Geometric Optical And Radiative Transfer Research Articles

LAI estimation based on physical model combining airborne LiDAR waveform and Sentinel-2 imagery.

Leaf area index (LAI) is an important biophysical parameter of vegetation and serves as a significant indicator for assessing forest ecosystems. Multi-source remote sensing data enables large-scale and dynamic surface observations, providing effective data for quantifying various indices in forest and evaluating ecosystem changes. However, employing single-source remote sensing spectral or LiDAR waveform data poses limitations for LAI inversion, making the integration of multi-source remote sensing data a trend. Currently, the fusion of active and passive remote sensing data for LAI inversion primarily relies on empirical models, which are mainly constructed based on field measurements and do not provide a good explanation of the fusion mechanism. In this study, we aimed to estimate LAI based on physical model using both spectral imagery and LiDAR waveform, exploring whether data fusion improved the accuracy of LAI inversion. Specifically, based on the physical model geometric-optical and radiative transfer (GORT), a fusion strategy was designed for LAI inversion. To ensure inversion accuracy, we enhanced the data processing by introducing a constraint-based EM waveform decomposition method. Considering the spatial heterogeneity of canopy/ground reflectivity ratio in regional forests, calculation strategy was proposed to improve this parameter in inversion model. The results showed that the constraint-based EM waveform decomposition method improved the decomposition accuracy with an average 12% reduction in RMSE, yielding more accurate waveform energy parameters. The proposed calculation strategy for the canopy/ground reflectivity ratio, considering dynamic variation of parameter, effectively enhanced previous research that relied on a fixed value, thereby improving the inversion accuracy that increasing on the correlation by 5% to 10% and on R2 by 62.5% to 132.1%. Based on the inversion strategy we proposed, data fusion could effectively be used for LAI inversion. The inversion accuracy achieved using both spectral and LiDAR data (correlation=0.81, R2=0.65, RMSE=1.01) surpassed that of using spectral data or LiDAR alone. This study provides a new inversion strategy for large-scale and high-precision LAI inversion, supporting the field of LAI research.

Characterization of NDSI Variation: Implications for Snow Cover Mapping

The normalized difference snow index (NDSI) plays an important role in mapping snow cover with spaceborne visible and shortwave-infrared imagery. The NDSI variation depends on illuminating-viewing geometry and snow physical properties, including equivalent grain size (EGS), snow depth (SD) and impurity concentration, as well as fractional snow cover (FSC) within a mixed pixel; however, it is still not fully understood. To quantifiably characterize the pattern of snow NDSI variation, we use a light scattering model of snow to calculate bidirectional reflectance and consequent NDSI values for a wide range of illuminating-viewing geometries, SD, and EGS values. In the model designated bicontinuous snow model using Geometric Optics theory and Radiative Transfer Equation (bicontinuous-GO/RTE), snowpack is represented by a bicontinuous microstructure, and bidirectional reflectance is simulated based on geometric optics and vector radiative transfer equation. The discrete ordinates radiative transfer (DISORT) algorithm is used to simulate the soot concentration effect on snow NDSI. A soil spectral reflectance model (SOILSPECT) is utilized with the assumption of the linear spectral mixture of snow and soil to quantify the effect of FSC on NDSI. As for discontinuous forests, an analytical hybrid geometric-optical and radiative transfer (GORT) model in conjunction with the bicontinuous-GO/RTE model and the PROpriétésSPECTrales (PROSPECT) model is used to examine the effect of canopy cover, which is related to the maximal FSC viewable to satellites. Modeling results indicate that: 1) snow NDSI is comparably low at off-nadir viewing angles, and this effect is exacerbated by the decline in solar elevation but limited by large EGS; 2) snow NDSI increases with EGS yet becomes saturated at EGS of 500 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> ; 3) the effect of SD that is as low as 1 cm on NDSI is rarely noticeable; 4) the concentration of internally mixed soot up to 1 ppm has little reducing effect on snow NDSI (<0.05); 5) the angular effects on the NDSI thresholding method to identify binary snow cover for soil background are nonsignificantly pronounced in the cross plane (CP); 6) NDSI increases linearly with FSC for soil background at nadir as well as in the CP; 7) the SNOw-MAPping algorithm (SNOMAP) method could omit snow cover at large zenith angles for solar zenith angles <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt; 20^{\circ }$ </tex-math></inline-formula> in forests; and 8) forests complicate the nonlinear relationship between NDSI and canopy cover with fully snow-covered ground beneath canopies. These findings imply important uncertainty sources of binary and FSC mapping with NDSI.

Effects of leaf age and canopy structure on gross ecosystem production in a subtropical evergreen Chinese fir forest

Despite the advances in technologies to derive the maximum rate of Rubisco carboxylation (Vcmax) seasonality at large-scale, factors controlling the temporal dynamics of Vcmax is largely unknown without extensive field measurements at stand scale. In addition, state-of-the-art process-based terrestrial ecosystem models had not accounted the complex canopy structure for estimating radiation interception by vegetation. Both of which could lead to uncertainties in gross ecosystem production (GEP) estimations. Here, we examined the respective and combined effects of leaf age and canopy structure on GEP by integrating the Farquhar photosynthesis model with a two-leaf (sunlit and shaded) canopy radiation interception model based on the Geometric Optical and Radiative Transfer (GORT) theory. We observed that the Vcmax of new leaves was approximately 34.4% higher than that of mature leaves. Considering the seasonal dynamics of Vcmax caused by new leaf expansion, the average bias between the modeled GEP and that derived from the eddy covariance (EC) in the growing season at 8-day temporal scale was reduced from -8% to -1%, with slightly higher correlation (from 0.9 to 0.92) and reduction in root mean squared error (from 6.0 to 3.9 g C m−2 8d−1). Most importantly, the total effect of new leaf expansion and canopy gaps during the growing season was +322 g C m−2 yr−1 and -114 g C m−2 yr−1, which was approximately 22.5% and 8.1% of the total GEP, respectively. Thus, it is important to consider both leaf age and canopy structure when developing a robust terrestrial ecosystem model. We highlighted that Vcmax seasonality and realistic canopy radiation interception simulation are critical for accurate estimation of canopy GEP for evergreen forests.

Extraction of Multiple Building Heights Using ICESat/GLAS Full-Waveform Data Assisted by Optical Imagery

Although the Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimetry System (ICESat/GLAS) has been used for urban monitoring, however, previous studies focused on extracting the maximum building height within the footprint. In fact, the full-waveform recording of GLAS data makes it possible to extract multiple building heights. However, the uncertainty of the spatial distribution and reflectance creates considerable challenges for the fine inversion of multiple building heights within the footprint. In this letter, we proposed an inversion method of multiple building heights using GLAS data assisted by QuickBird imagery. First, the GLAS waveform and the auxiliary optical imagery were processed to extract some spectral, horizontal, and vertical information as the prior knowledge of the inversion model. Then, the multiple building heights were inversed from the optimal simulated waveform based on the 3-D geometric optical and radiative transfer (GORT) model. The building heights measured by airborne LiDAR were used to validate the inversed building heights. The results demonstrated that the proposed inversion method achieved the building height estimation accurately and precisely ( $R^{2} = 0.971$ , rRMSE = 13.2%, and $n = 430$ ). This letter may shed some light on extracting multiple-level heights within the footprint using satellite LiDAR full-waveform data.

Impacts of Leaf Age on Canopy Spectral Signature Variation in Evergreen Chinese Fir Forests

Significant gaps exist in our knowledge of the impact of leaf aging on canopy signal variability, which limits our understanding of vegetation status based on remotely sensed data. To understand the effects of leaf aging at the leaf and canopy scales, a combination of field, remote-sensing and physical modeling techniques was adopted to assess the canopy spectral signals of evergreen Cunninghamia forests. We observed an approximately 10% increase in Near-Infrared (NIR) reflectance for new leaves and a 35% increase in NIR transmittance for mature leaves from May to October. When variations in leaf optical properties (LOPs) of only mature leaves, or both new and mature leaves were considered, the Geometric Optical and Radiative Transfer (GORT) model-simulated canopy reflectance trajectory was more consistent with Landsat observations (R2 increased from 0.37 to 0.82~0.89 for NIR reflectance, and from 0.35 to 0.67~0.88 for EVI2, with a small RMSE (0.01 to 0.02)). This study highlights the importance of leaf age on leaf spectral signatures, and provides evidence of age-dependent LOPs that have important impacts on canopy reflectance in the NIR band and EVI2, which are used to monitor canopy dynamics and productivity, with important implications for RS and forest ecosystem ecology.

Estimation of coniferous forest aboveground biomass with aggregated airborne small-footprint LiDAR full-waveforms.

Forest aboveground biomass (AGB) is critical for assessing forest productivity and evaluating carbon sequestration rates. Discrete-return LiDAR has been widely used to estimate forest AGB, however, fewer studies have estimated the coniferous forest AGB using airborne small-footprint full-waveform LiDAR data. The objective of this study was to extract a suite of newly proposed metrics from airborne small-footprint full-waveform LiDAR data and to evaluate the ability of these metrics in estimating coniferous forest AGB. To achieve this goal, each waveform was first preprocessed, including de-noising, smoothing, and normalization. Next, all the waveforms within each plot were aggregated into a large pseudo waveform and the return energy profile was generated. Then, the foliage profile was retrieved from the return energy profile based on the Geometric Optical and Radiative Transfer (GORT) model. Finally, a series of new return energy profile metrics and foliage profile metrics were extracted to estimate forest AGB. Simple linear regression was conducted to assess the correlation between each LiDAR metric and forest AGB. Stepwise multiple regression analysis was then carried out to select important prediction metrics and establish the optimal forest AGB estimation model. Results indicated that both return energy profile and foliage profile based height-related metrics were strongly correlated to forest AGB. The energy weighted canopy height (HEweight) (R = 0.88) and foliage area weighted height (HFweight) (R = 0.89) all had the highest correlation coefficients with forest AGB in return energy profile metrics and foliage profile metrics respectively. Energy height percentiles and foliage height percentiles also had the ability to explain AGB variation. The energy-related metrics, foliage area-related metrics, and bounding volume-related metrics derived from the return energy profile and foliage profile were not all sensitive to forest AGB. This study also concluded that combining return energy profile metrics and foliage profile metrics could improve the accuracy of forest AGB estimation, and the optimal model contained the metrics of HFweight, HEweight, and VolumemaxHE, which is the product of the maximum canopy return energy profile amplitude (maxCE) and the maximum height of return energy profile (maxHE).

Modelling spectral reflectance of open cork oak woodland: a simulation analysis of the effects of vegetation structure and background

ABSTRACTThis study analyses the influence of vegetation structure (i.e. leaf area index and canopy cover) and seasonal background changes on moderate-resolution imaging spectrometer (MODIS)-simulated reflectance data in open woodland. Approximately monthly spectral reflectance and transmittance field measurements (May 2011 to October 2013) of cork oak tree leaves (Quercus suber) and of the herbaceous understorey were recorded in the region of Ribatejo, Portugal. The geometric-optical and radiative transfer (GORT) model was used to simulate MODIS response (red, near-infrared) and to calculate vegetation indices, investigating their response to changes in the structure of the overstorey vegetation and to seasonal changes in the understorey using scenarios corresponding to contrasting phenological status (dry season vs. wet season). The performance of normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) is discussed. Results showed that SAVI and EVI were very sensitive to the emergence of background vegetation in the wet season compared to NDVI and that shading effects lead to an opposing trend in the vegetation indices. The information provided by this research can be useful to improve our understanding of the temporal dynamic of vegetation, monitored by vegetation indices.

Retrieval of vertical LAI profiles over tropical rain forests using waveform lidar at La Selva, Costa Rica

This study explores the potential of waveform lidar in mapping the vertical and spatial distributions of leaf area index (LAI) over the tropical rain forest of La Selva Biological Station in Costa Rica. Vertical profiles of LAI were derived at 0.3m height intervals from the Laser Vegetation Imaging Sensor (LVIS) data using the Geometric Optical and Radiative Transfer (GORT) model. Cumulative LAI profiles obtained from LVIS were validated with data from 55 ground to canopy vertical transects using a modular field tower to destructively sample all vegetation. Our results showed moderate agreement between lidar and field derived LAI (r2=0.42, RMSE=1.91, bias=−0.32), which further improved when differences between lidar and tower footprint scales (r2=0.50, RMSE=1.79, bias=0.27) and distance of field tower from lidar footprint center (r2=0.63, RMSE=1.36, bias=0.0) were accounted for. Next, we mapped the spatial distribution of total LAI across the landscape and analyzed LAI variations over different land cover types. Mean values of total LAI were 1.74, 5.20, 5.41 and 5.62 over open pasture, secondary forests, regeneration forests after selective-logging and old-growth forests respectively. Lastly, we evaluated the sensitivities of our LAI retrieval model to variations in canopy/ground reflectance ratio and to waveform noise such as induced by topographic slopes. We found for both, that the effects were not significant for moderate LAI values (about 4). However model derivations of LAI might be inaccurate in areas of high-slope and high LAI (about 8) if ground return energies are low. This research suggests that large footprint waveform lidar can provide accurate vertical LAI profile estimates that do not saturate even at the high LAI levels in tropical rain forests and may be a useful tool for understanding the light transmittance within these canopies.

Assessment of the impacts of surface topography, off-nadir pointing and vegetation structure on vegetation lidar waveforms using an extended geometric optical and radiative transfer model

In order to prioritize the measurement requirements and accuracies of the two new lidar missions, a physical model is required for a fundamental understanding of the impact of surface topography, footprint size and off-nadir pointing on vegetation lidar waveforms and vegetation height retrieval. In this study, we extended a well developed Geometric Optical and Radiative Transfer (GORT) vegetation lidar model to take into account for the impacts of surface topography and off-nadir pointing on vegetation lidar waveforms and vegetation height retrieval and applied this extended model to assess the aforementioned impacts on vegetation lidar waveforms and height retrieval. Model simulation shows that surface topography and off-nadir pointing angle stretch waveforms and the stretching effect magnifies with footprint size, slope and off-nadir pointing angle. For an off-nadir pointing laser penetrating vegetation over a slope terrain, the waveform is either stretched or compressed based on the relative angle. The stretching effect also results in a disappearing ground peak return when slope or off-nadir pointing angle is larger than the “critical slope angle”, which is closely related to various vegetation structures and footprint size. Model simulation indicates that waveform shapes are affected by surface topography, off-nadir pointing angle and vegetation structure and it is difficult to remove topography effects from waveform extent based only on the shapes of waveform without knowing any surface topography information. Height error without correction of surface topography and off-nadir pointing angle is the smallest when the laser beams at the toward-slope direction and the largest from the opposite direction. Further simulation reveals within 20° of slope and off-nadir pointing angle, given the canopy height as roughly 25 m and the footprint size as 25 m, the error for vegetation height (RH100) ranges from − 2 m to greater than 12 m, and the error for the height at the medium energy return (RH50) from − 1 m to 4 m. The RH100 error caused by unknown surface topography and without correction of off-nadir pointing effect can be explained by an analytical formula as a function of vegetation height, surface topography, off-nadir pointing angle and footprint size as a first order approximation. RH50 is not much affected by topography, off-nadir pointing and footprint size. This forward model simulation can provide scientific guidance on prioritizing future lidar mission measurement requirements and accuracies.

A clumped-foliage canopy radiative transfer model for a Global Dynamic Terrestrial Ecosystem Model II: Comparison to measurements

In a previous paper, we developed an analytical clumped two-stream model (ACTS) of canopy radiative transfer from an analytical geometric-optical and radiative transfer (GORT) scheme ( Ni-Meister et al., 2010). The ACTS model accounts for clumping of foliage and the influence of trunks in vegetation canopies for modeling of photosynthesis, radiative fluxes and surface albedo in dynamic global vegetation models (DGVMs), and particularly for the Ent Dynamic Global Terrestrial Ecosystem Model (DGTEM). This study evaluates the gap probability and transmittance estimates from the ACTS model by comparing the modeled results with ground-based data, as well as with the original full GORT model and a layered Beer's law scheme. The ground data used in this study include vertical profile measurements of incident photosynthetically active radiation (PAR) in (1) mixed deciduous forests in Morgan-Monroe State Forest, IN, USA, (2) coniferous forests in central Canada, (3) mixed deciduous forests in Harvard Forest, MA, and (4) ground lidar measurements of the canopy gap fraction in woodland in Australia. The model comparisons with these measurements demonstrate that the ACTS model achieves better or similar performance compared to the full GORT and the layered Beer's law schemes with regard to agreements with field measurements and computational cost. The ACTS model has excellent accuracy and flexibility to model the canopy gap probability and transmittance for various forest scenarios. Also, it has advantages relative to the currently widely used two-stream scheme through better radiation estimation for photosynthesis by accounting for the impact of both vertical and horizontal structure heterogeneity of complex vegetation on radiative transfer. Currently the ACTS is being implemented in Ent and will be further tested for how it improves surface energy balance and carbon flux estimates.

Modeling the hemispherical scanning, below-canopy lidar and vegetation structure characteristics with a geometric-optical and radiative-transfer model

This study applied a hybrid canopy geometric optical and radiative transfer (GORT) model to study the vegetation structure characteristics and lidar signals from a terrestrial below-canopy lidar instrument, Echidna Validation Instrument (EVI), developed by CSIRO Australia. Off-nadir scans from the below-canopy lidar show strong laser energy returns from both leaves and tree trunks. The GORT model was modified to include the effect of both leaves and trunks on below-canopy lidar energy returns by treating the trunks as simple uniform cylinders extending to the middle of each tree crown. GORT was also extended to allow multiple canopy layers by convolution of the canopy gap probability profiles for individual canopy layers. The extended leaf-and-trunk GORT model was evaluated by comparing the modeled and EVI-derived gap probability profiles in a single-layer pine plantation and a two-layer eucalypt forest at the Tumbarumba flux tower site in southeastern New South Wales, Australia. Results show that the new leaf-and-trunk GORT model improves estimates of EVI-derived gap probability profiles. This study demonstrates the potential use of terrestrial upward-scanning hemispherical lidar to retrieve forest canopy structural information. A future goal is to link these terrestrial hemispherical lidar measurements to downward-looking airborne lidar, such as the Laser Vegetation Imaging Sensor (LVIS), and spaceborne lidar, such as the Geoscience Laser Altimeter System (GLAS) on ICESat, through a common model to provide large-area mapping of vegetation structural properties and biomass.

The spectral/temporal manifestation of forest succession in optical imagery: The potential of multitemporal imagery

Forest succession is a fundamental ecological process, which has significant implications for sustainable natural resource management as well as ecosystem biological, biophysical, and biogeochemical processes. Remote sensing is perhaps the only viable option to monitor succession in forest ecosystems over large areas in a timely and cost efficient manner. This study integrates the geometric-optical and radiative transfer (GORT) canopy reflectance model with the ZELIG forest ecosystem dynamics model to study the manifestation of forest succession in optical imagery. The spectral/temporal trajectories associated with forest succession are highly nonlinear. The rate of change in spectral space through time is faster during the early years of succession. The succession trajectories do not proceed linearly in the spectral space, but rather curvilinearly, and are strongly influenced by initial background/understory conditions and topography. The nonlinear nature of the trajectories implies that adequate characterization of forest development with remote sensing requires multiple images through time. Validation using multiple Landsat TM images for the H.J. Andrews Experimental Forest (HJA) in the Cascade Range of Oregon found the expected successional trajectories in successfully regenerated young stands. The simulated effects of topography on successional trajectories exist in the Landsat data. The primary challenge in using multitemporal imagery to monitor forest succession is to minimize the effects of various kinds of noise, including varying atmospheric effects, phenology, changes in sun and viewing angles, and topography. The current linkage of GORT–ZELIG only captures the spectral/temporal changes for young stands. Successional changes associated with mature and old-growth stands due to changes in the materials comprising the canopy are not captured. Further research is necessary to include changes associated with mature and old-growth forests, and to develop operational algorithms to utilize information from multitemporal imagery to monitor forest succession.

Modeling lidar waveforms in heterogeneous and discrete canopies

This study explores the relationship between laser waveforms and canopy structure parameters and the effects of the spatial arrangement of canopy structure on this relationship through a geometric optical model. Studying laser waveforms for such plant canopies is needed for the advanced retrieval of three-dimensional (3D) canopy structure parameters from the vegetation canopy lidar (VCL) mission. For discontinuous plant canopies, a hybrid geometric optical and radiative transfer (GORT) model describing the effects of 3D canopy structure parameters of discrete canopies on the radiation environment has been modified for use with lidar. The GORT model is first used to describe the canopy lidar waveforms as a function of canopy structure parameters and then validated using scanning lidar imager of canopies by echo recovery (SLICER) data collected in conifer forests in central Canada during the boreal ecosystem-atmosphere study (BOREAS). Model simulations show that the clumping in natural vegetation, such as leaves clustering into tree crowns causes larger gap probability and smaller waveforms for discontinuous plant canopies than for horizontally homogeneous plant canopies. Ignoring the clumping effect can result in significantly lower values for the estimated foliage amount in the profile and in turn lower estimated biomass. Because of clumping, only the gap probability and apparent vertical projected foliage profile can be directly retrieved from the canopy lidar data. The retrieval is sensitive to the ratio of the volume backscattering coefficients of the vegetation and background, and this ratio depends on canopy architecture as well as foliage spectral characteristics.

A Coupled Vegetation–Soil Bidirectional Reflectance Model for a Semiarid Landscape

A model for the bidirectional reflectance over soils is coupled with a vegetation reflectance model to predict the bidirectional reflectance of semiarid landscape. The vegetation model is a hybrid of geometric optical and radiative transfer (GORT) model for bidirectional reflectance over discontinuous plant canopies. The coupling is done by relaxing the assumption of Lambertian background into a non-Lambertian case in the vegetation GORT model. The non-Lambertian soil background is modeled by a soil BRDF model. The coupled model is validated with POLDER and AVHRR imagery collected in a semiarid site during the Jornada PROVE'97 campaign with good accuracy. It is a very simple but analytical model for bidirectional reflectance over a semiarid landscape and has the potential for surface parameter retrieval for the study of biosphere–atmosphere interaction.

Effect of canopy structure and the presence of snow on the albedo of boreal conifer forests

A Geometric‐Optical and Radiative Transfer (GORT) approach for modeling the radiation regime within plant canopies is capable of predicting temporal variation in the albedo of boreal conifer forests. Model predictions of daily surface albedo patterns and reflected solar radiation during the winter and summer seasons were validated using field measurements from two forest stands in the northern study area of BOReal Ecosystem‐Atmosphere Study (BOREAS) in 1995. The model is able to predict the “W” shape for the daily albedo over the sparse old jack pine forest stand during the snow season and the “bowl” shape of daily albedo during clear days in the summer. Results immediately following new snow and at the end of the snowmelt season indicate the sensitivity of overall forest albedos to the albedo of snow. Incorporation of time‐varying values for snow albedo may improve future efforts to estimate forest albedos in the winter.Forest albedos are a complicated function of the canopy structure, the presence or absence of snow on the ground and the angular distribution of irradiance. These effects differ for the visible, near‐infrared and midinfrared portions of the solar spectrum. Forest albedos vary dramatically as a function of canopy cover when snow covers the ground, but very little when snow is not present. It is found that for tree cover over about 70%, the presence of snow has little effect on albedo.

Transmission of solar radiation in boreal conifer forests: Measurements and models

A combined geometric‐optical and radiative transfer (GORT) model allows incorporation of multiple scales of clustering in conifer canopies on the estimation of radiation transmission. Consideration of clustering of branches into whorls is the latest addition to this model. Modification of the GORT model to include whorl orientation improves the ability to model the observed patterns of solar radiation transmission as a function of solar zenith angle and height in the canopy. Whorl orientation distributions are derived from multidirectional measurements using a geometric optical mutual shadowing model. For BOREAS test stands, model estimates and vertical measurements of photosynthetically active radiation transmittance within the canopy show (1) general decreases in transmission as solar zenith angles increase in the range of solar zenith angles dominated by beam irradiance, (2) increases in PAR transmission at very high solar zenith angles where diffuse skylight is dominant, (3) maximum scattering and absorption occur in the middle of the canopy. Model estimates match measurements from the forest floor, indicating the value of the model for providing radiation inputs to snowmelt models in forested landscapes.