Resume Details: Nilushika Jayawardhana |
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| Address : |
314/B, Jayasri Uyana,Pallekale, Kundasale Kandy Sri Lanka |
Email : |
nilushika.n.jayawardhana@gmail.com |
Web : |
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Phone : |
+94(0)789903283 |
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Willng to Relocation : |
No |
Desire Location: |
Sri Lanka |
Date Available : |
15-2-2015 |
Desire job Title:: |
Remote Sensing & GIS Analyst |
Year of Experience: |
1 |
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Education : |
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| BSc Special in Photogrammetry and Remote Sensing (Second Class Lower Division, GPA - 3.19 out of 4), Sabaragamuwa University of Sri Lanka. |
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GIS Skills : |
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COMPUTER LANGUAGES
Python
HTML, XML, Java
C++
SQL
Pascal, GWBasic
SOFTWARE
ERDAS Imagine, ENVI
Arc GIS, QGIS
AutoCAD, ZWCAD
MATLAB, R
TIMESAT
Cubist, See5
MRT, LDOPE Tool
NEST
Spirits
LISFLOOD_FP hydraulic model
RRI model
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Work Experience : |
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Research Intern (RS/GIS)
GRandD Unit 10th February to 06th June 2014
IWMI Headquarters, Sri Lanka.
Worked at GRandD (GIS Remote Sensing and Data Management Unit), IWMI (International Water Management Institute) as a Research intern, in the partial fulfillment of the requirement for the degree in BSc Surveying Sciences (Special in Photogrammetry and Remote Sensing).
Responsible for
Irrigated Area Monitoring Mapping Activities of South Asia and Africa.
Downloading Satellite data and preprocess them.
Developed the methodology for differentiate single, double and triple crop in South Asia, using the wavelet transformation in MATLAB environment.
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Remote Sensing Consultant
Research Section 1st July to 31st December 2014
IWMI Headquarters, Sri Lanka.
Worked as a Remote Sensing Consultant at Research Section, IWMI Under the Theme Water Availability, Risk and Resilience.
Responsible for
Drought Monitoring and Mapping of South Asia.
Downloading satellite data, applying satellite data corrections and Time series cloud removing
Crop phenology identification using Time Series Analysis.
Responsible for the preparation and handling of large datasets, spatial analysis and GIS Mapping
Analysis of the results
Synthesis of various remote sensing data, image processing and developing classification and regression tree approach to determine drought severity in R environment.
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