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联系Siddique U.询问您的工作详情
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经验
GIS analyst
5月 2010 - 5月 2012 (2 年)
Motivated and results oriented GIS professional with more than 10 years experience in geo-spatial and remote sensing analysis. Well versed in Quantitative analytical work particularly with Arc Map, QGIS and ENVI. Experienced with managing database and done multiple projects on LULC and LST changes. Expertise in advance satellite image processing, land cover mapping, web mapping and other geo spatial analysis.
教育
PhD
(4 年)
资质
PGD
UOP, Pakistan
2013
Dedicated and hardworking GIS analyst with 10 years of experience working with GIS software's for developing GIS databases, data creation and snapping tolerances, LULC and LST analysis. Regularly demonstrate skill in marketing, basic sales, and customer interaction. Critical thinker continuously looking at ways to improve processes and build business. Excel in editing, COGO, map production and creating vector property maps.
出版物
Spatio-temporal changes in the six major glaciers of the Chitral River basin
Journal of Mountain Science
In this study, we estimated the glacier dynamics in terms of Equilibrium Line Altitude (ELA), mass balance and the snout position variation using remote sensing data between 2001 and 2018. Six glaciers, having area ≥ 20 km2 each, situated in the Chitral region (Hindukush Mountains) were investigated in this study. Digital Elevation Model (DEM) and available cloud-free continuous series of Landsat and Sentinel the entire study area was a retreat of -231 ± 140 m.
Analysis and simulation of land cover changes and their impacts on land surface temperature
Journal of Environmental Mangament
In this study we assessed current and the future LULC and LST trends in the alpine environment of lower Himalayan region of Pakistan . Past patterns of LULC and LST were identified through the Support Vector Machine (SVM) and multi-spectral Landsat satellite images during 1987–2017 data period. The Cellular automata (CA) model and Artificial Neural Network (ANN) were applied to simulate future (years 2032 and 2047) LULC and LST changes, respectively, using their past patterns
Remote Sensing-Based Quantification of LULC and LST
Multidisciplinary Digital Publishing Institute
The current study assessed LULC and LST changes for the period of 1990-2017 using support vector machine (SVM) method. A combined cellular automata and artificial neural network (CA-ANN) prediction model was used for simulation of LULC changes for the period of 2032 and 2047 using transition potential matrix obtained from the data years of 2002 and 2017. The thermal bands of Landsat images from the years 1990, 2002 and 2017 were used for LST derivation and prediction using CA-ANN model.
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