Wulder, M.[Mike]
* 2004: Predicting Forest Age Classes from High Spatial Resolution Remotely Sensed Imagery Using Voronoi Polygon Aggregation
* 2010: Reducing the effects of misregistration and sun-surface-sensor geometry on the detection of forest harvest cut-blocks
* 2012: Modeling Forest Structural Parameters in the Mediterranean Pines of Central Spain using QuickBird-2 Imagery and Classification and Regression Tree Analysis (CART)
* 2012: Post-Fire Canopy Height Recovery in Canada's Boreal Forests Using Airborne Laser Scanner (ALS)
* 2013: Area-Based Mapping of Defoliation of Scots Pine Stands Using Airborne Scanning LiDAR
Includes: Wulder, M.[Mike] Wulder, M.
Wulder, M.A.
* 2001: Interpretation of partial harvest forest conditions in New Brunswick using Landsat TM enhanced wetness difference imagery (EWDI)
* 2003: Structural change detection in a disturbed conifer forest using a geometric optical reflectance model in multiple-forward mode
* 2006: Object-based Analysis of Ikonos-2 Imagery for Extraction of Forest Inventory Parameters
* 2011: Stability of Sample-Based Scanning-LiDAR-Derived Vegetation Metrics for Forest Monitoring
* 2012: Using multi-frequency radar and discrete-return LiDAR measurements to estimate above-ground biomass and biomass components in a coastal temperate forest
* 2013: Modeling Stand Height, Volume, and Biomass from Very High Spatial Resolution Satellite Imagery and Samples of Airborne LiDAR
* 2014: Characterization of aboveground biomass in an unmanaged boreal forest using Landsat temporal segmentation metrics
* 2014: Estimation of Airborne Lidar-Derived Tropical Forest Canopy Height Using Landsat Time Series in Cambodia
* 2014: Historical forest biomass dynamics modelled with Landsat spectral trajectories
* 2015: Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm
* 2015: Mapping the Risk of Forest Wind Damage Using Airborne Scanning LiDAR
* 2016: Optical remotely sensed time series data for land cover classification: A review
* 2018: Assessing Biodiversity in Boreal Forests with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging
* 2018: Combining Multi-Date Airborne Laser Scanning and Digital Aerial Photogrammetric Data for Forest Growth and Yield Modelling
* 2018: National Assessment of Wetland Status and Trends for Canada's Forested Ecosystems Using 33 Years of Earth Observation Satellite Data, A
* 2018: Reply to Vauhkonen: Comment on Tompalski et al. Combining Multi-Date Airborne Laser Scanning and Digital Aerial Photogrammetric Data for Forest Growth and Yield Modelling. Remote Sens. 2018, 10, 347
* 2018: Using Spatial Features to Reduce the Impact of Seasonality for Detecting Tropical Forest Changes from Landsat Time Series
* 2019: Comparison and Assessment of Regional and Global Land Cover Datasets for Use in CLASS over Canada
* 2020: Accurate derivation of stem curve and volume using backpack mobile laser scanning
* 2020: Under-canopy UAV laser scanning for accurate forest field measurements
* 2023: assessment approach for pixel-based image composites, An
Includes: Wulder, M.A. Wulder, M.A.[Michael A.]
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