GIScience

---

Areas of expertise:

• Web GIS Mapping
• Spatial Data Analytics Using R
• GIS Programming (Python, R, JavaScript)
• Terrain Analysis
• Graph Theory

---

Web GIS Mapping

Web GIS mapping using open-source software packages, such GeoServer, Leaflet for R (Demo)

---

Spatial Data Analytics Using R

Crime Analysis - Visualizing the spatial and temporal trends of criminal activity (Demo)

Twitter Analytics - Sentiment analysis of location-based Twitter data (Demo)

---

GIS Programming

Object Detection and Segmentation - Using Python, Tensorflow, Keras

---

Terrain Analysis

Wu, Q., Liu, H., Wang, S., Yu, B., Beck, R., & Hinkel, K. (2015). A localized contour tree method for deriving geometric and topological properties of complex surface depressions based on high-resolution topographic data. International Journal of Geographical Information Science, 29(12), 2041-2060. https://doi.org/10.1080/13658816.2015.1038719 (source code)

---

Graph Theory

Wu, Q., Lane, C. R., Wang, L., Vanderhoof, M. K., Christensen, J. R., & Liu, H. (2019). Efficient Delineation of Nested Depression Hierarchy in Digital Elevation Models for Hydrological Analysis Using Level‐Set Method. JAWRA Journal of the American Water Resources Association, 55(2), 354-368. https://doi.org/10.1111/1752-1688.12689 (source code)

---

Close

Remote Sensing

---

Areas of expertise:

• LiDAR Remote Sensing
• Microwave Remote Sensing
• Wetlands and Water Resources
• Deep Learning and Cloud Computing (Google Earth Engine, Microsoft Azure)

---

LiDAR Remote Sensing

Mapping Wetland Inundatation Dynamics

Wu, Q., Lane, C. R., Li, X., Zhao, K., Zhou, Y., Clinton, N., DeVries, B., Golden, H. E., & Lang, M. W. (2019). Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine. Remote Sensing of Environment, 228, 1-13. https://doi.org/10.1016/j.rse.2019.04.015 (source code)

---

Vernal Pool Detection

Wu, Q., Lane, C. R., & Liu, H. (2014). An effective method for detecting potential woodland vernal pools using high-resolution LiDAR data and aerial imagery. Remote Sensing, 6(11), 11444-11467. https://doi.org/10.3390/rs61111444

---

Automated sinkhole Mapping

Wu, Q., Deng, C., & Chen, Z. (2016). Automated delineation of karst sinkholes from LiDAR-derived digital elevation models. Geomorphology, 266, 1-10. https://doi.org/10.1016/j.geomorph.2016.05.006 (source code)

---

Coastal Change Analysis

Wu, Q., Su, H., Sherman, D. J., Liu, H., Wozencraft, J. M., Yu, B., & Chen, Z. (2016). A graph-based approach for assessing storm-induced coastal changes. International Journal of Remote Sensing, 37(20), 4854-4873. https://doi.org/10.1080/01431161.2016.1225180

---

Microwave Remote Sensing

Remote Sensing of Soil Moisture

Wu, Q., Liu, H., Wang, L., & Deng, C. (2016). Evaluation of AMSR2 soil moisture products over the contiguous United States using in situ data from the International Soil Moisture Network. International Journal of Applied Earth Observation and GeoInformation, 45, 187-199. https://doi.org/10.1016/j.jag.2015.10.011

---

Wetlands and Water Resources

Mapping Depressional Wetlands

Wu, Q., & Lane, C. R. (2016). Delineation and quantification of wetland depressions in the Prairie Pothole Region of North Dakota. Wetlands, 36(2), 215-227. https://doi.org/10.1007/s13157-015-0731-6 (source code)

---

Mapping Wetland Hydrologic Connectivity

Wu, Q., & Lane, C. R. (2017). Delineating wetland catchments and modeling hydrologic connectivity using lidar data and aerial imagery. Hydrology and Earth System Sciences, 21(7), 3579. https://doi.org/10.5194/hess-21-3579-2017 (source code)

---

Deep Learning and Cloud Computing

Deep Learning

Object Detection and Segmentation - Using Python, Tensorflow, Keras

---

Cloud Computing

Google Earth Engine - A planetary-scale platform for Earth science data & analysis (Demo)

---

Close

Environmental Modeling

---

Areas of expertise:

• Hydrological Modeling
• Modeling Wetland Water Storage
• Modeling Wetland Hydrologic Connectivity

---

Hydrological Modeling

Hydrological modeling of surface water using high-resolution LiDAR data.

---

Modeling Wetland Water Storage

Wu, Q., & Lane, C. R. (2016). Delineation and quantification of wetland depressions in the Prairie Pothole Region of North Dakota. Wetlands, 36(2), 215-227. https://doi.org/10.1007/s13157-015-0731-6 (source code)

---

Modeling Wetland Hydrologic Connectivity

Wu, Q., & Lane, C. R. (2017). Delineating wetland catchments and modeling hydrologic connectivity using lidar data and aerial imagery. Hydrology and Earth System Sciences, 21(7), 3579. https://doi.org/10.5194/hess-21-3579-2017 (source code)

---

Close

Software Tools

---

Check Figshare to see a complete list of software tools and packages I developed.

• Mapping Wetland Inundation Dynamics
• Delineating Nested Depression Hierarchy
• Wetland Hydrology Analyst
• Drumlin Extraction Toolbox
• Depression Identification Analyst

---

Mapping Wetland Inundation Dynamics

Wu, Q., Lane, C. R., Li, X., Zhao, K., Zhou, Y., Clinton, N., DeVries, B., Golden, H. E., & Lang, M. W. (2019). Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine. Remote Sensing of Environment, 228, 1-13. https://doi.org/10.1016/j.rse.2019.04.015 (source code)

---

Delineating Nested Depression Hierarchy

Wu, Q., Lane, C. R., Wang, L., Vanderhoof, M. K., Christensen, J. R., & Liu, H. (2019). Efficient Delineation of Nested Depression Hierarchy in Digital Elevation Models for Hydrological Analysis Using Level‐Set Method. JAWRA Journal of the American Water Resources Association, 55(2), 354-368. https://doi.org/10.1111/1752-1688.12689 (source code)

---

Wetland Hydrology Analyst

Wu, Q., & Lane, C. R. (2017). Delineating wetland catchments and modeling hydrologic connectivity using lidar data and aerial imagery. Hydrology and Earth System Sciences, 21(7), 3579. https://doi.org/10.5194/hess-21-3579-2017 (source code)

---

Drumlin Extraction Toolbox

Wang, S., Wu, Q.*, & Ward, D. (2017). Automated delineation and characterization of drumlins using a localized contour tree approach. International Journal of Applied Earth Observation and GeoInformation, 62, 144-156. https://doi.org/10.1016/j.jag.2017.06.006 (source code)

---

Depression Identification Analyst

Wu, Q., Liu, H., Wang, S., Yu, B., Beck, R., & Hinkel, K. (2015). A localized contour tree method for deriving geometric and topological properties of complex surface depressions based on high-resolution topographic data. International Journal of Geographical Information Science, 29(12), 2041-2060. https://doi.org/10.1080/13658816.2015.1038719 (source code)

---

Close

Tutorials, Maps, and Data

---

Tutorials

• eCognition Tutorials
• Drumlin Extraction Toolbox Demo
• Wetland Hydrology Analyst Demo

---

Maps

• Pipestem Soils Hydric Classification Map
• Pipestem Hydrologic Connectivity Map
• Pipestem Inundation Map
• Pipestem PHDI Graph

---

Data

• SSURGO Data 2014
• USA Soils Hydric Classification
• Soil Hydrology of the United States

---

Useful Resources

• Remote Sensing Journals
• GIS & Geography Journals
• Hydrology & Wetlands Journals
• Literature Search
• GIS & Remote Sensing Software
• Geography Jobs

---

Close

Google Slides Show

---

Click the image below to play Google Slides (link)

---

Close