Lindsay, John

GEOG*1300 Introduction to the Biophysical Environment
GEOG*2420 The Earth from Space
GEOG*3420 Remote Sensing of the Environment
GEOG*3430 Geomatics for Environmental Analysis
GEOG*3480 GIS and Spatial Analysis
GEOG*6090 Research Methods
GEOG*6550 Environmental Modelling

• LiDAR DEM de-noising algorithm development.
• Characterization of multi-scale topographic position indices and visualization methods.
• Development and testing of terrain indices for wetland mapping.
• Development of the open-source GIS Whitebox Geospatial Analysis Tools and WhiteboxTools
• Accounting for soil organic carbon in profitability maps
• Using airborne LiDAR to map sidewalk encroachment and accessibility

• Peer reviewer for: Water Resources Research, Applied Geography, Computers & Geosciences, Photogrammetric Engineering and Remote Sensing, Hydrological Processes, International Journal of Geographical Information Science, and Earth Surface Processes and Landforms.
• Associate Editor (Science & Techniques) The Great Lakes Geographer, 2000, 2002.
• Symposium Organizer, International Perspectives on Spatial Modelling in Catchment Research, University of Manchester, UK, June 25, 2007.

Newman*, D.R., Cockburn, J.M.H., Drǎguţ, L., Lindsay, J.B. 2022. Local scale optimization of geomorphometric land surface parameters using scale-standardized Gaussian scale-space Computers and Geosciences, 165, 105144 15 pp. DOI: 10.1016/j.cageo.2022.105144.

Newman*, D.R., Cockburn, J.M.H., Drǎguţ, L., Lindsay, J.B. 2022. Evaluating Scaling Frameworks for Multiscale Geomorphometric Analysis. Geomatics, 2, 36-51. https://doi.org/10.3390/geomatics2010003.

Eyre*, R, Lindsay, JB, Laamrani, L, Berg, A. 2021. Within-field yield prediction in cereal crops using LiDAR-derived topographic attributes with geographically weighted regression models. Remote Sensing, 13(20), 4152; https://doi.org/10.3390/rs13204152.

Van Nieuwenhuizen, N, Lindsay, JB, DeVries, B. 2021. Smoothing of Digital Elevation Models and the Alteration of Overland Flow Path Length Distributions. Hydrological Processes, 35(7), e14271, DOI: 10.1002/hyp.14271.

Alijani, Z, Lindsay, JB, Chabot, M., Rowlandson, T, and Berg, A 2021. Sensitivity of C-band SAR polarimetric variables to the directionality of surface roughness parameters. Remote Sensing. 13(11), 2210; https://doi.org/10.3390/rs13112210.

Van Nieuwenhuizen, N, Lindsay, JB, DeVries, B. 2021. Automated mapping of transportation embankments in fine-resolution LiDAR DEMs. Remote Sensing. 13(7), 1308; https://doi.org/10.3390/rs13071308.

Lindsay, JB, Yang, W, Hornby, DD. 2019. Drainage network analysis and structuring of topologically noisy vector stream data. ISPRS International Journal of Geo-Information. 8(9), 422; DOI: 10.3390/ijgi8090422.

Lindsay JB, Francioni A, Cockburn JMH. 2019. LiDAR DEM smoothing and the preservation of drainage features. Remote Sensing, 11(16), 1926; DOI: 10.3390/rs11161926.

Roberts KC, Lindsay JB, Berg AA. 2019. An analysis of ground-point classifiers for terrestrial LiDAR. Remote Sensing, 11(16), 1915; DOI: 10.3390/rs11161915.

Lindsay JB, Newman DR, Francioni* A. 2019. Scale-optimized surface roughness for topographic analysis. Geosciences, 9(7) 322. DOI: 10.3390/geosciences9070322.

Newman D, Lindsay JB, Cockburn JMH. 2018. Measuring hyperscale topographic anisotropy as a continuous landscape property. Geosciences, 8(8) 1-14. DOI: 10.3390/geosciences8080278.

Liu Y, Yang W, Shao H, Yu Z, Lindsay JB. 2018. Development of an integrated modelling system for evaluating water quantity and quality effects of individual wetlands in an agricultural watershed. Water, 10(6). DOI: 10.3390/w10060774.

Newman D, Lindsay JB, Cockburn JM. 2018. Evaluating metrics of local topographic position for multiscale geomorphometric analysis. Geomorphology, 312(1): 40-50. DOI: 10.1016/j.geomorph.2018.04.003

Chabot M, Lindsay J, Berg A, Rowlandson T. 2018. Comparing the use of terrestrial LiDAR scanners and pin profilers for deriving agricultural roughness statistics. Canadian Journal of Remote Sensing, 44(2). DOI: 10.1080/07038992.2018.1461559.

Shao H, Yang W, Lindsay JB, Liu Y, Yu Z, and Oginskyy A. 2017. An open source GIS-based decision support system for watershed evaluation of BMPs. Journal of the American Water ResourcesAssociation. 53(3): 521–531. DOI: 10.1111/1752-1688.12521

Fuss CE, Berg AA, Lindsay JB. 2016. DEM fusion using a modified k-means clustering algorithm. The International Journal of Digital Earth, 9(12): 1242-1255. DOI: 10.1080/17538947.2016.1208685

Woodrow K, Lindsay JB, Berg AA. 2016. Evaluating DEM conditioning techniques, elevation source data, and grid resolution for field-scale hydrological parameter extraction. Journal of Hydrology, 540: 1022-1029. DOI: 10.1016/j.jhydrol.2016.07.018

Lindsay JB. 2016. Whitebox GAT: A case study in geomorphometric analysis. Computers & Geosciences, 95: 75-84. DOI: 10.1016/j.cageo.2016.07.003

Lindsay JB. 2016. The practice of DEM stream burning revisited. Earth Surface Processes and Landforms, 41(5): 658–668. DOI: 10.1002/esp.3888

For a complete and updated list of Prof. Lindsay's publications please see the Geomorphometry and Hydrogeomatics Research Group website.

GIS, Geomorphometry, LiDAR Remote Sensing, Spatial Analysis, Hydrology

Prof. John Lindsay is looking for 1-2 new Masters students to join the Geomorphometry and Hydrogeomatics Research Group (GHRG) in Fall 2020. These fully-funded students will take on a research project related to the broad range of geomorphometry (digital terrain analysis), spatial hydrology, and LiDAR remote sensing topics actively studied within the GHRG. Specifically, Prof. Lindsay is looking for students to work on projects related to improving information extracted from the newly acquired Ontario LiDAR topographic data sets. Application areas include predictive soils mapping, soil organic carbon, and the study of accessibility in urban areas. Like all graduate members of the GHRG, the incoming students will be involved in the application and development of novel techniques for handling these data in spatial hydrological applications. GHRG students are provided advanced training in GIS and geomatics more broadly and have opportunity to gain experience with terrain mapping equipment, LiDAR data, and spatial analysis software (GIS and remote sensing). Interested applicants are encouraged to email Prof. Lindsay (jlindsay@uoguelph.ca) with a statement of interest and experience and an unofficial transcript.