The effect of soil anisotropy on the radiance field emerging from vegetation canopies

Pinty, B.; Verstraete, Michel M.; Gobron, Nadine

doi:10.1029/98gl00383

Cited by 19 publications

(5 citation statements)

References 17 publications

(14 reference statements)

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“…The simulated RPV model values were compared with an independent set of measurements and also presented an adequate Correlation Coefficient. Contrary to canopy reflectance measurements [13,15], our results presented the strongest anisotropy in the forward scattering direction indicating the importance and the dominant role of canopy architecture in the total reflectance signal of a scene. Higher correlations were found in lower zenith angles and especially at nadir position.…”

Section: Conclusion and Recommendationscontrasting

confidence: 47%

“…To date, the model was mainly used to simulate reflectance of non-flat canopy targets that present a backscattering behaviour, in contrast with single leaf characteristics. However, the spectral and directional scattering properties of leaves, which are spatially distributed inside a crown/canopy, and ground reflectance properties, are important factors contributing to the BRDF signal of a canopy target [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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RPV Model Parameters Based on Hyperspectral Bidirectional Reflectance Measurementsof Fagus sylvatica L. Leaves

et al. 2009

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Abstract:The bidirectional reflectance parametric and semi-empirical Rahman-PintyVerstraete (RPV) model was inverted based on Bidirectional Reflectance Factor (BRF) measurements of 60 Fagus sylvatica L. leaves in the optical domain between 400 nm and 2,500 nm. This was accomplished using data retrieved from the Compact Laboratory Spectro-Goniometer (CLabSpeG) with an azimuth and zenith angular step of 30 and 15 degrees, respectively. Wavelength depended RPV parameters describing the leaf reflectance shape (rho0), the curve convexity (k) and the dominant forward scattering (Θ) were derived using the RPVinversion-2 software (Joint Research Centre) package with Correlation Coefficient values between modelled and measured data varying between 0.71 and 0.99 for all wavelengths, azimuth and zenith positions. The RPV model parameters were compared with a set of leaves not participating in the inversion procedure and presented Correlation Coefficient values ranging between 0.64 and 0.94 suggesting that RPV could be also used for simulating single canopy elements such as leaves.

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Section: Conclusion and Recommendationscontrasting

confidence: 47%

Section: Introductionmentioning

confidence: 99%

RPV Model Parameters Based on Hyperspectral Bidirectional Reflectance Measurementsof Fagus sylvatica L. Leaves

et al. 2009

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“…The LAI estimates in forest plots and in reference parcels (extreme values) are shown in columns 7 and 8 of Table 1, respectively. Bare soil, rock and soil covered with different types of litter represented the ground conditions of all forest plots. In the presence of forest plots with sparse canopy or low LAI, an optical characterization of the understory was necessary (Pinty et al, 1998). The direct appraisal of the optical properties in the field was hindered by the heterogeneous ground conditions.…”

Section: Optical Measurementsmentioning

confidence: 99%

A model-based performance test for forest classifiers on remote-sensing imagery

Couturier

Gastellu-Etchegorry

Patiño

et al. 2009

Forest Ecology and Management

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Ambiguity between forest types on remote-sensing imagery is a major cause of errors found in accuracy assessments of forest inventorymaps. This paper presents a methodology, based on forest plot inventory, ground measurements and simulated imagery, for systematically quantifying these ambiguities in the sense of the minimum distance (MD), maximum likelihood (ML), and frequency-based (FB) classifiers. The method is tested with multi-spectral IKONOS images acquired on areas containing six major communities (oak, pine, fir, primary and secondary high tropical forests, and avocado plantation) of the National Forest Inventory (NFI) map in Mexico. A structural record of the canopy and optical measurements (leaf area index and soil reflectance) were performed on one plot of each class. Intra-class signal variation was modelled using the Discrete Anisotropic Radiative Transfer (DART) simulator of remote-sensing images. Atmospheric conditions were inferred from ground measurements on reference surfaces and leaf optical properties of each forest type were derived from the IKONOS forest signal. Next, all forest types were simulated, using a common environmental configuration, in order to quantify similarity among all forest types, according to MD, ML and FB classifiers. Classes were considered ambiguous when their dissimilarity was smaller than intra-class signal variation. DART proved useful in approximating the pixel value distribution and the ambiguity pattern measured on real forest imagery. In the case study, the oak forest and the secondary tropical forest were both distinguishable from all other classes using an MD classifier in a 25 m window size, whereas pine and primary tropical forests were ambiguous with three other classes using MD. By contrast, only two pairs of classes were found ambiguous for the ML classifier and only one for the FB classifier in that same window size. The avocado plantation was confounded with the primary tropical forest for all classifiers, presumably because the reflectance of both types of forest is governed by a deep canopy and a similar shadow area. We confronted the results of this study with the confusion matrix from the accuracy assessment of the NFI map. An asset of this model-basedmethod is its applicability to a variety of sensor types, eco-zones and class definitions

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“…The bidirectional reflectance property of vegetative earth surfaces results from factors such as the scattering process within the canopy layer, leaf angle distribution and orientation, thickness and size of leaves, crowns and their spatial distribution [3-5], as well as the underlying ground-soil properties such as roughness, color, and organic matter content [6]. Furthermore, not only the radiation transfer modeling community is interested in the BRDF of vegetation but also the computer graphics scientists [7-8].…”

Section: Introductionmentioning

confidence: 99%

A Compact Laboratory Spectro-Goniometer (CLabSpeG) to Assess the BRDF of Materials. Presentation, Calibration and Implementation on Fagus sylvatica L. Leaves

Biliouris

Verstraeten

Dutré

et al. 2007

Sensors

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The design and calibration of a new hyperspectral Compact Laboratory Spectro-Goniometer (CLabSpeG) is presented. CLabSpeG effectively measures the bidirectional reflectance Factor (BRF) of a sample, using a halogen light source and an Analytical Spectral Devices (ASD) spectroradiometer. The apparatus collects 4356 reflectance data readings covering the spectrum from 350 nm to 2500 nm by independent positioning of the sensor, sample holder, and light source. It has an azimuth and zenith resolution of 30 and 15 degrees, respectively. CLabSpeG is used to collect BRF data and extract Bidirectional Reflectance Distribution Function (BRDF) data of non-isotropic vegetation elements such as bark, soil, and leaves. Accurate calibration has ensured robust geometric accuracy of the apparatus, correction for the conicality of the light source, while sufficient radiometric stability and repeatability between measurements are obtained. The bidirectional reflectance data collection is automated and remotely controlled and takes approximately two and half hours for a BRF measurement cycle over a full hemisphere with 125 cm radius and 2.4 minutes for a single BRF acquisition. A specific protocol for vegetative leaf collection and measurement was established in order to investigate the possibility to extract BRDF values from Fagus sylvatica L. leaves under laboratory conditions. Drying leaf effects induce a reflectance change during the BRF measurements due to the laboratory illumination source. Therefore, the full hemisphere could not be covered with one leaf. Instead 12 BRF measurements per leaf were acquired covering all azimuth positions for a single light source zenith position. Data are collected in radiance format and reflectance is calculated by dividing the leaf cycle measurement with a radiance cycle of a Spectralon reference panel, multiplied by a Spectralon reflectance correction factor and a factor to correct for the conical effect of the light source. BRF results of measured leaves are presented.

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The effect of soil anisotropy on the radiance field emerging from vegetation canopies

Abstract: Formulation of the Problem

Cited by 19 publications

References 17 publications

RPV Model Parameters Based on Hyperspectral Bidirectional Reflectance Measurementsof Fagus sylvatica L. Leaves

RPV Model Parameters Based on Hyperspectral Bidirectional Reflectance Measurementsof Fagus sylvatica L. Leaves

A model-based performance test for forest classifiers on remote-sensing imagery

A Compact Laboratory Spectro-Goniometer (CLabSpeG) to Assess the BRDF of Materials. Presentation, Calibration and Implementation on Fagus sylvatica L. Leaves

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