Quantifying Multi-Decadal Change of Planted Forest Cover Using Airborne LiDAR and Landsat Imagery
Disclaimer: The PDF document is a copy of the final version of this manuscript that was subsequently accepted by the journal for publication. The paper has been through peer review, but it has not been subject to any additional copy-editing or journal specific formatting (so will look different from the final version of record, which may be accessed following the DOI above depending on your access situation). Abstract Airborne Light Detection and Ranging (LiDAR) data provide useful measurements of forest canopy structure but are often limited in spatial coverage. Satellite remote sensing data from Landsat can provide extensive spatial coverage of generalized forest information. A forest survey approach that integrates airborne LiDAR and satellite data would potentially capitalize upon these distinctive characteristics. In this study in coastal forests of British Columbia, the main objective was to determine the potential of Landsat imagery to accurately estimate forest canopy cover measured from small-footprint airborne LiDAR data in order to expand the LiDAR measurements to a larger area. Landsat-derived Tasseled Cap Angle (TCA) and spectral mixture analysis (SMA) endmember fractions (i.e. sunlit canopy, non-photosynthetic vegetation (NPV), shade and exposed soil) were compared to LiDAR-derived canopy cover estimates. Pixel-based analysis and object-based area-weighted error calculations were used to assess regression model performance. The best canopy cover estimate was obtained (in the object-based deciduous forest models) with a mean object size (MOS) of 2.5 hectares (adjusted R 2 = 0.86 and RMSE = 0.28). Overall, lower canopy cover estimation accuracy was obtained for coniferous forests compared to deciduous forests in both the pixel and object-based approaches.