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Resume Details: Anu Amulu


Address :

India

Email :

anu.ggnn@gmail.com

Web :

http://

Phone :
Fax :

Willng to Relocation :
Yes
Desire Location:
North Carolina
Date Available :
2-1-2016
Desire job Title::
Remote Sensing and GIS
Year of Experience:
4

 
Education :
 
Degree Institute Board/University % of Marks
M.Sc (Geo-spatial Science &Technology) I.S.T, J.N.T.U Campus. Hyderabad. J.N.T. University 76.7
B.Sc (Genetics, Micro-biology) M.N.R.Degree College, Hyderabad. Osmania University 66.5
 
GIS Skills :
 
• Very Good experience in Arc-GIS based Spatial Decision Support System & Quantum GIS.
• Excellent Business communication, inter-personnel and team co-ordination skills.
• Hands on experience in various Mapping and Image Processing Software Skills like ERDAS 8.5, 8.7, 9.1, Arc GIS 9, 9.1, 9.2, 9.3, 10 Quantum GIS & Auto CAD Map, ENVI Classic, Geomatica.
• Strong knowledge in Image Interpretation for analyzing the Impact and Temporal Changes in the LULC.
• Very Good Knowledge in GPS, DGPS.
• Experience in Knowledge Transition to peers.
• Conceptual understanding of Remote Sensing and GIS Mapping Experience in planning.
 
Work Experience :
 
• Geo-reference the Toposheets and Satellite Images.
• Digitize the Toposheet and Prepare the Different Layers.
• Prepare the base layers such as Base maps, Contour maps, Drainage maps from which main layers are prepared (TIN, DEM, SLOPE, ASPECT, Flow Direction, Flow Accumulation).
• Using thematic layers final action plan map prepared.
• Total of 18 layers are prepared in APIB project viz., SOIL, GEOMORPHOLOGY, LAND DEGRADATION, Land Irrigation, Land Productivity, Soil Erosion etc., in 1:10000m scale.
• Interpret the Satellite data and Prepare of LU/LC maps Supervised, Unsupervised Classification.
• Prepared the NDVI and Change Detection Maps for detecting the temporal changes.
• Prepare the Final Layout Maps.
• Trained national and international participants on ArcGIS, ERDAS, QGIS and GPS (Theoretical and Practical).
• Collecting data from Flux tower.
• Measurements should be integrated appropriately using modelling approaches.
• Enable upscaling in both Spatial and Temporal domains.
• Identifying the patterns of rice and cotton ecosystem responses in terms of CO2 flux.

 
 
 

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