J.P. Gastellu-Etchegorry

Modeling radiative transfer in heterogeneous 3-D vegetation canopies

Abstract The DART (discrete anisotropic radiative transfer) model simulates radiative transfer in heterogeneous 3-D scenes that may comprise different landscape features, i.e., leaves, grass, trunks, water, soil. The scene is divided into a rectangular cell matrix, i.e., building block for simulating larger scenes. Cells are parallelipipedic. Their optical properties are represented by individual scattering phase functions that are directly input into the model or are computed with optical and structural characteristics of elements within the cell. Radiation scattering and propagation are simulated with the exact kernel and discrete ordinate approaches; any set of discrete direction can be selected. In addition to topography and hot spot, leaf specular and first-order polarization mechanisms are modeled. Two major iterative steps are distinguished: 1) Cell illumination with direct sun radiation: Within cell multiple scattering is accurately simulated. 2) Interception and scattering of previously scattered radiation: Atmospheric radiation, possibly anisotropic, is input at this stage. Multiple scattering is stored as spherical harmonics expansions, for reducing computer memory constraints. The model iterates on step 2, for all cells, and stops with the energetic equilibrium. Two simple accelerating techniques can be used: 1) Gauss Seidel method, i.e., simulation of scattering with radiation already scattered at the iteration stage, and (2) decrease of the spherical harmonics expansion order with the iteration order. Moreover, convergence towards the energetic equilibrium is accelerated with an exponential fitting technique. This model predicts the bidirectional reflectance distribution function of 3-D canopies. Radiation components associated with leaf volume and surface mechanisms are distinguished. It gives also the radiation regime within canopies, for further determination of 3-D photosynthesis rates and primary production. Accurate modeling of multiple scattering within cells, combined with the fact that cells can have different x,y,z dimensions, is well adapted to remote sensing based studies, i.e., scenes with large dimensions. The model was successfully tested with homogeneous covers. Preliminary comparisons of simulated reflectance images with remotely acquired spectral images of a 3-D heterogeneous forest cover stressed the usefulness of the DART model for conducting studies with remotely acquired information.

Sensitivity of Texture of High Resolution Images of Forest to Biophysical and Acquisition Parameters

Abstract This article presents a quantitative analysis of the sensitivity of textural information of high resolution remote sensing images of a forest plantation ( Les Landes, France ) to a number of biophysical parameters: crown diameter, distance between trees and rows, tree positioning, leaf area index (LAI), and tree height. Influence of acquisition parameters (spatial resolution, spectral domain and viewing, and illumination configurations) is also investigated. The work is realized with the discrete anisotropic radiative transfer model (DART) simulated images with which we quantify texture with variograms. Results point out the complex dependency of variogram characteristics (range, sill, amplitude of oscillations) on biophysical and acquisition parameters. Neglect of spatial variations of the reflectance of canopy elements, as in most geometric–optical models, can lead to important errors. This stresses the interest of accurate radiative transfer models, such as DART. Although tree crown diameter is the most influential biophysic parameter, its influence may be totally masked by acquisition parameters. Finally, theoretical results were tested against high resolution airborne data (1.67 m resolution). Although encouraging results were obtained, this work both confirms the difficulty of extracting reliable texture information from real remote sensing data, and stresses the usefulness of radiative transfer models for studying the texture of high resolution satellite images.

Modeling BRF and Radiation Regime of Boreal and Tropical Forests: I. BRF

Abstract Monitoring of forest evolution and functioning with remote sensing depends on canopy BRF (bidirectional reflectance factor) sensitivity to biophysical parameters and to canopy PAR (photosynthetically active radiation) regime. Here, we study the canopy BRF of a tropical (Sumatra) and three boreal (Canada) forest sites, with the DART (discrete anisotropic radiative transfer) model. The behavior of PAR regime of these forests is analyzed in a companion article. We assessed the BRF sensitivity to some major experimental parameters (scale of analysis, viewing and illumination directions, sky radiation) and compared it with BRF sensitivity to commonly studied biophysical quantities: Leaf area index (LAI) and leaf optical properties. Simulations showed that BRF directional anisotropy is very large for all forests. For example, maximum relative reflectance difference with view zenith angle less than 25° is around 0.5 in the visible, 0.4 in the short wave infrared, and 0.25 in the near-infrared for tropical forest. We showed that this BRF variability associated with experimental conditions can hamper the remote detection of forest LAI and tree cover change such as deforestation of tropical forest. DART BRFs of the boreal sites were favorably compared with ground (PARABOLA) and airborne (POLDER) measured BRFs. This work stressed 1) the potential of the DART model, 2) the importance of accurate field data for validation approaches, and 3) the very strong influence of canopy architecture on forest BRF; for example, depending on forest sites, a LAI increase may imply that nadir near-infrared reflectance increases or decreases.

A Modeling Approach for Studying Forest Chlorophyll Content

Abstract Imaging spectroscopy from space is a potentially powerful tool for assessing vegetation chemistry with approaches that rely either on empirical relationships or on the inversion of reflectance models. However, this assessment can be erroneous if the 3-D spatial distribution of the vegetation is neglected. Sophisticated radiative transfer models are often required to account for the 3-D canopy architecture. Due to long computation times, however, these models are not well adapted to sensitivity analyses and numerical inversions that require hundred of calls of the merit function. This paper presents a methodology developed to simulate vegetation reflectance spectra quickly and accurately (i.e., without neglecting the 3-D canopy architecture). Canopy reflectance spectra are calculated by linearly interpolating spectra pre-computed with a coupled model: a 3-D canopy model (DART) and a leaf optical properties model (PROSPECT). This approach was successfully tested by studying the influence of forest architecture on the determination of leaf chlorophyll concentration (Chlf) from reflectance measurements. We considered the case of beech stands (Fagus sylvatica L.) of the Fontainebleau Forest, France. The leaf chlorophyll concentration was characterized by the position of the inflection point of the red edge (λi). Apart from Chlf, we considered four other influential factors on λi: the LAI (leaf area index), the viewing direction, the understory reflectance, and the canopy architecture (i.e., a theoretical turbid medium, a pole stand, and a mature stand). Results demonstrated the strong influence of canopy architecture. For example, the λi has larger values for mature stands than for pole stands (δλi>10 nm), whatever the LAI and the viewing directions. Thus, errors on Chlf can be larger than 23 μg/cm2 if canopy architecture is nelected.

A modeling approach to assess the robustness of spectrometric predictive equations for canopy chemistry

Abstract We present a 3D modeling approach to assess the robustness of remotely derived spectrometric equations predictive of forest chemistry (cellulose, lignin, proteins) to structural variables (tree ground cover, leaf area index: LAI, understory) and to the view direction. Our methodology uses two radiative transfer models that operate at leaf (PROSPECT) and canopy (DART) levels. It includes three stages: (1) simulation of short wave infrared bidirectional reflectances of a “reference scene” with constant architecture and variable chemistry; (2) establishment of predictive relationships of chemicals with stepwise regressions; (3) assessment of the robustness of these relationships for scenes with variable structures. Results stressed that predictive relationships are influenced by canopy structure and view direction. Their reliability decreases with increasing heterogeneity of the understory and also if tree cover or LAI decreases. On the other hand, they tend to remain robust if tree cover and LAI increase, that is if the influence of the understory on canopy reflectance decreases. Their reliability increases when the view direction becomes more oblique, except for the hotspot and specular directions. Finally, this work stresses factors that can explain the difficulty to establish robust predictive relationships with remote sensing data. It shows also that 3D modeling approaches can be an efficient tool for studying forest chemistry from space, for instance, in order to assess the domain of validity of predictive relationships.

An interpolation procedure for generalizing a look-up table inversion method

Abstract The inversion of physically based reflectance models is increasingly efficient for extracting vegetation variables from remote sensing images. It requires a vegetation reflectance model and an inversion method that are accurate and efficient. Usually, the complexity of reflectance models implies to use specific inversion methods (e.g., look-up table and neural network). Unfortunately, these methods are valid only for the view-sun directions for which they are designed. A developed look-up table based inversion method avoids this limitation: it generalizes any look-up table for any view-sun direction, and more generally for any input parameter value. It uses a look-up table made of c i coefficients of any analytical expression h that fits a set of reflectance values simulated by the Discrete Anisotropic Radiative Transfer (DART) model. Interpolation on coefficients c i allows h to give reflectance values for any input parameter value. We settled some options of the inversion method with sensitivity studies: tree covers are simulated with 4-tree scenes, expression h has six coefficients c i and the interpolation is the continuous first derivative interpolation method. Moreover, the robustness of the inversion method was validated. The ability to generalize a look-up table for any view-sun direction was successfully tested with the inversion of SPOT images of Fontainebleau (France) forest. LAI maps proved to be as accurate (i.e., RMSE≈1.3) as those obtained with classical relationships that are calibrated with in situ LAI measurements. Here, the advantage of our inversion method was to avoid this calibration.

Modeling BRF and Radiation Regime of Boreal and Tropical Forest: II. PAR Regime

Abstract Monitoring functioning of forest ecosystems with remote sensing depends on canopy BRF (bidirectional reflectance function) sensitivity to biophysical parameters and PAR (photosynthetically active radiation) regime. Here, we studied the 3-D PAR regime of tropical (Sumatra) and boreal (Canada) forests, with the DART (discrete anisotropic radiative transfer) model. We considered wide ranges of Sun off-nadir angles (θ s ), leaf area index (LAI), and leaf clumping. The BRF of these forests is analyzed in a companion article. Here, we also investigated the possibility to derive simple analytical expressions of PAR vertical profiles: We fitted DART simulated APAR (absorbed PAR) profiles with a modified Goudriaan law (1977) the extinction coefficient of which is multiplied by a factor α that accounts for canopy architecture. Similarly to BRF, the PAR regime is very influenced by canopy structure: for θ s =50°, α≅0.40 for tropical forest, α≅0.56 for coniferous boreal forest (OBS), and α≅0.86 for deciduous boreal forest (OA). Moreover, α strongly depends on θ s and LAI; for example, for tropical forest α decreases from 0.44 to 0.12 if θ s varies from 0° to 80°, and from 0.70 to 0.38 if LAI increases from 3 to 10. α decreases slightly with the increase of leaf clumpiness. The NDVI of tropical and boreal forest sites was rather related to the LAI and fAPAR of the upper canopy than to those of total canopy. Finally, we studied the impact of forest architecture on canopy photosynthesis with the coupling of DART with a leaf functioning model. Neglect of architecture can lead to errors as large as 25% in relative on forest CO 2 assimilation.

Retrieval of forest biophysical parameters from remote sensing images with the DART model

This paper presents a quantitative analysis of the sensitivity of textural information of high resolution remote sensing images of a forest plantation (Les Landes, France) with a number of biophysical characteristics: tree cover, crown diameter, distance between rows and leaf area index (LAI). The influence of spatial resolution and viewing and illumination configurations are also assessed. The work is conducted with directional images simulated with the DART (Discrete Anisotropic Radiative Transfer) model, whereas textural information is quantified by means of variograms. Finally, actual 1.67 m resolution spectral images provide a partial validation of the approach and results.

Assessment of forest canopy chemistry with ISM

An ISM (Imaging SpectroMeter) airborne campaign was organised in 1993 over a pine plantation (Les Landes, SW France). ISM is a spectrometer that operates in the near and mid infrared portions of the spectrum [800-3200 nm]. The study was aimed to assess the capability of this airborne spectrometer for obtaining information about the chemical composition (nitrogen, lignin, cellulose) of a tree canopy. Samples (needles) were collected in the field at the time of the ISM survey: 21 parcels were sampled with 5 samples per parcel. Once these samples were dried and reduced in powder, they were analysed for determining their chemical concentrations. Some relationships appeared between the age of the trees and the needle chemical concentrations. Spectral analyses of samples was conducted with an InfraAlyser 450 that operates with 19 spectral bands centred on the absorption bands of chemicals. Stepwise analyses of reflectances were conducted for determining statistical relations that relate chemical concentrations to reflectances (lignin r=71%; nitrogen r=78%; cellulose r=75%). ISM data were geometrically corrected for obtaining spectra of parcels where samples were collected. The objective was to determine if most efficient wavelengths for predicting chemical concentrations are the same at the laboratory and remote sensing level. This comparative analysis was conducted through regression analyses (lignin r=78%; nitrogen r=78%; cellulose r=84%).<<ETX>>

DART: 3-D model of optical satellite images and radiation budget

DART (Discrete Anisotropic Radiative Transfer) was developed in 1996 for simulating radiative transfer in 3D scenes. Since then, it was greatly improved to make it more accurate, comprehensive and operational (e.g., simulation of thermal infrared and atmospheric radiative transfer). Presently, a single DART simulation gives 2 major products. (1) 3-D radiation budget of the Earth-Atmosphere system. (2) Optical remote sensing images at any altitude from bottom up to top of the atmosphere, for many view directions, simultaneously in several spectral bands, from the visible up to thermal infrared. DART works with natural landscapes (i.e., forests, field mosaics, etc.) made of trees, grass, rivers, etc. and urban landscapes made of buildings, roads, etc. Topography is simulated with digital elevation models. Atmosphere (vertical profiles, etc.) and Earth surface (spectral reflectance, etc.) databases can be used, sensor characteristics can be accounted for, etc. Moreover, a Graphic User Interface (GUI) is used to input scene parameters and to display scene and DART simulations. Recent improvements of DART (patent (PCT/FR 02/01181)) are presented here.