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Urban Ecosystems

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21-03-2023

Investigating and predicting Land Surface Temperature (LST) based on remotely sensed data during 1987–2030 (A case study of Reykjavik city, Iceland)

Authors: Mohammad Mansourmoghaddam, Iman Rousta, Mohammadsadegh Zamani, Haraldur Olafsson

Published in: Urban Ecosystems | Issue 2/2023

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Abstract

Observing the state and changes of land cover (LC) is critical for assessing the status and changes of ecosystems such as urban development. Remote sensing can extract useful data from a large region regularly and at the same time. The present study has used Landsat 5 and 8 images to derive LC classification and Land Surface Temperature (LST) maps of Reykjavik city for four years 1987, 1995, 2006, and 2019. Satellite images were classified into four major land-cover classes using the Artificial Neural Network classification (ANN) algorithm. Then, calculations including the LST and the distance from the water were calculated for each study period. Finally, using the Markov chain, the prediction of land cover classes and LST classes for 2020 was obtained. The results have shown an average of 21.5% (8.2 km2), 71% (32.9 km2), -62.9% (43.7 km2), and 3.4% (2.6 km2) growth for Urban Areas (UA), Vegetation Land (VL), Bare Lands (BL), and Water Lands (WL) of Reykjavik evaluated LC classes. Reykjavik LC forecasting shows that UA will have had a 38.5% (8.2 km2), VL a -43% (34.1 km2), BL a 14.7% (3.8 km2), and WL a 14.8% (11.9 km2) by 2030. The mean of LST in this area has risen by 67.5% (7.7 °C) during this period. This is also supported by the findings of the analysis of the NDVI and LST connection. According to the research, the LST and the NDVI value of 0.1 to 0.7 have a positive relationship in this area. However, there is a negative correlation between LST with NDVI values of 0.8 and 0.9. The results showed that the mean of LST was also influenced by the distance from WL water bodies. The LST has grown by a mean of 0.7 of correlation as the distance from WL has increased. The results of this research will be useful in order to get an overview of the past and present situation and plan for the control of heat islands and land cover changes in Reykjavik.

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Amiri R, Weng Q, Alimohammadi A, Alavipanah SK (2009) Spatial–temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area Iran. Remote Sens Environ 113:2606–2617CrossRef

Amiro B et al (2006) The effect of post-fire stand age on the boreal forest energy balance. Agric for Meteorol 140:41–50CrossRef

ArcGIS 10.8 help, ESRI (2019) Environmental systems research institute. ESRI ArcGIS, Redlands, CA, USA

Arnalds O (2015) The soils of Iceland. SpringerCrossRef

Arvor D, Jonathan M, Meirelles MSP, Dubreuil V, Durieux L (2011) Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso Brazil. Int J Remote Sens 32:7847–7871CrossRef

Asadi M, Oshnooei-Nooshabadi A, Saleh S-a, Habibnezhad F, Sarafraz-Asbagh S, Van Genderen JL (2022) Simulation of urban sprawl by comparison cellular automata-Markov and ANN. Preprints 2022, 2022080119. https://www.preprints.org/manuscript/202208.0119/v1

Avdan U, Jovanovska G (2016) Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data. J Sens 2016:1–8CrossRef

Benito PR, Cuevas JA, de la Parra RB, Prieto F, del Barrio JG, Zavala MA (2010) Land Use Change in a Mediterranean Metropolitan Region and Its Periphery: Assessment of Conservation Policies through CORINE Land Cover Data and Markov Models. Forest Syst 19:315–328CrossRef

Bischof H, Schneider W, Pinz AJ (1992) Multispectral Classification of Landsat-Images Using Neural Networks. IEEE Transact Geosci Remote Sens 30:482–490CrossRef

Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press

Bokaie M, Zarkesh MK, Arasteh PD, Hosseini A (2016) Assessment of urban heat island based on the relationship between land surface temperature and land use/land cover in Tehran. Sustain Cities Soc 23:94–104CrossRef

Bolch T (2007) Climate change and glacier retreat in northern Tien Shan (Kazakhstan/Kyrgyzstan) using remote sensing data. Global Planet Chang 56:1–12CrossRef

Burns P, Nolin A (2014) Using atmospherically-corrected Landsat imagery to measure glacier area change in the Cordillera Blanca, Peru from 1987 to 2010. Remote Sens Environ 140:165–178CrossRef

Chen W, Tong DQ, Zhang S, Zhang X, Zhao H (2017) Local PM10 and PM2. 5 emission inventories from agricultural tillage and harvest in northeastern China. J Environ Sci 57:15–23CrossRef

Collobert R, Weston J (2008) A unified architecture for natural language processing: Deep neural networks with multitask learning. In: Proceedings of the 25th international conference on Machine learning, pp 160–167

Dadhich PN, Hanaoka S (2010) Remote sensing, GIS and Markov’s method for land use change detection and prediction of Jaipur district. J Geomatics 4:9–15

Dong N, You L, Cai W, Li G, Lin H (2018) Land use projections in China under global socioeconomic and emission scenarios: Utilizing a scenario-based land-use change assessment framework. Glob Environ Chang 50:164–177CrossRef

dos Santos AR et al (2017) Spatial and temporal distribution of urban heat islands. Sci Total Environ 605:946–956PubMedCrossRef

Eddudóttir SD, Erlendsson E, Gísladóttir G (2020) Landscape change in the Icelandic highland: A long-term record of the impacts of land use, climate and volcanism. Quaternary Sci Rev 240:106363CrossRef

Estoque RC, Pontius RG Jr, Murayama Y, Hou H, Thapa RB, Lasco RD, Villar MA (2018) Simultaneous Comparison and Assessment of Eight Remotely Sensed Maps of Philippine Forests. Int J Appl Earth Observ Geoinform 67:123–134CrossRef

Fatemi M, Narangifard M (2019) Monitoring LULC changes and its impact on the LST and NDVI in District 1 of Shiraz City. Arab J Geosci 12:1–12CrossRef

Frohn R, Autrey B, Lane C, Reif M (2011) Segmentation and object-oriented classification of wetlands in a karst Florida landscape using multi-season Landsat-7 ETM+ imagery. Int J Remote Sens 32:1471–1489CrossRef

Gómez C, White JC, Wulder MA (2016) Optical remotely sensed time series data for land cover classification: A review. ISPRS J Photogramm Remote Sens 116:55–72CrossRef

Grimmond C (2006) Progress in measuring and observing the urban atmosphere. Theoret Appl Climatol 84:3–22CrossRef

Guan D, Gao W, Watari K, Fukahori H (2008) Land use change of Kitakyushu based on landscape ecology and Markov model. J Geog Sci 18:455–468CrossRef

Guan Q, Shi X, Huang M, Lai C (2016) A hybrid parallel cellular automata model for urban growth simulation over GPU/CPU heterogeneous architectures. Int J Geogr Inf Sci 30:494–514CrossRef

Guha S, Govil H, Diwan P (2020) Monitoring LST-NDVI relationship using Premonsoon Landsat datasets. Adv Meteorol 2020:1–15CrossRef

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 The Egyptian Journal of Remote Sensing and Space. Science 22:81–93

Hereher ME (2017) Effect of land use/cover change on land surface temperatures-The Nile Delta Egypt. J African Earth Sci 126:75–83CrossRef

Huang S, Liu H, Dahal D, Jin S, Li S, Liu S (2016) Spatial Variations in Immediate Greenhouse Gases and Aerosol Emissions and Resulting Radiative Forcing from Wildfires in Interior Alaska. Theoretical Appl Climatol 123:581–592CrossRef

Iacono M, Levinson D, El-Geneidy A, Wasfi R (2015) A Markov chain model of land use change TeMA Journal of Land Use. Mobil Environ 8:263–276

Ishtiaque A, Shrestha M, Chhetri N (2017) Rapid urban growth in the Kathmandu Valley, Nepal: Monitoring land use land cover dynamics of a himalayan city with landsat imageries. Environments 4:72CrossRef

Jeyaseelan A (2003) Droughts & floods assessment and monitoring using remote sensing and GIS. In: Satellite remote sensing and GIS applications in agricultural meteorology, vol 291. World Meteorol. Org. Dehra Dun, India Geneva, Switz, 313.

Jia K, Liang S, Liu J, Li Q, Wei X, Yuan W, Yao Y (2015) Forest cover changes in the three-north shelter forest region of China during 1990 to 2005. J Environ Inform 26(2):112–120

Jiang J, Tian G (2010) Analysis of the Impact of Land Use/land Cover Change on Land Surface Temperature with Remote Sensing. Proc Environ Sci 2:571–575CrossRef

Jianping L, Bai Z, Feng G (2005) RS-and-GIS-supported forecast of grassland degradation in southwest Songnen plain by Markov model Geo-spatial Information. Science 8:104–109

Jin S, Yang L, Zhu Z (2011) Homer C (2017) A land cover change detection and classification protocol for updating Alaska NLCD 2001 to. Remote Sens Environ 195:44–55CrossRef

Kadavi PR, Lee C-W (2018) Land cover classification analysis of volcanic island in Aleutian Arc using an artificial neural network (ANN) and a support vector machine (SVM) from Landsat imagery. Geosci J 22:653–665CrossRef

Kang K-m, Kim SH, Kim D-j, Cho Y-K, Lee S-H (2014) Comparison of coastal sea surface temperature derived from ship-, air-, and space-borne thermal infrared systems. In: 13-18 Jul 2014 IEEE Geoscience and Remote Sensing Symposium. IEEE, Quebec City, QC, Canada, pp 4419–4422

Kavzoglu T, Colkesen I (2009) A kernel functions analysis for support vector machines for land cover classification. Int J Appl Earth Obs Geoinf 11:352–359

Keshavarzi A, Sarmadian F, Sadeghnejad M, Pezeshki P (2010a) Developing pedotransfer functions for estimating some soil properties using artificial neural network and multivariate regression approaches. Proenviron Promediu 3(6):322–330

Keshavarzi A, Sarmadian F, Tirado-Corbal R, Sadeghnejad M (2010b) A sensitivity analysis of ANN pedotransfer functions for spatial modeling of soil cation exchange capacity. ProEnvironment Promediu 3(6):331–342

Kumar S, Radhakrishnan N, Mathew S (2014) Land use change modelling using a Markov model and remote sensing Geomatics. Nat Hazards Risk 5:145–156CrossRef

Landsat, USGS (2015) 8 (L8) data users handbook. Department of the Interior US Geological Survey LSDS-1574 Version, 3

Li C, Wang J, Wang L, Hu L, Gong P (2014) Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery. Remote Sens 6:964–983CrossRef

Li X, Zhou Y, Asrar GR, Imhoff M, Li X (2017) The surface urban heat island response to urban expansion: A panel analysis for the conterminous United States. Sci Total Environ 605:426–435PubMedCrossRef

Lillesand T, Kiefer RW, Chipman J (2015) Remote sensing and image interpretation. John Wiley & Sons

Liu J, Xue Y, Ren K, Song J, Windmill C, Merritt P (2019a) High-performance time-series quantitative retrieval from satellite images on a gpu cluster. IEEE J Select Topics Appl Earth Observ Remote Sens 12:2810–2821CrossRef

Liu S, Su H, Cao G, Wang S, Guan Q (2019b) Learning from data: A post classification method for annual land cover analysis in urban areas. ISPRS J Photogramm Remote Sens 154:202–215CrossRef

Liu X et al (2018) High-Resolution Multi-Temporal Mapping of Global Urban Land Using Landsat Images Based on the Google Earth Engine Platform. Remote Sens Environ 209:227–239CrossRef

Liu Y, Zhang K, Li Z, Liu Z, Wang J, Huang P (2020) A hybrid runoff generation modelling framework based on spatial combination of three runoff generation schemes for semi-humid and semi-arid watersheds. J Hydrol 590:125440CrossRef

Logsdon MG, Bell EJ, Westerlund FV (1996) Probability mapping of land use change: A GIS interface for visualizing transition probabilities Computers. Environ Urban Syst 20:389–398CrossRef

Luo H, Liu C, Wu C, Guo X (2018) Urban Change Detection Based on Dempster-Shafer Theory for Multitemporal Very High-Resolution Imagery. Remote Sens 10:980CrossRef

Lyons MB, Phinn SR, Roelfsema CM (2012) Long term land cover and seagrass mapping using Landsat and object-based image analysis from 1972 to 2010 in the coastal environment of South East Queensland Australia. ISPRS J Photogramm Remote Sens 71:34–46CrossRef

Maleki M, Van Genderen JL, Tavakkoli-Sabour SM, Saleh SS, Babaee E (2020) Land use/cover change in Dinevar rural area of West Iran during 2000–2018 and its prediction for 2024 and 2030. Geogr Tech 15:93–105

Mansourmoghaddam M, Ghafarian Malamiri HR, Arabi Aliabad F, Fallah Tafti M, Haghani M, Shojaei S (2022a) The Separation of the Unpaved Roads and Prioritization of Paving These Roads Using UAV Images. Air Soil Water Res 15:11786221221086284CrossRef

Mansourmoghaddam M, GhafarianMalamiri HR, Rousta I, Olafsson H, Zhang H (2022b) Assessment of Palm Jumeirah Island’s Construction Effects on the Surrounding Water Quality and Surface Temperatures during 2001–2020. Water 14:634CrossRef

Mansourmoghaddam M, Rousta I, Ghaffarian H, Mokhtari MH (2022c) Evaluating the capability of spatial and spectral fusion in land-cover mapping enhancement. Earth Observ Geomatics Eng 6(1):61–174

Mansourmoghaddam M, Rousta I, Zamani M, Mokhtari MH, Karimi Firozjaei M, Alavipanah SK (2021) Study and prediction of land surface temperature changes of Yazd city: assessing the proximity and changes of land cover. J RS GIS Nat Resourc 12:1–27

Mansourmoghaddam M, Rousta I, Zamani MS, Mokhtari MH, Karimi Firozjaei M, Alavipanah SK (2022d) Investigating and modeling the effect of the composition and arrangement of the landscapes of Yazd city on the land surface temperature using machine learning and Landsat-8 and Sentinel-2 data. Iranian J Remote Sens GIS, in press. https://gisj.sbu.ac.ir/article_102195.html?lang=enCrossRef

Marzban F, Sodoudi S, Preusker R (2018) The influence of land-cover type on the relationship between NDVI–LST and LST-T air. Int J Remote Sens 39:1377–1398CrossRef

Mas J-F, Kolb M, Paegelow M, Olmedo MTC, Houet T (2014) Inductive pattern-based land use/cover change models: A comparison of four software packages. Environ Model Softw 51:94–111CrossRef

Miao R et al (2022) Effects of long-term grazing exclusion on plant and soil properties vary with position in dune systems in the Horqin Sandy Land. CATENA 209:105860CrossRef

Muller MR, Middleton J (1994) A Markov model of land-use change dynamics in the Niagara Region Ontario, Canada. Landsc Ecol 9:151–157

Olafsson H, Rousta I (2021) Influence of atmospheric patterns and North Atlantic Oscillation (NAO) on vegetation dynamics in Iceland using Remote Sensing European. J Remote Sens 54:351–363. https://doi.org/10.1080/22797254.2021.1931462CrossRef

Olafsson H, Rousta I (2022) Remote sensing analysis to map inter-regional spatio-temporal variations of the vegetation in Iceland during 2001–2018. Acta Geogr Slovenica 62(1):106–124

Pakdaman M (2013) Using MCSST method for measuring sea surface temperature with modis imagery and modeling and prediction of regional variations with least squares method (case study: Persian Gulf, Iran). In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol XL-1/W3, 2013 SMPR 2013, 5 – 8 October 2013. Tehran, Iran, pp 499–503

Pal S, Ziaul S (2017) Detection of land use and land cover change and land surface temperature in English Bazar urban centre. Egypt J Remote Sens Space Sci 20:125–145

Paul S, Li J, Wheate R, Li Y (2018) Application of object oriented image classification and Markov chain modeling for land use and land cover change analysis. J Environ Inf 31:30–40

Qianguo X, Chuqun C, Ping S, Jingkun Y, Shilin T (2006) Atmospheric Correction of Landsat Data for the Retrieval of Sea Surface Temperature in Coastal Waters. Acta Oceanol Sin 25:25–34

Quan Q, Liang W, Yan D, Lei J (2022) Influences of joint action of natural and social factors on atmospheric process of hydrological cycle in Inner Mongolia China. Urban Clim 41:101043CrossRef

Ranagalage M, Estoque RC, Handayani HH, Zhang X, Morimoto T, Tadono T, Murayama Y (2018a) Relation between Urban Volume and Land Surface Temperature: A Comparative Study of Planned and Traditional Cities in Japan. Sustainability 10:2366CrossRef

Ranagalage M, Estoque RC, Murayama Y (2017) An urban heat island study of the Colombo metropolitan area, Sri Lanka, based on Landsat data (1997–2017). ISPRS Int J Geo-Inform 6:189CrossRef

Ranagalage M, Estoque RC, Zhang X, Murayama Y (2018b) Spatial changes of urban heat island formation in the Colombo District Sri Lanka: Implications for Sustainability Planning. Sustainability 10:1367CrossRef

Reddy DS, Prasad PRC (2018) Prediction of Vegetation Dynamics Using NDVI Time Series Data and LSTM Modeling. Earth Syst Environ 4:409–419CrossRef

Richards J, Richards J (1999) Remote Sensing Digital Image. Analysis-Springer, BostonCrossRef

Rousta I, Mansourmoghaddam M, Olafsson H, Krzyszczak J, Baranowski P, Zhang H, Tkaczyk P (2022) Analysis of the Recent Trends in Vegetation Dynamics and Its Relationship with Climatological Factors Using Remote Sensing Data for Caspian Sea Watersheds in Iran. Int Agrophys 36:139–153CrossRef

Rousta I, Olafsson H, Nasserzadeh MH, Zhang H, Krzyszczak J, Baranowski P (2021) Dynamics of Daytime Land Surface Temperature (LST) Variabilities in the Middle East Countries During 2001–2018. Pure Appl Geophys 178:2357–2377. https://doi.org/10.1007/s00024-021-02765-4CrossRef

Rousta I et al (2018) Spatiotemporal analysis of land use/land cover and its effects on surface urban heat island using Landsat data: A case study of Metropolitan City Tehran (1988–2018). Sustainability 10:4433CrossRef

Schneider A, Friedl MA, Potere D (2010) Mapping global urban areas using MODIS 500-m data: New methods and datasets based on ‘urban ecoregions.’ Remote Sens Environ 114:1733–1746CrossRef

Sexton JO, Urban DL, Donohue MJ, Song C (2013) Long-term land cover dynamics by multi-temporal classification across the Landsat-5 record. Remote Sens Environ 128:246–258CrossRef

Song X-P, Sexton JO, Huang C, Channan S, Townshend JR (2016) Characterizing the magnitude, timing and duration of urban growth from time series of Landsat-based estimates of impervious cover. Remote Sens Environ 175:1–13CrossRef

Srivastava PK, Majumdar T, Bhattacharya AK (2009) Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data. Adv Space Res 43:1563–1574CrossRef

Strigul N, Florescu I, Welden AR, Michalczewski F (2012) Modelling of forest stand dynamics using Markov chains. Environ Model Softw 31:64–75CrossRef

Sultana S, Satyanarayana A (2018) Urban heat island intensity during winter over metropolitan cities of India using remote-sensing techniques: Impact of urbanization. Int J Remote Sens 39:6692–6730CrossRef

Sun X, Tan X, Chen K, Song S, Zhu X, Hou D (2020) Quantifying landscape-metrics impacts on urban green-spaces and water-bodies cooling effect: The study of Nanjing China. Urban Forestry Urban Greening 55:126838CrossRef

Sun Z, Di L, Fang H (2019) Using long short-term memory recurrent neural network in land cover classification on Landsat and Cropland data layer time series. Int J Remote Sens 40:593–614CrossRef

Tan X, Sun X, Huang C, Yuan Y, Hou D (2021) Comparison of cooling effect between green space and water body. Sustain Cities Soc 67:102711CrossRef

Thai LH, Hai TS, Thuy NT (2012) Image classification using support vector machine and artificial neural network International Journal of Information Technology and Computer. Science 4:32–38

Thomas RF, Kingsford RT, Lu Y, Hunter SJ (2011) Landsat mapping of annual inundation (1979–2006) of the Macquarie Marshes in semi-arid Australia. Int J Remote Sens 32:4545–4569CrossRef

Thompson WD, Walter SD (1988) A reappraisal of the kappa coefficient. J Clin Epidemiol 41:949–958PubMedCrossRef

Tian H, Huang N, Niu Z, Qin Y, Pei J, Wang J (2019) Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm. Remote Sens 11:820CrossRef

Tian H, Qin Y, Niu Z, Wang L, Ge S (2021) Summer Maize Mapping by Compositing Time Series Sentinel-1A Imagery Based on Crop Growth Cycles. J Indian Soc Remote Sens 49:2863–2874CrossRef

Tian H, Wang Y, Chen T, Zhang L, Qin Y (2021) Early-Season Mapping of Winter Crops Using Sentinel-2 Optical Imagery. Remote Sens 13:3822CrossRef

Tian T, Zhang Y, Dou H, Tong H (2017) Land-use classification with biologically inspired color descriptor and sparse coding spatial pyramid matching. Multimedia Tools Appl 76:22943–22958CrossRef

Tong S, Sun Y, Yang Y (2012) Generating a future land use change scenario with a modified population-coupled Markov cellular automata model. J Environ Inform 19(2):108–119

van Rees E (2013) Exelis visual information solutions. GeoInformatics 16(3):24

Verburg PH, Kok K, Pontius RG, Veldkamp A (2006) Modeling land-use and land-cover change. Land-use and land-cover change: local processes and global impacts. Springer, Berlin, Heidelberg, pp 117–135CrossRef

Verstegen JA, Karssenberg D, Van Der Hilst F, Faaij AP (2014) Identifying a land use change cellular automaton by Bayesian data assimilation. Environ Model Softw 53:121–136CrossRef

Wang M et al (2017) Comparison of Spatial Interpolation and Regression Analysis Models for an Estimation of Monthly near Surface Air Temperature in China. Remote Sens 9:1278CrossRef

Wang S et al (2021) Exploring the utility of radar and satellite-sensed precipitation and their dynamic bias correction for integrated prediction of flood and landslide hazards. J Hydrol 603:126964CrossRef

Wang Y, Ouyang W (2021) Investigating the heterogeneity of water cooling effect for cooler cities. Sustain Cities Soc 75:103281CrossRef

weatherbase (2018) REYKJAVIK, ICELAND. https://www.weatherbase.com/weather/weather-summary.php3?s=3040&cityname=Reykjavik,+Capital+Region,+Iceland&units. Accessed 18 Dec 2019

Weinzettel J, Hertwich EG, Peters GP, Steen-Olsen K, Galli A (2013) Affluence drives the global displacement of land use. Glob Environ Chang 23:433–438CrossRef

Weng Q (2001) Modeling urban growth effects on surface runoff with the integration of remote sensing and GIS. Environ Manage 28:737–748PubMedCrossRef

Weng Q, Lu D, Schubring J (2004) Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sens Environ 89:467–483CrossRef

Woodcock CE, Ozdogan M (2012) Trends in land cover mapping and monitoring. In: Land change science: Observing, monitoring and understanding trajectories of change on the earth’s surface. Springer, pp 367–377

Wu Z, Zhang Y (2019) Water Bodies’ Cooling Effects on Urban Land Daytime Surface Temperature: Ecosystem Service Reducing Heat Island Effect. Sustainability 11:787CrossRef

Xiu L-n, Liu X-n (2003) Current Status and Future Direction of the Study on Artificial Neural Network Classification Processing in Remote Sensing. Remote Sens Technol Appl 18:339–345

Yan J, Wang L, Song W, Chen Y, Chen X, Deng Z (2019) A time-series classification approach based on change detection for rapid land cover mapping. ISPRS J Photogramm Remote Sens 158:249–262CrossRef

Yoshida T, Omatu S (1994) Neural network approach to land cover mapping. IEEE Trans Geosci Remote Sens 32:1103–1109CrossRef

Yue Z, Zhou W, Li T (2021) Impact of the Indian Ocean dipole on evolution of the subsequent ENSO: Relative roles of dynamic and thermodynamic processes. J Clim 34:3591–3607CrossRef

Zare Naghadehi S, Asadi M, Maleki M, Tavakkoli-Sabour S-M, Van Genderen JL, Saleh S-S (2021) Prediction of Urban Area Expansion with Implementation of MLC SAM and SVMs’ Classifiers Incorporating Artificial Neural Network Using Landsat Data. ISPRS Int J Geo-Inform 10:513CrossRef

Zhang K et al (2019) The sensitivity of North American terrestrial carbon fluxes to spatial and temporal variation in soil moisture: An analysis using radar-derived estimates of root-zone soil moisture Journal of Geophysical Research. Biogeosciences 124:3208–3231CrossRef

Zhang Q, Ge L, Hensley S, Metternicht GI, Liu C, Zhang R (2022) PolGAN: A deep-learning-based unsupervised forest height estimation based on the synergy of PolInSAR and LiDAR data. ISPRS J Photogramm Remote Sens 186:123–139CrossRef

Zhang R, Tang C, Ma S, Yuan H, Gao L, Fan W (2011) Using Markov chains to analyze changes in wetland trends in arid Yinchuan Plain China. Math Comput Model 54:924–930CrossRef

Zhao T et al (2020) Soil moisture experiment in the Luan River supporting new satellite mission opportunities. Remote Sens Environ 240:111680CrossRef

Zhao T et al (2021) Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm. Remote Sens Environ 257:112321CrossRef

Zhou G, Song B, Liang P, Xu J, Yue T (2022a) Voids Filling of DEM with Multiattention Generative Adversarial Network Model. Remote Sens 14:1206CrossRef

Zhou G, Yang F, Xiao J (2022b) Study on Pixel Entanglement Theory for Imagery Classification. IEEE Trans Geosci Remote Sens 60:1–18

Ziaul S, Pal S (2016) Image based surface temperature extraction and trend detection in an urban area of West Bengal India. J Environ Geogr 9:13–25CrossRef

Title: Investigating and predicting Land Surface Temperature (LST) based on remotely sensed data during 1987–2030 (A case study of Reykjavik city, Iceland)
Authors: Mohammad Mansourmoghaddam
Iman Rousta
Mohammadsadegh Zamani
Haraldur Olafsson
Publication date: 21-03-2023
Publisher: Springer US
Published in: Urban Ecosystems / Issue 2/2023
Print ISSN: 1083-8155
Electronic ISSN: 1573-1642
DOI: https://doi.org/10.1007/s11252-023-01337-9

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