ON CANOPY SPECTRAL INVARIANTS AND HYPERSPECTRAL RAY TRACING

ABSTRACT In this paper we present a method for the efficient simulation of canopy hyperspectral reflectance using Monte Carlo Ray Tracing. The method essentially describes the scattered radiation in terms of spectral invariants that gives an expression as a series of powers of leaf single scattering albedo. This can then be post-processed to describe the scattering regime for arbitrary leaf spectral functions. The spectral invariant expression is explored to interpret some of its features. Some practical uses of this include the use of truncated ray tracing methods that can be adjusted for unsampled scattering orders by consideration of energy conservation

Development of the Ames Global Hyperspectral Synthetic Data Set: Surface Bidirectional Reflectance Distribution Function

This study introduces the Ames Global Hyperspectral Synthetic Data set (AGHSD), in particular the surface bidirectional reflectance distribution function (BRDF) product, to support the NASA Surface Biology and Geology (SBG) mission development. The data set is generated based on the corresponding multispectral BRDF products from NASA\u27s MODIS satellite sensor. Based on theories of radiative transfer in vegetation canopies, we derive a simple but robust relationship that indicates that the hyperspectral surface BRDF can be accurately approximated as a weighted sum of the soil surface reflectance, the leaf single albedo, and the canopy scattering coefficient, where the weights or coefficients are spectrally invariant and thus readily estimated from the multispectral MODIS products. We validate the algorithm with simulations by a Monte Carlo Ray Tracing model and find the results highly consistent with the theoretic derivation. Using reflectance spectra of soil and vegetation derived from existing spectral libraries, we apply the algorithm to generate the AGHSD BRDF product at 1 km and 8-day resolutions for the year of 2019. The data set is biogeochemically and biogeophysically coherent and consistent, and serves the goal to support the SBG community in developing sciences and applications for the future global imaging spectroscopy mission

Canopy structure: the link between optical and lidar remote sensing through canopy spectral invariants

Canopy structure and chemistry are the dominant factors that determine the radiation budget of vegetation. One approach to understand the role of canopy structure and disentangle it from canopy chemistry is the canopy spectral invariants theory, or p-theory. Using p-theory, the bidirectional reflectance factor (BRF) recorded by sensors can be simulated using a few spectrally-invariant variables and leaf single scattering albedo. The p-theory is originally developed for the optical domain and there are several hallenges associated with it, such as the assumption of black soil, its requirements for narrowband spectral information (e.g. hyperspectral), and limitations in very dense forests. The main question of this study is can we extend the oncepts of p-theory to lidar to overcome these limitations? To answer this question, we developed the theoretical framework in which variables associated with p-theory in the optical domain can be estimated using lidar point clouds and full-waveform information. To verify this framework, we conduct a series of experiments using the DART Monte Carlo ray-tracing model and vegetation scenes with known canopy chemistry and structure such as those offered in the Radiation Transfer Model Intercomparison (RAMI) project. Our reliminary results show that there is a strong link between information provided by optical and lidar sensors through p-theory. We show that information derived from lidar and some fixed, universal canopy chemistry (i.e. dry matter, water, and chlorophyll content) are sufficient to simulate the optical signature of a canopy with high accuracy. The results of this study advance our theoretical understanding of light interaction with canopy elements and also have significant implications for lidar-optical data fusion.Published versio

Assessing the contribution of understory sun-induced chlorophyll fluorescence through 3-D radiative transfer modelling and field data

A major international effort has been made to monitor sun-induced chlorophyll fluorescence (SIF) from space as a proxy for the photosynthetic activity of terrestrial vegetation. However, the effect of spatial heterogeneity on the SIF retrievals from canopy radiance derived from images with medium and low spatial resolution remains uncharacterised. In images from forest and agricultural landscapes, the background comprises a mixture of soil and understory and can generate confounding effects that limit the interpretation of the SIF at the canopy level. This paper aims to improve the understanding of SIF from coarse spatial resolutions in heterogeneous canopies by considering the separated contribution of tree crowns, understory and background components, using a modified version of the FluorFLIGHT radiative transfer model (RTM). The new model is compared with others through the RAMI model intercomparison framework and is validated with airborne data. The airborne campaign includes high-resolution data collected over a tree-grass ecosystem with the HyPlant imaging spectrometer within the FLuorescence EXplorer (FLEX) preparatory missions. Field data measurements were collected from plots with a varying fraction of tree and understory vegetation cover. The relationship between airborne SIF calculated from pure tree crowns and aggregated pixels shows the effect of the understory at different resolutions. For a pixel size smaller than the mean crown size, the impact of the background was low (R2 > 0.99; NRMSE 0.2). This study demonstrates that using a 3D RTM model improves the calculation of SIF significantly (R2 = 0.83, RMSE = 0.03 mW m−2 sr−1 nm−1) when the specific contribution of the soil and understory layers are accounted for, in comparison with the SIF calculated from mixed pixels that considers only one layer as background (R2 = 0.4, RMSE = 0.28 mW m−2 sr−1 nm−1). These results demonstrate the need to account for the contribution of SIF emitted by the understory in the quantification of SIF within tree crowns and within the canopy from aggregated pixels in heterogeneous forest canopies

Quantitative estimation of vegetation traits and temporal dynamics using 3-D radiative transfer models, high-resolution hyperspectral images and satellite imagery

Large-scale monitoring of vegetation dynamics by remote sensing is key to detecting early signs of vegetation decline. Spectral-based indicators of phys-iological plant traits (PTs) have the potential to quantify variations in pho-tosynthetic pigments, chlorophyll fluorescence emission, and structural changes of vegetation as a function of stress. However, the specific response of PTs to disease-induced decline in heterogeneous canopies remains largely unknown, which is critical for the early detection of irreversible damage at different scales. Four specific objectives were defined in this research: i) to assess the feasibility of modelling the incidence and severity of Phytophthora cinnamomi and Xylella fastidiosa based on PTs and biophysical properties of vegetation; ii) to assess non-visual early indicators, iii) to retrieve PT using radiative transfer models (RTM), high-resolution imagery and satellite observations; and iv) to establish the basis for scaling up PTs at different spatial resolutions using RTM for their retrieval in different vegetation co-vers. This thesis integrates different approaches combining field data, air- and space-borne imagery, and physical and empirical models that allow the retrieval of indicators and the evaluation of each component’s contribution to understanding temporal variations of disease-induced symptoms in heter-ogeneous canopies. Furthermore, the effects associated with the understory are introduced, showing not only their impact but also providing a compre-hensive model to account for it. Consequently, a new methodology has been established to detect vegetation health processes and the influence of biotic and abiotic factors, considering different components of the canopy and their impact on the aggregated signal. It is expected that, using the presented methods, existing remote sensors and future developments, the ability to detect and assess vegetation health globally will have a substantial impact not only on socio-economic factors, but also on the preservation of our eco-system as a whole

The Laegeren site: an augmented forest laboratory combining 3-D reconstruction and radiative transfer models for trait-based assessment of functional diversity

Given the increased pressure on forests and their diversity in the context of global change, new ways of monitoring diversity are needed. Remote sensing has the potential to inform essential biodiversity variables on the global scale, but validation of data and products, particularly in remote areas, is difficult. We show how radiative transfer (RT) models, parameterized with a detailed 3-D forest reconstruction based on laser scanning, can be used to upscale leaf-level information to canopy scale. The simulation approach is compared with actual remote sensing data, showing very good agreement in both the spectral and spatial domains. In addition, we compute a set of physiological and morphological traits from airborne imaging spectroscopy and laser scanning data and show how these traits can be used to estimate the functional richness of a forest at regional scale. The presented RT modeling framework has the potential to prototype and validate future spaceborne observation concepts aimed at informing variables of biodiversity, while the trait-based mapping of diversity could augment in situ networks of diversity, providing effective spatiotemporal gap filling for a comprehensive assessment of changes to diversity

Measuring forests with dual wavelength lidar: A simulation study over topography

Accurate measurements of biophysical parameters are essential for understanding the distribution and dynamics of global vegetation, which exerts an influence on the carbon cycle and atmospheric circulation. Spaceborne, large footprint lidar has been shown to be a valuable tool. It is capable of measuring denser forests than other existing remote methods. However large-footprint lidar struggles to separate ground and canopy signals over topography and in the presence of short vegetation. This prevents the physically-based measurement of forest properties (such as canopy height and cover) at an acceptable accuracy (sub 10 m root mean square error for height) without the use of external data. The necessary external datasets are not yet available at a global scale at high accuracy. In this paper the issues of measuring forests with large-footprint, monochromatic lidar are presented. A number of subtle effects, such as shadows beneath crowns, can hamper the reliable measurement of forests. It is proposed that a dual wavelength lidar will allow the separation of canopy from ground returns in these situations and so allow the physically-based measurement of forests. An initial algorithm is developed and tested with Monte-Carlo ray tracer simulations as a proof of concept. Some refinements are needed to make the method more robust, but the initial form was found to determine the start of the ground return over steep slopes and a range of forest densities, canopy heights and vertical structures with a root mean square error (RMSE) of 2.7 m and mean bias of 67 cm for canopies with covers below 99%. This resulted in canopy height RMSE of 2.88 m with a bias of −23 cm. Such a system will allow measurement of a much broader range of forests than is possible with monochromatic lidar and could form a second generation spaceborne lidar mission

Understanding the measurement of forests with waveform lidar

The measurement of forests is essential for monitoring and predicting the role and response of the land surface to global climate change. Globally consistent and frequent measurements can only be made by satellites; unfortunately many current system’s measurements saturate at moderate canopy densities and are not directly related to forest properties, requiring tenuous empirical relationships that are insensitive to many of the Earth’s most important, Carbon rich forests. Lidar (laser radar) is a relatively new technology that offers the potential to make direct measurements of forest height, vertical density and, when ground based, explicit measurements of structure. In addition measurements do not saturate until much higher forest densities. In recent years there has been much interest in the measurement of forests by lidar, with a number of airborne and terrestrial and one spaceborne lidar developed. Measuring a forest leaf by leaf is impractical and very tedious, so more rapid ground based methods are needed to collect data to validate satellite derived estimates. These rapid methods are themselves not directly related to forest properties causing uncertainty in any validation of remotely sensed estimates. This thesis uses Monte Carlo ray tracing to simulate the measurement of forests by full waveform lidars over explicit geometric forest models for both above and below canopy instruments. Existing methods for deriving forest properties from measurements are tested against the known truth of these simulated forests, a process impossible in reality. Causes of disagreements are explored and new methods developed to attempt to overcome any shortcomings. These new methods include dual wavelength lidar for correcting satellite based measurements for topography and a voxel based method for more directly relating terrestrial lidar signals to forest properties