state of permafrost in the nordic area during the international polar year 2007–2009; Permafrost Periglac. 21 156–181, https://doi.org/10.1002/ppp.687.

  • Deluigi N, Lambiel C and Kanevski M 2017 Data-driven mapping of the potential mountain permafrost distribution; Sci. Total Environ. 590–591 370–380, https://doi.org/10.1016/j.scitotenv.2017.02.041.

    Article  Google Scholar 

  • Dimri D B, Baranwal M and Biswas U K 1983 Integrated geophysical studies in Tsokar Basin, District Ladakh, J and K, India; Indian Miner. 37 39–46.

    Google Scholar 

  • Fu P and Rich P M 2002 A geometric solar radiation model with applications in agriculture and forestry; Comput. Electron. Agric. 37 25–35, https://doi.org/10.1016/S0168-1699(02)00115-1.

    Article  Google Scholar 

  • Gao T, Zhang T, Cao L, Kang S and Sillanpää M 2016 Reduced winter runoff in a mountainous permafrost region in the northern Tibetan Plateau; Cold Reg. Sci. Technol. 126 36–43, https://doi.org/10.1016/j.coldregions.2016.03.007.

    Article  Google Scholar 

  • Garg A and Tai K 2013 Comparison of statistical and machine learning methods in modelling of data with multicollinearity; Int. J. Model. Identif. Control 18 295–312.

    Article  Google Scholar 

  • Gisnås K, Etzelmüller B, Farbrot H, Schuler T V and Westermann S 2013 CryoGRID 1.0: Permafrost Distribution in Norway estimated by a Spatial Numerical Model; Permafrost Periglac. 24 2–19, https://doi.org/10.1002/ppp.1765.

  • Gruber S 2012 Derivation and analysis of a high-resolution estimate of global permafrost zonation; Cryosphere 6 221–233, https://doi.org/10.5194/tc-6-221-2012.

    Article  Google Scholar 

  • Gruber S, Fleiner R, Guegan E, Panday P, Schmid M O, Stumm D, Wester P, Zhang Y and Zhao L 2017 Review article: Inferring permafrost and permafrost thaw in the mountains of the Hindu Kush Himalaya region; Cryosphere 11 81–99, https://doi.org/10.5194/tc-11-81-2017.

    Article  Google Scholar 

  • Gruber S and Hoelzle M 2001 Statistical modelling of mountain permafrost distribution: local calibration and incorporation of remotely sensed data; Permafrost Periglac. 12 69–77, https://doi.org/10.1002/ppp.374.

  • Gulbrandsen M L, Minsley B J, Ball L B and Hansen T M 2016 Semiautomatic mapping of permafrost in the Yukon Flats, Alaska; Geophys. Res. Lett. 43 12,131–12,137, https://doi.org/10.1002/2016GL071334.

    Article  Google Scholar 

  • Hall D K 1982 A review of the utility of remote sensing in Alaskan permafrost studies; IEEE Trans. Geosci. Remote Sens. GE-20 390–394, https://doi.org/10.1109/TGRS.1982.350460.

  • Hassan W, Jeelani G, Dimri A P and Nusser M 2023 Assessment of spatial extent of permafrost in Upper Indus Basin (UIB); J. Mt. Sci. 20(6) 1508–1525, https://doi.org/10.1007/s11629-023-7985-x.

    Article  Google Scholar 

  • Heginbottom J A, Brown J, Melnikov E S and Ferrians Jr O.J 1993 Circumarctic map of permafrost and ground ice conditions; In: Sixth International Conference on Permafrost., Beijing, China, pp. 1132–1136.

  • Hjort J, Karjalainen O, Aalto J, Westermann S, Romanovsky V E, Nelson F E, Etzelmüller B and Luoto M 2018 Degrading permafrost puts Arctic infrastructure at risk by mid-century; Nat. Commun. 9, https://doi.org/10.1038/s41467-018-07557-4.

  • Jimenez-Munoz J C, Cristobal J, Sobrino J A, Sòria G, Ninyerola M and Pons X 2009 Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data; IEEE Trans. Geosci. Remote Sens. 47 339–349, https://doi.org/10.1109/TGRS.2008.2007125.

    Article  Google Scholar 

  • Jorgenson M T, Yoshikawa K, Kanevskiy M and Shur Y 2008 Permafrost characteristics of Alaska; Fairbanks, AK: University of Alaska Fairbanks, Institute of Northern Engineering, 1 sheet, scale 1:7,200,000.

  • Kalinicheva S 2018 Identification and mapping of permafrost and thaliks using satellite images (on the example of the Olekmo–Charskiy Highland in Southern Yakutia); Proceedings of the International Conference “InterCarto/InterGIS” 24 71–82, https://doi.org/10.24057/2414-9179-2018-2-24-71-82.

  • Kalinicheva S V, Fedorov A N and Zhelezniak M N 2019 Mapping mountain permafrost landscapes in Siberia using Landsat thermal imagery; Geosci. J. 9, https://doi.org/10.3390/geosciences9010004.

  • Kalinicheva S V and Shestakova A A 2021 Using thermal remote sensing in the classification of mountain permafrost landscapes; J. Mt. Sci. 18 635–645, https://doi.org/10.1007/s11629-020-6475-7.

    Article  Google Scholar 

  • Kellerer-Pirklbauer A 2019 Long-term monitoring of sporadic permafrost at the eastern margin of the European Alps; Hochreichart. Permafrost. Periglac. 30(4) 260–277, https://doi.org/10.1002/ppp.2021.

    Article  Google Scholar 

  • Kendall M G 1938 A New measure of rank correlation; Biometrika 30 81–93, https://doi.org/10.1093/biomet/30.1-2.81.

    Article  Google Scholar 

  • Khalid S, Khalil T and Nasreen S 2014 A survey of feature selection and feature extraction techniques in machine learning; In: 2014 Science and Information Conference, pp. 372–378.

  • Khan M A R, Singh S, Pandey P, Bhardwaj A, Ali S N, Chaturvedi V and Ray P K C 2021 Modelling Permafrost distribution in Western Himalaya using remote sensing and field observations; Remote Sens. 13 4403, https://doi.org/10.3390/rs13214403.

    Article  Google Scholar 

  • Kopecký M, Macek M and Wild J 2021 Topographic wetness index calculation guidelines based on measured soil moisture and plant species composition; Sci. Total Environ. 757 143785, https://doi.org/10.1016/j.scitotenv.2020.143785.

  • Kozachenko L F and Leonenko N N 1987 Sample estimate of the entropy of a random vector; Probl. Peredachi Inf. 23 9–16.

    Google Scholar 

  • L8 User Handbook 2016 Landsat 8 (L8) Data Users Handbook; Department of the Interior U.S. Geological Survey.

  • Langer M, Westermann S and Boike J 2010 Spatial and temporal variations of summer surface temperatures of wet polygonal tundra in Siberia – implications for MODIS LST based permafrost monitoring; Remote Sens. Environ. 114 2059–2069, https://doi.org/10.1016/j.rse.2010.04.012.

    Article  Google Scholar 

  • Lee C and Lee G G 2006 Information gain and divergence-based feature selection for machine learning-based text categorisation; Comm. Com. Inf. Sci. 42 155–165.

    Google Scholar 

  • Leverington D W and Duguay C R 1997 A neural network method to determine the presence or absence of Permafrost near Mayo, Yukon Territory, Canada; Permafrost Periglac. 8 205–215, https://doi.org/10.1002/(SICI)1099-1530(199732)8:2%3c205::AID-PPP252%3e3.0.CO;2-5.

    Article  Google Scholar 

  • Li Z-L, Wu H, Wang N, Qiu S, Sobrino J A and Wan Z et al. 2012 Land surface emissivity retrieval from satellite data; Int. J. Remote Sens. 34 3084–3127, https://doi.org/10.1080/01431161.2012.716540.

    Article  Google Scholar 

  • Liu L and Zhang Y 2011 Urban heat island analysis using the Landsat TM Data and ASTER Data: A case study in Hong Kong; Remote Sens. 3(7) 1535–1552, https://doi.org/10.3390/rs3071535.

    Article  Google Scholar 

  • Mayewski P A, Jeschke P A and Ahmad N 1981 An active rock glacier, Wavball Pass, Jammu and Kashmir Himalayas, India; J. Glaciol. 27 201–202, https://doi.org/10.1017/s0022143000011394.

    Article  Google Scholar 

  • Morrissey L A, Strong L L and Card D H 1986 Mapping permafrost in the boreal forest with Thematic Mapper satellite data; Photogramm. Eng. Rem. Sens. 52 1513–1520.

    Google Scholar 

  • Mukherji A, Molden D, Nepal S, Rasul G and Wagnon P 2015 Himalayan waters at the crossroads: issues and challenges; Int. J. Water Resour. Dev. 31 151–160, https://doi.org/10.1080/07900627.2015.1040871.

    Article  Google Scholar 

  • NASA J 2013 NASA shuttle radar topography mission global 1 arc second ; NASA EOSDIS Land Processes DAAC.

  • Neinavaz E, Skidmore A K and Darvishhzadeh R 2020 Effects of prediction accuracy of the proportion of vegetation cover on land surface emissivity and temperature using the NDVI threshold method; Int. J. Appl. Earth Obs. 85 101984.

    Google Scholar 

  • Obu J 2021 How much of the Earth’s surface is underlain by permafrost?; J. Geophys. Res. Earth 126, https://doi.org/10.1029/2021JF006123.

  • Owen L A and England J 1998 Observations on rock glaciers in the Himalayas and Karakoram Mountains of northern Pakistan and India; Geomorphology 26 199–213, https://doi.org/10.1016/S0169-555X(98)00059-2.

    Article  Google Scholar 

  • Panda S K, Prakash A, Jorgenson M T and Solie D N 2012 Near-surface permafrost distribution mapping using logistic regression and remote sensing in interior Alaska; GIScience Remote Sens. 49 346–363, https://doi.org/10.2747/1548-1603.49.3.346.

  • Pandey A, Rai A, Gupta S K, Shukla D P and Dimri A P 2021 Integrated approach for effective debris mapping in glacierised regions of Chandra River Basin, Western Himalayas, India; Sci. Total Environ. 779 146492, https://doi.org/10.1016/j.scitotenv.2021.146492.

  • Pandey S, Clarke J, Nema P, Bonaccorsi R, Som S, Sharma M, Phartiyal B, Rajamani S, Mogul R, Martin-Torres J, Vaishampayan P, Blank J, Steller L, Srivastava A, Singh R, McGuirk S, Zorzano M P, Güttler J M, Mendaza T, Soria-Salinas A, Ahmad S, Ansari A, Singh V K, Mungi C and Bapat N 2019 Ladakh: Diverse, high-altitude extreme environments for off-earth analogue and astrobiology research; Int. J. Astrobiol., https://doi.org/10.1017/S1473550419000119.

    Article  Google Scholar 

  • Pastick N J, Jorgenson M T, Wylie B K, Nield S J, Johnson K D and Finley A O 2015 Distribution of near-surface permafrost in Alaska: Estimates of present and future conditions; Remote Sens. Environ. 168 301–315, https://doi.org/10.1016/j.rse.2015.07.019.

    Article  Google Scholar 

  • Persson A, Hasan A, Tang J and Pilesjö P 2012 Modelling Flow Routing in Permafrost Landscapes with TWI: An Evaluation against site-specific wetness measurements; Trans. GIS 16 701–713, https://doi.org/10.1111/j.1467-9671.2012.01338.x.

    Article  Google Scholar 

  • Pepin N, Bradley R S, Diaz H F, Baraer M, Caceres E B, Forsythe N, Fowler H, Greenwood G, Hashmi M Z, Liu X D, Miller J R, Ning L, Ohmura A, Palazzi E, Rangwala I, Schöner W, Severskiy I, Shahgedanova M, Wang M B, Williamson S N and Yang D Q 2015 Elevation-dependent warming in mountain regions of the world; Nat. Clim. Change 5 424–430, https://doi.org/10.1038/nclimate2563.

    Article  Google Scholar 

  • Philip G and Mazari R K 2000 Shrinking lake basins in the proximity of the Indus Suture Zone of northwestern Himalaya: A case study of Tso Kar and Startsapuk Tso, using 1RS-1C data; Int. J. Remote Sens. 21 2973–2984, https://doi.org/10.1080/01431160050144901.

    Article  Google Scholar 

  • Ran Y, Li X and Cheng G 2018 Climate warming over the past half century has led to thermal degradation of permafrost on the Qinghai–Tibet Plateau; Cryosphere 12 595–608, https://doi.org/10.5194/tc-12-595-2018.

    Article  Google Scholar 

  • Rana K S, Sharma R C, Tiwari V and Kumar R 2018 Assessment of surface water quality of the Himalayan lake Beni Tal, India; Curr. Res. Hydrol. Res. 2018, https://doi.org/10.29011/CRHR-102.

    Article  Google Scholar 

  • Rastogi S and Nawani P C 1976 Permafrost areas in Tso Kar Basin; In: Symposium on the Contribution of Earth Sciences Towards the Research and Development Activities in the N.R., pp. 1–7.

  • Rastogi S P and Narayan S 1999 Permafrost areas in Tso Kar Basin; In: Symposium for Snow, Ice and Glaciers, Geol. Surv. India, Special Publ. 53, Lucknow, India, pp. 315–319.

  • Reger R D and Hubbard T D 2010 Reconnaissance interpretation of 1978–1983 permafrost; Alaska Highway Corridor, Robertson River to Tetlin Junction, Alaska, https://doi.org/10.14509/19743.

    Article  Google Scholar 

  • Romanovsky V E, Drozdov D S, Oberman N G, Malkova G V, Kholodov A L, Marchenko S S, Moskalenko N G, Sergeev DO, Ukraintseva N G, Abramov A A, Gilichinsky D A and Vasiliev A A 2010 Thermal state of permafrost in Russia; Permafrost Periglac. 21 136–155, https://doi.org/10.1002/ppp.683.

  • Ross B C 2014 Mutual information between discrete and continuous data sets (ed.) Marinazzo D, PLoS One 9 e87357, https://doi.org/10.1371/journal.pone.0087357.

  • Runge A, Nitze I and Grosse G 2022 Remote sensing annual dynamics of rapid permafrost thaw disturbances with LandTrendr; Remote Sens. Environ. 268 112752, https://doi.org/10.1016/j.rse.2021.112752.

  • Samala R K, Chan H P, Hadjiiski L and Koneru S 2020 Hazards of data leakage in machine learning: A study on classification of breast cancer using deep neural networks; In: Medical Imaging 2020: Computer-Aided Diagnosis 1131416.

  • Satorra A and Bentler P M 1994 Corrections to test statistics and standard errors in covariance structure analysis; In: Latent variables analysis: Applications for developmental research (eds) A von Eye and C C Clogg, Thousand Oaks, CA: Sage, pp. 399–419.

  • Schmid M O, Baral P, Gruber S, Shahi S, Shrestha T, Stumm D and Wester P 2015 Assessment of permafrost distribution maps in the Hindu Kush Himalayan region using rock glaciers mapped in Google Earth; Cryosphere 9 2089–2099, https://doi.org/10.5194/tc-9-2089-2015.

    Article  Google Scholar 

  • Schuur E A G, Vogel J G, Crummer K G, Lee H, Sickman J O and Osterkamp T E 2009 The effect of permafrost thaw on old carbon release and net carbon exchange from tundra; Nature 459 556–559, https://doi.org/10.1038/nature08031.

    Article  Google Scholar 

  • Scott C A, Zhang F, Mukherji A, Immerzeel W, Mustafa D and Bharati L 2019 Water in the Hindu Kush Himalaya; In: The Hindu Kush Himalaya Assessment (eds) Wester P, Mishra A, Mukherji A and Shrestha A B, Cham: Springer International Publishing, pp. 257–299, https://doi.org/10.1007/978-3-319-92288-1_8.

  • Sharma R C, Kumar R and Chaudhary S 2018 Physico-chemical and microbiological assessment of surface water quality of a Himalayan Wetland Deoria Tal, India; Civ. Environ. Res. 10 59–75.

    Google Scholar 

  • Shi Y, Niu F, Yang C, Che T, Lin Z and Luo J 2018 Permafrost presence/absence mapping of the Qinghai-Tibet Plateau based on multi-source remote sensing data; Remote Sens. 10, https://doi.org/10.3390/rs10020309.

  • Smith S L, Romanovsky V E, Lewkowicz A G, Burn C R, Allard M, Clow G D, Yoshikawa K and Throop J 2010 Thermal state of permafrost in North America: A contribution to the international polar year; Permafrost Periglac. 21 117–135, https://doi.org/10.1002/ppp.690.

  • Snyder W C, Wan Z, Zhang Y and Feng Y-Z 1998 Classification-based emissivity for land surface temperature measurement from space; Int. J. Remote Sens. 19 2753–2774, https://doi.org/10.1080/014311698214497.

    Article  Google Scholar 

  • Sobrino J A and Raissouni N 2000 Toward remote sensing methods for land cover dynamic monitoring: Application to Morocco; Int. J. Remote Sens. 21 353–366, https://doi.org/10.1080/014311600210876.

    Article  Google Scholar 

  • Solorio-Fernández S, Carrasco-Ochoa J A and Martinez-Trinidad J F 2020 A review of unsupervised feature selection methods; Artif. Intell. Rev. 53 907–948.

    Article  Google Scholar 

  • Tamocai C, Canadell J G, Schuur E A G, Kuhry P, Mazhitova G and Zimov S 2009 Soil organic carbon pools in the northern circumpolar permafrost region; Global Biogeochem. Cy. 23, https://doi.org/10.1029/2008GB003327.

  • Valor E and Caselles V 1996 Mapping land surface emissivity from NDVI: Application to European, African, and South American Areas; Remote Sens. Environ. 57 167–184, https://doi.org/10.1016/0034-4257(96)00039-9.

    Article  Google Scholar 

  • Van de Griend A A and Owe M 1993 On the relationship between thermal emissivity and the normalised difference vegetation index for natural surfaces; Int. J. Remote Sens. 14 1119–1131, https://doi.org/10.1080/01431169308904400.

    Article  Google Scholar 

  • Van Everdingen R O and Association I P et al. 1998 Multi-language glossary of permafrost and related ground-ice terms, Arctic Inst. of North America University of Calgary.

  • Vieira G, Bockheim J, Guglielmin M, Balks M, Abramov A A, Boelhouwers J, Cannone N, Ganzert L, Gilichinsky D A, Goryachkin S, López-Martínez J, Meiklejohn I, Raffi R, Ramos M, Schaefer C, Serrano E, Simas F, Sletten R and Wagner D 2010 Thermal state of permafrost and active-layer monitoring in the antarctic: Advances during the international polar year 2007–2009; Permafrost Periglac. 21 182–197, https://doi.org/10.1002/ppp.685.

  • Wan Z, Zhang Y, Zhang Q and Li Z-L 2004 Quality assessment and validation of the MODIS global land surface temperature; Int. J. Remote Sens. 25 261–274, https://doi.org/10.1080/0143116031000116417.

    Article  Google Scholar 

  • Wang X, Liu L, Zhao L, Wu T, Li Z and Liu G 2017 Mapping and inventorying active rock glaciers in the northern Tien Shan of China using satellite SAR interferometry; The Cryosphere 11 997–1014, https://doi.org/10.5194/tc-11-997-2017.

    Article  Google Scholar 

  • Wani J M, Thayyen R J, Gruber S, Ojha C S P and Stumm D 2020 Single-year thermal regime and inferred permafrost occurrence in the upper Ganglass catchment of the cold-arid Himalaya, Ladakh, India; Sci. Total Environ. 703 134631, https://doi.org/10.1016/j.scitotenv.2019.134631.

  • Wani J M, Dimri A P and Thayyen R 2023 Permafrost in the Upper Indus Basin: An active layer dynamics; J. Earth Syst. Sci. 132(61), https://doi.org/10.1007/s12040-023-02074-5.

  • Westermann S, Elberling B, Højlund Pedersen S, Stendel M, Hansen B U and Liston G E 2015 Future permafrost conditions along environmental gradients in Zackenberg, Greenland; The Cryosphere 9 719–735, https://doi.org/10.5194/tc-9-719-2015.

    Article  Google Scholar 

  • Wünnemann B, Reinhardt C, Kotlia B S and Riedel F 2008 Observations on the relationship between lake formation, permafrost activity and lithalsa development during the last 20,000 years in the Tso Kar basin, Ladakh, India; Permafrost Periglac. 19 341–358, https://doi.org/10.1002/ppp.631.

    Article  Google Scholar 

  • Yan K and Zhang D 2015 Feature selection and analysis on correlated gas sensor data with recursive feature elimination; Sensor. Actuat. B-Chem. 212 353–363.

    Article  Google Scholar 

  • Zamli K Z, Alkazemi B Y and Kendall G 2016 A Tabu Search hyper-heuristic strategy for t-way test suite generation; In: Appl. Soft Comput. archive., pp. 57–74.

  • Zhao L, Wu Q, Marchenko S S and Sharkhuu N 2010 Thermal state of permafrost and active layer in central Asia during the international polar year; Permafrost Periglac. 21 198–207, https://doi.org/10.1002/ppp.688.

  • Zou D, Lin Z, Tonghua W, Xiaodong W, Qiangqiang P, Yongping Q and Zhiwei W 2015 Assessing the applicability of MODIS land surface temperature products in continuous permafrost regions in the central Tibetan Plateau; J. Glaciol. Geocryol. 37 308–317.

    Google Scholar 

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Acknowledgements

APD acknowledges the National Mission on Himalayan Studies (NMHS) funded by the Ministry of Environment, Forest and Climate Change (MoEF&CC) for the project grant GBPNI/NMHS-2019-20/MG, dated 22-08-2019.

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Authors and Affiliations

  1. School of Environmental Sciences, Jawaharlal Nehru University, New Delhi, India

    Aayushi Pandey, Bankim Chandra Yadav, John Mohd Wani & A P Dimri

  2. Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, India

    Bankim Chandra Yadav

Authors
  1. Aayushi Pandey
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  2. Bankim Chandra Yadav
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  3. John Mohd Wani
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  4. A P Dimri
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Contributions

APD conceived the study and provided overall guidance during the processing and analysis. AP, BCY and JMW developed the modelling strategy, and conducted the analysis with support from APD. JMW and APD carried out the fieldwork. All authors contributed to the writing of the manuscript.

Corresponding author

Correspondence to A P Dimri.

Additional information

Communicated by George Mathew

Corresponding editor: George Mathew

Appendix

Appendix

  1. (1)

    Ridge Regression

  2. (2)

    Logistic Regression

  3. (3)

    Stochastic Gradient Descent Learning

  4. (4)

    Perceptron Classifier

  5. (5)

    Passive Aggressive

  6. (6)

    Linear Discriminant Analysis

  7. (7)

    Quadratic Discriminant Analysis

  8. (8)

    Gaussian Naïve Bayes

  9. (9)

    Bernoulli’s Naïve Bayes

  10. (10)

    Ada Boost Regressor

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Pandey, A., Yadav, B.C., Wani, J.M. et al. Permafrost estimation model in Upper Indus Basin. J Earth Syst Sci 132, 156 (2023). https://doi.org/10.1007/s12040-023-02176-0

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  • DOI: https://doi.org/10.1007/s12040-023-02176-0

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