The planned NASA Hyperspectral Infrared Imager (HyspIRI) mission, equipped with an imaging spectrometer that has the capability of monitoring ecosystems globally, will provide an unprecedented opportunity to address scientiﬁc challenges related to ecosystem function and change. However, uncertainty remains around the impact of subpixel vegetation structure, in combination with the point spread function, on pixel-level imaging spectroscopy data. We estimated structural parameters, e.g., leaf area index (LAI), canopy cover, and tree location, from HyspIRI spectral data, with the goal of assessing how subpixel variation in these parameters impact pixel-level imaging spectroscopy data. The ﬁne-scale variability of real vegetation structure makes this a challenging endeavor. Therefore, we utilized a simulation-based approach to counter the time-consuming and often destructive sampling needs of vegetation structural analysis and to simultaneously generate synthetic HyspIRI data pre-launch. Three virtual scenes were constructed, corresponding to the actual vegetation structure in the National Ecological Observatory Network’s (NEON) Paciﬁc Southwest Domain (Fresno, CA). These included an oak savanna, a dense coniferous forest, and a conifer-manzanita-mixed forest. Simulated spectroscopy data for these scenes were then generated using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. Simulations ﬁrst were used to verify the physical model, virtual scene geometrical information, and simulation parameters. This was followed by simulations of HyspIRI data, where within-pixel structural variability was introduced, e.g., by iteratively changing per-pixel canopy cover and tree placement, tree clustering, leaf area index (LAI), etc., between simulation runs for the virtual scenes. Finally, narrow-band vegetation indices (VIs) were extracted from the data in an attempt to describe the variability of the subpixel structural parameters; this was done in order to assess VI robustness to changes in structural “levels”, as well as placement of trees/canopies within the instrument’s instantaneous ﬁeld-of-view (IFOV). Our ultimate goal is not only to better understand how such subpixel variability inﬂuence imaging spectroscopy outputs, but also to better estimate vegetation structural parameters using spectra. We constructed regression models for LAI (R<sup>2</sup> = 0.92) and canopy cover (R<sup>2</sup> = 0.97) with narrow-band VIs via this simulation approach. Our models ultimately are intended to improve the HyspIRI mission’s ability to monitor global vegetation structure.
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 veg- etation 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 de- veloped 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 short- wavelength 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.
In a typical waveform light detection and ranging (lidar) system, the received pulse can be represented by the convolution of the system impulse response, the outgoing pulse, and the underlying signal representing actual target interactions. Deconvolution is the process of removing the contribution of the system impulse response and outgoing pulse from the received signal, so that the true interactions may be seen. In many examples, deconvolution has been shown to expose fine structure within the waveform, which may be used to improve accuracy when estimating the vertical location of certain features. For instance, the exact location of the ground may be more accurately determined by separating the response of the ground from that of understory vegetation or vegetative ground cover. However, in order for the deconvolution to be successful, the impulse response and outgoing pulse must be known, and many deconvolution methods are sensitive to small errors in the estimation of these inputs. In this study, we propose a deconvolution method that uses a flat target response in place of the impulse response and outgoing pulse.
With the development of increasingly advanced airborne sensing systems, there is a growing need to support
sensor system design, modeling, and product-algorithm development with explicit 3D structural ground truth
commensurate to the scale of acquisition. Terrestrial laser scanning is one such technique which could provide
this structural information. Commercial instrumentation to suit this purpose has existed for some time now, but
cost can be a prohibitive barrier for some applications. As such we recently developed a unique laser scanning
system from readily-available components, supporting low cost, highly portable, and rapid measurement of
below-canopy 3D forest structure. Tools were developed to automatically reconstruct tree stem models as an
initial step towards virtual forest scene generation. The objective of this paper is to assess the potential of this
hardware/algorithm suite to reconstruct 3D stem information for a single scan of a New England hardwood forest
site. Detailed tree stem structure (e.g., taper, sweep, and lean) is recovered for trees of varying diameter, species,
and range from the sensor. Absolute stem diameter retrieval accuracy is 12.5%, with a 4.5% overestimation bias
likely due to the LiDAR beam divergence.