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Change detection on land use/land cover and land surface temperature using spatiotemporal data of Landsat: a case study of Gaza Strip

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Abstract

Human and natural activities are considered one of several factors that lead to global environmental changes. Urbanization and population growth are the most prevailing issues facing scientists around the world which further affect the environment and temperature. According to statistics, the population in Gaza Strip, a small and besieged area, will be over 2.4 million and the land demands will exceed the sustainable capacity of land use by 2023. In this study, geographic information system (GIS) and remote sensing (RS) techniques were applied to estimate temporal change detection of land use/land cover (LULC) and land surface temperature (LST) for Gaza Strip between 2000 and 2017 and to indicate the relationship between land changes and demography changes and LST in the same period. In this study, two kinds of pixel-based classifiers, i.e., maximum likelihood (ML) and support vector machine (SVM), have been performed to extract LULC changes. While for LST, it was calculated by using Normalized Difference Vegetation Index (NDVI) and Surface Emissivity equations. The results show clear decrease between 2000 and 2017 in bare land (67.19%) compared to an increase in urban (13.12%) and crop and vegetation (4.95%). Furthermore, the increase in population is directly proportional to the increase in urban area through this period. In addition to that, LST in bare lands has the highest temperature in July and September (summer, autumn) and the lowest in January (winter) due to seasonal effects.

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References

  • Abuelaish B, Olmedo MTC (2016) Scenario of land use and land cover change in the Gaza Strip using remote sensing and GIS models. Arab J Geosci 9(4):274

    Article  Google Scholar 

  • Amiri R, Weng O, Alimohammadi A, Alavipanah A (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

    Article  Google Scholar 

  • Arsiso BK, Tsidu GM, Stoffberg GH, Tadesse T (2018) Influence of urbanization-driven land use/cover change on climate: the case of Addis Ababa, Ethiopia. Phys Chem Earth A/B/C 105:212–223

    Article  Google Scholar 

  • Ayanlade A, Jegede O (2015) Evaluation of the intensity of the daytime surface urban heat island: how can remote sensing help? Int J Image Data Fusion 6:348–365. https://doi.org/10.1080/19479832.2014.985618

    Article  Google Scholar 

  • Barnes DK, Arnold R (2001) Competition, sub-lethal mortality and diversity on Southern Ocean coastal rock communities. Polar Biol 24(6):447–454

    Article  Google Scholar 

  • Bhatta B (2010) Analysis of urban growth and sprawl from remote sensing data. Springer Science & Business Media.

  • Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on Computational learning theory. ACM, pp 144–152

  • Campbell BM, ed. (1996) The Miombo in Transition: Woodlands and Welfare in Africa. Bogor, Indonesia: Cifor

  • Chadchan J, Shankar R (2012) An analysis of urban growth trends in the post-economic reforms period in India. Int J Sustain Built Environ 1(1):36–49

    Article  Google Scholar 

  • Chandra S, Sharma D, Dubey SK (2018) Linkage of urban expansion and land surface temperature using geospatial techniques for Jaipur City, India. Arab J Geosci 11(2):31

    Article  Google Scholar 

  • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    Google Scholar 

  • Epstein RM, Hundert EM (2002) Defining and assessing professional competence. Jama 287(2):226–235

    Article  Google Scholar 

  • Erdas Inc. (1999) Erdas field guide. Erdas Inc., Atlanta

    Google Scholar 

  • 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(4):127

    Article  Google Scholar 

  • Foley JA, DeFries R, Asner GP, Barford C, Bonan G, Carpenter SR et al (2005) Global consequences of land use. Science 309(5734):570–574

    Article  Google Scholar 

  • Foody GM, Mathur A (2004) A relative evaluation of multiclass image classification by support vector machines. IEEE Trans Geosci Remote Sens 42:1335–1343

    Article  Google Scholar 

  • Foody GM, Campbell NA, Trodd NM, Wood TF (1992) Derivation and applications of probabi- listic measures of class membership from the maximum-likelihood classification. Photogramm Eng Remote Sens 58:1335–1341

    Google Scholar 

  • Gopal S, Woodcock C (1996) Remote sensing of forest change using artificial neural networks. IEEE Trans Geosci Remote Sens 34(2):398–404

    Article  Google Scholar 

  • Grimm NB, Faeth SH, Golubiewski NE, Redman CL, Wu J, Bai X, Briggs JM (2008) Global change and the ecology of cities. Science 319(5864):756–760

    Article  Google Scholar 

  • Grimmond CSB (2006) Progress in measuring and observing the urban atmosphere. Theor Appl Climatol 84(1-3):3–22

    Article  Google Scholar 

  • Hathout S (2002) The use of GIS for monitoring and predicting urban growth in East and West St Paul, Winnipeg, Manitoba, Canada. J Environ Manag 66(3):229–238

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Jia Y, Harman M (2011) An analysis and survey of the development of mutation testing. IEEE Trans Softw Eng 37(5):649–678

    Article  Google Scholar 

  • Jiménez-Muñoz JC, Cristóbal J, Sobrino JA, Sòria G, Ninyerola M, Pons X (2008) Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data. IEEE Trans Geosci Remote Sens 47(1):339–349

    Article  Google Scholar 

  • Kandissounon GA, Kalra A, Ahmad S (2018) Integrating system dynamics and remote sensing to estimate future water usage and average surface runoff in Lagos, Nigeria. Civil Eng J 4(2):378–393

    Article  Google Scholar 

  • Katpatal YB, Kute A, Satapathy DR (2008) Surface-and air-temperature studies in relation to land use/land cover of Nagpur urban area using Landsat 5 TM data. J Urban Plann Dev 134(3):110–118

    Article  Google Scholar 

  • Kayet N, Pathak K (2015) Remote sensing and GIS based land use/land cover change detection mapping in Saranda Forest, Jharkhand, India. Int Res J Earth Sci 3:1–6

    Google Scholar 

  • Lejeune Q, Davin EL, Guillod BP, Seneviratne SI (2015) Influence of Amazonian deforestation on the future evolution of regional surface fluxes, circulation, surface temperature and precipitation. Clim Dyn 44:2769–2786

    Article  Google Scholar 

  • Li J, Wang X, Wang X, Ma M, Zhang H (2009) Remote sensing evaluation of urban heat island and its spatial pattern of the Shanghai metropolitan area, China. Ecol Complex 6:413–420

    Article  Google Scholar 

  • Liu G, Zhang Q, Li G, Doronzo DM (2016) Response of land cover types to land surface temperature derived from Landsat-5 TM in Nanjing Metropolitan Region, China. Environ Earth Sci 75(20):1386

    Article  Google Scholar 

  • Markham BL, Barker JL (1985) Spectral characterization of the Landsat Thematic Mapper sensors. Int. J. Remote Sens 6(5):697–716

    Article  Google Scholar 

  • Mehdipour V, Memarianfard M (2017) Application of support vector machine and gene expression programming on tropospheric ozone prognosticating for Tehran metropolitan. Civil Eng J 3(8):557–567

    Article  Google Scholar 

  • Mir AA, Ahmed P, Bhat PA, Singh H (2016) Analyzing Land Use / Land Cover Change Using Remote Sensing and GIS Techniques in Pohru Watershed of Kashmir Valley 16:104–11

  • Mountrakis G, Im J, Ogole C (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3):247–259

    Article  Google Scholar 

  • Mujabar S, Rao V (2018) Estimation and analysis of land surface temperature of Jubail Industrial City, Saudi Arabia, by using remote sensing and GIS technologies. Arab J Geosci 11(23):742

    Article  Google Scholar 

  • Muttitanon W, Tripathi N (2005) Land use/land cover changes in the coastal zone of Ban Don Bay, Thailand using Landsat 5 TM data. Int J Remote Sens 26:2311–2323

    Article  Google Scholar 

  • Pal M, Mather PM (2005) Support vector machines for classification in remote sensing. Int J Remote Sens 26:1007–1011

    Article  Google Scholar 

  • Palestinian Central Bureau of Statistics (PCBS) (2013) Statistical Yearbook of Palestine 2013, No 14. Ramallah – Palestine Google Scholar

  • Petropoulos GP, Griffiths HM, Kalivas DP (2014) Quantifying spatial and temporal vegetation recovery dynamics following a wildfire event in a Mediterranean landscape using EO data and GIS. Appl Geogr 50:120–131

    Article  Google Scholar 

  • Prasad TV, Ahson SI (2006) Visualization of microarray gene expression data. Bioinformation 1(4):141–145

    Article  Google Scholar 

  • Qiao Z, Tian G, Xiao L (2013) Diurnal and seasonal impacts of urbanization on the urban thermal environment: a case study of Beijing using MODIS data. ISPRS J Photogramm Remote Sens 85:93–101. https://doi.org/10.1016/j.isprsjprs.2013.08.010

    Article  Google Scholar 

  • Qin Z, Karnieli A (1999) Progress in the remote sensing of land surface temperature and ground emissivity using NOAA-AVHRR data. Int J Remote Sens 20(12):2367–2393

    Article  Google Scholar 

  • Raj A, Vijayan N (2012) Analysis of landuselandcover changes of Kazhakuttam block based on GIS. In Green Technologies (ICGT), 2012 International Conference on. IEEE, pp 143–146

  • Sahebjalal E, Dashtekian K (2013) Analysis of land use land covers changes using normalized difference vegetation index (NDVI) differencing and classification methods. Afr J Agric Res 8:4614–4622

    Article  Google Scholar 

  • Schneider A (2012) Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach. Remote Sens Environ 124:689–704

    Article  Google Scholar 

  • Shang X, Chisholm LA (2014) Classification of Australian native forest species using hyperspectral remote sensing and machine-learning classification algorithms. IEEE J Sel Top Appl Earth Observ Remote Sens 7(6):2481–2489

    Article  Google Scholar 

  • Sobrino JA, Jiménez-Muñoz JC (2014) Minimum configuration of thermal infrared bands for land surface temperature and emissivity estimation in the context of potential future missions. Remote Sens Environ 148:158–167

    Article  Google Scholar 

  • Taskin Kaya G, Musaoglu N, Ersoy OK (2011). Damage assessment of 2010 Haiti earthquake with post-earthquake satellite image by support vector selection and adaptation. Photogrammetric Engineering & Remote Sensing, 77(10):1025–1035

  • Torrens, P.M.; Alberti, M.; (2000) Measuring sprawl. (CASA Working Papers 27). Centre for Advanced Spatial Analysis (UCL): London, UK.

  • Toure SI, Stow DA, Shih HC, Weeks J, Lopez-Carr D (2018) Land cover and land use change analysis using multi-spatial resolution data and object-based image analysis. Remote Sens Environ 210:259–268

    Article  Google Scholar 

  • Vapnik V (1979) Estimation of Dependences Based on Empirical Data. Nauka, Moscow, pp. 5165–5184, 27 (in Russian) (English translation: Springer Verlag, New York, 1982)

  • Wang XW, Nie D, Lu BL (2014) Emotional state classification from EEG data using machine learning approach. Neurocomputing 129:94–106

    Article  Google Scholar 

  • Wu C, Murray AT (2003) Estimating impervious surface distribution by spectral mixture analysis. Remote Sens Environ 84(4):493–505

    Article  Google Scholar 

  • Xian G, Crane M (2006) An analysis of urban thermal characteristics and associated land cover in Tampa Bay and Las Vegas using Landsat satellite data. Remote Sens Environ 104(2):147–156

    Article  Google Scholar 

  • Xu Y, Qin Z, Lv J (2008) Comparative analysis of urban heat island and associated land cover change based in Suzhou City using Landsat data. 2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing, 316–319. https://doi.org/10.1109/ETTandGRS.2008.224

  • Xu Y, Qin Z, Shen Y (2012) Study on the estimation of nearsurface air temperature from MODIS data by statistical methods. Int J Remote 33:7629–7643

    Article  Google Scholar 

  • Zhang F, Tiyip T, Kung H, Johnson VC, Maimaitiyiming M, Zhou M, Wang J (2016) Dynamics of land surface temperature (LST) in response to land use and land cover (LULC) changes in the Weigan and Kuqa river oasis, Xinjiang, China. Arab J Geosci 9(7):499

    Article  Google Scholar 

  • Zhou D, Bousquet O, Lal TN, Weston J, Schölkopf B (2004) Learning with local and global consistency. In: Advances in neural information processing systems, pp 321–328

    Google Scholar 

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Acknowledgments

We would like to acknowledge the support and facilities provided by UPM in this study as well as NASA for the provision of the free Landsat data. Comments from anonymous reviewers are also gratefully acknowledged in improving this paper.

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Authors and Affiliations

  1. Department of Civil Engineering and Geospatial Information Science Research Centre (GISRC), Faculty of Engineering, Universiti Putra Malaysia (UPM), 43400, Serdang, Selangor, Malaysia

    Aldhshan Shaban R. S. & Helmi Zulhaidi Mohd Shafri

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  1. Aldhshan Shaban R. S.
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  2. Helmi Zulhaidi Mohd Shafri
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Correspondence to Helmi Zulhaidi Mohd Shafri.

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Editorial handling: Domenico M. Doronzo

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Aldhshan, S.R.S., Shafri, H.Z. Change detection on land use/land cover and land surface temperature using spatiotemporal data of Landsat: a case study of Gaza Strip. Arab J Geosci 12, 443 (2019). https://doi.org/10.1007/s12517-019-4597-4

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