Canadian Forest Service Publications

Large-area mapping of Canadian boreal forest cover, height, biomass and other structural attributes using Landsat composites and lidar plots. 2018. Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Zald, H.S. Remote Sensing of Environment, 209, pp. 90–106.

Year: 2018

Available from: Pacific Forestry Centre

Catalog ID: 39092

Language: English

CFS Availability: PDF (download)

Available from the Journal's Web site.
DOI: 10.1016/j.rse.2017.12.020

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Abstract

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 R2 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.

Plain Language Summary

Spatially comprehensive, timely, and accurate estimation of forest structural attributes, such as canopy cover, canopy height, and aboveground biomass, are required to inform on economic, ecological, and functional aspects of forest ecosystems. In this context, 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. Highlights - Framework for large-area mapping of forest structural attributes; Key response variables such as canopy cover, stand height, basal area, stem volume, and aboveground biomass; Landsat pixel composites and LiDAR plots data combined with an imputation model; R2 4 in 0.49-0.61 for key forest attributes when validating on >20,000 lidar plots; 30 m resolution forest attributes maps generated for Canadian boreal zone; Relevance of the predictors provided insights on key model drivers.

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