Model Based Change Detection of Water Body Using Landsat Imagery: A Case Study of Rajshahi Bangladesh DOI: 10.32526/ennrj.18.4.2020.33

Main Article Content

Tofayel Ahammad
Hafizur Rahaman
B.M. Refat Faisal
Nasrin Sultana

Abstract

The aim of this study is to detect water bodies which include ponds, lake, river, canal, irrigation land etc. from Landsat TM (Thematic Mapper) &OLI (Operational Land Imager) data by Pixel Based Model (PBM) for the year 2010 and 2017 and to identify the changed area of water body from 2010 to 2017. Rajshahi division of Bangladesh was selected as study area for this case study. Landsat 5 data acquired on 21 & 28 January 2010 and Landsat 8 data acquired on 15 & 24 January 2017 were used as satellite imagery for this analysis. Two images of each month were then mosaicked to find a new image which cover whole Rajshahidivision. Lowest and highest pixel of the water body for the each band was investigated. Then a Pixel Based Model (PBM) was formed by using the entireLandsat band and run the model. The result was shown a binary image which includes water body and non-water body. Also common area, newly formed area& abolished area of water bodywere identified by using PBMmodel. Theresult indicates that water land is decreased 3.74% in 2017 than 2010 , 56.52% area of water body  converted into non-water body in 2017 and  45.33%  new water body is formed in 2017. The unchanged area of water body is 1, 02,671hectors which is present both in 2010 & 2017.

Article Details

How to Cite
Ahammad, T., Rahaman, H. ., Refat Faisal, B. ., & Sultana, N. . (2020). Model Based Change Detection of Water Body Using Landsat Imagery: A Case Study of Rajshahi Bangladesh: DOI: 10.32526/ennrj.18.4.2020.33. Environment and Natural Resources Journal, 18(4), 345–355. Retrieved from https://ph02.tci-thaijo.org/index.php/ennrj/article/view/227215
Section
Original Research Articles

References

Alam MS, Uddin K. A Study of morphological changes in the coastal areas and Offshore Islands of Bangladesh using remote sensing. Scientific and Academic Publishing 2013;2(1):15-8.

Alsdorf DE, Rodríguez E, Lettenmaier DP. Measuring surface water from space. Reviews of Geophysics 2007;45(2): RG2002(1-24).

Acharya TD, Lee DH, Yang IT, Lee JK. Identification of water bodies in a Landsat 8 OLI image using a J48 decision tree. Sensors 2016;16:1075.

Chave P. The EU water framework directive: An introduction. IWA Publishing; 2001. p. 208.

Chignell SM, Anderson RS, Evangelista PH, Laituri MJ, Merritt DM. Multi-temporal independent component analysis and Landsat 8 for delineating maximum extent of the 2013. Colorado Front Range Flood. Remote Sensing 2015;7:9822-43.

Du Z, Linghu B, Ling F, Li W, Tian W, Wang H, Gui Y, Sun B, Zhang X. Estimating surface water area changes using time-series Landsat data in the Qingjiang River Basin, China. Journal of Appllied Remote Sensing 2012;6:063609.

Feyisa GL, Meilby H, Fensholt R, Proud SR. Automated water extraction index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment 2014;140:23-35.

Hatfield JL, Prueger JH. Value of using different vegetation indices to quantify agricultural crop characteristics at different growth stages under varying management practices. Remote Sensing 2010;2:562-78.

Hassan Z, Shabbir R, Ahmad SS, Malik AH, Aziz N, Buttand A, Erum S. Dynamics of land use and land cover change (LULCC) using geospatial techniques: A case study of Islamabad Pakistan. Springer Plus 2016;5,812(1-11).

Jiang H, Feng M, Zhu Y, Lu N, Huang J, Xiao T. An automated method for extracting rivers and lakes from Landsat imagery. Remote Sensing 2014;6:5067-89.

Jain S, Singh RD, Jain MK, Lohani AK. Delineation of flood-prone areas using remote sensing techniques. Water Resource Management 2005;19:333-47.

Jaafari S, Nazarisamani A. comparison between land use/land cover mapping through Landsat and Google Earth Imagery. American-Eurasian Journal of Agricultural and Environmental Sciences 2013;13(6):763-68.

Jawak SD, Kulkarni K, Luis AJ. A review on extraction of lakes from remotely sensed optical satellite data with a special focus on Cryospheric Lakes. Advances in Remote Sensing 2015;4:196-213.

Kaplan G, Avdan U. Object-based water body extraction model using Sentinel-2 satellite imagery. European Journal of Remote Sensing 2017;50:137-43.

Mcfeeters SK. The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing 1996;17:1425-32.

Miah S. Rajshahi Division [internet]. 2006 Available from: http://en.banglapedia.org/index.php?title=Rajshahi_Division.

Nicholas R, Goodwin, Lisa JC, Robert JD, Flood N, Tindall D. Cloud and cloud shadow screening across Queensland, Australia: An automated method for Landsat TM/ETM+ time series. Remote Sensing of Environment 2013;134:50-65.

Ozesmi SL, Bauer ME. Satellite remote sensing of wetlands. Wetlands Ecological Management 2014;10:381-402.

Reis S. Analyzing land use/land cover changes using remote sensing and GIS in Rize, North-East Turkey. Sensors 2008;8:6188-202.

Rokni K, Ahmad A, Selamat A, Hazini S. Water feature extraction and change detection using Multitemporal Landsat Imagery. Remote Sensing 2014;6:4173-89.

Rover J, Ji L, Wylie BK, Tieszen LL. Establishing water body areal extent trends in interior Alaska from multi-temporal Landsat data. Remote Sensing Letters 2012;3:595-604.

Rebelo LM, Finlayson CM, Nagabhatla N. Remote sensing and GIS for wetland inventory, mapping and change analysis. Journal of Environmental Management 2009;90:2144-53.

Shen L, Li C. Water Body Extraction from Landsat ETM+ Imagery Using Adaboost Algorithm. Proceedings of 18th International Conference on Geoinformatics; Beijing: China; 2010. p. 1-4.

Selim M. Change Detection Analysis using new nano satellite imagery. International Journal of Engineering and Advanced Technology 2018;7:1-10.

Sekertekin A, Marangoz AM, Akcin H. Pixel-based classification analysis of land use land cover using Sentinel-2 and Landsat-8 data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2017;XLII-4/W6: 91-3.

Trishchenko AP, Cihlar J, Zhanqing Li. Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors. Remote Sensing of Environment 2001;81:1-18.

Verpoorter C, Kutser T, Tranvik L. Automated mapping of water bodies using Landsat multispectral data. Limnology and Oceanography: Methods 2012;10:1037-50.

Wilson EH, Sader SA. Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment 2002;80:385-96.

Xu H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing 2006;27:3025-33.

Yang L, Tian S, Yu L, Ye F, Qian J, Qian Y. Deep learning for extracting water body from Landsat imagery. International Journal of Innovative Computing, Information and Control 2015;11:1913-29.

Zoran M, Stefan S. Climatic Changes Effects on Spectral Vegetation Indices for Forested Areas Analysis from Satellite Data. Proceedings of the 2nd Environmental Physics Conference; Alexandria: Egypt; 2006. p. 73-83.

Zhou ZG, Tang P, Zhou M. Detecting anomaly regions in satellite image time series based on seasonal autocorrelation analysis. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2016;III-3:303-10.