Mapping carbon-thermal environments for comprehending real-time scenarios

Srivastava, Chitra; Bharat, Alka

doi:doi.org/10.1007/s11600-024-01387-3

Artykuł - szczegóły

Tytuł artykułu

Mapping carbon-thermal environments for comprehending real-time scenarios

Autorzy

Srivastava Chitra , Bharat Alka

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

doi.org/10.1007/s11600-024-01387-3

Warianty tytułu

Języki publikacji

Abstrakty

Urbanization and land cover change (LCC) drive carbon emission, which in turn contributes to a substantial rise of about 1 °C in the global average surface temperature. Conducting a crucial study of carbon emissions (CEs) and land surface temperature (LST) change is vital for reducing global warming below 1.5 °C to avoid extreme events on cities, resources, and ecosystems. This study examines connections between land cover (LC), CE, and LST. Hotspot mapping pinpoints CE hotspots on LC types with spatial clustering. LST depicts gradual temperature rise for each LC. Eventually, the notational rubric method compares real-time situations involving CE and LST in composite carbon-thermal emission and absorption map. The solutions provided in the rubric are situation-based and nature-centric. This study presents novel mapping method of composite CE and absorption map by combining both land cover emissions and absorptions and fossil fuel emissions. Development of relational rubric gives an integrated approach to understand complex relationship between LCC CE and surface temperature.

Słowa kluczowe

carbon emission land surface temperature land cover change hotspot mapping rubric

emisja dwutlenku węgla temperatura powierzchni ziemi zmiany pokrycia terenu mapowanie punktów aktywnych rubryka

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2025

Tom

Vol. 73, no. 1

Strony

933--953

Opis fizyczny

Bibliogr. 148 poz.

Twórcy

autor

Srivastava Chitra

ar.chitrasrivastava@gmail.com

Department of Architecture and Planning, Maulana Azad National Institute of Technology (An Institute of National Importance), Bhopal, Madhya Pradesh 462003, India

https://orcid.org/0009-0009-0857-4512

autor

Bharat Alka

alka_bharat@yahoo.com

Department of Architecture and Planning, Maulana Azad National Institute of Technology (An Institute of National Importance), Bhopal, Madhya Pradesh 462003, India

https://orcid.org/0000-0003-2421-1884

Bibliografia

1. Acquaye AA, Wiedmann T, Feng K, Crawford RH, Barrett J, Kuylenstierna J, Duffy AP, Koh SCL, McQueen-Mason S (2011) Identification of ‘carbon hot-spots’ and quantification of GHG intensities in the biodiesel supply chain using hybrid LCA and structural path analysis. Environ Sci Technol 45(6):2471-2478. https://doi.org/10.1021/es103410q
2. Agarwal DS, Bharat A (2023) Nature-based solutions for flood- drought mitigation using a composite framework: a case-based approach. J Water Clim Change 14(3):778-795. https://doi.org/10.2166/wcc.2023.369
3. Agarwal DS, Bharat A, Kjeldsen TR, Adeyeye K (2024) Assessing impact of nature based solutions on peak flow using HEC- HMS. Water Resour Manage 38(3):1125-1140. https://doi.org/10.1007/s11269-023-03712-9
4. Alahmad B, Tomasso LP, Al-Hemoud A, James P, Koutrakis P (2020) Spatial distribution of land surface temperatures in Kuwait: Urban heat and cool Islands. Int J Environ Res Public Health 17(9):2993. https://doi.org/10.3390/ijerph17092993
5. Ali G, Pumijumnong N, Cui S (2018) Valuation and validation of carbon sources and sinks through land cover/use change analysis: the case of Bangkok metropolitan area. Land Use Policy 70:471-478. https://doi.org/10.1016/j.landusepol.2017.11.003
6. Anderson JR, Hardy EE, Roach JT, Witmer RE (1976) A land use and land cover classification system for use with remote sen¬sor data. In: Professional Paper. https://doi.org/10.3133/pp964
7. Antoszewski P, Świerk D, Krzyżaniak M (2020) Statistical review of quality parameters of blue-green infrastructure elements important in mitigating the effect of the urban heat island in the temperate climate (C) zone. Int J Environ Res Public Health 17(19):1-36. https://doi.org/10.3390/ijerph17197093
8. Arneth A, Sitch S, Pongratz J, Stocker BD, Ciais P, Poulter B, Bayer AD, Bondeau A, Calle L, Chini LP, Gasser T, Fader M, Friedlingstein P, Kato E, Li W, Lindeskog M, Nabel JEMS, Pugh TAM, Robertson E, Viovy N, Yue C, Zaehle S (2017) Historical carbon dioxide emissions caused by land- use changes are possibly larger than assumed. Nat Geosci 10(2):79-84. https://doi.org/10.1038/ngeo2882
9. Avellan T, Gremillion P (2019) Constructed wetlands for resource recovery in developing countries. Renew Sustain Energy Rev 99:42-57. https://doi.org/10.1016/j.rser.2018.09.024
10. Balbus J, Brody M, Burton I, Campos M, Carson I, Carter TR, Cohen S, Oude Essink G, Feenstra JF, Hlohowskyj I, Hulme M, Iglesias A, Klein R, Linder S, Logan C, Malcolm JR, Markham A, Moomaw WR, Nicholls R, Yates D (1998) Handbook on methods for climate change impact assessment and adaptation strategies. https://wedocs.unep.org/20.500.11822/32746
11. Bhattacharjee S, Chen J (2020) Prediction of satellite-based column CO2 concentration by combining emission inventory and LULC information. IEEE Trans Geosci Remote Sens 58(12):8285-8300. https://doi.org/10.1109/TGRS.2020.2985047
12. Bindi M, Brown S (UK), Camilloni I, Diedhiou A Ivory C, Djalante R, (USA), LEK. Francois, Engelbrecht (South Africa), JG (France), Hijioka Yasuaki (Japan), Mehrotra Shagun(USA/ India), Payne Antony(UK), Seneviratne Sonia I (Switzerland), Thomas Adelle (Bahamas), (UK), WR, Guangsheng Z (China) (2022) Impacts of 1.5°C global warming on natural and human systems. In: Global warming of 1.5°C, Cambridge University Press, 175-312. https://doi.org/10.1017/9781009157940.005
13. Bossio DA, Cook-Patton SC, Ellis PW, Fargione J, Sanderman J, Smith P, Wood S, Zomer RJ, von Unger M, Emmer IM, Griscom BW (2020) The role of soil carbon in natural climate
14. Solutions. Nat Sustain 3(5):391-398. https://doi.org/10.1038/s41893-020-0491-z
15. Bun R, Nahorski Z, Horabik-Pyzel J, Danylo O, See L, Charkovska N, Topylko P, Halushchak M, Lesiv M, Valakh M, Kinakh V (2019) Development of a high-resolution spatial inventory of greenhouse gas emissions for Poland from stationary and mobile sources. Mitig Adapt Strat Glob Change 24(6):853- 880. https://doi.org/10.1007/s11027-018-9791-2
16. Burian SJ, Brown MJ, McPherson TN (2002) Evaluation of land use/ land cover datasets for urban watershed modeling. Water Sci Technol 45(9):269-276. https://doi.org/10.2166/wst.2002.0256
17. Buttler K (2013) Band combinations for Landsat 8. In: ArcGIS Blog, 1-3. https://www.esri.com/arcgis-blog/products/product/imagery/band-combinations-for-landsat-8/
18. Chen Y (2020) Effects of land use cover change on carbon emissions and ecosystem services in Chengyu urban agglomeration, China. Stoch Env Res Risk Assess 34(8):1197-1215. https://doi.org/10.1007/s00477-020-01819-8
19. Chen J, Zhao F, Zeng N, Oda T (2020) Comparing a global high- resolution downscaled fossil fuel CO2 emission dataset to local inventory-based estimates over 14 global cities. Carbon Balance Manag 15:1. https://doi.org/10.1186/s13021-020-00146-3
20. Chen L, Msigwa G, Yang M, Osman AI, Fawzy S, Rooney DW, Yap PS (2022) Strategies to achieve a carbon neutral society: a review. Environ Chem Lett 20(4):2277-2310. https://doi.org/10.1007/s10311-022-01435-8
21. Christina N (2019) Greenhouse gases, facts and information. In: national geographic. https://www.nationalgeographic.com/environment/global-warming/deforestation/
22. Cui Y, Li L, Chen L, Zhang Y, Cheng L, Zhou X, Yang X (2018) Land- use carbon emissions estimation for the Yangtze River Delta Urban Agglomeration using 1994-2016 Landsat image data. Remote Sens 10(9):1334. https://doi.org/10.3390/rs10091334
23. De Carvalho RM, Szlafsztein CF (2019) Urban vegetation loss and ecosystem services: the influence on climate regulation and noise and air pollution. Environ Pollut 245:844-852. https://doi.org/10.1016/J.ENVPOL.2018.10.114
24. de la Barrera F, Reyes-Paecke S (2021) Green infrastructure to mitigate extreme temperatures in Cities BT. In: Palme M, Salvati A (eds) Urban microclimate modelling for comfort and energy studies. Springer International Publishing, Cham, pp 403-417. https://doi.org/10.1007/978-3-030-65421-4_19
25. Denton F, Wilbanks TJ, Abeysinghe AC, Burton I, Gao Q, Lemos MC, Masui T, O’Brien KL, Warner K, Bhadwal S, Leal W, Van Ypersele JP, Wright SB (2015) Climate-resilient pathways: adaptation, mitigation, and sustainable development. Clim Change 2014 Impacts Adapt Vulnerability Part A Glob Sect Asp. https://doi.org/10.1017/CBO9781107415379.025
26. Deshpande MV, Kumar N, Pillai D, Krishna VV, Jain M (2023) Green- house gas emissions from agricultural residue burning have increased by 75% since 2011 across India. Sci Total Environ 904(September):166944. https://doi.org/10.1016/j.scitotenv.2023.166944
27. Dewa DD, Buchori I (2023) Impacts of rapid urbanization on spatial dynamics of land use-based carbon emission and surface temperature changes in the Semarang Metropolitan Region, Indonesia. Environ Monit Assess 195(2):259. https://doi.org/10.1007/s10661-022-10839-6
28. Doost ZH, Yaseen ZM (2023) The impact of land use and land cover on groundwater fluctuations using remote sensing and geographical information system: representative case study in Afghani- stan. Environ Dev Sustain 0123456789. https://doi.org/10.1007/s10668-023-04253-2
29. EPA (2022) Basics of climate change | US EPA. In: Epa. https://www.epa.gov/climatechange-science/basics-climate-change
30. Eyring V, Gillett N (2021) Chapter 3: human influence on the climate system. In: Sixth assessment report. https://doi.org/10.1017/ 9781009157896.005.423
31. Fagodiya RK, Pathak H (2023) Reducing carbon emissions from agriculture for environmental security. Indian Farm 73(June):48-52
32. Fan L, Wang J, Han D, Gao J, Yao Y (2023) Research on promoting carbon sequestration of urban green space distribution characteristics and planting design models in Xi’an. Sustainability (switzerland) 15(1):572. https://doi.org/10.3390/su15010572
33. Fang JY, Guo ZD, Piao SL, Chen AP (2007) Terrestrial vegetation carbon sinks in China, 1981-2000. Sci China, Ser D Earth Sci 50(9):1341-1350. https://doi.org/10.1007/s11430-007-0049-1
34. Fatema S, Chakrabarty A (2020) Land use/land cover change with impact on land surface temperature: a case study of MKDA planning area, West Bengal, India. Geogr Environ Sustain 13(4):43-53. https://doi.org/10.24057/2071-9388-2020-62
35. Fattah MA, Morshed SR, Morshed SY (2021a) Impacts of land use- based carbon emission pattern on surface temperature dynamics: experience from the urban and suburban areas of Khulna, Bangladesh. Remote Sens Appl Soc Environ 22:100508. https://doi.org/10.1016/j.rsase.2021.100508
36. Fattah MA, Morshed SR, Morshed SY (2021b) Multi-layer perceptron-Markov chain-based artificial neural network for model- ling future land-specific carbon emission pattern and its influences on surface temperature. SN Appl Sci 3(3):1-22. https:// doi.org/10.1007/s42452-021-04351-8
37. Fawzy S, Osman AI, Doran J, Rooney DW (2020) Strategies for mitigation of climate change: a review. Envi- ron Chem Lett 18(6):2069-2094. https://doi.org/10.1007/s10311-020-01059-w
38. Friedlingstein P, Houghton RA, Marland G, Hackler J, Boden TA, Conway TJ, Canadell JG, Raupach MR, Ciais P, Le Quere C (2010) Update on CO2 emissions. Nat Geosci 3(12):811-812. https://doi.org/10.1038/ngeo1022
39. Friedlingstein P, O’Sullivan M, Jones MW, Andrew RM, Hauck J, Olsen A, Peters GP, Peters W, Pongratz J, Sitch S, Le Quere C, Canadell JG, Ciais P, Jackson RB, Alin S, Aragao LEOC, Arneth A, Arora V, Bates NR, Becker M, Benoit-Cattin A, Bittig HC, Bopp L, Bultan S, Chandra N, Chevallier F, Chini LP, Evans W, Florentie L, Forster PM, Gasser T, Gehlen M, Gilfillan D, Gkritzalis T, Gregor L, Gruber N, Harris I, Hartung K, Haverd V, Houghton RA, Ilyina T, Jain AK, Joetzjer E, Kadono K, Kato E, Kitidis V, Korsbakken JI, Landschutzer P, Lefevre N, Len- ton A, Lienert S, Liu Z, Lombardozzi D, Marland G, Metzl N, Munro DR, Nabel JMS, Nakaoka S-I, Niwa Y, O’Brien K, Ono T, Palmer PI, Pierrot D, Poulter B, Resplandy L, Robertson E, Rodenbeck C, Schwinger J, Seferian R, Skjelvan I, Smith AJP, Sutton AJ, Tanhua T, Tans PP, Tian H, Tilbrook B, van der Werf G, Vuichard N, Walker AP, Wanninkhof R, Watson AJ, Willis D, Wiltshire AJ, Wenping Y, Yue Xu, Zaehle S (2020) Global Car- bon Budget 2020. Earth Syst Sci Data 12(4):3269-3340. https://doi.org/10.5194/essd-12-3269-2020
40. Friedlingstein, P., O’sullivan, M., Jones, M. W., Andrew, R. M., Gregor, L., Hauck, J., Le Quere, C., Luijkx, I. T., Olsen, A., Peters, G. P., Peters, W., Pongratz, J., Schwingshackl, C., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Alkama, R., _ Zheng, B. (2022). Global Carbon Budget 2022. Earth Sys¬tem Science Data, 14(11), 4811-4900. https://doi.org/10.5194/essd-14-4811-2022
41. Galagoda RU, Jayasinghe GY, Halwatura RU, Rupasinghe HT (2018) The impact of urban green infrastructure as a sustainable approach towards tropical micro-climatic changes and human thermal comfort. Urban for Urban Green 34:1-9. https://doi.org/10.1016/J.UFUG.2018.05.008
42. Ganzenmuller R, Bultan S, Winkler K, Fuchs R, Zabel F, Pongratz J (2022) Land-use change emissions based on high-resolution activity data substantially lower than previously estimated. Environ Res Lett 17(6):064050. https://doi.org/10.1088/1748-9326/ac70d8
43. Gogoi PP, Vinoj V, Swain D, Roberts G, Dash J, Tripathy S (2019) Land use and land cover change effect on surface temperature over Eastern India. Sci Rep 9(1):8859. https://doi.org/10.1038/s41598-019-45213-z
44. Gohain KJ, Goswami A, Mohammad P, Kumar S (2023a) Modelling relationship between land use land cover changes, land surface temperature and urban heat island in Indore city of central India. Theor Appl Climatol 0123456789. https://doi.org/10.1007/s00704-023-04371-x
45. Gohain KJ, Goswami A, Mohammad P, Kumar S (2023b) Modelling relationship between land use land cover changes, land surface temperature and urban heat island in Indore city of central India. Theoret Appl Climatol 151(3-4):1981-2000. https://doi.org/10.1007/s00704-023-04371-x
46. Guerri G, Crisci A, Messeri A, Congedo L, Munafo M, Morabito M (2021) Thermal summer diurnal hot-spot analysis: the role of local urban features layers. Remote Sens 13(3):1-29. https://doi.org/10.3390/rs13030538
47. Gupta P, Bharat A (2022a) Developing sustainable development index as a tool for appropriate urban land take. Environ Dev Sustain 24(11):13378-13406. https://doi.org/10.1007/s10668-021-01992-y
48. Gupta P, Bharat A (2022b) An integrated approach for capturing intermediate demographic changes. J Urban Plan Dev 148(2):04022018. https://doi.org/10.1061/(asce)up.1943-5444.0000810
49. Gupta P, Bharat A (2023) A hybrid scale to relate natural and built environments: a pragmatic approach to sustainable cities. Int J Sust Dev World 30(1):95-110. https://doi.org/10.1080/13504509.2022.2123410
50. Gupta N, Mathew A, Khandelwal S (2019) Analysis of cooling effect of water bodies on land surface temperature in nearby region: a case study of Ahmedabad and Chandigarh cities in India. Egypt J Remote Sens Space Sci 22(1):81-93. https://doi.org/10.1016/j.ejrs.2018.03.007
51. Harris and Gibbs (2021) Quantifying Carbon Fluxes in the World’s Forests | World Resources Institute. In: Wri. https://www.wri.org/insights/forests-absorb-twice-much-carbon-they-emit-each-year
52. Hines S (2006) Greenhouse effect. Landsc Archit 96(1):78-85. https:// doi.org/10.4324/9780203888469-36
53. Houghton RA, House JI, Pongratz J, Van Der Werf GR, Defries RS, Hansen MC, Le Quere C, Ramankutty N (2012) Carbon emissions from land use and land-cover change. Biogeosciences 9(12):5125-5142. https://doi.org/10.5194/bg-9-5125-2012
54. Imran HM, Hossain A, Shammas MI, Das MK, Islam MR, Rahman K, Almazroui M (2022) Land surface temperature and human thermal comfort responses to land use dynamics in Chittagong city of Bangladesh. Geomat Nat Haz Risk 13(1):2283-2312. https://doi.org/10.1080/19475705.2022.2114384
55. International Energy Agency (IEA) (2022) Global CO2 emissions rebounded to their highest level in history in 2021. Global energy review: CO2 emissions in 2021, 1-3. https://www.iea.org/news/global-co2-emissions-rebounded-to-their-highest-level-in-histo ry-in-2021
56. IPCC (2001) TAR AR3 third assesment report-workgroup II: impacts, adaptation & vulnerability. Ipcc, 10032. http://www.ipcc.ch/ipccreports/tar/wg2/index.htm
57. IPCC (2006) 2006 IPCC guidelines for national greenhouse gas inventories 1-66
58. IPCC (2019) Foreword technical and preface. In: Climate change and land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems
59. IPCC (2022) Framing and Context. Global Warming of 1.5°C. Cam¬bridge University Press, Cham, pp 49-92. https://doi.org/10.1017/9781009157940.003
60. Kahangwa C, Nahonyo C, Sangu G (2020) Monitoring land cover change using remote sensing (rs) and geographical information system (gis): a case of golden pride and geita gold mines, Tan¬zania. J Geogr Inf Syst 12(05):387-410. https://doi.org/10.4236/jgis.2020.125024
61. Kamal M, Muhammad FH, Mahardhika SA (2020) Effect of image radiometric correction levels of Landsat images to the land cover maps resulted from maximum likelihood classification. E3S Web Conf 153:02004. https://doi.org/10.1051/e3sconf/202015302004
62. Karra K, Kontgis C, Statman-Weil Z, Mazzariello JC, Mathis M, Brumby SP (2021) Global land use/land cover with Sentinel 2 and deep learning. International Geoscience and Remote Sensing Symposium (IGARSS), p 4704-4707, July 2021. https://doi.org/10.1109/IGARSS47720.2021.9553499
63. Khan IU, Vachal K, Ebrahimi S, Wadhwa SS (2023) Hotspot analysis of single-vehicle lane departure crashes in North Dakota. IATSS Res 47(1):25-34. https://doi.org/10.1016/j.iatssr.2022.12.003
64. Kibria G, Haroon AKY, Nugegoda D (2018) Low-carbon development (LCD) pathways in Australia, Bangladesh, China and India—a review. J Clim Change 4:49-61. https://doi.org/10.3233/JCC-180006
65. Kumar E, Das A, Ranjan RG, Pathak K, Verma A (2009) Energy and carbon emissions profiles of 54 South Asian Cities. www.ukinindia.fco.gov.uk
66. Kumari A, Karthikeyan S (2023) Comparative performance of maxi- mum likelihood and minimum distance classifiers on land use and land cover analysis of Varanasi District (India) BT. In: Woungang I, Dhurandher SK, Pattanaik KK, Verma A, Verma P (eds) Advanced network technologies and intelligent computing. Springer Nature, Switzerland, pp 476-484
67. Kumari A, Thendiyath R (2022) Linking satellite-based forest cover change with rainfall and land surface temperature in Kerala, India. Environ Dev Sustain 24(9):11282-11300. https://doi.org/10.1007/s10668-021-01908-w
68. Kumari P, Sasanka T, Osuri KK (2023) Land use land cover representation through supervised machine learning methods: sensitivity on simulation of urban thunderstorms in the east coast of India. Nat Hazards 116(1):295-317. https://doi.org/10.1007/s11069-022-05674-4
69. Li X, Liu Z, Li S, Li Y, Wang W (2023) Urban land carbon emission and carbon emission intensity prediction based on patch-generating land use simulation model and grid with multiple scenarios in Tianjin. Land 12(12). https://doi.org/10.3390/land12122160
70. Lindsey R, Luann D (2024) Climate Change_ Global Temperature _ NOAA Climate.gov. https://www.climate.gov/news-features/understanding-climate/climate-change-global-temperature
71. Mavrakou T, Polydoros A, Cartalis C, Santamouris M (2018) Recognition of thermal hot and cold spots in Urban areas in support of mitigation plans to counteract overheating: application for Ath- ens. Climate 6(1):16. https://doi.org/10.3390/cli6010016
72. McGlynn E, Li S, Berger MF, AmendHarper MKL (2022) Addressing uncertainty and bias in land use, land use change, and forestry greenhouse gas inventories. Clim Change 170(1-2):1-25. https://doi.org/10.1007/s10584-021-03254-2
73. McHugh ML (2012) Interrater reliability The_kappa statistic. Biochem Medica 22(3):276
74. Minank R, Langhammer J, Hanus J (2019) Radiometrie and atmospheric corrections of multispectral gMCA Camera for UAV
75. spectroscopy. Remote Sens 11(20):2428. https://doi.org/10.3390/ rs11202428
76. Mistry R, Mehrotra S (2023) Spatio-temporal variation of the daytime surface temperature in local climate zones, forming cool island in Bhopal. J Indian Soc Remote Sens 51(4):713-731. https://doi.org/10.1007/s12524-022-01658-w
77. Mohammad P, Goswami A (2022a) Exploring different indicators for quantifying surface urban heat and cool island together: a case study over two metropolitan cities of India. Environ Dev Sustain. https://doi.org/10.1007/s10668-022-02509-x
78. Mohammad P, Goswami A (2022b) Spatial variation of surface urban heat island magnitude along the urban-rural gradient of four rapidly growing Indian cities. Geocarto Int 37(15):4269-4291. https://doi.org/10.1080/10106049.2021.1886338
79. Monteiro A, Ankrah J, Madureira H, Pacheco MO (2022) Climate risk mitigation and adaptation concerns in urban areas: a systematic review of the impact of IPCC assessment reports. Climate 10(8):115. https://doi.org/10.3390/cli10080115
80. Mostafa W, Magd Z, Abo Khashaba SM, Abdelaziz B, Hendawy E, Elfadaly A, Nabil M, Kucher DE, Chen S, Mohamed ES (2023) Impacts of human activities on urban sprawl and land surface temperature in rural areas, a case study of El-Reyad District, Kafrelsheikh Governorate, Egypt. Sustainability (switzerland) 15(18):13497. https://doi.org/10.3390/su151813497
81. Mukherjee F, Singh D (2020) Assessing Land use-land cover change and its impact on land surface temperature using LANDSAT data: a comparison of two urban areas in India. Earth Syst Environ 4(2):385-407. https://doi.org/10.1007/s41748-020-00155-9
82. Murtaza KO, Romshoo SA (2014) Determining the suitability and accuracy of various statistical algorithms for satellite data classification. Int J Geomat Geosci 4(14):585-599
83. Naikoo MW, Rihan M, IshtiaqueShahfahad M (2020) Analyses of land use land cover (LULC) change and built-up expansion in the suburb of a metropolitan city: spatio-temporal analysis of Delhi NCR using landsat datasets. J Urban Manag 9(3):347-359. https://doi.org/10.1016/j.jum.2020.05.004
84. Nanaki EA, Koroneos CJ (2016) Climate change mitigation and deployment of electric vehicles in urban areas. Renew Energy 99:1153-1160. https://doi.org/10.1016/j.renene.2016.08.006
85. NASA, Shaftel H, Jackson R, Callery S (2019) Mitigation and adaptation | solutions—climate change: vital signs of the planet. In: Global Climate Change, NASA. https://climate.nasa.gov/solutions/adaptation-mitigation/
86. Nayak AK, Shahid M, Shukla AK, Kumar A, Raja R, Tripathi R, Panda BB (2012) Climate change: greenhouse gas emission in rice farming and mitigation options. Clim Change Greenh Gas Emiss Rice Farm Mitig Options 1:17-32
87. NOAA/Earth Systems Research Laboratory (2009) Climate change largely irreversible for next 1.000 years, NOAA Reports. In: ScienceDaily. https://www.sciencedaily.com/releases/2009/01/090127163403.htm
88. Oda T, Maksyutov S (2011) A very high-resolution (1kmX1 km) global fossil fuel CO2 emission inventory derived using a point source database and satellite observations of nighttime lights. Atmos Chem Phys 11(2):543-556. https://doi.org/10.5194/acp-11-543-2011
89. Oda T, Maksyutov S, Andres RJ (2018) The open-source data inventory for Anthropogenic CO2, version 2016 (ODIAC2016): a global monthly fossil fuel CO2 gridded emissions data product for tracer transport simulations and surface flux inversions. Earth Syst Sci Data 10(1):87-107. https://doi.org/10.5194/essd-10-87-2018
90. Oda T, Bun R, Kinakh V, Topylko P, Halushchak M, Marland G, Lauvaux T, Jonas M, Maksyutov S, Nahorski Z, Lesiv M, Danylo O, Horabik-Pyzel J (2019) Errors and uncertainties spectroscopy. Remote Sens 11(20):2428. https://doi.org/10.3390/ rs11202428
91. Mistry R, Mehrotra S (2023) Spatio-temporal variation of the daytime surface temperature in local climate zones, forming cool island in Bhopal. J Indian Soc Remote Sens 51(4):713-731. https://doi.org/10.1007/s12524-022-01658-w
92. Mohammad P, Goswami A (2022a) Exploring different indicators for quantifying surface urban heat and cool island together: a case study over two metropolitan cities of India. Environ Dev Sustain. https://doi.org/10.1007/s10668-022-02509-x
93. Mohammad P, Goswami A (2022b) Spatial variation of surface urban heat island magnitude along the urban-rural gradient of four rapidly growing Indian cities. Geocarto Int 37(15):4269-4291. https://doi.org/10.1080/10106049.2021.1886338
94. Monteiro A, Ankrah J, Madureira H, Pacheco MO (2022) Climate risk mitigation and adaptation concerns in urban areas: a system- atic review of the impact of IPCC assessment reports. Climate 10(8):115. https://doi.org/10.3390/cli10080115
95. Mostafa W, Magd Z, Abo Khashaba SM, Abdelaziz B, Hendawy E, Elfadaly A, Nabil M, Kucher DE, Chen S, Mohamed ES (2023) Impacts of human activities on urban sprawl and land surface temperature in rural areas, a case study of El-Reyad District, Kafrelsheikh Governorate, Egypt. Sustainability (switzerland) 15(18):13497. https://doi.org/10.3390/su151813497
96. Mukherjee F, Singh D (2020) Assessing Land use-land cover change and its impact on land surface temperature using LANDSAT data: a comparison of two urban areas in India. Earth Syst Environ 4(2):385-407. https://doi.org/10.1007/s41748-020-00155-9
97. Murtaza KO, Romshoo SA (2014) Determining the suitability and accuracy of various statistical algorithms for satellite data classification. Int J Geomat Geosci 4(14):585-599
98. Naikoo MW, Rihan M, IshtiaqueShahfahad M (2020) Analyses of land use land cover (LULC) change and built-up expansion in the suburb of a metropolitan city: spatio-temporal analysis of Delhi NCR using landsat datasets. J Urban Manag 9(3):347-359. https://doi.org/10.1016/j.jum.2020.05.004
99. Nanaki EA, Koroneos CJ (2016) Climate change mitigation and deployment of electric vehicles in urban areas. Renew Energy 99:1153-1160. https://doi.org/10.1016/j.renene.2016.08.006
100. NASA, Shaftel H, Jackson R, Callery S (2019) Mitigation and adaptation | solutions—climate change: vital signs of the planet. In: Global Climate Change, NASA. https://climate.nasa.gov/solutions/adaptation-mitigation/
101. Nayak AK, Shahid M, Shukla AK, Kumar A, Raja R, Tripathi R, Panda BB (2012) Climate change: greenhouse gas emission in rice farming and mitigation options. Clim Change Greenh Gas Emiss Rice Farm Mitig Options 1:17-32
102. NOAA/Earth Systems Research Laboratory (2009) Climate change largely irreversible for next 1.000 years, NOAA Reports. In: ScienceDaily. https://www.sciencedaily.com/releases/2009/01/ 090127163403.htm
103. Oda T, Maksyutov S (2011) A very high-resolution (1kmX1 km) global fossil fuel CO2 emission inventory derived using a point source database and satellite observations of nighttime lights. Atmos Chem Phys 11(2):543-556. https://doi.org/10.5194/acp-11-543-2011
104. Oda T, Maksyutov S, Andres RJ (2018) The open-source data inventory for Anthropogenic CO2, version 2016 (ODIAC2016): a global monthly fossil fuel CO2 gridded emissions data product for tracer transport simulations and surface flux inversions. Earth Syst Sci Data 10(1):87-107. https://doi.org/10.5194/essd-10-87-2018
105. Oda T, Bun R, Kinakh V, Topylko P, Halushchak M, Marland G, Lauvaux T, Jonas M, Maksyutov S, Nahorski Z, Lesiv M, Danylo O, Horabik-Pyzel J (2019) Errors and uncertainties in a gridded carbon dioxide emissions inventory. Mitig Adapt Strat Glob Change 24(6):1007-1050. https://doi.org/10.1007/ s11027-019-09877-2
106. Ohara K (2022) Physical drivers of climate change. Clim Change Anthropocene I:19-41. https://doi.org/10.1016/b978-0-12-820308-8.00011-8
107. Osterreicher D, Sattler S (2018) Maintaining comfortable summer- time indoor temperatures by means of passive design measures to mitigate the urban heat island effect—a sensitivity analysis for residential buildings in the City of Vienna. Urban Sci 2(3):66. https://doi.org/10.3390/urbansci2030066
108. Padhee KA, Whitbread A (2022) Indian agriculture: The route post- CoP 26. https://www.downtoearth.org.in/blog/climate-change/indian-agriculture-the-route-post-cop-26-81154
109. Patel S, Joshi JP, Bhatt B (2017) An assessment of spatio-temporal variability of land surface temperature using MODIS data: a study of Gujarat state, India. Geogr Compass 11(4):e12312-e12312. https://doi.org/10.1111/gec3.12312
110. Patil MB, Desai CG, Umrikar BN (2012) Image classification tool for land use/land cover analysis : a comparative study of maximum likelihood. Int J Geol Earth Environ Sci 2(3):189-196
111. Peng X, Wu W, Zheng Y, Sun J, Hu T, Wang P (2020) Correlation analysis of land surface temperature and topographic elements in Hangzhou, China. Sci Rep 10(1):10451. https://doi.org/10.1038/s41598-020-67423-6
112. Qiu S, Zhu Z, Olofsson P, Woodcock CE, Jin S (2023) Evaluation of Landsat image compositing algorithms. Remote Sens Environ 285:113375. https://doi.org/10.1016/J.RSE.2022.113375
113. Ramankutty N, Gibbs HK, Achard F, Defries R, Foley JA, Houghton RA (2007) Challenges to estimating carbon emissions from tropical deforestation. Glob Change Biol 13:51-66
114. Rasul A, Balzter H, Smith C (2015) Spatial variation of the daytime Surface Urban Cool Island during the dry season in Erbil, Iraqi Kurdistan, from Landsat 8. Urban Clim 14:176-186. https://doi.org/10.1016/J.UCLIM.2015.09.001
115. Ritchie H, Roser M (2024) Urbanization. Our World in Data. https://ourworldindata.org/urbanization
116. Rong T, Zhang P, Jing W, Zhang Y, Li Y, Yang D, Yang J, Chang H, Ge L (2020) Carbon dioxide emissions and their driving forces of land use change based on Economic Contributive Coefficient (ECC) and Ecological Support Coefficient (ESC) in the lower yellow river region (1995-2018). Energies 13(10):2600. https://doi.org/10.3390/en13102600
117. Rosenzweig C, Solecki W, Romero-Lankao P, Mehrotra S, Dhakal S, Bowman T, Ibrahim SA (2018) Climate change and cities: second assessment report of the urban climate change research network: summary for city leaders. In: Rosenzweig C, Solecki WD, Romero-Lankao P, Mehrotra S, Dhakal S, Ali Ibrahim S (eds) Climate change and cities: second assessment report of the urban climate change research network. Cambridge University Press, pp xvii-xlii. https://doi.org/10.1017/9781316563878.007
118. Sahana M, Ahmed R, Sajjad H (2016) Analyzing land surface temperature distribution in response to land use/land cover change using split window algorithm and spectral radiance model in Sundarban Biosphere Reserve, India. Model Earth Syst Environ 2(2):1-11. https://doi.org/10.1007/s40808-016-0135-5
119. Sanchez-Aparicio M, Andres-Anaya P, Del Pozo S, Laguela S (2020) Retrieving land surface temperature from satellite imagery with a novel combined strategy. Remote Sens 12(2):277. https://doi.org/10.3390/rs12020277
120. Schwaab J, Meier R, Mussetti G, Seneviratne S, Burgi C, Davin EL (2021) The role of urban trees in reducing land surface temperatures in European cities. Nat Commun 12(1):1-11. https://doi.org/10.1038/s41467-021-26768-w
121. Seyam MMH, Haque MR, Rahman MM (2023) Identifying the land use land cover (LULC) changes using remote sensing and GIS approach: a case study at Bhaluka in Mymensingh, Bangla- desh. Case Stud Chem Environ Eng 7(December 2022):100293. https://doi.org/10.1016/j.cscee.2022.100293
122. Sha Z, Bai Y, Li R, Lan H, Zhang X, Li J, Liu X, Chang S, Xie Y (2022) The global carbon sink potential of terrestrial vegeta- tion can be increased substantially by optimal land management. Commun Earth Environ 3(1):1-10. https://doi.org/10.1038/s43247-021-00333-1
123. Sharma A, Kale GD (2022) Assessment of urbanization impact on urban heat island effect and rainfall for the Surat city. Acta Geophys 70(1):243-264. https://doi.org/10.1007/s11600-021-00715-1
124. Sharma J, Prasad R, Mishra VN, Yadav VP, Bala R (2018) Land use and land cover classification of multispectral Landsat-8 satellite imagery using discrete wavelet transform. Int Arch Photogramm Remote Sens Sp Inf Sci XLII-5(November):703-706. https://doi.org/10.5194/isprs-archives-xlii-5-703-2018
125. Shivakumar BR, Rajashekararadhya SV (2018) An investigation on land cover mapping capability of classical and fuzzy based maxi- mum likelihood classifiers. Int J Eng Technol 7(2):939. https://doi.org/10.14419/ijet.v7i2.10743
126. Sobrino JA, Jimenez-Munoz JC, Paolini L (2004) Land surface temperature retrieval from LANDSAT TM 5. Remote Sens Environ 90(4):434—440. https://doi.org/10.1016Zj.rse.2004.02.003
127. Solomon S, Plattner GK, Knutti R, Friedlingstein P (2009) Irreversible climate change due to carbon dioxide emissions. Proc Natl Acad Sci USA 106(6):1704-1709. https://doi.org/10.1073/pnas.0812721106
128. Sun S, Lin D, Operario D (2021) Managing tourism emissions through optimizing the tourism demand mix: concept and analysis. Prev Med Rep 22(January):101350
129. Tagore R (2013) Global warming. Pestology 37(10):8
130. Tiwari AK, Kanchan R (2024) Analytical study on the relationship among land surface temperature, land use/land cover and spectral indices using geospatial techniques. Discov Environ 2(1):1. https://doi.org/10.1007/s44274-023-00021-1
131. Tyagi N, Sahoo S (2022) Dynamics of land surface temperature (LST) and their relation with urban biophysical components in Gora- khpur (India) urban area: a GIS and statistical based analysis for sustainable planning. Arab J Geosci 15(10):1010. https://doi.org/10.1007/s12517-022-10242-y
132. UNEP (2015) Cities and climate change | UNEP - UN Environment Programme. In: UN Environment Programme. https://www.unep. org/explore-topics/resource-efficiency/what-we-do/cities/cities-and-climate-change
133. UN DESA (2023) SDGs Report 2023. In: The sustainable development goals report 2023: Special Edition, p 80. https://unstats.un.org/sdgs/report/2023/
134. Venditti B (2022a) Visualizing the material impact of global urbanization—Climate Champions. In: Visual Capitalist. https://climatechampions.unfccc.int/visualizing-the-material-impact-of-global-urbanization/#main
135. Venditti B (2022b) More people living in cities will double global consumption _ World Economic Forum. In: Visual Capitalist. https://www.weforum.org/agenda/2022/04/global-urbanization-material-consumption/#
136. Wang K, Li X, Lyu X, Dang D, Dou H, Li M, Liu S, Cao W (2022) Optimizing the land use and land cover pattern to increase its contribution to carbon neutrality. Remote Sens 14(19):4751. https://doi.org/10.3390/rs14194751
137. Wang F, Harindintwali JD, Wei K, Shan Y, Mi Z, Costello MJ, Grun¬wald S, Feng Z, Wang F, Guo Y, Wu X, Kumar P, Kaumlstner M, Feng X, Kang S, Liu Z, Fu Y, Zhao W, Ouyang C, Shen J, Wang H, Chang SX, Evans DL, Wang R, Zhu C, Xiang L, Rinklebe J, Du M, Huang L, Bai Z, Li S, Lal R, Elsner M, Wigneron J-P, Florindo F, Jiang X, Shaheen SM, Zhong X, Bol R, Vasques GM, Li X, Pfautsch S, Wang M, He X, Agathokleous E, Du H, Yan H, Kengara FO, Brahushi F, Long X-E, Pereira P, Ok YS, Rillig MC, Jeppesen E, Yan X, Jiao N, Han B, Schaumlffer A, Chen JM, Zhu Y, Cheng H, Amelung W, Spoumltl C, Zhu J, Tiedje JM (2023a) Climate change: strategies for mitigation and adaptation. Innov Geosci 1(1):100015. https://doi.org/10.59717/j.xinn-geo.2023.100015
138. Wang L, Wang G, Chen T, Liu J (2023b) The regulating effect of urban large planar water bodies on residential heat islands: a case study of Meijiang Lake in Tianjin. Land 12(12):2126. https://doi.org/10.3390/land12122126
139. World Bank Group (2021) World Bank Group climate change action plan 2021-2025 : supporting green, resilient, and inclusive devel- opment. World Bank, Washington, DC. www.worldbank.org
140. World Bank (2023) Climate Change Overview_ Development news.
141. https://www.worldbank.org/en/topic/climatechange/overview#1 Wu Z, Zhang Y (2019) Water bodies’ cooling effects on urban land daytime surface temperature: ecosystem service reducing heat island effect. Sustainability (switzerland) 11(3):1-11. https://doi.org/10.3390/su11030787
142. Xu J (2023) Study on spatiotemporal distribution characteristics and driving factors of carbon emission in Anhui Province. Sci Rep 13(1):1-12. https://doi.org/10.1038/s41598-023-41507-5
143. Xu H, Croot P, Zhang C (2021) Discovering hidden spatial patterns and their associations with controlling factors for potentially toxic elements in topsoil using hot spot analysis and K-means clustering analysis. Environ Int 151(March):106456. https://doi.org/10.1016/j.envint.2021.106456
144. Xue J, Zhang X, Chen S, Hu B, Wang N, Shi Z (2024) Quantifying the agreement and accuracy characteristics of four satellite-based LULC products for cropland classification in China. J Integr Agric 23(1):283-297. https://doi.Org/10.1016/J.JIA.2023.06.005
145. Yang X, Li Y, Luo Z, Chan PW (2017) The urban cool island phenomenon in a high-rise high-density city and its mechanisms. Int J Climatol 37(2):890-904. https://doi.org/10.1002/joc.4747
146. Ye C, Ming T (2023) Land use carbon emissions estimation and carbon emissions control strategy effect scenario simulation in Zhejiang province. Heliyon 9(11):e20783. https://doi.org/10.1016/j.heliyon.2023.e20783
147. Yu Z, Chen L, Tong H, Chen L, Zhang T, Li L, Yuan L, Xiao J, Wu R, Bai L, Shi S (2022) Spatial correlations of land-use carbon emissions in the Yangtze River Delta region: a perspective from social network analysis. Ecol Ind 142(July):109147. https://doi.org/10.1016/j.ecolind.2022.109147
148. Zhou Y, Chen M, Tang Z, Mei Z (2021) Urbanization, land use change, and carbon emissions: quantitative assessments for city-level carbon emissions in Beijing-Tianjin-Hebei region. Sustain Cities Soc. https://doi.org/10.1016/j.scs.2020.102701

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Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).

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