References
- ABMI. “3 X 7 Photoplot Land Cover Data.” Accessed December 27 2020. https://abmi.ca/home/data-analytics/da-top/da-product-overview/Advanced-Landcover-Prediction-and-Habitat-Assessment–ALPHA–Products/Photoplot-Land-Cover-Data-Training-and-Validation.html.
- Adam, E., O. Mutanga, and D. Rugege. 2010. “Multispectral and Hyperspectral Remote Sensing for Identification and Mapping of Wetland Vegetation: A Review.” Wetlands Ecology and Management 18 (3): 281–296. doi:https://doi.org/10.1007/s11273-009-9169-z.
- Aep, G. O. A. 2020. Alberta Wetland Mapping Standards and Guidelines: Mapping Wetlands at an Inventory Scale V1.0. Edmonton, Canada: Government of Alberta - Alberta Environment and Parks.
- Ågren, A. M., W. Lidberg, M. Strömgren, J. Ogilvie, and P. A. Arp. 2014. “Evaluating Digital Terrain Indices for Soil Wetness Mapping–a Swedish Case Study.” Hydrology and Earth System Sciences 18 (9): 3623–3634. doi:https://doi.org/10.5194/hess-18-3623-2014.
- Agresti, A. 1996. An Introduction to Categorical Data Analysis. New York, N.Y.: Wiley.
- Alberta Biodiversity Monitoring Institute. 2018. Human Footprint Inventory 2018 (Version 1). Alberta, Canada: Geospatial Center, Alberta Biodiversity and Monitoring Insitute.
- Alberta Environment and Sustainable Resource Development (ESRD). 2015. “Alberta Wetland Classification System. Water Policy Branch, Policy and Planning Division, Edmonton, AB.”
- Amani, M., B. Brisco, S. Majid Afshar, M. Mirmazloumi, S. Mahdavi, S. M. J. Mirzadeh, W. Huang, and J. Granger. 2019a. “A Generalized Supervised Classification Scheme to Produce Provincial Wetland Inventory Maps: An Application of Google Earth Engine for Big Geo Data Processing.” Big Earth Data 3 (4): 378–394. doi:https://doi.org/10.1080/20964471.2019.1690404.
- Amani, M., B. Salehi, S. Mahdavi, and B. Brisco. 2018. “Spectral Analysis of Wetlands Using Multi-source Optical Satellite Imagery.” ISPRS Journal of Photogrammetry and Remote Sensing 144: 119–136. doi:https://doi.org/10.1016/j.isprsjprs.2018.07.005.
- Amani, M., B. Salehi, S. Mahdavi, and J. Granger. 2017a. “Spectral Analysis of Wetlands in Newfoundland Using Sentinel 2A and Landsat 8 Imagery.” Proceedings of the IGTF, Baltimore, Maryland.
- Amani, M., B. Salehi, S. Mahdavi, J. Granger, and B. Brisco. 2017b. “Wetland Classification in Newfoundland and Labrador Using Multi-source SAR and Optical Data Integration.” GIScience & Remote Sensing 54 (6): 779–796. doi:https://doi.org/10.1080/15481603.2017.1331510.
- Amani, M., S. Mahdavi, M. Afshar, B. Brisco, W. Huang, S. M. J. Mirzadeh, L. White, S. Banks, J. Montgomery, and C. Hopkinson. 2019b. “Canadian Wetland Inventory Using Google Earth Engine: The First Map and Preliminary Results.” Remote Sensing 11 (7): 842. doi:https://doi.org/10.3390/rs11070842.
- Assessment, Millennium Ecosystem. 2005. Ecosystems and Human Well-being: Wetlands and Water. Washington, DC: World resources institute.
- AW3D. 2020. “High-resolution Digital 3D Map Covering the Entire Global Land Area.” Accessed January 11 2021.
- Bartzen, B. A., K. W. Dufour, R. G. Clark, and F. Dale Caswell. 2010. “Trends in Agricultural Impact and Recovery of Wetlands in Prairie Canada.” Ecological Applications 20 (2): 525–538. doi:https://doi.org/10.1890/08-1650.1.
- Belgiu, M., and D. Lucian. 2016. “Random Forest in Remote Sensing: A Review of Applications and Future Directions.” ISPRS Journal of Photogrammetry and Remote Sensing 114: 24–31. doi:https://doi.org/10.1016/j.isprsjprs.2016.01.011.
- Berhane, T. M., C. R. Lane, W. Qiusheng, B. C. Autrey, O. A. Anenkhonov, V. V. Chepinoga, and H. Liu. 2018. “Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory.” Remote Sensing 10 (4): 580. doi:https://doi.org/10.3390/rs10040580.
- Boehner, J., R. Koethe, O. Conrad, J. Gross, A. Ringeler, and T. Selige. 2001. “Soil Regionalisation by Means of Terrain Analysis and Process Parameterisation.” Soil Classification 7: 213.
- Bourgeau-Chavez, L., K. Riordan, R. Powell, N. Miller, and M. Nowels. 2009. “Improving Wetland Characterization with Multi-sensor, Multi-temporal SAR and Optical/infrared Data Fusion.”
- Bradley, J. V. 1968. Distribution-Free Statistical Test. EnglewoodCliffs: New Jersy: Prentice-Hall.
- Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. doi:https://doi.org/10.1023/A:1010933404324.
- Bwangoy, J.-R. B., M. C. Hansen, D. P. Roy, D. G. Gianfranco, and C. O. Justice. 2010. “Wetland Mapping in the Congo Basin Using Optical and Radar Remotely Sensed Data and Derived Topographical Indices.” Remote Sensing of Environment 114 (1): 73–86. doi:https://doi.org/10.1016/j.rse.2009.08.004.
- Chen, D., and D. Stow. 2002. “The Effect of Training Strategies on Supervised Classification at Different Spatial Resolutions.” Photogrammetric Engineering and Remote Sensing 68 (11): 1155–1162.
- Congalton, R. G., and K. Green. 2019. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. CRC press.
- Conrad, O., B. Bechtel, M. Bock, H. Dietrich, E. Fischer, L. Gerlitz, J. Wehberg, V. Wichmann, and B. Jürgen. 2015. “System for Automated Geoscientific Analyses (SAGA) V. 2.1. 4.” Geoscientific Model Development Discussions 8 (7): 1991–2007.
- Corcoran, J. M., J. F. Knight, and A. L. Gallant. 2013. “Influence of Multi-source and Multi-temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota.” Remote Sensing 5 (7): 3212–3238. doi:https://doi.org/10.3390/rs5073212.
- DeVries, B., M. W. Chengquan Huang, J. W. Lang, W. H. Jones, I. F. Creed, and M. L. Carroll. 2017. “Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery.” Remote Sensing 9 (8): 807. doi:https://doi.org/10.3390/rs9080807.
- Dixon, B., and N. Candade. 2008. “Multispectral Landuse Classification Using Neuralnetworks and Support Vector Machines: One or the Other, or Both? .” International Journal of Remote Sensing 29 (4): 1185–1206. doi:https://doi.org/10.1080/01431160701294661.
- Du, Y., Y. Zhang, F. Ling, Q. Wang, L. Wenbo, and L. Xiaodong. 2016. “Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band.” Remote Sensing 8 (4): 354. doi:https://doi.org/10.3390/rs8040354.
- Environment and Climate Change Canada. “The Government of Canada and Ducks Unlimited Canada Invest $1.5 Million for Wetland Conservation in Quebe.” Government of Canada., Accessed 7 January 2020
- ESA. 2019. “Sentinel Online: Acquisition Modes.” Accessed 23 March 2021. https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/acquisition-modes
- ESRI. 2020. ArcGIS Desktop: Release 10.8.1. Redlands, CA: Environmental Systems Research Institute.
- Fisette, T., P. Rollin, Z. Aly, L. Campbell, B. Daneshfar, P. Filyer, A. Smith, A. Davidson, J. Shang, and I. Jarvis. 2013. AAFC Annual Crop Inventory. Paper presented at the 2013 Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Fairfax, USA.
- Franklin, S. E., E. M. Skeries, M. A. Stefanuk, and O. S. Ahmed. 2018. “Wetland Classification Using Radarsat-2 SAR Quad-polarization and Landsat-8 OLI Spectral Response Data: A Case Study in the Hudson Bay Lowlands Ecoregion.” International Journal of Remote Sensing 39 (6): 1615–1627. doi:https://doi.org/10.1080/01431161.2017.1410295.
- Gardner, R. C., and C. Finlayson. “Global Wetland Outlook: State of the World’s Wetlands and Their Services to People.” Ramsar Convention Secretariat. https://ssrn.com/abstract=3261606.
- Ghimire, B., J. Rogan, V. R. Galiano, P. Panday, and N. Neeti. 2012. “An Evaluation of Bagging, Boosting, and Random Forests for Land-Cover Classification in Cape Cod, Massachusetts, USA.” GIScience & Remote Sensing 49 (5): 623–643. doi:https://doi.org/10.2747/1548-1603.49.5.623.
- Gislason, P. O., J. A. Benediktsson, and J. R. Sveinsson. 2006. “Random Forests for Land Cover Classification.” Pattern Recognition Letters 27 (4): 294–300. doi:https://doi.org/10.1016/j.patrec.2005.08.011.
- Gorelick, N., M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore. 2017. “Google Earth Engine: Planetary-scale Geospatial Analysis for Everyone.” Remote Sensing of Environment 202: 18–27. doi:https://doi.org/10.1016/j.rse.2017.06.031.
- Government of Alberta. “Alberta Merged Wetland Inventory.” Alberta Environment and Parks. https://geodiscover.alberta.ca/geoportal/rest/metadata/item/bfa8b3fdf0df4ec19f7f648689237969/html.
- Große-Stoltenberg, C., H. André, C. Werner, J. Oldeland, and J. Thiele. 2016. “Evaluation of Continuous VNIR-SWIR Spectra versus Narrowband Hyperspectral Indices to Discriminate the Invasive Acacia Longifolia within a Mediterranean Dune Ecosystem.” Remote Sensing 8 (4): 334. doi:https://doi.org/10.3390/rs8040334.
- Gulácsi, A., and F. Kovács. 2020. “Sentinel-1-imagery-based high-resolution water cover detection on wetlands, Aided by Google Earth Engine.” Remote Sensing 12 (10):1614.
- Henderson, F. M., and A. J. Lewis. 2008. “Radar Detection of Wetland Ecosystems: A Review.” International Journal of Remote Sensing 29 (20): 5809–5835. doi:https://doi.org/10.1080/01431160801958405.
- Higginbottom, T. P., C. D. Field, A. E. Rosenburgh, A. Wright, E. Symeonakis, and S. J. M. Caporn. 2018. “High-resolution Wetness Index Mapping: A Useful Tool for Regional Scale Wetland Management.” Ecological Informatics 48: 89–96. doi:https://doi.org/10.1016/j.ecoinf.2018.08.003.
- Hird, J. N., E. R. DeLancey, G. J. McDermid, and J. Kariyeva. 2017. “Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping.” Remote Sensing 9 (12): 1315. doi:https://doi.org/10.3390/rs9121315.
- Hsu, C.-W., C.-C. Chang, and C.-J. Lin. 2003. “A practical guide to support vector classification. 2003.” In.
- Huang, C., L. S. Davis, and J. R. G. Townshend. 2002. “An Assessment of Support Vector Machines for Land Cover Classification.” International Journal of Remote Sensing 23 (4): 725–749. doi:https://doi.org/10.1080/01431160110040323.
- Huang, W., B. DeVries, C. Huang, M. W. Lang, J. W. Jones, I. F. Creed, and M. L. Carroll. 2018. “Automated Extraction of Surface Water Extent from Sentinel-1 Data.” Remote Sensing 10 (5):797.
- Immitzer, M., C. Atzberger, and T. Koukal. 2012. “Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data.” Remote Sensing 4 (9): 2661–2693. doi:https://doi.org/10.3390/rs4092661.
- Jahncke, R., B. Leblon, P. Bush, and L. Armand. 2018. “Mapping Wetlands in Nova Scotia with Multi-beam RADARSAT-2 Polarimetric SAR, Optical Satellite Imagery, and Lidar Data.” International Journal of Applied Earth Observation and Geoinformation 68: 139–156. doi:https://doi.org/10.1016/j.jag.2018.01.012.
- Kamusoko, C. 2019. Remote Sensing Image Classification in R. Singapore Pte Ltd.: Springer Geography.
- Kau, L.-J., and T.-L. Lee. 2013. “An HSV Model-based approach for the sharpening of color images”. Paper presented at the 2013 IEEE International Conference on Systems, Man, and Cybernetics, USA.
- Lang, M., G. McCarty, R. Oesterling, and I.-Y. Yeo. 2013. “Topographic Metrics for Improved Mapping of Forested Wetlands.” Wetlands 33 (1): 141–155. doi:https://doi.org/10.1007/s13157-012-0359-8.
- Mahdianpari, M., B. Brisco, J. E. Granger, F. Mohammadimanesh, B. Salehi, S. Banks, S. Homayouni, L. Bourgeau-Chavez, and Q. Weng. 2020a. “The Second Generation Canadian Wetland Inventory Map at 10 Meters Resolution Using Google Earth Engine.” Canadian Journal of Remote Sensing 46 (3): 360–375. doi:https://doi.org/10.1080/07038992.2020.1802584.
- Mahdianpari, M., B. Salehi, F. Mohammadimanesh, B. Brisco, S. Homayouni, E. Gill, E. R. DeLancey, and L. Bourgeau-Chavez. 2020b. “Big Data for a Big Country: The First Generation of Canadian Wetland Inventory Map at a Spatial Resolution of 10-m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform.” Canadian Journal of Remote Sensing 46 (1): 15–33. doi:https://doi.org/10.1080/07038992.2019.1711366.
- Mahdianpari, M., B. Salehi, F. Mohammadimanesh, S. Homayouni, and E. Gill. 2019. “The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 M Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform.” Remote Sensing 11 (1): 43. doi:https://doi.org/10.3390/rs11010043.
- Mattivi, P., F. Franci, A. Lambertini, and G. Bitelli. 2019. “TWI Computation: A Comparison of Different Open Source GISs.” Open Geospatial Data, Software and Standards 4 (1): 1–12. doi:https://doi.org/10.1186/s40965-019-0066-y.
- McLachlan, G. J. 1992. Discriminant Analysis and Statistical Pattern Recognition. New York: John Wiley & Sons.
- Millard, K., P. Kirby, S. Nandlall, A. Behnamian, S. Banks, and F. Pacini. 2020. “Using Growing-Season Time Series Coherence for Improved Peatland Mapping: Comparing the Contributions of Sentinel-1 and RADARSAT-2 Coherence in Full and Partial Time Series.” Remote Sensing 12 (15):2465.
- Moore, I. D., A. Lewis, and J. C. Gallant. 1993. “Terrain Attributes: Estimation Methods and Scale Effects.” In Modelling Change in Environmental Systems, edited by A. J. Jakeman, M. B. Beck, and M. McAleer, 189–214. London: Wiley.
- Moore, I. D., R. B. Grayson, and A. R. Ladson. 1991. “Digital Terrain Modelling: A Review of Hydrological, Geomorphological, and Biological Applications.” Hydrological Processes 5 (1): 3–30. doi:https://doi.org/10.1002/hyp.3360050103.
- Mountrakis, G., I. Jungho, and C. Ogole. 2011. “Support Vector Machines in Remote Sensing: A Review.” ISPRS Journal of Photogrammetry and Remote Sensing 66 (3): 247–259. doi:https://doi.org/10.1016/j.isprsjprs.2010.11.001.
- National Wetlands Working Group. 1997. The Canadian Wetland Classification System. Waterloo, Ontario: University of Waterloo.
- Natural Regions Committee. 2006. Natural Regions and Subregions of Alberta. D. J. Downing and W. W. Pettapiece edited by: Government of Alberta, Alberta.
- Niculescu, S., C. Lardeux, I. Grigoras, J. Hanganu, and L. David. 2016. “Synergy between LiDAR, RADARSAT-2, and Spot-5 Images for the Detection and Mapping of Wetland Vegetation in the Danube Delta.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9 (8): 3651–3666. doi:https://doi.org/10.1109/JSTARS.2016.2545242.
- Niculescu, S., J.-B. Boissonnat, C. Lardeux, D. Roberts, J. Hanganu, A. Billey, A. Constantinescu, and M. Doroftei. 2020. “Synergy of High-Resolution Radar and Optical Images Satellite for Identification and Mapping of Wetland Macrophytes on the Danube Delta.” Remote Sensing 12 (14): 2188. doi:https://doi.org/10.3390/rs12142188.
- Olthof, I., and T. Rainville. 2020. “Evaluating Simulated RADARSAT Constellation Mission (RCM) Compact Polarimetry for Open-Water and Flooded-Vegetation Wetland Mapping.” Remote Sensing 12 (9): 1476. doi:https://doi.org/10.3390/rs12091476.
- Onojeghuo, A. O., and A. R. Onojeghuo. 2017. “Object-based Habitat Mapping Using Very High Spatial Resolution Multispectral and Hyperspectral Imagery with LiDAR Data.” International Journal of Applied Earth Observation and Geoinformation 59: 79–91. doi:https://doi.org/10.1016/j.jag.2017.03.007.
- Ozesmi, S. L., and M. E. Bauer. 2002. “Satellite Remote Sensing of Wetlands.” Wetlands Ecology and Management 10 (5): 381–402. doi:https://doi.org/10.1023/A:1020908432489.
- Pal, M. 2005. “Random Forest Classifier for Remote Sensing Classification.” International Journal of Remote Sensing 26 (1): 217–222. doi:https://doi.org/10.1080/01431160412331269698.
- Pal, M., and P. M. Mather. 2005. “Support Vector Machines for Classification in Remote Sensing.” International Journal of Remote Sensing 26 (5): 1007–1011. doi:https://doi.org/10.1080/01431160512331314083.
- Pelletier, C., S. Valero, J. Inglada, N. Champion, and G. Dedieu. 2016. “Assessing the Robustness of Random Forests to Map Land Cover with High Resolution Satellite Image Time Series over Large Areas.” Remote Sensing of Environment 187: 156–168. doi:https://doi.org/10.1016/j.rse.2016.10.010.
- Powers, D. M. 2011. “Evaluation: From Precision, Recall and F-measure to ROC, Informedness, Markedness and Correlation.”
- R Core Team. 2020. “R: A language and environment for statistical computing.” In. Vienna, Austria: R Foundation for Statistical Computing.
- Raduła, M. W., T. H. Szymura, and M. Szymura. 2018. “Topographic Wetness Index Explains Soil Moisture Better than Bioindication with Ellenberg’s Indicator Values.” Ecological Indicators 85: 172–179. doi:https://doi.org/10.1016/j.ecolind.2017.10.011.
- Rodriguez-Galiano, B., V. F. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez. 2012. “An Assessment of the Effectiveness of a Random Forest Classifier for Land-cover Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 67: 93–104. doi:https://doi.org/10.1016/j.isprsjprs.2011.11.002.
- Saga, G. I. S. 2013. “System for Automated Geoscientific Analyses.” In Available At: Www.Saga-gis.Org/en/index.Html
- Schell, J. A., and D. Deering. 1973. “Monitoring Vegetation Systems in the Great Plains with ERTS.” NASA Special Publication 351: 309.
- Sörensen, R., U. Zinko, and J. Seibert. 2006. “On the Calculation of the Topographic Wetness Index: Evaluation of Different Methods Based on Field Observations.”
- Steinberg, D., and P. Colla. 2009. “CART: classification and regression trees.” The top ten algorithms in data mining 9:179.
- Sui, Y., H. Bin, and F. Tianjiao. 2019. “Energy-based Cloud Detection in Multispectral Images Based on the SVM Technique.” International Journal of Remote Sensing 40 (14): 5530–5543. doi:https://doi.org/10.1080/01431161.2019.1580788.
- Tadono, T., H. Ishida, F. Oda, S. Naito, K. Minakawa, and H. Iwamoto. 2014. “Precise Global DEM Generation by ALOS PRISM.” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2 (4): 71. doi:https://doi.org/10.5194/isprsannals-II-4-71-2014.
- Tickner, D., J. J. Opperman, R. Abell, M. Acreman, A. H. Arthington, S. E. Bunn, S. J. Cooke, et al. 2020. “Bending the Curve of Global Freshwater Biodiversity Loss: An Emergency Recovery Plan.” BioScience 70 (4): 330–342. doi:https://doi.org/10.1093/biosci/biaa002.
- TNTMips. 2014. Raster & Image Processing: Smoothing and Noise Removal Filters. edited by Micro Images, USA.
- Vapnik, V. N., and V. Vapnik. 1998. Statistical Learning Theory. New York: Wiley.
- Wang, X., X. Xiao, Z. Zou, L. Hou, Y. Qin, J. Dong, R. B. Doughty, B. Chen, X. Zhang, and Y. Chen. 2020. “Mapping Coastal Wetlands of China Using Time Series Landsat Images in 2018 and Google Earth Engine.” ISPRS Journal of Photogrammetry and Remote Sensing 163: 312–326. doi:https://doi.org/10.1016/j.isprsjprs.2020.03.014.
- Weise, K., R. Höfer, J. Franke, A. Guelmami, W. Simonson, J. Muro, B. O’Connor, et al. 2020. “Wetland Extent Tools for SDG 6.6.1 Reporting from the Satellite-based Wetland Observation Service (SWOS).” Remote Sensing of Environment 247 :111892. doi:https://doi.org/10.1016/j.rse.2020.111892.
- Wen, L., and M. Hughes. 2020. “Coastal Wetland Mapping Using Ensemble Learning Algorithms: A Comparative Study of Bagging, Boosting and Stacking Techniques.” Remote Sensing 12 (10): 1683. doi:https://doi.org/10.3390/rs12101683.
- Wu, Q., C. R. Lane, L. Xuecao, K. Zhao, Y. Zhou, N. Clinton, B. DeVries, H. E. Golden, and M. W. Lang. 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. doi:https://doi.org/10.1016/j.rse.2019.04.015.
- WWF. 2020. “Living Planet Report 2020 - Bending the Curve of Biodiversity Loss.” In Gland, edited by R. E. A. Almond, M. Grooten, and T. Petersen (pp. 1–159). Switzerland: WWF.
- Yang, X., S. Zhao, X. Qin, N. Zhao, and L. Liang. 2017. “Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 M Resolution via NDWI-Based Image Sharpening.” Remote Sensing 9 (6): 596. doi:https://doi.org/10.3390/rs9060596.
- Yommy, A. S., R. Liu, and S. Wu. 2015. SAR image despeckling using refined Lee filter. Paper presented at the 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics, USA.