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Attributes that describe forest structure, such as height, canopy cover, volume, and biomass, are required to inform forest inventories and monitoring programs. Light Detection and Ranging (lidar) has been successfully demonstrated as a means to derive a suite of forest structure attributes at the plot level; however, these acquisitions are often constrained to limited spatial extents and to a given point in time. Sample based approaches for model development can accommodate the spatial limitations of lidar acquisitions when characterizing large areas. The combination of lidar plot data and time-series satellite imagery is well suited to provide spatially extensive, and temporally dense, information on forest structure and related dynamics over very large areas. In this research, we combine lidar plot-derived information with Landsat pixel-based composites to produce annual forest structure estimates from 1984 to 2016 over 650 million ha of Canada's forest ecosystems using a nearest neighbor imputation approach with a Random Forests-based distance metric. Imputed variables included lidar metrics of height (e.g., mean height, standard deviation of height) and cover, as well as area-based modelled inventory estimates of Lorey's height, basal area, stem volume, and biomass. Models were validated using reserved validation plots, with model R 2 ranging from 0.62 to 0.64 for lidar metrics of height and cover, and R 2 of 0.67, 0.68, 0.71, and 0.70 for Lorey's height, basal area, volume and biomass, respectively. Unique to this study was the assessment of model extension through time, with model performance for imputing lidar metrics evaluated at the forest stand-level using independent lidar data representing a latitudinal gradient of forest conditions and that was not used in model development. The period evaluated was 2006–2012, with R 2 values ranging from 0.36 to 0.66 for height metrics, and 0.47–0.77 for cover metrics. Ultimately, we show how deriving forest structural estimates on an annual basis enables the analysis of both the dynamics and regional trends of undisturbed forest, as well as regenerating stands following stand-replacing disturbances (i.e., fire, harvesting). [ABSTRACT FROM AUTHOR]

Passive optical remotely sensed images such as those from the Landsat satellites enable the development of spatially comprehensive, well-calibrated reflectance measures that support large-area mapping. In recent years, as an alternative to field plot data, the use of Light Detection and Ranging (lidar) acquisitions for calibration and validation purposes in combination with such satellite reflectance data to model a range of forest structural response variables has become well established. In this research, we use a predictive modeling approach to map forest structural attributes over the ~ 552 million ha boreal forest of Canada. For model calibration and independent validation we utilize airborne lidar-derived measurements of forest vertical structure (known as lidar plots) obtained in 2010 via a > 25,000 km transect-based national survey. Models were developed linking the lidar plot structural variables to wall-to-wall 30-m spatial resolution surface reflectance composites derived from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus imagery. Spectral indices extracted from the composites, disturbance information (years since disturbance and type), as well as geographic position and topographic variables (i.e., elevation, slope, radiation, etc.) were considered as predictor variables. A nearest neighbor imputation approach based on the Random Forest framework was used to predict a total of 10 forest structural attributes. The model was developed and validated on > 80,000 lidar plots, with R 2 values ranging from 0.49 to 0.61 for key response variables such as canopy cover, stand height, basal area, stem volume, and aboveground biomass. Additionally, a predictor variable importance analysis confirmed that spectral indices, elevation, and geographic coordinates were key sources of information, ultimately offering an improved understanding of the driving variables for large-area forest structure modeling. This study demonstrates the integration of airborne lidar and Landsat-derived reflectance products to generate detailed and spatially extensive maps of forest structure. The methods are portable to map other attributes of interest (based upon calibration data) through access to Landsat or other appropriate optical remotely-sensed data sources, thereby offering unique opportunities for science, monitoring, and reporting programs. [ABSTRACT FROM AUTHOR]