Despite the active research, terrestrial laser scanning (TLS) has remained underutilized for forest structure assessment due to reliance of processing algorithms on high-resolution data, which may be costly and time-consuming to collect. Operational inventories, however, necessitate maximizing sample size while minimizing time and cost. The objective of this study was to assess the performance of a novel technique that enables stem reconstruction from low-resolution, single-scan TLS data in an effort to satisfy performance criteria against operational acquisition constraints. Instead of utilizing the curvature of the tree stem, e.g., by circle or cylinder fitting, we take advantage of the sensor-object geometry and reduce the dimensionality of the modeling to a series of one-dimensional (1-D) line fits. This allowed robust recovery of tree stem structure in a range of New England forest types, for tree stems which subtended at least an angular width of 15 mradthe beam divergence of our system. Assessment was performed by projecting the three-dimensional (3-D) data onto two-dimensional (2-D) images and evaluating the per-point classification accuracies using manually digitized truth maps. Manual forest inventory measurements were also collected for each 20 × 20 m plot and compared to measurements derived automatically. Good retrievals of stem location (R 2 = 0.99, RMSE = 0.16 m) and diameter at breast height (DBH) (R 2 = 0.80, RMSE = 6.0 cm) were achieved. This study demonstrates that low-resolution sensors may be effective in providing data for operational forest inventories constrained by sample size, time, and cost.
Consistent and scalable estimation of vegetation structural parameters from imaging spectroscopy is essential to remote sensing for ecosystem studies, with applications to a wide range of biophysical assessments. To support global vegetation assessment, NASA has proposed the Hyperspectral Infrared Imager (HyspIRI) imaging spectrometer, which measures the randiance 380-2500nm in 10nm contiguous bands with 60m ground sample distance (GSD). However, because of the large pixel size on the ground, there is uncertainty as to the effects of vegetation structure on observed radiance. This research evaluates linkages between vegetation structure and imaging spectroscopy. Specifically, we assess the impact of within-pixel vegetation density and position on large-footprint spectral radiances.To achieve this objective, three virtual forest scenes were constructed, which correspond to the actual vegetation structure of the National Ecological Observatory Network (NEON) Pacific Southwest domain (PSW; D17; Fresno, CA). These were used to simulate anticipated HyspIRI data (60m GSD) using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model, a first-principles synthetic image generation model developed by the Rochester Institute of Technology. Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) and NEON's high-resolution imaging spectrometer (NIS) data were used to verify the geometric parameters and physical models. Multiple simulated HyspIRI data sets were generated by varying within-pixel structural variables, such as forest density, position, and distribution of trees, in order to assess the impact of sub-pixel structural variation on observed HyspIRI data.Results indicate that HyspIRI is sensitive to sub-pixel vegetation density variation in the visible to shortwavelength infrared spectrum due to vegetation structural changes, and associated pigment and water content variation. This has implications for improving the system's suitability for consistent global vegetation structural assessments by adapting calibration strategies to account for this sub-pixel variation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.