Contribution of topographic features and categorization uncertainty for a tree species classification in the boreal biome of Northern Ontario

References

• Abidine, A. Z. E., P. Y. Bernier, J. D. Stewart, and A. P. Plamondon. 1994. “Water Stress Preconditioning of Black Spruce Seedlings from Lowland and Upland Sites.” Canadian Journal of Botany 72 (10): 1511–21.
   Google Scholar
• Airborne Imaging. 2018. “Final Report for Project Cochrane LiDar.” Calgary, Alberta.
   Google Scholar
• Altamash, B., D. A. MacLean, and C. R. Hennigar. 2019, 2. “Growth-Mortality Attributes and Species Composition Determine Carbon Sequestration and Dynamics of Old Stand Types in the Acadian Forest of New Brunswick, Canada.” Annals of Forest Science 76 (2). doi:10.1007/s13595-019-0821-3.
   PubMed Web of Science ®Google Scholar
• Beaudoin, A., P. Y. Bernier, L. Guindon, P. Villemaire, X. J. Guo, G. Stinson, T. Bergeron, S. Magnussen, and R. J. Hall. 2014. “Mapping Attributes of Canada’s Forests at Moderate Resolution Through KNN and MODIS Imagery.” Canadian Journal of Forest Research 44 (5): 521–532. doi:10.1139/cjfr-2013-0401.
   Web of Science ®Google Scholar
• Beaudoin, A., P. Y. Bernier, P. Villemaire, L. Guindon, and X. Jing Guo. 2018. “Tracking Forest Attributes Across Canada Between 2001 and 2011 Using a K Nearest Neighbors Mapping Approach Applied to MODIS Imagery.” Canadian Journal of Forest Research 48 (1): 85–93.
   Google Scholar
• Beguin, J., G. Arne Fuglstad, N. Mansuy, and D. Paré. 2017. “Predicting Soil Properties in the Canadian Boreal Forest with Limited Data: Comparison of Spatial and Non-Spatial Statistical Approaches.” Geoderma 306 (May): 195–205. doi:10.1016/j.geoderma.2017.06.016.
   Google Scholar
• Bing, T., C. Zhou, X. Liao, X. Zhi, Y. Peng, and O. Xianfeng. 2020. “Hierarchical Structure-Based Noisy Labels Detection for Hyperspectral Image Classification.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 13: 2183–2199. doi:10.1109/JSTARS.2020.2994162.
   Google Scholar
• Boulanger, Y., A. R. Taylor, D. T. Price, D. Cyr, E. McGarrigle, W. Rammer, G. Sainte-Marie, A. Beaudoin, L. Guindon, and N. Mansuy. 2017. “Climate Change Impacts on Forest Landscapes Along the Canadian Southern Boreal Forest Transition Zone.” Landscape Ecology 32 (7): 1415–1431. doi:10.1007/s10980-016-0421-7.
   Web of Science ®Google Scholar
• Brandon, H., H. Hung Chak, J. Zhang, A. Knudby, C. E. Bulmer, and M. G. Schmidt. 2016. “An Overview and Comparison of Machine-Learning Techniques for Classification Purposes in Digital Soil Mapping.” Geoderma 265: 62–77. doi:10.1016/j.geoderma.2015.11.014.
   Web of Science ®Google Scholar
• Bravo-Oviedo, A., H. Pretzsch, C. Ammer, E. Andenmatten, A. Barbati, S. Barreiro, P. Brang, et al. 2014. “European Mixed Forests: Definition and Research Perspectives.” Forest Systems 23 (3): 518–533. doi:10.5424/fs/2014233-06256.
   Web of Science ®Google Scholar
• Brungard, C. W., J. L. Boettinger, M. C. Duniway, S. A. Wills, and T. C. Edwards. 2015. “Machine Learning for Predicting Soil Classes in Three Semi-Arid Landscapes.” Geoderma 239: 68–83. doi:10.1016/j.geoderma.2014.09.019.
   Web of Science ®Google Scholar
• Carbonneau, M. A., V. Cheplygina, E. Granger, and G. Gagnon. 2018. “Multiple Instance Learning: A Survey of Problem Characteristics and Applications.” Pattern Recognition 77: 329–353. doi:10.1016/j.patcog.2017.10.009.
   Web of Science ®Google Scholar
• Chambers, D., C. Périé, N. Casajus, and S. de Blois. 2013. “Challenges in Modelling the Abundance of 105 Tree Species in Eastern North America Using Climate, Edaphic, and Topographic Variables.” Forest Ecology and Management 291: 20–29. doi:10.1016/j.foreco.2012.10.046.
   Web of Science ®Google Scholar
• Chiang, S., and M. Valdez. 2019. Tree Species Classification by Integrating Satellite Imagery and Topographic Variables Using Maximum Entropy Method in a Mongolian Forest. Forests 10 (11): 961. doi:10.3390/f10110961.
   Web of Science ®Google Scholar
• Cohen, J. 1960. “A Coefficient of Agreement for Nominal Scales.” Educational and Psychological Measurement XX (1): 37–46. doi:10.1177/001316446002000104.
   Web of Science ®Google Scholar
• Conrad, O., B. Bechtel, M. Bock, H. Dietrich, E. Fischer, L. Gerlitz, J. Wehberg, V. Wichmann, and J. Böhner. 2015. “System for Automated Geoscientific Analyses (SAGA) V. 2.1.4.” Geoscientific Model Development 8 (7): 1991–2007. doi:10.5194/gmd-8-1991-2015.
   Web of Science ®Google Scholar
• Curtis, E., and M. G. Wing. 2012. “Tree Location Measurement Accuracy with a Mapping-Grade GPS Receiver Under Forest Canopy.” Forest Science 58 (6): 567–576. doi:10.5849/forsci.11-015.
   Web of Science ®Google Scholar
• Dalponte, M., L. Bruzzone, and D. Gianelle. 2012. “Tree Species Classification in the Southern Alps Based on the Fusion of Very High Geometrical Resolution Multispectral/Hyperspectral Images and LiDar Data.” Remote Sensing of Environment 123: 258–270. doi:10.1016/j.rse.2012.03.013.
   Web of Science ®Google Scholar
• Dalponte, M., H. Ole Orka, T. Gobakken, D. Gianelle, and E. Naesset. 2013. “Tree Species Classification in Boreal Forests with Hyperspectral Data.” IEEE Transactions on Geoscience & Remote Sensing 51 (5): 2632–2645. doi:10.1109/TGRS.2012.2216272.
   Web of Science ®Google Scholar
• Dalponte, M., L. Theodor Ene, M. Marconcini, T. Gobakken, and E. Næsset. 2015. “Semi-Supervised SVM for Individual Tree Crown Species Classification.” Isprs Journal of Photogrammetry & Remote Sensing 110: 77–87. doi:10.1016/j.isprsjprs.2015.10.010.
   Web of Science ®Google Scholar
• Damien, S.M., C. E. Woodcock, and M. A. Friedl. 2018. “Canadian Boreal Forest Greening and Browning Trends: An Analysis of Biogeographic Patterns and the Relative Roles of Disturbance versus Climate Drivers.” Environmental Research Letters 13 (1): 1. doi:https://doi.org/10.1088/1748-9326/aa9b88.
   Web of Science ®Google Scholar
• Denneler, B., Y. Bergeron, and Y. Bégin. 1999. “An Attempt to Explain the Distribution of the Tree Species Composing the Riparian Forests of Lake Duparquet, Southern Boreal Region of Quebec, Canada.” Canadian Journal of Botany 77 (12): 1744–1755. doi:10.1139/cjb-77-12-1744.
   Google Scholar
• Digital Globe. 2016. “World View- 2 Design and Specifications,” 2. www.digitalglobe.com.
   Google Scholar
• Etzrodt, N., R. Zimmermann, and O. Conrad. 2002. “Upscaling Water Cycle Parameters Using Geomorphometric Terrain Parameters and Topographic Indices Derived from Interferometric DEM.” European Space Agency, (Special Publication) ESA SP, no. 475: 251–254.
   Google Scholar
• Fassnacht, F. E., H. Latifi, K. Stereńczak, A. Modzelewska, M. Lefsky, L. T. Waser, C. Straub, and A. Ghosh. 2016. “Review of Studies on Tree Species Classification from Remotely Sensed Data.” Remote Sensing of Environment 186: 64–87. doi:10.1016/j.rse.2016.08.013.
   Web of Science ®Google Scholar
• Franklin, S. E. 2018. “Pixel-And Object-Based Multispectral Classification of Forest Tree Species from Small Unmanned Aerial Vehicles.” Journal of Unmanned Vehicle Systems 6 (4): 195–211. doi:10.1139/juvs-2017-0022.
   Web of Science ®Google Scholar
• Franklin, S. E. 2020. “Interpretation and Use of Geomorphometry in Remote Sensing: A Guide and Review of Integrated Applications.” International Journal of Remote Sensing 41 (19): 7700–7733. doi:10.1080/01431161.2020.1792577.
   Web of Science ®Google Scholar
• Gallant, J. C., and T. I. Dowling. 2003, 12. “A Multiresolution Index of Valley Bottom Flatness for Mapping Depositional Areas.” Water Resources Research 39 (12). https://doi.org/10.1029/2002WR001426.
   Web of Science ®Google Scholar
• Garcia, M., S. Saatchi, A. Ferraz, C. Alberto Silva, S. Ustin, A. Koltunov, and H. Balzter. 2017. “Impact of Data Model and Point Density on Aboveground Forest Biomass Estimation from Airborne LiDar.” Carbon Balance and Management 12 (1): 1–18. doi:10.1186/s13021-017-0073-1.
   PubMedGoogle Scholar
• Gimaret-Carpentier, C., R. Pélissier, J.P. Pascal, and F. Houllier. 1998. “Sampling Strategies for the Assessment of Tree Species Diversity.” Journal of Vegetation Science 9 (2): 161–172. doi:10.2307/3237115.
   Web of Science ®Google Scholar
• Haynes, K. M., J. Smart, B. Disher, O. Carpino, and W. L. Quinton. 2021. “The Role of Hummocks in Re-Establishing Black Spruce Forest Following Permafrost Thaw.” Ecohydrology 14 (3): 3. doi:https://doi.org/10.1002/eco.2273.
   Web of Science ®Google Scholar
• Hu, B., Li, Q., and Hall, G Brent. 2021. A decision-level fusion approach to tree species classification from multi-source remotely sensed data. ISPRS Open Journal of Photogrammetry and Remote Sensing 1. doi: 10.1016/j.ophoto.2021.100002.
   Google Scholar
• Kuhn, M. 2007. “Variable Importance Using the Caret Package.”
   Google Scholar
• Kuhn, M. 2008. “Building Predictive Models in R Using the Caret Package.” Journal of Statistical Software 28: 5. doi:10.18637/jss.v028.i05.
   Web of Science ®Google Scholar
• Kuhn, M. 2013. “Inconsistent Results Between Caret+kernlab Versions.” R-Help. 2013. https://r.789695.n4.nabble.com/Inconsistent-results-between-caret-kernlab-versions-td4680500.html.
   Google Scholar
• Kulha, N., L. Pasanen, L. Holmström, L. De Grandpré, T. Kuuluvainen, and T. Aakala. 2019. “At What Scales and Why Does Forest Structure Vary in Naturally Dynamic Boreal Forests? An Analysis of Forest Landscapes on Two Continents.” Ecosystems 22 (4): 709–724. doi:10.1007/s10021-018-0297-2.
   Web of Science ®Google Scholar
• Kumar, U., V. Kumar, and J. N. Kapur. 1986. “Normalized Measures of Entropy.” International Journal of General Systems 12 (1): 55–69. doi:10.1080/03081078608934927.
   Web of Science ®Google Scholar
• Lindberg, E., J. Holmgren, and H. Olsson. 2021. “Classification of Tree Species Classes in a Hemi-Boreal Forest from Multispectral Airborne Laser Scanning Data Using a Mini Raster Cell Method.” International Journal of Applied Earth Observation and Geoinformation 100 (April): 102334. doi:10.1016/j.jag.2021.102334.
   Google Scholar
• Lin, H. T., C. Jen Lin, and R. C. Weng. 2007. “A Note on Platt’s Probabilistic Outputs for Support Vector Machines.” Machine Learning 68 (3): 267–276. doi:10.1007/s10994-007-5018-6.
   Web of Science ®Google Scholar
• Liuzzo, L., V. Puleo, S. Nizza, and G. Freni. 2020. “Parameterization of a Bayesian Normalized Difference Water Index for Surface Water Detection.” Geosciences (Switzerland) 10 (7): 1–18. doi:10.3390/geosciences10070260.
   PubMed Web of Science ®Google Scholar
• McHugh, M. L. 2012. “Lessons in Biostatistics Interrater Reliability: The Kappa Statistic.” Biochemica Medica 22 (3): 276–282. doi:10.11613/BM.2012.031.
   PubMed Web of Science ®Google Scholar
• Minasny, B., Ö. Berglund, J. Connolly, C. Hedley, F. de Vries, A. Gimona, B. Kempen, et al. 2019. “Digital Mapping of Peatlands – a Critical Review.” Earth-Science Reviews 196 (June 2018): 102870. doi:10.1016/j.earscirev.2019.05.014.
   Google Scholar
• Modzelewska, A., F. Ewald Fassnacht, and K. Stereńczak. 2020. “Tree Species Identification Within an Extensive Forest Area with Diverse Management Regimes Using Airborne Hyperspectral Data.” International Journal of Applied Earth Observation and Geoinformation 84 (August 2019): 101960. doi:10.1016/j.jag.2019.101960.
   Google Scholar
• Monserud, R. A., and R. Leemans. 1992. “Comparing Global Vegetation Maps with the Kappa Statistic.” Ecological Modelling 62 (4): 275–293. doi:10.1016/0304-3800(92)90003-W.
   Web of Science ®Google Scholar
• Mueller, K. E., D. M. Eissenstat, S. E. Hobbie, J. Oleksyn, A. M. Jagodzinski, P. B. Reich, O. A. Chadwick, and J. Chorover. 2012. “Tree Species Effects on Coupled Cycles of Carbon, Nitrogen, and Acidity in Mineral Soils at a Common Garden Experiment.” Biogeochemistry 111 (1–3): 601–614. doi:10.1007/s10533-011-9695-7.
   Web of Science ®Google Scholar
• Natesan, S., C. Armenakis, and U. Vepakomma. 2020. “Individual Tree Species Identification Using Dense Convolutional Network (Densenet) on Multitemporal RGB Images from UAV.” Journal of Unmanned Vehicle Systems 8 (4): 310–333. doi:10.1139/juvs-2020-0014.
   Web of Science ®Google Scholar
• Paloniemi, J. 2018. “FRI Field Guide.” Forest Resources Inventory, Ontario Ministry of Natural Resources and Forestry, Ontario.
   Google Scholar
• Pitkänen, S. 1998. “The Use of Diversity Indices to Assess the Diversity of Vegetation in Managed Boreal Forests.” Forest Ecology and Management 112 (1–2): 121–137. doi:10.1016/S0378-1127(98)00319-3.
   Web of Science ®Google Scholar
• Pittman, R., B. Hu, and K. Webster. 2021. “Improvement of Soil Property Mapping in the Great Clay Belt of Northern Ontario Using Multi-Source Remotely Sensed Data.” Geoderma 381. doi:10.1016/j.geoderma.2020.114761.
   Web of Science ®Google Scholar
• Qin, Y., X. Xiao, J. Dong, G. Zhang, M. Shimada, J. Liu, L. Chungan, W. Kou, and B. Moore. 2015. “Forest Cover Maps of China in 2010 from Multiple Approaches and Data Sources: PALSAR, Landsat, MODIS, FRA, and NFI.” Isprs Journal of Photogrammetry & Remote Sensing 109: 1–16. doi:10.1016/j.isprsjprs.2015.08.010.
   Web of Science ®Google Scholar
• Roulston, M. S., and L. A. Smith. 2002. “Evaluating Probabilistic Forecasts Using Information Theory.” Monthly Weather Review 130 (6): 1653–1660. doi:10.1175/1520-0493(2002)130<1653:EPFUIT>2.0.CO;2.
   Web of Science ®Google Scholar
• Ryuzo, Y., M. Shirasawa, and R. J. Pike. 2002. “Visualizing Topography by Openness: A New Application of Image Processing to Digital Elevation Models.” Photogrammetric Engineering & Remote Sensing 68 (3): 257–265.
   Web of Science ®Google Scholar
• SAGA Development Team. 2020. “System for Automated Geoscientific Analysis (SAGA).” http://www.saga-gis.org/en/index.html.
   Google Scholar
• Sage, A. J., U. Genschel, and D. Nettleton. 2020. “Tree Aggregation for Random Forest Class Probability Estimation.” Statistical Analysis and Data Mining 13 (2): 134–150. doi:10.1002/sam.11446.
   Web of Science ®Google Scholar
• Sheeren, D., M. Fauvel, V. Josipovíc, M. Lopes, C. Planque, J. Willm, and J. François Dejoux. 2016. “Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series.” Remote Sensing 8 (9): 1–29. doi:10.3390/rs8090734.
   Web of Science ®Google Scholar
• Stage, A. 1976. “An Expression for the Effect of Aspect, Slope, and Habitat Type on Tree Growth“ Forest Science 22 (4): 457–460.
   Web of Science ®Google Scholar
• Strahler, A., Logan, T., and Bryant, N. 1978. “Improving Forest Cover Classification Accuracy from Landsat by Incorporating Topographic Information.“ Proceedings of the 12th International Symposium on Remote Sensing of the Environment. 927–942.
   Google Scholar
• Suratno, A., C. Seielstad, and L. Queen. 2009. “Tree Species Identification in Mixed Coniferous Forest Using Airborne Laser Scanning.” Isprs Journal of Photogrammetry & Remote Sensing 64 (6): 683–693. doi:10.1016/j.isprsjprs.2009.07.001.
   Web of Science ®Google Scholar
• Thomas, R. L., and R. C. Anderson. 1993. “Influence of Topography on Stand Composition in a Midwestern Ravine Forest.” The American Midland Naturalist 130 (1): 1–12. doi:10.2307/2426270.
   Web of Science ®Google Scholar
• Wang, K., T. Wang, and X. Liu. 2018. “A Review: Individual Tree Species Classification Using Integrated Airborne LiDar and Optical Imagery with a Focus on the Urban Environment.” Forests 10 (1): 1–18. doi:10.3390/f10010001.
   Web of Science ®Google Scholar
• Yun Chun, W., M. Chieh Shih, and T. Yu Kang. 2021. “Using Normalized Entropy to Measure Uncertainty of Rankings for Network Meta-Analyses.” Medical Decision Making 41 (6): 706–713. doi:10.1177/0272989X21999023.
   PubMed Web of Science ®Google Scholar
• Zhang, H. H., J. Ahn, X. Lin, and C. Park. 2006. “Gene Selection Using Support Vector Machines with Non-Convex Penalty.” Bioinformatics 22 (1): 88–95. doi:10.1093/bioinformatics/bti736.
   PubMed Web of Science ®Google Scholar
• Zhu, A. X. 1997. “Measuring Uncertainty in Class Assignment for Natural Resource Maps Under Fuzzy Logic.” Photogrammetric Engineering & Remote Sensing 63 (10): 1195–1202.
   Web of Science ®Google Scholar
• Zhu, X., and D. Liu. 2014. “Accurate Mapping of Forest Types Using Dense Seasonal Landsat Time-Series.” Isprs Journal of Photogrammetry & Remote Sensing 96: 1–11. doi:10.1016/j.isprsjprs.2014.06.012.
   Web of Science ®Google Scholar