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Consistent and scalable estimation of vegetation structural parameters—essential to understanding forest ecosystems—is widely investigated through remote sensing imaging spectroscopy. NASA’s proposed spaceborne mission, the Hyperspectral Infrared Imager (HyspIRI), will measure spectral radiance from 380 to 2500 nm in 10-nm contiguous bands with a 60-m ground sample distance (GSD) and provide a global benchmark from which future changes can be assessed. The historic foci of spectrometers have been foliar/canopy biochemistry and species classification; however, given the relatively large GSD of a spaceborne instrument, there is uncertainty as to the effects of subpixel vegetation structure on observed radiance. This paper, therefore, evaluates the linkages between the within-pixel vegetation structure and imaging spectroscopy signals at the pixel level. We constructed a realistic virtual forest scene representing the National Ecological Observatory Network (NEON) Pacific Southwest domain site. Anticipated HyspIRI data (60-m GSD) for this site were then simulated using the physics-driven Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. Both the models were first validated via comparison to overflow classic Airborne Visible/Infrared Imaging Spectrometer and NEON’s imaging spectrometer (NIS). Then, to assess the impact of within-pixel: 1) tree canopy cover (CC); 2) tree positioning; and 3) distribution on large-footprint HyspIRI signals, we generated the variations of the baseline virtual forest scene and measured the anticipated spectral radiance using DIRSIG. Results indicate that HyspIRI is sensitive to subpixel vegetation structural variation in the visible to a short-wavelength infrared spectrum due to vegetation structural changes. This has implications for improving the system’s suitability for consistent global vegetation structural assessments by adapting calibration strategies to account for this subpixel variation.

Terrestrial laser scanning has demonstrated increasing potential for rapid comprehensive measurement of forest structure, especially when multiple scans are spatially registered in order to reduce the limitations of occlusion. Although marker-based registration techniques (based on retro-reflective spherical targets) are commonly used in practice, a blind marker-free approach is preferable, insofar as it supports rapid operational data acquisition. To support these efforts, we extend the pairwise registration approach of our earlier work, and develop a graph-theoretical framework to perform blind marker-free global registration of multiple point cloud data sets. Pairwise pose estimates are weighted based on their estimated error, in order to overcome pose conflict while exploiting redundant information and improving precision. The proposed approach was tested for eight diverse New England forest sites, with 25 scans collected at each site. Quantitative assessment was provided via a novel embedded confidence metric, with a mean estimated root-mean-square error of 7.2 cm and 89% of scans connected to the reference node. This paper assesses the validity of the embedded multiview registration confidence metric and evaluates the performance of the proposed registration algorithm.

Terrestrial laser scanning (TLS) has emerged as an effective tool for rapid comprehensive measurement of object structure. Registration of TLS data is an important prerequisite to overcome the limitations of occlusion. However, due to the high dissimilarity of point cloud data collected from disparate viewpoints in the forest environment, adequate marker-free registration approaches have not been developed. The majority of studies instead rely on the utilization of artificial tie points (e.g., reflective tooling balls) placed within a scene to aid in coordinate transformation. We present a technique for generating view-invariant feature descriptors that are intrinsic to the point cloud data and, thus, enable blind marker-free registration in forest environments. To overcome the limitation of initial pose estimation, we employ a voting method to blindly determine the optimal pairwise transformation parameters, without an a priori estimate of the initial sensor pose. To provide embedded error metrics, we developed a set theory framework in which a circular transformation is traversed between disjoint tie point subsets. This provides an upper estimate of the Root Mean Square Error (RMSE) confidence associated with each pairwise transformation. Output RMSE errors are commensurate with the RMSE of input tie points locations. Thus, while the mean output $\text{RMSE}=16.3\ \text{cm}$ , improved results could be achieved with a more precise laser scanning system. This study 1) quantifies the RMSE of the proposed marker-free registration approach, 2) assesses the validity of embedded confidence metrics using receiver operator characteristic (ROC) curves, and 3) informs optimal sample spacing considerations for TLS data collection in New England forests. While the implications for rapid, accurate, and precise forest inventory are obvious, the conceptual framework outlined here could potentially be extended to built environments.