home icon user icon
Welcome to International Network for Natural Sciences | INNS Pub.
Home My Account
Submit Manuscript
double_slash
breadcumb_bg

Identification of land use/cover changes mapping in an urban area using satellite imagery & support vector machine algorithm (case study: Some’esara)

Paper Details

Research Paper 01/07/2015
Views (222) Download (7)
current_issue_feature_image
publication_file

Identification of land use/cover changes mapping in an urban area using satellite imagery & support vector machine algorithm (case study: Some’esara)

Neda Musavi
J. Bio. Env. Sci.7( 1), 543-556, July 2015.
Certificate: JBES 2015 [Generate Certificate]
Key: Change detectionGilan provincePost classification methodSome'esara townshipSupport vector machine (SVM)

Abstract

Land-use change processes present a variety of trajectories depending on local conditions, the regional context and external influences. This study is an in-depth analysis of spatial and temporal land-use change in a township mountain area for the data period 1989 to 2014 in northwest of Iran. Presently, unplanned changes of land use have become a major problem. Most land use changes occur without a clear and logical planning with little attention to their environmental impact. Since those changes in land use take place in large and extensive areas, so, remote sensing technology is a necessary and valuable tool for land use change detection. Some’esara Township with 1254.543 square kilometer and possible changes are investigated in two times, from 1989 to 2014. For accuracy assessment of this method, after collecting ground truth data, which are carried out through field visiting, Google Earth images and aerial photographs, overall accuracy and Kappa coefficient are used. Overall accuracies of the maps obtained through classification using SVM method for TM, ETM+ images are 93%, 95% respectively, that state high accuracy of this algorithm in classification of satellites images. During 1989 to 2014. The methods enabled four periods to be identified revealing a distinctive evolution in land use, in which urban consolidation is present consistently, together with rotation of the wetland typology e involving marsh degradation, gains from agro-forest land or sparsely vegetated areas and the appearance of urban areas.

VIEWS 6
Cover PDF

Alavi Panah K. 2005. Application of remote sensing in geosciences (earth sciences) University of Tehran Press 478.

Bonyad AE, Hajyghaderi T. 2007. Inventorying and Mapping of Natural Forest Stands of Zanjan Province Using Landsat ETM+ Image Data. Journal of Science and Technology of Agriculture and Natural Resources 42(11), 627-638.

Burges C. 1998. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167.

Campbell JB. 1996. Introduction to remote sensing (2nd ed.). London: Taylor and Francis.

Campbell JB. 1987. Introduction to remote sensing. The Guilford Press.

Carrao H, Goncalves P, Caetano M. 2008. Contribution of multispectral and multitemporal information from MODIS images to land cover classification. Remote Sensing of Environment 112, 986–997.

Castillejo-González L, López-Granados F, García-Ferrer A, Peña-Barragán JM, Jurado-Expósito M, de la Orden MS. 2009. Object- and pixel-based analysis for mapping crops and their agro-environmental associated measures using QuickBird imagery. Computers and Electronics in Agriculture 68(2), 207–215.

Chan JC, Chan KP, Yeh AGO. 2001. Detecting the nature of change in an urban environment: comparison of machine learning algorithms. Photogrammetric Engineering and Remote Sensing 67, 213–225.

Chavez PSJR, Mackinnon DJ. 1994. Automatic detection of vegetation changes in the southwestern United States using remotely sensed images. Photogrammetric Engineering and Remote Sensing 60, 571-583.

Chintan AS, Arora MK, Pramod KV. 2004. Unsupervised classification of hyperspectral data: An ICA mixture model based approach. International Journal of Remote Sensing 25, 481-487.

Council Directive 92/43/EEC. 1992. Of the Conservation of Natural Habitats and Wild Flora and Fauna the Council of the European Communities.

Dixon B, Candade N. 2008. Multispectral land use classification using neural networks and support vector machines: one or the other, or both?, International Journal of Remote Sensing 29, 1185– 1206.

Du Y, Teillet PM, Cihlar J. 2002. Radiometric normalization of multitemporal high-resolution satellite images with quality control for land cover change detection. Remote Sensing of Environment 82, 123–134.

ENVI User’s Guide. 2014. ENVI user’s manual, ITT Visual Information Solutions. Fassnacht, K. S., Cohen, W. B., & Spies, T. A. 2006. Key issues in making and using satellite-based maps in ecology: A primer. Forest Ecology and Management 222, 167-181.

Fassnacht KS, Cohen WB, Spies TA. 2006. Key issues in making and using satellite-based maps in ecology: A primer. Forest Ecology and Management 222, 167-181.

Foody GM. 2000. Mapping land cover from remotely sensed data with a softened feed forward neural network classification, Journal of Intelligent and robotic Systems 29, 433-449.

Gautam RS, Singh D, Mittal A. 2008. Application of SVM on satellite images to detect hotspots in Jharia coal field region of India [J]. Advances in Space Research 41(11), 1784-1792.

Geneletti D, Gorte BGH. 2003. A method for object oriented land cover classification combining Land sat TM and aerial photographs. International journal of Remote sensing 24, 1273-1286.

Gualtieri JA, Cromp RF. 1998. Support vector machines for hyperspectral remote sensing classification. Proceedings of the 27th AIPR Workshop: Advances in Computer Assisted Recognition, Washington, DC 27, 221-232.

Hsu CW, Chang CC, Lin CJ. 2008. A practical guide to support vector classification, http://www.csie.ntu. edu.tw/cjlin/papers/guide/guide.pdf, 16p.

Hsu CW, Lin CJ. 2002. A comparison on methods for multi-class support vector machines. IEEE Transactions on Neural Network 13, 415-425.

Huang C, Davis LS, Townshend JRG. 2002. An assessment of support vector machines for land cover classification. International Journal of Remote Sensing 23, 725-749.

Huang C, Song K, Kim S, Townshend JRG, Davis P, Masek JG. 2008. Use of dark object concept and support vector machines to automate forest cover change analysis. Remote Sensing of Environment 112, 970–985.

Joachims T. 1998a. Making large scale SVM learning practical. In Advances in Kernel Methods-Support Vector Learning, edited by B. Scholkopf, C. Burges and A. Smola (New York: MIT Press).

Joachims T. 1998b. Text categorization with support vector machines-learning with many relevant features. In Proceedings of European Conference on Machine Learning, Chemnitz, Germany, April 10, 1998 (Berlin: Springer) 137-142.

Joyce AT, Ivey JH, Burns GS. 1980. The Use of Landsat MSS Data for Detecting Land Use Changes in Forestland. 14th International Symposium Remote Sensing of Environment. Ann Arbor. Michigan 12 pp.

Karimi Y, Prasher SO, Patel RM, Kim SH. 2006. Application of support vector machines technology for weed and nitrogen stress detection in corn. Computers and Electronics in Agriculture 51, 99-109.

Kavzoglu T, Colkesen I. 2009. A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation 11, 352-359.

Khorram S, Biging GS, Chrisman NR, Congalton RG, Dobson JE, Ferguson RL. 1999. Accuracy assessment of remote sensingderived change detection. Bethesda: American Society of Photo-grammetry and Remote Sensing.

King RB. 2002. Land cover mapping principles: a return to interpretation fundamentals, international journal of Remote Sensing 23.

Lambin EF, Ehrlich D. 1997. Land-cover changes in Sub-Saharan Africa 1982–1991: Application of a change index based on remotely sensed surface temperature and vegetation indices at a continental scale. Remote Sensing Environment 61(2), 181–200.

Li DC, Liu CW. 2010. A class possibility based kernel to increase classification accuracy for small data sets using support vector machines. Expert Systems with Applications 37, 3104–3110.

Lillesand TM, Kiefer RW. 1994. Remote sensing and image interpretation (4th ed.). New York Wiley.

Lu D, Mausel P, Brondizio E, Moran E. 2004. Change detection techniques. INT. J. Remore Sensing 25(12), 2365–2407 pp.

Lu D, Weng Q. 2007. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing 28(5), 823–870.

Mas JF. 1999. Monitoring Land-Cover Changes: A Comparison of Change Detection Techniques, International Journal Remote Sensing 20(1), 139-152.

Mendoza JE, Etter R. 2002. Multitemporal analysis (1940–1996) of land cover changes in the southwestern Bogota highplain (Colombia). Landscape and Urban Planning 59(3), 147–158.

Muchoney DM, Haack B. 1994. Change detection for monitoring forest defoliation. Photogrammetric Engineering and Remote Sensing 60, 1243–1251.

Nemmourit Chibani Y. 2006, Multiple support vector machines for land cover change detection: An application for mapping urban extensions [J]. ISPRS Journal of Photogrammetry & Remote Sensing 61(2), 125-133.

Pal M, Mather PM. 2005. Some issues in the classification of DAIS hyperspectral data. International Journal of Remote Sensing 27, 2895–2916.

Palmer AR, Van Rooyen AF. 1998. Detecting vegetation change in the southern Kalahari using Landsat TM data. Journal of Arid Environments 39, 143–153.

Petropoulos GP, Kontoes C, Keramitsoglou I. 2011. Burnt Area Delineation from a uni-temporal perspective based on Landsat TM imagery classification using Support Vector Machines. International Journal of Applied Earth Observation and Geoinformation 70–80.

Plaza J, Plaza A, Perez R, Martinez P. 2005. Automated generation of semilabeled training samples for nonlinear neural network-based abundance estimation in hyperspectral data. IGARSS 2005, Seoul (S. Korea) 1261-1264.

Plaza J, Plaza A, Perez R, Martinez P. 2009. On the use of small training sets for neural network-based characterisation of mixed pixels in remotely sensed hyperspectral images. Pattern Recognition 42, 3032-3045.

Ram B, Kolarkar AS. 1993. Remote sensing application in monitoring land-use changes in arid Rajasthan. International Journal of Remote Sensing 14(17), 3191-3220.

Rembold F, Carnicelli S, Nori M, Ferrari A. 2000. Use of aerial photographs, landsat TM imagery and multidisciplinary field survey for landcover change analysis in the lakes region (Ethiopia). International Journal of Applied Earth Observation and Geoinformation 2(34), 181-189.

Sadek SHA. 1993. Use of landsat imagery for monitoring agricultural expansion of East and West Nile Delta, Egypt. Egyptian Journal of Soil Sciences 33(1), 23-24.

Sanchez-Hernandez C, Boyd DS, Foody GM. 2007. Mapping specific habitats from remotely sensed imagery: Support vector machine and support vector data description based classification of coastal saltmarsh habitats. Ecological Informatics 2, 83–88.

Singh A. 1989. Digital change detection techniques using remotely sensed data. International Journal of Remote Sensing 10, 989-1003.

Skole DL. 1994. Data on global land-cover change: acquisition, assessment and analysis. In: W. B. Meyer, B. L. Turner II (Eds.), Changes in land use and land cover: a global perspective 437-471. Cam-bridge: Cambridge University Press.

Stow DA, Chen DM, Parrott R. 1996. Enhancement, identification and quantification of land cover change. In Morain, S. A., & Lopez Barose, S.V. (Eds.), Raster imagery in geographical information systems 307-312. Santa Fe: One Word.

Su Lihong Chooping MS, Rango A. 2007. Support vector machines for recognition of semi-arid vegetation types using MISR multiangle imagery [J]. Remote Sensing of Environment 107(102), 299-311.

Sunar F. 1996. An Analysis of changes in a Multi-data set: a case study in the Ikitelli area Istanbul Turkey. Int. Journal of Remote Sensing 9, 20-34l.

Thomas IL, Benning VM, Ching NP. 1987. Classification of remotely sensed images. Bristol: Adam Hilger.

Trebar M, Steele N. 2008. Application of distributed SVM architectures in classifying forest data cover types [J]. Computers and Electronics in Agriculture 63 (2), 119-130.

Vapnik VN. 1995. The Nature of Statistical Learning Theory (New York: Springer Verlag) 188p.

Vitousek PM. 1994. Beyond global warming: ecology and global change. Ecology 75, 1861-1876.

Yao X, Tham LG, Dai FC. 2008. Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China, Geomorphology 101, 572-582.

Yuan D, Elvidge CD, Lunetta RS. 1999. Survey of multi-spectral methods for land cover change analysis. In R. S. Lunetta, & C. D. Elvidge (Eds.), Remote sensing change detection: Environmental monitoring methods and applications, 21-39. London: Taylor & Francis.

Yuan F, Bauer ME, Heinert NJ, Holden GR. 2005. Multi-level land cover mapping of the Twin cities (Minnesota) Metropolitan area with multiseasonal landsat TM/ETM + data, Geocarto International 20 (2), 5-14.

Submit Manuscript