{"id":33376,"date":"2026-06-15T09:33:33","date_gmt":"2026-06-15T13:33:33","guid":{"rendered":"https:\/\/carleton.ca\/geography\/?post_type=cu_people&p=33376"},"modified":"2026-06-15T10:32:44","modified_gmt":"2026-06-15T14:32:44","slug":"sassan-mohammady","status":"publish","type":"cu_people","link":"https:\/\/carleton.ca\/geography\/people\/sassan-mohammady\/","title":{"rendered":"Sassan Mohammady"},"content":{"rendered":"
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My academic path includes a Bachelor\u2019s degree in Geomatics and Surveying Engineering, two Master\u2019s degrees in Geospatial Engineering (1: GIS, University of Tehran and 2: Geoinformatics Engineering, Politecnico di Milano), and a PhD in Environment and Sustainability at the University of Saskatchewan.<\/p>\n\n\n\n

I have a deep understanding of long and short-term optical remote sensing using cloud-based and client-based systems. In my first master thesis, I used Artificial neural network and adaptive neuro-fuzzy inference-based systems to predict land use change on Landsat imageries. In my second master thesis, I combined Landsat imageries with Sentinel 2A imageries to analysis urban sprawl. In my PhD research at the University of Saskatchewan, using cloud based mapping platform (i.e., Google earth engine) and Landsat imageries, I mapped Chlorophyll-a, as the main index of phytoplankton biomass which is one of the main indices of lake water quality globally, across all the lakes (>32,000 lakes) inside the Lake Winnipeg watershed, Canada (~1,000,000 square kilometers) between 1984 to 2023. For the same region and same period, I used Google earth engine and Landsat imageries to map inundated areas, and from which, derived wetlands and wetlandscape properties across the region.<\/p>\n\n\n\n

I possess a deep understanding and experience in machine learning (classification and regression using artificial
neural networks, random forest), deep learning (e.g., CNN) and input-output interactions, and programming
languages including Python (TensorFlow, PyTorch, Xarray), R, MATLAB, and GEE JavaScript API. In my master\u2019s
thesis, I have used Artificial neural network and adaptive neuro-fuzzy inference-based systems to predict the land
use changes from Landsat and Sentinel 2A imageries and environmental variables. I have also a deep understanding
of time-series statistical analysis, such as trend detection using Mann-Kendall test and mean, median, standard
deviation and variance analysis, which are critical for long-term ecological monitoring. In my PhD degree, I used
machine learning (i.e., random forest) to explore this relationship between local and regional variables impacting lake water quality. Furthermore, I have predicted wetland ecosystem conditions in urban areas using neural networks and GIS-driven environmental predictors.<\/p>\n\n\n\n

I have authored several peer-reviewed publications on these topics and invite you to review my Google Scholar profile for further details. I have been a lecturer and instructor of geomatics courses (e.g., Introduction to GIS, Introduction to Remote sensing, introduction to photogrammetry and close-range remote sensing) at the diploma and bachelor\u2019s degree for more than 4 years in Iran between 2014-2019.<\/p>\n\n\n\n

Peer-Reviewed Journal Articles<\/strong><\/p>\n\n\n\n

1. Mohammady, S., Creed, I.F. (2025). A Pan-Watershed Chlorophyll-a Concentration Dataset for 27,313 Lakes Draining into Lake Winnipeg (Canada), 1984-2023. Data in Brief, 112035. https:\/\/doi.org\/10.1016\/j.dib.2025.112035<\/p>\n\n\n\n

2. Mohammady, S., Creed, I.F. (2025). Human activities and climate override local catchment characteristics in explaining long-term phytoplankton trends in Prairie lakes. Science of the Total Environment, 1000, 180439.
https:\/\/doi.org\/10.1016\/j.scitotenv.2025.180439<\/p>\n\n\n\n

3. Fendereski, F, Ma, S, Mohammady, S, Creed, I, Spence, C. (2025). Tracking changes in wetlandscape properties of the Lake Winnipeg Watershed using Landsat inundation products (1984\u20132020). International Journal of Applied Earth Observation and Geoinformation, 136, 104376. https:\/\/doi.org\/10.1016\/j.jag.2025.104376<\/p>\n\n\n\n

4. Erratt, K. J., Mohammady, S., Lee, T.S., Carney, V., Sanderson, K., Lesage, C.L., Nwaishi, F.C., Creed. I.F. (2024). Expert-assisted statistical learning techniques for assessing wetland conditions in urban landscapes, Ecological Indicators, 169, 112932. https:\/\/doi.org\/10.1016\/j.ecolind.2024.112932<\/p>\n\n\n\n

5. Mohammady, S., Erratt, K.J., Creed, I.F. (2024). Tracking Phytoplankton Biomass Amid Wildfire Smoke Interference Using Landsat 8 OLI. Remote Sens., 16, 3605. https:\/\/doi.org\/10.3390\/rs16193605<\/p>\n\n\n\n

6. Mohammady, S., Delavar, M.R. (2016). Urban Sprawl Assessment and Modeling Using Landsat Images and GIS, Modeling Earth Systems and Environment, 2(3), 1-14. https:\/\/doi.org\/10.1007\/s40808-016-0209-4<\/p>\n\n\n\n

7. Mohammady, S. (2016). Optimization of Adaptive Neuro Fuzzy Inference System Based Urban Growth Model, City, Territory and Architecture, 3(1), 1-15. https:\/\/doi.org\/10.1186\/s40410-016- 0039-8<\/p>\n\n\n\n

8. Mohammady, S., Delavar, M. R. (2015). Parameters Sensitivity Analyses in Agent based Urban Growth Models, Environmental Research, Engineering and Management, 71 (2), 22-30. https:\/\/doi.org\/10.5755\/j01.erem.71.2.11504<\/p>\n\n\n\n

9. Mohammady, S., Delavar, M R. (2015). Urban Sprawl Monitoring, Modern Applied
Science, 9(8), 1-12. https:\/\/doi.org\/10.5539\/mas.v9n8p1<\/a><\/p>\n\n\n\n

Technical Reports<\/strong><\/p>\n\n\n\n

1. Lee, T.S., Creed, I.F., Mohammady, S., Sanderson, K., Erratt, K.J., Lesage. Modelled Aquatic Condition Index for Calgary Wetlands. DOI 10, w13152036. https:\/\/www.rockies.ca\/files\/reports\/Modelled_ACI_31.10.2023_Final_v2.pdf<\/a><\/p>\n\n\n\n

Conference Papers and Proceedings: Peer-reviewed<\/strong><\/p>\n\n\n\n

1. Mohammady, S., Creed, I.F. Discriminating the Drivers of Phytoplankton Biomass in the Upland Lakes that Drain into Lake Winnipeg. 46th Canadian Symposium on Remote Sensing, June 2025<\/p>\n\n\n\n

2. Fendereski, F, Ma, S, Mohammady, S, Creed, I, Tracking Changes in Ephemeral Inundated Areas within the Lake Winnipeg Watershed over the Last Three Decades (1984-2020), Society of Wetland Science (SWS) 2021 Annual Meeting, June 2021<\/p>\n\n\n\n

3. Fendereski, F, Ma, S, Mohammady, S, Creed, I, Climate Change Impacts on Wetlandscapes, AGU Fall Meeting, December 2021<\/p>\n\n\n\n

Oral Presentations and Posters<\/strong><\/p>\n\n\n\n

1. Mohammady, S., Creed, I.F. Discriminating the Drivers of Phytoplankton Biomass in the Upland Lakes that Drain into Lake Winnipeg. 46th Canadian Symposium on Remote Sensing, June 2025<\/p>\n\n\n\n

2. Mohammady, S., Delavar, M. R., and Pahlavani, P.: Urban growth modeling using an artificial neural network a case study of Sanandaj city, Iran, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL- 2\/W3, 203-208, doi:10.5194\/isprsarchives-XL-2-W3-203-2014, 2014.(http:\/www.int-arch-photogramm- remote-sens-spatial-infsci.net\/XL-2- W3\/203\/2014\/isprsarchives-XL-2-W3-203-2014.html)<\/p>\n\n\n\n

3. Mohammady, S., Delavar, M. R., and Pijanowski, B. C.: Urban growth modeling using ANFIS algorithm: a case study for Sanandaj city, Iran, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1\/W3, 493- 498, doi:10.5194\/isprsarchives-XL-1-W3-493-2013, 2013. (http:\/\/www.int-arch-photogramm-remote-sens- spatial-infsci.net\/XL-1- W3\/493\/2013\/isprsarchives-XL-1-W3-493-2013.html)







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