Geometrical Optics Model Research Articles

REAL AND SIMULATED WAVEFORM RECORDING LIDAR DATA IN BOREAL JUVENILE FOREST VEGETATION

Abstract. Airborne small-footprint LiDAR is replacing field measurements in regional-level forest inventories, but auxiliary field work is still required for the optimal management of young stands. Waveform (WF) recording sensors can provide a more detailed description of the vegetation compared to discrete return (DR) systems. Furthermore, knowing the shape of the signal facilitates comparisons between real data and those obtained with simulation tools. We performed a quantitative validation of a Monte Carlo ray tracing (MCRT) -based LiDAR simulator against real data and used simulations and empirical data to study the WF recording LiDAR for the classification of boreal juvenile forest vegetation. Geometric-optical models of three common species were used as input for the MCRT model. Simulated radiometric and geometric WF features were in good agreement with the real data, and interspecies differences were preserved. We used the simulator to study the effects of sensor parameters on species classification performance. An increase in footprint size improved the classification accuracy up to a certain footprint size, while the emitted pulse width and the WF sampling rate had minor effects. Analyses on empirical data showed small improvement in performance compared to existing studies, when classifying seedling stand vegetation to four operational classes. The results on simulator validation serve as a basis for the future use of simulation models e.g. in LiDAR survey planning or in the simulation of synthetic training data, while the empirical findings clarify the potential of WF LiDAR data in the inventory chain for the operational forest management planning in Finland.

Optical photon transport in powdered‐phosphor scintillators. Part II. Calculation of single‐scattering transport parameters

Monte Carlo methods based on the Boltzmann transport equation (BTE) have previously been used to model light transport in powdered-phosphor scintillator screens. Physically motivated guesses or, alternatively, the complexities of Mie theory have been used by some authors to provide the necessary inputs of transport parameters. The purpose of Part II of this work is to: (i) validate predictions of modulation transform function (MTF) using the BTE and calculated values of transport parameters, against experimental data published for two Gd2O2S:Tb screens; (ii) investigate the impact of size-distribution and emission spectrum on Mie predictions of transport parameters; (iii) suggest simpler and novel geometrical optics-based models for these parameters and compare to the predictions of Mie theory. A computer code package called phsphr is made available that allows the MTF predictions for the screens modeled to be reproduced and novel screens to be simulated. The transport parameters of interest are the scattering efficiency (Q sct), absorption efficiency (Q abs), and the scatter anisotropy (g). Calculations of these parameters are made using the analytic method of Mie theory, for spherical grains of radii 0.1-5.0 μm. The sensitivity of the transport parameters to emission wavelength is investigated using an emission spectrum representative of that of Gd2O2S:Tb. The impact of a grain-size distribution in the screen on the parameters is investigated using a Gaussian size-distribution (σ = 1%, 5%, or 10% of mean radius). Two simple and novel alternative models to Mie theory are suggested: a geometrical optics and diffraction model (GODM) and an extension of this (GODM+). Comparisons to measured MTF are made for two commercial screens: Lanex Fast Back and Lanex Fast Front (Eastman Kodak Company, Inc.). The Mie theory predictions of transport parameters were shown to be highly sensitive to both grain size and emission wavelength. For a phosphor screen structure with a distribution in grain sizes and a spectrum of emission, only the average trend of Mie theory is likely to be important. This average behavior is well predicted by the more sophisticated of the geometrical optics models (GODM+) and in approximate agreement for the simplest (GODM). The root-mean-square differences obtained between predicted MTF and experimental measurements, using all three models (GODM, GODM+, Mie), were within 0.03 for both Lanex screens in all cases. This is excellent agreement in view of the uncertainties in screen composition and optical properties. If Mie theory is used for calculating transport parameters for light scattering and absorption in powdered-phosphor screens, care should be taken to average out the fine-structure in the parameter predictions. However, for visible emission wavelengths (λ < 1.0 μm) and grain radii (a > 0.5 μm), geometrical optics models for transport parameters are an alternative to Mie theory. These geometrical optics models are simpler and lead to no substantial loss in accuracy.

Estimation of forest canopy nitrogen content based on remote sensing

Hypespectral data was used to estimate leaf and canopy nitrogen content. Erf-BP, an improved model based on the Gaussian error function of BP neural network, was used to develop remote sensing models for estimating leaf nitrogen content. Then the scaling conversion function during downscales from canopy to leaf spectral was derived according to principles of geometric optics model. These relations were used during downscales from the canopy reflectance of Hyperion image to leaf spectral for leaf nitrogen content estimation. Finally, forest structural parameter leaf area index (LAI) was used to obtain canopy nitrogen content from leaf level. The results showed that the best Erf-BP. neural network model with testing accuracy of 76.8597% includes 8 neurons in hidden layer. Using scaling conversion function to estimate canopy spectra at 670nm and 865nm, correlations (R-2) between modeling spectra and measurements were 0.5203 and 0.4117 respectively. Correlation coefficient between estimated leaf nitrogen content and measurements was 0.7019. This method provides a good reference for more rapid and accurate estimation of leaf and canopy nitrogen.

Unbiasing a Stochastic Endmember Interpolator Using ENVI Object-BasedClassifiers, a Farquhar's Single Voxel Leaf Photosynthetic ResponseExplanatory Model and Boolean Time Series Statistics for ForecastingShade-Canopied Simulium damnosum s.l. Larval Habitats in Burkina Faso

Endmember spectra recovered from sub-meter resolution data [e.g., Quick Bird visible and near infra-red (NIR) 0.61m wavebands ratio] of an arthropod-related infectious disease aquatic larval habitat can act as a dependent variable within a least squares estimation algorithm. By so doing, seasonal endemic transmission -oriented risk variables can be accurately interpolated. Spectral mixing, however, is a problem inherent to multi-dimensional canopy-oriented arthropod-related infectious disease larval habitat feature attributes resulting in few image sub-pixel spectra representing pure targets. This can lead to a biased endmember target signature due to spectrally unquantitated mixed sub-pixel radiance originating from different canopy-oriented larval habitat object types. An erroneous endmember larval habitat signature will render inconsistent residual forecasts in a stochastic/deterministic interpolator. In this analyses we spectrally extracted and decomposed multiple canopied endmembers surface-oriented sub-meter resolution pixel reflectance values derived from a georeferenced QuickBird imaged canopied larval habitat of Similium damnosum s.l. (Figure 1), a black fly vector of onchocerciasis in an epidemiological riverine study site in Burkina Faso. We employed ENVI object-based classifiers, a 3-Dimensional radiative transfer equation and the Li-Strahler geometric-optical model to perform the pixel decomposition. Thereafter, the georeferenced larval habitat and the within canopy radiance values (e.g., Precambrian rock) were spectrally isolated and weighed using a robust Successive Progression Algorithm (SPA) within a Boolean domain. The decomposed endmember then rendered a robust spectral signature in ArcGIS which was subsequently kriged to identify unknown, unsampled productive S. damnosum s.l. larval habitats along a Burkina Faso river system using a blind study format. The validation model revealed a 100% correlation among the predicted georeferenced productive black fly habitat sites based on the seasonal-sampled larval density counts values.

High-Resolution Near-Infrared Polarimetry of a Circumstellar Disk around UX Tau A

Abstract We present $ H$-band polarimetric imagery of UX Tau A taken with HiCIAO/AO188 on the Subaru Telescope. UX Tau A has been classified as a pre-transitional disk object, with a gap structure separating its inner and outer disks. Our imagery taken with the 0$ \rlap{\hskip1pt .}''$ 15 (21 AU) radius coronagraphic mask has revealed a strongly polarized circumstellar disk surrounding UX Tau A, which extends to 120 AU, at a spatial resolution of 0$ \rlap{\hskip1pt .}''$ 1 (14 AU). It is inclined by 46$ ^{\circ}$$ \pm$ 2$ ^{\circ}$ , since the west side is nearest. Although SED modeling and sub-millimeter imagery have suggested the presence of a gap in the disk, with the inner edge of the outer disk estimated to be located at 25–30 AU, we detect no evidence of a gap at the limit of our inner working angle (23 AU) at the near-infrared wavelength. We attribute the observed strong polarization (up to 66%) to light scattering by dust grains in the disk. However, neither polarization models of the circumstellar disk based on Rayleigh-scattering nor Mie-scattering approximations were consistent with the observed azimuthal profile of the polarization degrees of the disk. Instead, a geometric optics model of the disk with nonspherical grains with radii of 30 $ \mu$ m is consistent with the observed profile. We suggest that the dust grains have experienced frequent collisional coagulations, and have grown in the circumstellar disk of UX Tau A.

Estimation of Forest Leaf Area Index from Remote Sensing Data Using the Algorithm Based on Geometric-optical Model

Estimation of Forest Leaf Area Index from Remote Sensing Data Using the Algorithm Based on Geometric-optical Model

Comparative Study Between Measurement and Predictions Using Geometrical Optics and Uniform Theory of Diffraction for Case of Non-Line-of-Sight (NLOS) in Indoor Environment

This paper describes the investigation and comparison of the accuracy of a deterministic model for a WLAN system in the indoor environment. The measurement system consisted of a spectrum analyzer and a log-periodic antenna. Non-line-of-sight (NLOS) propagation (in furnished rooms) was investigated. All the measurement sites mentioned in this paper are located in the Division of Information Technology at Universiti Putra Malaysia. The furnished rooms mentioned above were a teaching laboratory and a computer laboratory. The measured path losses were compared with the results obtained using deterministic models, namely the geometrical optics model and the uniform theory of diffraction model with the aid of image theory. The predicted results showed good agreement with the measured data for the NLOS environment, with an absolute mean error that ranged between 1.61 and 3.07 dB.

Augmenting forest inventory attributes with geometric optical modelling in support of regional susceptibility assessments to bark beetle infestations

Assessment of the susceptibility of forests to mountain pine beetle (Dendroctonus ponderosae Hopkins) infestation is based upon an understanding of the characteristics that predispose the stands to attack. These assessments are typically derived from conventional forest inventory data; however, this information often represents only managed forest areas. It does not cover areas such as forest parks or conservation regions and is often not regularly updated resulting in an inability to assess forest susceptibility. To address these shortcomings, we demonstrate how a geometric optical model (GOM) can be applied to Landsat-5 Thematic Mapper (TM) imagery (30m spatial resolution) to estimate stand-level susceptibility to mountain pine beetle attack. Spectral mixture analysis was used to determine the proportion of sunlit canopy and background, and shadow of each Landsat pixel enabling per pixel estimates of attributes required for model inversion. Stand structural attributes were then derived from inversion of the geometric optical model and used as basis for susceptibility mapping. Mean stand density estimated by the geometric optical model was 2753 (standard deviation±308) stems per hectare and mean horizontal crown radius was 2.09 (standard deviation±0.11) metres. When compared to equivalent forest inventory attributes, model predictions of stems per hectare and crown radius were shown to be reasonably estimated using a Kruskal–Wallis ANOVA (p<0.001). These predictions were then used to create a large area map that provided an assessment of the forest area susceptible to mountain pine beetle damage.

X-ray phase contrast imaging of objects with subpixel-size inhomogeneities: a geometrical optics model

Several x-ray phase contrast extraction algorithms use a set of images acquired along the rocking curve of a perfect flat analyzer crystal to study the internal structure of objects. By measuring the angular shift of the rocking curve peak, one can determine the local deflections of the x-ray beam propagated through a sample. Additionally, some objects determine a broadening of the crystal rocking curve, which can be explained in terms of multiple refraction of x rays by many subpixel-size inhomogeneities contained in the sample. This fact may allow us to differentiate between materials and features characterized by different refraction properties. In the present work we derive an expression for the beam broadening in the form of a linear integral of the quantity related to statistical properties of the dielectric susceptibility distribution function of the object.

Accurate geometrical optics model for single-lens stereovision system using a prism

In this paper, we proposed a new method for analyzing the image formation of a prism. The prism was considered as a single optical system composed of some planes. By analyzing each plane individually and then combining them together, we derived a transformation matrix which can express the relationship between an object point and its image by the refraction of a prism. We also explained how to use this matrix for epipolar geometry and three-dimensional point reconstruction. Our method is based on optical geometry and could be used in a multiocular prism. Experimentation results are presented to prove the accuracy of our method is better than former researchers' and is comparable with that of the multicamera stereovision system.

Examining a reduced jet-medium coupling in Pb+Pb collisions at the Large Hadron Collider

Recent data on the nuclear modification factor ${R}_{AA}$ of jet fragments in $2.76$ ATeV Pb+Pb collisions at the Large Hadron Collider (LHC) indicate that the jet-medium coupling in a quark-gluon plasma (QGP) is reduced at LHC energies and not compatible with the coupling deduced from data at the Relativistic Hadron Collider (RHIC). We estimate the reduction factor from a combined fit to the available data on ${R}_{AA}(\sqrt{s},{p}_{T},b)$ and the elliptic flow ${v}_{2}(\sqrt{s},{p}_{T},b)$ at $\sqrt{s}=0.2,2.76$ ATeV over a transverse momentum range ${p}_{T}\ensuremath{\sim}10--100$ GeV and a broad impact parameter, $b$, range. We use a simple analytic ``polytrope'' model ($dE/dx=\ensuremath{-}\ensuremath{\kappa}{E}^{a}{x}^{z}{T}^{c}$) to investigate the dynamical jet-energy loss model dependence. Varying $a=0--1$ interpolates between weakly coupled and strongly coupled models of jet-energy dependence while $z=0--2$ covers a wide range of possible jet-path dependencies from elastic and radiative to holographic string mechanisms. Our fit to LHC data indicates an approximate 40$%$ reduction of the coupling $\ensuremath{\kappa}$ from RHIC to LHC and excludes energy-loss models characterized by a jet-energy exponent with $a&gt;1/3$. In particular, the rapid rise of ${R}_{AA}$ with ${p}_{T}\ensuremath{\ge}10$ GeV combined with the slow variation of the asymptotic ${v}_{2}({p}_{T})$ at the LHC rules out popular exponential geometric optics models ($a=1$). The LHC data are compatible with $0\ensuremath{\le}a\ensuremath{\le}1/3$ pQCD-like energy-loss models where the jet-medium coupling is reduced by approximately 10$%$ between RHIC and LHC.

CHARACTERISING VEGETATED SURFACES USING MODIS MULTIANGULAR SATELLITE DATA

Abstract. Bidirectional Reflectance Distribution Functions (BRDF) seek to represent variations in surface reflectance resulting from changes in a satellite's view and solar illumination angles. BRDF representations have been widely used to assist in the characterisation of vegetation. However BRDF effects are often noisy, difficult to interpret and are the spatial integral of all the individual surface features present in a pixel. This paper describes the results of an approach to understanding how BRDF effects can be used to characterise vegetation. The implementation of the Ross Thick Li Sparse BRDF model using MODIS is a stable, mature data product with a 10 year history and is a ready data source. Using this dataset, a geometric optical model is proposed that seeks to interpret the BRDF effects in terms of Normalised Difference Vegetation Index (NDVI) and a height-to-width ratio of the vegetation components. The height-to-width ratio derived from this model seeks to represent the dependence of NDVI to changes in view zenith angle as a single numeric value. The model proposed within this paper has been applied to MODIS pixels in central Australia for areas in excess of 18,000 km2. The study area is predominantly arid and sparsely vegetated which provides a level of temporal and spatial homogeneity. The selected study area also minimises the effects associated with mutual obscuration of vegetation which is not considered by the model. The results are represented as a map and compared to NDVI derived from MODIS and NDVI derived from Landsat mosaics developed for Australia's National Carbon Accounting System (NCAS). The model reveals additional information not obvious in reflectance data. For example, the height-to-width ratio is able to reveal vegetation features in arid areas that do not have an accompanying significant increase in NDVI derived from MODIS, i.e. the height-to-width ratio reveals vegetation which is otherwise only apparent in NDVI derived from the higher resolution Landsat NCAS dataset.

Lidar calibration and validation for geometric-optical modeling with Landsat imagery

There is a paucity of detailed and timely forest inventory information available for Canada's large, remote northern boreal forests. The Canadian National Forest Inventory program has derived a limited set of attributes from a Landsat-based land cover product representing circa year 2000 conditions. Of the required inventory attributes, forest vertical structure (e.g., tree height) is critical for terrestrial biomass and carbon modeling and to date, is unavailable for these remote areas. In this study, we develop a large-area, fine-scale (25m) mapping solution to estimate tree height (mean, dominant, and Lorey's height) across Canada's northern forests by integrating lidar data (representing 0.27% of the study area), and Landsat imagery (representing 100% of the study area), using a geometric-optical modeling technique. First, spectral mixture analysis (SMA) was used to extract image endmembers and generate fraction images. Second, lidar data were used to calibrate the inverted geometric-optical model by adjusting the model's three key fractional inputs: sunlit crown, sunlit background, and shade fraction, based upon the SMA derived images. The heterogeneity of the study area, spanning 2.16 million ha, made it challenging to directly and accurately decompose mixed Landsat image pixels into the canopy and background fractions used for the Li–Strahler geometric-optical model inversion. As a result we developed a novel method to use the lidar plot data to facilitate the calculation of these fractions in an accurate and automated manner. The average estimation errors for mean, dominant, and Lorey's height were 4.9m, 4.1m, and 4.7m, respectively when compared to the lidar data, with the best result achieved using dominant tree height, where the average error was 3.5m for over 80% of the forested area. Using this approach of optical remotely sensed data calibrated and validated with lidar height estimates, we generate and evaluate wall-to-wall estimates of tree height that can subsequently be used as inputs for biomass and carbon modeling.

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.

A unified canopy bidirectional reflectance (BRDF) model for row ceops

Row sowing is a basic crop sowing method in China, and thus an accurate Bidirectional Reflectance Distribution Function (BRDF) model of row crops is the foundation for describing the canopy bidirectional reflectance characteristics and estimating crop ecological parameters. Because of the macroscopically geometric difference, the row crop is usually regarded as a transition between continuous and discrete vegetation in previous studies. Were row treated as the unit for calculating the four components in the Geometric Optical model (GO model), the formula would be too complex and difficult to retrieve. This study focuses on the microscopic structure of row crops. Regarding the row crop as a result of leaves clumped at canopy scale, we apply clumping index to link continuous vegetation and row crops. Meanwhile, the formula of clumping index is deduced theoretically. Then taking leaf as the basic unit, we calculate the four components of the GO model and develop a BRDF model for continuous vegetation, which is gradually extended to the unified BRDF model for row crops. It is of great importance to introduce clumping index into BRDF model. In order to evaluate the performance of the unified BRDF model, the canopy BRDF data collected in field experiment, “Watershed Allied Telemetry Experiment Research (WATER)”, from May 30th to July 1st, 2008 are used as the validation dataset for the simulated values. The results show that the unified model proposed in this paper is able to accurately describe the non-isotropic characteristics of canopy reflectance for row crops. In addition, the model is simple and easy to retrieve. In general, there is no irreconcilable conflict between continuous and discrete vegetation, so understanding their common and individual characteristics is advantageous for simulating canopy BRDF. It is proven that the four components of the GO model is the basic motivational factor for bidirectional reflectance of all vegetation types.

Spatial and temporal variations of forest LAI in China during 2000–2010

Forests are crucial terrestrial ecosystems. Their leaf area index (LAI) is a key parameter determining the exchange of matter and energy between the atmosphere and the ground surface. In this study, MOD 09A1 and MCD 43A1 data were input into an inversion model based on the 4-scale geometric optical model to retrieve 8-d 500 m LAI products in China during the period 2000 to 2010. The resulting LAI product was validated using LAI measured in 6 typical areas. The spatial and temporal variations of forest LAI and its relationships with temperature and precipitation were analyzed. The results show that the accuracy of the 500 m LAI product was above 70% in the 6 typical areas, indicating the reliability of this product. From 2000 to 2010, forest LAI in northeast, north, and south central China showed increasing trends. However, forest LAI in southeast and parts of southwest China showed downward trends, mainly because of the significant decrease observed during the period 2008 to 2010. Annual mean forest LAI positively correlated with annual mean temperature (AMT) in northeastern China and negatively correlated with AMT in southwest China. It had positive correlations with annual total precipitation in central south and north China. The abnormal climate conditions in 2001 and 2009 caused forest LAI to be obviously lower than normal in regions south of the Qinling Mountains and Huaihe River. Annual mean LAI decreased by more than 1.0 in some areas.

Forest Canopy Cover and Height From MISR in Topographically Complex Southwestern US Landscapes Assessed With High Quality Reference Data

This study addresses the retrieval of spatially contiguous canopy cover and height estimates in southwestern US forests via inversion of a geometric-optical (GO) model against surface bidirectional reflectance factor (BRF) estimates from the Multi-angle Imaging SpectroRadiometer (MISR). Model inversion can provide such maps if good estimates of the background bidirectional reflectance distribution function (BRDF) are avail- able. The study area is in the Sierra National Forest in the Sierra Nevada of California. Tree number density, mean crown radius, and fractional cover reference estimates were obtained via analysis of QuickBird 0.6 m spatial resolution panchromatic imagery using the CANopy Analysis with Panchromatic Imagery (CANAPI) algorithm, while RH50, RH75 and RH100 (50%, 75%, and 100% energy return) height data were obtained from the NASA Laser Vegetation Imaging Sensor (LVIS), a full waveform light detection and ranging (lidar) instrument. These canopy parameters were used to drive a modified version of the simple GO model (SGM), accurately reproducing patterns of MISR 672 nm band surface reflectance (mean RMSE = 0.011, mean R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.82, N = 1048). Cover and height maps were obtained through model inversion against MISR 672 nm reflectance estimates on a 250 m grid. The free parameters were tree number density and mean crown radius. RMSE values with respect to reference data for the cover and height retrievals were 0.05 and 6.65 m, respectively, with R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.54 and 0.49. MISR can thus provide maps of forest cover and height in areas of topographic variation although refinements are required to improve retrieval precision.

Retrieval of forest canopy attributes based on a geometric-optical model using airborne LiDAR and optical remote-sensing data

In the retrieval of forest canopy attributes using a geometric-optical model, the spectral scene reflectance of each component should be known as prior knowledge. Generally, these reflectances were acquired by a foregone survey using an analytical spectral device. This article purposed to retrieve the forest structure parameters using light detection and ranging (LiDAR) data, and used a linear spectrum decomposition model to determine the reflectances of the spectral scene components, which are regarded as prior knowledge in the retrieval of forest canopy cover and effective plant area index (PAIe) using a simplified Li–Strahler geometric-optical model based on a Satellites Pour l'Observation de la Terre 5 (SPOT-5) high-resolution geometry (HRG) image. The airborne LiDAR data are first used to retrieve the forest structure parameters and then the proportion of the SPOT pixel not covered by crown or shadow Kg of each pixel in the sample was calculated, which was used to extract the reflectances of the spectral scene components by a linear spectrum decomposition model. Finally, the forest canopy cover and PAIe are retrieved by the geometric-optical model. As the acquired time of SPOT-5 image and measured data has a discrepancy of about 2 months, the retrieved result of forest canopy cover needs a further validation. The relatively high value of R 2 between the retrieval result of PAIe and the measurements indicates the efficiency of our methods.

Directional Effects on Land Surface Temperature Estimation From Meteosat Second Generation for Savanna Landscapes

Structured canopies can show pronounced directional effects which influence land surface temperature (LST) estimates from thermal infrared satellite data. The effects depend on illumination and viewing geometries, because changes in these two geometries effectively cause the sensor to “see” different fractions of the canopy and the “background” surface (bare soil or low vegetation). Furthermore, parts of these two components will be in shadow, depending on the specific geometry of the canopy and its structure. This paper investigates these directional effects for a specific savanna site in West Africa and extends the findings to areas with denser tree crown cover. This is achieved by modeling the combined effects of the structured surface with a geometric optics model. The model assumes that the surface consists of four components: shaded and sunlit tree canopies and shaded and sunlit backgrounds. The brightness temperatures of these four surface components are provided by in situ measurements at the validation site, and emissivities are taken from the Land Surface Analysis Satellite Applications Facility (LSA-SAF) project. The LST modeling is performed for the geometry of the geostationary Meteosat Second Generation and for nadir geometry. Analyses of the temperature differences between the LST estimates for the two geometries show that, in many cases, the directional effects exceed 1°C within a day and that the timing and the sign of the effects change with season. Directional errors due to structured canopies are currently not considered in error estimates of operationally available LST products, e.g., the LSA-SAF LST product or the Moderate Resolution Imaging Spectroradiometer (MODIS) LST/emissivity products.

Error Analysis of Triangular Laser Measurement System &lt;sup&gt;&lt;/sup&gt;

Analysis for error in triangular laser measuring system of three-dimensional profile. Analysis of the basic principle of laser triangulation method, and setting up the geometric optics model. Study on errors caused by image processing, expounding errors change caused by system structure parameters in detail, and given the methods to eliminating or reducing the errors with different factors.