Journal of Natural Environment

Comparison of the accuracy of pixel-based and object-oriented methods in land use classification (case study: Samalghan Watershed)

Document Type : Research Paper

Authors

1 department of water

2 Associate Prof. Department of Water Science Engineering, University of Birjand

3 3Associate Prof. Department of Watershed Management, Faculty of Natural Resources and Environment, University of Birjand

Abstract

Planning and optimal use of resources and controlling and unprincipled changes in the future, requires studying the extent of change and destruction of resources. In fact, planners for principled decisions must have a full knowledge of land use, detection, prediction of land use change and land cover in order to better manage natural resources in the long time. The aim of this study was to evaluate the accuracy of different supervised classification algorithms of basic and object-oriented pixels in land use extraction in Samalghan watershed in three periods of time 1987, 2002 and 2019. The results showed that the support vector machine algorithms for the images of 1987 and 2019 and the neural network for the 2002 image in the pixel-based classification method have the highest overall accuracy and kappa coefficient. Also, the most obvious change that can be seen by comparing the prepared user maps is the change in the level of land uses with the growth of residential areas, thus this expansion has been continuously associated with a decrease in rangeland land use. Thus, from the years of 1987 to 2019, the residential land use area increased by more than 9.197 km2 and dryland lands during these years increased by 130.89 km2, irrigated agricultural lands also increased from 44.45 km2 and Rangeland use has also decreased by 272.3 km2.

Keywords

Agarwal, S., Vailshery, LS,, Jaganmohan, M., Nagendra, H., 2013. Mapping urban tree species using very high resolution satellite imagery: comparing pixel-based and object-based approaches. ISPRS International Journal of Geo-Information 2:220-236.
Akar, O., Güngör, O., 2012. Classification of multispectral images using Random Forest algorithm. Journal of Geodesy and Geoinformation, 1(2): 105-112.
Blaschke, T., 2009. Object based image analysis for remote sensing, ISPRS Journal of Photogrammetry and Remote Sensing, journal homepage: www.elsevier.com/locate/isprsjprs.pp.10-21.
Breiman, L., 2001. Random forests Machine Learning 45(l), 5-32.
Brian, W. S., C. Qi., B. Michael., 2011. A comparison of classification techniques to support land cover and land use analysis in tropical coastal zones. Applied Geography, 31: 525-532.
Chowdhury, M., Hasan, M. E., Abdullah-Al-Mamun, M. M., 2020. Land use/land cover change assessment of Halda watershed using remote sensing and GIS. The Egyptian Journal of Remote Sensing and Space Science, 23(1), 63-75.
Duarte, D.C.O., Zanetti, J., Junior, J.G., Medeiros, N.G., 2016. Comparison of supervised classification methods of Maximum Likelihood image, Minimum Distance, Parallelepiped and Neural network in images of Unmanned Air Vehicle (UAV) in Viçosa-MG. Proceedings XVII GEOINFO, November 27-30, Campos do Jord˜ao, Brazil.
Duro, DC., Franklin, SE., Dubé, MG., 2012. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment, 118: 259-272.
Esetlili, M.T.; Balcik, F. B.; Sanli, F. B.; Kalkan, K.; Ustuner, M.; Goksel, C.;  Kurucu, Y., 2018. Comparison of Object and Pixel-Based Classifications for Mapping Crops Using Rapideye Imagery: A Case Study of Menemen Plain, Turkey, International Journal of Environment and Geoinformatics, 5(2), 231-243.
FAO., 2012. FAO Statistical Yearbook 2012. Rome: FAO.
Gahegan ,M., German, G., West, G., 1999. Improving Neural Network Performance on the Classification of Complex Geographic Datasets, Journal of Geographical Systems, No. 1, pp. 3- 22.
Hastie, T., 2001. The elements of statistical learning: data mining, inference, and prediction, In Springer series in statistics New York, xvi: p. 533.
Hopkins, P.F., Maclean, A.L., Lillesand, T.M., 1988. Assessment of thematic mapper imagery for forestry application under lake states conditions, Photogrameteric Engineering and Remote Sensing, 54 (1): 61-68.
Ildormi, A., Nori. H., Naderi, M., Aghabeigi, S., Zeinvand, H., 2017. Land use Change Prediction using Markov Chain and CA Markov Model (Case Study: Gareen Watershed). Journal of Watershed Management Research, 8(16): 232-240.
Jensen, J., 2005. Introductory digital image processing: A remote sensing perspective (3rd ed.). Upper Saddle River, NJ: Prentice Hall. 526 pp.
Khazaei, M., Zare, M., Mokhtari, M.H., Rashtian, A., Arabi Aliabad, F., 2019. Comparison of of Different Classification Methods in Terms of Accuracy for Land Use Mapping: ACase Study of the City of Yazd. Journal of Geographical Research on Desert Areas, 7 (1), 165-178.(In Persian)
Li, M., Ma, L., Blaschke, T., Cheng, L., Tiede, D., 2016. A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments. International Journal of Applied Earth Observation and Geoinformation, 49, 87-98.
Li, M., Ma, L., Blaschke, T., Cheng, L., Tiede, D., 2016. A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments. International Journal of Applied Earth Observation and Geoinformation, 49: 87-98.
Lichtblau, E., Oswald, C.J., 2019. Classification of impervious land-use features using object-based image analysis and data fusion. Computers, Environment and Urban Systems, 75, 103-116.
Luo, G., Yin, C., Chen, X., Xu, W., Lu, L., 2010. “Combining System Dynamic Model and CLUE-s Model to Improve Land Use Scenario Analyses at Regional Scale: A Case Study of Sangong Watershed in Xinjiang, China”. Ecological Complexity, 7: 198-207.
Ma, L., Cheng, L., Li, M., Liu, Y., Ma, X., 2015. Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 102: 14-27.
Md Abdullah, AY., Masrur, A., Adnan, MSG., Baky, M., Al A, Hassan, QK., Dewan, A., 2019. SpatioTemporal Patterns of Land Use/Land Cover Change in the Heterogeneous Coastal Region of Bangladesh between 1990 and 2017. Remote Sensing, 11(7): 790.
Myint, S.W., Gober, P., Brazel, A., ClarkeGrossman, S., Weng, Q., 2011. Per- pixel vs object-based classification of urban land cover extraction using high spatial resolution imagery. Remot sensing of environment, 115(5), 1145-1161.
Richards, JA., 1995. Remote Sensing Digital Image Analysis, An Introduction 2nd Edition, Springer, ISBN. 0-387-8.
Robert, A.S. 1987, Techniques for Image Processing and Classification. Remote Sensing University of Arizona, Academic Press, New York, 387p.
Sabzghabaei, G., Jafarzadeh, K., Dashti, S., Khanghah, SY., Baleshti, MB., 2017. Land use change detection using remote sensing and GIS (Case study: Qhaemshahr city). Journal of Environmental Science and Technology 19.
Shalkoff, R.J., 1997. Artificial Neural Networks, McGraw-Hill Companies Pub, New yourk.
Strahler, AH., 1980. The use of prior probabilities in maximum likelihood classification of remotely sensed data. Remote Sensing of Environment 10:135-163.
Suykens, JA., Vandewalle, J., 1999. Least squares support vector machine classifiers. Neural Processing Letters 9:293-300.
Wijaya ,A., 2005. Application of Multi-Stage Classification to Detect Illegal Logging with the Use of MultiSource Data, MSc. Thesis, ITC, Enschede, The Netherlands.
Wijaya, A., Budiharto, R.S., Tosiani, A., Murdiyarso, D., Verchot, L.V., 2015. Assessment of Large Scale Land Cover Change Classifications and Drivers of Deforestation in Indonesia. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(7), 557-573.
Yan, G., 2003. Pixel based and object oriented image analysis for coal fire research. Enschede Holanda.
Yan, GAO., 2003. Pixel Based and Object Oriented Image for Coal Fire Research, http://www.ITC.com (accessed in July 2008). pp. 3-99.
Yoo, C., Han, D., Im, J., Bechtel, B., 2019. Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images. ISPRS Journal of Photogrammetry and Remote Sensing, 157, 155-170.
Journal of Natural Environment
Volume 73, Issue 4
March 2021
Pages 687-700