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Remote Sensing for Assessing Environment Situation

BALWOIS’2010 Ohrid, 25-29 May 2010. Remote Sensing for Assessing Environment Situation. Neki Frasheri, Betim Cico, Hakik Paci, Faculty of Information Technology, Polytechnic University of Tirana Jozef Bushati University of Shkodra. Why Remote Sensing.

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Remote Sensing for Assessing Environment Situation

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  1. BALWOIS’2010 Ohrid, 25-29 May 2010 Remote Sensing for Assessing Environment Situation Neki Frasheri, Betim Cico, Hakik Paci, Faculty of Information Technology, Polytechnic University of Tirana Jozef Bushati University of Shkodra

  2. Why Remote Sensing • Easy identification of significant environmental changes, in particular water bodies and vegetation • An early example – Semani beach Combination of sea regression / transgression in Semani beach correlated with a hidden tectonic fault. An object built in stere now is situated deep in water Frasheri, Cico, Paci, Bushati

  3. Identification of Water Bodies • Used Landsat images, band NIR • The same band from three different years combined as image RGB • Water bodies remain black • Terra remains variations of white gray • Changes of water bodies identified by color • Combination of only one or two basic colors Frasheri, Cico, Paci, Bushati

  4. W E D Water Body Case 1 • Changes in Buna River Erosion / Deposition in Buna River delta area and expansion of Wetland (Red: water transgression; Blue/Green: water regression / deposition Frasheri, Cico, Paci, Bushati

  5. Water Body Case 2 • Changes in Prespa Lakes Loss of water in Macro Prespa Lake Shore changes in Prespa (Blue: water regression / sedimentation; red strips from errors in early images) Today 2001 Sedimentation in Micro Prespa Lake from Devolli River Frasheri, Cico, Paci, Bushati

  6. Cloudy area Identification of Flows 2010 • Flows of 2010 in Drini and Buna Rivers identified in low resolution images of MODIS Frasheri, Cico, Paci, Bushati

  7. Analysis of Images in Time Domain • CHERS application framework initiated in FP7 SEE-GRID-SCI project • Trend analysis in time for NDVI values of each pixel Frasheri, Cico, Paci, Bushati

  8. Issues with Trend Analysis • Only linear trend may lead to wrong conclusions • Reversal tendencies visible in second order trend Linear trend indicates continuous decrease. Reality has tendency for improvement Linear trend indicates continuous increase. Reality has tendency for degradation Frasheri, Cico, Paci, Bushati

  9. Trend Analysis Case 1 • MODIS images • Second order trend of NDVI Interpretation palette Frasheri, Cico, Paci, Bushati

  10. Trend Analysis Case 2 • MODIS images • Linear trend of NDVI Prespa area Shkodra area Interpretation palette Frasheri, Cico, Paci, Bushati

  11. Conclusions • Combination of bands of satellite images in different time represents a 3D data structure that permits identification of environmental changes and their character. • We experimented the analysis of changes of water bodies, and the trend in time of NDVI, producing false color images that represent their variations. • Analysis of images and supervised classification would allow to clearly integrate in GIS interested areas. • The methodology can be used for the whole range of environmental parameters obtained from satellite images. Frasheri, Cico, Paci, Bushati

  12. Thank You • Questions ? Frasheri, Cico, Paci, Bushati

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