Abstract
Airborne laser scanning (ALS) systems tuned to the near-infrared (NIR; 1064 nm) wavelength have become the best available data source for characterizing vegetation structure. Proliferation of multi-spectral ALS (M-ALS) data with lasers tuned at two additional wavelengths (commonly 532 nm; green, and 1550 nm; short-wave infrared (SWIR)) has promoted interest in the benefit of additional wavelengths for forest inventory modelling. In this study, structural and intensity based M-ALS metrics were derived from wavelengths independently and combined to assess their value for modelling forest inventory attributes (Lorey’s height (HL), gross volume (V), and basal area (BA)) and overstorey species diversity (Shannon index (H), Simpson index (D), and species richness (R)) in a diverse mixed-wood forest in Ontario, Canada. The area-based approach (ABA) to forest attribute modelling was used, where structural- and intensity-based metrics were calculated and used as inputs for random forest models. Structural metrics from the SWIR channel (SWIRstruc) were found to be the most accurate for H and R (%RMSE = 14.3 and 14.9), and NIRstruc were most accurate for V (%RMSE = 20.4). The addition of intensity metrics marginally increased the accuracy of HL models for SWIR and combined channels (%RMSE = 7.5). Additionally, a multi-resolution (0.5, 1, 2 m) voxel analysis was performed, where intensity data were used to calculate a suite of spectral indices. Plot-level summaries of spectral indices from each voxel resolution alone, as well as combined with structural metrics from the NIR wavelength, were used as random forest predictors. The addition of structural metrics from the NIR band reduced %RMSE for all models with HL, BA, and V realizing the largest improvements. Intensity metrics were found to be important variables in the 1 m and 2 m voxel models for D and H. Overall, results indicated that structural metrics were the most appropriate. However, the inclusion of intensity metrics, and continued testing of their potential for modelling diversity indices is warranted, given minor improvements when included. Continued analyses using M-ALS intensity metrics and voxel-based indices would help to better understand the value of these data, and their future role in forest management.
Highlights
Airborne laser scanning (ALS) is a globally established technology for building enhanced forest inventories (EFIs) and supplementing effective forest management [1,2]
By comparing the two approaches, we investigate the potential benefit of multi-spectral ALS (M-ALS) vegetation indices for improving forest attribute model predictions using an area-based approach (ABA), as well as better understand the potential value of voxel-based vegetation indices derived from M-ALS intensity data
Our study focused on the potential benefits of point cloud intensity metrics and voxelized indices for estimating forest inventory attributes and overstorey species diversity
Summary
Airborne laser scanning (ALS) is a globally established technology for building enhanced forest inventories (EFIs) and supplementing effective forest management [1,2]. ALS point clouds are most often derived using near-infrared (NIR; most commonly 1064 nm) tuned lasers. NIR-tuned ALS systems have become common place, improving the cost-effectiveness of characterizing vegetation structure and generating best available geo-spatial terrain information. Since the adoption of ALS technologies in forestry, point cloud-derived height data have been used to characterize forest structure. In a review on the role of radiometric ALS correction and calibration, Kashani et al [5] highlighted the interdisciplinary value of intensity data for classifying natural and urban cover surfaces [6]. Intensity data have been shown to improve the separation of typical landcover surfaces such as concrete and vegetation [7], though careful attention to intensity correction and calibration methods is a prerequisite [8]. The lack of a calibrated intensity response makes the development of models that utilize intensity limited, as they are unable to be reliably transferred between ALS acquisitions and across different sensors and locations
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