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:: Volume 7, Issue 14 (10-2019) ::
PEC 2019, 7(14): 253-274 Back to browse issues page
vegetation change detection using multi-temporal remotly sensed data during recent three decades by artificial intelligence technique (Case study: protected area of Bashgol)
Behzad Rayegani Dr, Hamid Goshtasb *
, meigooni1959@gmail.com
Abstract:   (233 Views)
Quantitative and qualitative information of vegetation and its changes in duration of time as a basic foundation of determination of  habitat quality, priority of protected area and also determination of price of ecosystem services in order to optimum management of natural resources and sustainable development is a very important technical point. In other hand, researchers are interested in remote sensing as an efficient tool to access timely and accurate information about land coverage especially vegetation coverage. In current study post classification comparison method among different methods of change detection was used because of possibility of achieving optimum accuracy by using an efficient and accurate classification method. In order to determine vegetation classes by field-based resources and Landsat images and also, slope-based and distance-based vegetation indices derived from these images, two artificial neural networks; percentage of vegetation cover (overall accuracy 94.3% and mean square error 5.7% for test data) and dry weight of standing biomass (overall accuracy 86.6% and mean square error 11.4% for test data) were built and vegetation maps according to these qualified models were prepared. Results of this research show high capacity of the artificial intelligence technique in vegetation classification if, the average number of field-based samples and variety of images in terms of time were possible. By the vegetation classification maps made, change detection maps with pixel by pixel comparison that show change classes from class … to class … in three duration of time; 2000-2015, 1986-2000 and 1986-2015 were prepared. Accuracy of these maps is totally depended on classification accuracy and demonstrated qualitative enhancement in a wide area of case study after conservation.
 
Keywords: : multi temporal images, post comparison classification, artificial neural network, Landsat images
Full-Text [PDF 1359 kb]   (73 Downloads)    
Type of Study: Research | Subject: Special
Received: 2017/10/22 | Accepted: 2018/12/23 | Published: 2019/09/30
References
1. Alatorre, L., C, Sanchez-Carrille, S., Miramontes-Beltran, S., Medina, R. J, Torres-olave, M., E, Bravo, L., C, Wiebe, L., C, Grarados, A., Adams, D., K, Sanchez, E., UC, M. 2016. Temporal changes of NDVI for qualitative environmental assessment of mangroves: shrimp farming impact on the health decline of the arid mangroves in the Gulfe of California (1990-2010), Journal of Arid Enviroments, NO. 125, pp. 98-109
2. Afifi, H.A. 2011. Evaluating of change detection techniques for monitoring land-cover changes: a case Study in New Burg El-Arab area, Alexandria Engineering Journal. Vol. 50, pp. 187-195
3. Bandari, A.K, Kummar, A., Singh, G.K. 2012. Feature extraction using normalized difference vegetation index (NDVI): a case study of Jabalpur city, Procedia Technogy, vol. 2(2012), pp. 612-621
4. Congalton, R., Green, K. 2007. Accuracy of Remotly Sensed Data, 2th Edition, Boca Raton, London, Newyork: Taylor & Francis Group, 183 pp
5. Fatiha, B., Abdelkader, A., Hamami, L., Elaihar, M. 2013. Spatio temporal analysis of vegetation indices from molti-dates satellite images: application to a semi arid area in Algheria, TerraGreen 13 International Conference, Energy Procedia, Vol. 36, pp. 667-675
6. Gomaz, C., White, J.C., Wulder, M. 2016. Optical Remotly Sensed Time Series Data for Landcover Classification: A Rview, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 16, pp. 55-72
7. Kerr, J.T., Ostrovsky, M. 2003. From space to Species: ecological application for remote sensing, Trend in Ecology and Evolution, Vol. 18, Issue. 6, pp. 299-305
8. Kirui, K.B, Kairo, J.G, Bosire., J, Viergever, K.M, Rudra, S, Huxham, M., Briers, R.A. 2013. Mappingof mangrove forest land cover change along the Kenya coastline using landsat imagery, Journal of Ocean & Coastal Management, Vol. 83, pp. 19-24
9. Lary, D.J., Alavi, A.H., Gandomi, A.H., Walker, A.L. 2016. Machin Learning in Geosience and Remote Sensing, Geosience Frontiers, Volum 7, Issue 1, pp. 3-10
10. Sarp, G. 2012. Determination of vegetation change using thematic mapper imagery in Afsin-Elbistan lignite basin; SE, Turkey, Procedia Technology, Vol. 1(2012), pp.407-411
11. USGS, Earthexplorer. 2015. available on: www.Landsat.usgs.nasa.gov
12. Vogelman, E. James., L-Gallant, Alisa., Hua, Shi., Zhu, Z. 2016. Perspectives on Monitoring Gradual Change Across the Continiuity of Landsat Sensors Using Time-Series Data, Remote Sensing of Environment, pp. 1-13
13. Xiaolu, S., Bo, C.H. 2011. Change detection using change vector analysis from landsat TM images in Wuhab, Procedia Environment Sciences, Vol. 11(2011), pp. 238-244
14. Yuan, F., Bouer, M.E., Heinert, N.J., Holden, G.R. 2005. Mapping of the Twin Cities (Minnesota) Metropolitan area with multi-seasonal landsat TM/ETM+ data, Print of paper accepted for publication in Geocarto International
15. Zhu, Z., 2017. Change Detection Using Landsat Time Series: A Review of Frequencies, Preprocessing, Algorithms, and Application, ISPRS Journal of Photogrammetry and Remote Sensing 130, pp. 370-384
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Rayegani B, Goshtasb H. vegetation change detection using multi-temporal remotly sensed data during recent three decades by artificial intelligence technique (Case study: protected area of Bashgol). PEC. 2019; 7 (14) :253-274
URL: http://pec.gonbad.ac.ir/article-1-391-en.html


Volume 7, Issue 14 (10-2019) Back to browse issues page
مجله حفاظت زیست بوم گیاهان Journal of Plant Ecosystem Conservation
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