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Boston, Massachusetts, United States
November 13, 2017

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Wenqiao Zhang

** ******* **, *** **, Allston, MA, 02134




Master of art in Remote Sensing & Geographic Information System Expected January 2018 GPA: 3.76/4.0

Main Courses:

Regional Energy Modeling, Advanced Topics in Remote Sensing, Geographic Information Systems, Spatial Analysis Using Geographic Information Systems, Digital Image Processing – Remote Sensing, Information Structures with Python, Multivariate Analysis for Geographers, Modeling and Monitoring Terrestrial Ecosystem Processes


Bachelor of National Geographical Monitoring June 2016 GPA: 3.88/4.0

Main Courses:

Geography (Physical & Human), Land Cover and Land Use, Economic Geography & District Planning, Spatial-Temporal Database, Principles of Geographical Information System, Error Processing of Spatial Data, Digital Sensor Network Technology, Digital Image Processing, Principle and Application of Remote Sensing, Geographical Data Analysis and Modeling, Remote Sensing Monitoring of Water Conservancy etc. Work Experience

Boston University: Research Assistant June 2017

• Continue change detection and classification (CCDC) time series analysis

• Used QGIS to find disturbance (land cover change) in Mexico Geographic Information Center in Zhejiang, China: Data Analyst March - May 2016

• Analyzed the results of the First National Geographic Census for different application like disaster monitoring, resource conservation, urban planning, economic development, etc.

• Used geospatial analysis, statistical modeling, and linear regression including: network analysis, hydrology analysis, proximity analysis, etc. by ArcGIS

• Wrote reports and produced maps assisting with government policy making Project & Research Experiences

Geographically Weighted Regression for Geospatial Analysis (Boston University) March – May 2017

• Studied how spatial neighborhood characteristics could affect the retail price of gasoline in the Greater Boston area

• Used the number of competitors around a gas station, the distance to the highways and the income in the area around as neighborhoods factors

• Used geospatial methods including: spatial join, buffer, select tools in ArcMap to rank these characteristics and produced maps

Change Detection based on Landsat data (Boston University) March – May 2017 Deforestation in Bolivia from 1986 to 2014

• Studied deforestation in the central part of Bolivia from 1986 to 2014

• Used Random Forest to classify Landsat images of different years to identify deforestation area

• Used multi-date classification to detect change (deforestation)

• Used the confusion matrix to assess accuracy and estimate deforestation area

• Produced classification maps and deforestation maps Project of Geospatial Analysis (Boston University) September – December 2016 Gas leak and its impact on trees in Quincy

• Studied how gas leak impacted on trees in the Quincy area by using several geospatial methods, including: hot spot analysis, point density, buffer to explore the spatial relationship between the gas leak points and the affected trees through ArcGIS

• Used georeference tool to digitalize pipeline map of Boston

• Produced corresponding thematic maps by ArcMap

• Calculated the average declined percentage of each tree species to figure out which kind of tree is more vulnerable to the gas leaks

Amazon Drought and Seasonality (Boston University) October – December 2016

• Explored two droughts in the Amazon region using remote sensing data MODIS vegetation index product and TRMM precipitation data

• Calculated and showed the spatial patterns of the standardized anomalies of dry season mean EVI and mean precipitation for drought year 2010 and non-drought year 2008 using Matlab

• Explored the seasonality of the wet equatorial Amazonian rainforests by BU MODIS LAI data, CERES surface PAR data and TRMM precipitation data

• Got the profiles of LAI, PAR and precipitation over the wet equatorial Amazonian rainforests Exploitation and Utilization of Low-slope Hilly Land Resource March – June 2016

• Established the evaluation model of exploiting and utilizing low-slope hilly resources by multi-factor comprehensive evaluation method by ArcGIS Engine

• Quantified the features of low-slope hilly resources by geospatial analysis methods and produced corresponding thematic maps by ArcMap

• Analyzed the results and obtained information to support regional planning and policymaking Published Article in ICCSET March 2015

Anti-Excessive Filtering Model Based on Sliding Window

• Addressed the problem that ground points in cloud of LiDAR has been excessively filtered

• Proposed an improved model based on the traditional sliding window model, combining with optimized rules on determining standard elevation value, tolerance of elevation difference and dynamic thresholds Innovative Experiment of Undergraduate March 2014

Single Tree 3D Modeling Based On the Point Cloud Data Collected from Terrestrial Laser Scanners

• Studied on segmentation of point cloud data of a single tree, extraction and optimization of the framework of branches, surface reconstruction of a single tree model, in order to achieve real three dimensional modeling and database storage of the typical forest

• Learned and explored the related algorithms to acquire geometric and topological information from the unorganized LiDAR data and procedure for point cloud segmentation Technical Skills

• Basic Software: Microsoft Office

• Professional Software: ArcGIS, QGIS, ERDAS, ENVI, etc.

• Data Analysis tool: Matlab, R, SPSS, etc.

• Programming: C, C++, Python

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