Deriving and validating Leaf Area Index (LAI) at multiple spatial scales through lidar remote sensing: A case study in Sierra National Forest, CA

The ability to validate airborne lidar LAI data collected at different spatial scales to the available ground measurements allows further upscaled validation using global lidar datasets provided by spaceborne lidar, such as the Geoscience Laser Altimeter System (GLAS). In the absence of adequate ground validation plots coincident with GLAS footprints, GLAS LAI validation is examined using geographically limited but spatially continuous LVIS data. Under favorable conditions, significantly the absence of slopes greater than ~ 20°, the comparison between LVIS and GLAS LAI values obtained using a recursive algorithm constrained by independently validated LAI limits exposes the capability of GLAS as an accurate standalone LAI sensor (r2 = 0.69, bias = − 0.05 and RMSE = 0.33). The correlation comparison between LVIS and GLAS LAI estimates not only significantly exceed those associated with equivalent space borne passive remote sensing datasets, such as MODIS (r2 = 0.20, bias = − 0.16 and RMSE = 0.67) but also offers significant advantages to future research including the prospective validation of regional and global LAI products and data comparison with ecosystem model inputs. The encountered effectiveness of these relationships allows the implementation of a scaling-up strategy where ground-based LAI observations are related to aircraft observations of LAI, which in turn are used to validate GLAS LAI derived from coincident data. Successful implementation of this strategy paves the way for the future recovery of vertical LAI profiles on a global scale and opens up the potential for fusion studies to incorporate widely available and spatially abundant passive optical datasets.