Abstract
The aggravating deforestation, industrialization, and urbanization are becoming the principal causes for environmental challenges worldwide. As a result, satellite-based remote sensing helps to explore the environmental challenges spatially and temporally. This investigation analyzed the spatiotemporal variability in land surface temperature (LST) and its link with elevation in the Amhara region, Ethiopia. The Moderate Resolution Imaging Spectroradiometer (MODIS) LST data (2001–2020) were used. The pixel-based linear regression model was used to explore the spatiotemporal variability of LST changes. Furthermore, Sen’s slope and Mann-Kendall trend test were used to determine the magnitude of temporal shifts of the areal average LST and evaluate trends in areal average LST, respectively. Coefficient of variation (CV) was also used to analyze spatial and temporal variability in seasonal and annual LST. The seasonal LST CV varied from 1.096–10.72%, 0.7–11.06%, 1.29–14.76%, and 2.19–10.35% for average autumn (September to November), summer (June to August), spring (March to May), and winter (December to February) seasons, respectively. The highest inter-annual variability was observed in the eastern, northern, and south-western districts than that in the other parts. The seasonal spatial LST trend varied from −0.7–0.16, −0.4–0.224, 0.6–0.19, and −0.6–0.32 for average autumn, summer, spring, and winter seasons, respectively. Besides, the annual spatial LST slope varied from −0.58 to 0.17. Negative slopes were found in the central, mid-western, and mid-northern districts in annual LST, unlike the other parts. The annual variations of mean areal LST decreased insignificantly at the rate of 0.046°C year−1 (P<0.05). However, the inter-annual variability trend of annual LST increased significantly. Generally, the LST is tremendously variable in space and time and negatively correlated with elevation.
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References
Aina YA, Adam EM, Ahmed F (2017) Spatiotemporal variations in the impacts of urban land use types on urban heat island effects: the case of Riyadh, Saudi Arabia. Int Arch Photogramm Remote Sens Spat Inf Sci - ISPRS Arch 42:9–14. https://doi.org/10.5194/isprs-archives-XLII-3-W2-9-2017
Alemu MM, Bawoke GT (2020) Analysis of spatial variability and temporal trends of rainfall in Amhara Region, Ethiopia. J Water Clim Chang 11:1505–1520. https://doi.org/10.2166/wcc.2019.084
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 113:2606–2617. https://doi.org/10.1016/j.rse.2009.07.021
Ayalew D (2012) Variability of rainfall and its current trend in Amhara region, Ethiopia. Afr J Agric Res 7:1475–1486. https://doi.org/10.5897/ajar11.698
Bahari NIS, Muharam FM, Zulkafli Z, Mazlan N, Husin NA (2021) Modified linear scaling and quantile mapping mean bias correction of modis land surface temperature for surface air temperature estimation for the lowland areas of peninsular malaysia. Remote Sens 13. https://doi.org/10.3390/rs13132589
Benali A, Carvalho AC, Nunes JP, Carvalhais N, Santos A (2012) Estimating air surface temperature in Portugal using MODIS LST data. Remote Sens Environ 124:108–121. https://doi.org/10.1016/j.rse.2012.04.024
Berger C, Rosentreter J, Voltersen M, Baumgart C, Schmullius C, Hese S (2017) Spatio-temporal analysis of the relationship between 2D/3D urban site characteristics and land surface temperature. Remote Sens Environ 193:225–243. https://doi.org/10.1016/j.rse.2017.02.020
Bosilovich MG (2006) A comparison of MODIS land surface temperature with in situ observations. Geophys Res Lett 33:1–5. https://doi.org/10.1029/2006GL027519
Cai Y, Zhang H, Zheng P, Pan W (2016) Quantifying the impact of land use/land cover changes on the urban heat island: a case study of the natural wetlands distribution area of Fuzhou City, China. Wetlands 36:285–298. https://doi.org/10.1007/s13157-016-0738-7
Chen X, Zhang X, Church JA, Watson CS, King MA, Monselesan D, Legresy B, Harig C (2017) The increasing rate of global mean sea-level rise during 1993-2014. Nat Clim Chang 7:492–495. https://doi.org/10.1038/nclimate3325
Dawit M, Halefom A, Teshome A, Sisay E, Shewayirga B, Dananto M (2019) Changes and variability of precipitation and temperature in the Guna Tana watershed, Upper Blue Nile Basin, Ethiopia. Model Earth Syst Environ 5:1395–1404. https://doi.org/10.1007/s40808-019-00598-8
Degefu MA, Bewket W (2014) Variability and trends in rainfall amount and extreme event indices in the Omo-Ghibe River Basin, Ethiopia. Reg Environ Chang 14:799–810. https://doi.org/10.1007/s10113-013-0538-z
Fabeku BB, Balogun IA, Abdul-azeez S (2018) Spatio-temporal variability in land surface temperature and its relationship with vegetation types over Ibadan, south-western Nigeria. Atmos Clim Sci 08:318–336. https://doi.org/10.4236/acs.2018.83021
Farina A (2012) Exploring the relationship between land surface temperature and vegetation abundance for urban heat island mitigation in Seville. LUMA-GIS Thesis nr, Spain
Fathizad H, Tazeh M, Kalantari S, Shojaei S (2017) The investigation of spatiotemporal variations of land surface temperature based on land use changes using NDVI in southwest of Iran. J Afr Earth Sci 134:249–256. https://doi.org/10.1016/j.jafrearsci.2017.06.007
Fernandes R, Leblanc SG (2005) Parametric (modified least squares) and non-parametric (Theil-Sen) linear regressions for predicting biophysical parameters in the presence of measurement errors. Remote Sens Environ 95:303–316. https://doi.org/10.1016/j.rse.2005.01.005
Fonseka HPU, Zhang H, Sun Y, Su H, Lin H, Lin Y (2019) Urbanization and its impacts on land surface temperature in Colombo Metropolitan Area, Sri Lanka, from 1988 to 2016. Remote Sens 11:1–18. https://doi.org/10.3390/rs11080926
Gebrehiwot T, Veen AVD (2013) Assessing the evidence of climate variability in the northern part of Ethiopia. J Dev Agric Econ 5:104–119. https://doi.org/10.5897/JDAE12.056
Gidey E, Dikinya O, Sebego R, Segosebe E, Zenebe A (2018) Analysis of the long-term agricultural drought onset, cessation, duration, frequency, severity and spatial extent using Vegetation Health Index (VHI) in Raya and its environs, Northern Ethiopia. Environ Syst Res 7:1–18. https://doi.org/10.1186/s40068-018-0115-z
Güneralp B, Zhou Y, Ürge-Vorsatz D, Gupta M, Yu S, Patel PL, Fragkias M, Li X, Seto KC (2017) Global scenarios of urban density and its impacts on building energy use through 2050. Proc Natl Acad Sci U S A 114:8945–8950. https://doi.org/10.1073/pnas.1606035114
Hamlaoui-Moulai L, Mesbah M, Souag-Gamane D, Medjerab A (2013) Detecting hydro-climatic change using spatiotemporal analysis of rainfall time series in Western Algeria. Nat Hazards 65:1293–1311. https://doi.org/10.1007/s11069-012-0411-2
He J, Zhao W, Li A, Wen F, Yu D (2018) The impact of the terrain effect on land surface temperature variation based on Landsat-8 observations in mountainous areas. Int J Remote Sens 40:1808–1827. https://doi.org/10.1080/01431161.2018.1466082
Ibitoye MO, Aderibigbe OG, Adegboyega SA, Adebola AO (2017) Spatio-temporal analysis of land surface temperature variations in the rapidly developing Akure and its environs, southwestern Nigeria using Landsat data. Ethiop J Environ Stud Manag 10:389. https://doi.org/10.4314/ejesm.v10i3.9
Jiang Y, Fu P, Weng Q (2015) Assessing the impacts of urbanization-associated land use/cover change on land surface temperature and surface moisture: a case study in the midwestern united states. Remote Sens 7:4880–4898. https://doi.org/10.3390/rs70404880
Julien Y, Sobrino JA (2010) Comparison of cloud-reconstruction methods for time series of composite NDVI data. Remote Sens Environ 114:618–625. https://doi.org/10.1016/j.rse.2009.11.001
Julien Y, Sobrino JA, Verhoef W (2006) Changes in land surface temperatures and NDVI values over Europe between 1982 and 1999. Remote Sens Environ 103:43–55. https://doi.org/10.1016/j.rse.2006.03.011
Justice CO, Vermote E, Townshend JRG, Defries R, Roy DP, Hall DK, Salomonson VV, Privette JL, Riggs G, Strahler A, Lucht W, Myneni RB, Knyazikhin Y, Running SW, Nemani RR, Wan Z, Huete AR, Van Leeuwen W, Wolfe RE, Barnsley MJ (1998) The moderate resolution imaging spectroradiometer (MODIS): land remote sensing for global change research. IEEE Trans Geosci Remote Sens 36:1228–1249. https://doi.org/10.1109/36.701075
Khanal S, Kc K, Fulton JP, Shearer S, Ozkan E (2020) Remote sensing in agriculture accomplishments , limitations , and opportunities. Remote Sens 12:1–29
Khandelwal S, Goyal R, Kaul N, Mathew A (2017) Assessment of land surface temperature variation due to change in elevation of area surrounding Jaipur, India. Egypt J Remote Sens Sp Sci 21:87–94. https://doi.org/10.1016/j.ejrs.2017.01.005
Khorchani M, Vicente-Serrano SM, Azorin-Molina C, Garcia M, Martin-Hernandez N, Peña-Gallardo M, El Kenawy A, Domínguez-Castro F (2018) Trends in LST over the peninsular Spain as derived from the AVHRR imagery data. Glob Planet Chang 166:75–93. https://doi.org/10.1016/j.gloplacha.2018.04.006
Li ZL, Tang BH, Wu H, Ren H, Yan G, Wan Z, Trigo IF, Sobrino JA (2013) Satellite-derived land surface temperature: current status and perspectives. Remote Sens Environ 131:14–37. https://doi.org/10.1016/j.rse.2012.12.008
Li X, Zhou Y, Asrar GR, Zhu Z (2018) Creating a seamless 1 km resolution daily land surface temperature dataset for urban and surrounding areas in the conterminous United States. Remote Sens Environ 206:84–97. https://doi.org/10.1016/j.rse.2017.12.010
Li J, Pei Y, Zhao S, Xiao R, Sang X, Zhang C (2020) A review of remote sensing for environmental monitoring in China. Remote Sens 12:1–25. https://doi.org/10.3390/rs12071130
Liang L, Sun Q, Luo X, Wang J, Zhang L, Deng M, Di L, Liu Z (2017) Long-term spatial and temporal variations of vegetative drought based on vegetation condition index in China. Ecosphere 8:8. https://doi.org/10.1002/ecs2.1919
Libiseller C, Grimvall A (2002) Performance of partial Mann-Kendall tests for trend detection in the presence of covariates. Environmetrics 13:71–84. https://doi.org/10.1002/env.507
Luintel N, Ma W, Ma Y, Wang B, Subba S (2019) Spatial and temporal variation of daytime and nighttime MODIS land surface temperature across Nepal. Atmos Ocean Sci Lett 12:305–312. https://doi.org/10.1080/16742834.2019.1625701
Maffei C, Alfieri SM, Menenti M (2018) Relating spatiotemporal patterns of forest fires burned area and duration to diurnal land surface temperature anomalies. Remote Sens 10:1–20. https://doi.org/10.3390/rs10111777
Mann HB (1945) Non-parametric test against trend. Econometrica 13:245–259 http://www.economist.com/node/18330371?story%7B_%7Did=18330371
Mildrexler DJ, Cohen WB, Running SW (2018) Thermal anomalies detect critical global land surface changes. J Appl Meteorol Climatol 57:391–411
Miles V, Esau I (2020) Surface urban heat islands in 57 cities across different climates in northern Fennoscandia. Urban Clim 31:100575. https://doi.org/10.1016/j.uclim.2019.100575
Mirzaei M, Verrelst J, Arbabi M, Shaklabadi Z (2020) Urban heat island monitoring and impacts on citizen ’ s general health status in Isfahan metropolis: a remote sensing and field survey approach. Remote Sens 12:1–17
Muthoni FK, OdongoVO OJ, Mugalavai EM, Mourice SK, Hoesche-Zeledon I, Mwila M, Bekunda M (2019) Long-term spatial-temporal trends and variability of rainfall over Eastern and Southern Africa. Theor Appl Climatol 137:1869–1882. https://doi.org/10.1007/s00704-018-2712-1
Ngie A, Abutaleb K, Ahmed F, Darwish A, Ahmed M (2014) Assessment of urban heat island using satellite remotely sensed imagery: a review. South African Geogr J 96:198–214. https://doi.org/10.1080/03736245.2014.924864
NourEldeen N, Mao K, Yuan Z, Shen X, Xu T, Qin Z (2020) Analysis of the spatiotemporal change in land surface temperature for a long-term sequence in Africa (2003-2017). Remote Sens 12:1–25. https://doi.org/10.3390/rs12030488
Parvez IM, Aina YA, Balogun AL (2019) The influence of urban form on the spatiotemporal variations in land surface temperature in an arid coastal city. Geocarto Int 36:640–659. https://doi.org/10.1080/10106049.2019.1622598
Peng J, Ma J, Liu Q, Liu Y, Hu Y, Li Y, Yue Y (2018) Spatial-temporal change of land surface temperature across 285 cities in China: an urban-rural contrast perspective. Sci Total Environ 635:487–497. https://doi.org/10.1016/j.scitotenv.2018.04.105
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 10:1–16. https://doi.org/10.1038/s41598-020-67423-6
Phan TN, Kappas M, Tran TP (2018) Land surface temperature variation due to changes in elevation in Northwest Vietnam. Climate 6:1–19. https://doi.org/10.3390/cli6020028
Phan TN, Kappas M, Nguyen KT, Tran TP, Tran QV, Emam AR (2019) Evaluation of MODIS land surface temperature products for daily air surface temperature estimation in northwest Vietnam. Int J Remote Sens 40:5544–5562. https://doi.org/10.1080/01431161.2019.1580789
Porter PS, Rao ST, Hogrefe C (2002) Linear trend analysis: a comparison of methods. Atmos Environ 36:3055–3056. https://doi.org/10.1016/S1352-2310(02)00189-9
Qian X, Liang L, Shen Q, Sun Q, Zhang L, Liu Z, Zhao S, Qin Z (2016) Drought trends based on the VCI and its correlation with climate factors in the agricultural areas of China from 1982 to 2010. Environ Monit Assess 188:188. https://doi.org/10.1007/s10661-016-5657-9
Qiao Z, Liu L, Qin Y, Xu X, Wang B, Liu Z (2020) The impact of urban renewal on land surface temperature changes: A case study in the main city of Guangzhou, China. Remote Sens 12:1–15. https://doi.org/10.3390/rs12050794
Qureshi S, Alavipanah SK, Konyushkova M, Mijani N, Fathololomi S, Firozjaei MK, Homaee M, Hamzeh S, Kakroodi AA (2020) A remotely sensed assessment of surface ecological change over the Gomishan Wetland, Iran. Remote Sens 12:1–24. https://doi.org/10.3390/RS12182989
Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63:1379–1389. https://doi.org/10.1080/01621459.1968.10480934
Shwetha HR, Kumar DN (2016) Prediction of high spatio-temporal resolution land surface temperature under cloudy conditions using microwave vegetation index and ANN. ISPRS J Photogramm Remote Sens 117:40–55. https://doi.org/10.1016/j.isprsjprs.2016.03.011
Singh RB, Grover A, Zhan J (2014) Inter-seasonal variations of surface temperature in the urbanized environment of Delhi using Landsat thermal data. Energies 7:1811–1828. https://doi.org/10.3390/en7031811
Thorne PW, Donat MG, Dunn RJH, Williams CN, Alexander LV, Caesar J, Durre I, Harris I, Hausfather Z, Jones PD, Menne MJ, Rohde R, Vose RS, Davy R, Lawrimore JH, Peterson TC, Rennie JJ (2016) Reassessing changes in diurnal temperature range: intercomparison and evaluation of existing global data set estimates. Journal of Geophysical Research: Atmospheres Research 121:5138–5158. https://doi.org/10.1002/2015JD024584.Received
Tomlinson CJ, Chapman L, Thornes JE, Baker C (2011) Remote sensing land surface temperature for meteorology and climatology: a review. Meteorol Appl 18:296–306. https://doi.org/10.1002/met.287
Xiao H, Kopeck M, Guo S, Guan Y, Cai D, Zhang C (2018) Responses of urban land surface temperature on land cover: a comparative study of Vienna and Madrid. Sustain 10:260. https://doi.org/10.3390/su10020260
Yang H, Xi C, Zhao X, Mao P, Wang Z, Shi Y, He T, Li Z (2020a) Measuring the urban land surface temperature variations under Zhengzhou city expansion using Landsat-like data. Remote Sens 12:1–21. https://doi.org/10.3390/rs12050801
Yang C, Zhan Q, Gao S, Liu H (2020b) Characterizing the spatial and temporal variation of the land surface temperature hotspots in Wuhan from a local scale. Geo Spatial Inf Sci 23:327–340. https://doi.org/10.1080/10095020.2020.1834882
Ye C, Wang M, Li J (2017) Derivation of the characteristics of the surface urban heat island in the Greater Toronto area using thermal infrared remote sensing derivation of the characteristics of the surface urban heat. Remote Sens Lett 8:637–646. https://doi.org/10.1080/2150704X.2017.1312025
Zhang Q, Singh VP, Li J, Chen X (2011) Analysis of the periods of maximum consecutive wet days in China. J Geophys Res Atmos 116. https://doi.org/10.1029/2011JD016088
Zhou D, Li D, Sun G, Zhang L, Liu Y, Hao L (2016) Contrasting effects of urbanization and agriculture on surface temperature in eastern China Decheng. Journal of Geophysical Research: Atmospheres Research 175:4449–4238. https://doi.org/10.1038/175238c0
Zhu X, Wang X, Yan D, Liu Z, Zhou Y (2018) Analysis of remotely-sensed ecological indexes’ influence on urban thermal environment dynamic using an integrated ecological index: a case study of Xi’an, China. Int J Remote Sens 40:3421–3447. https://doi.org/10.1080/01431161.2018.1547448
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Getachew Bayable: Participated in data analysis, interpretation, and writing the paper.
Getnet Alemu Desta: Participated in data analysis, interpretation, and writing the paper.
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Bayable, ., Alemu, G. Spatiotemporal variability of land surface temperature in north-western Ethiopia. Environ Sci Pollut Res 29, 2629–2641 (2022). https://doi.org/10.1007/s11356-021-15763-9
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DOI: https://doi.org/10.1007/s11356-021-15763-9