My research interests focus upon the development and application of spatial analysis techniques (i.e. GIS and remote sensing) to the study of the physical environment. Much of my work has focused on applications in the areas of hydrology, geomorphology, and environmental contamination. I am interested in digital terrain modelling and analysis and in the study of error in environmental modelling applications. I have several ongoing research projects that involve topographic data derived from laser altimetry (LiDAR). GIS software development and spatial analysis algorithm design have always been important components of much of my research.
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
Bhamjee R, Lindsay JB, Cockburn J. 2016. Monitoring ephemeral headwater streams: A paired sensor approach. Hydrological Processes, 30(6): 888–898. DOI: 10.1002/hyp.10677
Lindsay JB. 2016. Efficient hybrid breaching-filling sink removal methods for flow path enforcement in digital elevation models. Hydrological Processes, 30(6): 846–857. DOI: 10.1002/hyp.10648
Lindsay JB, Cockburn J, Russell H. 2015. An integral image approach to performing multi-scale topographic position analysis. , 245: 51–61. DOI: 10.1016/j.geomorph.2015.05.025
Molder B, Cockburn J, Berg A, Lindsay J, Woodrow K. 2015. Sediment-assisted nutrient transfer from a small, no-till, tile drained watershed in southwestern Ontario, Canada. Agricultural Water Management, 152: 31–40. DOI: 10.1016/j.agwat.2014.12.010
Lindsay JB, Dhun K. 2015. Modelling surface drainage patterns in altered landscapes using LiDAR. International Journal of Geographical Information Science, 29: 1–15. DOI: 10.1080/13658816.2014.975715
Peirce SE, Lindsay JB. 2015. Characterizing ephemeral streams in a southern Ontario watershed using electrical resistance sensors. Hydrological Processes, 29: 103–111. DOI: 10.1002/hyp.10136
Goulsbra C, Evans M, Lindsay J. 2014. Temporary streams in a peatland catchment: Pattern, timing, and controls on stream network expansion and contraction. Earth Surface Processes and Landforms, 39: 790–803. DOI: 10.1002/esp.3533
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 (email@example.com) with a statement of interest and experience and an unofficial transcript.
Graduate Students Supervised
|M.A.||Lanthier, Colton||Developing an open-source spatial decision support system to conduct accurate capacity analysis of rooftop photovoltaic potential at the city-scale.|
|M.Sc.||Lisso, Laura||Research interests: geomatics and hydrology.|
|M.Sc.||Owen, Garnet||Digital Elevation Model (DEM) Preprocessing Techniques for Geomorphometric and Hydrological Modelling Applications.|
|M.Sc.||2010||Bhamjee, Rashaad [M.Sc.]||Assessing spatial and temporal variability in ephemeral stream flow in Southern Ontario.|
|M.Sc.||2011||Dhun, Kimberly||Application of LiDAR DEM to the modelling of surface drainage patterns in human modified landscapes.|
|M.Sc.||2012||Ahrens, Beau||Landscape characterization using digital elevation models.|
|M.Sc.||2012||Peirce, Sarah||Characterization of ephemeral streams using electrical resistance (ER) sensors.|
|M.Sc.||2013||Fuss, Colleen||Increasing digital elevation model resolution using multi-observation, quad-polar, RADARSAT-2 imagery and data fusion techniques.|
|M.Sc.||2014||Woodrow, Kathryn||Isolated Catchment Mapping in Southwestern Ontario Morainal Landscapes|
|Ph.D.||2015||Bhamjee, Rashaad [PhD]||Low-Flow Hydrology In Canadian Headwater Areas.|
|M.Sc.||2016||Chabot (Cowan), Melanie||Characterizing Agricultural Surface roughness using a terrestrial laser scanner: Implications for soil moisture retrieval from remote sensing products.|
|M.Sc.||2017||Mallon, Christopher||Hydrologic-economic modeling of the cost-effectiveness and targeting of nutrient management in the Gully Creek Watershed, Ontario.|
|M.Sc.||2018||Newman, Daniel (MA)||Investigating hyperscale terrain analysis metrics and methods.|
|M.Sc.||2019||Francioni, Anthony||Using fine resolution LiDAR DEMs to create an algorithm that preserves significant edge-features such as microtopographic drainage channels by ways of a drainage-feature preserving smoothing filter.|
|M.Sc.||2019||Gudim, Simon||Impoundment Size Index (ISI): The use of potential impoundment size for the characterization of topographic incision in DEMs.|
|M.Sc.||2019||Roberts, Kevin||An Analysis of Ground-Point Classifiers for Terrestrial LiDAR.|
|M.Sc.||2021||Van Nieuwenhuizen, Nigel||An Analysis of Preprocessing Techniques for the Removal of Transportation Embankments and Surface Roughness in Fine-Resolution DEMs|
|Ph.D.||2022||Newman, Daniel||Development and assessment of spatially heterogeneous scaling methods for multiscale topographic characterization.|