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Comparison of land use/land cover change of fused image and multispectral image of landsat mission: a case study of Rajshahi, Bangladesh

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Abstract

To monitor the impacts on environment due to climate change and anthropogenic influences, it is important to have precise and accurate information regarding the land use/land cover (LULC) change. The current study has been carried out to understand the LULC pattern and changing scenario using remote sensing and GIS techniques. The data were used in two ways, i.e. first the multispectral image was used to classify by maximum likelihood (ML) classification system on both 1999 and 2016 images separately, and then compare the change history, and second, the multispectral image and panchromatic band was fused using Gram-Schmidt (GM) fusion technique to enhance the image quality. Then, the fused image of 1999 and 2016 was classified separately using the ML process. Several statistical analyses on fused image were carried to quantify the image quality. The classified images were compared to get the most accurate result. Four land use classes were detected, namely cultivated land, vegetation, built up area, and water body. During the classification, the vegetation, built up and cultivated land classes were biased and slightly deviated from the accuracy. The maximum difference between the classified panchromatic and multispectral images in 1999 for cultivated land was 12.2 km2, and the minimum difference was found for the water body, i.e. 0.8 km2. The similarly in 2016 presented the maximum difference for vegetation equal to 7.3 km2 and minimum equal to 4.4 km2 for the water body. Accuracy result shows that the fused image is better for classified proposes for analysis and decision making.

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Source: Geological Survey of Bangladesh)

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The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  • Agrawal S, Verma NK, Tamrakar P, Sircar P (2011) Content based color image classification using SVM. In: Eighth international conference on information technology: new generations, pp. 1090–1094. https://doi.org/10.1109/ITNG.2011.202

  • Ahmeduzzaman H, Kar S, Asad A (2012) A study on ground water fluctuation at Barind Area, Rajshahi. Int J Eng Res Appl (IJERA) 2(6):1465–1470

    Google Scholar 

  • Aik DHJ, Ismail MH, Muharam FM (2020) Land use/land cover changes and the relationship with land surface temperature using landsat and MODIS imageries in Cameron Highlands. Malays Land 9:372. https://doi.org/10.3390/land9100372

    Article  Google Scholar 

  • Alparone L, Wald L, Chanussot J, Thomas C, Gamba P, Bruce LM (2007) Comparison of pansharpening algorithms outcome of the 2006 GRS-S data-fusion contest. IEEE Trans Geosci Remote Sens 45(10):3012–3021

    Article  Google Scholar 

  • Alparone L, Aiazzi B, Baronti A, Garzelli A, Nencini P (2004) A golbal quality measurement of pan-sharpened multispectral imagery. IEEE Geosci Remote Sens Lett 1(4):313–317

    Article  Google Scholar 

  • Amolins K, Zhang Y, Dare P (2007) Wavelet based image fusion techniques—an introduction, review and comparison. ISPRS J Photogramm Remote Sens 62(4):249–263

    Article  Google Scholar 

  • Andualem TG, Belay G, Guadie A (2018) Land use change detection using remote sensing technology. J Earth SciClim Change 9:10. https://doi.org/10.4172/2157-7617.1000496

    Article  Google Scholar 

  • Anjali M, Bhirud SG (2009a) Image fusion of digital images. Int J Recent Trends Eng 2(3):146–148

    Google Scholar 

  • Anjali A, Pure NG, Meha S (2013) An overview of different image fusion methods for medical applications. Int J Sci Eng Res 4(7):2229–5518

    Google Scholar 

  • Anjali M, Bhirud SG (2009) Image Fusion of Digital Images. Int J Recent Trends Eng 2 (3): 146–148. https://pdfs.semanticscholar.org/82e0/b79bf7b70792f0b251638a0b8e2496194c14.pdf

  • Arefin R (2020) Groundwater potential zone identification at plio-pleistocene elevated tract, Bangladesh: AHP-GIS and remote sensing approach. Groundw Sustain Dev. https://doi.org/10.1016/j.gsd.2020.100340

    Article  Google Scholar 

  • Arefin R, Mohir MMI, Alam J (2020a) Watershed prioritization for soil and water conservation aspect using GIS and remote sensing: PCA-based approach at northern elevated tract Bangladesh. Appl Water Sci 10:91. https://doi.org/10.1007/s13201-020-1176-5

    Article  Google Scholar 

  • Arefin R, Meshram SG, Santos CAG et al (2020b) Hybrid modelling approach for water body change detection at Chalan Beel area in northern Bangladesh. Environ Earth Sci 79:442. https://doi.org/10.1007/s12665-020-09185-y

    Article  Google Scholar 

  • Briones RU, Ella VB, Bantayan NC (2016) Hydrologic impact evaluation of land use and land cover change in Palico watershed, Batangas, Philippines, using the SWAT model. J Environ Sci Manage 19(1):96–107

    Google Scholar 

  • Chander G, Markham B (2003) Revised Landsat-5 TM radiometric calibration procedures and post calibration dynamic ranges. IEEE Trans Geosci Remote Sens 41(11):2674–2677. https://doi.org/10.1109/TGRS.2003.818464

    Article  Google Scholar 

  • Chavez PS, Sides SC, Anderson JA (1991) Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT panchromatic. Photogramm Eng Remote Sens 57(3):295–303

    Google Scholar 

  • Clayton DG (1974) The gram-schmidt regression, Farebrother. Appl Stat 23:470–476

    Article  Google Scholar 

  • Clayton (1971) Gram-Schmidt orthogonalization. Appl Stat 20:335–338

  • Colwell JE, Weber FP (1981) Forest change detection. In: Proc 15th Int Symp on remote sensing of  environment. Ann Arbor, Michigan, pp 839–852

  • Congalton RG, Green K (1999) Assessing the accuracy of remotely sensed data: principles 317 and practices. CRC/Lewis Press, Boca Raton, p 137

    Google Scholar 

  • Coppin PR, Bauer ME (1996) Digital change detection in forest ecosystems with remote sensing imagery. Remote Sens Rev 13:207–234

    Article  Google Scholar 

  • Deepak KS, Parsai MP (2012) Different image fusion techniques—a critical review. Int J Mod Eng Res (IJMER) 2(5):4298–4301

    Google Scholar 

  • Devyani M, Deshmukh P, Malviya PAV (2015) Image fusion an application of digital image processing using wavelet transform. Int J Sci Eng Res 6(11):1247

    Google Scholar 

  • Ding Y, Elvidge CD, Lunetta RS (1998) Survey of multispectral methods for land cover change detection analysis. In: Ross S. Lunetta, Christopher D (eds) ElvidgeRemote sensing change detection: environmental monitoring methods and applications. Sleeping Bear Press, Inc., New York, N.Y, pp 21–39

    Google Scholar 

  • Du Q (2005) Unsupervised real time constrained linear discriminate analysis to hyper spectral image classification. Department of Electrical & Computer Engg, Mississippi state university, USA. Pattern Reorganization, pp 361–368. http://www.sciencedirect.com

  • Firouz AAW, Kalyankar NV, Ali AAZ (2011) The IHS Transformations Based Image Fusion. Computer Vision and Pattern Recognition (cs.CV). http://arxiv.org/abs/1107.3348

  • Franczyk J, Changk H (2009) The effects of climate change and urbanization on the runoff of 13 the rock creek basin in the Portland metropolitan area, Oregon, USA. Hydrol Process 23:805–815

    Article  Google Scholar 

  • Ghaffari G, Keesstra S, Ghodousi J, Ahmadi H (2009) SWAT-simulated hydrological impact 16 of land-use change in the Zanjanrood Basin. Northwest Iran Hydrol Process. https://doi.org/10.1002/hyp.7530

    Article  Google Scholar 

  • González-Audicana M, Otazu X, Fors O, Seco A (2005) Comparison between Mallat’s and the ‘à trous’ discrete wavelet transform based algorithms for the fusion of multispectral and panchromatic images. Int J Remote Sens 26(3):595–614

    Article  Google Scholar 

  • Guo Q, Chen S, Leung H, Liu S (2010) Covariance intersection based image fusion technique with application to pansharpening in remote sensing. Inf Sci 180(18):3434–3443

    Article  Google Scholar 

  • Haque MI, Basak R (2017) Land cover change detection using GIS and remote sensing techniques: a spatio-temporal study on Tanguar Haor, Sunamganj, Bangladesh. Egypt J Remote Sens Space Sci 20:251–263

    Google Scholar 

  • Healy RW, Cook PG (2002) Using groundwater levels to estimate recharge. Hydrogeol J 10:91–109

    Article  Google Scholar 

  • Ibrahim-Bathis K, Ahmed SA (2016) Geospatial technology for delineating groundwater potential zones in Doddahalla watershed of Chitradurga district, India. Egypt J Remote Sens Space Sci 19:223–234

    Google Scholar 

  • Im S, Brannan KM, Mostaghimi S (2003) Simulating hydrologic and water quality impacts on an urbanizing watershed. J Am Water Resour Assoc 39(6):1465–1479

    Article  Google Scholar 

  • Im S, Kim H, Kim C, Jang C (2009) Assessing the impacts of land use changes on watershed hydrology using MIKE SHE. Environ Geol 57:231. https://doi.org/10.1007/s00254-008-1303-3

    Article  Google Scholar 

  • Jagalingam P, Arkal VH (2017a) Comparison of various pan-sharpening methods using Quickbird-2 and Landsat-8 imagery. Arab J Geosci 10(5):119. https://doi.org/10.1007/s12517-017-2878-3

    Article  Google Scholar 

  • Jagalingam P, Arkal VH (2017b) Comparison of various pan-sharpening methods using Quickbird-2 and Landsat-8 imagery. Arab J Geosci 10(5):119. https://doi.org/10.1007/s12517-017-2878-3

    Article  Google Scholar 

  • Jain M, Tomar PS (2013) Review of image classification methods and techniques. Int J Eng Res Tech (IJERT) 2(8):852–858

    Google Scholar 

  • Jensen JR (1996) Introductory digital image processing: a remote sensing perspective, 2nd edn. Prentice Hall, Upper Saddle River, New Jersey, p 316

    Google Scholar 

  • Johnson RD, Kasischke ES (1998) Change vector analysis: a technique for the multispectral monitoring for land cover and condition. Int J Remote Sens 19:411–426

    Article  Google Scholar 

  • Kamruzzaman M, Rahman ATMS, Ahmed MS (2018) Spatio-temporal analysis of climatic variables in the western part of Bangladesh. Environ Dev Sustain 20:89–108. https://doi.org/10.1007/s10668-016-9872-x

    Article  Google Scholar 

  • Khoi DN, Suetsugi T (2014) Impact of climate and land-use changes on hydrological processes and sediment yield—a case study of the Be river catchment. Vietnam Hydrol Sci J 59(5):1095–1108

    Article  Google Scholar 

  • Klonus S, Ehlers M (2007) Image fusion using the Ehlers spectral characteristics preservation algorithm. Gisci Remote Sens 44(2):93–116. https://doi.org/10.2747/1548-1603.44.2.93

    Article  Google Scholar 

  • Konstantinos N, Dimitrios O (2015) Quality assessment of ten fusion techniques applied on Worldview-2. Eur J Remote Sens 48:141–167. https://doi.org/10.5721/EuJRS20154809

    Article  Google Scholar 

  • Laben CA, Brower BV (2000) Webster, both of N.Y. process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. Eastman Kodak Company, Rochester

    Google Scholar 

  • Laporterie-Dejean F, de Boissezon H, Flouzat G, Lefevre-Fonollosa MJ (2005) Thematic and statistical evaluations of five panchromatic/multispectral fusion methods on simulated PLEIADES-HR images. Inf Fusion 6(3):193–212

    Article  Google Scholar 

  • Lee JM, Park YS, Kum D, Jung Y, Kim B, Hwang SJ, Kim HB, Kim C, Lim KJ (2014) Assessing the effect of watershed slopes on recharge/baseflow and soil erosion. Paddy Water Environ 12(1):169–183

    Article  Google Scholar 

  • Li Z, Deng X, Wu F, Hasan SS (2015) Scenario analysis for water resources in response to land use change in the middle and upper reaches of the Heihe river basin. Sustainability 7(3):3086–3108

    Article  Google Scholar 

  • Li P, Hengpeng Li H, Yang G, Zhang Q, Diao Y (2018) Assessing the hydrologic impacts of land use change in the Taihu Lake Basin of China from 1985 to 2010. Water 10:1512. https://doi.org/10.3390/w10111512                                           

    Article  Google Scholar 

  • Lu D, Wend Q (2007) A survey of image classification methods and technology for improving classification performance. Department of Geography, Geology, and Anthropology, Indiana State University, USA, IJRS 28(5)

  • Mamta MM, Brijesh V (2016) Survey on different image fusion techniques. Int Res J Eng Tech (IRJET) 03(03):933–936

    Google Scholar 

  • Marcelino EV, Ventura FN, Formaggio AR, Fonseca LMG, Rosa ANCS (2003) Evaluation of image fusion techniques for the identification of landslide scars using satellite data. Geografia 28(3):431–445

    Google Scholar 

  • Markham BL, Barker JL (1987) Radiometric properties of U.S. processed Landsat MSS data. Remote Sens Environ 22:39–71

    Article  Google Scholar 

  • Markham BL, Storey JC, Williams DL, Irons JR (2004) Landsat sensor performance: history and current status. IEEE Trans Geosci Remote Sens 42:2691–2694

    Article  Google Scholar 

  • Maurer T (2013) How to pan-sharpen images using the gram-schmidt pan-sharpen method—a recipe. Int Arch Photogramm Remote Sens Spatial Inf Sci 11:239–244

    Article  Google Scholar 

  • Melih Ö, Copty NK, Saysel AK (2013) Modeling the impact of land use change on the hydrology of a rural watershed. J Hydrol 497:97–109

    Article  Google Scholar 

  • Miller SN, Kepner WG, Mehaffey MH, Hernandez M, Miller RC, Goodrich DC, Devonhold KK, Heggem DT, Miller WP (2002) Integrating landscape assessment and hydrologic modeling for land cover change analysis. J Am Water Resour Assoc 38(4):915–929

    Article  Google Scholar 

  • Mishra PK, Rai A, Rai SC (2019) Land use and land cover change detection using geospatial techniques in the Sikkim Himalaya, India. Egypt J Remote Sens Space Sci 1:1. https://doi.org/10.1016/j.ejrs.2019.02.001

    Article  Google Scholar 

  • Mora A, Santos TMA, Łukasik S, Silva JMN, Falcão AJ, Fonseca JM, Ribeiro RA (2017) Land cover classification from multispectral data using computational intelligence tools: a comparative study. Information 8(4):147. https://doi.org/10.3390/info8040147

    Article  Google Scholar 

  • Moustakidis S, Mallinis G, Koutsias N, Theocharis JB, Petridis V (2011) SVM-based fuzzy decision trees for classification of high spatial resolution remote sensing images. IEEE Trans Geosc Remote Sens 50(1):149–169. https://doi.org/10.1109/TGRS.2011.2159726

    Article  Google Scholar 

  • Nikolakopoulos KG (2005) Comparison of six fusion techniques for SPOT5 data. Proc IEEE Int Geosci Remote Sens Symp 4:2811–2814

    Google Scholar 

  • Nirmala P, Kishore R (2018) Multi sensor image fusion for surveillance applications using hybrid image fusion algorithm. Multimed Tools Appl 77(10):12405–12436

    Article  Google Scholar 

  • Petchprayoon P, Blanken PD, Ekkawatpanit C, Hussein K (2010) Hydrological impacts of land use/land cover change in a large river basin in central-northern Thailand. Int J Climatol 30(13):1917–1930

    Article  Google Scholar 

  • Rahman ATMS, Jahan CS, Mazumder QH (2017) Drought analysis and its implication in sustainable water resource management in Barind area, Bangladesh. J Geol Soc India 89:47–56. https://doi.org/10.1007/s12594-017-0557-3

    Article  Google Scholar 

  • Rokni K, Ahmad A, Solaimani K, Hazini S (2015) A new approach for surface water change detection: integration of pixel level image fusion and image classification techniques. Int J Appl Earth Obs Geoinf 34(1):226–234. https://doi.org/10.1016/j.jag.2014.08.014

    Article  Google Scholar 

  • Shutao L, Xudong K, Leyuan F (2017) Pixel-level image fusion: a survey of the state of the art. Inf Fusion 33:100–112  

    Article  Google Scholar 

  • Singh A (1989) Digital change detection techniques using remotely-sensed data. Int J Remote Sens 10:989–1003

    Article  Google Scholar 

  • Subramanian P, Alamelu NR, Aramudhan M (2015) Fusion of multispectral and panchromatic images and its quality assessment. J Eng Appl Sci 10(9):2–24

    Google Scholar 

  • Sweta K, Shah DU (2014) Comparative study of image fusion techniques based on spatial and transform domain. Int J Innov Res Sci Eng Technol 3(3):1–5

    Google Scholar 

  • Turner II, BL, Skole D, Sanderson S, Fischer G, Fresco L, Leemans R (1995) Land-use and land-cover change. In: Science/Research Plan. IGBP Report No. 35/HDP Report No. 7, Stockholm, Sweden, and Geneva, Switzerland

  • Ufade A, Kawade M (2013) Comparison of spatial domain and transform domain image fusion technique for restoration of blur images. Int Conf Recent Trends Eng Technol 2(1):290–299

    Google Scholar 

  • Vijay S, Katiyar SK (2016) Pixel-level image fusion techniques in remote sensing: a review. Spat Inf Res 24(4):475–483

    Article  Google Scholar 

  • Vora PD, Chudasama N (2015) Different image fusion techniques and parameters: a review. Int J Comput Sci Inf Technol 6(1):889–892

    Google Scholar 

  • Wald L (2000) Quality of high resolution synthesized images: is there a simple criterion. Proc Int Conf Fusion Earth Data 26–28, pp. 99–103. ffhal-00395027. https://hal.archives-ouvertes.fr/hal-00395027/document

  • Wang Z, Bovik AC (2002) A universal image quality index. IEEE Signal Process Lett 9(3):81–84

    Article  Google Scholar 

  • Wijesekaraa GN, Guptab CA, Valeoc, Hasbanid JG, Marceaue DJ (2010) Impact of land-use changes on the hydrological processes in the Elbow river watershed in southern Alberta. In: David A. Swayne, Yang W, Voinov AA, Rizzoli A, Filatova T (eds)International Environmental Modelling and Software Society (IEMSS) 2010 international congress on environmental modelling and software modelling for environment’s sake, fifth biennial meeting. Ottawa, Canada. http://www.iemss.org/iemss2010/index.php?n=Main.Proceedings

  • William ER, William BM, Turner BL (1994) Modeling land use and land cover as part of global environmental change. Clim Change 28:45–64

    Article  Google Scholar 

  • Xiangzhi B, Sheng G (2018) Weight strategy aided infrared and visible image fusion utilizing the center operator from opening and closing based toggle operator. Infrared Phys Technol 92:190–192

    Article  Google Scholar 

  • Zhang W, Mao L, Xu W (2009) Automatic image classification using the classification ant-colony algorithm. In: 2009 International conference on environmental science and information application technology, pp. 325-329. https://doi.org/10.1109/ESIAT.2009.280

  • Zhang L, Wang L, Lin W (2012) Semi-supervised biased maximum margin analysis for interactive image retrieval. IEEE Trans Image Process 21(4):2294–2308. https://doi.org/10.1109/TIP.2011.2177846

    Article  Google Scholar 

  • Zhanwen L, Yan F, Hang C, Licheng J (2017) A fusion algorithm for infrared and visible based on guided filtering and phase congruency in NSST domain. Opt Lasers Eng 97:71–77

    Article  Google Scholar 

  • Zhiqiang Z, Wang B, Sun L, Mingjie D (2016) Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with Gaussian and bilateral filters. Inf Fusion 30:15–26

    Article  Google Scholar 

  • Zhou Y, Xu YJ, Xiao W, Wang J, Huang Y, Yang H (2017) Climate change impacts on flow and suspended sediment yield in headwaters of high-latitude regions-a case study in China’s far northeast. Water 9(12):1–17

    Article  Google Scholar 

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Conceptualization, formal analysis: RA; Investigation: SGM, CAGS; Methodology: RA; Writing—original draft: RA; Writing—review and editing: SGM, CAGS.

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Correspondence to Sarita Gajbhiye Meshram.

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Arefin, R., Meshram, S.G. & Santos, C.A.G. Comparison of land use/land cover change of fused image and multispectral image of landsat mission: a case study of Rajshahi, Bangladesh. Environ Earth Sci 80, 578 (2021). https://doi.org/10.1007/s12665-021-09807-z

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